fix

fix: make sure openai provider works
2025-12-10 16:00:31 +01:00 · 2025-12-10 15:52:21 +01:00 · 2025-12-10 15:52:17 +01:00
333 changed files with 36772 additions and 43251 deletions
@@ -2,9 +2,8 @@
 # Copy this file to .env and fill in your values

 # LLM Configuration (Required)
-HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
-HINDSIGHT_API_LLM_MODEL=o3-mini
+HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
 HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1

 # API Configuration (Optional)
@@ -1,27 +0,0 @@
-#!/bin/bash
-# Pre-commit hook - runs all scripts in scripts/hooks/
-
-set -e
-
-REPO_ROOT="$(git rev-parse --show-toplevel)"
-HOOKS_DIR="$REPO_ROOT/scripts/hooks"
-
-if [ ! -d "$HOOKS_DIR" ]; then
-    exit 0
-fi
-
-echo ""
-echo "=== Running pre-commit hooks ==="
-echo ""
-
-# Run all executable scripts in hooks directory
-for hook in "$HOOKS_DIR"/*.sh; do
-    if [ -x "$hook" ]; then
-        echo "[hook] $(basename "$hook")"
-        (cd "$REPO_ROOT" && "$hook")
-    fi
-done
-
-echo ""
-echo "=== Pre-commit hooks completed ==="
-echo ""
@@ -0,0 +1,11 @@
+name: 'Setup pg0'
+description: 'Install pg0 embedded PostgreSQL'
+
+runs:
+  using: 'composite'
+  steps:
+    - name: Install pg0
+      shell: bash
+      run: |
+        curl -fsSL https://raw.githubusercontent.com/vectorize-io/pg0/main/install.sh | bash
+        echo "$HOME/.pg0/bin" >> $GITHUB_PATH
@@ -20,17 +20,18 @@ concurrency:
 jobs:
  build:
    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: hindsight-docs
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
          cache: npm
-          cache-dependency-path: package-lock.json
-      - uses: astral-sh/setup-uv@v4
-      - run: npm ci --workspace=hindsight-docs
-      - run: uv run generate-llms-full
-      - run: npm run build --workspace=hindsight-docs
+          cache-dependency-path: hindsight-docs/package-lock.json
+      - run: npm ci
+      - run: npm run build
      - uses: actions/upload-pages-artifact@v3
        with:
          path: hindsight-docs/build
@@ -38,14 +38,6 @@ jobs:
      working-directory: ./hindsight
      run: uv build --out-dir dist

-    - name: Build hindsight-litellm
-      working-directory: ./hindsight-integrations/litellm
-      run: uv build --out-dir dist
-
-    - name: Build hindsight-embed
-      working-directory: ./hindsight-embed
-      run: uv build --out-dir dist
-
    # Publish in order (client and api first, then hindsight-all which depends on them)
    - name: Publish hindsight-client to PyPI
      uses: pypa/gh-action-pypi-publish@release/v1
@@ -65,18 +57,6 @@ jobs:
        packages-dir: ./hindsight/dist
        skip-existing: true

-    - name: Publish hindsight-litellm to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-integrations/litellm/dist
-        skip-existing: true
-
-    - name: Publish hindsight-embed to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-embed/dist
-        skip-existing: true
-
    # Upload artifacts for GitHub release
    - name: Upload artifacts
      uses: actions/upload-artifact@v4
@@ -86,8 +66,6 @@ jobs:
          hindsight-clients/python/dist/*
          hindsight-api/dist/*
          hindsight/dist/*
-          hindsight-integrations/litellm/dist/*
-          hindsight-embed/dist/*
        retention-days: 1

  release-typescript-client:
@@ -102,29 +80,18 @@ jobs:
      with:
        node-version: '20'
        registry-url: 'https://registry.npmjs.org'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json

    - name: Install dependencies
-      run: npm ci --workspace=hindsight-clients/typescript
+      working-directory: ./hindsight-clients/typescript
+      run: npm ci

    - name: Build
-      run: npm run build --workspace=hindsight-clients/typescript
+      working-directory: ./hindsight-clients/typescript
+      run: npm run build

    - name: Publish to npm
      working-directory: ./hindsight-clients/typescript
-      run: |
-        set +e
-        OUTPUT=$(npm publish --access public 2>&1)
-        EXIT_CODE=$?
-        echo "$OUTPUT"
-        if [ $EXIT_CODE -ne 0 ]; then
-          if echo "$OUTPUT" | grep -q "cannot publish over"; then
-            echo "Package version already published, skipping..."
-            exit 0
-          fi
-          exit $EXIT_CODE
-        fi
+      run: npm publish --access public
      env:
        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}

@@ -139,65 +106,6 @@ jobs:
        path: hindsight-clients/typescript/*.tgz
        retention-days: 1

-  release-control-plane:
-    runs-on: ubuntu-latest
-    environment: npm
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        registry-url: 'https://registry.npmjs.org'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Install dependencies
-      run: npm ci
-
-    - name: Build TypeScript client (dependency)
-      run: npm run build --workspace=hindsight-clients/typescript
-
-    - name: Fix platform-specific native modules
-      run: |
-        # npm ci installs from lockfile which may have wrong platform binaries
-        # Delete hoisted native modules and reinstall for current platform
-        rm -rf node_modules/lightningcss node_modules/@tailwindcss
-        npm install lightningcss @tailwindcss/postcss @tailwindcss/node
-
-    - name: Build
-      run: npm run build --workspace=hindsight-control-plane
-
-    - name: Publish to npm
-      working-directory: ./hindsight-control-plane
-      run: |
-        set +e
-        OUTPUT=$(npm publish --access public 2>&1)
-        EXIT_CODE=$?
-        echo "$OUTPUT"
-        if [ $EXIT_CODE -ne 0 ]; then
-          if echo "$OUTPUT" | grep -q "cannot publish over"; then
-            echo "Package version already published, skipping..."
-            exit 0
-          fi
-          exit $EXIT_CODE
-        fi
-      env:
-        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-    - name: Pack for GitHub release
-      working-directory: ./hindsight-control-plane
-      run: npm pack
-
-    - name: Upload artifacts
-      uses: actions/upload-artifact@v4
-      with:
-        name: control-plane
-        path: hindsight-control-plane/*.tgz
-        retention-days: 1
-
  release-rust-cli:
    runs-on: ${{ matrix.os }}
    strategy:
@@ -262,7 +170,7 @@ jobs:
    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
      with:
-        tool-cache: true
+        tool-cache: false
        android: true
        dotnet: true
        haskell: true
@@ -287,7 +195,7 @@ jobs:
      id: get_version
      run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT

-    - name: Extract metadata for release tags
+    - name: Extract metadata
      id: meta
      uses: docker/metadata-action@v5
      with:
@@ -298,29 +206,7 @@ jobs:
          type=semver,pattern={{major}},value=${{ steps.get_version.outputs.VERSION }}
          type=raw,value=latest

-    # TODO: Re-enable smoke test when disk space issue is resolved
-    # # Step 1: Build for local testing (single platform, no push)
-    # # This creates an identical image to what will be released, just for one platform
-    # - name: Build image for testing
-    #   uses: docker/build-push-action@v6
-    #   with:
-    #     context: .
-    #     file: docker/standalone/Dockerfile
-    #     target: ${{ matrix.target }}
-    #     push: false
-    #     load: true
-    #     tags: ${{ matrix.image_name }}:test
-    #     cache-from: type=gha
-    #     cache-to: type=gha,mode=max
-
-    # # Step 2: Test the image before pushing anything
-    # - name: Smoke test - verify container starts
-    #   env:
-    #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-    #   run: ./scripts/docker-smoke-test.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"
-
-    # Build multi-platform and push to release tags
-    - name: Build and push release images
+    - name: Build and push
      uses: docker/build-push-action@v6
      with:
        context: .
@@ -333,9 +219,6 @@ jobs:

  release-helm-chart:
    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write

    steps:
    - uses: actions/checkout@v4
@@ -345,18 +228,12 @@ jobs:
      with:
        version: 'latest'

-    - name: Log in to GHCR
-      run: echo "${{ secrets.GITHUB_TOKEN }}" | helm registry login ghcr.io -u ${{ github.actor }} --password-stdin
-
    - name: Lint Helm chart
      run: helm lint helm/hindsight

    - name: Package Helm chart
      run: helm package helm/hindsight --destination ./helm-packages

-    - name: Push to GHCR OCI
-      run: helm push helm-packages/*.tgz oci://ghcr.io/${{ github.repository_owner }}/charts
-
    - name: Upload artifacts
      uses: actions/upload-artifact@v4
      with:
@@ -366,7 +243,7 @@ jobs:

  create-github-release:
    runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-rust-cli, release-docker-images, release-helm-chart]
    permissions:
      contents: write

@@ -389,12 +266,6 @@ jobs:
        name: typescript-client
        path: ./artifacts/typescript-client

-    - name: Download Control Plane
-      uses: actions/download-artifact@v4
-      with:
-        name: control-plane
-        path: ./artifacts/control-plane
-
    - name: Download Rust CLI (Linux)
      uses: actions/download-artifact@v4
      with:
@@ -426,12 +297,8 @@ jobs:
        cp artifacts/python-packages/hindsight-clients/python/dist/* release-assets/ || true
        cp artifacts/python-packages/hindsight-api/dist/* release-assets/ || true
        cp artifacts/python-packages/hindsight/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-integrations/litellm/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
        # TypeScript client
        cp artifacts/typescript-client/*.tgz release-assets/ || true
-        # Control Plane
-        cp artifacts/control-plane/*.tgz release-assets/ || true
        # Rust CLI binaries
        cp artifacts/rust-cli-linux/hindsight-linux-amd64 release-assets/ || true
        cp artifacts/rust-cli-darwin-amd64/hindsight-darwin-amd64 release-assets/ || true
@@ -440,11 +307,54 @@ jobs:
        cp artifacts/helm-chart/*.tgz release-assets/ || true
        ls -la release-assets/

+    - name: Generate release notes
+      run: |
+        cat << 'EOF' > release-notes.md
+        ## Quick Start
+
+        ```bash
+        # Install the CLI
+        curl -fsSL https://raw.githubusercontent.com/vectorize-io/hindsight/refs/heads/main/hindsight-cli/install.sh | bash
+
+        # Start the server
+        docker run -p 8888:8888 -p 9999:9999 \
+          -e HINDSIGHT_API_LLM_PROVIDER=openai \
+          -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
+          -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
+          ghcr.io/${{ github.repository_owner }}/hindsight:${{ steps.get_version.outputs.VERSION }}
+        ```
+
+        ## Docker Images
+        - `ghcr.io/${{ github.repository_owner }}/hindsight:${{ steps.get_version.outputs.VERSION }}` - Standalone (recommended)
+        - `ghcr.io/${{ github.repository_owner }}/hindsight-api:${{ steps.get_version.outputs.VERSION }}` - API only
+        - `ghcr.io/${{ github.repository_owner }}/hindsight-control-plane:${{ steps.get_version.outputs.VERSION }}` - Web UI only
+
+        ## CLI
+        ```bash
+        curl -fsSL https://raw.githubusercontent.com/vectorize-io/hindsight/refs/heads/main/hindsight-cli/install.sh | bash
+        ```
+
+        ## Python
+        ```bash
+        pip install hindsight-all  # or hindsight-api, hindsight-client
+        ```
+
+        ## TypeScript/JavaScript
+        ```bash
+        npm install @vectorize-io/hindsight-client
+        ```
+
+        ## Helm
+        ```bash
+        helm install hindsight oci://ghcr.io/${{ github.repository_owner }}/charts/hindsight --version ${{ steps.get_version.outputs.VERSION }}
+        ```
+        EOF
+
    - name: Create GitHub Release
      uses: softprops/action-gh-release@v2
      with:
        files: release-assets/*
-        generate_release_notes: true
+        body_path: release-notes.md
        draft: false
        prerelease: false
      env:
@@ -9,131 +9,6 @@ concurrency:
  cancel-in-progress: true

 jobs:
-  build-python-packages:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        include:
-          - name: hindsight-all
-            path: hindsight
-          - name: hindsight-api
-            path: hindsight-api
-          - name: hindsight-client
-            path: hindsight-clients/python
-          - name: hindsight-embed
-            path: hindsight-embed
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Build ${{ matrix.name }}
-      working-directory: ./${{ matrix.path }}
-      run: uv build
-
-  build-api-python-versions:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ['3.11', '3.12', '3.13']
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Build hindsight-api
-      working-directory: ./hindsight-api
-      run: uv build
-
-  build-typescript-client:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Install dependencies
-      run: npm ci --workspace=hindsight-clients/typescript
-
-    - name: Build TypeScript client
-      run: npm run build --workspace=hindsight-clients/typescript
-
-  build-control-plane:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Install SDK dependencies
-      run: npm ci --workspace=hindsight-clients/typescript
-
-    - name: Build SDK
-      run: npm run build --workspace=hindsight-clients/typescript
-
-    # Install control plane deps and fix hoisted lightningcss binary
-    # lightningcss gets hoisted to root node_modules, so we need to reinstall it there
-    - name: Install Control Plane dependencies
-      run: |
-        npm install --workspace=hindsight-control-plane
-        rm -rf node_modules/lightningcss node_modules/@tailwindcss
-        npm install lightningcss @tailwindcss/postcss @tailwindcss/node
-
-    - name: Build Control Plane
-      run: npm run build --workspace=hindsight-control-plane
-
-    - name: Verify standalone build
-      run: |
-        test -f hindsight-control-plane/standalone/server.js || exit 1
-        test -d hindsight-control-plane/standalone/node_modules || exit 1
-        node hindsight-control-plane/bin/cli.js --help
-
-    - name: Smoke test - verify server starts
-      run: |
-        cd hindsight-control-plane
-        node bin/cli.js --port 9999 &
-        SERVER_PID=$!
-        sleep 5
-        if curl -sf http://localhost:9999 > /dev/null 2>&1; then
-          echo "Server started successfully"
-          kill $SERVER_PID 2>/dev/null || true
-          exit 0
-        else
-          echo "Server failed to respond"
-          kill $SERVER_PID 2>/dev/null || true
-          exit 1
-        fi
-
  build-docs:
    runs-on: ubuntu-latest

@@ -144,14 +19,14 @@ jobs:
      uses: actions/setup-node@v4
      with:
        node-version: '20'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json

    - name: Install dependencies
-      run: npm ci --workspace=hindsight-docs
+      working-directory: ./hindsight-docs
+      run: npm ci

    - name: Build docs
-      run: npm run build --workspace=hindsight-docs
+      working-directory: ./hindsight-docs
+      run: npm run build

  build-rust-cli:
    runs-on: ubuntu-latest
@@ -175,13 +50,6 @@ jobs:
      working-directory: hindsight-cli
      run: cargo build --release

-    - name: Upload CLI artifact
-      uses: actions/upload-artifact@v4
-      with:
-        name: hindsight-cli
-        path: hindsight-cli/target/release/hindsight
-        retention-days: 1
-
  lint-helm-chart:
    runs-on: ubuntu-latest

@@ -214,7 +82,7 @@ jobs:
    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
      with:
-        tool-cache: true
+        tool-cache: false
        android: true
        dotnet: true
        haskell: true
@@ -232,26 +100,14 @@ jobs:
        file: docker/standalone/Dockerfile
        target: ${{ matrix.target }}
        push: false
-        load: false
-
-    # TODO: Re-enable smoke test when disk space issue is resolved
-    # - name: Smoke test - verify container starts
-    #   env:
-    #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-    #   run: ./scripts/docker-smoke-test.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"

  test-api:
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: groq
      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
    - uses: actions/checkout@v4
@@ -267,33 +123,16 @@ jobs:
      with:
        python-version-file: ".python-version"

+    - name: Install pg0
+      uses: ./.github/actions/setup-pg0
+
    - name: Build API
      working-directory: ./hindsight-api
      run: uv build

    - name: Install dependencies
      working-directory: ./hindsight-api
-      run: uv sync --extra test --no-install-project --index-strategy unsafe-best-match
-
-    - name: Cache HuggingFace models
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
-        restore-keys: |
-          ${{ runner.os }}-huggingface-
-
-    - name: Pre-download models
-      working-directory: ./hindsight-api
-      run: |
-        uv run python -c "
-        from sentence_transformers import SentenceTransformer, CrossEncoder
-        print('Downloading embedding model...')
-        SentenceTransformer('BAAI/bge-small-en-v1.5')
-        print('Downloading cross-encoder model...')
-        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-        print('Models downloaded successfully')
-        "
+      run: uv sync --extra test

    - name: Run tests
      working-directory: ./hindsight-api
@@ -307,8 +146,6 @@ jobs:
      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
    - uses: actions/checkout@v4
@@ -324,6 +161,9 @@ jobs:
      with:
        python-version-file: ".python-version"

+    - name: Install pg0
+      uses: ./.github/actions/setup-pg0
+
    - name: Build API
      working-directory: ./hindsight-api
      run: uv build
@@ -334,11 +174,11 @@ jobs:

    - name: Install client test dependencies
      working-directory: ./hindsight-clients/python
-      run: uv sync --extra test --index-strategy unsafe-best-match
+      run: uv sync --extra test

    - name: Install API dependencies
      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync

    - name: Create .env file
      run: |
@@ -383,8 +223,6 @@ jobs:
      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
    - uses: actions/checkout@v4
@@ -405,13 +243,16 @@ jobs:
      with:
        node-version: '20'

+    - name: Install pg0
+      uses: ./.github/actions/setup-pg0
+
    - name: Build API
      working-directory: ./hindsight-api
      run: uv build

    - name: Install API dependencies
      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync

    - name: Install TypeScript client dependencies
      working-directory: ./hindsight-clients/typescript
@@ -464,8 +305,6 @@ jobs:
      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
    - uses: actions/checkout@v4
@@ -493,13 +332,16 @@ jobs:
          hindsight-clients/rust/target
        key: ${{ runner.os }}-cargo-client-${{ hashFiles('hindsight-clients/rust/Cargo.lock') }}

+    - name: Install pg0
+      uses: ./.github/actions/setup-pg0
+
    - name: Build API
      working-directory: ./hindsight-api
      run: uv build

    - name: Install API dependencies
      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync

    - name: Create .env file
      run: |
@@ -535,184 +377,3 @@ jobs:
      run: |
        echo "=== API Server Logs ==="
        cat /tmp/api-server.log || echo "No API server log found"
-
-  test-litellm-integration:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-        prune-cache: false
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Build litellm integration
-      working-directory: ./hindsight-integrations/litellm
-      run: uv build
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/litellm
-      run: uv sync --extra dev
-
-    - name: Run tests
-      working-directory: ./hindsight-integrations/litellm
-      run: uv run pytest tests -v
-
-  test-embed:
-    runs-on: ubuntu-latest
-    env:
-      HINDSIGHT_EMBED_LLM_PROVIDER: groq
-      HINDSIGHT_EMBED_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_EMBED_LLM_MODEL: openai/gpt-oss-20b
-      # Prefer CPU-only PyTorch in CI
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-        prune-cache: false
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Install dependencies
-      working-directory: ./hindsight-embed
-      run: uv sync --index-strategy unsafe-best-match
-
-    - name: Cache HuggingFace models
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-embed-${{ hashFiles('hindsight-embed/pyproject.toml') }}
-        restore-keys: |
-          ${{ runner.os }}-huggingface-embed-
-          ${{ runner.os }}-huggingface-
-
-    - name: Run smoke test
-      working-directory: ./hindsight-embed
-      run: ./test.sh
-
-  test-doc-examples:
-    runs-on: ubuntu-latest
-    needs: build-rust-cli
-    env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
-      HINDSIGHT_API_URL: http://localhost:8888
-      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Download CLI artifact
-      uses: actions/download-artifact@v4
-      with:
-        name: hindsight-cli
-        path: /usr/local/bin
-
-    - name: Make CLI executable
-      run: chmod +x /usr/local/bin/hindsight
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-        prune-cache: false
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Build and install API
-      working-directory: ./hindsight-api
-      run: |
-        uv build
-        uv sync --no-install-project --index-strategy unsafe-best-match
-
-    - name: Install Python client dependencies
-      working-directory: ./hindsight-clients/python
-      run: uv sync --extra test --index-strategy unsafe-best-match
-
-    - name: Install TypeScript client
-      run: |
-        npm ci --workspace=hindsight-clients/typescript
-        npm run build --workspace=hindsight-clients/typescript
-
-    - name: Create .env file
-      run: |
-        cat > .env << EOF
-        HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
-        HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
-        EOF
-
-    - name: Start API server
-      run: |
-        ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
-        echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
-          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
-            echo "API server is ready after ${i}s"
-            break
-          fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
-            cat /tmp/api-server.log
-            exit 1
-          fi
-          sleep 1
-        done
-
-    - name: Run Python doc examples
-      working-directory: ./hindsight-clients/python
-      run: |
-        for f in ../../hindsight-docs/examples/api/*.py; do
-          echo "Running $f..."
-          uv run python "$f"
-        done
-
-    - name: Run Node.js doc examples
-      run: |
-        for f in hindsight-docs/examples/api/*.mjs; do
-          echo "Running $f..."
-          node "$f"
-        done
-
-    - name: Configure CLI
-      run: hindsight configure --api-url http://localhost:8888
-
-    - name: Run CLI doc examples
-      run: |
-        for f in hindsight-docs/examples/api/*.sh; do
-          echo "Running $f..."
-          bash "$f"
-        done
-
-    - name: Show API server logs
-      if: always()
-      run: |
-        echo "=== API Server Logs ==="
-        cat /tmp/api-server.log || echo "No API server log found"
@@ -9,9 +9,6 @@ wheels/
 # Virtual environments
 .venv

-# Node
-node_modules/
-
 # Environment variables
 .env

@@ -32,9 +29,6 @@ logs/

 .DS_Store

-# Generated docs files
-hindsight-docs/static/llms-full.txt
-

 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
@@ -14,7 +14,6 @@ This document captures architectural decisions and coding conventions for the Hi
 hindsight/              # Python package for embedded usage
 hindsight-api/          # FastAPI server (core memory engine)
 hindsight-cli/          # Rust CLI client
-hindsight-embed/        # Embedded CLI (no server needed)
 hindsight-control-plane/ # Next.js admin UI
 hindsight-docs/         # Docusaurus documentation site
 hindsight-dev/          # Development tools and benchmarks
@@ -146,8 +145,3 @@ Note: The maintained wrapper `hindsight_client.py` and `README.md` are preserved
 - PostgreSQL with pgvector extension
 - Schema managed via Alembic migrations in `hindsight-api/alembic/`, db migrations happen during api startup, no manual commands
 - Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
-
-# Branding
-## Colors
- Primary: gradient from #0074d9 to #009296
-
@@ -5,23 +5,13 @@ Thanks for your interest in contributing to Hindsight!
 ## Getting Started

 1. Fork and clone the repository
+2. Install dependencies:
   ```bash
-   git clone [email protected]:vectorize-io/hindsight.git
-   cd hindsight
+   cd hindsight-api && uv sync
   ```
-2. Set up your environment:
+3. Set up your environment:
   ```bash
-   cp .env.example .env
-   ```
-   Edit the .env to add LLM API key and config as required
-
-3. Install dependencies:
-   ```bash
-   # Python dependencies
-   uv sync --directory hindsight-api/
-
-   # Node dependencies (uses npm workspaces)
-   npm install
+   export OPENAI_API_KEY=your-key
   ```

 ## Development
@@ -1,15 +1,16 @@
 <div align="center">

-![Hindsight Banner](./hindsight-docs/static/img/banner.svg)
+# Hindsight

-[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://vectorize.io/hindsight/cloud)
+**Agent Memory that Works Like Human Memory**

-[![CI](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml)
-[![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
+[![CI](https://github.com/vectorize-io/hindsight/actions/workflows/test.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/test.yml)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-![PyPI - Downloads](https://img.shields.io/pypi/dm/hindsight-api?label=PyPI)
-![NPM Downloads](https://img.shields.io/npm/dm/%40vectorize-io%2Fhindsight-client?logoColor=orange&label=NPM&color=blue&link=https%3A%2F%2Fwww.npmjs.com%2Fpackage%2F%40vectorize-io%2Fhindsight-client)
+[![PyPI - hindsight-client](https://img.shields.io/pypi/v/hindsight-client?label=hindsight-client)](https://pypi.org/project/hindsight-client/)
+[![npm](https://img.shields.io/npm/v/@vectorize-io/hindsight-client)](https://www.npmjs.com/package/@vectorize-io/hindsight-client)
+[![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)

+[Documentation](https://vectorize-io.github.io/hindsight) • [Paper](./Hindsight.pdf) • [Examples](./examples)

 </div>

@@ -17,7 +18,7 @@

 ## What is Hindsight?

-Hindsight™ is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.
+Hindsight is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph.

 Hindsight addresses common challenges that have frustrated AI engineers building agents to automate tasks and assist users with conversational interfaces. Many of these challenges stem directly from a lack of memory.

@@ -25,48 +26,27 @@ Hindsight addresses common challenges that have frustrated AI engineers building
 - **Hallucinations:** Long term memory can be seeded with external knowledge to ground agent behavior in reliable sources to augment training data.
 - **Cognitive Overload:** As workflows get complex, retrievals, tool calls, user messages and agent responses can grow to fill the context window leading to context rot. Short term memory optimization allows agents to reduce tokens and focus context by removing irrelevant details.

-## How is Hindsight Different From Other Memory Systems?
+## How Hindsight Works

-![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
+![Overview](./hindsight-docs/static/img/hindsight-overview.png)

-Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
+Hindsight organizes memory into four networks to mimic the way human memory works:

 - **World:** Facts about the world ("The stove gets hot")
 - **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
 - **Opinion:** Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
 - **Observation:** Complex mental models derived by reflecting on facts and experiences ("Curling irons, ovens, and fire are also hot. I shouldn't touch those either.")

-Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
-
 Hindsight provides three simple methods to interact with the system:

 - **Retain:** Provide information to Hindsight that you want it to remember
 - **Recall:** Retrieve memories from Hindsight
 - **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.

-### Agent Memory That Learns
-
-A key goal of Hindsight is to build agent memory that enables agents to learn and improve over time. This is the role of the `reflect` operation which provides the agent to form broader opinions and observations over time.
-
-For example, imagine a product support agent that is helping a user troubleshoot a problem. It uses a `search-documentation` tool it found on an MCP server. Later in the conversation, the agent discovers that the documentation returned from the tool wasn't for the product the user was asking about. The agent now has an experience in its memory bank. And just like humans, we want that agent to learn from its experience.
-
-As the agent gains more experiences, `reflect` allows the agent to form observations about what worked, what didn't, and what to do differently the next time it encounters a similar task.
+Memories in Hindsight are stored in banks (e.g. memory banks). When memories are retained, they are transformed to construct a series of search indexes, time series data, and entity/relationship graphs.

 ---

-## Memory Performance & Accuracy
-
-Hindsight has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational
-AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of December 2025 is shown here:
-
-![Overview](./hindsight-docs/static/img/hindsight-bench.jpg)
-
-The benchmark performance data for Hindsight and GPT-4o (full context) have been reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
-
-A thorough examination of the techniques implemented in Hindsight and detailed breakdowns of benchmark performance are [available on arXiv](https://arxiv.org/abs/2512.12818). This research is currently being prepared for conference submission and the wider peer review process.
-
-The benchmark results from this research can be inspected in our [visual benchmark explorer](https://hindsight-benchmarks.vercel.app). As additional improvements are made to Hindsight, new benchmark data will be available for review using this same tool.
-
 ## Quick Start

 ### Docker (recommended)
@@ -74,11 +54,12 @@ The benchmark results from this research can be inspected in our [visual benchma
 ```bash
 export OPENAI_API_KEY=your-key

-docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
+docker run -p 8888:8888 -p 9999:9999 \
+  -e HINDSIGHT_API_LLM_PROVIDER=openai \
  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -e HINDSIGHT_API_LLM_MODEL=o3-mini \
+  -e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
+  ghcr.io/vectorize-io/hindsight
 ```

 API: http://localhost:8888  
@@ -87,7 +68,7 @@ UI: http://localhost:9999
 Install client:

 ```bash
-pip install hindsight-client -U
+pip install hindsight-client
 # or
 npm install @vectorize-io/hindsight-client
 ```
@@ -95,24 +76,25 @@ npm install @vectorize-io/hindsight-client
 Python example:

 ```python
-from hindsight_client import Hindsight
+from hindsight import HindsightClient

-client = Hindsight(base_url="http://localhost:8888")
+client = HindsightClient(base_url="http://localhost:8888")

-# Retain: Store information
-client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer")
+# Store
+client.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")

-# Recall: Search memories
-client.recall(bank_id="my-bank", query="What does Alice do?")
+# Query
+results = client.recall(bank_id="my-agent", query="What does Alice do?")

-# Reflect: Generate disposition-aware response
-client.reflect(bank_id="my-bank", query="Tell me about Alice")
+# Reflect
+response = client.reflect(bank_id="my-agent", query="Tell me about Alice")
+print(response.text)
 ```

 ### Python (embedded, no Docker)

 ```bash
-pip install hindsight-all -U
+pip install hindsight-all
 ```

 ```python
@@ -121,27 +103,27 @@ from hindsight import HindsightServer, HindsightClient

 with HindsightServer(
    llm_provider="openai",
-    llm_model="gpt-5-mini", 
+    llm_model="gpt-4o-mini", 
    llm_api_key=os.environ["OPENAI_API_KEY"]
 ) as server:
    client = HindsightClient(base_url=server.url)
-    client.retain(bank_id="my-bank", content="Alice works at Google")
-    results = client.recall(bank_id="my-bank", query="Where does Alice work?")
+    client.retain(bank_id="my-agent", content="Alice works at Google")
+    results = client.recall(bank_id="my-agent", query="Where does Alice work?")
 ```

-### Node.js / TypeScript
+### TypeScript

 ```bash
 npm install @vectorize-io/hindsight-client
 ```

-```javascript
-const { HindsightClient } = require('@vectorize-io/hindsight-client');
+```typescript
+import { HindsightClient } from '@vectorize-io/hindsight-client';

 const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });

-await client.retain('my-bank', 'Alice loves hiking in Yosemite');
-await client.recall('my-bank', 'What does Alice like?');
+await client.retain('my-agent', 'Alice loves hiking in Yosemite');
+const response = await client.recall('my-agent', 'What does Alice like?');
 ```

 ---
@@ -174,7 +156,7 @@ client.retain(

 Behind the scenes, the retain operation uses an LLM to extract key facts, temporal data, entities, and relationships. It passes these through a normalization process to transform extracted data into canonical entities, time series, and search indexes along with metadata. These representations create the pathways for accurate memory retrieval in the recall and reflect operations. 

-![Retain Operation](hindsight-docs/static/img/retain-operation.webp)
+![Retain Operation](hindsight-docs/static/img/retain-operation.png)

 ### Recall

@@ -189,7 +171,9 @@ client = Hindsight(base_url="http://localhost:8888")
 client.recall(bank_id="my-bank", query="What does Alice do?")

 # Temporal
-client.recall(bank_id="my-bank", query="What happened in June?")
+results = client.recall(bank_id="my-bank", query="What happened in June?")
+
+
 ```

 Recall performs 4 retrieval strategies in parallel:
@@ -198,7 +182,7 @@ Recall performs 4 retrieval strategies in parallel:
 - Graph: Entity/temporal/causal links
 - Temporal: Time range filtering

-![Retain Operation](hindsight-docs/static/img/recall-operation.webp)
+![Retain Operation](hindsight-docs/static/img/recall-operation.png)

 The individual results from the retrievals are merged, then ordered by relevance using reciprocal rank fusion and a cross-encoder reranking model.

@@ -224,29 +208,36 @@ client = Hindsight(base_url="http://localhost:8888")
 client.reflect(bank_id="my-bank", query="What should I know about Alice?")
 ```

-![Retain Operation](hindsight-docs/static/img/reflect-operation.webp)
+![Retain Operation](hindsight-docs/static/img/reflect-operation.png)
+
+## Integrations
+
+### Examples
+
+[Examples Repo]([./examples](https://github.com/vectorize-io/hindsight-cookbook)) includes:
+
+- Basic usage
+- Multi-session conversations
+- Temporal queries
+- Entity reasoning
+- Opinion tracking
+- Production setup (Docker Compose + monitoring)

 ---

 ## Resources

-**Documentation:** 
- [https://hindsight.vectorize.io](https://hindsight.vectorize.io)
+**Documentation:** [vectorize-io.github.io/hindsight](https://vectorize-io.github.io/hindsight)

 **Clients:**
 - [Python](http://hindsight.vectorize.io/sdks/python)
 - [Node.js](http://hindsight.vectorize.io/sdks/nodejs)
- [REST API](https://hindsight.vectorize.io/api-reference)
- [CLI](https://hindsight.vectorize.io/sdks/cli)
+- [REST API](http://hindsight.vectorize.io/api-reference)

 **Community:**
 - [Slack](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
 - [GitHub Issues](https://github.com/vectorize-io/hindsight/issues)

---
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=vectorize-io/hindsight&type=date&legend=top-left)](https://www.star-history.com/#vectorize-io/hindsight&type=date&legend=top-left)
 ---

 ## Contributing
@@ -259,4 +250,4 @@ MIT — see [LICENSE](./LICENSE)

 ---

-Built by [Vectorize.io](https://vectorize.io)
+Built by [Vectorize.io](https://vectorize.io)
@@ -2,19 +2,16 @@
 # Supports building API-only, Control Plane-only, or both
 #
 # Build args:
-#   INCLUDE_API=true/false       - Include API (default: true)
-#   INCLUDE_CP=true/false        - Include Control Plane (default: true)
-#   PRELOAD_ML_MODELS=true/false - Pre-download ML models during build (default: true)
+#   INCLUDE_API=true/false  - Include API (default: true)
+#   INCLUDE_CP=true/false   - Include Control Plane (default: true)
 #
 # Examples:
-#   docker build -t hindsight .                                          # Both (standalone)
-#   docker build -t hindsight-api --build-arg INCLUDE_CP=false .         # API only
-#   docker build -t hindsight-cp --build-arg INCLUDE_API=false .         # Control Plane only
-#   docker build -t hindsight --build-arg PRELOAD_ML_MODELS=false .      # Skip ML model preload
+#   docker build -t hindsight .                                    # Both (standalone)
+#   docker build -t hindsight-api --build-arg INCLUDE_CP=false .   # API only
+#   docker build -t hindsight-cp --build-arg INCLUDE_API=false .   # Control Plane only

 ARG INCLUDE_API=true
 ARG INCLUDE_CP=true
-ARG PRELOAD_ML_MODELS=true

 # =============================================================================
 # Stage: API Builder
@@ -46,9 +43,6 @@ RUN uv sync
 # Copy source code (alembic migrations are inside hindsight_api/)
 COPY hindsight-api/hindsight_api ./hindsight_api

-# Install the local package (uv sync only installed dependencies, not the package itself)
-RUN uv pip install -e .
-
 # =============================================================================
 # Stage: SDK Builder (needed for Control Plane)
 # =============================================================================
@@ -57,15 +51,13 @@ FROM node:20-slim AS sdk-builder
 ARG INCLUDE_CP
 RUN if [ "$INCLUDE_CP" != "true" ]; then echo "Skipping SDK build" && exit 0; fi

-WORKDIR /app
+WORKDIR /app/sdk

-# Copy root package files for npm workspaces
-COPY package.json package-lock.json ./
-COPY hindsight-clients/typescript/ ./hindsight-clients/typescript/
+COPY hindsight-clients/typescript/package*.json ./
+RUN npm ci

-# Install and build SDK using workspace (--ignore-scripts skips git hooks setup)
-RUN npm ci --ignore-scripts -w @vectorize-io/hindsight-client
-RUN npm run build -w @vectorize-io/hindsight-client
+COPY hindsight-clients/typescript/ ./
+RUN npm run build

 # =============================================================================
 # Stage: Control Plane Builder
@@ -75,48 +67,30 @@ FROM node:20-slim AS cp-builder
 ARG INCLUDE_CP
 RUN if [ "$INCLUDE_CP" != "true" ]; then echo "Skipping CP build" && exit 0; fi

-# Create directory structure matching the monorepo layout
-# This is required because build:standalone script expects .next/standalone/memory-poc/hindsight-control-plane
-WORKDIR /app/memory-poc/hindsight-control-plane
+WORKDIR /app
+
+# Copy built SDK
+COPY --from=sdk-builder /app/sdk /app/sdk

 # Install Control Plane dependencies
 # Only copy package.json (not package-lock.json) to ensure npm installs
 # correct platform-specific native bindings for lightningcss/tailwindcss
 COPY hindsight-control-plane/package.json ./
-# Remove the file: dependency on SDK (we'll copy it directly later)
-RUN sed -i '/"@vectorize-io\/hindsight-client":/d' package.json
 RUN npm install

 # Copy Control Plane source (excluding node_modules via .dockerignore)
 COPY hindsight-control-plane/ ./
 # Remove package-lock.json to avoid conflicts with installed native bindings
-# Also remove the file: dependency from package.json (restored by COPY above)
-RUN rm -f package-lock.json && sed -i '/"@vectorize-io\/hindsight-client":/d' package.json
+RUN rm -f package-lock.json

-# Copy built SDK directly into node_modules (more reliable than npm link in Docker)
-COPY --from=sdk-builder /app/hindsight-clients/typescript ./node_modules/@vectorize-io/hindsight-client
+# Link SDK (temporary for build)
+RUN cd /app/sdk && npm link && cd /app && npm link @vectorize-io/hindsight-client

-# Build Control Plane - run next build first, then custom standalone copy
-# (The build:standalone script expects a specific path structure that differs in Docker)
-RUN npm exec -- next build
+# Build Control Plane
+RUN npm run build

-# Create standalone directory structure manually
-# Note: Must exclude node_modules from find to avoid wrong server.js from next/dist/experimental/testmode/
-# Note: Must explicitly copy .next since glob * doesn't match hidden directories
-RUN STANDALONE_ROOT=$(find .next/standalone -path '*/node_modules' -prune -o -name 'server.js' -print | head -1 | xargs dirname) && \
-    mkdir -p standalone && \
-    cp -r "$STANDALONE_ROOT"/* standalone/ && \
-    cp -r "$STANDALONE_ROOT"/.next standalone/.next && \
-    # Copy node_modules if separate from app dir (monorepo structure)
-    if [ -d ".next/standalone/node_modules" ] && [ "$STANDALONE_ROOT" != ".next/standalone" ]; then \
-      cp -r .next/standalone/node_modules standalone/node_modules; \
-    fi && \
-    cp -r .next/static standalone/.next/static && \
-    mkdir -p standalone/public && \
-    cp -r public/* standalone/public/ 2>/dev/null || true && \
-    # Verify required files exist
-    test -f standalone/server.js || (echo "ERROR: server.js missing!" && exit 1) && \
-    test -f standalone/.next/BUILD_ID || (echo "ERROR: BUILD_ID missing!" && exit 1)
+# Create public directory if it doesn't exist
+RUN mkdir -p public

 # =============================================================================
 # Stage: Final Image - API Only
@@ -125,16 +99,14 @@ FROM python:3.11-slim AS api-only

 WORKDIR /app

-# Install pg0 dependencies (procps provides 'kill' command needed by pg0)
-# Note: libicu version varies by Debian version - try common versions in order
+# Install pg0 dependencies
 RUN apt-get update && apt-get install -y \
    curl \
-    procps \
    libxml2 \
    libssl3 \
    libgssapi-krb5-2 \
    libossp-uuid16 \
-    && (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
+    && apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
    && rm -rf /var/lib/apt/lists/* \
    && pip install --no-cache-dir uv

@@ -155,24 +127,45 @@ RUN mkdir -p /app/data && chown -R hindsight:hindsight /app
 USER hindsight

 # Set PATH for hindsight user
-ENV PATH="/app/api/.venv/bin:${PATH}"
+ENV PATH="/home/hindsight/.hindsight/bin:/app/api/.venv/bin:${PATH}"

-# Pre-cache PostgreSQL binaries by starting/stopping pg0-embedded
+# Install pg0 binary
+RUN mkdir -p /home/hindsight/.hindsight/bin && \
+    ARCH=$(uname -m) && \
+    if [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ]; then \
+        PG0_BINARY="pg0-linux-aarch64-gnu"; \
+    elif [ "$ARCH" = "x86_64" ]; then \
+        PG0_BINARY="pg0-linux-x86_64-gnu"; \
+    else \
+        echo "Unsupported architecture: $ARCH" && exit 1; \
+    fi && \
+    echo "Installing pg0 binary: $PG0_BINARY" && \
+    for i in 1 2 3 4 5; do \
+        curl -fsSL -o /home/hindsight/.hindsight/bin/pg0 \
+            "https://github.com/vectorize-io/pg0/releases/latest/download/$PG0_BINARY" && \
+        chmod +x /home/hindsight/.hindsight/bin/pg0 && \
+        break || (echo "Retry $i failed, waiting..." && sleep 10); \
+    done && \
+    /home/hindsight/.hindsight/bin/pg0 --version
+
+# Pre-download PostgreSQL binaries
 ENV PG0_HOME=/home/hindsight/.pg0-cache
+RUN pg0 start --help && \
+    (pg0 start --name hindsight --port 5555 --username hindsight --password hindsight --database hindsight && \
+    sleep 2 && \
+    pg0 stop --name hindsight && \
+    echo "PostgreSQL pre-cached to $PG0_HOME") || echo "Pre-download skipped"

 ENV PG0_HOME=/home/hindsight/.pg0

-# Pre-download ML models to avoid runtime download (conditional)
-ARG PRELOAD_ML_MODELS
-RUN if [ "$PRELOAD_ML_MODELS" = "true" ]; then \
-    /app/api/.venv/bin/python -c "\
+# Pre-download ML models to avoid runtime download
+RUN /app/api/.venv/bin/python -c "\
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Models cached successfully')"; \
-    else echo "Skipping ML model preload"; fi
+print('Models cached successfully')"

 EXPOSE 8888

@@ -181,7 +174,6 @@ ENV HINDSIGHT_API_PORT=8888
 ENV HINDSIGHT_API_LOG_LEVEL=info
 ENV HINDSIGHT_ENABLE_API=true
 ENV HINDSIGHT_ENABLE_CP=false
-ENV PYTHONUNBUFFERED=1

 CMD ["/app/start-all.sh"]

@@ -193,13 +185,13 @@ FROM node:20-alpine AS cp-only
 WORKDIR /app

 # Copy built SDK
-COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk
+COPY --from=sdk-builder /app/sdk /app/sdk

 # Copy Control Plane standalone build
 WORKDIR /app/control-plane
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/standalone ./
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/.next/static ./.next/static
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/public ./public
+COPY --from=cp-builder /app/.next/standalone ./
+COPY --from=cp-builder /app/.next/static ./.next/static
+COPY --from=cp-builder /app/public ./public

 WORKDIR /app

@@ -226,16 +218,14 @@ FROM python:3.11-slim AS standalone

 WORKDIR /app

-# Install Node.js, curl, uv, and pg0 dependencies (procps provides 'kill' command needed by pg0)
-# Note: libicu version varies by Debian version - try common versions in order
+# Install Node.js, curl, uv, and pg0 dependencies
 RUN apt-get update && apt-get install -y \
    curl \
-    procps \
    libxml2 \
    libssl3 \
    libgssapi-krb5-2 \
    libossp-uuid16 \
-    && (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
+    && apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
    && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
    && apt-get install -y nodejs \
    && rm -rf /var/lib/apt/lists/* \
@@ -248,13 +238,13 @@ RUN useradd -m -s /bin/bash hindsight
 COPY --from=api-builder /app/api /app/api

 # Copy built SDK
-COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk
+COPY --from=sdk-builder /app/sdk /app/sdk

 # Copy Control Plane standalone build
 WORKDIR /app/control-plane
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/standalone ./
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/.next/static ./.next/static
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/public ./public
+COPY --from=cp-builder /app/.next/standalone ./
+COPY --from=cp-builder /app/.next/static ./.next/static
+COPY --from=cp-builder /app/public ./public

 WORKDIR /app

@@ -269,31 +259,45 @@ RUN mkdir -p /app/data && chown -R hindsight:hindsight /app
 USER hindsight

 # Set PATH for hindsight user
-ENV PATH="/app/api/.venv/bin:${PATH}"
+ENV PATH="/home/hindsight/.hindsight/bin:/app/api/.venv/bin:${PATH}"

-# Pre-cache PostgreSQL binaries by starting/stopping pg0-embedded
+# Install pg0 binary
+RUN mkdir -p /home/hindsight/.hindsight/bin && \
+    ARCH=$(uname -m) && \
+    if [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ]; then \
+        PG0_BINARY="pg0-linux-aarch64-gnu"; \
+    elif [ "$ARCH" = "x86_64" ]; then \
+        PG0_BINARY="pg0-linux-x86_64-gnu"; \
+    else \
+        echo "Unsupported architecture: $ARCH" && exit 1; \
+    fi && \
+    echo "Installing pg0 binary: $PG0_BINARY" && \
+    for i in 1 2 3 4 5; do \
+        curl -fsSL -o /home/hindsight/.hindsight/bin/pg0 \
+            "https://github.com/vectorize-io/pg0/releases/latest/download/$PG0_BINARY" && \
+        chmod +x /home/hindsight/.hindsight/bin/pg0 && \
+        break || (echo "Retry $i failed, waiting..." && sleep 10); \
+    done && \
+    /home/hindsight/.hindsight/bin/pg0 --version
+
+# Pre-download PostgreSQL binaries
 ENV PG0_HOME=/home/hindsight/.pg0-cache
-RUN /app/api/.venv/bin/python -c "\
-from pg0 import Pg0; \
-print('Pre-caching PostgreSQL binaries...'); \
-pg = Pg0(name='hindsight', port=5555, username='hindsight', password='hindsight', database='hindsight'); \
-pg.start(); \
-pg.stop(); \
-print('PostgreSQL pre-cached to PG0_HOME')" || echo "Pre-download skipped"
+RUN pg0 start --help && \
+    (pg0 start --name hindsight --port 5555 --username hindsight --password hindsight --database hindsight && \
+    sleep 2 && \
+    pg0 stop --name hindsight && \
+    echo "PostgreSQL pre-cached to $PG0_HOME") || echo "Pre-download skipped"

 ENV PG0_HOME=/home/hindsight/.pg0

-# Pre-download ML models to avoid runtime download (conditional)
-ARG PRELOAD_ML_MODELS
-RUN if [ "$PRELOAD_ML_MODELS" = "true" ]; then \
-    /app/api/.venv/bin/python -c "\
+# Pre-download ML models to avoid runtime download
+RUN /app/api/.venv/bin/python -c "\
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Models cached successfully')"; \
-    else echo "Skipping ML model preload"; fi
+print('Models cached successfully')"

 EXPOSE 8888 9999

@@ -304,7 +308,6 @@ ENV NODE_ENV=production
 ENV HINDSIGHT_CP_DATAPLANE_API_URL=http://localhost:8888
 ENV HINDSIGHT_ENABLE_API=true
 ENV HINDSIGHT_ENABLE_CP=true
-ENV PYTHONUNBUFFERED=1

 CMD ["/app/start-all.sh"]

@@ -1,6 +1,9 @@
 #!/bin/bash
 set -e

+echo "🚀 Starting Hindsight..."
+echo ""
+
 # Service flags (default to true if not set)
 ENABLE_API="${HINDSIGHT_ENABLE_API:-true}"
 ENABLE_CP="${HINDSIGHT_ENABLE_CP:-true}"
@@ -23,31 +26,32 @@ PIDS=()
 # Start API if enabled
 if [ "$ENABLE_API" = "true" ]; then
    cd /app/api
-    # Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
-    hindsight-api &
+    hindsight-api 2>&1 | sed -u 's/^/[api] /' &
    API_PID=$!
    PIDS+=($API_PID)

    # Wait for API to be ready
+    echo "⏳ Waiting for API..."
    for i in {1..60}; do
        if curl -sf http://localhost:8888/health &>/dev/null; then
+            echo "✅ API is ready"
            break
        fi
        sleep 1
    done
 else
-    echo "API disabled (HINDSIGHT_ENABLE_API=false)"
+    echo "⏭️  API disabled (HINDSIGHT_ENABLE_API=false)"
 fi

 # Start Control Plane if enabled
 if [ "$ENABLE_CP" = "true" ]; then
    echo "🎛️  Starting Control Plane..."
    cd /app/control-plane
-    PORT=9999 node server.js &
+    PORT=9999 node server.js 2>&1 | grep -v -E "^[[:space:]]*(▲|✓|-|$)" | sed -u 's/^/[control-plane] /' &
    CP_PID=$!
    PIDS+=($CP_PID)
 else
-    echo "Control Plane disabled (HINDSIGHT_ENABLE_CP=false)"
+    echo "⏭️  Control Plane disabled (HINDSIGHT_ENABLE_CP=false)"
 fi

 # Print status
@@ -0,0 +1,135 @@
+HINDSIGHT HELM CHART INSTALLATION GUIDE
+=====================================
+
+PREREQUISITES
+-------------
+- Kubernetes cluster (1.19+)
+- kubectl configured
+- Helm 3.x installed
+- PostgreSQL database with pgvector extension (if not using bundled PostgreSQL)
+
+BASIC INSTALLATION
+------------------
+
+1. Install with default values (requires external PostgreSQL):
+
+   helm install hindsight ./hindsight \
+     --set postgresql.external.host=your-postgres-host \
+     --set postgresql.external.password=your-password \
+     --set api.secrets.MEMORY_LLM_API_KEY=your-api-key
+
+2. Install with custom values file:
+
+   helm install hindsight ./hindsight -f hindsight/values-production.yaml
+
+3. Install in a specific namespace:
+
+   kubectl create namespace hindsight
+   helm install hindsight ./hindsight -n hindsight
+
+CONFIGURATION OPTIONS
+---------------------
+
+Development setup (using values-development.yaml):
+   helm install hindsight ./hindsight -f hindsight/values-development.yaml
+
+Production setup (using values-production.yaml):
+   helm install hindsight ./hindsight -f hindsight/values-production.yaml
+
+Custom LLM provider:
+   helm install hindsight ./hindsight \
+     --set api.env.MEMORY_LLM_PROVIDER=openai \
+     --set api.env.MEMORY_LLM_MODEL=gpt-4 \
+     --set api.secrets.MEMORY_LLM_API_KEY=sk-your-key
+
+Enable ingress:
+   helm install hindsight ./hindsight \
+     --set ingress.enabled=true \
+     --set ingress.hosts[0].host=hindsight.example.com
+
+Enable autoscaling:
+   helm install hindsight ./hindsight \
+     --set autoscaling.enabled=true \
+     --set autoscaling.minReplicas=2 \
+     --set autoscaling.maxReplicas=10
+
+UPGRADE
+-------
+
+Upgrade existing installation:
+   helm upgrade hindsight ./hindsight
+
+Upgrade with new values:
+   helm upgrade hindsight ./hindsight -f hindsight/values-production.yaml
+
+UNINSTALL
+---------
+
+Remove the Helm release:
+   helm uninstall hindsight
+
+Remove with namespace:
+   helm uninstall hindsight -n hindsight
+
+TESTING
+-------
+
+Test the installation with dry-run:
+   helm install hindsight ./hindsight --dry-run --debug
+
+Validate templates:
+   helm template hindsight ./hindsight
+
+Lint the chart:
+   helm lint ./hindsight
+
+ACCESSING THE SERVICES
+----------------------
+
+Port-forward control plane:
+   kubectl port-forward svc/hindsight-control-plane 3000:3000
+
+Port-forward API:
+   kubectl port-forward svc/hindsight-api 8888:8888
+
+Get service URLs:
+   helm status hindsight
+
+DATABASE INITIALIZATION
+-----------------------
+
+NOTE: Database migrations now run automatically when the API service starts.
+      You typically don't need to run migrations manually.
+
+If you want to pre-initialize the database before deploying (optional):
+   kubectl run hindsight-init --rm -it --restart=Never \
+     --image=hindsight/api:latest \
+     --env="DATABASE_URL=postgresql://user:pass@host:5432/hindsight" \
+     -- python -c "from hindsight.migrations import run_migrations; run_migrations()"
+
+TROUBLESHOOTING
+---------------
+
+Check pod status:
+   kubectl get pods -l app.kubernetes.io/name=hindsight
+
+View logs for API:
+   kubectl logs -l app.kubernetes.io/component=api
+
+View logs for control plane:
+   kubectl logs -l app.kubernetes.io/component=control-plane
+
+Describe a pod:
+   kubectl describe pod <pod-name>
+
+Check configuration:
+   kubectl get configmap hindsight-config -o yaml
+   kubectl get secret hindsight-secret -o yaml
+
+NOTES
+-----
+- Make sure PostgreSQL has pgvector extension enabled
+- Run database migrations before first use
+- Configure proper resource limits for production
+- Use external secrets management for production
+- Enable TLS/SSL for production deployments
@@ -1,6 +0,0 @@
-dependencies:
- name: postgresql
-  repository: https://charts.bitnami.com/bitnami
-  version: 15.5.38
-digest: sha256:f67c7612736803ece8a669f8ca6b0555f3b78557bc0ecb732aa2e43f0df7750d
-generated: "2025-12-10T17:20:57.058794+01:00"
@@ -1,9 +1,9 @@
 apiVersion: v2
 name: hindsight
-description: Hindsight helm chart
+description: A Helm chart for Hindsight - temporal-semantic-entity memory system for AI agents
 type: application
-version: 0.1.12
-appVersion: "0.1.12"
+version: 0.1.0
+appVersion: "0.1.0"
 keywords:
  - ai
  - memory
@@ -1,182 +0,0 @@
-# Hindsight Helm Chart
-
-Helm chart for deploying Hindsight - a temporal-semantic-entity memory system for AI agents.
-
-## Prerequisites
-
- Kubernetes 1.19+
- Helm 3.0+
- PostgreSQL database (external or bundled)
-
-## Quick Start
-
-```bash
-# Update dependencies first
-helm dependency update ./helm/hindsight
-
-# Install (PostgreSQL included by default)
-export OPENAI_API_KEY="sk-your-openai-key"
-helm upgrade hindsight --install ./helm/hindsight -n hindsight --create-namespace \
-  --set api.secrets.HINDSIGHT_API_LLM_API_KEY="$OPENAI_API_KEY"
-```
-
-To use an external database instead:
-
-```bash
-helm install hindsight ./helm/hindsight -n hindsight --create-namespace \
-  --set api.secrets.HINDSIGHT_API_LLM_API_KEY="sk-your-openai-key" \
-  --set postgresql.enabled=false \
-  --set postgresql.external.host=my-postgres.example.com \
-  --set postgresql.external.password=mypassword
-```
-
-## Installation
-
-### Add the repository (if published)
-
-```bash
-helm repo add hindsight https://your-helm-repo.com
-helm repo update
-```
-
-### Install with custom values file
-
-Create a `values-override.yaml`:
-
-```yaml
-api:
-  secrets:
-    HINDSIGHT_API_LLM_API_KEY: "sk-your-openai-key"
-
-postgresql:
-  external:
-    host: "my-postgres.example.com"
-    password: "mypassword"
-```
-
-Then install:
-
-```bash
-helm install hindsight ./helm/hindsight -n hindsight --create-namespace -f values-override.yaml
-```
-
-## Configuration
-
-### Key Values
-
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `version` | Default image tag for all components | `0.1.0` |
-| `api.enabled` | Enable the API component | `true` |
-| `api.image.repository` | API image repository | `hindsight/api` |
-| `api.image.tag` | API image tag (defaults to `version`) | - |
-| `api.service.port` | API service port | `8888` |
-| `controlPlane.enabled` | Enable the control plane | `true` |
-| `controlPlane.image.repository` | Control plane image repository | `hindsight/control-plane` |
-| `controlPlane.image.tag` | Control plane image tag (defaults to `version`) | - |
-| `controlPlane.service.port` | Control plane service port | `3000` |
-| `postgresql.enabled` | Deploy PostgreSQL as subchart | `true` |
-| `postgresql.external.host` | External PostgreSQL host | `postgresql` |
-| `postgresql.external.port` | External PostgreSQL port | `5432` |
-| `postgresql.external.database` | Database name | `hindsight` |
-| `postgresql.external.username` | Database username | `hindsight` |
-| `ingress.enabled` | Enable ingress | `false` |
-| `autoscaling.enabled` | Enable HPA | `false` |
-
-### Environment Variables
-
-All environment variables in `api.env` and `controlPlane.env` are automatically added to the respective pods. Sensitive values should go in `api.secrets` or `controlPlane.secrets`.
-
-```yaml
-api:
-  env:
-    HINDSIGHT_API_LLM_PROVIDER: "openai"
-    HINDSIGHT_API_LLM_MODEL: "gpt-4"
-  secrets:
-    HINDSIGHT_API_LLM_API_KEY: "your-api-key"
-    HINDSIGHT_API_LLM_BASE_URL: "https://api.openai.com/v1"
-
-controlPlane:
-  env:
-    NODE_ENV: "production"
-  secrets: {}
-```
-
-### External Database
-
-To connect to an external PostgreSQL database:
-
-```yaml
-postgresql:
-  enabled: false
-  external:
-    host: "my-postgres.example.com"
-    port: 5432
-    database: "hindsight"
-    username: "hindsight"
-    password: "your-password"
-```
-
-### Ingress
-
-To expose the services via ingress:
-
-```yaml
-ingress:
-  enabled: true
-  className: "nginx"
-  annotations:
-    cert-manager.io/cluster-issuer: "letsencrypt-prod"
-  hosts:
-    - host: hindsight.example.com
-      paths:
-        - path: /
-          pathType: Prefix
-          service: controlPlane
-        - path: /api
-          pathType: Prefix
-          service: api
-  tls:
-    - secretName: hindsight-tls
-      hosts:
-        - hindsight.example.com
-```
-
-## Upgrading
-
-```bash
-helm upgrade hindsight ./helm/hindsight -n hindsight
-```
-
-## Uninstalling
-
-```bash
-helm uninstall hindsight -n hindsight
-```
-
-## Components
-
-The chart deploys:
-
- **API**: The main Hindsight API server for memory operations
- **Control Plane**: Web UI for managing agents and viewing memories
-
-## Development
-
-### Lint the chart
-
-```bash
-helm lint ./helm/hindsight
-```
-
-### Template locally
-
-```bash
-helm template hindsight ./helm/hindsight --debug
-```
-
-### Dry run installation
-
-```bash
-helm install hindsight ./helm/hindsight --dry-run --debug
-```
@@ -1,2 +1,71 @@
-Hindsight installed. Access the control plane:
-  kubectl port-forward -n {{ .Release.Namespace }} svc/{{ include "hindsight.fullname" . }}-control-plane 3000:3000
+Thank you for installing {{ .Chart.Name }}!
+
+Your release is named {{ .Release.Name }}.
+
+To learn more about the release, try:
+
+  $ helm status {{ .Release.Name }}
+  $ helm get all {{ .Release.Name }}
+
+{{- if .Values.ingress.enabled }}
+
+The application is accessible via the following URL(s):
+{{- range .Values.ingress.hosts }}
+  - http{{ if $.Values.ingress.tls }}s{{ end }}://{{ .host }}
+{{- end }}
+
+{{- else }}
+
+1. Get the Control Plane URL by running these commands:
+{{- if contains "NodePort" .Values.controlPlane.service.type }}
+  export NODE_PORT=$(kubectl get --namespace {{ .Release.Namespace }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ include "hindsight.fullname" . }}-control-plane)
+  export NODE_IP=$(kubectl get nodes --namespace {{ .Release.Namespace }} -o jsonpath="{.items[0].status.addresses[0].address}")
+  echo "Control Plane URL: http://$NODE_IP:$NODE_PORT"
+{{- else if contains "LoadBalancer" .Values.controlPlane.service.type }}
+  NOTE: It may take a few minutes for the LoadBalancer IP to be available.
+  You can watch the status by running 'kubectl get --namespace {{ .Release.Namespace }} svc -w {{ include "hindsight.fullname" . }}-control-plane'
+  export SERVICE_IP=$(kubectl get svc --namespace {{ .Release.Namespace }} {{ include "hindsight.fullname" . }}-control-plane --template "{{"{{ range (index .status.loadBalancer.ingress 0) }}{{.}}{{ end }}"}}")
+  echo "Control Plane URL: http://$SERVICE_IP:{{ .Values.controlPlane.service.port }}"
+{{- else if contains "ClusterIP" .Values.controlPlane.service.type }}
+  export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "app.kubernetes.io/component=control-plane,app.kubernetes.io/instance={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}")
+  export CONTAINER_PORT=$(kubectl get pod --namespace {{ .Release.Namespace }} $POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}")
+  echo "Control Plane URL: http://127.0.0.1:3000"
+  kubectl --namespace {{ .Release.Namespace }} port-forward $POD_NAME 3000:$CONTAINER_PORT
+{{- end }}
+
+2. Get the API URL by running these commands:
+{{- if contains "NodePort" .Values.api.service.type }}
+  export NODE_PORT=$(kubectl get --namespace {{ .Release.Namespace }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ include "hindsight.fullname" . }}-api)
+  export NODE_IP=$(kubectl get nodes --namespace {{ .Release.Namespace }} -o jsonpath="{.items[0].status.addresses[0].address}")
+  echo "API URL: http://$NODE_IP:$NODE_PORT"
+{{- else if contains "LoadBalancer" .Values.api.service.type }}
+  NOTE: It may take a few minutes for the LoadBalancer IP to be available.
+  You can watch the status by running 'kubectl get --namespace {{ .Release.Namespace }} svc -w {{ include "hindsight.fullname" . }}-api'
+  export SERVICE_IP=$(kubectl get svc --namespace {{ .Release.Namespace }} {{ include "hindsight.fullname" . }}-api --template "{{"{{ range (index .status.loadBalancer.ingress 0) }}{{.}}{{ end }}"}}")
+  echo "API URL: http://$SERVICE_IP:{{ .Values.api.service.port }}"
+{{- else if contains "ClusterIP" .Values.api.service.type }}
+  export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "app.kubernetes.io/component=api,app.kubernetes.io/instance={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}")
+  export CONTAINER_PORT=$(kubectl get pod --namespace {{ .Release.Namespace }} $POD_NAME -o jsonpath="{.spec.containers[0].ports[0].containerPort}")
+  echo "API URL: http://127.0.0.1:8888"
+  kubectl --namespace {{ .Release.Namespace }} port-forward $POD_NAME 8888:$CONTAINER_PORT
+{{- end }}
+
+{{- end }}
+
+{{- if not .Values.postgresql.enabled }}
+
+NOTE: You are using an external PostgreSQL database.
+Please ensure that:
+  1. The database is accessible from the cluster
+  2. The pgvector extension is enabled
+
+Database migrations run automatically when the API service starts.
+
+If you want to pre-initialize the database before deploying (optional):
+  kubectl run --namespace {{ .Release.Namespace }} hindsight-init --rm -it --restart=Never \
+    --image={{ .Values.api.image.repository }}:{{ .Values.api.image.tag }} \
+    --env="DATABASE_URL={{ include "hindsight.databaseUrl" . }}" \
+    -- python -c "from hindsight.migrations import run_migrations; run_migrations()"
+{{- end }}
+
+For more information, visit: https://github.com/yourusername/hindsight
@@ -98,7 +98,7 @@ Generate database URL
 {{- if .Values.databaseUrl }}
 {{- .Values.databaseUrl }}
 {{- else if .Values.postgresql.enabled }}
-{{- printf "postgresql://%s:%s@%s-postgresql:%d/%s" .Values.postgresql.auth.username .Values.postgresql.auth.password (include "hindsight.fullname" .) (.Values.postgresql.service.port | int) .Values.postgresql.auth.database }}
+{{- printf "postgresql://%s:%s@%s-postgresql:%d/%s" .Values.postgresql.auth.username .Values.postgresql.auth.password (include "hindsight.fullname" .) (.Values.postgresql.primary.service.port | int) .Values.postgresql.auth.database }}
 {{- else }}
 {{- printf "postgresql://%s:$(POSTGRES_PASSWORD)@%s:%d/%s" .Values.postgresql.external.username .Values.postgresql.external.host (.Values.postgresql.external.port | int) .Values.postgresql.external.database }}
 {{- end }}
@@ -15,6 +15,7 @@ spec:
  template:
    metadata:
      annotations:
+        checksum/config: {{ include (print $.Template.BasePath "/configmap.yaml") . | sha256sum }}
        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
        {{- with .Values.podAnnotations }}
        {{- toYaml . | nindent 8 }}
@@ -31,7 +32,7 @@ spec:
      - name: api
        securityContext:
          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.api.image.repository }}:{{ .Values.api.image.tag | default .Values.version }}"
+        image: "{{ .Values.api.image.repository }}:{{ .Values.api.image.tag }}"
        imagePullPolicy: {{ .Values.api.image.pullPolicy }}
        ports:
        - name: http
@@ -47,16 +48,29 @@ spec:
              name: {{ include "hindsight.fullname" . }}-secret
              key: postgres-password
        {{- end }}
-        {{- range $key, $value := .Values.api.env }}
-        - name: {{ $key }}
-          value: {{ $value | quote }}
-        {{- end }}
-        {{- range $key, $value := .Values.api.secrets }}
-        - name: {{ $key }}
+        - name: HINDSIGHT_API_LLM_PROVIDER
+          valueFrom:
+            configMapKeyRef:
+              name: {{ include "hindsight.fullname" . }}-config
+              key: llm-provider
+        - name: HINDSIGHT_API_LLM_MODEL
+          valueFrom:
+            configMapKeyRef:
+              name: {{ include "hindsight.fullname" . }}-config
+              key: llm-model
+        {{- if and .Values.api.secrets (hasKey .Values.api.secrets "HINDSIGHT_API_LLM_API_KEY") }}
+        - name: HINDSIGHT_API_LLM_API_KEY
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.fullname" $ }}-secret
-              key: {{ $key }}
+              name: {{ include "hindsight.fullname" . }}-secret
+              key: llm-api-key
+        {{- end }}
+        {{- if and .Values.api.secrets (hasKey .Values.api.secrets "HINDSIGHT_API_LLM_BASE_URL") }}
+        - name: HINDSIGHT_API_LLM_BASE_URL
+          valueFrom:
+            secretKeyRef:
+              name: {{ include "hindsight.fullname" . }}-secret
+              key: llm-base-url
        {{- end }}
        livenessProbe:
          {{- toYaml .Values.api.livenessProbe | nindent 10 }}
@@ -0,0 +1,15 @@
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: {{ include "hindsight.fullname" . }}-config
+  labels:
+    {{- include "hindsight.labels" . | nindent 4 }}
+data:
+  # API configuration
+  llm-provider: {{ .Values.api.env.HINDSIGHT_API_LLM_PROVIDER | quote }}
+  llm-model: {{ .Values.api.env.HINDSIGHT_API_LLM_MODEL | quote }}
+
+  # Control plane configuration
+  node-env: {{ .Values.controlPlane.env.NODE_ENV | quote }}
+  hostname: {{ .Values.controlPlane.env.HINDSIGHT_CP_HOSTNAME | quote }}
+  control-plane-port: {{ .Values.controlPlane.env.HINDSIGHT_CP_PORT | quote }}
@@ -15,7 +15,7 @@ spec:
  template:
    metadata:
      annotations:
-        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
+        checksum/config: {{ include (print $.Template.BasePath "/configmap.yaml") . | sha256sum }}
        {{- with .Values.podAnnotations }}
        {{- toYaml . | nindent 8 }}
        {{- end }}
@@ -31,26 +31,30 @@ spec:
      - name: control-plane
        securityContext:
          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.controlPlane.image.repository }}:{{ .Values.controlPlane.image.tag | default .Values.version }}"
+        image: "{{ .Values.controlPlane.image.repository }}:{{ .Values.controlPlane.image.tag }}"
        imagePullPolicy: {{ .Values.controlPlane.image.pullPolicy }}
        ports:
        - name: http
          containerPort: {{ .Values.controlPlane.service.targetPort }}
          protocol: TCP
        env:
+        - name: NODE_ENV
+          valueFrom:
+            configMapKeyRef:
+              name: {{ include "hindsight.fullname" . }}-config
+              key: node-env
+        - name: HINDSIGHT_CP_HOSTNAME
+          valueFrom:
+            configMapKeyRef:
+              name: {{ include "hindsight.fullname" . }}-config
+              key: hostname
+        - name: HINDSIGHT_CP_PORT
+          valueFrom:
+            configMapKeyRef:
+              name: {{ include "hindsight.fullname" . }}-config
+              key: control-plane-port
        - name: HINDSIGHT_CP_DATAPLANE_API_URL
          value: {{ include "hindsight.apiUrl" . | quote }}
-        {{- range $key, $value := .Values.controlPlane.env }}
-        - name: {{ $key }}
-          value: {{ $value | quote }}
-        {{- end }}
-        {{- range $key, $value := .Values.controlPlane.secrets }}
-        - name: {{ $key }}
-          valueFrom:
-            secretKeyRef:
-              name: {{ include "hindsight.fullname" $ }}-secret
-              key: {{ $key }}
-        {{- end }}
        livenessProbe:
          {{- toYaml .Values.controlPlane.livenessProbe | nindent 10 }}
        readinessProbe:
@@ -1,19 +0,0 @@
-{{- if .Values.postgresql.enabled }}
-apiVersion: v1
-kind: Service
-metadata:
-  name: {{ include "hindsight.fullname" . }}-postgresql
-  labels:
-    {{- include "hindsight.labels" . | nindent 4 }}
-    app.kubernetes.io/component: postgresql
-spec:
-  type: ClusterIP
-  ports:
-  - port: {{ .Values.postgresql.service.port }}
-    targetPort: postgresql
-    protocol: TCP
-    name: postgresql
-  selector:
-    {{- include "hindsight.selectorLabels" . | nindent 4 }}
-    app.kubernetes.io/component: postgresql
-{{- end }}
@@ -1,85 +0,0 @@
-{{- if .Values.postgresql.enabled }}
-apiVersion: apps/v1
-kind: StatefulSet
-metadata:
-  name: {{ include "hindsight.fullname" . }}-postgresql
-  labels:
-    {{- include "hindsight.labels" . | nindent 4 }}
-    app.kubernetes.io/component: postgresql
-spec:
-  serviceName: {{ include "hindsight.fullname" . }}-postgresql
-  replicas: 1
-  selector:
-    matchLabels:
-      {{- include "hindsight.selectorLabels" . | nindent 6 }}
-      app.kubernetes.io/component: postgresql
-  template:
-    metadata:
-      labels:
-        {{- include "hindsight.selectorLabels" . | nindent 8 }}
-        app.kubernetes.io/component: postgresql
-    spec:
-      containers:
-      - name: postgresql
-        image: "{{ .Values.postgresql.image.repository }}:{{ .Values.postgresql.image.tag }}"
-        imagePullPolicy: {{ .Values.postgresql.image.pullPolicy }}
-        ports:
-        - name: postgresql
-          containerPort: 5432
-          protocol: TCP
-        env:
-        - name: POSTGRES_USER
-          value: {{ .Values.postgresql.auth.username | quote }}
-        - name: POSTGRES_PASSWORD
-          value: {{ .Values.postgresql.auth.password | quote }}
-        - name: POSTGRES_DB
-          value: {{ .Values.postgresql.auth.database | quote }}
-        - name: PGDATA
-          value: /var/lib/postgresql/data/pgdata
-        livenessProbe:
-          exec:
-            command:
-            - pg_isready
-            - -U
-            - {{ .Values.postgresql.auth.username }}
-            - -d
-            - {{ .Values.postgresql.auth.database }}
-          initialDelaySeconds: 30
-          periodSeconds: 10
-          timeoutSeconds: 5
-          failureThreshold: 3
-        readinessProbe:
-          exec:
-            command:
-            - pg_isready
-            - -U
-            - {{ .Values.postgresql.auth.username }}
-            - -d
-            - {{ .Values.postgresql.auth.database }}
-          initialDelaySeconds: 5
-          periodSeconds: 5
-          timeoutSeconds: 3
-          failureThreshold: 3
-        resources:
-          {{- toYaml .Values.postgresql.resources | nindent 10 }}
-        volumeMounts:
-        - name: data
-          mountPath: /var/lib/postgresql/data
-  {{- if .Values.postgresql.persistence.enabled }}
-  volumeClaimTemplates:
-  - metadata:
-      name: data
-    spec:
-      accessModes: ["ReadWriteOnce"]
-      {{- if .Values.postgresql.persistence.storageClass }}
-      storageClassName: {{ .Values.postgresql.persistence.storageClass | quote }}
-      {{- end }}
-      resources:
-        requests:
-          storage: {{ .Values.postgresql.persistence.size }}
-  {{- else }}
-      volumes:
-      - name: data
-        emptyDir: {}
-  {{- end }}
-{{- end }}
@@ -6,12 +6,14 @@ metadata:
    {{- include "hindsight.labels" . | nindent 4 }}
 type: Opaque
 data:
-  {{- range $key, $value := .Values.api.secrets }}
-  {{ $key }}: {{ $value | b64enc | quote }}
+  {{- if and .Values.api.secrets (hasKey .Values.api.secrets "MEMORY_LLM_API_KEY") }}
+  llm-api-key: {{ .Values.api.secrets.MEMORY_LLM_API_KEY | b64enc | quote }}
  {{- end }}
-  {{- range $key, $value := .Values.controlPlane.secrets }}
-  {{ $key }}: {{ $value | b64enc | quote }}
+  {{- if and .Values.api.secrets (hasKey .Values.api.secrets "MEMORY_LLM_BASE_URL") }}
+  llm-base-url: {{ .Values.api.secrets.MEMORY_LLM_BASE_URL | b64enc | quote }}
  {{- end }}
-  {{- if and (not .Values.postgresql.enabled) .Values.postgresql.external.password }}
+  {{- if not .Values.postgresql.enabled }}
+  {{- if .Values.postgresql.external.password }}
  postgres-password: {{ .Values.postgresql.external.password | b64enc | quote }}
  {{- end }}
+  {{- end }}
@@ -1,8 +1,5 @@
 # Default values for hindsight

-# Chart version - use this to set a consistent image tag across all components
-version: "0.1.1"
-
 # Global settings
 replicaCount: 1

@@ -11,9 +8,9 @@ api:
  enabled: true
  replicaCount: 1
  image:
-    repository: ghcr.io/vectorize-io/hindsight-api
+    repository: hindsight/api
    pullPolicy: IfNotPresent
-    # tag defaults to .Values.version if not specified
+    tag: "latest"

  service:
    type: ClusterIP
@@ -32,7 +29,7 @@ api:
  # Liveness and readiness probes
  livenessProbe:
    httpGet:
-      path: /health
+      path: /
      port: 8888
    initialDelaySeconds: 30
    periodSeconds: 10
@@ -41,7 +38,7 @@ api:

  readinessProbe:
    httpGet:
-      path: /health
+      path: /
      port: 8888
    initialDelaySeconds: 10
    periodSeconds: 5
@@ -50,7 +47,7 @@ api:

  # Environment variables
  env:
-    #HINDSIGHT_API_LLM_PROVIDER: "groq"
+    HINDSIGHT_API_LLM_PROVIDER: "groq"
    HINDSIGHT_API_LLM_MODEL: "openai/gpt-oss-120b"

  # Secret environment variables
@@ -63,9 +60,9 @@ controlPlane:
  enabled: true
  replicaCount: 1
  image:
-    repository: ghcr.io/vectorize-io/hindsight-control-plane
+    repository: hindsight/hindsight-control-plane
    pullPolicy: IfNotPresent
-    # tag defaults to .Values.version if not specified
+    tag: "latest"

  service:
    type: ClusterIP
@@ -81,9 +78,10 @@ controlPlane:
      cpu: 250m
      memory: 512Mi

-  # Liveness and readiness probes (TCP check)
+  # Liveness and readiness probes
  livenessProbe:
-    tcpSocket:
+    httpGet:
+      path: /
      port: 3000
    initialDelaySeconds: 30
    periodSeconds: 10
@@ -91,7 +89,8 @@ controlPlane:
    failureThreshold: 3

  readinessProbe:
-    tcpSocket:
+    httpGet:
+      path: /
      port: 3000
    initialDelaySeconds: 10
    periodSeconds: 5
@@ -107,43 +106,21 @@ controlPlane:
 # PostgreSQL configuration
 postgresql:
  # Set to true to deploy PostgreSQL as part of this chart
-  enabled: true
-
-  image:
-    repository: ankane/pgvector
-    tag: latest
-    pullPolicy: IfNotPresent
-
-  auth:
-    username: "hindsight"
-    password: "hindsight"
-    database: "hindsight"
-
-  service:
-    port: 5432
-
-  persistence:
-    enabled: true
-    size: 8Gi
-    # storageClass: ""
-
-  resources:
-    limits:
-      cpu: 1000m
-      memory: 1Gi
-    requests:
-      cpu: 250m
-      memory: 256Mi
+  enabled: false

  # External PostgreSQL connection details
-  # Only used if postgresql.enabled is false
+  # If postgresql.enabled is false, provide external database details
  external:
    host: "postgresql"
    port: 5432
    database: "hindsight"
    username: "hindsight"
+    # Password should be provided via secret
    # password: ""

+# Database URL (auto-generated from postgresql config if not provided)
+# databaseUrl: "postgresql://user:pass@host:5432/database"
+
 # Ingress configuration
 ingress:
  enabled: false
@@ -1,137 +1 @@
-# Hindsight API
-
-**Memory System for AI Agents** — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.
-
-Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.
-
-## Installation
-
-```bash
-pip install hindsight-api
-```
-
-## Quick Start
-
-### Run the Server
-
-```bash
-# Set your LLM provider
-export HINDSIGHT_API_LLM_PROVIDER=openai
-export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
-
-# Start the server (uses embedded PostgreSQL by default)
-hindsight-api
-```
-
-The server starts at http://localhost:8888 with:
- REST API for memory operations
- MCP server at `/mcp` for tool-use integration
-
-### Use the Python API
-
-```python
-from hindsight_api import MemoryEngine
-
-# Create and initialize the memory engine
-memory = MemoryEngine()
-await memory.initialize()
-
-# Create a memory bank for your agent
-bank = await memory.create_memory_bank(
-    name="my-assistant",
-    background="A helpful coding assistant"
-)
-
-# Store a memory
-await memory.retain(
-    memory_bank_id=bank.id,
-    content="The user prefers Python for data science projects"
-)
-
-# Recall memories
-results = await memory.recall(
-    memory_bank_id=bank.id,
-    query="What programming language does the user prefer?"
-)
-
-# Reflect with reasoning
-response = await memory.reflect(
-    memory_bank_id=bank.id,
-    query="Should I recommend Python or R for this ML project?"
-)
-```
-
-## CLI Options
-
-```bash
-hindsight-api --help
-
-# Common options
-hindsight-api --port 9000          # Custom port (default: 8888)
-hindsight-api --host 127.0.0.1     # Bind to localhost only
-hindsight-api --workers 4          # Multiple worker processes
-hindsight-api --log-level debug    # Verbose logging
-```
-
-## Configuration
-
-Configure via environment variables:
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `HINDSIGHT_API_DATABASE_URL` | PostgreSQL connection string | `pg0` (embedded) |
-| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `groq`, `gemini`, `ollama` | `openai` |
-| `HINDSIGHT_API_LLM_API_KEY` | API key for LLM provider | - |
-| `HINDSIGHT_API_LLM_MODEL` | Model name | `gpt-4o-mini` |
-| `HINDSIGHT_API_HOST` | Server bind address | `0.0.0.0` |
-| `HINDSIGHT_API_PORT` | Server port | `8888` |
-
-### Example with External PostgreSQL
-
-```bash
-export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
-export HINDSIGHT_API_LLM_PROVIDER=groq
-export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
-
-hindsight-api
-```
-
-## Docker
-
-```bash
-docker run --rm -it -p 8888:8888 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## MCP Server
-
-For local MCP integration without running the full API server:
-
-```bash
-hindsight-local-mcp
-```
-
-This runs a stdio-based MCP server that can be used directly with MCP-compatible clients.
-
-## Key Features
-
- **Multi-Strategy Retrieval (TEMPR)** — Semantic, keyword, graph, and temporal search combined with RRF fusion
- **Entity Graph** — Automatic entity extraction and relationship tracking
- **Temporal Reasoning** — Native support for time-based queries
- **Disposition Traits** — Configurable skepticism, literalism, and empathy influence opinion formation
- **Three Memory Types** — World facts, bank actions, and formed opinions with confidence scores
-
-## Documentation
-
-Full documentation: [https://hindsight.vectorize.io](https://hindsight.vectorize.io)
-
- [Installation Guide](https://hindsight.vectorize.io/developer/installation)
- [Configuration Reference](https://hindsight.vectorize.io/developer/configuration)
- [API Reference](https://hindsight.vectorize.io/api-reference)
- [Python SDK](https://hindsight.vectorize.io/sdks/python)
-
-## License
-
-Apache 2.0
+# Memory 
@@ -3,29 +3,26 @@ Memory System for AI Agents.

 Temporal + Semantic Memory Architecture using PostgreSQL with pgvector.
 """
-
-from .config import HindsightConfig, get_config
-from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
-from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
-from .engine.llm_wrapper import LLMConfig
 from .engine.memory_engine import MemoryEngine
 from .engine.search.trace import (
-    EntryPoint,
-    LinkInfo,
-    NodeVisit,
-    PruningDecision,
-    QueryInfo,
-    SearchPhaseMetrics,
-    SearchSummary,
    SearchTrace,
+    QueryInfo,
+    EntryPoint,
+    NodeVisit,
    WeightComponents,
+    LinkInfo,
+    PruningDecision,
+    SearchSummary,
+    SearchPhaseMetrics,
 )
 from .engine.search.tracer import SearchTracer
-from .models import RequestContext
+from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
+from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
+from .engine.llm_wrapper import LLMConfig
+from .config import HindsightConfig, get_config

 __all__ = [
    "MemoryEngine",
-    "RequestContext",
    "HindsightConfig",
    "get_config",
    "SearchTrace",
@@ -2,19 +2,20 @@
 Alembic environment configuration for SQLAlchemy with pgvector.
 Uses synchronous psycopg2 driver for migrations to avoid pgbouncer issues.
 """
-
 import logging
 import os
+import sys
 from pathlib import Path

+from sqlalchemy import pool, engine_from_config
+from sqlalchemy.engine import Connection
+
 from alembic import context
 from dotenv import load_dotenv
-from sqlalchemy import engine_from_config, pool

 # Import your models here
 from hindsight_api.models import Base

-
 # Load environment variables based on HINDSIGHT_API_DATABASE_URL env var or default to local
 def load_env():
    """Load environment variables from .env"""
@@ -29,7 +30,6 @@ def load_env():
    if env_file.exists():
        load_dotenv(env_file)

-
 load_env()

 # this is the Alembic Config object, which provides
@@ -109,9 +109,6 @@ def run_migrations_online() -> None:

    get_database_url()  # Process and set the database URL in config

-    # Check if we're targeting a specific schema (for multi-tenant isolation)
-    target_schema = config.get_main_option("target_schema")
-
    connectable = engine_from_config(
        config.get_section(config.config_ini_section, {}),
        prefix="sqlalchemy.",
@@ -124,34 +121,17 @@ def run_migrations_online() -> None:
    def set_read_write_mode(dbapi_connection, connection_record):
        cursor = dbapi_connection.cursor()
        cursor.execute("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE")
-        # If targeting a specific schema, set search_path
-        # Include public in search_path for access to shared extensions (pgvector)
-        if target_schema:
-            cursor.execute(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"')
-            cursor.execute(f'SET search_path TO "{target_schema}", public')
        cursor.close()

    with connectable.connect() as connection:
        # Also explicitly set read-write mode on this connection
        connection.execute(text("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE"))
-
-        # If targeting a specific schema, set search_path
-        # Include public in search_path for access to shared extensions (pgvector)
-        if target_schema:
-            connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"'))
-            connection.execute(text(f'SET search_path TO "{target_schema}", public'))
-
        connection.commit()  # Commit the SET command

-        # Configure context with version_table_schema if using a specific schema
-        context_opts = {
-            "connection": connection,
-            "target_metadata": target_metadata,
-        }
-        if target_schema:
-            context_opts["version_table_schema"] = target_schema
-
-        context.configure(**context_opts)
+        context.configure(
+            connection=connection,
+            target_metadata=target_metadata
+        )

        with context.begin_transaction():
            context.run_migrations()
@@ -5,150 +5,120 @@ Revises:
 Create Date: 2025-11-27 11:54:19.228030

 """
+from typing import Sequence, Union

-from collections.abc import Sequence
-
-import sqlalchemy as sa
 from alembic import op
-from pgvector.sqlalchemy import Vector
+import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
+from pgvector.sqlalchemy import Vector
+

 # revision identifiers, used by Alembic.
-revision: str = "5a366d414dce"
-down_revision: str | Sequence[str] | None = None
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
+revision: str = '5a366d414dce'
+down_revision: Union[str, Sequence[str], None] = None
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    """Upgrade schema - create all tables from scratch."""

    # Enable required extensions
-    op.execute("CREATE EXTENSION IF NOT EXISTS vector")
+    op.execute('CREATE EXTENSION IF NOT EXISTS vector')

    # Create banks table
    op.create_table(
-        "banks",
-        sa.Column("bank_id", sa.Text(), nullable=False),
-        sa.Column("name", sa.Text(), nullable=True),
-        sa.Column(
-            "personality",
-            postgresql.JSONB(astext_type=sa.Text()),
-            server_default=sa.text("'{}'::jsonb"),
-            nullable=False,
-        ),
-        sa.Column("background", sa.Text(), nullable=True),
-        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.PrimaryKeyConstraint("bank_id", name=op.f("pk_banks")),
+        'banks',
+        sa.Column('bank_id', sa.Text(), nullable=False),
+        sa.Column('name', sa.Text(), nullable=True),
+        sa.Column('personality', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
+        sa.Column('background', sa.Text(), nullable=True),
+        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.PrimaryKeyConstraint('bank_id', name=op.f('pk_banks'))
    )

    # Create documents table
    op.create_table(
-        "documents",
-        sa.Column("id", sa.Text(), nullable=False),
-        sa.Column("bank_id", sa.Text(), nullable=False),
-        sa.Column("original_text", sa.Text(), nullable=True),
-        sa.Column("content_hash", sa.Text(), nullable=True),
-        sa.Column(
-            "metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
-        ),
-        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.PrimaryKeyConstraint("id", "bank_id", name=op.f("pk_documents")),
+        'documents',
+        sa.Column('id', sa.Text(), nullable=False),
+        sa.Column('bank_id', sa.Text(), nullable=False),
+        sa.Column('original_text', sa.Text(), nullable=True),
+        sa.Column('content_hash', sa.Text(), nullable=True),
+        sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
+        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.PrimaryKeyConstraint('id', 'bank_id', name=op.f('pk_documents'))
    )
-    op.create_index("idx_documents_bank_id", "documents", ["bank_id"])
-    op.create_index("idx_documents_content_hash", "documents", ["content_hash"])
+    op.create_index('idx_documents_bank_id', 'documents', ['bank_id'])
+    op.create_index('idx_documents_content_hash', 'documents', ['content_hash'])

    # Create async_operations table
    op.create_table(
-        "async_operations",
-        sa.Column(
-            "operation_id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False
-        ),
-        sa.Column("bank_id", sa.Text(), nullable=False),
-        sa.Column("operation_type", sa.Text(), nullable=False),
-        sa.Column("status", sa.Text(), server_default="pending", nullable=False),
-        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("completed_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column("error_message", sa.Text(), nullable=True),
-        sa.Column(
-            "result_metadata",
-            postgresql.JSONB(astext_type=sa.Text()),
-            server_default=sa.text("'{}'::jsonb"),
-            nullable=False,
-        ),
-        sa.PrimaryKeyConstraint("operation_id", name=op.f("pk_async_operations")),
-        sa.CheckConstraint(
-            "status IN ('pending', 'processing', 'completed', 'failed')", name="async_operations_status_check"
-        ),
+        'async_operations',
+        sa.Column('operation_id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
+        sa.Column('bank_id', sa.Text(), nullable=False),
+        sa.Column('operation_type', sa.Text(), nullable=False),
+        sa.Column('status', sa.Text(), server_default='pending', nullable=False),
+        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('completed_at', postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column('error_message', sa.Text(), nullable=True),
+        sa.Column('result_metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
+        sa.PrimaryKeyConstraint('operation_id', name=op.f('pk_async_operations')),
+        sa.CheckConstraint("status IN ('pending', 'processing', 'completed', 'failed')", name='async_operations_status_check')
    )
-    op.create_index("idx_async_operations_bank_id", "async_operations", ["bank_id"])
-    op.create_index("idx_async_operations_status", "async_operations", ["status"])
-    op.create_index("idx_async_operations_bank_status", "async_operations", ["bank_id", "status"])
+    op.create_index('idx_async_operations_bank_id', 'async_operations', ['bank_id'])
+    op.create_index('idx_async_operations_status', 'async_operations', ['status'])
+    op.create_index('idx_async_operations_bank_status', 'async_operations', ['bank_id', 'status'])

    # Create entities table
    op.create_table(
-        "entities",
-        sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
-        sa.Column("canonical_name", sa.Text(), nullable=False),
-        sa.Column("bank_id", sa.Text(), nullable=False),
-        sa.Column(
-            "metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
-        ),
-        sa.Column("first_seen", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("last_seen", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("mention_count", sa.Integer(), server_default="1", nullable=False),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_entities")),
+        'entities',
+        sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
+        sa.Column('canonical_name', sa.Text(), nullable=False),
+        sa.Column('bank_id', sa.Text(), nullable=False),
+        sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
+        sa.Column('first_seen', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('last_seen', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('mention_count', sa.Integer(), server_default='1', nullable=False),
+        sa.PrimaryKeyConstraint('id', name=op.f('pk_entities'))
    )
-    op.create_index("idx_entities_bank_id", "entities", ["bank_id"])
-    op.create_index("idx_entities_canonical_name", "entities", ["canonical_name"])
-    op.create_index("idx_entities_bank_name", "entities", ["bank_id", "canonical_name"])
+    op.create_index('idx_entities_bank_id', 'entities', ['bank_id'])
+    op.create_index('idx_entities_canonical_name', 'entities', ['canonical_name'])
+    op.create_index('idx_entities_bank_name', 'entities', ['bank_id', 'canonical_name'])
    # Create unique index on (bank_id, LOWER(canonical_name)) for entity resolution
-    op.execute("CREATE UNIQUE INDEX idx_entities_bank_lower_name ON entities (bank_id, LOWER(canonical_name))")
+    op.execute('CREATE UNIQUE INDEX idx_entities_bank_lower_name ON entities (bank_id, LOWER(canonical_name))')

    # Create memory_units table
    op.create_table(
-        "memory_units",
-        sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
-        sa.Column("bank_id", sa.Text(), nullable=False),
-        sa.Column("document_id", sa.Text(), nullable=True),
-        sa.Column("text", sa.Text(), nullable=False),
-        sa.Column("embedding", Vector(384), nullable=True),
-        sa.Column("context", sa.Text(), nullable=True),
-        sa.Column("event_date", postgresql.TIMESTAMP(timezone=True), nullable=False),
-        sa.Column("occurred_start", postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column("occurred_end", postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column("mentioned_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column("fact_type", sa.Text(), server_default="world", nullable=False),
-        sa.Column("confidence_score", sa.Float(), nullable=True),
-        sa.Column("access_count", sa.Integer(), server_default="0", nullable=False),
-        sa.Column(
-            "metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
-        ),
-        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["document_id", "bank_id"],
-            ["documents.id", "documents.bank_id"],
-            name="memory_units_document_fkey",
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("id", name=op.f("pk_memory_units")),
-        sa.CheckConstraint(
-            "fact_type IN ('world', 'bank', 'opinion', 'observation')", name="memory_units_fact_type_check"
-        ),
-        sa.CheckConstraint(
-            "confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)",
-            name="memory_units_confidence_range_check",
-        ),
+        'memory_units',
+        sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
+        sa.Column('bank_id', sa.Text(), nullable=False),
+        sa.Column('document_id', sa.Text(), nullable=True),
+        sa.Column('text', sa.Text(), nullable=False),
+        sa.Column('embedding', Vector(384), nullable=True),
+        sa.Column('context', sa.Text(), nullable=True),
+        sa.Column('event_date', postgresql.TIMESTAMP(timezone=True), nullable=False),
+        sa.Column('occurred_start', postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column('occurred_end', postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column('mentioned_at', postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column('fact_type', sa.Text(), server_default='world', nullable=False),
+        sa.Column('confidence_score', sa.Float(), nullable=True),
+        sa.Column('access_count', sa.Integer(), server_default='0', nullable=False),
+        sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
+        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.ForeignKeyConstraint(['document_id', 'bank_id'], ['documents.id', 'documents.bank_id'], name='memory_units_document_fkey', ondelete='CASCADE'),
+        sa.PrimaryKeyConstraint('id', name=op.f('pk_memory_units')),
+        sa.CheckConstraint("fact_type IN ('world', 'bank', 'opinion', 'observation')", name='memory_units_fact_type_check'),
+        sa.CheckConstraint("confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)", name='memory_units_confidence_range_check'),
        sa.CheckConstraint(
            "(fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
            "(fact_type = 'observation') OR "
            "(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL)",
-            name="confidence_score_fact_type_check",
-        ),
+            name='confidence_score_fact_type_check'
+        )
    )

    # Add search_vector column for full-text search
@@ -158,41 +128,18 @@ def upgrade() -> None:
        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
    """)

-    op.create_index("idx_memory_units_bank_id", "memory_units", ["bank_id"])
-    op.create_index("idx_memory_units_document_id", "memory_units", ["document_id"])
-    op.create_index("idx_memory_units_event_date", "memory_units", [sa.text("event_date DESC")])
-    op.create_index("idx_memory_units_bank_date", "memory_units", ["bank_id", sa.text("event_date DESC")])
-    op.create_index("idx_memory_units_access_count", "memory_units", [sa.text("access_count DESC")])
-    op.create_index("idx_memory_units_fact_type", "memory_units", ["fact_type"])
-    op.create_index("idx_memory_units_bank_fact_type", "memory_units", ["bank_id", "fact_type"])
-    op.create_index(
-        "idx_memory_units_bank_type_date", "memory_units", ["bank_id", "fact_type", sa.text("event_date DESC")]
-    )
-    op.create_index(
-        "idx_memory_units_opinion_confidence",
-        "memory_units",
-        ["bank_id", sa.text("confidence_score DESC")],
-        postgresql_where=sa.text("fact_type = 'opinion'"),
-    )
-    op.create_index(
-        "idx_memory_units_opinion_date",
-        "memory_units",
-        ["bank_id", sa.text("event_date DESC")],
-        postgresql_where=sa.text("fact_type = 'opinion'"),
-    )
-    op.create_index(
-        "idx_memory_units_observation_date",
-        "memory_units",
-        ["bank_id", sa.text("event_date DESC")],
-        postgresql_where=sa.text("fact_type = 'observation'"),
-    )
-    op.create_index(
-        "idx_memory_units_embedding",
-        "memory_units",
-        ["embedding"],
-        postgresql_using="hnsw",
-        postgresql_ops={"embedding": "vector_cosine_ops"},
-    )
+    op.create_index('idx_memory_units_bank_id', 'memory_units', ['bank_id'])
+    op.create_index('idx_memory_units_document_id', 'memory_units', ['document_id'])
+    op.create_index('idx_memory_units_event_date', 'memory_units', [sa.text('event_date DESC')])
+    op.create_index('idx_memory_units_bank_date', 'memory_units', ['bank_id', sa.text('event_date DESC')])
+    op.create_index('idx_memory_units_access_count', 'memory_units', [sa.text('access_count DESC')])
+    op.create_index('idx_memory_units_fact_type', 'memory_units', ['fact_type'])
+    op.create_index('idx_memory_units_bank_fact_type', 'memory_units', ['bank_id', 'fact_type'])
+    op.create_index('idx_memory_units_bank_type_date', 'memory_units', ['bank_id', 'fact_type', sa.text('event_date DESC')])
+    op.create_index('idx_memory_units_opinion_confidence', 'memory_units', ['bank_id', sa.text('confidence_score DESC')], postgresql_where=sa.text("fact_type = 'opinion'"))
+    op.create_index('idx_memory_units_opinion_date', 'memory_units', ['bank_id', sa.text('event_date DESC')], postgresql_where=sa.text("fact_type = 'opinion'"))
+    op.create_index('idx_memory_units_observation_date', 'memory_units', ['bank_id', sa.text('event_date DESC')], postgresql_where=sa.text("fact_type = 'observation'"))
+    op.create_index('idx_memory_units_embedding', 'memory_units', ['embedding'], postgresql_using='hnsw', postgresql_ops={'embedding': 'vector_cosine_ops'})

    # Create BM25 full-text search index on search_vector
    op.execute("""
@@ -211,149 +158,116 @@ def upgrade() -> None:
        FROM memory_units
    """)

-    op.create_index("idx_memory_units_bm25_bank", "memory_units_bm25", ["bank_id"])
-    op.create_index("idx_memory_units_bm25_text_vector", "memory_units_bm25", ["text_vector"], postgresql_using="gin")
+    op.create_index('idx_memory_units_bm25_bank', 'memory_units_bm25', ['bank_id'])
+    op.create_index('idx_memory_units_bm25_text_vector', 'memory_units_bm25', ['text_vector'], postgresql_using='gin')

    # Create entity_cooccurrences table
    op.create_table(
-        "entity_cooccurrences",
-        sa.Column("entity_id_1", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("entity_id_2", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("cooccurrence_count", sa.Integer(), server_default="1", nullable=False),
-        sa.Column(
-            "last_cooccurred", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False
-        ),
-        sa.ForeignKeyConstraint(
-            ["entity_id_1"],
-            ["entities.id"],
-            name=op.f("fk_entity_cooccurrences_entity_id_1_entities"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["entity_id_2"],
-            ["entities.id"],
-            name=op.f("fk_entity_cooccurrences_entity_id_2_entities"),
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("entity_id_1", "entity_id_2", name=op.f("pk_entity_cooccurrences")),
-        sa.CheckConstraint("entity_id_1 < entity_id_2", name="entity_cooccurrence_order_check"),
+        'entity_cooccurrences',
+        sa.Column('entity_id_1', postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column('entity_id_2', postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column('cooccurrence_count', sa.Integer(), server_default='1', nullable=False),
+        sa.Column('last_cooccurred', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.ForeignKeyConstraint(['entity_id_1'], ['entities.id'], name=op.f('fk_entity_cooccurrences_entity_id_1_entities'), ondelete='CASCADE'),
+        sa.ForeignKeyConstraint(['entity_id_2'], ['entities.id'], name=op.f('fk_entity_cooccurrences_entity_id_2_entities'), ondelete='CASCADE'),
+        sa.PrimaryKeyConstraint('entity_id_1', 'entity_id_2', name=op.f('pk_entity_cooccurrences')),
+        sa.CheckConstraint('entity_id_1 < entity_id_2', name='entity_cooccurrence_order_check')
    )
-    op.create_index("idx_entity_cooccurrences_entity1", "entity_cooccurrences", ["entity_id_1"])
-    op.create_index("idx_entity_cooccurrences_entity2", "entity_cooccurrences", ["entity_id_2"])
-    op.create_index("idx_entity_cooccurrences_count", "entity_cooccurrences", [sa.text("cooccurrence_count DESC")])
+    op.create_index('idx_entity_cooccurrences_entity1', 'entity_cooccurrences', ['entity_id_1'])
+    op.create_index('idx_entity_cooccurrences_entity2', 'entity_cooccurrences', ['entity_id_2'])
+    op.create_index('idx_entity_cooccurrences_count', 'entity_cooccurrences', [sa.text('cooccurrence_count DESC')])

    # Create memory_links table
    op.create_table(
-        "memory_links",
-        sa.Column("from_unit_id", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("to_unit_id", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("link_type", sa.Text(), nullable=False),
-        sa.Column("entity_id", postgresql.UUID(as_uuid=True), nullable=True),
-        sa.Column("weight", sa.Float(), server_default="1.0", nullable=False),
-        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["entity_id"], ["entities.id"], name=op.f("fk_memory_links_entity_id_entities"), ondelete="CASCADE"
-        ),
-        sa.ForeignKeyConstraint(
-            ["from_unit_id"],
-            ["memory_units.id"],
-            name=op.f("fk_memory_links_from_unit_id_memory_units"),
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(
-            ["to_unit_id"],
-            ["memory_units.id"],
-            name=op.f("fk_memory_links_to_unit_id_memory_units"),
-            ondelete="CASCADE",
-        ),
-        sa.CheckConstraint(
-            "link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')",
-            name="memory_links_link_type_check",
-        ),
-        sa.CheckConstraint("weight >= 0.0 AND weight <= 1.0", name="memory_links_weight_check"),
+        'memory_links',
+        sa.Column('from_unit_id', postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column('to_unit_id', postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column('link_type', sa.Text(), nullable=False),
+        sa.Column('entity_id', postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column('weight', sa.Float(), server_default='1.0', nullable=False),
+        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.ForeignKeyConstraint(['entity_id'], ['entities.id'], name=op.f('fk_memory_links_entity_id_entities'), ondelete='CASCADE'),
+        sa.ForeignKeyConstraint(['from_unit_id'], ['memory_units.id'], name=op.f('fk_memory_links_from_unit_id_memory_units'), ondelete='CASCADE'),
+        sa.ForeignKeyConstraint(['to_unit_id'], ['memory_units.id'], name=op.f('fk_memory_links_to_unit_id_memory_units'), ondelete='CASCADE'),
+        sa.CheckConstraint("link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')", name='memory_links_link_type_check'),
+        sa.CheckConstraint('weight >= 0.0 AND weight <= 1.0', name='memory_links_weight_check')
    )
    # Create unique constraint using COALESCE for nullable entity_id
-    op.execute(
-        "CREATE UNIQUE INDEX idx_memory_links_unique ON memory_links (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid))"
-    )
-    op.create_index("idx_memory_links_from_unit", "memory_links", ["from_unit_id"])
-    op.create_index("idx_memory_links_to_unit", "memory_links", ["to_unit_id"])
-    op.create_index("idx_memory_links_entity", "memory_links", ["entity_id"])
-    op.create_index("idx_memory_links_link_type", "memory_links", ["link_type"])
+    op.execute("CREATE UNIQUE INDEX idx_memory_links_unique ON memory_links (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid))")
+    op.create_index('idx_memory_links_from_unit', 'memory_links', ['from_unit_id'])
+    op.create_index('idx_memory_links_to_unit', 'memory_links', ['to_unit_id'])
+    op.create_index('idx_memory_links_entity', 'memory_links', ['entity_id'])
+    op.create_index('idx_memory_links_link_type', 'memory_links', ['link_type'])

    # Create unit_entities table
    op.create_table(
-        "unit_entities",
-        sa.Column("unit_id", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("entity_id", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["entity_id"], ["entities.id"], name=op.f("fk_unit_entities_entity_id_entities"), ondelete="CASCADE"
-        ),
-        sa.ForeignKeyConstraint(
-            ["unit_id"], ["memory_units.id"], name=op.f("fk_unit_entities_unit_id_memory_units"), ondelete="CASCADE"
-        ),
-        sa.PrimaryKeyConstraint("unit_id", "entity_id", name=op.f("pk_unit_entities")),
+        'unit_entities',
+        sa.Column('unit_id', postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column('entity_id', postgresql.UUID(as_uuid=True), nullable=False),
+        sa.ForeignKeyConstraint(['entity_id'], ['entities.id'], name=op.f('fk_unit_entities_entity_id_entities'), ondelete='CASCADE'),
+        sa.ForeignKeyConstraint(['unit_id'], ['memory_units.id'], name=op.f('fk_unit_entities_unit_id_memory_units'), ondelete='CASCADE'),
+        sa.PrimaryKeyConstraint('unit_id', 'entity_id', name=op.f('pk_unit_entities'))
    )
-    op.create_index("idx_unit_entities_unit", "unit_entities", ["unit_id"])
-    op.create_index("idx_unit_entities_entity", "unit_entities", ["entity_id"])
+    op.create_index('idx_unit_entities_unit', 'unit_entities', ['unit_id'])
+    op.create_index('idx_unit_entities_entity', 'unit_entities', ['entity_id'])


 def downgrade() -> None:
    """Downgrade schema - drop all tables."""

    # Drop tables in reverse dependency order
-    op.drop_index("idx_unit_entities_entity", table_name="unit_entities")
-    op.drop_index("idx_unit_entities_unit", table_name="unit_entities")
-    op.drop_table("unit_entities")
+    op.drop_index('idx_unit_entities_entity', table_name='unit_entities')
+    op.drop_index('idx_unit_entities_unit', table_name='unit_entities')
+    op.drop_table('unit_entities')

-    op.drop_index("idx_memory_links_link_type", table_name="memory_links")
-    op.drop_index("idx_memory_links_entity", table_name="memory_links")
-    op.drop_index("idx_memory_links_to_unit", table_name="memory_links")
-    op.drop_index("idx_memory_links_from_unit", table_name="memory_links")
-    op.execute("DROP INDEX IF EXISTS idx_memory_links_unique")
-    op.drop_table("memory_links")
+    op.drop_index('idx_memory_links_link_type', table_name='memory_links')
+    op.drop_index('idx_memory_links_entity', table_name='memory_links')
+    op.drop_index('idx_memory_links_to_unit', table_name='memory_links')
+    op.drop_index('idx_memory_links_from_unit', table_name='memory_links')
+    op.execute('DROP INDEX IF EXISTS idx_memory_links_unique')
+    op.drop_table('memory_links')

-    op.drop_index("idx_entity_cooccurrences_count", table_name="entity_cooccurrences")
-    op.drop_index("idx_entity_cooccurrences_entity2", table_name="entity_cooccurrences")
-    op.drop_index("idx_entity_cooccurrences_entity1", table_name="entity_cooccurrences")
-    op.drop_table("entity_cooccurrences")
+    op.drop_index('idx_entity_cooccurrences_count', table_name='entity_cooccurrences')
+    op.drop_index('idx_entity_cooccurrences_entity2', table_name='entity_cooccurrences')
+    op.drop_index('idx_entity_cooccurrences_entity1', table_name='entity_cooccurrences')
+    op.drop_table('entity_cooccurrences')

    # Drop BM25 materialized view and index
-    op.drop_index("idx_memory_units_bm25_text_vector", table_name="memory_units_bm25")
-    op.drop_index("idx_memory_units_bm25_bank", table_name="memory_units_bm25")
-    op.execute("DROP MATERIALIZED VIEW IF EXISTS memory_units_bm25")
+    op.drop_index('idx_memory_units_bm25_text_vector', table_name='memory_units_bm25')
+    op.drop_index('idx_memory_units_bm25_bank', table_name='memory_units_bm25')
+    op.execute('DROP MATERIALIZED VIEW IF EXISTS memory_units_bm25')

-    op.drop_index("idx_memory_units_embedding", table_name="memory_units")
-    op.drop_index("idx_memory_units_observation_date", table_name="memory_units")
-    op.drop_index("idx_memory_units_opinion_date", table_name="memory_units")
-    op.drop_index("idx_memory_units_opinion_confidence", table_name="memory_units")
-    op.drop_index("idx_memory_units_bank_type_date", table_name="memory_units")
-    op.drop_index("idx_memory_units_bank_fact_type", table_name="memory_units")
-    op.drop_index("idx_memory_units_fact_type", table_name="memory_units")
-    op.drop_index("idx_memory_units_access_count", table_name="memory_units")
-    op.drop_index("idx_memory_units_bank_date", table_name="memory_units")
-    op.drop_index("idx_memory_units_event_date", table_name="memory_units")
-    op.drop_index("idx_memory_units_document_id", table_name="memory_units")
-    op.drop_index("idx_memory_units_bank_id", table_name="memory_units")
-    op.execute("DROP INDEX IF EXISTS idx_memory_units_text_search")
-    op.drop_table("memory_units")
+    op.drop_index('idx_memory_units_embedding', table_name='memory_units')
+    op.drop_index('idx_memory_units_observation_date', table_name='memory_units')
+    op.drop_index('idx_memory_units_opinion_date', table_name='memory_units')
+    op.drop_index('idx_memory_units_opinion_confidence', table_name='memory_units')
+    op.drop_index('idx_memory_units_bank_type_date', table_name='memory_units')
+    op.drop_index('idx_memory_units_bank_fact_type', table_name='memory_units')
+    op.drop_index('idx_memory_units_fact_type', table_name='memory_units')
+    op.drop_index('idx_memory_units_access_count', table_name='memory_units')
+    op.drop_index('idx_memory_units_bank_date', table_name='memory_units')
+    op.drop_index('idx_memory_units_event_date', table_name='memory_units')
+    op.drop_index('idx_memory_units_document_id', table_name='memory_units')
+    op.drop_index('idx_memory_units_bank_id', table_name='memory_units')
+    op.execute('DROP INDEX IF EXISTS idx_memory_units_text_search')
+    op.drop_table('memory_units')

-    op.execute("DROP INDEX IF EXISTS idx_entities_bank_lower_name")
-    op.drop_index("idx_entities_bank_name", table_name="entities")
-    op.drop_index("idx_entities_canonical_name", table_name="entities")
-    op.drop_index("idx_entities_bank_id", table_name="entities")
-    op.drop_table("entities")
+    op.execute('DROP INDEX IF EXISTS idx_entities_bank_lower_name')
+    op.drop_index('idx_entities_bank_name', table_name='entities')
+    op.drop_index('idx_entities_canonical_name', table_name='entities')
+    op.drop_index('idx_entities_bank_id', table_name='entities')
+    op.drop_table('entities')

-    op.drop_index("idx_async_operations_bank_status", table_name="async_operations")
-    op.drop_index("idx_async_operations_status", table_name="async_operations")
-    op.drop_index("idx_async_operations_bank_id", table_name="async_operations")
-    op.drop_table("async_operations")
+    op.drop_index('idx_async_operations_bank_status', table_name='async_operations')
+    op.drop_index('idx_async_operations_status', table_name='async_operations')
+    op.drop_index('idx_async_operations_bank_id', table_name='async_operations')
+    op.drop_table('async_operations')

-    op.drop_index("idx_documents_content_hash", table_name="documents")
-    op.drop_index("idx_documents_bank_id", table_name="documents")
-    op.drop_table("documents")
+    op.drop_index('idx_documents_content_hash', table_name='documents')
+    op.drop_index('idx_documents_bank_id', table_name='documents')
+    op.drop_table('documents')

-    op.drop_table("banks")
+    op.drop_table('banks')

    # Drop extensions (optional - comment out if you want to keep them)
    # op.execute('DROP EXTENSION IF EXISTS vector')
@@ -5,18 +5,18 @@ Revises: 5a366d414dce
 Create Date: 2025-11-28 00:00:00.000000

 """
+from typing import Sequence, Union

-from collections.abc import Sequence
-
-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql

+
 # revision identifiers, used by Alembic.
-revision: str = "b7c4d8e9f1a2"
-down_revision: str | Sequence[str] | None = "5a366d414dce"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
+revision: str = 'b7c4d8e9f1a2'
+down_revision: Union[str, Sequence[str], None] = '5a366d414dce'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
@@ -24,47 +24,47 @@ def upgrade() -> None:

    # Create chunks table with single text PK (bank_id_document_id_chunk_index)
    op.create_table(
-        "chunks",
-        sa.Column("chunk_id", sa.Text(), nullable=False),
-        sa.Column("document_id", sa.Text(), nullable=False),
-        sa.Column("bank_id", sa.Text(), nullable=False),
-        sa.Column("chunk_index", sa.Integer(), nullable=False),
-        sa.Column("chunk_text", sa.Text(), nullable=False),
-        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["document_id", "bank_id"],
-            ["documents.id", "documents.bank_id"],
-            name="chunks_document_fkey",
-            ondelete="CASCADE",
-        ),
-        sa.PrimaryKeyConstraint("chunk_id", name=op.f("pk_chunks")),
+        'chunks',
+        sa.Column('chunk_id', sa.Text(), nullable=False),
+        sa.Column('document_id', sa.Text(), nullable=False),
+        sa.Column('bank_id', sa.Text(), nullable=False),
+        sa.Column('chunk_index', sa.Integer(), nullable=False),
+        sa.Column('chunk_text', sa.Text(), nullable=False),
+        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.ForeignKeyConstraint(['document_id', 'bank_id'], ['documents.id', 'documents.bank_id'], name='chunks_document_fkey', ondelete='CASCADE'),
+        sa.PrimaryKeyConstraint('chunk_id', name=op.f('pk_chunks'))
    )

    # Add indexes for efficient queries
-    op.create_index("idx_chunks_document_id", "chunks", ["document_id"])
-    op.create_index("idx_chunks_bank_id", "chunks", ["bank_id"])
+    op.create_index('idx_chunks_document_id', 'chunks', ['document_id'])
+    op.create_index('idx_chunks_bank_id', 'chunks', ['bank_id'])

    # Add chunk_id column to memory_units (nullable, as existing records won't have chunks)
-    op.add_column("memory_units", sa.Column("chunk_id", sa.Text(), nullable=True))
+    op.add_column('memory_units', sa.Column('chunk_id', sa.Text(), nullable=True))

    # Add foreign key constraint to chunks table
    op.create_foreign_key(
-        "memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="SET NULL"
+        'memory_units_chunk_fkey',
+        'memory_units',
+        'chunks',
+        ['chunk_id'],
+        ['chunk_id'],
+        ondelete='SET NULL'
    )

    # Add index on chunk_id for efficient lookups
-    op.create_index("idx_memory_units_chunk_id", "memory_units", ["chunk_id"])
+    op.create_index('idx_memory_units_chunk_id', 'memory_units', ['chunk_id'])


 def downgrade() -> None:
    """Remove chunks table and chunk_id from memory_units."""

    # Drop index and foreign key from memory_units
-    op.drop_index("idx_memory_units_chunk_id", table_name="memory_units")
-    op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
-    op.drop_column("memory_units", "chunk_id")
+    op.drop_index('idx_memory_units_chunk_id', table_name='memory_units')
+    op.drop_constraint('memory_units_chunk_fkey', 'memory_units', type_='foreignkey')
+    op.drop_column('memory_units', 'chunk_id')

    # Drop chunks table indexes and table
-    op.drop_index("idx_chunks_bank_id", table_name="chunks")
-    op.drop_index("idx_chunks_document_id", table_name="chunks")
-    op.drop_table("chunks")
+    op.drop_index('idx_chunks_bank_id', table_name='chunks')
+    op.drop_index('idx_chunks_document_id', table_name='chunks')
+    op.drop_table('chunks')
@@ -5,35 +5,35 @@ Revises: b7c4d8e9f1a2
 Create Date: 2025-12-02 00:00:00.000000

 """
+from typing import Sequence, Union

-from collections.abc import Sequence
-
-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql

+
 # revision identifiers, used by Alembic.
-revision: str = "c8e5f2a3b4d1"
-down_revision: str | Sequence[str] | None = "b7c4d8e9f1a2"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
+revision: str = 'c8e5f2a3b4d1'
+down_revision: Union[str, Sequence[str], None] = 'b7c4d8e9f1a2'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    """Add retain_params JSONB column to documents table."""

    # Add retain_params column to store parameters passed during retain
-    op.add_column("documents", sa.Column("retain_params", postgresql.JSONB(), nullable=True))
+    op.add_column('documents', sa.Column('retain_params', postgresql.JSONB(), nullable=True))

    # Add index for efficient queries on retain_params
-    op.create_index("idx_documents_retain_params", "documents", ["retain_params"], postgresql_using="gin")
+    op.create_index('idx_documents_retain_params', 'documents', ['retain_params'], postgresql_using='gin')


 def downgrade() -> None:
    """Remove retain_params column from documents table."""

    # Drop index
-    op.drop_index("idx_documents_retain_params", table_name="documents")
+    op.drop_index('idx_documents_retain_params', table_name='documents')

    # Drop column
-    op.drop_column("documents", "retain_params")
+    op.drop_column('documents', 'retain_params')
@@ -5,49 +5,44 @@ Revises: c8e5f2a3b4d1
 Create Date: 2024-12-04 15:00:00.000000

 """
+from alembic import op
+import sqlalchemy as sa

-from alembic import context, op

 # revision identifiers, used by Alembic.
-revision = "d9f6a3b4c5e2"
-down_revision = "c8e5f2a3b4d1"
+revision = 'd9f6a3b4c5e2'
+down_revision = 'c8e5f2a3b4d1'
 branch_labels = None
 depends_on = None


-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
 def upgrade():
-    schema = _get_schema_prefix()
-
    # Drop old check constraint FIRST (before updating data)
-    op.drop_constraint("memory_units_fact_type_check", "memory_units", type_="check")
+    op.drop_constraint('memory_units_fact_type_check', 'memory_units', type_='check')

    # Update existing 'bank' values to 'experience'
-    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
+    op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
    # Also update any 'interactions' values (in case of partial migration)
-    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")
+    op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")

    # Create new check constraint with 'experience' instead of 'bank'
    op.create_check_constraint(
-        "memory_units_fact_type_check", "memory_units", "fact_type IN ('world', 'experience', 'opinion', 'observation')"
+        'memory_units_fact_type_check',
+        'memory_units',
+        "fact_type IN ('world', 'experience', 'opinion', 'observation')"
    )


 def downgrade():
-    schema = _get_schema_prefix()
-
    # Drop new check constraint FIRST
-    op.drop_constraint("memory_units_fact_type_check", "memory_units", type_="check")
+    op.drop_constraint('memory_units_fact_type_check', 'memory_units', type_='check')

    # Update 'experience' back to 'bank'
-    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")
+    op.execute("UPDATE memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")

    # Recreate old check constraint
    op.create_check_constraint(
-        "memory_units_fact_type_check", "memory_units", "fact_type IN ('world', 'bank', 'opinion', 'observation')"
+        'memory_units_fact_type_check',
+        'memory_units',
+        "fact_type IN ('world', 'bank', 'opinion', 'observation')"
    )
@@ -8,49 +8,22 @@ Migrate disposition traits from Big Five (openness, conscientiousness, extravers
 agreeableness, neuroticism, bias_strength with 0-1 float values) to the new 3-trait
 system (skepticism, literalism, empathy with 1-5 integer values).
 """
+from typing import Sequence, Union

-from collections.abc import Sequence
-
+from alembic import op
 import sqlalchemy as sa
-from alembic import context, op
+

 # revision identifiers, used by Alembic.
-revision: str = "e0a1b2c3d4e5"
-down_revision: str | Sequence[str] | None = "rename_personality"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def _get_target_schema() -> str:
-    """Get the target schema name (tenant schema or 'public')."""
-    schema = context.config.get_main_option("target_schema")
-    return schema if schema else "public"
+revision: str = 'e0a1b2c3d4e5'
+down_revision: Union[str, Sequence[str], None] = 'rename_personality'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    """Convert Big Five disposition to 3-trait disposition."""
    conn = op.get_bind()
-    schema = _get_schema_prefix()
-    target_schema = _get_target_schema()
-
-    # Check if disposition column exists (should have been created by previous migration)
-    result = conn.execute(
-        sa.text("""
-        SELECT column_name
-        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
-    )
-    if not result.fetchone():
-        # Column doesn't exist yet (shouldn't happen but be safe)
-        return

    # Update all existing banks to use the new disposition format
    # Convert from old format to new format with reasonable mappings:
@@ -58,54 +31,32 @@ def upgrade() -> None:
    # - literalism: derived from conscientiousness (detail-oriented people are more literal)
    # - empathy: derived from agreeableness + inverse of neuroticism
    # Default all to 3 (neutral) for simplicity
-    conn.execute(
-        sa.text(f"""
-        UPDATE {schema}banks
-        SET disposition = '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
+    conn.execute(sa.text("""
+        UPDATE banks
+        SET disposition = '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
        WHERE disposition IS NOT NULL
-    """)
-    )
+    """))

    # Update the default for new banks
-    conn.execute(
-        sa.text(f"""
-        ALTER TABLE {schema}banks
-        ALTER COLUMN disposition SET DEFAULT '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
-    """)
-    )
+    conn.execute(sa.text("""
+        ALTER TABLE banks
+        ALTER COLUMN disposition SET DEFAULT '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
+    """))


 def downgrade() -> None:
    """Convert back to Big Five disposition."""
    conn = op.get_bind()
-    schema = _get_schema_prefix()
-    target_schema = _get_target_schema()
-
-    # Check if disposition column exists
-    result = conn.execute(
-        sa.text("""
-        SELECT column_name
-        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
-    )
-    if not result.fetchone():
-        return

    # Revert to Big Five format with default values
-    conn.execute(
-        sa.text(f"""
-        UPDATE {schema}banks
-        SET disposition = '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
+    conn.execute(sa.text("""
+        UPDATE banks
+        SET disposition = '{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}'::jsonb
        WHERE disposition IS NOT NULL
-    """)
-    )
+    """))

    # Update the default for new banks
-    conn.execute(
-        sa.text(f"""
-        ALTER TABLE {schema}banks
-        ALTER COLUMN disposition SET DEFAULT '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
-    """)
-    )
+    conn.execute(sa.text("""
+        ALTER TABLE banks
+        ALTER COLUMN disposition SET DEFAULT '{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}'::jsonb
+    """))
@@ -5,81 +5,61 @@ Revises: d9f6a3b4c5e2
 Create Date: 2024-12-04

 """
+from typing import Sequence, Union

-from collections.abc import Sequence
-
+from alembic import op
 import sqlalchemy as sa
-from alembic import context, op
 from sqlalchemy.dialects import postgresql

+
 # revision identifiers, used by Alembic.
-revision: str = "rename_personality"
-down_revision: str | Sequence[str] | None = "d9f6a3b4c5e2"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_target_schema() -> str:
-    """Get the target schema name (tenant schema or 'public')."""
-    schema = context.config.get_main_option("target_schema")
-    return schema if schema else "public"
+revision: str = 'rename_personality'
+down_revision: Union[str, Sequence[str], None] = 'd9f6a3b4c5e2'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    """Rename personality column to disposition in banks table (if it exists)."""
    conn = op.get_bind()
-    target_schema = _get_target_schema()

    # Check if 'personality' column exists (old database)
-    result = conn.execute(
-        sa.text("""
+    result = conn.execute(sa.text("""
        SELECT column_name
        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'personality'
-    """),
-        {"schema": target_schema},
-    )
+        WHERE table_name = 'banks' AND column_name = 'personality'
+    """))
    has_personality = result.fetchone() is not None

    # Check if 'disposition' column exists (new database)
-    result = conn.execute(
-        sa.text("""
+    result = conn.execute(sa.text("""
        SELECT column_name
        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
-    )
+        WHERE table_name = 'banks' AND column_name = 'disposition'
+    """))
    has_disposition = result.fetchone() is not None

    if has_personality and not has_disposition:
        # Old database: rename personality -> disposition
-        op.alter_column("banks", "personality", new_column_name="disposition")
+        op.alter_column('banks', 'personality', new_column_name='disposition')
    elif not has_personality and not has_disposition:
        # Neither exists (shouldn't happen, but be safe): add disposition column
-        op.add_column(
-            "banks",
-            sa.Column(
-                "disposition",
-                postgresql.JSONB(astext_type=sa.Text()),
-                server_default=sa.text("'{}'::jsonb"),
-                nullable=False,
-            ),
-        )
+        op.add_column('banks', sa.Column(
+            'disposition',
+            postgresql.JSONB(astext_type=sa.Text()),
+            server_default=sa.text("'{}'::jsonb"),
+            nullable=False
+        ))
    # else: disposition already exists, nothing to do


 def downgrade() -> None:
    """Revert disposition column back to personality."""
    conn = op.get_bind()
-    target_schema = _get_target_schema()
-    result = conn.execute(
-        sa.text("""
+    result = conn.execute(sa.text("""
        SELECT column_name
        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
-    )
+        WHERE table_name = 'banks' AND column_name = 'disposition'
+    """))
    if result.fetchone():
-        op.alter_column("banks", "disposition", new_column_name="personality")
+        op.alter_column('banks', 'disposition', new_column_name='personality')
@@ -3,10 +3,8 @@ Unified API module for Hindsight.

 Provides both HTTP REST API and MCP (Model Context Protocol) server.
 """
-
 import logging
 from typing import Optional
-
 from fastapi import FastAPI

 from hindsight_api import MemoryEngine
@@ -19,7 +17,7 @@ def create_app(
    http_api_enabled: bool = True,
    mcp_api_enabled: bool = False,
    mcp_mount_path: str = "/mcp",
-    initialize_memory: bool = True,
+    initialize_memory: bool = True
 ) -> FastAPI:
    """
    Create and configure the unified Hindsight API application.
@@ -49,8 +47,10 @@ def create_app(
    # Import and create HTTP API if enabled
    if http_api_enabled:
        from .http import create_app as create_http_app
-
-        app = create_http_app(memory=memory, initialize_memory=initialize_memory)
+        app = create_http_app(
+            memory=memory,
+            initialize_memory=initialize_memory
+        )
        logger.info("HTTP REST API enabled")
    else:
        # Create minimal FastAPI app
@@ -77,15 +77,15 @@ def create_app(

 # Re-export commonly used items for backwards compatibility
 from .http import (
-    CreateBankRequest,
-    DispositionTraits,
-    MemoryItem,
    RecallRequest,
-    RecallResponse,
    RecallResult,
+    RecallResponse,
+    MemoryItem,
+    RetainRequest,
    ReflectRequest,
    ReflectResponse,
-    RetainRequest,
+    CreateBankRequest,
+    DispositionTraits,
 )

 __all__ = [
@@ -4,34 +4,27 @@ import json
 import logging
 import os
 from contextvars import ContextVar
+from typing import Optional

 from fastmcp import FastMCP
-
 from hindsight_api import MemoryEngine
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
-from hindsight_api.models import RequestContext

 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
-_log_level_map = {
-    "critical": logging.CRITICAL,
-    "error": logging.ERROR,
-    "warning": logging.WARNING,
-    "info": logging.INFO,
-    "debug": logging.DEBUG,
-    "trace": logging.DEBUG,
-}
+_log_level_map = {"critical": logging.CRITICAL, "error": logging.ERROR, "warning": logging.WARNING,
+                  "info": logging.INFO, "debug": logging.DEBUG, "trace": logging.DEBUG}
 logging.basicConfig(
    level=_log_level_map.get(_log_level_str, logging.INFO),
-    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"
 )
 logger = logging.getLogger(__name__)

 # Context variable to hold the current bank_id from the URL path
-_current_bank_id: ContextVar[str | None] = ContextVar("current_bank_id", default=None)
+_current_bank_id: ContextVar[Optional[str]] = ContextVar("current_bank_id", default=None)


-def get_current_bank_id() -> str | None:
+def get_current_bank_id() -> Optional[str]:
    """Get the current bank_id from context (set from URL path)."""
    return _current_bank_id.get()

@@ -68,10 +61,9 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
        """
        try:
            bank_id = get_current_bank_id()
-            if bank_id is None:
-                return "Error: No bank_id configured"
-            await memory.retain_batch_async(
-                bank_id=bank_id, contents=[{"content": content, "context": context}], request_context=RequestContext()
+            await memory.put_batch_async(
+                bank_id=bank_id,
+                contents=[{"content": content, "context": context}]
            )
            return "Memory stored successfully"
        except Exception as e:
@@ -95,16 +87,12 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
        """
        try:
            bank_id = get_current_bank_id()
-            if bank_id is None:
-                return "Error: No bank_id configured"
            from hindsight_api.engine.memory_engine import Budget
-
            search_result = await memory.recall_async(
                bank_id=bank_id,
                query=query,
                fact_type=list(VALID_RECALL_FACT_TYPES),
-                budget=Budget.LOW,
-                request_context=RequestContext(),
+                budget=Budget.LOW
            )

            results = [
@@ -113,7 +101,7 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
                    "text": fact.text,
                    "type": fact.fact_type,
                    "context": fact.context,
-                    "occurred_start": fact.occurred_start,
+                    "event_date": fact.event_date,
                }
                for fact in search_result.results[:max_results]
            ]
@@ -133,7 +121,11 @@ class MCPMiddleware:
        self.app = app
        self.memory = memory
        self.mcp_server = create_mcp_server(memory)
-        self.mcp_app = self.mcp_server.http_app()
+        # Use sse_app - http_app requires lifespan management that's complex with middleware
+        import warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", DeprecationWarning)
+            self.mcp_app = self.mcp_server.sse_app()

    async def __call__(self, scope, receive, send):
        if scope["type"] != "http":
@@ -145,7 +137,7 @@ class MCPMiddleware:
        # Strip any mount prefix (e.g., /mcp) that FastAPI might not have stripped
        root_path = scope.get("root_path", "")
        if root_path and path.startswith(root_path):
-            path = path[len(root_path) :] or "/"
+            path = path[len(root_path):] or "/"

        # Also handle case where mount path wasn't stripped (e.g., /mcp/...)
        if path.startswith("/mcp/"):
@@ -181,7 +173,10 @@ class MCPMiddleware:
                    body = message.get("body", b"")
                    if body and b"/messages" in body:
                        # Rewrite /messages to /{bank_id}/messages in SSE endpoint event
-                        body = body.replace(b"data: /messages", f"data: /{bank_id}/messages".encode())
+                        body = body.replace(
+                            b"data: /messages",
+                            f"data: /{bank_id}/messages".encode()
+                        )
                        message = {**message, "body": body}
                await send(message)

@@ -192,19 +187,15 @@ class MCPMiddleware:
    async def _send_error(self, send, status: int, message: str):
        """Send an error response."""
        body = json.dumps({"error": message}).encode()
-        await send(
-            {
-                "type": "http.response.start",
-                "status": status,
-                "headers": [(b"content-type", b"application/json")],
-            }
-        )
-        await send(
-            {
-                "type": "http.response.body",
-                "body": body,
-            }
-        )
+        await send({
+            "type": "http.response.start",
+            "status": status,
+            "headers": [(b"content-type", b"application/json")],
+        })
+        await send({
+            "type": "http.response.body",
+            "body": body,
+        })


 def create_mcp_app(memory: MemoryEngine):
@@ -1,96 +0,0 @@
-"""
-Banner display for Hindsight API startup.
-
-Shows the logo and tagline with gradient colors.
-"""
-
-# Gradient colors: #0074d9 -> #009296
-GRADIENT_START = (0, 116, 217)  # #0074d9
-GRADIENT_END = (0, 146, 150)  # #009296
-
-# Pre-generated logo (generated by test-logo.py)
-LOGO = """\
-  \033[38;2;9;127;184m\u2584\033[0m\033[48;2;8;130;178m\033[38;2;5;133;186m\u2584\033[0m       \033[48;2;10;143;160m\033[38;2;10;143;165m\u2584\033[0m\033[38;2;7;140;156m\u2584\033[0m
- \033[38;2;8;125;192m\u2584\033[0m \033[38;2;3;132;191m\u2580\033[0m\033[38;2;2;133;192m\u2584\033[0m \033[38;2;3;132;180m\u2584\033[0m\033[38;2;1;137;184m\u2584\033[0m\033[38;2;3;133;174m\u2584\033[0m \033[38;2;3;142;176m\u2584\033[0m\033[38;2;4;142;169m\u2580\033[0m \033[38;2;10;144;164m\u2584\033[0m
-\033[38;2;6;121;195m\u2580\033[0m\033[38;2;5;128;203m\u2580\033[0m\033[48;2;5;124;195m\033[38;2;3;125;200m\u2584\033[0m\033[38;2;2;126;196m\u2584\033[0m\033[48;2;3;128;188m\033[38;2;1;131;196m\u2584\033[0m\033[48;2;0;152;219m\033[38;2;2;131;191m\u2584\033[0m\033[38;2;1;141;196m\u2580\033[0m\033[38;2;1;135;183m\u2580\033[0m\033[38;2;1;148;198m\u2580\033[0m\033[48;2;1;156;202m\033[38;2;2;135;180m\u2584\033[0m\033[48;2;4;134;169m\033[38;2;1;137;177m\u2584\033[0m\033[38;2;3;138;173m\u2584\033[0m\033[48;2;6;137;165m\033[38;2;2;140;170m\u2584\033[0m\033[38;2;7;144;169m\u2580\033[0m\033[38;2;7;139;158m\u2580\033[0m
-   \033[48;2;2;128;202m\033[38;2;2;124;201m\u2584\033[0m\033[48;2;1;130;201m\033[38;2;0;135;212m\u2584\033[0m\033[38;2;2;128;196m\u2584\033[0m \033[48;2;2;142;204m\033[38;2;7;138;199m\u2584\033[0m \033[38;2;1;135;186m\u2584\033[0m\033[48;2;1;142;186m\033[38;2;2;144;194m\u2584\033[0m\033[48;2;3;138;176m\033[38;2;2;134;176m\u2584\033[0m
- \033[48;2;8;118;200m\033[38;2;8;121;209m\u2584\033[0m\033[38;2;3;121;203m\u2580\033[0m \033[38;2;3;122;192m\u2580\033[0m\033[38;2;1;138;216m\u2580\033[0m\033[48;2;0;138;210m\033[38;2;3;128;198m\u2584\033[0m\033[48;2;0;126;188m\033[38;2;2;131;198m\u2584\033[0m\033[48;2;0;142;205m\033[38;2;3;132;193m\u2584\033[0m\033[38;2;1;140;196m\u2580\033[0m  \033[38;2;4;134;175m\u2580\033[0m\033[48;2;13;135;167m\033[38;2;8;136;174m\u2584\033[0m """
-
-
-def _interpolate_color(start: tuple, end: tuple, t: float) -> tuple:
-    """Interpolate between two RGB colors."""
-    return (
-        int(start[0] + (end[0] - start[0]) * t),
-        int(start[1] + (end[1] - start[1]) * t),
-        int(start[2] + (end[2] - start[2]) * t),
-    )
-
-
-def gradient_text(text: str, start: tuple = GRADIENT_START, end: tuple = GRADIENT_END) -> str:
-    """Render text with a gradient color effect."""
-    result = []
-    length = len(text)
-    for i, char in enumerate(text):
-        if char == " ":
-            result.append(" ")
-        else:
-            t = i / max(length - 1, 1)
-            r, g, b = _interpolate_color(start, end, t)
-            result.append(f"\033[38;2;{r};{g};{b}m{char}")
-    result.append("\033[0m")
-    return "".join(result)
-
-
-def print_banner():
-    """Print the Hindsight startup banner."""
-    print(LOGO)
-    tagline = gradient_text("Hindsight: Agent Memory That Works Like Human Memory")
-    print(f"\n  {tagline}\n")
-
-
-def color(text: str, t: float = 0.0) -> str:
-    """Color text using gradient position (0.0 = start, 1.0 = end)."""
-    r, g, b = _interpolate_color(GRADIENT_START, GRADIENT_END, t)
-    return f"\033[38;2;{r};{g};{b}m{text}\033[0m"
-
-
-def color_start(text: str) -> str:
-    """Color text with gradient start color (#0074d9)."""
-    return color(text, 0.0)
-
-
-def color_end(text: str) -> str:
-    """Color text with gradient end color (#009296)."""
-    return color(text, 1.0)
-
-
-def color_mid(text: str) -> str:
-    """Color text with gradient middle color."""
-    return color(text, 0.5)
-
-
-def dim(text: str) -> str:
-    """Dim/gray text."""
-    return f"\033[38;2;128;128;128m{text}\033[0m"
-
-
-def print_startup_info(
-    host: str,
-    port: int,
-    database_url: str,
-    llm_provider: str,
-    llm_model: str,
-    embeddings_provider: str,
-    reranker_provider: str,
-    mcp_enabled: bool = False,
-):
-    """Print styled startup information."""
-    print(color_start("Starting Hindsight API..."))
-    print(f"  {dim('URL:')} {color(f'http://{host}:{port}', 0.2)}")
-    print(f"  {dim('Database:')} {color(database_url, 0.4)}")
-    print(f"  {dim('LLM:')} {color(f'{llm_provider} / {llm_model}', 0.6)}")
-    print(f"  {dim('Embeddings:')} {color(embeddings_provider, 0.8)}")
-    print(f"  {dim('Reranker:')} {color(reranker_provider, 1.0)}")
-    if mcp_enabled:
-        print(f"  {dim('MCP:')} {color_end('enabled at /mcp')}")
-    print()
@@ -3,10 +3,10 @@ Centralized configuration for Hindsight API.

 All environment variables and their defaults are defined here.
 """
-
-import logging
 import os
 from dataclasses import dataclass
+from typing import Optional
+import logging

 logger = logging.getLogger(__name__)

@@ -29,18 +29,11 @@ ENV_HOST = "HINDSIGHT_API_HOST"
 ENV_PORT = "HINDSIGHT_API_PORT"
 ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
-ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
-ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
-ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
-
-# Optimization flags
-ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
-ENV_LAZY_RERANKER = "HINDSIGHT_API_LAZY_RERANKER"

 # Default values
 DEFAULT_DATABASE_URL = "pg0"
-DEFAULT_LLM_PROVIDER = "openai"
-DEFAULT_LLM_MODEL = "gpt-5-mini"
+DEFAULT_LLM_PROVIDER = "groq"
+DEFAULT_LLM_MODEL = "openai/gpt-oss-20b"

 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
@@ -52,28 +45,6 @@ DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
 DEFAULT_LOG_LEVEL = "info"
 DEFAULT_MCP_ENABLED = True
-DEFAULT_GRAPH_RETRIEVER = "bfs"  # Options: "bfs", "mpfp"
-DEFAULT_MCP_LOCAL_BANK_ID = "mcp"
-
-# Default MCP tool descriptions (can be customized via env vars)
-DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.
-
-Use this tool PROACTIVELY whenever the user shares:
- Personal facts, preferences, or interests
- Important events or milestones
- User history, experiences, or background
- Decisions, opinions, or stated preferences
- Goals, plans, or future intentions
- Relationships or people mentioned
- Work context, projects, or responsibilities"""
-
-DEFAULT_MCP_RECALL_DESCRIPTION = """Search memories to provide personalized, context-aware responses.
-
-Use this tool PROACTIVELY to:
- Check user's preferences before making suggestions
- Recall user's history to provide continuity
- Remember user's goals and context
- Personalize responses based on past interactions"""

 # Required embedding dimension for database schema
 EMBEDDING_DIMENSION = 384
@@ -88,19 +59,19 @@ class HindsightConfig:

    # LLM
    llm_provider: str
-    llm_api_key: str | None
+    llm_api_key: Optional[str]
    llm_model: str
-    llm_base_url: str | None
+    llm_base_url: Optional[str]

    # Embeddings
    embeddings_provider: str
    embeddings_local_model: str
-    embeddings_tei_url: str | None
+    embeddings_tei_url: Optional[str]

    # Reranker
    reranker_provider: str
    reranker_local_model: str
-    reranker_tei_url: str | None
+    reranker_tei_url: Optional[str]

    # Server
    host: str
@@ -108,42 +79,34 @@ class HindsightConfig:
    log_level: str
    mcp_enabled: bool

-    # Recall
-    graph_retriever: str
-
-    # Optimization flags
-    skip_llm_verification: bool
-    lazy_reranker: bool
-
    @classmethod
    def from_env(cls) -> "HindsightConfig":
        """Create configuration from environment variables."""
        return cls(
            # Database
            database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
+
            # LLM
            llm_provider=os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER),
            llm_api_key=os.getenv(ENV_LLM_API_KEY),
            llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
            llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
+
            # Embeddings
            embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
            embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
            embeddings_tei_url=os.getenv(ENV_EMBEDDINGS_TEI_URL),
+
            # Reranker
            reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
            reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
            reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
+
            # Server
            host=os.getenv(ENV_HOST, DEFAULT_HOST),
            port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
            log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
            mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
-            # Recall
-            graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
-            # Optimization flags
-            skip_llm_verification=os.getenv(ENV_SKIP_LLM_VERIFICATION, "false").lower() == "true",
-            lazy_reranker=os.getenv(ENV_LAZY_RERANKER, "false").lower() == "true",
        )

    def get_llm_base_url(self) -> str:
@@ -175,8 +138,7 @@ class HindsightConfig:
        """Configure Python logging based on the log level."""
        logging.basicConfig(
            level=self.get_python_log_level(),
-            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-            force=True,  # Override any existing configuration
+            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"
        )

    def log_config(self) -> None:
@@ -185,7 +147,6 @@ class HindsightConfig:
        logger.info(f"LLM: provider={self.llm_provider}, model={self.llm_model}")
        logger.info(f"Embeddings: provider={self.embeddings_provider}")
        logger.info(f"Reranker: provider={self.reranker_provider}")
-        logger.info(f"Graph retriever: {self.graph_retriever}")


 def get_config() -> HindsightConfig:
@@ -1,204 +0,0 @@
-"""
-Daemon mode support for Hindsight API.
-
-Provides idle timeout and lockfile management for running as a background daemon.
-"""
-
-import asyncio
-import fcntl
-import logging
-import os
-import sys
-import time
-from pathlib import Path
-
-logger = logging.getLogger(__name__)
-
-# Default daemon configuration
-DEFAULT_DAEMON_PORT = 8889
-DEFAULT_IDLE_TIMEOUT = 0  # 0 = no auto-exit (hindsight-embed passes its own timeout)
-LOCKFILE_PATH = Path.home() / ".hindsight" / "daemon.lock"
-DAEMON_LOG_PATH = Path.home() / ".hindsight" / "daemon.log"
-
-
-class IdleTimeoutMiddleware:
-    """ASGI middleware that tracks activity and exits after idle timeout."""
-
-    def __init__(self, app, idle_timeout: int = DEFAULT_IDLE_TIMEOUT):
-        self.app = app
-        self.idle_timeout = idle_timeout
-        self.last_activity = time.time()
-        self._checker_task = None
-
-    async def __call__(self, scope, receive, send):
-        # Update activity timestamp on each request
-        self.last_activity = time.time()
-        await self.app(scope, receive, send)
-
-    def start_idle_checker(self):
-        """Start the background task that checks for idle timeout."""
-        self._checker_task = asyncio.create_task(self._check_idle())
-
-    async def _check_idle(self):
-        """Background task that exits the process after idle timeout."""
-        # If idle_timeout is 0, don't auto-exit
-        if self.idle_timeout <= 0:
-            return
-
-        while True:
-            await asyncio.sleep(30)  # Check every 30 seconds
-            idle_time = time.time() - self.last_activity
-            if idle_time > self.idle_timeout:
-                logger.info(f"Idle timeout reached ({self.idle_timeout}s), shutting down daemon")
-                # Give a moment for any in-flight requests
-                await asyncio.sleep(1)
-                os._exit(0)
-
-
-class DaemonLock:
-    """
-    File-based lock to prevent multiple daemon instances.
-
-    Uses fcntl.flock for atomic locking on Unix systems.
-    """
-
-    def __init__(self, lockfile: Path = LOCKFILE_PATH):
-        self.lockfile = lockfile
-        self._fd = None
-
-    def acquire(self) -> bool:
-        """
-        Try to acquire the daemon lock.
-
-        Returns True if lock acquired, False if another daemon is running.
-        """
-        self.lockfile.parent.mkdir(parents=True, exist_ok=True)
-
-        try:
-            self._fd = open(self.lockfile, "w")
-            fcntl.flock(self._fd.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-            # Write PID for debugging
-            self._fd.write(str(os.getpid()))
-            self._fd.flush()
-            return True
-        except (IOError, OSError):
-            # Lock is held by another process
-            if self._fd:
-                self._fd.close()
-                self._fd = None
-            return False
-
-    def release(self):
-        """Release the daemon lock."""
-        if self._fd:
-            try:
-                fcntl.flock(self._fd.fileno(), fcntl.LOCK_UN)
-                self._fd.close()
-            except Exception:
-                pass
-            finally:
-                self._fd = None
-                # Remove lockfile
-                try:
-                    self.lockfile.unlink()
-                except Exception:
-                    pass
-
-    def is_locked(self) -> bool:
-        """Check if the lock is held by another process."""
-        if not self.lockfile.exists():
-            return False
-
-        try:
-            fd = open(self.lockfile, "r")
-            fcntl.flock(fd.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-            # We got the lock, so no one else has it
-            fcntl.flock(fd.fileno(), fcntl.LOCK_UN)
-            fd.close()
-            return False
-        except (IOError, OSError):
-            return True
-
-    def get_pid(self) -> int | None:
-        """Get the PID of the daemon holding the lock."""
-        if not self.lockfile.exists():
-            return None
-        try:
-            with open(self.lockfile, "r") as f:
-                return int(f.read().strip())
-        except (ValueError, IOError):
-            return None
-
-
-def daemonize():
-    """
-    Fork the current process into a background daemon.
-
-    Uses double-fork technique to properly detach from terminal.
-    """
-    # First fork
-    pid = os.fork()
-    if pid > 0:
-        # Parent exits
-        sys.exit(0)
-
-    # Create new session
-    os.setsid()
-
-    # Second fork to prevent zombie processes
-    pid = os.fork()
-    if pid > 0:
-        sys.exit(0)
-
-    # Redirect standard file descriptors to log file
-    DAEMON_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
-
-    sys.stdout.flush()
-    sys.stderr.flush()
-
-    # Redirect stdin to /dev/null
-    with open("/dev/null", "r") as devnull:
-        os.dup2(devnull.fileno(), sys.stdin.fileno())
-
-    # Redirect stdout/stderr to log file
-    log_fd = open(DAEMON_LOG_PATH, "a")
-    os.dup2(log_fd.fileno(), sys.stdout.fileno())
-    os.dup2(log_fd.fileno(), sys.stderr.fileno())
-
-
-def check_daemon_running(port: int = DEFAULT_DAEMON_PORT) -> bool:
-    """Check if a daemon is running and responsive on the given port."""
-    import socket
-
-    try:
-        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        sock.settimeout(1)
-        result = sock.connect_ex(("127.0.0.1", port))
-        sock.close()
-        return result == 0
-    except Exception:
-        return False
-
-
-def stop_daemon(port: int = DEFAULT_DAEMON_PORT) -> bool:
-    """Stop a running daemon by sending SIGTERM to the process."""
-    lock = DaemonLock()
-    pid = lock.get_pid()
-
-    if pid is None:
-        return False
-
-    try:
-        import signal
-
-        os.kill(pid, signal.SIGTERM)
-        # Wait for process to exit
-        for _ in range(50):  # Wait up to 5 seconds
-            time.sleep(0.1)
-            try:
-                os.kill(pid, 0)  # Check if process exists
-            except OSError:
-                return True  # Process exited
-        return False
-    except OSError:
-        return False
@@ -7,30 +7,24 @@ This package contains all the implementation details of the memory engine:
 - Supporting modules: embeddings, cross_encoder, entity_resolver, etc.
 """

-from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
+from .memory_engine import MemoryEngine
 from .db_utils import acquire_with_retry
 from .embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
-from .llm_wrapper import LLMConfig
-from .memory_engine import (
-    MemoryEngine,
-    UnqualifiedTableError,
-    fq_table,
-    get_current_schema,
-    validate_sql_schema,
-)
-from .response_models import MemoryFact, RecallResult, ReflectResult
+from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
 from .search.trace import (
-    EntryPoint,
-    LinkInfo,
-    NodeVisit,
-    PruningDecision,
-    QueryInfo,
-    SearchPhaseMetrics,
-    SearchSummary,
    SearchTrace,
+    QueryInfo,
+    EntryPoint,
+    NodeVisit,
    WeightComponents,
+    LinkInfo,
+    PruningDecision,
+    SearchSummary,
+    SearchPhaseMetrics,
 )
 from .search.tracer import SearchTracer
+from .llm_wrapper import LLMConfig
+from .response_models import RecallResult, ReflectResult, MemoryFact

 __all__ = [
    "MemoryEngine",
@@ -55,9 +49,4 @@ __all__ = [
    "RecallResult",
    "ReflectResult",
    "MemoryFact",
-    # Schema safety utilities
-    "fq_table",
-    "get_current_schema",
-    "validate_sql_schema",
-    "UnqualifiedTableError",
 ]
@@ -5,19 +5,19 @@ Provides an interface for reranking with different backends.

 Configuration via environment variables - see hindsight_api.config for all env var names.
 """
-
+from abc import ABC, abstractmethod
+from typing import List, Tuple, Optional
 import logging
 import os
-from abc import ABC, abstractmethod

 import httpx

 from ..config import (
-    DEFAULT_RERANKER_LOCAL_MODEL,
-    DEFAULT_RERANKER_PROVIDER,
-    ENV_RERANKER_LOCAL_MODEL,
    ENV_RERANKER_PROVIDER,
+    ENV_RERANKER_LOCAL_MODEL,
    ENV_RERANKER_TEI_URL,
+    DEFAULT_RERANKER_PROVIDER,
+    DEFAULT_RERANKER_LOCAL_MODEL,
 )

 logger = logging.getLogger(__name__)
@@ -47,7 +47,7 @@ class CrossEncoderModel(ABC):
        pass

    @abstractmethod
-    def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+    def predict(self, pairs: List[Tuple[str, str]]) -> List[float]:
        """
        Score query-document pairs for relevance.

@@ -72,7 +72,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
    - Trained for passage re-ranking
    """

-    def __init__(self, model_name: str | None = None):
+    def __init__(self, model_name: Optional[str] = None):
        """
        Initialize local SentenceTransformers cross-encoder.

@@ -101,10 +101,15 @@ class LocalSTCrossEncoder(CrossEncoderModel):
            )

        logger.info(f"Reranker: initializing local provider with model {self.model_name}")
-        self._model = CrossEncoder(self.model_name)
+        # Disable lazy loading (meta tensors) which causes issues with newer transformers/accelerate
+        # Setting low_cpu_mem_usage=False and device_map=None ensures tensors are fully materialized
+        self._model = CrossEncoder(
+            self.model_name,
+            model_kwargs={"low_cpu_mem_usage": False, "device_map": None},
+        )
        logger.info("Reranker: local provider initialized")

-    def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+    def predict(self, pairs: List[Tuple[str, str]]) -> List[float]:
        """
        Score query-document pairs for relevance.

@@ -117,7 +122,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        if self._model is None:
            raise RuntimeError("Reranker not initialized. Call initialize() first.")
        scores = self._model.predict(pairs, show_progress_bar=False)
-        return scores.tolist() if hasattr(scores, "tolist") else list(scores)
+        return scores.tolist() if hasattr(scores, 'tolist') else list(scores)


 class RemoteTEICrossEncoder(CrossEncoderModel):
@@ -153,8 +158,8 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
        self.batch_size = batch_size
        self.max_retries = max_retries
        self.retry_delay = retry_delay
-        self._client: httpx.Client | None = None
-        self._model_id: str | None = None
+        self._client: Optional[httpx.Client] = None
+        self._model_id: Optional[str] = None

    @property
    def provider_name(self) -> str:
@@ -163,7 +168,6 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
    def _request_with_retry(self, method: str, url: str, **kwargs) -> httpx.Response:
        """Make an HTTP request with automatic retries on transient errors."""
        import time
-
        last_error = None
        delay = self.retry_delay

@@ -178,18 +182,14 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
            except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout) as e:
                last_error = e
                if attempt < self.max_retries:
-                    logger.warning(
-                        f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
-                    )
+                    logger.warning(f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
                    time.sleep(delay)
                    delay *= 2  # Exponential backoff
            except httpx.HTTPStatusError as e:
                # Retry on 5xx server errors
                if e.response.status_code >= 500 and attempt < self.max_retries:
                    last_error = e
-                    logger.warning(
-                        f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
-                    )
+                    logger.warning(f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
                    time.sleep(delay)
                    delay *= 2
                else:
@@ -214,7 +214,7 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
        except httpx.HTTPError as e:
            raise RuntimeError(f"Failed to connect to TEI server at {self.base_url}: {e}")

-    def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+    def predict(self, pairs: List[Tuple[str, str]]) -> List[float]:
        """
        Score query-document pairs using the remote TEI reranker.

@@ -234,7 +234,7 @@ class RemoteTEICrossEncoder(CrossEncoderModel):

        # Process in batches
        for i in range(0, len(pairs), self.batch_size):
-            batch = pairs[i : i + self.batch_size]
+            batch = pairs[i:i + self.batch_size]

            # TEI rerank endpoint expects query and texts separately
            # All pairs in a batch should have the same query for optimal performance
@@ -292,11 +292,15 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
    if provider == "tei":
        url = os.environ.get(ENV_RERANKER_TEI_URL)
        if not url:
-            raise ValueError(f"{ENV_RERANKER_TEI_URL} is required when {ENV_RERANKER_PROVIDER} is 'tei'")
+            raise ValueError(
+                f"{ENV_RERANKER_TEI_URL} is required when {ENV_RERANKER_PROVIDER} is 'tei'"
+            )
        return RemoteTEICrossEncoder(base_url=url)
    elif provider == "local":
        model = os.environ.get(ENV_RERANKER_LOCAL_MODEL)
        model_name = model or DEFAULT_RERANKER_LOCAL_MODEL
        return LocalSTCrossEncoder(model_name=model_name)
    else:
-        raise ValueError(f"Unknown reranker provider: {provider}. Supported: 'local', 'tei'")
+        raise ValueError(
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei'"
+        )
@@ -1,11 +1,9 @@
 """
 Database utility functions for connection management with retry logic.
 """
-
 import asyncio
 import logging
 from contextlib import asynccontextmanager
-
 import asyncpg

 logger = logging.getLogger(__name__)
@@ -56,14 +54,16 @@ async def retry_with_backoff(
        except retryable_exceptions as e:
            last_exception = e
            if attempt < max_retries:
-                delay = min(base_delay * (2**attempt), max_delay)
+                delay = min(base_delay * (2 ** attempt), max_delay)
                logger.warning(
                    f"Database operation failed (attempt {attempt + 1}/{max_retries + 1}): {e}. "
                    f"Retrying in {delay:.1f}s..."
                )
                await asyncio.sleep(delay)
            else:
-                logger.error(f"Database operation failed after {max_retries + 1} attempts: {e}")
+                logger.error(
+                    f"Database operation failed after {max_retries + 1} attempts: {e}"
+                )
    raise last_exception


@@ -83,7 +83,6 @@ async def acquire_with_retry(pool: asyncpg.Pool, max_retries: int = DEFAULT_MAX_
    Yields:
        An asyncpg connection
    """
-
    async def acquire():
        return await pool.acquire()

@@ -8,20 +8,20 @@ the database schema (pgvector column defined as vector(384)).

 Configuration via environment variables - see hindsight_api.config for all env var names.
 """
-
+from abc import ABC, abstractmethod
+from typing import List, Optional
 import logging
 import os
-from abc import ABC, abstractmethod

 import httpx

 from ..config import (
-    DEFAULT_EMBEDDINGS_LOCAL_MODEL,
-    DEFAULT_EMBEDDINGS_PROVIDER,
-    EMBEDDING_DIMENSION,
-    ENV_EMBEDDINGS_LOCAL_MODEL,
    ENV_EMBEDDINGS_PROVIDER,
+    ENV_EMBEDDINGS_LOCAL_MODEL,
    ENV_EMBEDDINGS_TEI_URL,
+    DEFAULT_EMBEDDINGS_PROVIDER,
+    DEFAULT_EMBEDDINGS_LOCAL_MODEL,
+    EMBEDDING_DIMENSION,
 )

 logger = logging.getLogger(__name__)
@@ -52,7 +52,7 @@ class Embeddings(ABC):
        pass

    @abstractmethod
-    def encode(self, texts: list[str]) -> list[list[float]]:
+    def encode(self, texts: List[str]) -> List[List[float]]:
        """
        Generate 384-dimensional embeddings for a list of texts.

@@ -75,7 +75,7 @@ class LocalSTEmbeddings(Embeddings):
    embeddings matching the database schema.
    """

-    def __init__(self, model_name: str | None = None):
+    def __init__(self, model_name: Optional[str] = None):
        """
        Initialize local SentenceTransformers embeddings.

@@ -123,7 +123,7 @@ class LocalSTEmbeddings(Embeddings):

        logger.info(f"Embeddings: local provider initialized (dim: {model_dim})")

-    def encode(self, texts: list[str]) -> list[list[float]]:
+    def encode(self, texts: List[str]) -> List[List[float]]:
        """
        Generate 384-dimensional embeddings for a list of texts.

@@ -172,8 +172,8 @@ class RemoteTEIEmbeddings(Embeddings):
        self.batch_size = batch_size
        self.max_retries = max_retries
        self.retry_delay = retry_delay
-        self._client: httpx.Client | None = None
-        self._model_id: str | None = None
+        self._client: Optional[httpx.Client] = None
+        self._model_id: Optional[str] = None

    @property
    def provider_name(self) -> str:
@@ -182,7 +182,6 @@ class RemoteTEIEmbeddings(Embeddings):
    def _request_with_retry(self, method: str, url: str, **kwargs) -> httpx.Response:
        """Make an HTTP request with automatic retries on transient errors."""
        import time
-
        last_error = None
        delay = self.retry_delay

@@ -197,18 +196,14 @@ class RemoteTEIEmbeddings(Embeddings):
            except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout) as e:
                last_error = e
                if attempt < self.max_retries:
-                    logger.warning(
-                        f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
-                    )
+                    logger.warning(f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
                    time.sleep(delay)
                    delay *= 2  # Exponential backoff
            except httpx.HTTPStatusError as e:
                # Retry on 5xx server errors
                if e.response.status_code >= 500 and attempt < self.max_retries:
                    last_error = e
-                    logger.warning(
-                        f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
-                    )
+                    logger.warning(f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
                    time.sleep(delay)
                    delay *= 2
                else:
@@ -233,7 +228,7 @@ class RemoteTEIEmbeddings(Embeddings):
        except httpx.HTTPError as e:
            raise RuntimeError(f"Failed to connect to TEI server at {self.base_url}: {e}")

-    def encode(self, texts: list[str]) -> list[list[float]]:
+    def encode(self, texts: List[str]) -> List[List[float]]:
        """
        Generate embeddings using the remote TEI server.

@@ -253,7 +248,7 @@ class RemoteTEIEmbeddings(Embeddings):

        # Process in batches
        for i in range(0, len(texts), self.batch_size):
-            batch = texts[i : i + self.batch_size]
+            batch = texts[i:i + self.batch_size]

            try:
                response = self._request_with_retry(
@@ -283,11 +278,15 @@ def create_embeddings_from_env() -> Embeddings:
    if provider == "tei":
        url = os.environ.get(ENV_EMBEDDINGS_TEI_URL)
        if not url:
-            raise ValueError(f"{ENV_EMBEDDINGS_TEI_URL} is required when {ENV_EMBEDDINGS_PROVIDER} is 'tei'")
+            raise ValueError(
+                f"{ENV_EMBEDDINGS_TEI_URL} is required when {ENV_EMBEDDINGS_PROVIDER} is 'tei'"
+            )
        return RemoteTEIEmbeddings(base_url=url)
    elif provider == "local":
        model = os.environ.get(ENV_EMBEDDINGS_LOCAL_MODEL)
        model_name = model or DEFAULT_EMBEDDINGS_LOCAL_MODEL
        return LocalSTEmbeddings(model_name=model_name)
    else:
-        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei'")
+        raise ValueError(
+            f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei'"
+        )
@@ -4,14 +4,12 @@ Entity extraction and resolution for memory system.
 Uses spaCy for entity extraction and implements resolution logic
 to disambiguate entities across memory units.
 """
-
-from datetime import UTC, datetime
-from difflib import SequenceMatcher
-
 import asyncpg
-
+from typing import List, Dict, Optional, Set, Any
+from difflib import SequenceMatcher
+from datetime import datetime, timezone
 from .db_utils import acquire_with_retry
-from .memory_engine import fq_table
+

 # Load spaCy model (singleton)
 _nlp = None
@@ -34,11 +32,11 @@ class EntityResolver:
    async def resolve_entities_batch(
        self,
        bank_id: str,
-        entities_data: list[dict],
+        entities_data: List[Dict],
        context: str,
        unit_event_date,
        conn=None,
-    ) -> list[str]:
+    ) -> List[str]:
        """
        Resolve multiple entities in batch (MUCH faster than sequential).

@@ -64,38 +62,36 @@ class EntityResolver:
        else:
            return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)

-    async def _resolve_entities_batch_impl(
-        self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
-    ) -> list[str]:
+    async def _resolve_entities_batch_impl(self, conn, bank_id: str, entities_data: List[Dict], context: str, unit_event_date) -> List[str]:
        # Query ALL candidates for this bank
        all_entities = await conn.fetch(
-            f"""
+            """
            SELECT canonical_name, id, metadata, last_seen, mention_count
-            FROM {fq_table("entities")}
+            FROM entities
            WHERE bank_id = $1
            """,
-            bank_id,
+            bank_id
        )

        # Build entity ID to name mapping for co-occurrence lookups
-        entity_id_to_name = {row["id"]: row["canonical_name"].lower() for row in all_entities}
+        entity_id_to_name = {row['id']: row['canonical_name'].lower() for row in all_entities}

        # Query ALL co-occurrences for this bank's entities in one query
        # This builds a map of entity_id -> set of co-occurring entity names
        all_cooccurrences = await conn.fetch(
-            f"""
+            """
            SELECT ec.entity_id_1, ec.entity_id_2, ec.cooccurrence_count
-            FROM {fq_table("entity_cooccurrences")} ec
-            WHERE ec.entity_id_1 IN (SELECT id FROM {fq_table("entities")} WHERE bank_id = $1)
-               OR ec.entity_id_2 IN (SELECT id FROM {fq_table("entities")} WHERE bank_id = $1)
+            FROM entity_cooccurrences ec
+            WHERE ec.entity_id_1 IN (SELECT id FROM entities WHERE bank_id = $1)
+               OR ec.entity_id_2 IN (SELECT id FROM entities WHERE bank_id = $1)
            """,
-            bank_id,
+            bank_id
        )

        # Build co-occurrence map: entity_id -> set of co-occurring entity names (lowercase)
-        cooccurrence_map: dict[str, set[str]] = {}
+        cooccurrence_map: Dict[str, Set[str]] = {}
        for row in all_cooccurrences:
-            eid1, eid2 = row["entity_id_1"], row["entity_id_2"]
+            eid1, eid2 = row['entity_id_1'], row['entity_id_2']
            # Add both directions
            if eid1 not in cooccurrence_map:
                cooccurrence_map[eid1] = set()
@@ -109,24 +105,22 @@ class EntityResolver:

        # Build candidate map for each entity text
        all_candidates = {}  # Maps entity_text -> list of candidates
-        entity_texts = list(set(e["text"] for e in entities_data))
+        entity_texts = list(set(e['text'] for e in entities_data))

        for entity_text in entity_texts:
            matching = []
            entity_text_lower = entity_text.lower()
            for row in all_entities:
-                canonical_name = row["canonical_name"]
-                ent_id = row["id"]
-                metadata = row["metadata"]
-                last_seen = row["last_seen"]
-                mention_count = row["mention_count"]
+                canonical_name = row['canonical_name']
+                ent_id = row['id']
+                metadata = row['metadata']
+                last_seen = row['last_seen']
+                mention_count = row['mention_count']
                canonical_lower = canonical_name.lower()
                # Match if exact or substring match
-                if (
-                    entity_text_lower == canonical_lower
-                    or entity_text_lower in canonical_lower
-                    or canonical_lower in entity_text_lower
-                ):
+                if (entity_text_lower == canonical_lower or
+                    entity_text_lower in canonical_lower or
+                    canonical_lower in entity_text_lower):
                    matching.append((ent_id, canonical_name, metadata, last_seen, mention_count))
            all_candidates[entity_text] = matching

@@ -136,10 +130,10 @@ class EntityResolver:
        entities_to_create = []  # (idx, entity_data, event_date)

        for idx, entity_data in enumerate(entities_data):
-            entity_text = entity_data["text"]
-            nearby_entities = entity_data.get("nearby_entities", [])
+            entity_text = entity_data['text']
+            nearby_entities = entity_data.get('nearby_entities', [])
            # Use per-entity date if available, otherwise fall back to batch-level date
-            entity_event_date = entity_data.get("event_date", unit_event_date)
+            entity_event_date = entity_data.get('event_date', unit_event_date)

            candidates = all_candidates.get(entity_text, [])

@@ -152,13 +146,17 @@ class EntityResolver:
            best_candidate = None
            best_score = 0.0

-            nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}
+            nearby_entity_set = {e['text'].lower() for e in nearby_entities if e['text'] != entity_text}

            for candidate_id, canonical_name, metadata, last_seen, mention_count in candidates:
                score = 0.0

                # 1. Name similarity (0-0.5)
-                name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
+                name_similarity = SequenceMatcher(
+                    None,
+                    entity_text.lower(),
+                    canonical_name.lower()
+                ).ratio()
                score += name_similarity * 0.5

                # 2. Co-occurring entities (0-0.3)
@@ -171,10 +169,8 @@ class EntityResolver:
                # 3. Temporal proximity (0-0.2)
                if last_seen and entity_event_date:
                    # Normalize timezone awareness for comparison
-                    event_date_utc = (
-                        entity_event_date if entity_event_date.tzinfo else entity_event_date.replace(tzinfo=UTC)
-                    )
-                    last_seen_utc = last_seen if last_seen.tzinfo else last_seen.replace(tzinfo=UTC)
+                    event_date_utc = entity_event_date if entity_event_date.tzinfo else entity_event_date.replace(tzinfo=timezone.utc)
+                    last_seen_utc = last_seen if last_seen.tzinfo else last_seen.replace(tzinfo=timezone.utc)
                    days_diff = abs((event_date_utc - last_seen_utc).total_seconds() / 86400)
                    if days_diff < 7:
                        temporal_score = max(0, 1.0 - (days_diff / 7))
@@ -196,13 +192,13 @@ class EntityResolver:
        # Batch update existing entities
        if entities_to_update:
            await conn.executemany(
-                f"""
-                UPDATE {fq_table("entities")} SET
+                """
+                UPDATE entities SET
                    mention_count = mention_count + 1,
                    last_seen = $2
                WHERE id = $1::uuid
                """,
-                entities_to_update,
+                entities_to_update
            )

        # Batch create new entities using COPY + INSERT for maximum speed
@@ -212,7 +208,7 @@ class EntityResolver:
            # For duplicates, we only insert once and reuse the ID
            unique_entities = {}  # lowercase_name -> (entity_data, event_date, [indices])
            for idx, entity_data, event_date in entities_to_create:
-                name_lower = entity_data["text"].lower()
+                name_lower = entity_data['text'].lower()
                if name_lower not in unique_entities:
                    unique_entities[name_lower] = (entity_data, event_date, [idx])
                else:
@@ -226,31 +222,31 @@ class EntityResolver:
            indices_map = []  # Maps result index -> list of original indices

            for name_lower, (entity_data, event_date, indices) in unique_entities.items():
-                entity_names.append(entity_data["text"])
+                entity_names.append(entity_data['text'])
                entity_dates.append(event_date)
                indices_map.append(indices)

            # Batch INSERT ... ON CONFLICT with RETURNING
            # This is much faster than individual inserts
            rows = await conn.fetch(
-                f"""
-                INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
+                """
+                INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
                SELECT $1, name, event_date, event_date, 1
                FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
                ON CONFLICT (bank_id, LOWER(canonical_name))
                DO UPDATE SET
-                    mention_count = {fq_table("entities")}.mention_count + 1,
+                    mention_count = entities.mention_count + 1,
                    last_seen = EXCLUDED.last_seen
                RETURNING id
                """,
                bank_id,
                entity_names,
-                entity_dates,
+                entity_dates
            )

            # Map returned IDs back to original indices
            for result_idx, row in enumerate(rows):
-                entity_id = row["id"]
+                entity_id = row['id']
                for original_idx in indices_map[result_idx]:
                    entity_ids[original_idx] = entity_id

@@ -261,7 +257,7 @@ class EntityResolver:
        bank_id: str,
        entity_text: str,
        context: str,
-        nearby_entities: list[dict],
+        nearby_entities: List[Dict],
        unit_event_date,
    ) -> str:
        """
@@ -280,9 +276,9 @@ class EntityResolver:
        async with acquire_with_retry(self.pool) as conn:
            # Find candidate entities with similar name
            candidates = await conn.fetch(
-                f"""
+                """
                SELECT id, canonical_name, metadata, last_seen
-                FROM {fq_table("entities")}
+                FROM entities
                WHERE bank_id = $1
                  AND (
                    canonical_name ILIKE $2
@@ -291,14 +287,14 @@ class EntityResolver:
                  )
                ORDER BY mention_count DESC
                """,
-                bank_id,
-                entity_text,
-                f"%{entity_text}%",
+                bank_id, entity_text, f"%{entity_text}%"
            )

            if not candidates:
                # New entity - create it
-                return await self._create_entity(conn, bank_id, entity_text, unit_event_date)
+                return await self._create_entity(
+                    conn, bank_id, entity_text, unit_event_date
+                )

            # Score candidates based on:
            # 1. Name similarity
@@ -310,27 +306,31 @@ class EntityResolver:
            best_score = 0.0
            best_name_similarity = 0.0

-            nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}
+            nearby_entity_set = {e['text'].lower() for e in nearby_entities if e['text'] != entity_text}

            for row in candidates:
-                candidate_id = row["id"]
-                canonical_name = row["canonical_name"]
-                metadata = row["metadata"]
-                last_seen = row["last_seen"]
+                candidate_id = row['id']
+                canonical_name = row['canonical_name']
+                metadata = row['metadata']
+                last_seen = row['last_seen']
                score = 0.0

                # 1. Name similarity (0-1)
-                name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
+                name_similarity = SequenceMatcher(
+                    None,
+                    entity_text.lower(),
+                    canonical_name.lower()
+                ).ratio()
                score += name_similarity * 0.5

                # 2. Co-occurring entities (0-0.5)
                # Get entities that co-occurred with this candidate before
                # Use the materialized co-occurrence cache for fast lookup
                co_entity_rows = await conn.fetch(
-                    f"""
+                    """
                    SELECT e.canonical_name, ec.cooccurrence_count
-                    FROM {fq_table("entity_cooccurrences")} ec
-                    JOIN {fq_table("entities")} e ON (
+                    FROM entity_cooccurrences ec
+                    JOIN entities e ON (
                        CASE
                            WHEN ec.entity_id_1 = $1 THEN ec.entity_id_2
                            WHEN ec.entity_id_2 = $1 THEN ec.entity_id_1
@@ -338,9 +338,9 @@ class EntityResolver:
                    )
                    WHERE ec.entity_id_1 = $1 OR ec.entity_id_2 = $1
                    """,
-                    candidate_id,
+                    candidate_id
                )
-                co_entities = {r["canonical_name"].lower() for r in co_entity_rows}
+                co_entities = {r['canonical_name'].lower() for r in co_entity_rows}

                # Check overlap with nearby entities
                overlap = len(nearby_entity_set & co_entities)
@@ -366,19 +366,20 @@ class EntityResolver:
            if best_score > threshold:
                # Update entity
                await conn.execute(
-                    f"""
-                    UPDATE {fq_table("entities")}
+                    """
+                    UPDATE entities
                    SET mention_count = mention_count + 1,
                        last_seen = $1
                    WHERE id = $2
                    """,
-                    unit_event_date,
-                    best_candidate,
+                    unit_event_date, best_candidate
                )
                return best_candidate
            else:
                # Not confident - create new entity
-                return await self._create_entity(conn, bank_id, entity_text, unit_event_date)
+                return await self._create_entity(
+                    conn, bank_id, entity_text, unit_event_date
+                )

    async def _create_entity(
        self,
@@ -403,19 +404,16 @@ class EntityResolver:
            Entity ID
        """
        entity_id = await conn.fetchval(
-            f"""
-            INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
+            """
+            INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
            VALUES ($1, $2, $3, $4, 1)
            ON CONFLICT (bank_id, LOWER(canonical_name))
            DO UPDATE SET
-                mention_count = {fq_table("entities")}.mention_count + 1,
+                mention_count = entities.mention_count + 1,
                last_seen = EXCLUDED.last_seen
            RETURNING id
            """,
-            bank_id,
-            entity_text,
-            event_date,
-            event_date,
+            bank_id, entity_text, event_date, event_date
        )
        return entity_id

@@ -431,27 +429,25 @@ class EntityResolver:
        async with acquire_with_retry(self.pool) as conn:
            # Insert unit-entity link
            await conn.execute(
-                f"""
-                INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
+                """
+                INSERT INTO unit_entities (unit_id, entity_id)
                VALUES ($1, $2)
                ON CONFLICT DO NOTHING
                """,
-                unit_id,
-                entity_id,
+                unit_id, entity_id
            )

            # Update co-occurrence cache: find other entities in this unit
            rows = await conn.fetch(
-                f"""
+                """
                SELECT entity_id
-                FROM {fq_table("unit_entities")}
+                FROM unit_entities
                WHERE unit_id = $1 AND entity_id != $2
                """,
-                unit_id,
-                entity_id,
+                unit_id, entity_id
            )

-            other_entities = [row["entity_id"] for row in rows]
+            other_entities = [row['entity_id'] for row in rows]

            # Update co-occurrences for each pair
            for other_entity_id in other_entities:
@@ -473,19 +469,18 @@ class EntityResolver:
            entity_id_1, entity_id_2 = entity_id_2, entity_id_1

        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
+            """
+            INSERT INTO entity_cooccurrences (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
            VALUES ($1, $2, 1, NOW())
            ON CONFLICT (entity_id_1, entity_id_2)
            DO UPDATE SET
-                cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
+                cooccurrence_count = entity_cooccurrences.cooccurrence_count + 1,
                last_cooccurred = NOW()
            """,
-            entity_id_1,
-            entity_id_2,
+            entity_id_1, entity_id_2
        )

-    async def link_units_to_entities_batch(self, unit_entity_pairs: list[tuple[str, str]], conn=None):
+    async def link_units_to_entities_batch(self, unit_entity_pairs: List[tuple[str, str]], conn=None):
        """
        Link multiple memory units to entities in batch (MUCH faster than sequential).

@@ -504,15 +499,15 @@ class EntityResolver:
        else:
            return await self._link_units_to_entities_batch_impl(conn, unit_entity_pairs)

-    async def _link_units_to_entities_batch_impl(self, conn, unit_entity_pairs: list[tuple[str, str]]):
+    async def _link_units_to_entities_batch_impl(self, conn, unit_entity_pairs: List[tuple[str, str]]):
        # Batch insert all unit-entity links
        await conn.executemany(
-            f"""
-            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
+            """
+            INSERT INTO unit_entities (unit_id, entity_id)
            VALUES ($1, $2)
            ON CONFLICT DO NOTHING
            """,
-            unit_entity_pairs,
+            unit_entity_pairs
        )

        # Build map of unit -> entities for co-occurrence calculation
@@ -529,7 +524,7 @@ class EntityResolver:
            entity_list = list(entity_ids)  # Convert set to list for iteration
            # For each pair of entities in this unit, create co-occurrence
            for i, entity_id_1 in enumerate(entity_list):
-                for entity_id_2 in entity_list[i + 1 :]:
+                for entity_id_2 in entity_list[i+1:]:
                    # Skip if same entity (shouldn't happen with set, but be safe)
                    if entity_id_1 == entity_id_2:
                        continue
@@ -540,20 +535,20 @@ class EntityResolver:

        # Batch update co-occurrences
        if cooccurrence_pairs:
-            now = datetime.now(UTC)
+            now = datetime.now(timezone.utc)
            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
+                """
+                INSERT INTO entity_cooccurrences (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
                VALUES ($1, $2, $3, $4)
                ON CONFLICT (entity_id_1, entity_id_2)
                DO UPDATE SET
-                    cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
+                    cooccurrence_count = entity_cooccurrences.cooccurrence_count + 1,
                    last_cooccurred = EXCLUDED.last_cooccurred
                """,
-                [(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs],
+                [(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs]
            )

-    async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> list[str]:
+    async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> List[str]:
        """
        Get all units that mention an entity.

@@ -566,23 +561,22 @@ class EntityResolver:
        """
        async with acquire_with_retry(self.pool) as conn:
            rows = await conn.fetch(
-                f"""
+                """
                SELECT unit_id
-                FROM {fq_table("unit_entities")}
+                FROM unit_entities
                WHERE entity_id = $1
                ORDER BY unit_id
                LIMIT $2
                """,
-                entity_id,
-                limit,
+                entity_id, limit
            )
-            return [row["unit_id"] for row in rows]
+            return [row['unit_id'] for row in rows]

    async def get_entity_by_text(
        self,
        bank_id: str,
        entity_text: str,
-    ) -> str | None:
+    ) -> Optional[str]:
        """
        Find an entity by text (for query resolution).

@@ -595,15 +589,14 @@ class EntityResolver:
        """
        async with acquire_with_retry(self.pool) as conn:
            row = await conn.fetchrow(
-                f"""
-                SELECT id FROM {fq_table("entities")}
+                """
+                SELECT id FROM entities
                WHERE bank_id = $1
                  AND canonical_name ILIKE $2
                ORDER BY mention_count DESC
                LIMIT 1
                """,
-                bank_id,
-                entity_text,
+                bank_id, entity_text
            )

-            return row["id"] if row else None
+            return row['id'] if row else None
@@ -1,592 +0,0 @@
-"""Abstract interface for MemoryEngine public methods.
-
-This module defines the public API that HTTP endpoints and extensions should use
-to interact with the memory system. All methods require a RequestContext for
-authentication when a TenantExtension is configured.
-"""
-
-from abc import ABC, abstractmethod
-from datetime import datetime
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from hindsight_api.engine.memory_engine import Budget
-    from hindsight_api.engine.response_models import RecallResult, ReflectResult
-    from hindsight_api.models import RequestContext
-
-
-class MemoryEngineInterface(ABC):
-    """
-    Abstract interface for the Memory Engine.
-
-    This defines the public API that should be used by HTTP endpoints and extensions.
-    All methods require a RequestContext for authentication.
-    """
-
-    # =========================================================================
-    # Health & Status
-    # =========================================================================
-
-    @abstractmethod
-    async def health_check(self) -> dict:
-        """
-        Check the health of the memory system.
-
-        Returns:
-            Dict with 'status' key ('healthy' or 'unhealthy') and additional info.
-        """
-        ...
-
-    # =========================================================================
-    # Core Memory Operations
-    # =========================================================================
-
-    @abstractmethod
-    async def retain_batch_async(
-        self,
-        bank_id: str,
-        contents: list[dict[str, Any]],
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Retain a batch of memory items.
-
-        Args:
-            bank_id: The memory bank ID.
-            contents: List of content dicts with 'content', optional 'event_date',
-                     'context', 'metadata', 'document_id'.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with processing results.
-        """
-        ...
-
-    @abstractmethod
-    async def recall_async(
-        self,
-        bank_id: str,
-        query: str,
-        *,
-        budget: "Budget | None" = None,
-        max_tokens: int = 4096,
-        enable_trace: bool = False,
-        fact_type: list[str] | None = None,
-        question_date: datetime | None = None,
-        include_entities: bool = False,
-        max_entity_tokens: int = 500,
-        include_chunks: bool = False,
-        max_chunk_tokens: int = 8192,
-        request_context: "RequestContext",
-    ) -> "RecallResult":
-        """
-        Recall memories relevant to a query.
-
-        Args:
-            bank_id: The memory bank ID.
-            query: The search query.
-            budget: Search budget (LOW, MID, HIGH).
-            max_tokens: Maximum tokens in response.
-            enable_trace: Include trace information.
-            fact_type: Filter by fact types.
-            question_date: Context date for temporal relevance.
-            include_entities: Include entity observations.
-            max_entity_tokens: Max tokens for entity observations.
-            include_chunks: Include raw chunks.
-            max_chunk_tokens: Max tokens for chunks.
-            request_context: Request context for authentication.
-
-        Returns:
-            RecallResult with matching memories.
-        """
-        ...
-
-    @abstractmethod
-    async def reflect_async(
-        self,
-        bank_id: str,
-        query: str,
-        *,
-        budget: "Budget | None" = None,
-        context: str | None = None,
-        request_context: "RequestContext",
-    ) -> "ReflectResult":
-        """
-        Reflect on a query and generate a thoughtful response.
-
-        Args:
-            bank_id: The memory bank ID.
-            query: The question to reflect on.
-            budget: Search budget for retrieving context.
-            context: Additional context for the reflection.
-            request_context: Request context for authentication.
-
-        Returns:
-            ReflectResult with generated response and supporting facts.
-        """
-        ...
-
-    # =========================================================================
-    # Bank Management
-    # =========================================================================
-
-    @abstractmethod
-    async def list_banks(
-        self,
-        *,
-        request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
-        """
-        List all memory banks.
-
-        Args:
-            request_context: Request context for authentication.
-
-        Returns:
-            List of bank info dicts.
-        """
-        ...
-
-    @abstractmethod
-    async def get_bank_profile(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Get bank profile including disposition and background.
-
-        Args:
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Bank profile dict.
-        """
-        ...
-
-    @abstractmethod
-    async def update_bank_disposition(
-        self,
-        bank_id: str,
-        disposition: dict[str, int],
-        *,
-        request_context: "RequestContext",
-    ) -> None:
-        """
-        Update bank disposition traits.
-
-        Args:
-            bank_id: The memory bank ID.
-            disposition: Dict with trait values.
-            request_context: Request context for authentication.
-        """
-        ...
-
-    @abstractmethod
-    async def merge_bank_background(
-        self,
-        bank_id: str,
-        new_info: str,
-        *,
-        update_disposition: bool = True,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Merge new background information into bank profile.
-
-        Args:
-            bank_id: The memory bank ID.
-            new_info: New background information to merge.
-            update_disposition: Whether to infer disposition from background.
-            request_context: Request context for authentication.
-
-        Returns:
-            Updated background info.
-        """
-        ...
-
-    @abstractmethod
-    async def delete_bank(
-        self,
-        bank_id: str,
-        *,
-        fact_type: str | None = None,
-        request_context: "RequestContext",
-    ) -> dict[str, int]:
-        """
-        Delete a bank or its memories.
-
-        Args:
-            bank_id: The memory bank ID.
-            fact_type: If specified, only delete memories of this type.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with deletion counts.
-        """
-        ...
-
-    # =========================================================================
-    # Memory Units
-    # =========================================================================
-
-    @abstractmethod
-    async def list_memory_units(
-        self,
-        bank_id: str,
-        *,
-        fact_type: str | None = None,
-        search_query: str | None = None,
-        limit: int = 100,
-        offset: int = 0,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        List memory units with pagination.
-
-        Args:
-            bank_id: The memory bank ID.
-            fact_type: Filter by fact type.
-            search_query: Full-text search query.
-            limit: Maximum results.
-            offset: Pagination offset.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with 'items', 'total', 'limit', 'offset'.
-        """
-        ...
-
-    @abstractmethod
-    async def delete_memory_unit(
-        self,
-        unit_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Delete a specific memory unit.
-
-        Args:
-            unit_id: The memory unit ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Deletion result.
-        """
-        ...
-
-    @abstractmethod
-    async def get_graph_data(
-        self,
-        bank_id: str,
-        *,
-        fact_type: str | None = None,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Get graph data for visualization.
-
-        Args:
-            bank_id: The memory bank ID.
-            fact_type: Filter by fact type.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with nodes, edges, table_rows, total_units.
-        """
-        ...
-
-    # =========================================================================
-    # Documents
-    # =========================================================================
-
-    @abstractmethod
-    async def list_documents(
-        self,
-        bank_id: str,
-        *,
-        search_query: str | None = None,
-        limit: int = 100,
-        offset: int = 0,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        List documents with pagination.
-
-        Args:
-            bank_id: The memory bank ID.
-            search_query: Search query.
-            limit: Maximum results.
-            offset: Pagination offset.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with 'items', 'total', 'limit', 'offset'.
-        """
-        ...
-
-    @abstractmethod
-    async def get_document(
-        self,
-        document_id: str,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any] | None:
-        """
-        Get a specific document.
-
-        Args:
-            document_id: The document ID.
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Document dict or None if not found.
-        """
-        ...
-
-    @abstractmethod
-    async def delete_document(
-        self,
-        document_id: str,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, int]:
-        """
-        Delete a document and its memory units.
-
-        Args:
-            document_id: The document ID.
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with deletion counts.
-        """
-        ...
-
-    @abstractmethod
-    async def get_chunk(
-        self,
-        chunk_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any] | None:
-        """
-        Get a specific chunk.
-
-        Args:
-            chunk_id: The chunk ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Chunk dict or None if not found.
-        """
-        ...
-
-    # =========================================================================
-    # Entities
-    # =========================================================================
-
-    @abstractmethod
-    async def list_entities(
-        self,
-        bank_id: str,
-        *,
-        limit: int = 100,
-        request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
-        """
-        List entities for a bank.
-
-        Args:
-            bank_id: The memory bank ID.
-            limit: Maximum results.
-            request_context: Request context for authentication.
-
-        Returns:
-            List of entity dicts.
-        """
-        ...
-
-    @abstractmethod
-    async def get_entity_observations(
-        self,
-        bank_id: str,
-        entity_id: str,
-        *,
-        limit: int = 10,
-        request_context: "RequestContext",
-    ) -> list[Any]:
-        """
-        Get observations for an entity.
-
-        Args:
-            bank_id: The memory bank ID.
-            entity_id: The entity ID.
-            limit: Maximum observations.
-            request_context: Request context for authentication.
-
-        Returns:
-            List of EntityObservation objects.
-        """
-        ...
-
-    @abstractmethod
-    async def regenerate_entity_observations(
-        self,
-        bank_id: str,
-        entity_id: str,
-        entity_name: str,
-        *,
-        request_context: "RequestContext",
-    ) -> None:
-        """
-        Regenerate observations for an entity.
-
-        Args:
-            bank_id: The memory bank ID.
-            entity_id: The entity ID.
-            entity_name: The entity's canonical name.
-            request_context: Request context for authentication.
-        """
-        ...
-
-    # =========================================================================
-    # Statistics & Operations
-    # =========================================================================
-
-    @abstractmethod
-    async def get_bank_stats(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Get statistics about memory nodes and links for a bank.
-
-        Args:
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with node_counts, link_counts, link_counts_by_fact_type,
-            link_breakdown, and operations stats.
-        """
-        ...
-
-    @abstractmethod
-    async def get_entity(
-        self,
-        bank_id: str,
-        entity_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any] | None:
-        """
-        Get entity details including metadata and observations.
-
-        Args:
-            bank_id: The memory bank ID.
-            entity_id: The entity ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Entity dict with id, canonical_name, mention_count, first_seen,
-            last_seen, metadata, and observations. None if not found.
-        """
-        ...
-
-    @abstractmethod
-    async def list_operations(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
-        """
-        List async operations for a bank.
-
-        Args:
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            List of operation dicts with id, task_type, status, etc.
-        """
-        ...
-
-    @abstractmethod
-    async def cancel_operation(
-        self,
-        bank_id: str,
-        operation_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Cancel a pending async operation.
-
-        Args:
-            bank_id: The memory bank ID.
-            operation_id: The operation ID to cancel.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with success status and message.
-
-        Raises:
-            ValueError: If operation not found.
-        """
-        ...
-
-    @abstractmethod
-    async def update_bank(
-        self,
-        bank_id: str,
-        *,
-        name: str | None = None,
-        background: str | None = None,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Update bank name and/or background.
-
-        Args:
-            bank_id: The memory bank ID.
-            name: New bank name (optional).
-            background: New background text (optional, replaces existing).
-            request_context: Request context for authentication.
-
-        Returns:
-            Updated bank profile dict.
-        """
-        ...
-
-    @abstractmethod
-    async def submit_async_retain(
-        self,
-        bank_id: str,
-        contents: list[dict[str, Any]],
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Submit a batch retain operation to run asynchronously.
-
-        Args:
-            bank_id: The memory bank ID.
-            contents: List of content dicts to retain.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with operation_id and items_count.
-        """
-        ...
@@ -1,19 +1,12 @@
 """
 LLM wrapper for unified configuration across providers.
 """
-
-import asyncio
-import json
-import logging
 import os
 import time
-from typing import Any
-
-import httpx
-from google import genai
-from google.genai import errors as genai_errors
-from google.genai import types as genai_types
-from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError
+import asyncio
+from typing import Optional, Any, Dict, List
+from openai import AsyncOpenAI, RateLimitError, APIError, APIStatusError, APIConnectionError, LengthFinishReasonError
+import logging

 # Seed applied to every Groq request for deterministic behavior.
 DEFAULT_LLM_SEED = 4242
@@ -35,15 +28,14 @@ class OutputTooLongError(Exception):
    to allow callers to handle output length issues without depending on
    provider-specific implementations.
    """
-
    pass


 class LLMProvider:
    """
-    Unified LLM provider.
+    Unified LLM provider using OpenAI-compatible API.

-    Supports OpenAI, Groq, Ollama (OpenAI-compatible), and Gemini.
+    Supports OpenAI, Groq, and Ollama (any OpenAI-compatible endpoint).
    """

    def __init__(
@@ -58,7 +50,7 @@ class LLMProvider:
        Initialize LLM provider.

        Args:
-            provider: Provider name ("openai", "groq", "ollama", "gemini").
+            provider: Provider name ("openai", "groq", "ollama").
            api_key: API key.
            base_url: Base URL for the API.
            model: Model name.
@@ -71,9 +63,11 @@ class LLMProvider:
        self.reasoning_effort = reasoning_effort

        # Validate provider
-        valid_providers = ["openai", "groq", "ollama", "gemini"]
+        valid_providers = ["openai", "groq", "ollama"]
        if self.provider not in valid_providers:
-            raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")
+            raise ValueError(
+                f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}"
+            )

        # Set default base URLs
        if not self.base_url:
@@ -86,50 +80,22 @@ class LLMProvider:
        if self.provider != "ollama" and not self.api_key:
            raise ValueError(f"API key not found for {self.provider}")

-        # Create client based on provider
-        if self.provider == "gemini":
-            self._gemini_client = genai.Client(api_key=self.api_key)
-            self._client = None
-        elif self.provider == "ollama":
+        # Create OpenAI-compatible client for all providers
+        if self.provider == "ollama":
            self._client = AsyncOpenAI(api_key="ollama", base_url=self.base_url, max_retries=0)
-            self._gemini_client = None
        else:
-            # Only pass base_url if it's set (OpenAI uses default URL otherwise)
-            client_kwargs = {"api_key": self.api_key, "max_retries": 0}
-            if self.base_url:
-                client_kwargs["base_url"] = self.base_url
-            self._client = AsyncOpenAI(**client_kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
-            self._gemini_client = None
+            self._client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url, max_retries=0)

-    async def verify_connection(self) -> None:
-        """
-        Verify that the LLM provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            logger.info(
-                f"Verifying LLM: provider={self.provider}, model={self.model}, base_url={self.base_url or 'default'}..."
-            )
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=100,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-            )
-            # If we get here without exception, the connection is working
-            logger.info(f"LLM verified: {self.provider}/{self.model}")
-        except Exception as e:
-            raise RuntimeError(f"LLM connection verification failed for {self.provider}/{self.model}: {e}") from e
+        logger.info(
+            f"Initialized LLM: provider={self.provider}, model={self.model}, base_url={self.base_url}"
+        )

    async def call(
        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
+        messages: List[Dict[str, str]],
+        response_format: Optional[Any] = None,
+        max_completion_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
        scope: str = "memory",
        max_retries: int = 10,
        initial_backoff: float = 1.0,
@@ -159,67 +125,26 @@ class LLMProvider:
        """
        async with _global_llm_semaphore:
            start_time = time.time()
-
-            # Handle Gemini provider separately
-            if self.provider == "gemini":
-                return await self._call_gemini(
-                    messages, response_format, max_retries, initial_backoff, max_backoff, skip_validation, start_time
-                )
-
-            # Handle Ollama with native API for structured output (better schema enforcement)
-            if self.provider == "ollama" and response_format is not None:
-                return await self._call_ollama_native(
-                    messages,
-                    response_format,
-                    max_completion_tokens,
-                    temperature,
-                    max_retries,
-                    initial_backoff,
-                    max_backoff,
-                    skip_validation,
-                    start_time,
-                )
+            import json

            call_params = {
                "model": self.model,
                "messages": messages,
            }

-            # Check if model supports reasoning parameter (o1, o3, gpt-5 families)
-            model_lower = self.model.lower()
-            is_reasoning_model = any(x in model_lower for x in ["gpt-5", "o1", "o3", "deepseek"])
-
-            # For GPT-4 and GPT-4.1 models, cap max_completion_tokens to 32000
-            # For GPT-4o models, cap to 16384
-            is_gpt4_model = any(x in model_lower for x in ["gpt-4.1", "gpt-4-"])
-            is_gpt4o_model = "gpt-4o" in model_lower
            if max_completion_tokens is not None:
-                if is_gpt4o_model and max_completion_tokens > 16384:
-                    max_completion_tokens = 16384
-                elif is_gpt4_model and max_completion_tokens > 32000:
-                    max_completion_tokens = 32000
-                # For reasoning models, max_completion_tokens includes reasoning + output tokens
-                # Enforce minimum of 16000 to ensure enough space for both
-                if is_reasoning_model and max_completion_tokens < 16000:
-                    max_completion_tokens = 16000
                call_params["max_completion_tokens"] = max_completion_tokens
-
-            # GPT-5/o1/o3 family doesn't support custom temperature (only default 1)
-            if temperature is not None and not is_reasoning_model:
+            if temperature is not None:
                call_params["temperature"] = temperature

-            # Set reasoning_effort for reasoning models (OpenAI gpt-5, o1, o3)
-            if is_reasoning_model:
-                call_params["reasoning_effort"] = self.reasoning_effort
-
            # Provider-specific parameters
            if self.provider == "groq":
                call_params["seed"] = DEFAULT_LLM_SEED
-                extra_body = {"service_tier": "auto"}
-                # Only add reasoning parameters for reasoning models
-                if is_reasoning_model:
-                    extra_body["include_reasoning"] = False
-                call_params["extra_body"] = extra_body
+                call_params["extra_body"] = {
+                    "service_tier": "auto",
+                    "reasoning_effort": self.reasoning_effort,
+                    "include_reasoning": False,
+                }

            last_exception = None

@@ -227,46 +152,20 @@ class LLMProvider:
                try:
                    if response_format is not None:
                        # Add schema to system message for JSON mode
-                        if hasattr(response_format, "model_json_schema"):
+                        if hasattr(response_format, 'model_json_schema'):
                            schema = response_format.model_json_schema()
                            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"

-                            if call_params["messages"] and call_params["messages"][0].get("role") == "system":
-                                call_params["messages"][0]["content"] += schema_msg
-                            elif call_params["messages"]:
-                                call_params["messages"][0]["content"] = (
-                                    schema_msg + "\n\n" + call_params["messages"][0]["content"]
-                                )
+                            if call_params['messages'] and call_params['messages'][0].get('role') == 'system':
+                                call_params['messages'][0]['content'] += schema_msg
+                            elif call_params['messages']:
+                                call_params['messages'][0]['content'] = schema_msg + "\n\n" + call_params['messages'][0]['content']

-                        call_params["response_format"] = {"type": "json_object"}
+                        call_params['response_format'] = {"type": "json_object"}
                        response = await self._client.chat.completions.create(**call_params)

                        content = response.choices[0].message.content
-
-                        # Log raw LLM response for debugging JSON parse issues
-                        try:
-                            json_data = json.loads(content)
-                        except json.JSONDecodeError as json_err:
-                            # Truncate content for logging (first 500 and last 200 chars)
-                            content_preview = content[:500] if content else "<empty>"
-                            if content and len(content) > 700:
-                                content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                            logger.warning(
-                                f"JSON parse error from LLM response (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                                f"  Model: {self.provider}/{self.model}\n"
-                                f"  Content length: {len(content) if content else 0} chars\n"
-                                f"  Content preview: {content_preview!r}\n"
-                                f"  Finish reason: {response.choices[0].finish_reason if response.choices else 'unknown'}"
-                            )
-                            # Retry on JSON parse errors - LLM may return valid JSON on next attempt
-                            if attempt < max_retries:
-                                backoff = min(initial_backoff * (2**attempt), max_backoff)
-                                await asyncio.sleep(backoff)
-                                last_exception = json_err
-                                continue
-                            else:
-                                logger.error(f"JSON parse error after {max_retries + 1} attempts, giving up")
-                                raise
+                        json_data = json.loads(content)

                        if skip_validation:
                            result = json_data
@@ -282,8 +181,8 @@ class LLMProvider:
                    if duration > 10.0:
                        ratio = max(1, usage.completion_tokens) / usage.prompt_tokens
                        cached_tokens = 0
-                        if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
-                            cached_tokens = getattr(usage.prompt_tokens_details, "cached_tokens", 0) or 0
+                        if hasattr(usage, 'prompt_tokens_details') and usage.prompt_tokens_details:
+                            cached_tokens = getattr(usage.prompt_tokens_details, 'cached_tokens', 0) or 0
                        cache_info = f", cached_tokens={cached_tokens}" if cached_tokens > 0 else ""
                        logger.info(
                            f"slow llm call: model={self.provider}/{self.model}, "
@@ -296,19 +195,14 @@ class LLMProvider:
                except LengthFinishReasonError as e:
                    logger.warning(f"LLM output exceeded token limits: {str(e)}")
                    raise OutputTooLongError(
-                        "LLM output exceeded token limits. Input may need to be split into smaller chunks."
+                        f"LLM output exceeded token limits. Input may need to be split into smaller chunks."
                    ) from e

                except APIConnectionError as e:
                    last_exception = e
                    if attempt < max_retries:
-                        status_code = getattr(e, "status_code", None) or getattr(
-                            getattr(e, "response", None), "status_code", None
-                        )
-                        logger.warning(
-                            f"Connection error, retrying... (attempt {attempt + 1}/{max_retries + 1}) - status_code={status_code}, message={e}"
-                        )
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
+                        logger.warning(f"Connection error, retrying... (attempt {attempt + 1}/{max_retries + 1})")
+                        backoff = min(initial_backoff * (2 ** attempt), max_backoff)
                        await asyncio.sleep(backoff)
                        continue
                    else:
@@ -316,14 +210,14 @@ class LLMProvider:
                        raise

                except APIStatusError as e:
-                    # Fast fail only on 401 (unauthorized) and 403 (forbidden) - these won't recover with retries
-                    if e.status_code in (401, 403):
-                        logger.error(f"Auth error (HTTP {e.status_code}), not retrying: {str(e)}")
+                    # Fast fail on 4xx client errors (except 429 rate limit and 498 which is treated as server error)
+                    if 400 <= e.status_code < 500 and e.status_code not in (429, 498):
+                        logger.error(f"Client error (HTTP {e.status_code}), not retrying: {str(e)}")
                        raise

                    last_exception = e
                    if attempt < max_retries:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
+                        backoff = min(initial_backoff * (2 ** attempt), max_backoff)
                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
                        sleep_time = backoff + jitter
                        await asyncio.sleep(sleep_time)
@@ -337,306 +231,55 @@ class LLMProvider:

            if last_exception:
                raise last_exception
-            raise RuntimeError("LLM call failed after all retries with no exception captured")
-
-    async def _call_ollama_native(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any,
-        max_completion_tokens: int | None,
-        temperature: float | None,
-        max_retries: int,
-        initial_backoff: float,
-        max_backoff: float,
-        skip_validation: bool,
-        start_time: float,
-    ) -> Any:
-        """
-        Call Ollama using native API with JSON schema enforcement.
-
-        Ollama's native API supports passing a full JSON schema in the 'format' parameter,
-        which provides better structured output control than the OpenAI-compatible API.
-        """
-        # Get the JSON schema from the Pydantic model
-        schema = response_format.model_json_schema() if hasattr(response_format, "model_json_schema") else None
-
-        # Build the base URL for Ollama's native API
-        # Default OpenAI-compatible URL is http://localhost:11434/v1
-        # Native API is at http://localhost:11434/api/chat
-        base_url = self.base_url or "http://localhost:11434/v1"
-        if base_url.endswith("/v1"):
-            native_url = base_url[:-3] + "/api/chat"
-        else:
-            native_url = base_url.rstrip("/") + "/api/chat"
-
-        # Build request payload
-        payload = {
-            "model": self.model,
-            "messages": messages,
-            "stream": False,
-        }
-
-        # Add schema as format parameter for structured output
-        if schema:
-            payload["format"] = schema
-
-        # Add optional parameters with optimized defaults for Ollama
-        # Benchmarking shows num_ctx=16384 + num_batch=512 is optimal
-        options = {
-            "num_ctx": 16384,  # 16k context window for larger prompts
-            "num_batch": 512,  # Optimal batch size for prompt processing
-        }
-        if max_completion_tokens:
-            options["num_predict"] = max_completion_tokens
-        if temperature is not None:
-            options["temperature"] = temperature
-        payload["options"] = options
-
-        last_exception = None
-
-        async with httpx.AsyncClient(timeout=300.0) as client:
-            for attempt in range(max_retries + 1):
-                try:
-                    response = await client.post(native_url, json=payload)
-                    response.raise_for_status()
-
-                    result = response.json()
-                    content = result.get("message", {}).get("content", "")
-
-                    # Parse JSON response
-                    try:
-                        json_data = json.loads(content)
-                    except json.JSONDecodeError as json_err:
-                        content_preview = content[:500] if content else "<empty>"
-                        if content and len(content) > 700:
-                            content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                        logger.warning(
-                            f"Ollama JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                            f"  Model: ollama/{self.model}\n"
-                            f"  Content length: {len(content) if content else 0} chars\n"
-                            f"  Content preview: {content_preview!r}"
-                        )
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = json_err
-                            continue
-                        else:
-                            raise
-
-                    # Validate against Pydantic model or return raw JSON
-                    if skip_validation:
-                        return json_data
-                    else:
-                        return response_format.model_validate(json_data)
-
-                except httpx.HTTPStatusError as e:
-                    last_exception = e
-                    if attempt < max_retries:
-                        logger.warning(
-                            f"Ollama HTTP error (attempt {attempt + 1}/{max_retries + 1}): {e.response.status_code}"
-                        )
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        logger.error(f"Ollama HTTP error after {max_retries + 1} attempts: {e}")
-                        raise
-
-                except httpx.RequestError as e:
-                    last_exception = e
-                    if attempt < max_retries:
-                        logger.warning(f"Ollama connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        logger.error(f"Ollama connection error after {max_retries + 1} attempts: {e}")
-                        raise
-
-                except Exception as e:
-                    logger.error(f"Unexpected error during Ollama call: {type(e).__name__}: {e}")
-                    raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Ollama call failed after all retries")
-
-    async def _call_gemini(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None,
-        max_retries: int,
-        initial_backoff: float,
-        max_backoff: float,
-        skip_validation: bool,
-        start_time: float,
-    ) -> Any:
-        """Handle Gemini-specific API calls."""
-        # Convert OpenAI-style messages to Gemini format
-        system_instruction = None
-        gemini_contents = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                if system_instruction:
-                    system_instruction += "\n\n" + content
-                else:
-                    system_instruction = content
-            elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
-            else:
-                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            if system_instruction:
-                system_instruction += schema_msg
-            else:
-                system_instruction = schema_msg
-
-        # Build generation config
-        config_kwargs = {}
-        if system_instruction:
-            config_kwargs["system_instruction"] = system_instruction
-        if response_format is not None:
-            config_kwargs["response_mime_type"] = "application/json"
-            config_kwargs["response_schema"] = response_format
-
-        generation_config = genai_types.GenerateContentConfig(**config_kwargs) if config_kwargs else None
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._gemini_client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=generation_config,
-                )
-
-                content = response.text
-
-                # Handle empty response
-                if content is None:
-                    block_reason = None
-                    if hasattr(response, "candidates") and response.candidates:
-                        candidate = response.candidates[0]
-                        if hasattr(candidate, "finish_reason"):
-                            block_reason = candidate.finish_reason
-
-                    if attempt < max_retries:
-                        logger.warning(f"Gemini returned empty response (reason: {block_reason}), retrying...")
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        raise RuntimeError(f"Gemini returned empty response after {max_retries + 1} attempts")
-
-                if response_format is not None:
-                    json_data = json.loads(content)
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Log slow calls
-                duration = time.time() - start_time
-                if duration > 10.0 and hasattr(response, "usage_metadata") and response.usage_metadata:
-                    usage = response.usage_metadata
-                    logger.info(
-                        f"slow llm call: model={self.provider}/{self.model}, "
-                        f"input_tokens={usage.prompt_token_count}, output_tokens={usage.candidates_token_count}, "
-                        f"time={duration:.3f}s"
-                    )
-
-                return result
-
-            except json.JSONDecodeError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    logger.warning("Gemini returned invalid JSON, retrying...")
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Gemini returned invalid JSON after {max_retries + 1} attempts")
-                    raise
-
-            except genai_errors.APIError as e:
-                # Fast fail only on 401 (unauthorized) and 403 (forbidden) - these won't recover with retries
-                if e.code in (401, 403):
-                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
-                    raise
-
-                # Retry on retryable errors (rate limits, server errors, and other client errors like 400)
-                if e.code in (400, 429, 500, 502, 503, 504) or (e.code and e.code >= 500):
-                    last_exception = e
-                    if attempt < max_retries:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                        await asyncio.sleep(backoff + jitter)
-                    else:
-                        logger.error(f"Gemini API error after {max_retries + 1} attempts: {str(e)}")
-                        raise
-                else:
-                    logger.error(f"Gemini API error: {type(e).__name__}: {str(e)}")
-                    raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Gemini call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Gemini call failed after all retries")
+            raise RuntimeError(f"LLM call failed after all retries with no exception captured")

    @classmethod
    def for_memory(cls) -> "LLMProvider":
        """Create provider for memory operations from environment variables."""
        provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq")
        api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
-        if not api_key:
-            raise ValueError("HINDSIGHT_API_LLM_API_KEY environment variable is required")
        base_url = os.getenv("HINDSIGHT_API_LLM_BASE_URL", "")
        model = os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b")

-        return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="low")
+        return cls(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            reasoning_effort="low"
+        )

    @classmethod
    def for_answer_generation(cls) -> "LLMProvider":
        """Create provider for answer generation. Falls back to memory config if not set."""
        provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
        api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY"))
-        if not api_key:
-            raise ValueError(
-                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_ANSWER_LLM_API_KEY environment variable is required"
-            )
        base_url = os.getenv("HINDSIGHT_API_ANSWER_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
        model = os.getenv("HINDSIGHT_API_ANSWER_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))

-        return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="high")
+        return cls(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            reasoning_effort="high"
+        )

    @classmethod
    def for_judge(cls) -> "LLMProvider":
        """Create provider for judge/evaluator operations. Falls back to memory config if not set."""
        provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
        api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY"))
-        if not api_key:
-            raise ValueError(
-                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_JUDGE_LLM_API_KEY environment variable is required"
-            )
        base_url = os.getenv("HINDSIGHT_API_JUDGE_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
        model = os.getenv("HINDSIGHT_API_JUDGE_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))

-        return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="high")
+        return cls(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            reasoning_effort="high"
+        )


 # Backwards compatibility alias
@@ -4,12 +4,11 @@ Query analysis abstraction for the memory system.
 Provides an interface for analyzing natural language queries to extract
 structured information like temporal constraints.
 """
-
+from abc import ABC, abstractmethod
+from typing import Optional
+from datetime import datetime, timedelta
 import logging
 import re
-from abc import ABC, abstractmethod
-from datetime import datetime, timedelta
-
 from pydantic import BaseModel, Field

 logger = logging.getLogger(__name__)
@@ -21,7 +20,6 @@ class TemporalConstraint(BaseModel):

    Represents a time range with start and end dates.
    """
-
    start_date: datetime = Field(description="Start of the time range (inclusive)")
    end_date: datetime = Field(description="End of the time range (inclusive)")

@@ -35,9 +33,9 @@ class QueryAnalysis(BaseModel):

    Contains extracted structured information like temporal constraints.
    """
-
-    temporal_constraint: TemporalConstraint | None = Field(
-        default=None, description="Extracted temporal constraint, if any"
+    temporal_constraint: Optional[TemporalConstraint] = Field(
+        default=None,
+        description="Extracted temporal constraint, if any"
    )


@@ -60,7 +58,9 @@ class QueryAnalyzer(ABC):
        pass

    @abstractmethod
-    def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
+    def analyze(
+        self, query: str, reference_date: Optional[datetime] = None
+    ) -> QueryAnalysis:
        """
        Analyze a natural language query.

@@ -95,10 +95,11 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        """Load dateparser (lazy import)."""
        if self._search_dates is None:
            from dateparser.search import search_dates
-
            self._search_dates = search_dates

-    def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
+    def analyze(
+        self, query: str, reference_date: Optional[datetime] = None
+    ) -> QueryAnalysis:
        """
        Analyze query using dateparser.

@@ -125,9 +126,9 @@ class DateparserQueryAnalyzer(QueryAnalyzer):

        # Use dateparser's search_dates to find temporal expressions
        settings = {
-            "RELATIVE_BASE": reference_date,
-            "PREFER_DATES_FROM": "past",
-            "RETURN_AS_TIMEZONE_AWARE": False,
+            'RELATIVE_BASE': reference_date,
+            'PREFER_DATES_FROM': 'past',
+            'RETURN_AS_TIMEZONE_AWARE': False,
        }

        results = self._search_dates(query, settings=settings)
@@ -136,8 +137,11 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
            return QueryAnalysis(temporal_constraint=None)

        # Filter out false positives (common words parsed as dates)
-        false_positives = {"do", "may", "march", "will", "can", "sat", "sun", "mon", "tue", "wed", "thu", "fri"}
-        valid_results = [(text, date) for text, date in results if text.lower() not in false_positives or len(text) > 3]
+        false_positives = {'do', 'may', 'march', 'will', 'can', 'sat', 'sun', 'mon', 'tue', 'wed', 'thu', 'fri'}
+        valid_results = [
+            (text, date) for text, date in results
+            if text.lower() not in false_positives or len(text) > 3
+        ]

        if not valid_results:
            return QueryAnalysis(temporal_constraint=None)
@@ -149,94 +153,84 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        start_date = parsed_date.replace(hour=0, minute=0, second=0, microsecond=0)
        end_date = parsed_date.replace(hour=23, minute=59, second=59, microsecond=999999)

-        return QueryAnalysis(temporal_constraint=TemporalConstraint(start_date=start_date, end_date=end_date))
+        return QueryAnalysis(
+            temporal_constraint=TemporalConstraint(
+                start_date=start_date,
+                end_date=end_date
+            )
+        )

-    def _extract_period(self, query: str, reference_date: datetime) -> TemporalConstraint | None:
+    def _extract_period(
+        self, query: str, reference_date: datetime
+    ) -> Optional[TemporalConstraint]:
        """
        Extract period-based temporal expressions (week, month, year, weekend).

        These need special handling as they represent date ranges, not single dates.
        Supports multiple languages.
        """
-
        def constraint(start: datetime, end: datetime) -> TemporalConstraint:
            return TemporalConstraint(
                start_date=start.replace(hour=0, minute=0, second=0, microsecond=0),
-                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999),
+                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999)
            )

        # Yesterday patterns (English, Spanish, Italian, French, German)
-        if re.search(r"\b(yesterday|ayer|ieri|hier|gestern)\b", query, re.IGNORECASE):
+        if re.search(r'\b(yesterday|ayer|ieri|hier|gestern)\b', query, re.IGNORECASE):
            d = reference_date - timedelta(days=1)
            return constraint(d, d)

        # Today patterns
-        if re.search(r"\b(today|hoy|oggi|aujourd\'?hui|heute)\b", query, re.IGNORECASE):
+        if re.search(r'\b(today|hoy|oggi|aujourd\'?hui|heute)\b', query, re.IGNORECASE):
            return constraint(reference_date, reference_date)

        # "a couple of days ago" / "a few days ago" patterns
        # These are imprecise so we create a range
-        if re.search(r"\b(a\s+)?couple\s+(of\s+)?days?\s+ago\b", query, re.IGNORECASE):
+        if re.search(r'\b(a\s+)?couple\s+(of\s+)?days?\s+ago\b', query, re.IGNORECASE):
            # "a couple of days" = approximately 2 days, give range of 1-3 days
            return constraint(reference_date - timedelta(days=3), reference_date - timedelta(days=1))

-        if re.search(r"\b(a\s+)?few\s+days?\s+ago\b", query, re.IGNORECASE):
+        if re.search(r'\b(a\s+)?few\s+days?\s+ago\b', query, re.IGNORECASE):
            # "a few days" = approximately 3-4 days, give range of 2-5 days
            return constraint(reference_date - timedelta(days=5), reference_date - timedelta(days=2))

        # "a couple of weeks ago" / "a few weeks ago" patterns
-        if re.search(r"\b(a\s+)?couple\s+(of\s+)?weeks?\s+ago\b", query, re.IGNORECASE):
+        if re.search(r'\b(a\s+)?couple\s+(of\s+)?weeks?\s+ago\b', query, re.IGNORECASE):
            # "a couple of weeks" = approximately 2 weeks, give range of 1-3 weeks
            return constraint(reference_date - timedelta(weeks=3), reference_date - timedelta(weeks=1))

-        if re.search(r"\b(a\s+)?few\s+weeks?\s+ago\b", query, re.IGNORECASE):
+        if re.search(r'\b(a\s+)?few\s+weeks?\s+ago\b', query, re.IGNORECASE):
            # "a few weeks" = approximately 3-4 weeks, give range of 2-5 weeks
            return constraint(reference_date - timedelta(weeks=5), reference_date - timedelta(weeks=2))

        # "a couple of months ago" / "a few months ago" patterns
-        if re.search(r"\b(a\s+)?couple\s+(of\s+)?months?\s+ago\b", query, re.IGNORECASE):
+        if re.search(r'\b(a\s+)?couple\s+(of\s+)?months?\s+ago\b', query, re.IGNORECASE):
            # "a couple of months" = approximately 2 months, give range of 1-3 months
            return constraint(reference_date - timedelta(days=90), reference_date - timedelta(days=30))

-        if re.search(r"\b(a\s+)?few\s+months?\s+ago\b", query, re.IGNORECASE):
+        if re.search(r'\b(a\s+)?few\s+months?\s+ago\b', query, re.IGNORECASE):
            # "a few months" = approximately 3-4 months, give range of 2-5 months
            return constraint(reference_date - timedelta(days=150), reference_date - timedelta(days=60))

        # Last week patterns (English, Spanish, Italian, French, German)
-        if re.search(
-            r"\b(last\s+week|la\s+semana\s+pasada|la\s+settimana\s+scorsa|la\s+semaine\s+derni[eè]re|letzte\s+woche)\b",
-            query,
-            re.IGNORECASE,
-        ):
+        if re.search(r'\b(last\s+week|la\s+semana\s+pasada|la\s+settimana\s+scorsa|la\s+semaine\s+derni[eè]re|letzte\s+woche)\b', query, re.IGNORECASE):
            start = reference_date - timedelta(days=reference_date.weekday() + 7)
            return constraint(start, start + timedelta(days=6))

        # Last month patterns
-        if re.search(
-            r"\b(last\s+month|el\s+mes\s+pasado|il\s+mese\s+scorso|le\s+mois\s+dernier|letzten?\s+monat)\b",
-            query,
-            re.IGNORECASE,
-        ):
+        if re.search(r'\b(last\s+month|el\s+mes\s+pasado|il\s+mese\s+scorso|le\s+mois\s+dernier|letzten?\s+monat)\b', query, re.IGNORECASE):
            first = reference_date.replace(day=1)
            end = first - timedelta(days=1)
            start = end.replace(day=1)
            return constraint(start, end)

        # Last year patterns
-        if re.search(
-            r"\b(last\s+year|el\s+a[ñn]o\s+pasado|l\'anno\s+scorso|l\'ann[ée]e\s+derni[eè]re|letztes?\s+jahr)\b",
-            query,
-            re.IGNORECASE,
-        ):
+        if re.search(r'\b(last\s+year|el\s+a[ñn]o\s+pasado|l\'anno\s+scorso|l\'ann[ée]e\s+derni[eè]re|letztes?\s+jahr)\b', query, re.IGNORECASE):
            year = reference_date.year - 1
            return constraint(datetime(year, 1, 1), datetime(year, 12, 31))

        # Last weekend patterns
-        if re.search(
-            r"\b(last\s+weekend|el\s+fin\s+de\s+semana\s+pasado|lo\s+scorso\s+fine\s+settimana|le\s+week-?end\s+dernier|letztes?\s+wochenende)\b",
-            query,
-            re.IGNORECASE,
-        ):
+        if re.search(r'\b(last\s+weekend|el\s+fin\s+de\s+semana\s+pasado|lo\s+scorso\s+fine\s+settimana|le\s+week-?end\s+dernier|letztes?\s+wochenende)\b', query, re.IGNORECASE):
            days_since_sat = (reference_date.weekday() + 2) % 7
            if days_since_sat == 0:
                days_since_sat = 7
@@ -245,22 +239,22 @@ class DateparserQueryAnalyzer(QueryAnalyzer):

        # Month + Year patterns (e.g., "June 2024", "junio 2024", "giugno 2024")
        month_patterns = {
-            "january|enero|gennaio|janvier|januar": 1,
-            "february|febrero|febbraio|f[ée]vrier|februar": 2,
-            "march|marzo|mars|m[äa]rz": 3,
-            "april|abril|aprile|avril": 4,
-            "may|mayo|maggio|mai": 5,
-            "june|junio|giugno|juin|juni": 6,
-            "july|julio|luglio|juillet|juli": 7,
-            "august|agosto|ao[uû]t": 8,
-            "september|septiembre|settembre|septembre": 9,
-            "october|octubre|ottobre|octobre|oktober": 10,
-            "november|noviembre|novembre": 11,
-            "december|diciembre|dicembre|d[ée]cembre|dezember": 12,
+            'january|enero|gennaio|janvier|januar': 1,
+            'february|febrero|febbraio|f[ée]vrier|februar': 2,
+            'march|marzo|mars|m[äa]rz': 3,
+            'april|abril|aprile|avril': 4,
+            'may|mayo|maggio|mai': 5,
+            'june|junio|giugno|juin|juni': 6,
+            'july|julio|luglio|juillet|juli': 7,
+            'august|agosto|ao[uû]t': 8,
+            'september|septiembre|settembre|septembre': 9,
+            'october|octubre|ottobre|octobre|oktober': 10,
+            'november|noviembre|novembre': 11,
+            'december|diciembre|dicembre|d[ée]cembre|dezember': 12,
        }

        for pattern, month_num in month_patterns.items():
-            match = re.search(rf"\b({pattern})\s+(\d{{4}})\b", query, re.IGNORECASE)
+            match = re.search(rf'\b({pattern})\s+(\d{{4}})\b', query, re.IGNORECASE)
            if match:
                year = int(match.group(2))
                start = datetime(year, month_num, 1)
@@ -285,7 +279,11 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
    - Model size: ~80M params (~300MB download)
    """

-    def __init__(self, model_name: str = "google/flan-t5-small", device: str = "cpu"):
+    def __init__(
+        self,
+        model_name: str = "google/flan-t5-small",
+        device: str = "cpu"
+    ):
        """
        Initialize T5 query analyzer.

@@ -306,10 +304,11 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
            return

        try:
-            from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+            from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
        except ImportError:
            raise ImportError(
-                "transformers is required for TransformerQueryAnalyzer. Install it with: pip install transformers"
+                "transformers is required for TransformerQueryAnalyzer. "
+                "Install it with: pip install transformers"
            )

        logger.info(f"Loading query analyzer model: {self.model_name}...")
@@ -323,7 +322,9 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        """Lazy load the T5 model for temporal extraction (calls load())."""
        self.load()

-    def _extract_with_rules(self, query: str, reference_date: datetime) -> TemporalConstraint | None:
+    def _extract_with_rules(
+        self, query: str, reference_date: datetime
+    ) -> Optional[TemporalConstraint]:
        """
        Extract temporal expressions using rule-based patterns.

@@ -331,7 +332,6 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        patterns that need model-based extraction.
        """
        import re
-
        query_lower = query.lower()

        def get_last_weekday(weekday: int) -> datetime:
@@ -343,60 +343,50 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        def constraint(start: datetime, end: datetime) -> TemporalConstraint:
            return TemporalConstraint(
                start_date=start.replace(hour=0, minute=0, second=0, microsecond=0),
-                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999),
+                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999)
            )

        # Yesterday
-        if re.search(r"\byesterday\b", query_lower):
+        if re.search(r'\byesterday\b', query_lower):
            d = reference_date - timedelta(days=1)
            return constraint(d, d)

        # Last week
-        if re.search(r"\blast\s+week\b", query_lower):
+        if re.search(r'\blast\s+week\b', query_lower):
            start = reference_date - timedelta(days=reference_date.weekday() + 7)
            return constraint(start, start + timedelta(days=6))

        # Last month
-        if re.search(r"\blast\s+month\b", query_lower):
+        if re.search(r'\blast\s+month\b', query_lower):
            first = reference_date.replace(day=1)
            end = first - timedelta(days=1)
            start = end.replace(day=1)
            return constraint(start, end)

        # Last year
-        if re.search(r"\blast\s+year\b", query_lower):
+        if re.search(r'\blast\s+year\b', query_lower):
            y = reference_date.year - 1
            return constraint(datetime(y, 1, 1), datetime(y, 12, 31))

        # Last weekend
-        if re.search(r"\blast\s+weekend\b", query_lower):
+        if re.search(r'\blast\s+weekend\b', query_lower):
            sat = get_last_weekday(5)
            return constraint(sat, sat + timedelta(days=1))

        # Last <weekday>
-        weekdays = {"monday": 0, "tuesday": 1, "wednesday": 2, "thursday": 3, "friday": 4, "saturday": 5, "sunday": 6}
+        weekdays = {'monday': 0, 'tuesday': 1, 'wednesday': 2, 'thursday': 3,
+                    'friday': 4, 'saturday': 5, 'sunday': 6}
        for name, num in weekdays.items():
-            if re.search(rf"\blast\s+{name}\b", query_lower):
+            if re.search(rf'\blast\s+{name}\b', query_lower):
                d = get_last_weekday(num)
                return constraint(d, d)

        # Month + Year: "June 2024", "in March 2023"
-        months = {
-            "january": 1,
-            "february": 2,
-            "march": 3,
-            "april": 4,
-            "may": 5,
-            "june": 6,
-            "july": 7,
-            "august": 8,
-            "september": 9,
-            "october": 10,
-            "november": 11,
-            "december": 12,
-        }
+        months = {'january': 1, 'february': 2, 'march': 3, 'april': 4, 'may': 5,
+                  'june': 6, 'july': 7, 'august': 8, 'september': 9, 'october': 10,
+                  'november': 11, 'december': 12}
        for name, num in months.items():
-            match = re.search(rf"\b{name}\s+(\d{{4}})\b", query_lower)
+            match = re.search(rf'\b{name}\s+(\d{{4}})\b', query_lower)
            if match:
                year = int(match.group(1))
                if num == 12:
@@ -407,7 +397,9 @@ class TransformerQueryAnalyzer(QueryAnalyzer):

        return None

-    def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
+    def analyze(
+        self, query: str, reference_date: Optional[datetime] = None
+    ) -> QueryAnalysis:
        """
        Analyze query for temporal expressions.

@@ -443,11 +435,11 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        last_saturday = get_last_weekday(5)

        # Build prompt for T5
-        prompt = f"""Today is {reference_date.strftime("%Y-%m-%d")}. Extract date range or "none".
+        prompt = f"""Today is {reference_date.strftime('%Y-%m-%d')}. Extract date range or "none".

 June 2024 = 2024-06-01 to 2024-06-30
-yesterday = {yesterday.strftime("%Y-%m-%d")} to {yesterday.strftime("%Y-%m-%d")}
-last Saturday = {last_saturday.strftime("%Y-%m-%d")} to {last_saturday.strftime("%Y-%m-%d")}
+yesterday = {yesterday.strftime('%Y-%m-%d')} to {yesterday.strftime('%Y-%m-%d')}
+last Saturday = {last_saturday.strftime('%Y-%m-%d')} to {last_saturday.strftime('%Y-%m-%d')}
 what is the weather = none
 {query} ="""

@@ -456,7 +448,13 @@ what is the weather = none
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with self._no_grad():
-            outputs = self._model.generate(**inputs, max_new_tokens=30, num_beams=3, do_sample=False, temperature=1.0)
+            outputs = self._model.generate(
+                **inputs,
+                max_new_tokens=30,
+                num_beams=3,
+                do_sample=False,
+                temperature=1.0
+            )

        result = self._tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

@@ -468,14 +466,14 @@ what is the weather = none
        """Get torch.no_grad context manager."""
        try:
            import torch
-
            return torch.no_grad()
        except ImportError:
            from contextlib import nullcontext
-
            return nullcontext()

-    def _parse_generated_output(self, result: str, reference_date: datetime) -> TemporalConstraint | None:
+    def _parse_generated_output(
+        self, result: str, reference_date: datetime
+    ) -> Optional[TemporalConstraint]:
        """
        Parse T5 generated output into TemporalConstraint.

@@ -494,8 +492,7 @@ what is the weather = none
        try:
            # Parse "YYYY-MM-DD to YYYY-MM-DD"
            import re
-
-            pattern = r"(\d{4}-\d{2}-\d{2})\s+to\s+(\d{4}-\d{2}-\d{2})"
+            pattern = r'(\d{4}-\d{2}-\d{2})\s+to\s+(\d{4}-\d{2}-\d{2})'
            match = re.search(pattern, result, re.IGNORECASE)

            if match:
@@ -516,7 +513,7 @@ what is the weather = none

                return TemporalConstraint(start_date=start_date, end_date=end_date)

-        except (ValueError, AttributeError):
+        except (ValueError, AttributeError) as e:
            return None

        return None
@@ -6,9 +6,9 @@ API response models should be kept separate and convert from these core models t
 API stability even if internal models change.
 """

-from typing import Any
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field, ConfigDict

-from pydantic import BaseModel, ConfigDict, Field

 # Valid fact types for recall operations (excludes 'observation' which is internal)
 VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "opinion"])
@@ -23,12 +23,17 @@ class DispositionTraits(BaseModel):
    - literalism: 1=flexible interpretation, 5=literal interpretation (how strictly to interpret information)
    - empathy: 1=detached, 5=empathetic (how much to consider emotional context)
    """
-
    skepticism: int = Field(ge=1, le=5, description="How skeptical vs trusting (1=trusting, 5=skeptical)")
    literalism: int = Field(ge=1, le=5, description="How literally to interpret information (1=flexible, 5=literal)")
    empathy: int = Field(ge=1, le=5, description="How much to consider emotional context (1=detached, 5=empathetic)")

-    model_config = ConfigDict(json_schema_extra={"example": {"skepticism": 3, "literalism": 3, "empathy": 3}})
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "skepticism": 3,
+            "literalism": 3,
+            "empathy": 3
+        }
+    })


 class MemoryFact(BaseModel):
@@ -38,44 +43,38 @@ class MemoryFact(BaseModel):
    This represents a unit of information stored in the memory system,
    including both the content and metadata.
    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "id": "123e4567-e89b-12d3-a456-426614174000",
-                "text": "Alice works at Google on the AI team",
-                "fact_type": "world",
-                "entities": ["Alice", "Google"],
-                "context": "work info",
-                "occurred_start": "2024-01-15T10:30:00Z",
-                "occurred_end": "2024-01-15T10:30:00Z",
-                "mentioned_at": "2024-01-15T10:30:00Z",
-                "document_id": "session_abc123",
-                "metadata": {"source": "slack"},
-                "chunk_id": "bank123_session_abc123_0",
-                "activation": 0.95,
-            }
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "id": "123e4567-e89b-12d3-a456-426614174000",
+            "text": "Alice works at Google on the AI team",
+            "fact_type": "world",
+            "entities": ["Alice", "Google"],
+            "context": "work info",
+            "occurred_start": "2024-01-15T10:30:00Z",
+            "occurred_end": "2024-01-15T10:30:00Z",
+            "mentioned_at": "2024-01-15T10:30:00Z",
+            "document_id": "session_abc123",
+            "metadata": {"source": "slack"},
+            "chunk_id": "bank123_session_abc123_0",
+            "activation": 0.95
        }
-    )
+    })

    id: str = Field(description="Unique identifier for the memory fact")
    text: str = Field(description="The actual text content of the memory")
    fact_type: str = Field(description="Type of fact: 'world', 'experience', 'opinion', or 'observation'")
-    entities: list[str] | None = Field(None, description="Entity names mentioned in this fact")
-    context: str | None = Field(None, description="Additional context for the memory")
-    occurred_start: str | None = Field(None, description="ISO format date when the event started occurring")
-    occurred_end: str | None = Field(None, description="ISO format date when the event ended occurring")
-    mentioned_at: str | None = Field(None, description="ISO format date when the fact was mentioned/learned")
-    document_id: str | None = Field(None, description="ID of the document this memory belongs to")
-    metadata: dict[str, str] | None = Field(None, description="User-defined metadata")
-    chunk_id: str | None = Field(
-        None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)"
-    )
+    entities: Optional[List[str]] = Field(None, description="Entity names mentioned in this fact")
+    context: Optional[str] = Field(None, description="Additional context for the memory")
+    occurred_start: Optional[str] = Field(None, description="ISO format date when the event started occurring")
+    occurred_end: Optional[str] = Field(None, description="ISO format date when the event ended occurring")
+    mentioned_at: Optional[str] = Field(None, description="ISO format date when the fact was mentioned/learned")
+    document_id: Optional[str] = Field(None, description="ID of the document this memory belongs to")
+    metadata: Optional[Dict[str, str]] = Field(None, description="User-defined metadata")
+    chunk_id: Optional[str] = Field(None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)")


 class ChunkInfo(BaseModel):
    """Information about a chunk."""
-
    chunk_text: str = Field(description="The raw chunk text")
    chunk_index: int = Field(description="Index of the chunk within the document")
    truncated: bool = Field(default=False, description="Whether the chunk was truncated due to token limits")
@@ -88,33 +87,35 @@ class RecallResult(BaseModel):
    Contains a list of matching memory facts and optional trace information
    for debugging and transparency.
    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "results": [
-                    {
-                        "id": "123e4567-e89b-12d3-a456-426614174000",
-                        "text": "Alice works at Google on the AI team",
-                        "fact_type": "world",
-                        "context": "work info",
-                        "occurred_start": "2024-01-15T10:30:00Z",
-                        "occurred_end": "2024-01-15T10:30:00Z",
-                        "activation": 0.95,
-                    }
-                ],
-                "trace": {"query": "What did Alice say about machine learning?", "num_results": 1},
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "results": [
+                {
+                    "id": "123e4567-e89b-12d3-a456-426614174000",
+                    "text": "Alice works at Google on the AI team",
+                    "fact_type": "world",
+                    "context": "work info",
+                    "occurred_start": "2024-01-15T10:30:00Z",
+                    "occurred_end": "2024-01-15T10:30:00Z",
+                    "activation": 0.95
+                }
+            ],
+            "trace": {
+                "query": "What did Alice say about machine learning?",
+                "num_results": 1
            }
        }
-    )
+    })

-    results: list[MemoryFact] = Field(description="List of memory facts matching the query")
-    trace: dict[str, Any] | None = Field(None, description="Trace information for debugging")
-    entities: dict[str, "EntityState"] | None = Field(
-        None, description="Entity states for entities mentioned in results (keyed by canonical name)"
+    results: List[MemoryFact] = Field(description="List of memory facts matching the query")
+    trace: Optional[Dict[str, Any]] = Field(None, description="Trace information for debugging")
+    entities: Optional[Dict[str, "EntityState"]] = Field(
+        None,
+        description="Entity states for entities mentioned in results (keyed by canonical name)"
    )
-    chunks: dict[str, ChunkInfo] | None = Field(
-        None, description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
+    chunks: Optional[Dict[str, ChunkInfo]] = Field(
+        None,
+        description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
    )


@@ -125,35 +126,37 @@ class ReflectResult(BaseModel):
    Contains the formulated answer, the facts it was based on (organized by type),
    and any new opinions that were formed during the reflection process.
    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "text": "Based on my knowledge, machine learning is being actively used in healthcare...",
-                "based_on": {
-                    "world": [
-                        {
-                            "id": "123e4567-e89b-12d3-a456-426614174000",
-                            "text": "Machine learning is used in medical diagnosis",
-                            "fact_type": "world",
-                            "context": "healthcare",
-                            "occurred_start": "2024-01-15T10:30:00Z",
-                            "occurred_end": "2024-01-15T10:30:00Z",
-                        }
-                    ],
-                    "experience": [],
-                    "opinion": [],
-                },
-                "new_opinions": ["Machine learning has great potential in healthcare"],
-            }
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "text": "Based on my knowledge, machine learning is being actively used in healthcare...",
+            "based_on": {
+                "world": [
+                    {
+                        "id": "123e4567-e89b-12d3-a456-426614174000",
+                        "text": "Machine learning is used in medical diagnosis",
+                        "fact_type": "world",
+                        "context": "healthcare",
+                        "occurred_start": "2024-01-15T10:30:00Z",
+                        "occurred_end": "2024-01-15T10:30:00Z"
+                    }
+                ],
+                "experience": [],
+                "opinion": []
+            },
+            "new_opinions": [
+                "Machine learning has great potential in healthcare"
+            ]
        }
-    )
+    })

    text: str = Field(description="The formulated answer text")
-    based_on: dict[str, list[MemoryFact]] = Field(
+    based_on: Dict[str, List[MemoryFact]] = Field(
        description="Facts used to formulate the answer, organized by type (world, experience, opinion)"
    )
-    new_opinions: list[str] = Field(default_factory=list, description="List of newly formed opinions during reflection")
+    new_opinions: List[str] = Field(
+        default_factory=list,
+        description="List of newly formed opinions during reflection"
+    )


 class Opinion(BaseModel):
@@ -163,12 +166,12 @@ class Opinion(BaseModel):
    Opinions represent the bank's formed perspectives on topics,
    with a confidence level indicating strength of belief.
    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {"text": "Machine learning has great potential in healthcare", "confidence": 0.85}
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "text": "Machine learning has great potential in healthcare",
+            "confidence": 0.85
        }
-    )
+    })

    text: str = Field(description="The opinion text")
    confidence: float = Field(description="Confidence score between 0.0 and 1.0")
@@ -181,15 +184,15 @@ class EntityObservation(BaseModel):
    Observations are objective facts synthesized from multiple memory facts
    about an entity, without personality influence.
    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {"text": "John is detail-oriented and works at Google", "mentioned_at": "2024-01-15T10:30:00Z"}
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "text": "John is detail-oriented and works at Google",
+            "mentioned_at": "2024-01-15T10:30:00Z"
        }
-    )
+    })

    text: str = Field(description="The observation text")
-    mentioned_at: str | None = Field(None, description="ISO format date when this observation was created")
+    mentioned_at: Optional[str] = Field(None, description="ISO format date when this observation was created")


 class EntityState(BaseModel):
@@ -198,22 +201,20 @@ class EntityState(BaseModel):

    Contains observations synthesized from facts about the entity.
    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "entity_id": "123e4567-e89b-12d3-a456-426614174000",
-                "canonical_name": "John",
-                "observations": [
-                    {"text": "John is detail-oriented", "mentioned_at": "2024-01-15T10:30:00Z"},
-                    {"text": "John works at Google on the AI team", "mentioned_at": "2024-01-14T09:00:00Z"},
-                ],
-            }
+    model_config = ConfigDict(json_schema_extra={
+        "example": {
+            "entity_id": "123e4567-e89b-12d3-a456-426614174000",
+            "canonical_name": "John",
+            "observations": [
+                {"text": "John is detail-oriented", "mentioned_at": "2024-01-15T10:30:00Z"},
+                {"text": "John works at Google on the AI team", "mentioned_at": "2024-01-14T09:00:00Z"}
+            ]
        }
-    )
+    })

    entity_id: str = Field(description="Unique identifier for the entity")
    canonical_name: str = Field(description="Canonical name of the entity")
-    observations: list[EntityObservation] = Field(
-        default_factory=list, description="List of observations about this entity"
+    observations: List[EntityObservation] = Field(
+        default_factory=list,
+        description="List of observations about this entity"
    )
@@ -12,16 +12,23 @@ This package contains modular components for the retain operation:
 - fact_storage: Handle fact insertion into database
 """

-from . import (
-    chunk_storage,
-    deduplication,
-    embedding_processing,
-    entity_processing,
-    fact_extraction,
-    fact_storage,
-    link_creation,
+from .types import (
+    RetainContent,
+    ExtractedFact,
+    ProcessedFact,
+    ChunkMetadata,
+    EntityRef,
+    CausalRelation,
+    RetainBatch
 )
-from .types import CausalRelation, ChunkMetadata, EntityRef, ExtractedFact, ProcessedFact, RetainBatch, RetainContent
+
+from . import fact_extraction
+from . import embedding_processing
+from . import deduplication
+from . import entity_processing
+from . import link_creation
+from . import chunk_storage
+from . import fact_storage

 __all__ = [
    # Types
@@ -5,12 +5,9 @@ bank profile utilities for disposition and background management.
 import json
 import logging
 import re
-from typing import TypedDict
-
+from typing import Dict, Optional, TypedDict
 from pydantic import BaseModel, Field
-
 from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
 from ..response_models import DispositionTraits

 logger = logging.getLogger(__name__)
@@ -24,7 +21,6 @@ DEFAULT_DISPOSITION = {

 class BankProfile(TypedDict):
    """Type for bank profile data."""
-
    name: str
    disposition: DispositionTraits
    background: str
@@ -32,7 +28,6 @@ class BankProfile(TypedDict):

 class BackgroundMergeResponse(BaseModel):
    """LLM response for background merge with disposition inference."""
-
    background: str = Field(description="Merged background in first person perspective")
    disposition: DispositionTraits = Field(description="Inferred disposition traits (skepticism, literalism, empathy)")

@@ -52,11 +47,11 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
    async with acquire_with_retry(pool) as conn:
        # Try to get existing bank
        row = await conn.fetchrow(
-            f"""
+            """
            SELECT name, disposition, background
-            FROM {fq_table("banks")} WHERE bank_id = $1
+            FROM banks WHERE bank_id = $1
            """,
-            bank_id,
+            bank_id
        )

        if row:
@@ -66,26 +61,36 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
                disposition_data = json.loads(disposition_data)

            return BankProfile(
-                name=row["name"], disposition=DispositionTraits(**disposition_data), background=row["background"]
+                name=row["name"],
+                disposition=DispositionTraits(**disposition_data),
+                background=row["background"]
            )

        # Bank doesn't exist, create with defaults
        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("banks")} (bank_id, name, disposition, background)
+            """
+            INSERT INTO banks (bank_id, name, disposition, background)
            VALUES ($1, $2, $3::jsonb, $4)
            ON CONFLICT (bank_id) DO NOTHING
            """,
            bank_id,
            bank_id,  # Default name is the bank_id
            json.dumps(DEFAULT_DISPOSITION),
-            "",
+            ""
        )

-        return BankProfile(name=bank_id, disposition=DispositionTraits(**DEFAULT_DISPOSITION), background="")
+        return BankProfile(
+            name=bank_id,
+            disposition=DispositionTraits(**DEFAULT_DISPOSITION),
+            background=""
+        )


-async def update_bank_disposition(pool, bank_id: str, disposition: dict[str, int]) -> None:
+async def update_bank_disposition(
+    pool,
+    bank_id: str,
+    disposition: Dict[str, int]
+) -> None:
    """
    Update bank disposition traits.

@@ -99,18 +104,24 @@ async def update_bank_disposition(pool, bank_id: str, disposition: dict[str, int

    async with acquire_with_retry(pool) as conn:
        await conn.execute(
-            f"""
-            UPDATE {fq_table("banks")}
+            """
+            UPDATE banks
            SET disposition = $2::jsonb,
                updated_at = NOW()
            WHERE bank_id = $1
            """,
            bank_id,
-            json.dumps(disposition),
+            json.dumps(disposition)
        )


-async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, update_disposition: bool = True) -> dict:
+async def merge_bank_background(
+    pool,
+    llm_config,
+    bank_id: str,
+    new_info: str,
+    update_disposition: bool = True
+) -> dict:
    """
    Merge new background information with existing background using LLM.
    Normalizes to first person ("I") and resolves conflicts.
@@ -131,7 +142,12 @@ async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, u
    current_background = profile["background"]

    # Use LLM to merge backgrounds and optionally infer disposition
-    result = await _llm_merge_background(llm_config, current_background, new_info, infer_disposition=update_disposition)
+    result = await _llm_merge_background(
+        llm_config,
+        current_background,
+        new_info,
+        infer_disposition=update_disposition
+    )

    merged_background = result["background"]
    inferred_disposition = result.get("disposition")
@@ -141,8 +157,8 @@ async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, u
        if inferred_disposition:
            # Update both background and disposition
            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
+                """
+                UPDATE banks
                SET background = $2,
                    disposition = $3::jsonb,
                    updated_at = NOW()
@@ -150,19 +166,19 @@ async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, u
                """,
                bank_id,
                merged_background,
-                json.dumps(inferred_disposition),
+                json.dumps(inferred_disposition)
            )
        else:
            # Update only background
            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
+                """
+                UPDATE banks
                SET background = $2,
                    updated_at = NOW()
                WHERE bank_id = $1
                """,
                bank_id,
-                merged_background,
+                merged_background
            )

    response = {"background": merged_background}
@@ -172,7 +188,12 @@ async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, u
    return response


-async def _llm_merge_background(llm_config, current: str, new_info: str, infer_disposition: bool = False) -> dict:
+async def _llm_merge_background(
+    llm_config,
+    current: str,
+    new_info: str,
+    infer_disposition: bool = False
+) -> dict:
    """
    Use LLM to intelligently merge background information.
    Optionally infer Big Five disposition traits from the merged background.
@@ -252,19 +273,25 @@ Merged background:"""
                    response_format=BackgroundMergeResponse,
                    scope="bank_background",
                    temperature=0.3,
-                    max_completion_tokens=8192,
+                    max_completion_tokens=8192
                )
                logger.info(f"Successfully got structured response: background={parsed.background[:100]}")

                # Convert Pydantic model to dict format
-                return {"background": parsed.background, "disposition": parsed.disposition.model_dump()}
+                return {
+                    "background": parsed.background,
+                    "disposition": parsed.disposition.model_dump()
+                }
            except Exception as e:
                logger.warning(f"Structured output failed, falling back to manual parsing: {e}")
                # Fall through to manual parsing below

        # Manual parsing fallback or non-disposition merge
        content = await llm_config.call(
-            messages=messages, scope="bank_background", temperature=0.3, max_completion_tokens=8192
+            messages=messages,
+            scope="bank_background",
+            temperature=0.3,
+            max_completion_tokens=8192
        )

        logger.info(f"LLM response for background merge (first 500 chars): {content[:500]}")
@@ -283,7 +310,7 @@ Merged background:"""
            # Method 2: Extract from markdown code blocks
            if result is None:
                # Remove markdown code blocks
-                code_block_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", content, re.DOTALL)
+                code_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL)
                if code_block_match:
                    try:
                        result = json.loads(code_block_match.group(1))
@@ -294,9 +321,7 @@ Merged background:"""
            # Method 3: Find nested JSON structure
            if result is None:
                # Look for JSON object with nested structure
-                json_match = re.search(
-                    r'\{[^{}]*"background"[^{}]*"disposition"[^{}]*\{[^{}]*\}[^{}]*\}', content, re.DOTALL
-                )
+                json_match = re.search(r'\{[^{}]*"background"[^{}]*"disposition"[^{}]*\{[^{}]*\}[^{}]*\}', content, re.DOTALL)
                if json_match:
                    try:
                        result = json.loads(json_match.group())
@@ -310,7 +335,7 @@ Merged background:"""
                # Fallback: use new_info as background with default disposition
                return {
                    "background": new_info if new_info else current if current else "",
-                    "disposition": DEFAULT_DISPOSITION.copy(),
+                    "disposition": DEFAULT_DISPOSITION.copy()
                }

            # Validate disposition values
@@ -362,9 +387,9 @@ async def list_banks(pool) -> list:
    """
    async with acquire_with_retry(pool) as conn:
        rows = await conn.fetch(
-            f"""
+            """
            SELECT bank_id, name, disposition, background, created_at, updated_at
-            FROM {fq_table("banks")}
+            FROM banks
            ORDER BY updated_at DESC
            """
        )
@@ -376,15 +401,13 @@ async def list_banks(pool) -> list:
            if isinstance(disposition_data, str):
                disposition_data = json.loads(disposition_data)

-            result.append(
-                {
-                    "bank_id": row["bank_id"],
-                    "name": row["name"],
-                    "disposition": disposition_data,
-                    "background": row["background"],
-                    "created_at": row["created_at"].isoformat() if row["created_at"] else None,
-                    "updated_at": row["updated_at"].isoformat() if row["updated_at"] else None,
-                }
-            )
+            result.append({
+                "bank_id": row["bank_id"],
+                "name": row["name"],
+                "disposition": disposition_data,
+                "background": row["background"],
+                "created_at": row["created_at"].isoformat() if row["created_at"] else None,
+                "updated_at": row["updated_at"].isoformat() if row["updated_at"] else None,
+            })

        return result
@@ -3,16 +3,20 @@ Chunk storage for retain pipeline.

 Handles storage of document chunks in the database.
 """
-
 import logging
+from typing import List, Dict, Optional

-from ..memory_engine import fq_table
 from .types import ChunkMetadata

 logger = logging.getLogger(__name__)


-async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[ChunkMetadata]) -> dict[int, str]:
+async def store_chunks_batch(
+    conn,
+    bank_id: str,
+    document_id: str,
+    chunks: List[ChunkMetadata]
+) -> Dict[int, str]:
    """
    Store document chunks in the database.

@@ -43,21 +47,24 @@ async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[

    # Batch insert all chunks
    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("chunks")} (chunk_id, document_id, bank_id, chunk_text, chunk_index)
+        """
+        INSERT INTO chunks (chunk_id, document_id, bank_id, chunk_text, chunk_index)
        SELECT * FROM unnest($1::text[], $2::text[], $3::text[], $4::text[], $5::integer[])
        """,
        chunk_ids,
        [document_id] * len(chunk_texts),
        [bank_id] * len(chunk_texts),
        chunk_texts,
-        chunk_indices,
+        chunk_indices
    )

    return chunk_id_map


-def map_facts_to_chunks(facts_chunk_indices: list[int], chunk_id_map: dict[int, str]) -> list[str | None]:
+def map_facts_to_chunks(
+    facts_chunk_indices: List[int],
+    chunk_id_map: Dict[int, str]
+) -> List[Optional[str]]:
    """
    Map fact chunk indices to chunk IDs.

@@ -3,17 +3,22 @@ Deduplication logic for retain pipeline.

 Checks for duplicate facts using semantic similarity and temporal proximity.
 """
-
 import logging
+from datetime import datetime
+from typing import List
 from collections import defaultdict
-from datetime import UTC

 from .types import ProcessedFact

 logger = logging.getLogger(__name__)


-async def check_duplicates_batch(conn, bank_id: str, facts: list[ProcessedFact], duplicate_checker_fn) -> list[bool]:
+async def check_duplicates_batch(
+    conn,
+    bank_id: str,
+    facts: List[ProcessedFact],
+    duplicate_checker_fn
+) -> List[bool]:
    """
    Check which facts are duplicates using batched time-window queries.

@@ -42,12 +47,16 @@ async def check_duplicates_batch(conn, bank_id: str, facts: list[ProcessedFact],

        # Defensive: if both are None (shouldn't happen), use now()
        if fact_date is None:
-            from datetime import datetime
-
-            fact_date = datetime.now(UTC)
+            from datetime import datetime, timezone
+            fact_date = datetime.now(timezone.utc)

        # Round to 12-hour bucket to group similar times
-        bucket_key = fact_date.replace(hour=(fact_date.hour // 12) * 12, minute=0, second=0, microsecond=0)
+        bucket_key = fact_date.replace(
+            hour=(fact_date.hour // 12) * 12,
+            minute=0,
+            second=0,
+            microsecond=0
+        )
        time_buckets[bucket_key].append((idx, fact))

    # Process each bucket in batch
@@ -59,7 +68,14 @@ async def check_duplicates_batch(conn, bank_id: str, facts: list[ProcessedFact],
        embeddings = [item[1].embedding for item in bucket_items]

        # Check duplicates for this time bucket
-        dup_flags = await duplicate_checker_fn(conn, bank_id, texts, embeddings, bucket_date, time_window_hours=24)
+        dup_flags = await duplicate_checker_fn(
+            conn,
+            bank_id,
+            texts,
+            embeddings,
+            bucket_date,
+            time_window_hours=24
+        )

        # Map results back to original indices
        for idx, is_dup in zip(indices, dup_flags):
@@ -68,7 +84,10 @@ async def check_duplicates_batch(conn, bank_id: str, facts: list[ProcessedFact],
    return all_is_duplicate


-def filter_duplicates(facts: list[ProcessedFact], is_duplicate_flags: list[bool]) -> list[ProcessedFact]:
+def filter_duplicates(
+    facts: List[ProcessedFact],
+    is_duplicate_flags: List[bool]
+) -> List[ProcessedFact]:
    """
    Filter out duplicate facts based on duplicate flags.

@@ -3,8 +3,9 @@ Embedding processing for retain pipeline.

 Handles augmenting fact texts with temporal information and generating embeddings.
 """
-
 import logging
+from typing import List
+from datetime import datetime

 from . import embedding_utils
 from .types import ExtractedFact
@@ -12,7 +13,7 @@ from .types import ExtractedFact
 logger = logging.getLogger(__name__)


-def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list[str]:
+def augment_texts_with_dates(facts: List[ExtractedFact], format_date_fn) -> List[str]:
    """
    Augment fact texts with readable dates for better temporal matching.

@@ -36,7 +37,10 @@ def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list
    return augmented_texts


-async def generate_embeddings_batch(embeddings_model, texts: list[str]) -> list[list[float]]:
+async def generate_embeddings_batch(
+    embeddings_model,
+    texts: List[str]
+) -> List[List[float]]:
    """
    Generate embeddings for a batch of texts.

@@ -50,6 +54,9 @@ async def generate_embeddings_batch(embeddings_model, texts: list[str]) -> list[
    if not texts:
        return []

-    embeddings = await embedding_utils.generate_embeddings_batch(embeddings_model, texts)
+    embeddings = await embedding_utils.generate_embeddings_batch(
+        embeddings_model,
+        texts
+    )

    return embeddings
@@ -4,11 +4,12 @@ Embedding generation utilities for memory units.

 import asyncio
 import logging
+from typing import List

 logger = logging.getLogger(__name__)


-def generate_embedding(embeddings_backend, text: str) -> list[float]:
+def generate_embedding(embeddings_backend, text: str) -> List[float]:
    """
    Generate embedding for text using the provided embeddings backend.

@@ -26,7 +27,7 @@ def generate_embedding(embeddings_backend, text: str) -> list[float]:
        raise Exception(f"Failed to generate embedding: {str(e)}")


-async def generate_embeddings_batch(embeddings_backend, texts: list[str]) -> list[list[float]]:
+async def generate_embeddings_batch(embeddings_backend, texts: List[str]) -> List[List[float]]:
    """
    Generate embeddings for multiple texts using the provided embeddings backend.

@@ -46,7 +47,7 @@ async def generate_embeddings_batch(embeddings_backend, texts: list[str]) -> lis
        embeddings = await loop.run_in_executor(
            None,  # Use default thread pool
            embeddings_backend.encode,
-            texts,
+            texts
        )
        return embeddings
    except Exception as e:
@@ -3,18 +3,24 @@ Entity processing for retain pipeline.

 Handles entity extraction, resolution, and link creation for stored facts.
 """
-
 import logging
+from typing import List, Tuple, Dict, Any
+from uuid import UUID

+from .types import ProcessedFact, EntityRef, EntityLink
 from . import link_utils
-from .types import EntityLink, ProcessedFact

 logger = logging.getLogger(__name__)


 async def process_entities_batch(
-    entity_resolver, conn, bank_id: str, unit_ids: list[str], facts: list[ProcessedFact], log_buffer: list[str] = None
-) -> list[EntityLink]:
+    entity_resolver,
+    conn,
+    bank_id: str,
+    unit_ids: List[str],
+    facts: List[ProcessedFact],
+    log_buffer: List[str] = None
+) -> List[EntityLink]:
    """
    Process entities for all facts and create entity links.

@@ -47,7 +53,8 @@ async def process_entities_batch(
    fact_dates = [fact.occurred_start if fact.occurred_start is not None else fact.mentioned_at for fact in facts]
    # Convert EntityRef objects to dict format expected by link_utils
    entities_per_fact = [
-        [{"text": entity.name, "type": "CONCEPT"} for entity in (fact.entities or [])] for fact in facts
+        [{'text': entity.name, 'type': 'CONCEPT'} for entity in (fact.entities or [])]
+        for fact in facts
    ]

    # Use existing link_utils function for entity processing
@@ -60,13 +67,16 @@ async def process_entities_batch(
        "",  # context (not used in current implementation)
        fact_dates,
        entities_per_fact,
-        log_buffer,  # Pass log_buffer for detailed logging
+        log_buffer  # Pass log_buffer for detailed logging
    )

    return entity_links


-async def insert_entity_links_batch(conn, entity_links: list[EntityLink]) -> None:
+async def insert_entity_links_batch(
+    conn,
+    entity_links: List[EntityLink]
+) -> None:
    """
    Insert entity links in batch.

@@ -4,17 +4,16 @@ Fact extraction from text using LLM.
 Extracts semantic facts, entities, and temporal information from text.
 Uses the LLMConfig wrapper for all LLM calls.
 """
-
-import asyncio
-import json
 import logging
+import os
+import json
 import re
+import asyncio
 from datetime import datetime, timedelta
-from typing import Literal
-
-from pydantic import BaseModel, ConfigDict, Field, field_validator
-
-from ..llm_wrapper import LLMConfig, OutputTooLongError
+from typing import List, Dict, Optional, Literal
+from openai import AsyncOpenAI
+from pydantic import BaseModel, Field, field_validator, ConfigDict
+from ..llm_wrapper import OutputTooLongError, LLMConfig


 def _sanitize_text(text: str) -> str:
@@ -32,12 +31,11 @@ def _sanitize_text(text: str) -> str:
        return text
    # Remove surrogate characters (U+D800 to U+DFFF) using regex
    # These are invalid in UTF-8 and cause encoding errors
-    return re.sub(r"[\ud800-\udfff]", "", text)
+    return re.sub(r'[\ud800-\udfff]', '', text)


 class Entity(BaseModel):
    """An entity extracted from text."""
-
    text: str = Field(
        description="The specific, named entity as it appears in the fact. Must be a proper noun or specific identifier."
    )
@@ -50,46 +48,42 @@ class Fact(BaseModel):
    This is what fact_extraction returns and what the rest of the pipeline expects.
    Combined fact text format: "what | when | where | who | why"
    """
-
    # Required fields
    fact: str = Field(description="Combined fact text: what | when | where | who | why")
    fact_type: Literal["world", "experience", "opinion"] = Field(description="Perspective: world/experience/opinion")

    # Optional temporal fields
-    occurred_start: str | None = None
-    occurred_end: str | None = None
-    mentioned_at: str | None = None
+    occurred_start: Optional[str] = None
+    occurred_end: Optional[str] = None
+    mentioned_at: Optional[str] = None

    # Optional location field
-    where: str | None = Field(
-        None, description="WHERE the fact occurred or is about (specific location, place, or area)"
-    )
+    where: Optional[str] = Field(None, description="WHERE the fact occurred or is about (specific location, place, or area)")

    # Optional structured data
-    entities: list[Entity] | None = None
-    causal_relations: list["CausalRelation"] | None = None
+    entities: Optional[List[Entity]] = None
+    causal_relations: Optional[List['CausalRelation']] = None


 class CausalRelation(BaseModel):
    """Causal relationship between facts."""
-
    target_fact_index: int = Field(
        description="Index of the related fact in the facts array (0-based). "
-        "This creates a directed causal link to another fact in the extraction."
+                   "This creates a directed causal link to another fact in the extraction."
    )
    relation_type: Literal["causes", "caused_by", "enables", "prevents"] = Field(
        description="Type of causal relationship: "
-        "'causes' = this fact directly causes the target fact, "
-        "'caused_by' = this fact was caused by the target fact, "
-        "'enables' = this fact enables/allows the target fact, "
-        "'prevents' = this fact prevents/blocks the target fact"
+                   "'causes' = this fact directly causes the target fact, "
+                   "'caused_by' = this fact was caused by the target fact, "
+                   "'enables' = this fact enables/allows the target fact, "
+                   "'prevents' = this fact prevents/blocks the target fact"
    )
    strength: float = Field(
        description="Strength of causal relationship (0.0 to 1.0). "
-        "1.0 = direct/strong causation, 0.5 = moderate, 0.3 = weak/indirect",
+                   "1.0 = direct/strong causation, 0.5 = moderate, 0.3 = weak/indirect",
        ge=0.0,
        le=1.0,
-        default=1.0,
+        default=1.0
    )


@@ -98,7 +92,9 @@ class ExtractedFact(BaseModel):

    model_config = ConfigDict(
        json_schema_mode="validation",
-        json_schema_extra={"required": ["what", "when", "where", "who", "why", "fact_type"]},
+        json_schema_extra={
+            "required": ["what", "when", "where", "who", "why", "fact_type"]
+        }
    )

    # ==========================================================================
@@ -107,43 +103,43 @@ class ExtractedFact(BaseModel):

    what: str = Field(
        description="WHAT happened - COMPLETE, DETAILED description with ALL specifics. "
-        "NEVER summarize or omit details. Include: exact actions, objects, quantities, specifics. "
-        "BE VERBOSE - capture every detail that was mentioned. "
-        "Example: 'Emily got married to Sarah at a rooftop garden ceremony with 50 guests attending and a live jazz band playing' "
-        "NOT: 'A wedding happened' or 'Emily got married'"
+                   "NEVER summarize or omit details. Include: exact actions, objects, quantities, specifics. "
+                   "BE VERBOSE - capture every detail that was mentioned. "
+                   "Example: 'Emily got married to Sarah at a rooftop garden ceremony with 50 guests attending and a live jazz band playing' "
+                   "NOT: 'A wedding happened' or 'Emily got married'"
    )

    when: str = Field(
        description="WHEN it happened - ALWAYS include temporal information if mentioned. "
-        "Include: specific dates, times, durations, relative time references. "
-        "Examples: 'on June 15th, 2024 at 3pm', 'last weekend', 'for the past 3 years', 'every morning at 6am'. "
-        "Write 'N/A' ONLY if absolutely no temporal context exists. Prefer converting to absolute dates when possible."
+                   "Include: specific dates, times, durations, relative time references. "
+                   "Examples: 'on June 15th, 2024 at 3pm', 'last weekend', 'for the past 3 years', 'every morning at 6am'. "
+                   "Write 'N/A' ONLY if absolutely no temporal context exists. Prefer converting to absolute dates when possible."
    )

    where: str = Field(
        description="WHERE it happened or is about - SPECIFIC locations, places, areas, regions if applicable. "
-        "Include: cities, neighborhoods, venues, buildings, countries, specific addresses when mentioned. "
-        "Examples: 'downtown San Francisco at a rooftop garden venue', 'at the user's home in Brooklyn', 'online via Zoom', 'Paris, France'. "
-        "Write 'N/A' ONLY if absolutely no location context exists or if the fact is completely location-agnostic."
+                   "Include: cities, neighborhoods, venues, buildings, countries, specific addresses when mentioned. "
+                   "Examples: 'downtown San Francisco at a rooftop garden venue', 'at the user's home in Brooklyn', 'online via Zoom', 'Paris, France'. "
+                   "Write 'N/A' ONLY if absolutely no location context exists or if the fact is completely location-agnostic."
    )

    who: str = Field(
        description="WHO is involved - ALL people/entities with FULL context and relationships. "
-        "Include: names, roles, relationships to user, background details. "
-        "Resolve coreferences (if 'my roommate' is later named 'Emily', write 'Emily, the user's college roommate'). "
-        "BE DETAILED about relationships and roles. "
-        "Example: 'Emily (user's college roommate from Stanford, now works at Google), Sarah (Emily's partner of 5 years, software engineer)' "
-        "NOT: 'my friend' or 'Emily and Sarah'"
+                   "Include: names, roles, relationships to user, background details. "
+                   "Resolve coreferences (if 'my roommate' is later named 'Emily', write 'Emily, the user's college roommate'). "
+                   "BE DETAILED about relationships and roles. "
+                   "Example: 'Emily (user's college roommate from Stanford, now works at Google), Sarah (Emily's partner of 5 years, software engineer)' "
+                   "NOT: 'my friend' or 'Emily and Sarah'"
    )

    why: str = Field(
        description="WHY it matters - ALL emotional, contextual, and motivational details. "
-        "Include EVERYTHING: feelings, preferences, motivations, observations, context, background, significance. "
-        "BE VERBOSE - capture all the nuance and meaning. "
-        "FOR ASSISTANT FACTS: MUST include what the user asked/requested that led to this interaction! "
-        "Example (world): 'The user felt thrilled and inspired, has always dreamed of an outdoor ceremony, mentioned wanting a similar garden venue, was particularly moved by the intimate atmosphere and personal vows' "
-        "Example (assistant): 'User asked how to fix slow API performance with 1000+ concurrent users, expected 70-80% reduction in database load' "
-        "NOT: 'User liked it' or 'To help user'"
+                   "Include EVERYTHING: feelings, preferences, motivations, observations, context, background, significance. "
+                   "BE VERBOSE - capture all the nuance and meaning. "
+                   "FOR ASSISTANT FACTS: MUST include what the user asked/requested that led to this interaction! "
+                   "Example (world): 'The user felt thrilled and inspired, has always dreamed of an outdoor ceremony, mentioned wanting a similar garden venue, was particularly moved by the intimate atmosphere and personal vows' "
+                   "Example (assistant): 'User asked how to fix slow API performance with 1000+ concurrent users, expected 70-80% reduction in database load' "
+                   "NOT: 'User liked it' or 'To help user'"
    )

    # ==========================================================================
@@ -152,17 +148,17 @@ class ExtractedFact(BaseModel):

    fact_kind: str = Field(
        default="conversation",
-        description="'event' = specific datable occurrence (set occurred dates), 'conversation' = general info (no occurred dates)",
+        description="'event' = specific datable occurrence (set occurred dates), 'conversation' = general info (no occurred dates)"
    )

    # Temporal fields - optional
-    occurred_start: str | None = Field(
+    occurred_start: Optional[str] = Field(
        default=None,
-        description="WHEN the event happened (ISO timestamp). Only for fact_kind='event'. Leave null for conversations.",
+        description="WHEN the event happened (ISO timestamp). Only for fact_kind='event'. Leave null for conversations."
    )
-    occurred_end: str | None = Field(
+    occurred_end: Optional[str] = Field(
        default=None,
-        description="WHEN the event ended (ISO timestamp). Only for events with duration. Leave null for conversations.",
+        description="WHEN the event ended (ISO timestamp). Only for events with duration. Leave null for conversations."
    )

    # Classification (CRITICAL - required)
@@ -172,15 +168,16 @@ class ExtractedFact(BaseModel):
    )

    # Entities - extracted from fact content
-    entities: list[Entity] | None = Field(
+    entities: Optional[List[Entity]] = Field(
        default=None,
-        description="Named entities, objects, AND abstract concepts from the fact. Include: people names, organizations, places, significant objects (e.g., 'coffee maker', 'car'), AND abstract concepts/themes (e.g., 'friendship', 'career growth', 'loss', 'celebration'). Extract anything that could help link related facts together.",
+        description="Named entities, objects, AND abstract concepts from the fact. Include: people names, organizations, places, significant objects (e.g., 'coffee maker', 'car'), AND abstract concepts/themes (e.g., 'friendship', 'career growth', 'loss', 'celebration'). Extract anything that could help link related facts together."
    )
-    causal_relations: list[CausalRelation] | None = Field(
-        default=None, description="Causal links to other facts. Can be null."
+    causal_relations: Optional[List[CausalRelation]] = Field(
+        default=None,
+        description="Causal links to other facts. Can be null."
    )

-    @field_validator("entities", mode="before")
+    @field_validator('entities', mode='before')
    @classmethod
    def ensure_entities_list(cls, v):
        """Ensure entities is always a list (convert None to empty list)."""
@@ -188,7 +185,7 @@ class ExtractedFact(BaseModel):
            return []
        return v

-    @field_validator("causal_relations", mode="before")
+    @field_validator('causal_relations', mode='before')
    @classmethod
    def ensure_causal_relations_list(cls, v):
        """Ensure causal_relations is always a list (convert None to empty list)."""
@@ -201,11 +198,11 @@ class ExtractedFact(BaseModel):
        parts = [self.what]

        # Add 'who' if not N/A
-        if self.who and self.who.upper() != "N/A":
+        if self.who and self.who.upper() != 'N/A':
            parts.append(f"Involving: {self.who}")

        # Add 'why' if not N/A
-        if self.why and self.why.upper() != "N/A":
+        if self.why and self.why.upper() != 'N/A':
            parts.append(self.why)

        if len(parts) == 1:
@@ -216,11 +213,12 @@ class ExtractedFact(BaseModel):

 class FactExtractionResponse(BaseModel):
    """Response containing all extracted facts."""
-
-    facts: list[ExtractedFact] = Field(description="List of extracted factual statements")
+    facts: List[ExtractedFact] = Field(
+        description="List of extracted factual statements"
+    )


-def chunk_text(text: str, max_chars: int) -> list[str]:
+def chunk_text(text: str, max_chars: int) -> List[str]:
    """
    Split text into chunks, preserving conversation structure when possible.

@@ -234,6 +232,7 @@ def chunk_text(text: str, max_chars: int) -> list[str]:
    Returns:
        List of text chunks, roughly under max_chars
    """
+    import json
    from langchain_text_splitters import RecursiveCharacterTextSplitter

    # If text is small enough, return as-is
@@ -257,21 +256,21 @@ def chunk_text(text: str, max_chars: int) -> list[str]:
        is_separator_regex=False,
        separators=[
            "\n\n",  # Paragraph breaks
-            "\n",  # Line breaks
-            ". ",  # Sentence endings
-            "! ",  # Exclamations
-            "? ",  # Questions
-            "; ",  # Semicolons
-            ", ",  # Commas
-            " ",  # Words
-            "",  # Characters (last resort)
+            "\n",    # Line breaks
+            ". ",    # Sentence endings
+            "! ",    # Exclamations
+            "? ",    # Questions
+            "; ",    # Semicolons
+            ", ",    # Commas
+            " ",     # Words
+            "",      # Characters (last resort)
        ],
    )

    return splitter.split_text(text)


-def _chunk_conversation(turns: list[dict], max_chars: int) -> list[str]:
+def _chunk_conversation(turns: List[dict], max_chars: int) -> List[str]:
    """
    Chunk a conversation array at turn boundaries, preserving complete turns.

@@ -282,6 +281,7 @@ def _chunk_conversation(turns: list[dict], max_chars: int) -> list[str]:
    Returns:
        List of JSON-serialized chunks, each containing complete turns
    """
+    import json

    chunks = []
    current_chunk = []
@@ -315,10 +315,10 @@ async def _extract_facts_from_chunk(
    total_chunks: int,
    event_date: datetime,
    context: str,
-    llm_config: "LLMConfig",
+    llm_config: 'LLMConfig',
    agent_name: str = None,
-    extract_opinions: bool = False,
-) -> list[dict[str, str]]:
+    extract_opinions: bool = False
+) -> List[Dict[str, str]]:
    """
    Extract facts from a single chunk (internal helper for parallel processing).

@@ -333,9 +333,7 @@ async def _extract_facts_from_chunk(
        # Opinion extraction uses a separate prompt (not this one)
        fact_types_instruction = "Extract ONLY 'opinion' type facts (formed opinions, beliefs, and perspectives). DO NOT extract 'world' or 'assistant' facts."
    else:
-        fact_types_instruction = (
-            "Extract ONLY 'world' and 'assistant' type facts. DO NOT extract opinions - those are extracted separately."
-        )
+        fact_types_instruction = "Extract ONLY 'world' and 'assistant' type facts. DO NOT extract opinions - those are extracted separately."

    prompt = f"""Extract facts from text into structured format with FOUR required dimensions - BE EXTREMELY DETAILED.

@@ -536,8 +534,10 @@ WHAT TO EXTRACT vs SKIP
 ✅ EXTRACT: User preferences (ALWAYS as separate facts!), feelings, plans, events, relationships, achievements
 ❌ SKIP: Greetings, filler ("thanks", "cool"), purely structural statements"""

-    import logging

+
+
+    import logging
    from openai import BadRequestError

    logger = logging.getLogger(__name__)
@@ -548,11 +548,11 @@ WHAT TO EXTRACT vs SKIP

    # Sanitize input text to prevent Unicode encoding errors (e.g., unpaired surrogates)
    sanitized_chunk = _sanitize_text(chunk)
-    sanitized_context = _sanitize_text(context) if context else "none"
+    sanitized_context = _sanitize_text(context) if context else 'none'

    # Build user message with metadata and chunk content in a clear format
    # Format event_date with day of week for better temporal reasoning
-    event_date_formatted = event_date.strftime("%A, %B %d, %Y")  # e.g., "Monday, June 10, 2024"
+    event_date_formatted = event_date.strftime('%A, %B %d, %Y')  # e.g., "Monday, June 10, 2024"
    user_message = f"""Extract facts from the following text chunk.
 {memory_bank_context}

@@ -566,7 +566,16 @@ Text:
    for attempt in range(max_retries):
        try:
            extraction_response_json = await llm_config.call(
-                messages=[{"role": "system", "content": prompt}, {"role": "user", "content": user_message}],
+                messages=[
+                    {
+                        "role": "system",
+                        "content": prompt
+                    },
+                    {
+                        "role": "user",
+                        "content": user_message
+                    }
+                ],
                response_format=FactExtractionResponse,
                scope="memory_extract_facts",
                temperature=0.1,
@@ -592,7 +601,7 @@ Text:
                    )
                    return []

-            raw_facts = extraction_response_json.get("facts", [])
+            raw_facts = extraction_response_json.get('facts', [])
            if not raw_facts:
                logger.debug(
                    f"LLM response missing 'facts' field or returned empty list. "
@@ -613,48 +622,48 @@ Text:
                # Helper to get non-empty value
                def get_value(field_name):
                    value = llm_fact.get(field_name)
-                    if value and value != "" and value != [] and value != {} and str(value).upper() != "N/A":
+                    if value and value != '' and value != [] and value != {} and str(value).upper() != 'N/A':
                        return value
                    return None

                # NEW FORMAT: what, when, who, why (all required)
-                what = get_value("what")
-                when = get_value("when")
-                who = get_value("who")
-                why = get_value("why")
+                what = get_value('what')
+                when = get_value('when')
+                who = get_value('who')
+                why = get_value('why')

                # Fallback to old format if new fields not present
                if not what:
-                    what = get_value("factual_core")
+                    what = get_value('factual_core')
                if not what:
                    logger.warning(f"Skipping fact {i}: missing 'what' field")
                    continue

                # Critical field: fact_type
                # LLM uses "assistant" but we convert to "experience" for storage
-                fact_type = llm_fact.get("fact_type")
+                fact_type = llm_fact.get('fact_type')

                # Convert "assistant" → "experience" for storage
-                if fact_type == "assistant":
-                    fact_type = "experience"
+                if fact_type == 'assistant':
+                    fact_type = 'experience'

                # Validate fact_type (after conversion)
-                if fact_type not in ["world", "experience", "opinion"]:
+                if fact_type not in ['world', 'experience', 'opinion']:
                    # Try to fix common mistakes - check if they swapped fact_type and fact_kind
-                    fact_kind = llm_fact.get("fact_kind")
-                    if fact_kind == "assistant":
-                        fact_type = "experience"
-                    elif fact_kind in ["world", "experience", "opinion"]:
+                    fact_kind = llm_fact.get('fact_kind')
+                    if fact_kind == 'assistant':
+                        fact_type = 'experience'
+                    elif fact_kind in ['world', 'experience', 'opinion']:
                        fact_type = fact_kind
                    else:
                        # Default to 'world' if we can't determine
-                        fact_type = "world"
+                        fact_type = 'world'
                        logger.warning(f"Fact {i}: defaulting to fact_type='world'")

                # Get fact_kind for temporal handling (but don't store it)
-                fact_kind = llm_fact.get("fact_kind", "conversation")
-                if fact_kind not in ["conversation", "event", "other"]:
-                    fact_kind = "conversation"
+                fact_kind = llm_fact.get('fact_kind', 'conversation')
+                if fact_kind not in ['conversation', 'event', 'other']:
+                    fact_kind = 'conversation'

                # Build combined fact text from the 4 dimensions: what | when | who | why
                fact_data = {}
@@ -673,20 +682,20 @@ Text:

                # Add temporal fields
                # For events: occurred_start/occurred_end (when the event happened)
-                if fact_kind == "event":
-                    occurred_start = get_value("occurred_start")
-                    occurred_end = get_value("occurred_end")
+                if fact_kind == 'event':
+                    occurred_start = get_value('occurred_start')
+                    occurred_end = get_value('occurred_end')
                    if occurred_start:
-                        fact_data["occurred_start"] = occurred_start
+                        fact_data['occurred_start'] = occurred_start
                        # For point events: if occurred_end not set, default to occurred_start
                        if occurred_end:
-                            fact_data["occurred_end"] = occurred_end
+                            fact_data['occurred_end'] = occurred_end
                        else:
-                            fact_data["occurred_end"] = occurred_start
+                            fact_data['occurred_end'] = occurred_start

                # Add entities if present (validate as Entity objects)
                # LLM sometimes returns strings instead of {"text": "..."} format
-                entities = get_value("entities")
+                entities = get_value('entities')
                if entities:
                    # Validate and normalize each entity
                    validated_entities = []
@@ -694,34 +703,38 @@ Text:
                        if isinstance(ent, str):
                            # Normalize string to Entity object
                            validated_entities.append(Entity(text=ent))
-                        elif isinstance(ent, dict) and "text" in ent:
+                        elif isinstance(ent, dict) and 'text' in ent:
                            try:
                                validated_entities.append(Entity.model_validate(ent))
                            except Exception as e:
                                logger.warning(f"Invalid entity {ent}: {e}")
                    if validated_entities:
-                        fact_data["entities"] = validated_entities
+                        fact_data['entities'] = validated_entities

                # Add causal relations if present (validate as CausalRelation objects)
                # Filter out invalid relations (missing required fields)
-                causal_relations = get_value("causal_relations")
+                causal_relations = get_value('causal_relations')
                if causal_relations:
                    validated_relations = []
                    for rel in causal_relations:
-                        if isinstance(rel, dict) and "target_fact_index" in rel and "relation_type" in rel:
+                        if isinstance(rel, dict) and 'target_fact_index' in rel and 'relation_type' in rel:
                            try:
                                validated_relations.append(CausalRelation.model_validate(rel))
                            except Exception as e:
                                logger.warning(f"Invalid causal relation {rel}: {e}")
                    if validated_relations:
-                        fact_data["causal_relations"] = validated_relations
+                        fact_data['causal_relations'] = validated_relations

                # Always set mentioned_at to the event_date (when the conversation/document occurred)
-                fact_data["mentioned_at"] = event_date.isoformat()
+                fact_data['mentioned_at'] = event_date.isoformat()

                # Build Fact model instance
                try:
-                    fact = Fact(fact=combined_text, fact_type=fact_type, **fact_data)
+                    fact = Fact(
+                        fact=combined_text,
+                        fact_type=fact_type,
+                        **fact_data
+                    )
                    chunk_facts.append(fact)
                except Exception as e:
                    logger.error(f"Failed to create Fact model for fact {i}: {e}")
@@ -740,9 +753,7 @@ Text:
        except BadRequestError as e:
            last_error = e
            if "json_validate_failed" in str(e):
-                logger.warning(
-                    f"          [1.3.{chunk_index + 1}] Attempt {attempt + 1}/{max_retries} failed with JSON validation error: {e}"
-                )
+                logger.warning(f"          [1.3.{chunk_index + 1}] Attempt {attempt + 1}/{max_retries} failed with JSON validation error: {e}")
                if attempt < max_retries - 1:
                    logger.info(f"          [1.3.{chunk_index + 1}] Retrying...")
                    continue
@@ -761,8 +772,8 @@ async def _extract_facts_with_auto_split(
    context: str,
    llm_config: LLMConfig,
    agent_name: str = None,
-    extract_opinions: bool = False,
-) -> list[dict[str, str]]:
+    extract_opinions: bool = False
+) -> List[Dict[str, str]]:
    """
    Extract facts from a chunk with automatic splitting if output exceeds token limits.

@@ -783,7 +794,6 @@ async def _extract_facts_with_auto_split(
        List of fact dictionaries extracted from the chunk (possibly from sub-chunks)
    """
    import logging
-
    logger = logging.getLogger(__name__)

    try:
@@ -796,9 +806,9 @@ async def _extract_facts_with_auto_split(
            context=context,
            llm_config=llm_config,
            agent_name=agent_name,
-            extract_opinions=extract_opinions,
+            extract_opinions=extract_opinions
        )
-    except OutputTooLongError:
+    except OutputTooLongError as e:
        # Output exceeded token limits - split the chunk in half and retry
        logger.warning(
            f"Output too long for chunk {chunk_index + 1}/{total_chunks} "
@@ -814,7 +824,7 @@ async def _extract_facts_with_auto_split(
        search_start = max(0, mid_point - search_range)
        search_end = min(len(chunk), mid_point + search_range)

-        sentence_endings = [". ", "! ", "? ", "\n\n"]
+        sentence_endings = ['. ', '! ', '? ', '\n\n']
        best_split = mid_point

        for ending in sentence_endings:
@@ -828,7 +838,8 @@ async def _extract_facts_with_auto_split(
        second_half = chunk[best_split:].strip()

        logger.info(
-            f"Split chunk {chunk_index + 1} into two sub-chunks: {len(first_half)} chars and {len(second_half)} chars"
+            f"Split chunk {chunk_index + 1} into two sub-chunks: "
+            f"{len(first_half)} chars and {len(second_half)} chars"
        )

        # Process both halves recursively (in parallel)
@@ -841,7 +852,7 @@ async def _extract_facts_with_auto_split(
                context=context,
                llm_config=llm_config,
                agent_name=agent_name,
-                extract_opinions=extract_opinions,
+                extract_opinions=extract_opinions
            ),
            _extract_facts_with_auto_split(
                chunk=second_half,
@@ -851,8 +862,8 @@ async def _extract_facts_with_auto_split(
                context=context,
                llm_config=llm_config,
                agent_name=agent_name,
-                extract_opinions=extract_opinions,
-            ),
+                extract_opinions=extract_opinions
+            )
        ]

        sub_results = await asyncio.gather(*sub_tasks)
@@ -862,7 +873,9 @@ async def _extract_facts_with_auto_split(
        for sub_result in sub_results:
            all_facts.extend(sub_result)

-        logger.info(f"Successfully extracted {len(all_facts)} facts from split chunk {chunk_index + 1}")
+        logger.info(
+            f"Successfully extracted {len(all_facts)} facts from split chunk {chunk_index + 1}"
+        )

        return all_facts

@@ -874,7 +887,7 @@ async def extract_facts_from_text(
    agent_name: str,
    context: str = "",
    extract_opinions: bool = False,
-) -> tuple[list[Fact], list[tuple[str, int]]]:
+) -> tuple[List[Fact], List[tuple[str, int]]]:
    """
    Extract semantic facts from conversational or narrative text using LLM.

@@ -907,7 +920,7 @@ async def extract_facts_from_text(
            context=context,
            llm_config=llm_config,
            agent_name=agent_name,
-            extract_opinions=extract_opinions,
+            extract_opinions=extract_opinions
        )
        for i, chunk in enumerate(chunks)
    ]
@@ -925,10 +938,8 @@ async def extract_facts_from_text(
 # ============================================================================

 # Import types for the orchestration layer (note: ExtractedFact here is different from the Pydantic model above)
-
-from .types import CausalRelation as CausalRelationType
-from .types import ChunkMetadata, RetainContent
-from .types import ExtractedFact as ExtractedFactType
+from .types import RetainContent, ExtractedFact as ExtractedFactType, ChunkMetadata, CausalRelation as CausalRelationType
+from typing import Tuple

 logger = logging.getLogger(__name__)

@@ -937,8 +948,11 @@ SECONDS_PER_FACT = 10


 async def extract_facts_from_contents(
-    contents: list[RetainContent], llm_config, agent_name: str, extract_opinions: bool = False
-) -> tuple[list[ExtractedFactType], list[ChunkMetadata]]:
+    contents: List[RetainContent],
+    llm_config,
+    agent_name: str,
+    extract_opinions: bool = False
+) -> Tuple[List[ExtractedFactType], List[ChunkMetadata]]:
    """
    Extract facts from multiple content items in parallel.

@@ -971,7 +985,7 @@ async def extract_facts_from_contents(
            context=item.context,
            llm_config=llm_config,
            agent_name=agent_name,
-            extract_opinions=extract_opinions,
+            extract_opinions=extract_opinions
        )
        fact_extraction_tasks.append(task)

@@ -979,8 +993,8 @@ async def extract_facts_from_contents(
    all_fact_results = await asyncio.gather(*fact_extraction_tasks)

    # Step 3: Flatten and convert to typed objects
-    extracted_facts: list[ExtractedFactType] = []
-    chunks_metadata: list[ChunkMetadata] = []
+    extracted_facts: List[ExtractedFactType] = []
+    chunks_metadata: List[ChunkMetadata] = []

    global_chunk_idx = 0
    global_fact_idx = 0
@@ -994,7 +1008,7 @@ async def extract_facts_from_contents(
                chunk_text=chunk_text,
                fact_count=chunk_fact_count,
                content_index=content_index,
-                chunk_index=global_chunk_idx,
+                chunk_index=global_chunk_idx
            )
            chunks_metadata.append(chunk_metadata)
            global_chunk_idx += 1
@@ -1015,21 +1029,18 @@ async def extract_facts_from_contents(
                        fact_type=fact_from_llm.fact_type,
                        entities=[e.text for e in (fact_from_llm.entities or [])],
                        # occurred_start/end: from LLM only, leave None if not provided
-                        occurred_start=_parse_datetime(fact_from_llm.occurred_start)
-                        if fact_from_llm.occurred_start
-                        else None,
-                        occurred_end=_parse_datetime(fact_from_llm.occurred_end)
-                        if fact_from_llm.occurred_end
-                        else None,
+                        occurred_start=_parse_datetime(fact_from_llm.occurred_start) if fact_from_llm.occurred_start else None,
+                        occurred_end=_parse_datetime(fact_from_llm.occurred_end) if fact_from_llm.occurred_end else None,
                        causal_relations=_convert_causal_relations(
-                            fact_from_llm.causal_relations or [], global_fact_idx
+                            fact_from_llm.causal_relations or [],
+                            global_fact_idx
                        ),
                        content_index=content_index,
                        chunk_index=chunk_global_idx,
                        context=content.context,
                        # mentioned_at: always the event_date (when the conversation/document occurred)
                        mentioned_at=content.event_date,
-                        metadata=content.metadata,
+                        metadata=content.metadata
                    )

                    extracted_facts.append(extracted_fact)
@@ -1045,14 +1056,13 @@ async def extract_facts_from_contents(
 def _parse_datetime(date_str: str):
    """Parse ISO datetime string."""
    from dateutil import parser as date_parser
-
    try:
        return date_parser.isoparse(date_str)
    except Exception:
        return None


-def _convert_causal_relations(relations_from_llm, fact_start_idx: int) -> list[CausalRelationType]:
+def _convert_causal_relations(relations_from_llm, fact_start_idx: int) -> List[CausalRelationType]:
    """
    Convert causal relations from LLM format to ExtractedFact format.

@@ -1063,13 +1073,13 @@ def _convert_causal_relations(relations_from_llm, fact_start_idx: int) -> list[C
        causal_relation = CausalRelationType(
            relation_type=rel.relation_type,
            target_fact_index=fact_start_idx + rel.target_fact_index,
-            strength=rel.strength,
+            strength=rel.strength
        )
        causal_relations.append(causal_relation)
    return causal_relations


-def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainContent]) -> None:
+def _add_temporal_offsets(facts: List[ExtractedFactType], contents: List[RetainContent]) -> None:
    """
    Add time offsets to preserve fact ordering within each content.

@@ -3,19 +3,22 @@ Fact storage for retain pipeline.

 Handles insertion of facts into the database.
 """
-
-import json
 import logging
+import json
+from typing import List, Optional
+from uuid import UUID

-from ..memory_engine import fq_table
 from .types import ProcessedFact

 logger = logging.getLogger(__name__)


 async def insert_facts_batch(
-    conn, bank_id: str, facts: list[ProcessedFact], document_id: str | None = None
-) -> list[str]:
+    conn,
+    bank_id: str,
+    facts: List[ProcessedFact],
+    document_id: Optional[str] = None
+) -> List[str]:
    """
    Insert facts into the database in batch.

@@ -59,7 +62,7 @@ async def insert_facts_batch(
        contexts.append(fact.context)
        fact_types.append(fact.fact_type)
        # confidence_score is only for opinion facts
-        confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
+        confidence_scores.append(1.0 if fact.fact_type == 'opinion' else None)
        access_counts.append(0)  # Initial access count
        metadata_jsons.append(json.dumps(fact.metadata))
        chunk_ids.append(fact.chunk_id)
@@ -68,8 +71,8 @@ async def insert_facts_batch(

    # Batch insert all facts
    results = await conn.fetch(
-        f"""
-        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+        """
+        INSERT INTO memory_units (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
                                 context, fact_type, confidence_score, access_count, metadata, chunk_id, document_id)
        SELECT $1, * FROM unnest(
            $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
@@ -90,10 +93,10 @@ async def insert_facts_batch(
        access_counts,
        metadata_jsons,
        chunk_ids,
-        document_ids,
+        document_ids
    )

-    unit_ids = [str(row["id"]) for row in results]
+    unit_ids = [str(row['id']) for row in results]
    return unit_ids


@@ -108,20 +111,25 @@ async def ensure_bank_exists(conn, bank_id: str) -> None:
        bank_id: Bank identifier
    """
    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("banks")} (bank_id, disposition, background)
+        """
+        INSERT INTO banks (bank_id, disposition, background)
        VALUES ($1, $2::jsonb, $3)
        ON CONFLICT (bank_id) DO UPDATE
        SET updated_at = NOW()
        """,
        bank_id,
        '{"skepticism": 3, "literalism": 3, "empathy": 3}',
-        "",
+        ""
    )


 async def handle_document_tracking(
-    conn, bank_id: str, document_id: str, combined_content: str, is_first_batch: bool, retain_params: dict | None = None
+    conn,
+    bank_id: str,
+    document_id: str,
+    combined_content: str,
+    is_first_batch: bool,
+    retain_params: Optional[dict] = None
 ) -> None:
    """
    Handle document tracking in the database.
@@ -143,13 +151,14 @@ async def handle_document_tracking(
    # Only delete on the first batch to avoid deleting data we just inserted
    if is_first_batch:
        await conn.fetchval(
-            f"DELETE FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2 RETURNING id", document_id, bank_id
+            "DELETE FROM documents WHERE id = $1 AND bank_id = $2 RETURNING id",
+            document_id, bank_id
        )

    # Insert document (or update if exists from concurrent operations)
    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, metadata, retain_params)
+        """
+        INSERT INTO documents (id, bank_id, original_text, content_hash, metadata, retain_params)
        VALUES ($1, $2, $3, $4, $5, $6)
        ON CONFLICT (id, bank_id) DO UPDATE
        SET original_text = EXCLUDED.original_text,
@@ -163,5 +172,5 @@ async def handle_document_tracking(
        combined_content,
        content_hash,
        json.dumps({}),  # Empty metadata dict
-        json.dumps(retain_params) if retain_params else None,
+        json.dumps(retain_params) if retain_params else None
    )
@@ -3,16 +3,20 @@ Link creation for retain pipeline.

 Handles creation of temporal, semantic, and causal links between facts.
 """
-
 import logging
+from typing import List

+from .types import ProcessedFact, CausalRelation
 from . import link_utils
-from .types import ProcessedFact

 logger = logging.getLogger(__name__)


-async def create_temporal_links_batch(conn, bank_id: str, unit_ids: list[str]) -> int:
+async def create_temporal_links_batch(
+    conn,
+    bank_id: str,
+    unit_ids: List[str]
+) -> int:
    """
    Create temporal links between facts.

@@ -29,10 +33,20 @@ async def create_temporal_links_batch(conn, bank_id: str, unit_ids: list[str]) -
    if not unit_ids:
        return 0

-    return await link_utils.create_temporal_links_batch_per_fact(conn, bank_id, unit_ids, log_buffer=[])
+    return await link_utils.create_temporal_links_batch_per_fact(
+        conn,
+        bank_id,
+        unit_ids,
+        log_buffer=[]
+    )


-async def create_semantic_links_batch(conn, bank_id: str, unit_ids: list[str], embeddings: list[list[float]]) -> int:
+async def create_semantic_links_batch(
+    conn,
+    bank_id: str,
+    unit_ids: List[str],
+    embeddings: List[List[float]]
+) -> int:
    """
    Create semantic links between facts.

@@ -53,10 +67,20 @@ async def create_semantic_links_batch(conn, bank_id: str, unit_ids: list[str], e
    if len(unit_ids) != len(embeddings):
        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and embeddings ({len(embeddings)})")

-    return await link_utils.create_semantic_links_batch(conn, bank_id, unit_ids, embeddings, log_buffer=[])
+    return await link_utils.create_semantic_links_batch(
+        conn,
+        bank_id,
+        unit_ids,
+        embeddings,
+        log_buffer=[]
+    )


-async def create_causal_links_batch(conn, unit_ids: list[str], facts: list[ProcessedFact]) -> int:
+async def create_causal_links_batch(
+    conn,
+    unit_ids: List[str],
+    facts: List[ProcessedFact]
+) -> int:
    """
    Create causal links between facts.

@@ -84,9 +108,9 @@ async def create_causal_links_batch(conn, unit_ids: list[str], facts: list[Proce
            # Convert CausalRelation objects to dicts
            relations_dicts = [
                {
-                    "relation_type": rel.relation_type,
-                    "target_fact_index": rel.target_fact_index,
-                    "strength": rel.strength,
+                    'relation_type': rel.relation_type,
+                    'target_fact_index': rel.target_fact_index,
+                    'strength': rel.strength
                }
                for rel in fact.causal_relations
            ]
@@ -94,6 +118,10 @@ async def create_causal_links_batch(conn, unit_ids: list[str], facts: list[Proce
        else:
            causal_relations_per_fact.append([])

-    link_count = await link_utils.create_causal_links_batch(conn, unit_ids, causal_relations_per_fact)
+    link_count = await link_utils.create_causal_links_batch(
+        conn,
+        unit_ids,
+        causal_relations_per_fact
+    )

    return link_count
@@ -2,12 +2,12 @@
 Link creation utilities for temporal, semantic, and entity links.
 """

-import logging
 import time
-from datetime import UTC, datetime, timedelta
+import logging
+from typing import List
+from datetime import timedelta, datetime, timezone
 from uuid import UUID

-from ..memory_engine import fq_table
 from .types import EntityLink

 logger = logging.getLogger(__name__)
@@ -19,7 +19,7 @@ def _normalize_datetime(dt):
        return None
    if dt.tzinfo is None:
        # Naive datetime - assume UTC
-        return dt.replace(tzinfo=UTC)
+        return dt.replace(tzinfo=timezone.utc)
    return dt


@@ -54,26 +54,24 @@ def compute_temporal_links(
        try:
            time_lower = unit_event_date_norm - timedelta(hours=time_window_hours)
        except OverflowError:
-            time_lower = datetime.min.replace(tzinfo=UTC)
+            time_lower = datetime.min.replace(tzinfo=timezone.utc)
        try:
            time_upper = unit_event_date_norm + timedelta(hours=time_window_hours)
        except OverflowError:
-            time_upper = datetime.max.replace(tzinfo=UTC)
+            time_upper = datetime.max.replace(tzinfo=timezone.utc)

        # Filter candidates within this unit's time window
        matching_neighbors = [
-            (row["id"], row["event_date"])
+            (row['id'], row['event_date'])
            for row in candidates
-            if time_lower <= _normalize_datetime(row["event_date"]) <= time_upper
+            if time_lower <= _normalize_datetime(row['event_date']) <= time_upper
        ][:10]  # Limit to top 10

        for recent_id, recent_event_date in matching_neighbors:
            # Calculate temporal proximity weight
-            time_diff_hours = abs(
-                (unit_event_date_norm - _normalize_datetime(recent_event_date)).total_seconds() / 3600
-            )
+            time_diff_hours = abs((unit_event_date_norm - _normalize_datetime(recent_event_date)).total_seconds() / 3600)
            weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
-            links.append((unit_id, str(recent_id), "temporal", weight, None))
+            links.append((unit_id, str(recent_id), 'temporal', weight, None))

    return links

@@ -101,17 +99,17 @@ def compute_temporal_query_bounds(
    try:
        min_date = min(all_dates) - timedelta(hours=time_window_hours)
    except OverflowError:
-        min_date = datetime.min.replace(tzinfo=UTC)
+        min_date = datetime.min.replace(tzinfo=timezone.utc)

    try:
        max_date = max(all_dates) + timedelta(hours=time_window_hours)
    except OverflowError:
-        max_date = datetime.max.replace(tzinfo=UTC)
+        max_date = datetime.max.replace(tzinfo=timezone.utc)

    return min_date, max_date


-def _log(log_buffer, message, level="info"):
+def _log(log_buffer, message, level='info'):
    """Helper to log to buffer if available, otherwise use logger.

    Args:
@@ -119,7 +117,7 @@ def _log(log_buffer, message, level="info"):
        message: The log message
        level: 'info', 'debug', 'warning', or 'error'. Debug messages are not added to buffer.
    """
-    if level == "debug":
+    if level == 'debug':
        # Debug messages only go to logger, not to buffer
        logger.debug(message)
        return
@@ -127,23 +125,23 @@ def _log(log_buffer, message, level="info"):
    if log_buffer is not None:
        log_buffer.append(message)
    else:
-        if level == "info":
+        if level == 'info':
            logger.info(message)
        else:
-            logger.log(logging.WARNING if level == "warning" else logging.ERROR, message)
+            logger.log(logging.WARNING if level == 'warning' else logging.ERROR, message)


 async def extract_entities_batch_optimized(
    entity_resolver,
    conn,
    bank_id: str,
-    unit_ids: list[str],
-    sentences: list[str],
+    unit_ids: List[str],
+    sentences: List[str],
    context: str,
-    fact_dates: list,
-    llm_entities: list[list[dict]],
-    log_buffer: list[str] = None,
-) -> list[tuple]:
+    fact_dates: List,
+    llm_entities: List[List[dict]],
+    log_buffer: List[str] = None,
+) -> List[tuple]:
    """
    Process LLM-extracted entities for ALL facts in batch.

@@ -173,19 +171,15 @@ async def extract_entities_batch_optimized(
            formatted_entities = []
            for ent in entity_list:
                # Handle both Entity objects and dicts
-                if hasattr(ent, "text"):
+                if hasattr(ent, 'text'):
                    # Entity objects only have 'text', default type to 'CONCEPT'
-                    formatted_entities.append({"text": ent.text, "type": "CONCEPT"})
+                    formatted_entities.append({'text': ent.text, 'type': 'CONCEPT'})
                elif isinstance(ent, dict):
-                    formatted_entities.append({"text": ent.get("text", ""), "type": ent.get("type", "CONCEPT")})
+                    formatted_entities.append({'text': ent.get('text', ''), 'type': ent.get('type', 'CONCEPT')})
            all_entities.append(formatted_entities)

        total_entities = sum(len(ents) for ents in all_entities)
-        _log(
-            log_buffer,
-            f"  [6.1] Process LLM entities: {total_entities} entities from {len(sentences)} facts in {time.time() - substep_start:.3f}s",
-            level="debug",
-        )
+        _log(log_buffer, f"  [6.1] Process LLM entities: {total_entities} entities from {len(sentences)} facts in {time.time() - substep_start:.3f}s", level='debug')

        # Step 2: Resolve entities in BATCH (much faster!)
        substep_start = time.time()
@@ -201,19 +195,13 @@ async def extract_entities_batch_optimized(
                continue

            for local_idx, entity in enumerate(entities):
-                all_entities_flat.append(
-                    {
-                        "text": entity["text"],
-                        "type": entity["type"],
-                        "nearby_entities": entities,
-                    }
-                )
+                all_entities_flat.append({
+                    'text': entity['text'],
+                    'type': entity['type'],
+                    'nearby_entities': entities,
+                })
                entity_to_unit.append((unit_id, local_idx, fact_date))
-        _log(
-            log_buffer,
-            f"    [6.2.1] Prepare entities: {len(all_entities_flat)} entities in {time.time() - substep_6_2_1_start:.3f}s",
-            level="debug",
-        )
+        _log(log_buffer, f"    [6.2.1] Prepare entities: {len(all_entities_flat)} entities in {time.time() - substep_6_2_1_start:.3f}s", level='debug')

        # Resolve ALL entities in one batch call
        if all_entities_flat:
@@ -222,7 +210,7 @@ async def extract_entities_batch_optimized(

            # Add per-entity dates to entity data for batch resolution
            for idx, (unit_id, local_idx, fact_date) in enumerate(entity_to_unit):
-                all_entities_flat[idx]["event_date"] = fact_date
+                all_entities_flat[idx]['event_date'] = fact_date

            # Resolve ALL entities in ONE batch call (much faster than sequential buckets)
            # INSERT ... ON CONFLICT handles any race conditions at the DB level
@@ -231,14 +219,10 @@ async def extract_entities_batch_optimized(
                entities_data=all_entities_flat,
                context=context,
                unit_event_date=None,  # Not used when per-entity dates provided
-                conn=conn,  # Use main transaction connection
+                conn=conn  # Use main transaction connection
            )

-            _log(
-                log_buffer,
-                f"    [6.2.2] Resolve entities: {len(all_entities_flat)} entities in single batch in {time.time() - substep_6_2_2_start:.3f}s",
-                level="debug",
-            )
+            _log(log_buffer, f"    [6.2.2] Resolve entities: {len(all_entities_flat)} entities in single batch in {time.time() - substep_6_2_2_start:.3f}s", level='debug')

            # [6.2.3] Create unit-entity links in BATCH
            substep_6_2_3_start = time.time()
@@ -255,24 +239,12 @@ async def extract_entities_batch_optimized(

            # Batch insert all unit-entity links (MUCH faster!)
            await entity_resolver.link_units_to_entities_batch(unit_entity_pairs, conn=conn)
-            _log(
-                log_buffer,
-                f"    [6.2.3] Create unit-entity links (batched): {len(unit_entity_pairs)} links in {time.time() - substep_6_2_3_start:.3f}s",
-                level="debug",
-            )
+            _log(log_buffer, f"    [6.2.3] Create unit-entity links (batched): {len(unit_entity_pairs)} links in {time.time() - substep_6_2_3_start:.3f}s", level='debug')

-            _log(
-                log_buffer,
-                f"  [6.2] Entity resolution (batched): {len(all_entities_flat)} entities resolved in {time.time() - step_6_2_start:.3f}s",
-                level="debug",
-            )
+            _log(log_buffer, f"  [6.2] Entity resolution (batched): {len(all_entities_flat)} entities resolved in {time.time() - step_6_2_start:.3f}s", level='debug')
        else:
            unit_to_entity_ids = {}
-            _log(
-                log_buffer,
-                f"  [6.2] Entity resolution (batched): 0 entities in {time.time() - step_6_2_start:.3f}s",
-                level="debug",
-            )
+            _log(log_buffer, f"  [6.2] Entity resolution (batched): 0 entities in {time.time() - step_6_2_start:.3f}s", level='debug')

        # Step 3: Create entity links between units that share entities
        substep_start = time.time()
@@ -281,44 +253,39 @@ async def extract_entities_batch_optimized(
        for entity_ids in unit_to_entity_ids.values():
            all_entity_ids.update(entity_ids)

-        _log(log_buffer, f"  [6.3] Creating entity links for {len(all_entity_ids)} unique entities...", level="debug")
+        _log(log_buffer, f"  [6.3] Creating entity links for {len(all_entity_ids)} unique entities...", level='debug')

        # Find all units that reference these entities (ONE batched query)
        entity_to_units = {}
        if all_entity_ids:
            query_start = time.time()
            import uuid
-
            entity_id_list = [uuid.UUID(eid) if isinstance(eid, str) else eid for eid in all_entity_ids]
            rows = await conn.fetch(
-                f"""
+                """
                SELECT entity_id, unit_id
-                FROM {fq_table("unit_entities")}
+                FROM unit_entities
                WHERE entity_id = ANY($1::uuid[])
                """,
-                entity_id_list,
-            )
-            _log(
-                log_buffer,
-                f"      [6.3.1] Query unit_entities: {len(rows)} rows in {time.time() - query_start:.3f}s",
-                level="debug",
+                entity_id_list
            )
+            _log(log_buffer, f"      [6.3.1] Query unit_entities: {len(rows)} rows in {time.time() - query_start:.3f}s", level='debug')

            # Group by entity_id
            group_start = time.time()
            for row in rows:
-                entity_id = row["entity_id"]
+                entity_id = row['entity_id']
                if entity_id not in entity_to_units:
                    entity_to_units[entity_id] = []
-                entity_to_units[entity_id].append(row["unit_id"])
-            _log(log_buffer, f"      [6.3.2] Group by entity_id: {time.time() - group_start:.3f}s", level="debug")
+                entity_to_units[entity_id].append(row['unit_id'])
+            _log(log_buffer, f"      [6.3.2] Group by entity_id: {time.time() - group_start:.3f}s", level='debug')

        # Create bidirectional links between units that share entities
        # OPTIMIZATION: Limit links per entity to avoid N² explosion
        # Only link each new unit to the most recent MAX_LINKS_PER_ENTITY units
        MAX_LINKS_PER_ENTITY = 50  # Limit to prevent explosion when entity appears in many facts
        link_gen_start = time.time()
-        links: list[EntityLink] = []
+        links: List[EntityLink] = []
        new_unit_set = set(unit_ids)  # Units from this batch

        def to_uuid(val) -> UUID:
@@ -332,52 +299,27 @@ async def extract_entities_batch_optimized(

            # Link new units to each other (within batch) - also limited
            # For very common entities, limit within-batch links too
-            new_units_to_link = (
-                new_units[-MAX_LINKS_PER_ENTITY:] if len(new_units) > MAX_LINKS_PER_ENTITY else new_units
-            )
+            new_units_to_link = new_units[-MAX_LINKS_PER_ENTITY:] if len(new_units) > MAX_LINKS_PER_ENTITY else new_units
            for i, unit_id_1 in enumerate(new_units_to_link):
-                for unit_id_2 in new_units_to_link[i + 1 :]:
-                    links.append(
-                        EntityLink(
-                            from_unit_id=to_uuid(unit_id_1), to_unit_id=to_uuid(unit_id_2), entity_id=entity_uuid
-                        )
-                    )
-                    links.append(
-                        EntityLink(
-                            from_unit_id=to_uuid(unit_id_2), to_unit_id=to_uuid(unit_id_1), entity_id=entity_uuid
-                        )
-                    )
+                for unit_id_2 in new_units_to_link[i+1:]:
+                    links.append(EntityLink(from_unit_id=to_uuid(unit_id_1), to_unit_id=to_uuid(unit_id_2), entity_id=entity_uuid))
+                    links.append(EntityLink(from_unit_id=to_uuid(unit_id_2), to_unit_id=to_uuid(unit_id_1), entity_id=entity_uuid))

            # Link new units to LIMITED existing units (most recent)
            existing_to_link = existing_units[-MAX_LINKS_PER_ENTITY:]  # Take most recent
            for new_unit in new_units:
                for existing_unit in existing_to_link:
-                    links.append(
-                        EntityLink(
-                            from_unit_id=to_uuid(new_unit), to_unit_id=to_uuid(existing_unit), entity_id=entity_uuid
-                        )
-                    )
-                    links.append(
-                        EntityLink(
-                            from_unit_id=to_uuid(existing_unit), to_unit_id=to_uuid(new_unit), entity_id=entity_uuid
-                        )
-                    )
+                    links.append(EntityLink(from_unit_id=to_uuid(new_unit), to_unit_id=to_uuid(existing_unit), entity_id=entity_uuid))
+                    links.append(EntityLink(from_unit_id=to_uuid(existing_unit), to_unit_id=to_uuid(new_unit), entity_id=entity_uuid))

-        _log(
-            log_buffer, f"      [6.3.3] Generate {len(links)} links: {time.time() - link_gen_start:.3f}s", level="debug"
-        )
-        _log(
-            log_buffer,
-            f"  [6.3] Entity link creation: {len(links)} links for {len(all_entity_ids)} unique entities in {time.time() - substep_start:.3f}s",
-            level="debug",
-        )
+        _log(log_buffer, f"      [6.3.3] Generate {len(links)} links: {time.time() - link_gen_start:.3f}s", level='debug')
+        _log(log_buffer, f"  [6.3] Entity link creation: {len(links)} links for {len(all_entity_ids)} unique entities in {time.time() - substep_start:.3f}s", level='debug')

        return links

    except Exception as e:
        logger.error(f"Failed to extract entities in batch: {str(e)}")
        import traceback
-
        traceback.print_exc()
        raise

@@ -385,9 +327,9 @@ async def extract_entities_batch_optimized(
 async def create_temporal_links_batch_per_fact(
    conn,
    bank_id: str,
-    unit_ids: list[str],
+    unit_ids: List[str],
    time_window_hours: int = 24,
-    log_buffer: list[str] = None,
+    log_buffer: List[str] = None,
 ) -> int:
    """
    Create temporal links for multiple units, each with their own event_date.
@@ -414,18 +356,15 @@ async def create_temporal_links_batch_per_fact(
        # Get the event_date for each new unit
        fetch_dates_start = time_mod.time()
        rows = await conn.fetch(
-            f"""
+            """
            SELECT id, event_date
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE id::text = ANY($1)
            """,
-            unit_ids,
-        )
-        new_units = {str(row["id"]): row["event_date"] for row in rows}
-        _log(
-            log_buffer,
-            f"      [7.1] Fetch event_dates for {len(unit_ids)} units: {time_mod.time() - fetch_dates_start:.3f}s",
+            unit_ids
        )
+        new_units = {str(row['id']): row['event_date'] for row in rows}
+        _log(log_buffer, f"      [7.1] Fetch event_dates for {len(unit_ids)} units: {time_mod.time() - fetch_dates_start:.3f}s")

        # Fetch ALL potential temporal neighbors in ONE query (much faster!)
        # Get time range across all units with overflow protection
@@ -433,9 +372,9 @@ async def create_temporal_links_batch_per_fact(

        fetch_neighbors_start = time_mod.time()
        all_candidates = await conn.fetch(
-            f"""
+            """
            SELECT id, event_date
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE bank_id = $1
              AND event_date BETWEEN $2 AND $3
              AND id::text != ALL($4)
@@ -444,48 +383,24 @@ async def create_temporal_links_batch_per_fact(
            bank_id,
            min_date,
            max_date,
-            unit_ids,
-        )
-        _log(
-            log_buffer,
-            f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s",
+            unit_ids
        )
+        _log(log_buffer, f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s")

        # Filter and create links in memory (much faster than N queries)
        link_gen_start = time_mod.time()
        links = compute_temporal_links(new_units, all_candidates, time_window_hours)
-
-        # Also compute temporal links WITHIN the new batch (new units to each other)
-        if len(new_units) > 1:
-            # Convert new_units dict to candidate format for within-batch linking
-            new_unit_items = list(new_units.items())
-            for i, (unit_id, event_date) in enumerate(new_unit_items):
-                unit_event_date_norm = _normalize_datetime(event_date)
-
-                # Compare with other new units (only those after this one to avoid duplicates)
-                for j in range(i + 1, len(new_unit_items)):
-                    other_id, other_event_date = new_unit_items[j]
-                    other_event_date_norm = _normalize_datetime(other_event_date)
-
-                    # Check if within time window
-                    time_diff_hours = abs((unit_event_date_norm - other_event_date_norm).total_seconds() / 3600)
-                    if time_diff_hours <= time_window_hours:
-                        weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
-                        # Create bidirectional links
-                        links.append((unit_id, other_id, "temporal", weight, None))
-                        links.append((other_id, unit_id, "temporal", weight, None))
-
        _log(log_buffer, f"      [7.3] Generate {len(links)} temporal links: {time_mod.time() - link_gen_start:.3f}s")

        if links:
            insert_start = time_mod.time()
            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                """
+                INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
                VALUES ($1, $2, $3, $4, $5)
                ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
                """,
-                links,
+                links
            )
            _log(log_buffer, f"      [7.4] Insert {len(links)} temporal links: {time_mod.time() - insert_start:.3f}s")

@@ -494,7 +409,6 @@ async def create_temporal_links_batch_per_fact(
    except Exception as e:
        logger.error(f"Failed to create temporal links: {str(e)}")
        import traceback
-
        traceback.print_exc()
        raise

@@ -502,11 +416,11 @@ async def create_temporal_links_batch_per_fact(
 async def create_semantic_links_batch(
    conn,
    bank_id: str,
-    unit_ids: list[str],
-    embeddings: list[list[float]],
+    unit_ids: List[str],
+    embeddings: List[List[float]],
    top_k: int = 5,
    threshold: float = 0.7,
-    log_buffer: list[str] = None,
+    log_buffer: List[str] = None,
 ) -> int:
    """
    Create semantic links for multiple units efficiently.
@@ -530,26 +444,22 @@ async def create_semantic_links_batch(

    try:
        import time as time_mod
-
        import numpy as np

        # Fetch ALL existing units with embeddings in ONE query
        fetch_start = time_mod.time()
        all_existing = await conn.fetch(
-            f"""
+            """
            SELECT id, embedding
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE bank_id = $1
              AND embedding IS NOT NULL
              AND id::text != ALL($2)
            """,
            bank_id,
-            unit_ids,
-        )
-        _log(
-            log_buffer,
-            f"      [8.1] Fetch {len(all_existing)} existing embeddings (1 query): {time_mod.time() - fetch_start:.3f}s",
+            unit_ids
        )
+        _log(log_buffer, f"      [8.1] Fetch {len(all_existing)} existing embeddings (1 query): {time_mod.time() - fetch_start:.3f}s")

        # Convert to numpy for vectorized similarity computation
        compute_start = time_mod.time()
@@ -557,16 +467,15 @@ async def create_semantic_links_batch(

        if all_existing:
            # Convert existing embeddings to numpy array
-            existing_ids = [str(row["id"]) for row in all_existing]
+            existing_ids = [str(row['id']) for row in all_existing]
            # Stack embeddings as 2D array: (num_embeddings, embedding_dim)
            embedding_arrays = []
            for row in all_existing:
-                raw_emb = row["embedding"]
+                raw_emb = row['embedding']
                # Handle different pgvector formats
                if isinstance(raw_emb, str):
                    # Parse string format: "[1.0, 2.0, ...]"
                    import json
-
                    emb = np.array(json.loads(raw_emb), dtype=np.float32)
                elif isinstance(raw_emb, (list, tuple)):
                    emb = np.array(raw_emb, dtype=np.float32)
@@ -605,68 +514,33 @@ async def create_semantic_links_batch(

                    for idx in sorted_indices:
                        similar_id = existing_ids[idx]
-                        # Clamp to [0, 1] to handle floating point precision issues
-                        similarity = float(min(1.0, max(0.0, similarities[idx])))
-                        all_links.append((unit_id, similar_id, "semantic", similarity, None))
+                        similarity = float(similarities[idx])
+                        all_links.append((unit_id, similar_id, 'semantic', similarity, None))

-        # Also compute similarities WITHIN the new batch (new units to each other)
-        # Apply the same top_k limit per unit as we do for existing units
-        if len(unit_ids) > 1:
-            new_embeddings_matrix = np.array(embeddings)
-
-            for i, unit_id in enumerate(unit_ids):
-                # Compute similarities with all OTHER new units
-                other_indices = [j for j in range(len(unit_ids)) if j != i]
-                if not other_indices:
-                    continue
-
-                other_embeddings = new_embeddings_matrix[other_indices]
-                similarities = np.dot(other_embeddings, new_embeddings_matrix[i])
-
-                # Find top-k above threshold (same logic as existing units)
-                above_threshold = np.where(similarities >= threshold)[0]
-
-                if len(above_threshold) > 0:
-                    # Sort by similarity (descending) and take top-k
-                    sorted_local_indices = above_threshold[np.argsort(-similarities[above_threshold])][:top_k]
-
-                    for local_idx in sorted_local_indices:
-                        other_idx = other_indices[local_idx]
-                        other_id = unit_ids[other_idx]
-                        # Clamp to [0, 1] to handle floating point precision issues
-                        similarity = float(min(1.0, max(0.0, similarities[local_idx])))
-                        all_links.append((unit_id, other_id, "semantic", similarity, None))
-
-        _log(
-            log_buffer,
-            f"      [8.2] Compute similarities & generate {len(all_links)} semantic links: {time_mod.time() - compute_start:.3f}s",
-        )
+        _log(log_buffer, f"      [8.2] Compute similarities & generate {len(all_links)} semantic links: {time_mod.time() - compute_start:.3f}s")

        if all_links:
            insert_start = time_mod.time()
            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                """
+                INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
                VALUES ($1, $2, $3, $4, $5)
                ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
                """,
-                all_links,
-            )
-            _log(
-                log_buffer, f"      [8.3] Insert {len(all_links)} semantic links: {time_mod.time() - insert_start:.3f}s"
+                all_links
            )
+            _log(log_buffer, f"      [8.3] Insert {len(all_links)} semantic links: {time_mod.time() - insert_start:.3f}s")

        return len(all_links)

    except Exception as e:
        logger.error(f"Failed to create semantic links: {str(e)}")
        import traceback
-
        traceback.print_exc()
        raise


-async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: int = 50000):
+async def insert_entity_links_batch(conn, links: List[EntityLink], chunk_size: int = 50000):
    """
    Insert all entity links using COPY to temp table + INSERT for maximum speed.

@@ -682,6 +556,7 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i
    if not links:
        return

+    import uuid as uuid_mod
    import time as time_mod

    total_start = time_mod.time()
@@ -708,22 +583,28 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i
    convert_start = time_mod.time()
    records = []
    for link in links:
-        records.append((link.from_unit_id, link.to_unit_id, link.link_type, link.weight, link.entity_id))
+        records.append((
+            link.from_unit_id,
+            link.to_unit_id,
+            link.link_type,
+            link.weight,
+            link.entity_id
+        ))
    logger.debug(f"      [9.3] Convert {len(records)} records: {time_mod.time() - convert_start:.3f}s")

    # Bulk load using COPY (fastest method)
    copy_start = time_mod.time()
    await conn.copy_records_to_table(
-        "_temp_entity_links",
+        '_temp_entity_links',
        records=records,
-        columns=["from_unit_id", "to_unit_id", "link_type", "weight", "entity_id"],
+        columns=['from_unit_id', 'to_unit_id', 'link_type', 'weight', 'entity_id']
    )
    logger.debug(f"      [9.4] COPY {len(records)} records to temp table: {time_mod.time() - copy_start:.3f}s")

    # Insert from temp table with ON CONFLICT (single query for all rows)
    insert_start = time_mod.time()
-    await conn.execute(f"""
-        INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+    await conn.execute("""
+        INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
        SELECT from_unit_id, to_unit_id, link_type, weight, entity_id
        FROM _temp_entity_links
        ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
@@ -734,8 +615,8 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i

 async def create_causal_links_batch(
    conn,
-    unit_ids: list[str],
-    causal_relations_per_fact: list[list[dict]],
+    unit_ids: List[str],
+    causal_relations_per_fact: List[List[dict]],
 ) -> int:
    """
    Create causal links between facts based on LLM-extracted causal relationships.
@@ -763,7 +644,6 @@ async def create_causal_links_batch(

    try:
        import time as time_mod
-
        create_start = time_mod.time()

        # Build links list
@@ -775,12 +655,12 @@ async def create_causal_links_batch(
            from_unit_id = unit_ids[fact_idx]

            for relation in causal_relations:
-                target_idx = relation["target_fact_index"]
-                relation_type = relation["relation_type"]
-                strength = relation.get("strength", 1.0)
+                target_idx = relation['target_fact_index']
+                relation_type = relation['relation_type']
+                strength = relation.get('strength', 1.0)

                # Validate relation_type - must match database constraint
-                valid_types = {"causes", "caused_by", "enables", "prevents"}
+                valid_types = {'causes', 'caused_by', 'enables', 'prevents'}
                if relation_type not in valid_types:
                    logger.error(
                        f"Invalid relation_type '{relation_type}' (type: {type(relation_type).__name__}) "
@@ -805,25 +685,24 @@ async def create_causal_links_batch(
                # weight is the strength of the relationship
                links.append((from_unit_id, to_unit_id, relation_type, strength, None))

+
        if links:
            insert_start = time_mod.time()
            try:
                await conn.executemany(
-                    f"""
-                    INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                    """
+                    INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
                    VALUES ($1, $2, $3, $4, $5)
                    ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
                    """,
-                    links,
+                    links
                )
            except Exception as db_error:
                # Log the actual data being inserted for debugging
                logger.error(f"Database insert failed for causal links. Error: {db_error}")
                logger.error(f"Attempted to insert {len(links)} links. First few:")
                for i, link in enumerate(links[:3]):
-                    logger.error(
-                        f"  Link {i}: from={link[0]}, to={link[1]}, type='{link[2]}' (repr={repr(link[2])}), weight={link[3]}, entity={link[4]}"
-                    )
+                    logger.error(f"  Link {i}: from={link[0]}, to={link[1]}, type='{link[2]}' (repr={repr(link[2])}), weight={link[3]}, entity={link[4]}")
                raise

        return len(links)
@@ -831,6 +710,5 @@ async def create_causal_links_batch(
    except Exception as e:
        logger.error(f"Failed to create causal links: {str(e)}")
        import traceback
-
        traceback.print_exc()
        raise
@@ -3,15 +3,15 @@ Observation regeneration for retain pipeline.

 Regenerates entity observations as part of the retain transaction.
 """
-
 import logging
 import time
 import uuid
-from datetime import UTC, datetime
+from datetime import datetime, timezone
+from typing import List, Dict, Optional

-from ..memory_engine import fq_table
 from ..search import observation_utils
 from . import embedding_utils
+from ..db_utils import acquire_with_retry
 from .types import EntityLink

 logger = logging.getLogger(__name__)
@@ -19,12 +19,12 @@ logger = logging.getLogger(__name__)

 def utcnow():
    """Get current UTC time."""
-    return datetime.now(UTC)
+    return datetime.now(timezone.utc)


 # Simple dataclass-like container for facts (avoid importing from memory_engine)
 class MemoryFactForObservation:
-    def __init__(self, id: str, text: str, fact_type: str, context: str, occurred_start: str | None):
+    def __init__(self, id: str, text: str, fact_type: str, context: str, occurred_start: Optional[str]):
        self.id = id
        self.text = text
        self.fact_type = fact_type
@@ -33,7 +33,12 @@ class MemoryFactForObservation:


 async def regenerate_observations_batch(
-    conn, embeddings_model, llm_config, bank_id: str, entity_links: list[EntityLink], log_buffer: list[str] = None
+    conn,
+    embeddings_model,
+    llm_config,
+    bank_id: str,
+    entity_links: List[EntityLink],
+    log_buffer: List[str] = None
 ) -> None:
    """
    Regenerate observations for top entities in this batch.
@@ -56,7 +61,7 @@ async def regenerate_observations_batch(
        return

    # Count mentions per entity in this batch
-    entity_mention_counts: dict[str, int] = {}
+    entity_mention_counts: Dict[str, int] = {}
    for link in entity_links:
        if link.entity_id:
            entity_id = str(link.entity_id)
@@ -66,7 +71,11 @@ async def regenerate_observations_batch(
        return

    # Sort by mention count descending and take top N
-    sorted_entities = sorted(entity_mention_counts.items(), key=lambda x: x[1], reverse=True)
+    sorted_entities = sorted(
+        entity_mention_counts.items(),
+        key=lambda x: x[1],
+        reverse=True
+    )
    entities_to_process = [e[0] for e in sorted_entities[:TOP_N_ENTITIES]]

    obs_start = time.time()
@@ -76,28 +85,26 @@ async def regenerate_observations_batch(

    # Batch query for entity names
    entity_rows = await conn.fetch(
-        f"""
-        SELECT id, canonical_name FROM {fq_table("entities")}
+        """
+        SELECT id, canonical_name FROM entities
        WHERE id = ANY($1) AND bank_id = $2
        """,
-        entity_uuids,
-        bank_id,
+        entity_uuids, bank_id
    )
-    entity_names = {row["id"]: row["canonical_name"] for row in entity_rows}
+    entity_names = {row['id']: row['canonical_name'] for row in entity_rows}

    # Batch query for fact counts
    fact_counts = await conn.fetch(
-        f"""
+        """
        SELECT ue.entity_id, COUNT(*) as cnt
-        FROM {fq_table("unit_entities")} ue
-        JOIN {fq_table("memory_units")} mu ON ue.unit_id = mu.id
+        FROM unit_entities ue
+        JOIN memory_units mu ON ue.unit_id = mu.id
        WHERE ue.entity_id = ANY($1) AND mu.bank_id = $2
        GROUP BY ue.entity_id
        """,
-        entity_uuids,
-        bank_id,
+        entity_uuids, bank_id
    )
-    entity_fact_counts = {row["entity_id"]: row["cnt"] for row in fact_counts}
+    entity_fact_counts = {row['entity_id']: row['cnt'] for row in fact_counts}

    # Filter entities that meet the threshold
    entities_with_names = []
@@ -119,7 +126,8 @@ async def regenerate_observations_batch(
    for entity_id, entity_name in entities_with_names:
        try:
            obs_ids = await _regenerate_entity_observations(
-                conn, embeddings_model, llm_config, bank_id, entity_id, entity_name
+                conn, embeddings_model, llm_config,
+                bank_id, entity_id, entity_name
            )
            total_observations += len(obs_ids)
        except Exception as e:
@@ -127,14 +135,17 @@ async def regenerate_observations_batch(

    obs_time = time.time() - obs_start
    if log_buffer is not None:
-        log_buffer.append(
-            f"[11] Observations: {total_observations} observations for {len(entities_with_names)} entities in {obs_time:.3f}s"
-        )
+        log_buffer.append(f"[11] Observations: {total_observations} observations for {len(entities_with_names)} entities in {obs_time:.3f}s")


 async def _regenerate_entity_observations(
-    conn, embeddings_model, llm_config, bank_id: str, entity_id: str, entity_name: str
-) -> list[str]:
+    conn,
+    embeddings_model,
+    llm_config,
+    bank_id: str,
+    entity_id: str,
+    entity_name: str
+) -> List[str]:
    """
    Regenerate observations for a single entity.

@@ -155,18 +166,17 @@ async def _regenerate_entity_observations(

    # Get all facts mentioning this entity (exclude observations themselves)
    rows = await conn.fetch(
-        f"""
+        """
        SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.fact_type
-        FROM {fq_table("memory_units")} mu
-        JOIN {fq_table("unit_entities")} ue ON mu.id = ue.unit_id
+        FROM memory_units mu
+        JOIN unit_entities ue ON mu.id = ue.unit_id
        WHERE mu.bank_id = $1
          AND ue.entity_id = $2
          AND mu.fact_type IN ('world', 'experience')
        ORDER BY mu.occurred_start DESC
        LIMIT 50
        """,
-        bank_id,
-        entity_uuid,
+        bank_id, entity_uuid
    )

    if not rows:
@@ -175,42 +185,45 @@ async def _regenerate_entity_observations(
    # Convert to fact objects for observation extraction
    facts = []
    for row in rows:
-        occurred_start = row["occurred_start"].isoformat() if row["occurred_start"] else None
-        facts.append(
-            MemoryFactForObservation(
-                id=str(row["id"]),
-                text=row["text"],
-                fact_type=row["fact_type"],
-                context=row["context"],
-                occurred_start=occurred_start,
-            )
-        )
+        occurred_start = row['occurred_start'].isoformat() if row['occurred_start'] else None
+        facts.append(MemoryFactForObservation(
+            id=str(row['id']),
+            text=row['text'],
+            fact_type=row['fact_type'],
+            context=row['context'],
+            occurred_start=occurred_start
+        ))

    # Extract observations using LLM
-    observations = await observation_utils.extract_observations_from_facts(llm_config, entity_name, facts)
+    observations = await observation_utils.extract_observations_from_facts(
+        llm_config,
+        entity_name,
+        facts
+    )

    if not observations:
        return []

    # Delete old observations for this entity
    await conn.execute(
-        f"""
-        DELETE FROM {fq_table("memory_units")}
+        """
+        DELETE FROM memory_units
        WHERE id IN (
            SELECT mu.id
-            FROM {fq_table("memory_units")} mu
-            JOIN {fq_table("unit_entities")} ue ON mu.id = ue.unit_id
+            FROM memory_units mu
+            JOIN unit_entities ue ON mu.id = ue.unit_id
            WHERE mu.bank_id = $1
              AND mu.fact_type = 'observation'
              AND ue.entity_id = $2
        )
        """,
-        bank_id,
-        entity_uuid,
+        bank_id, entity_uuid
    )

    # Generate embeddings for new observations
-    embeddings = await embedding_utils.generate_embeddings_batch(embeddings_model, observations)
+    embeddings = await embedding_utils.generate_embeddings_batch(
+        embeddings_model, observations
+    )

    # Insert new observations
    current_time = utcnow()
@@ -218,8 +231,8 @@ async def _regenerate_entity_observations(

    for obs_text, embedding in zip(observations, embeddings):
        result = await conn.fetchrow(
-            f"""
-            INSERT INTO {fq_table("memory_units")} (
+            """
+            INSERT INTO memory_units (
                bank_id, text, embedding, context, event_date,
                occurred_start, occurred_end, mentioned_at,
                fact_type, access_count
@@ -234,19 +247,18 @@ async def _regenerate_entity_observations(
            current_time,
            current_time,
            current_time,
-            current_time,
+            current_time
        )
-        obs_id = str(result["id"])
+        obs_id = str(result['id'])
        created_ids.append(obs_id)

        # Link observation to entity
        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
+            """
+            INSERT INTO unit_entities (unit_id, entity_id)
            VALUES ($1, $2)
            """,
-            uuid.UUID(obs_id),
-            entity_uuid,
+            uuid.UUID(obs_id), entity_uuid
        )

    return created_ids
@@ -3,32 +3,31 @@ Main orchestrator for the retain pipeline.

 Coordinates all retain pipeline modules to store memories efficiently.
 """
-
 import logging
 import time
 import uuid
-from datetime import UTC, datetime
+from datetime import datetime, timezone
+from typing import List, Dict, Any, Optional

-from ..db_utils import acquire_with_retry
 from . import bank_utils
+from ..db_utils import acquire_with_retry


 def utcnow():
    """Get current UTC time."""
-    return datetime.now(UTC)
-
+    return datetime.now(timezone.utc)

+from .types import RetainContent, ExtractedFact, ProcessedFact, EntityLink
 from . import (
-    chunk_storage,
-    deduplication,
-    embedding_processing,
-    entity_processing,
    fact_extraction,
+    embedding_processing,
+    deduplication,
+    chunk_storage,
    fact_storage,
+    entity_processing,
    link_creation,
-    observation_regeneration,
+    observation_regeneration
 )
-from .types import ExtractedFact, ProcessedFact, RetainContent, RetainContentDict

 logger = logging.getLogger(__name__)

@@ -42,12 +41,12 @@ async def retain_batch(
    format_date_fn,
    duplicate_checker_fn,
    bank_id: str,
-    contents_dicts: list[RetainContentDict],
-    document_id: str | None = None,
+    contents_dicts: List[Dict[str, Any]],
+    document_id: Optional[str] = None,
    is_first_batch: bool = True,
-    fact_type_override: str | None = None,
-    confidence_score: float | None = None,
-) -> list[list[str]]:
+    fact_type_override: Optional[str] = None,
+    confidence_score: Optional[float] = None,
+) -> List[List[str]]:
    """
    Process a batch of content through the retain pipeline.

@@ -74,10 +73,10 @@ async def retain_batch(

    # Buffer all logs
    log_buffer = []
-    log_buffer.append(f"{'=' * 60}")
+    log_buffer.append(f"{'='*60}")
    log_buffer.append(f"RETAIN_BATCH START: {bank_id}")
    log_buffer.append(f"Batch size: {len(contents_dicts)} content items, {total_chars:,} chars")
-    log_buffer.append(f"{'=' * 60}")
+    log_buffer.append(f"{'='*60}")

    # Get bank profile
    profile = await bank_utils.get_bank_profile(pool, bank_id)
@@ -90,26 +89,23 @@ async def retain_batch(
            content=item["content"],
            context=item.get("context", ""),
            event_date=item.get("event_date") or utcnow(),
-            metadata=item.get("metadata", {}),
+            metadata=item.get("metadata", {})
        )
        contents.append(content)

    # Step 1: Extract facts from all contents
    step_start = time.time()
-    extract_opinions = fact_type_override == "opinion"
+    extract_opinions = (fact_type_override == 'opinion')

    extracted_facts, chunks = await fact_extraction.extract_facts_from_contents(
-        contents, llm_config, agent_name, extract_opinions
-    )
-    log_buffer.append(
-        f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s"
+        contents,
+        llm_config,
+        agent_name,
+        extract_opinions
    )
+    log_buffer.append(f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s")

    if not extracted_facts:
-        total_time = time.time() - start_time
-        logger.info(
-            f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s (nothing to store)"
-        )
        return [[] for _ in contents]

    # Apply fact_type_override if provided
@@ -134,7 +130,6 @@ async def retain_batch(

    # Group contents by document_id for document tracking and chunk storage
    from collections import defaultdict
-
    contents_by_doc = defaultdict(list)
    for idx, content_dict in enumerate(contents_dicts):
        doc_id = content_dict.get("document_id")
@@ -160,11 +155,7 @@ async def retain_batch(
                    if first_item.get("context"):
                        retain_params["context"] = first_item["context"]
                    if first_item.get("event_date"):
-                        retain_params["event_date"] = (
-                            first_item["event_date"].isoformat()
-                            if hasattr(first_item["event_date"], "isoformat")
-                            else str(first_item["event_date"])
-                        )
+                        retain_params["event_date"] = first_item["event_date"].isoformat() if hasattr(first_item["event_date"], "isoformat") else str(first_item["event_date"])
                    if first_item.get("metadata"):
                        retain_params["metadata"] = first_item["metadata"]

@@ -204,11 +195,7 @@ async def retain_batch(
                                if first_item.get("context"):
                                    retain_params["context"] = first_item["context"]
                                if first_item.get("event_date"):
-                                    retain_params["event_date"] = (
-                                        first_item["event_date"].isoformat()
-                                        if hasattr(first_item["event_date"], "isoformat")
-                                        else str(first_item["event_date"])
-                                    )
+                                    retain_params["event_date"] = first_item["event_date"].isoformat() if hasattr(first_item["event_date"], "isoformat") else str(first_item["event_date"])
                                if first_item.get("metadata"):
                                    retain_params["metadata"] = first_item["metadata"]

@@ -218,9 +205,7 @@ async def retain_batch(
                            document_ids_added.append(actual_doc_id)

            if document_ids_added:
-                log_buffer.append(
-                    f"[2.5] Document tracking: {len(document_ids_added)} documents in {time.time() - step_start:.3f}s"
-                )
+                log_buffer.append(f"[2.5] Document tracking: {len(document_ids_added)} documents in {time.time() - step_start:.3f}s")

            # Store chunks and map to facts for all documents
            step_start = time.time()
@@ -245,9 +230,7 @@ async def retain_batch(
                    for chunk_idx, chunk_id in chunk_id_map.items():
                        chunk_id_map_by_doc[(doc_id, chunk_idx)] = chunk_id

-                log_buffer.append(
-                    f"[3] Store chunks: {len(chunks)} chunks for {len(chunks_by_doc)} documents in {time.time() - step_start:.3f}s"
-                )
+                log_buffer.append(f"[3] Store chunks: {len(chunks)} chunks for {len(chunks_by_doc)} documents in {time.time() - step_start:.3f}s")

                # Map chunk_ids and document_ids to facts
                for fact, processed_fact in zip(extracted_facts, processed_facts):
@@ -282,9 +265,7 @@ async def retain_batch(
            is_duplicate_flags = await deduplication.check_duplicates_batch(
                conn, bank_id, processed_facts, duplicate_checker_fn
            )
-            log_buffer.append(
-                f"[4] Deduplication: {sum(is_duplicate_flags)} duplicates in {time.time() - step_start:.3f}s"
-            )
+            log_buffer.append(f"[4] Deduplication: {sum(is_duplicate_flags)} duplicates in {time.time() - step_start:.3f}s")

            # Filter out duplicates
            non_duplicate_facts = deduplication.filter_duplicates(processed_facts, is_duplicate_flags)
@@ -312,18 +293,14 @@ async def retain_batch(
            # Create semantic links
            step_start = time.time()
            embeddings_for_links = [fact.embedding for fact in non_duplicate_facts]
-            semantic_link_count = await link_creation.create_semantic_links_batch(
-                conn, bank_id, unit_ids, embeddings_for_links
-            )
+            semantic_link_count = await link_creation.create_semantic_links_batch(conn, bank_id, unit_ids, embeddings_for_links)
            log_buffer.append(f"[8] Semantic links: {semantic_link_count} links in {time.time() - step_start:.3f}s")

            # Insert entity links
            step_start = time.time()
            if entity_links:
                await entity_processing.insert_entity_links_batch(conn, entity_links)
-            log_buffer.append(
-                f"[9] Entity links: {len(entity_links) if entity_links else 0} links in {time.time() - step_start:.3f}s"
-            )
+            log_buffer.append(f"[9] Entity links: {len(entity_links) if entity_links else 0} links in {time.time() - step_start:.3f}s")

            # Create causal links
            step_start = time.time()
@@ -332,22 +309,34 @@ async def retain_batch(

            # Regenerate observations INSIDE transaction for atomicity
            await observation_regeneration.regenerate_observations_batch(
-                conn, embeddings_model, llm_config, bank_id, entity_links, log_buffer
+                conn,
+                embeddings_model,
+                llm_config,
+                bank_id,
+                entity_links,
+                log_buffer
            )

            # Map results back to original content items
-            result_unit_ids = _map_results_to_contents(contents, extracted_facts, is_duplicate_flags, unit_ids)
+            result_unit_ids = _map_results_to_contents(
+                contents, extracted_facts, is_duplicate_flags, unit_ids
+            )

        # Trigger background tasks AFTER transaction commits (opinion reinforcement only)
-        await _trigger_background_tasks(task_backend, bank_id, unit_ids, non_duplicate_facts)
+        await _trigger_background_tasks(
+            task_backend,
+            bank_id,
+            unit_ids,
+            non_duplicate_facts
+        )

        # Log final summary
        total_time = time.time() - start_time
-        log_buffer.append(f"{'=' * 60}")
+        log_buffer.append(f"{'='*60}")
        log_buffer.append(f"RETAIN_BATCH COMPLETE: {len(unit_ids)} units in {total_time:.3f}s")
        if document_ids_added:
            log_buffer.append(f"Documents: {', '.join(document_ids_added)}")
-        log_buffer.append(f"{'=' * 60}")
+        log_buffer.append(f"{'='*60}")

        logger.info("\n" + "\n".join(log_buffer) + "\n")

@@ -355,11 +344,11 @@ async def retain_batch(


 def _map_results_to_contents(
-    contents: list[RetainContent],
-    extracted_facts: list[ExtractedFact],
-    is_duplicate_flags: list[bool],
-    unit_ids: list[str],
-) -> list[list[str]]:
+    contents: List[RetainContent],
+    extracted_facts: List[ExtractedFact],
+    is_duplicate_flags: List[bool],
+    unit_ids: List[str]
+) -> List[List[str]]:
    """
    Map created unit IDs back to original content items.

@@ -387,19 +376,17 @@ def _map_results_to_contents(
 async def _trigger_background_tasks(
    task_backend,
    bank_id: str,
-    unit_ids: list[str],
-    facts: list[ProcessedFact],
+    unit_ids: List[str],
+    facts: List[ProcessedFact],
 ) -> None:
    """Trigger opinion reinforcement as background task (after transaction commits)."""
    # Trigger opinion reinforcement if there are entities
    fact_entities = [[e.name for e in fact.entities] for fact in facts]
    if any(fact_entities):
-        await task_backend.submit_task(
-            {
-                "type": "reinforce_opinion",
-                "bank_id": bank_id,
-                "created_unit_ids": unit_ids,
-                "unit_texts": [fact.fact_text for fact in facts],
-                "unit_entities": fact_entities,
-            }
-        )
+        await task_backend.submit_task({
+            'type': 'reinforce_opinion',
+            'bank_id': bank_id,
+            'created_unit_ids': unit_ids,
+            'unit_texts': [fact.fact_text for fact in facts],
+            'unit_entities': fact_entities
+        })
@@ -6,34 +6,11 @@ from content input to fact storage.
 """

 from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from typing import TypedDict
+from typing import List, Optional, Dict, Any
+from datetime import datetime
 from uuid import UUID


-class RetainContentDict(TypedDict, total=False):
-    """Type definition for content items in retain_batch_async.
-
-    Fields:
-        content: Text content to store (required)
-        context: Context about the content (optional)
-        event_date: When the content occurred (optional, defaults to now)
-        metadata: Custom key-value metadata (optional)
-        document_id: Document ID for this content item (optional)
-    """
-
-    content: str  # Required
-    context: str
-    event_date: datetime
-    metadata: dict[str, str]
-    document_id: str
-
-
-def _now_utc() -> datetime:
-    """Factory function for default event_date."""
-    return datetime.now(UTC)
-
-
@dataclass
 class RetainContent:
    """
@@ -41,11 +18,16 @@ class RetainContent:

    Represents a single piece of content to extract facts from.
    """
-
    content: str
    context: str = ""
-    event_date: datetime = field(default_factory=_now_utc)
-    metadata: dict[str, str] = field(default_factory=dict)
+    event_date: Optional[datetime] = None
+    metadata: Dict[str, str] = field(default_factory=dict)
+
+    def __post_init__(self):
+        """Ensure event_date is set."""
+        if self.event_date is None:
+            from datetime import datetime, timezone
+            self.event_date = datetime.now(timezone.utc)


@dataclass
@@ -55,7 +37,6 @@ class ChunkMetadata:

    Used to track which facts were extracted from which chunks.
    """
-
    chunk_text: str
    fact_count: int
    content_index: int  # Index of the source content
@@ -69,10 +50,9 @@ class EntityRef:

    Entities are extracted by the LLM during fact extraction.
    """
-
    name: str
-    canonical_name: str | None = None  # Resolved canonical name
-    entity_id: UUID | None = None  # Resolved entity ID
+    canonical_name: Optional[str] = None  # Resolved canonical name
+    entity_id: Optional[UUID] = None  # Resolved entity ID


@dataclass
@@ -82,7 +62,6 @@ class CausalRelation:

    Represents how one fact causes, enables, or prevents another.
    """
-
    relation_type: str  # "causes", "enables", "prevents", "caused_by"
    target_fact_index: int  # Index of the target fact in the batch
    strength: float = 1.0  # Strength of the causal relationship
@@ -95,21 +74,20 @@ class ExtractedFact:

    This is the raw output from fact extraction before processing.
    """
-
    fact_text: str
    fact_type: str  # "world", "experience", "opinion", "observation"
-    entities: list[str] = field(default_factory=list)
-    occurred_start: datetime | None = None
-    occurred_end: datetime | None = None
-    where: str | None = None  # WHERE the fact occurred or is about
-    causal_relations: list[CausalRelation] = field(default_factory=list)
+    entities: List[str] = field(default_factory=list)
+    occurred_start: Optional[datetime] = None
+    occurred_end: Optional[datetime] = None
+    where: Optional[str] = None  # WHERE the fact occurred or is about
+    causal_relations: List[CausalRelation] = field(default_factory=list)

    # Context from the content item
    content_index: int = 0  # Which content this fact came from
    chunk_index: int = 0  # Which chunk this fact came from
    context: str = ""
-    mentioned_at: datetime | None = None
-    metadata: dict[str, str] = field(default_factory=dict)
+    mentioned_at: Optional[datetime] = None
+    metadata: Dict[str, str] = field(default_factory=dict)


@dataclass
@@ -119,38 +97,37 @@ class ProcessedFact:

    Includes resolved entities, embeddings, and all necessary fields.
    """
-
    # Core fact data
    fact_text: str
    fact_type: str
-    embedding: list[float]
+    embedding: List[float]

    # Temporal data
-    occurred_start: datetime | None
-    occurred_end: datetime | None
+    occurred_start: Optional[datetime]
+    occurred_end: Optional[datetime]
    mentioned_at: datetime

    # Context and metadata
    context: str
-    metadata: dict[str, str]
+    metadata: Dict[str, str]

    # Location data
-    where: str | None = None
+    where: Optional[str] = None

    # Entities
-    entities: list[EntityRef] = field(default_factory=list)
+    entities: List[EntityRef] = field(default_factory=list)

    # Causal relations
-    causal_relations: list[CausalRelation] = field(default_factory=list)
+    causal_relations: List[CausalRelation] = field(default_factory=list)

    # Chunk reference
-    chunk_id: str | None = None
+    chunk_id: Optional[str] = None

    # Document reference (denormalized for query performance)
-    document_id: str | None = None
+    document_id: Optional[str] = None

    # DB fields (set after insertion)
-    unit_id: UUID | None = None
+    unit_id: Optional[UUID] = None

    @property
    def is_duplicate(self) -> bool:
@@ -159,8 +136,10 @@ class ProcessedFact:

    @staticmethod
    def from_extracted_fact(
-        extracted_fact: "ExtractedFact", embedding: list[float], chunk_id: str | None = None
-    ) -> "ProcessedFact":
+        extracted_fact: 'ExtractedFact',
+        embedding: List[float],
+        chunk_id: Optional[str] = None
+    ) -> 'ProcessedFact':
        """
        Create ProcessedFact from ExtractedFact.

@@ -172,12 +151,12 @@ class ProcessedFact:
        Returns:
            ProcessedFact ready for storage
        """
-        from datetime import datetime
+        from datetime import datetime, timezone

        # Use occurred dates only if explicitly provided by LLM
        occurred_start = extracted_fact.occurred_start
        occurred_end = extracted_fact.occurred_end
-        mentioned_at = extracted_fact.mentioned_at or datetime.now(UTC)
+        mentioned_at = extracted_fact.mentioned_at or datetime.now(timezone.utc)

        # Convert entity strings to EntityRef objects
        entities = [EntityRef(name=name) for name in extracted_fact.entities]
@@ -193,7 +172,7 @@ class ProcessedFact:
            metadata=extracted_fact.metadata,
            entities=entities,
            causal_relations=extracted_fact.causal_relations,
-            chunk_id=chunk_id,
+            chunk_id=chunk_id
        )


@@ -204,11 +183,10 @@ class EntityLink:

    Used for entity-based graph connections in the memory graph.
    """
-
    from_unit_id: UUID
    to_unit_id: UUID
    entity_id: UUID
-    link_type: str = "entity"
+    link_type: str = 'entity'
    weight: float = 1.0


@@ -219,25 +197,24 @@ class RetainBatch:

    Tracks all facts, chunks, and metadata for a batch operation.
    """
-
    bank_id: str
-    contents: list[RetainContent]
-    document_id: str | None = None
-    fact_type_override: str | None = None
-    confidence_score: float | None = None
+    contents: List[RetainContent]
+    document_id: Optional[str] = None
+    fact_type_override: Optional[str] = None
+    confidence_score: Optional[float] = None

    # Extracted data (populated during processing)
-    extracted_facts: list[ExtractedFact] = field(default_factory=list)
-    processed_facts: list[ProcessedFact] = field(default_factory=list)
-    chunks: list[ChunkMetadata] = field(default_factory=list)
+    extracted_facts: List[ExtractedFact] = field(default_factory=list)
+    processed_facts: List[ProcessedFact] = field(default_factory=list)
+    chunks: List[ChunkMetadata] = field(default_factory=list)

    # Results (populated after storage)
-    unit_ids_by_content: list[list[str]] = field(default_factory=list)
+    unit_ids_by_content: List[List[str]] = field(default_factory=list)

-    def get_facts_for_content(self, content_index: int) -> list[ExtractedFact]:
+    def get_facts_for_content(self, content_index: int) -> List[ExtractedFact]:
        """Get all extracted facts for a specific content item."""
        return [f for f in self.extracted_facts if f.content_index == content_index]

-    def get_chunks_for_content(self, content_index: int) -> list[ChunkMetadata]:
+    def get_chunks_for_content(self, content_index: int) -> List[ChunkMetadata]:
        """Get all chunks for a specific content item."""
        return [c for c in self.chunks if c.content_index == content_index]
@@ -3,27 +3,13 @@ Search module for memory retrieval.

 Provides modular search architecture:
 - Retrieval: 4-way parallel (semantic + BM25 + graph + temporal)
- Graph retrieval: Pluggable strategies (BFS, PPR)
 - Reranking: Pluggable strategies (heuristic, cross-encoder)
 """

-from .graph_retrieval import BFSGraphRetriever, GraphRetriever
-from .mpfp_retrieval import MPFPGraphRetriever
+from .retrieval import retrieve_parallel
 from .reranking import CrossEncoderReranker
-from .retrieval import (
-    ParallelRetrievalResult,
-    get_default_graph_retriever,
-    retrieve_parallel,
-    set_default_graph_retriever,
-)

 __all__ = [
    "retrieve_parallel",
-    "get_default_graph_retriever",
-    "set_default_graph_retriever",
-    "ParallelRetrievalResult",
-    "GraphRetriever",
-    "BFSGraphRetriever",
-    "MPFPGraphRetriever",
    "CrossEncoderReranker",
 ]
@@ -2,12 +2,15 @@
 Helper functions for hybrid search (semantic + BM25 + graph).
 """

-from typing import Any
-
-from .types import MergedCandidate, RetrievalResult
+from typing import List, Dict, Any, Tuple
+import asyncio
+from .types import RetrievalResult, MergedCandidate


-def reciprocal_rank_fusion(result_lists: list[list[RetrievalResult]], k: int = 60) -> list[MergedCandidate]:
+def reciprocal_rank_fusion(
+    result_lists: List[List[RetrievalResult]],
+    k: int = 60
+) -> List[MergedCandidate]:
    """
    Merge multiple ranked result lists using Reciprocal Rank Fusion.

@@ -70,14 +73,20 @@ def reciprocal_rank_fusion(result_lists: list[list[RetrievalResult]], k: int = 6
        sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True), start=1
    ):
        merged_candidate = MergedCandidate(
-            retrieval=all_retrievals[doc_id], rrf_score=rrf_score, rrf_rank=rrf_rank, source_ranks=source_ranks[doc_id]
+            retrieval=all_retrievals[doc_id],
+            rrf_score=rrf_score,
+            rrf_rank=rrf_rank,
+            source_ranks=source_ranks[doc_id]
        )
        merged_results.append(merged_candidate)

    return merged_results


-def normalize_scores_on_deltas(results: list[dict[str, Any]], score_keys: list[str]) -> list[dict[str, Any]]:
+def normalize_scores_on_deltas(
+    results: List[Dict[str, Any]],
+    score_keys: List[str]
+) -> List[Dict[str, Any]]:
    """
    Normalize scores based on deltas (min-max normalization within result set).

@@ -1,235 +0,0 @@
-"""
-Graph retrieval strategies for memory recall.
-
-This module provides an abstraction for graph-based memory retrieval,
-allowing different algorithms (BFS spreading activation, PPR, etc.) to be
-swapped without changing the rest of the recall pipeline.
-"""
-
-import logging
-from abc import ABC, abstractmethod
-
-from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
-from .types import RetrievalResult
-
-logger = logging.getLogger(__name__)
-
-
-class GraphRetriever(ABC):
-    """
-    Abstract base class for graph-based memory retrieval.
-
-    Implementations traverse the memory graph (entity links, temporal links,
-    causal links) to find relevant facts that might not be found by
-    semantic or keyword search alone.
-    """
-
-    @property
-    @abstractmethod
-    def name(self) -> str:
-        """Return identifier for this retrieval strategy (e.g., 'bfs', 'mpfp')."""
-        pass
-
-    @abstractmethod
-    async def retrieve(
-        self,
-        pool,
-        query_embedding_str: str,
-        bank_id: str,
-        fact_type: str,
-        budget: int,
-        query_text: str | None = None,
-        semantic_seeds: list[RetrievalResult] | None = None,
-        temporal_seeds: list[RetrievalResult] | None = None,
-    ) -> list[RetrievalResult]:
-        """
-        Retrieve relevant facts via graph traversal.
-
-        Args:
-            pool: Database connection pool
-            query_embedding_str: Query embedding as string (for finding entry points)
-            bank_id: Memory bank identifier
-            fact_type: Fact type to filter ('world', 'experience', 'opinion', 'observation')
-            budget: Maximum number of nodes to explore/return
-            query_text: Original query text (optional, for some strategies)
-            semantic_seeds: Pre-computed semantic entry points (from semantic retrieval)
-            temporal_seeds: Pre-computed temporal entry points (from temporal retrieval)
-
-        Returns:
-            List of RetrievalResult objects with activation scores set
-        """
-        pass
-
-
-class BFSGraphRetriever(GraphRetriever):
-    """
-    Graph retrieval using BFS-style spreading activation.
-
-    Starting from semantic entry points, spreads activation through
-    the memory graph (entity, temporal, causal links) using breadth-first
-    traversal with decaying activation.
-
-    This is the original Hindsight graph retrieval algorithm.
-    """
-
-    def __init__(
-        self,
-        entry_point_limit: int = 5,
-        entry_point_threshold: float = 0.5,
-        activation_decay: float = 0.8,
-        min_activation: float = 0.1,
-        batch_size: int = 20,
-    ):
-        """
-        Initialize BFS graph retriever.
-
-        Args:
-            entry_point_limit: Maximum number of entry points to start from
-            entry_point_threshold: Minimum semantic similarity for entry points
-            activation_decay: Decay factor per hop (activation *= decay)
-            min_activation: Minimum activation to continue spreading
-            batch_size: Number of nodes to process per batch (for neighbor fetching)
-        """
-        self.entry_point_limit = entry_point_limit
-        self.entry_point_threshold = entry_point_threshold
-        self.activation_decay = activation_decay
-        self.min_activation = min_activation
-        self.batch_size = batch_size
-
-    @property
-    def name(self) -> str:
-        return "bfs"
-
-    async def retrieve(
-        self,
-        pool,
-        query_embedding_str: str,
-        bank_id: str,
-        fact_type: str,
-        budget: int,
-        query_text: str | None = None,
-        semantic_seeds: list[RetrievalResult] | None = None,
-        temporal_seeds: list[RetrievalResult] | None = None,
-    ) -> list[RetrievalResult]:
-        """
-        Retrieve facts using BFS spreading activation.
-
-        Algorithm:
-        1. Find entry points (top semantic matches above threshold)
-        2. BFS traversal: visit neighbors, propagate decaying activation
-        3. Boost causal links (causes, enables, prevents)
-        4. Return visited nodes up to budget
-
-        Note: BFS finds its own entry points via embedding search.
-        The semantic_seeds and temporal_seeds parameters are accepted
-        for interface compatibility but not used.
-        """
-        async with acquire_with_retry(pool) as conn:
-            return await self._retrieve_with_conn(conn, query_embedding_str, bank_id, fact_type, budget)
-
-    async def _retrieve_with_conn(
-        self,
-        conn,
-        query_embedding_str: str,
-        bank_id: str,
-        fact_type: str,
-        budget: int,
-    ) -> list[RetrievalResult]:
-        """Internal implementation with connection."""
-
-        # Step 1: Find entry points
-        entry_points = await conn.fetch(
-            f"""
-            SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
-                   1 - (embedding <=> $1::vector) AS similarity
-            FROM {fq_table("memory_units")}
-            WHERE bank_id = $2
-              AND embedding IS NOT NULL
-              AND fact_type = $3
-              AND (1 - (embedding <=> $1::vector)) >= $4
-            ORDER BY embedding <=> $1::vector
-            LIMIT $5
-            """,
-            query_embedding_str,
-            bank_id,
-            fact_type,
-            self.entry_point_threshold,
-            self.entry_point_limit,
-        )
-
-        if not entry_points:
-            return []
-
-        # Step 2: BFS spreading activation
-        visited = set()
-        results = []
-        queue = [(RetrievalResult.from_db_row(dict(r)), r["similarity"]) for r in entry_points]
-        budget_remaining = budget
-
-        while queue and budget_remaining > 0:
-            # Collect a batch of nodes to process
-            batch_nodes = []
-            batch_activations = {}
-
-            while queue and len(batch_nodes) < self.batch_size and budget_remaining > 0:
-                current, activation = queue.pop(0)
-                unit_id = current.id
-
-                if unit_id not in visited:
-                    visited.add(unit_id)
-                    budget_remaining -= 1
-                    current.activation = activation
-                    results.append(current)
-                    batch_nodes.append(current.id)
-                    batch_activations[unit_id] = activation
-
-            # Batch fetch neighbors
-            if batch_nodes and budget_remaining > 0:
-                max_neighbors = len(batch_nodes) * 20
-                neighbors = await conn.fetch(
-                    f"""
-                    SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
-                           mu.mentioned_at, mu.access_count, mu.embedding, mu.fact_type,
-                           mu.document_id, mu.chunk_id,
-                           ml.weight, ml.link_type, ml.from_unit_id
-                    FROM {fq_table("memory_links")} ml
-                    JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
-                    WHERE ml.from_unit_id = ANY($1::uuid[])
-                      AND ml.weight >= $2
-                      AND mu.fact_type = $3
-                    ORDER BY ml.weight DESC
-                    LIMIT $4
-                    """,
-                    batch_nodes,
-                    self.min_activation,
-                    fact_type,
-                    max_neighbors,
-                )
-
-                for n in neighbors:
-                    neighbor_id = str(n["id"])
-                    if neighbor_id not in visited:
-                        parent_id = str(n["from_unit_id"])
-                        parent_activation = batch_activations.get(parent_id, 0.5)
-
-                        # Boost causal links
-                        link_type = n["link_type"]
-                        base_weight = n["weight"]
-
-                        if link_type in ("causes", "caused_by"):
-                            causal_boost = 2.0
-                        elif link_type in ("enables", "prevents"):
-                            causal_boost = 1.5
-                        else:
-                            causal_boost = 1.0
-
-                        effective_weight = base_weight * causal_boost
-                        new_activation = parent_activation * effective_weight * self.activation_decay
-
-                        if new_activation > self.min_activation:
-                            neighbor_result = RetrievalResult.from_db_row(dict(n))
-                            queue.append((neighbor_result, new_activation))
-
-        return results
@@ -1,439 +0,0 @@
-"""
-Meta-Path Forward Push (MPFP) graph retrieval.
-
-A sublinear graph traversal algorithm for memory retrieval over heterogeneous
-graphs with multiple edge types (semantic, temporal, causal, entity).
-
-Combines meta-path patterns from HIN literature with Forward Push local
-propagation from Approximate PPR.
-
-Key properties:
- Sublinear in graph size (threshold pruning bounds active nodes)
- Predefined patterns capture different retrieval intents
- All patterns run in parallel, results fused via RRF
- No LLM in the loop during traversal
-"""
-
-import asyncio
-import logging
-from collections import defaultdict
-from dataclasses import dataclass, field
-
-from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
-from .graph_retrieval import GraphRetriever
-from .types import RetrievalResult
-
-logger = logging.getLogger(__name__)
-
-
-# -----------------------------------------------------------------------------
-# Data Classes
-# -----------------------------------------------------------------------------
-
-
-@dataclass
-class EdgeTarget:
-    """A neighbor node with its edge weight."""
-
-    node_id: str
-    weight: float
-
-
-@dataclass
-class TypedAdjacency:
-    """Adjacency lists split by edge type."""
-
-    # edge_type -> from_node_id -> list of (to_node_id, weight)
-    graphs: dict[str, dict[str, list[EdgeTarget]]] = field(default_factory=dict)
-
-    def get_neighbors(self, edge_type: str, node_id: str) -> list[EdgeTarget]:
-        """Get neighbors for a node via a specific edge type."""
-        return self.graphs.get(edge_type, {}).get(node_id, [])
-
-    def get_normalized_neighbors(self, edge_type: str, node_id: str, top_k: int) -> list[EdgeTarget]:
-        """Get top-k neighbors with weights normalized to sum to 1."""
-        neighbors = self.get_neighbors(edge_type, node_id)[:top_k]
-        if not neighbors:
-            return []
-
-        total = sum(n.weight for n in neighbors)
-        if total == 0:
-            return []
-
-        return [EdgeTarget(node_id=n.node_id, weight=n.weight / total) for n in neighbors]
-
-
-@dataclass
-class PatternResult:
-    """Result from a single pattern traversal."""
-
-    pattern: list[str]
-    scores: dict[str, float]  # node_id -> accumulated mass
-
-
-@dataclass
-class MPFPConfig:
-    """Configuration for MPFP algorithm."""
-
-    alpha: float = 0.15  # teleport/keep probability
-    threshold: float = 1e-6  # mass pruning threshold (lower = explore more)
-    top_k_neighbors: int = 20  # fan-out limit per node
-
-    # Patterns from semantic seeds
-    patterns_semantic: list[list[str]] = field(
-        default_factory=lambda: [
-            ["semantic", "semantic"],  # topic expansion
-            ["entity", "temporal"],  # entity timeline
-            ["semantic", "causes"],  # reasoning chains (forward)
-            ["semantic", "caused_by"],  # reasoning chains (backward)
-            ["entity", "semantic"],  # entity context
-        ]
-    )
-
-    # Patterns from temporal seeds
-    patterns_temporal: list[list[str]] = field(
-        default_factory=lambda: [
-            ["temporal", "semantic"],  # what was happening then
-            ["temporal", "entity"],  # who was involved then
-        ]
-    )
-
-
-@dataclass
-class SeedNode:
-    """An entry point node with its initial score."""
-
-    node_id: str
-    score: float  # initial mass (e.g., similarity score)
-
-
-# -----------------------------------------------------------------------------
-# Core Algorithm
-# -----------------------------------------------------------------------------
-
-
-def mpfp_traverse(
-    seeds: list[SeedNode],
-    pattern: list[str],
-    adjacency: TypedAdjacency,
-    config: MPFPConfig,
-) -> PatternResult:
-    """
-    Forward Push traversal following a meta-path pattern.
-
-    Args:
-        seeds: Entry point nodes with initial scores
-        pattern: Sequence of edge types to follow
-        adjacency: Typed adjacency structure
-        config: Algorithm parameters
-
-    Returns:
-        PatternResult with accumulated scores per node
-    """
-    if not seeds:
-        return PatternResult(pattern=pattern, scores={})
-
-    scores: dict[str, float] = {}
-
-    # Initialize frontier with seed masses (normalized)
-    total_seed_score = sum(s.score for s in seeds)
-    if total_seed_score == 0:
-        total_seed_score = len(seeds)  # fallback to uniform
-
-    frontier: dict[str, float] = {s.node_id: s.score / total_seed_score for s in seeds}
-
-    # Follow pattern hop by hop
-    for edge_type in pattern:
-        next_frontier: dict[str, float] = {}
-
-        for node_id, mass in frontier.items():
-            if mass < config.threshold:
-                continue
-
-            # Keep α portion for this node
-            scores[node_id] = scores.get(node_id, 0) + config.alpha * mass
-
-            # Push (1-α) to neighbors
-            push_mass = (1 - config.alpha) * mass
-            neighbors = adjacency.get_normalized_neighbors(edge_type, node_id, config.top_k_neighbors)
-
-            for neighbor in neighbors:
-                next_frontier[neighbor.node_id] = next_frontier.get(neighbor.node_id, 0) + push_mass * neighbor.weight
-
-        frontier = next_frontier
-
-    # Final frontier nodes get their remaining mass
-    for node_id, mass in frontier.items():
-        if mass >= config.threshold:
-            scores[node_id] = scores.get(node_id, 0) + mass
-
-    return PatternResult(pattern=pattern, scores=scores)
-
-
-def rrf_fusion(
-    results: list[PatternResult],
-    k: int = 60,
-    top_k: int = 50,
-) -> list[tuple[str, float]]:
-    """
-    Reciprocal Rank Fusion to combine pattern results.
-
-    Args:
-        results: List of pattern results
-        k: RRF constant (higher = more uniform weighting)
-        top_k: Number of results to return
-
-    Returns:
-        List of (node_id, fused_score) tuples, sorted by score descending
-    """
-    fused: dict[str, float] = {}
-
-    for result in results:
-        if not result.scores:
-            continue
-
-        # Rank nodes by their score in this pattern
-        ranked = sorted(result.scores.keys(), key=lambda n: result.scores[n], reverse=True)
-
-        for rank, node_id in enumerate(ranked):
-            fused[node_id] = fused.get(node_id, 0) + 1.0 / (k + rank + 1)
-
-    # Sort by fused score and return top-k
-    sorted_results = sorted(fused.items(), key=lambda x: x[1], reverse=True)
-
-    return sorted_results[:top_k]
-
-
-# -----------------------------------------------------------------------------
-# Database Loading
-# -----------------------------------------------------------------------------
-
-
-async def load_typed_adjacency(pool, bank_id: str) -> TypedAdjacency:
-    """
-    Load all edges for a bank, split by edge type.
-
-    Single query, then organize in-memory for fast traversal.
-    """
-    async with acquire_with_retry(pool) as conn:
-        rows = await conn.fetch(
-            f"""
-            SELECT ml.from_unit_id, ml.to_unit_id, ml.link_type, ml.weight
-            FROM {fq_table("memory_links")} ml
-            JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-            WHERE mu.bank_id = $1
-              AND ml.weight >= 0.1
-            ORDER BY ml.from_unit_id, ml.weight DESC
-            """,
-            bank_id,
-        )
-
-    graphs: dict[str, dict[str, list[EdgeTarget]]] = defaultdict(lambda: defaultdict(list))
-
-    for row in rows:
-        from_id = str(row["from_unit_id"])
-        to_id = str(row["to_unit_id"])
-        link_type = row["link_type"]
-        weight = row["weight"]
-
-        graphs[link_type][from_id].append(EdgeTarget(node_id=to_id, weight=weight))
-
-    return TypedAdjacency(graphs=dict(graphs))
-
-
-async def fetch_memory_units_by_ids(
-    pool,
-    node_ids: list[str],
-    fact_type: str,
-) -> list[RetrievalResult]:
-    """Fetch full memory unit details for a list of node IDs."""
-    if not node_ids:
-        return []
-
-    async with acquire_with_retry(pool) as conn:
-        rows = await conn.fetch(
-            f"""
-            SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, access_count, embedding, fact_type, document_id, chunk_id
-            FROM {fq_table("memory_units")}
-            WHERE id = ANY($1::uuid[])
-              AND fact_type = $2
-            """,
-            node_ids,
-            fact_type,
-        )
-
-    return [RetrievalResult.from_db_row(dict(r)) for r in rows]
-
-
-# -----------------------------------------------------------------------------
-# Graph Retriever Implementation
-# -----------------------------------------------------------------------------
-
-
-class MPFPGraphRetriever(GraphRetriever):
-    """
-    Graph retrieval using Meta-Path Forward Push.
-
-    Runs predefined patterns in parallel from semantic and temporal seeds,
-    then fuses results via RRF.
-    """
-
-    def __init__(self, config: MPFPConfig | None = None):
-        """
-        Initialize MPFP retriever.
-
-        Args:
-            config: Algorithm configuration (uses defaults if None)
-        """
-        self.config = config or MPFPConfig()
-        self._adjacency_cache: dict[str, TypedAdjacency] = {}
-
-    @property
-    def name(self) -> str:
-        return "mpfp"
-
-    async def retrieve(
-        self,
-        pool,
-        query_embedding_str: str,
-        bank_id: str,
-        fact_type: str,
-        budget: int,
-        query_text: str | None = None,
-        semantic_seeds: list[RetrievalResult] | None = None,
-        temporal_seeds: list[RetrievalResult] | None = None,
-    ) -> list[RetrievalResult]:
-        """
-        Retrieve facts using MPFP algorithm.
-
-        Args:
-            pool: Database connection pool
-            query_embedding_str: Query embedding (used for fallback seed finding)
-            bank_id: Memory bank ID
-            fact_type: Fact type to filter
-            budget: Maximum results to return
-            query_text: Original query text (optional)
-            semantic_seeds: Pre-computed semantic entry points
-            temporal_seeds: Pre-computed temporal entry points
-
-        Returns:
-            List of RetrievalResult with activation scores
-        """
-        # Load typed adjacency (could cache per bank_id with TTL)
-        adjacency = await load_typed_adjacency(pool, bank_id)
-
-        # Convert seeds to SeedNode format
-        semantic_seed_nodes = self._convert_seeds(semantic_seeds, "similarity")
-        temporal_seed_nodes = self._convert_seeds(temporal_seeds, "temporal_score")
-
-        # If no semantic seeds provided, fall back to finding our own
-        if not semantic_seed_nodes:
-            semantic_seed_nodes = await self._find_semantic_seeds(pool, query_embedding_str, bank_id, fact_type)
-
-        # Run all patterns in parallel
-        tasks = []
-
-        # Patterns from semantic seeds
-        for pattern in self.config.patterns_semantic:
-            if semantic_seed_nodes:
-                tasks.append(
-                    asyncio.to_thread(
-                        mpfp_traverse,
-                        semantic_seed_nodes,
-                        pattern,
-                        adjacency,
-                        self.config,
-                    )
-                )
-
-        # Patterns from temporal seeds
-        for pattern in self.config.patterns_temporal:
-            if temporal_seed_nodes:
-                tasks.append(
-                    asyncio.to_thread(
-                        mpfp_traverse,
-                        temporal_seed_nodes,
-                        pattern,
-                        adjacency,
-                        self.config,
-                    )
-                )
-
-        if not tasks:
-            return []
-
-        # Gather pattern results
-        pattern_results = await asyncio.gather(*tasks)
-
-        # Fuse results
-        fused = rrf_fusion(pattern_results, top_k=budget)
-
-        if not fused:
-            return []
-
-        # Get top result IDs (don't exclude seeds - they may be highly relevant)
-        result_ids = [node_id for node_id, score in fused][:budget]
-
-        # Fetch full details
-        results = await fetch_memory_units_by_ids(pool, result_ids, fact_type)
-
-        # Add activation scores from fusion
-        score_map = {node_id: score for node_id, score in fused}
-        for result in results:
-            result.activation = score_map.get(result.id, 0.0)
-
-        # Sort by activation
-        results.sort(key=lambda r: r.activation or 0, reverse=True)
-
-        return results
-
-    def _convert_seeds(
-        self,
-        seeds: list[RetrievalResult] | None,
-        score_attr: str,
-    ) -> list[SeedNode]:
-        """Convert RetrievalResult seeds to SeedNode format."""
-        if not seeds:
-            return []
-
-        result = []
-        for seed in seeds:
-            score = getattr(seed, score_attr, None)
-            if score is None:
-                score = seed.activation or seed.similarity or 1.0
-            result.append(SeedNode(node_id=seed.id, score=score))
-
-        return result
-
-    async def _find_semantic_seeds(
-        self,
-        pool,
-        query_embedding_str: str,
-        bank_id: str,
-        fact_type: str,
-        limit: int = 20,
-        threshold: float = 0.3,
-    ) -> list[SeedNode]:
-        """Fallback: find semantic seeds via embedding search."""
-        async with acquire_with_retry(pool) as conn:
-            rows = await conn.fetch(
-                f"""
-                SELECT id, 1 - (embedding <=> $1::vector) AS similarity
-                FROM {fq_table("memory_units")}
-                WHERE bank_id = $2
-                  AND embedding IS NOT NULL
-                  AND fact_type = $3
-                  AND (1 - (embedding <=> $1::vector)) >= $4
-                ORDER BY embedding <=> $1::vector
-                LIMIT $5
-                """,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                threshold,
-                limit,
-            )
-
-        return [SeedNode(node_id=str(r["id"]), score=r["similarity"]) for r in rows]
@@ -6,7 +6,7 @@ about an entity, without personality influence.
 """

 import logging
-
+from typing import List, Dict, Any
 from pydantic import BaseModel, Field

 from ..response_models import MemoryFact
@@ -16,17 +16,18 @@ logger = logging.getLogger(__name__)

 class Observation(BaseModel):
    """An observation about an entity."""
-
    observation: str = Field(description="The observation text - a factual statement about the entity")


 class ObservationExtractionResponse(BaseModel):
    """Response containing extracted observations."""
-
-    observations: list[Observation] = Field(default_factory=list, description="List of observations about the entity")
+    observations: List[Observation] = Field(
+        default_factory=list,
+        description="List of observations about the entity"
+    )


-def format_facts_for_observation_prompt(facts: list[MemoryFact]) -> str:
+def format_facts_for_observation_prompt(facts: List[MemoryFact]) -> str:
    """Format facts as text for observation extraction prompt."""
    import json

@@ -34,7 +35,9 @@ def format_facts_for_observation_prompt(facts: list[MemoryFact]) -> str:
        return "[]"
    formatted = []
    for fact in facts:
-        fact_obj = {"text": fact.text}
+        fact_obj = {
+            "text": fact.text
+        }

        # Add context if available
        if fact.context:
@@ -89,7 +92,11 @@ def get_observation_system_message() -> str:
    return "You are an objective observer synthesizing facts about an entity. Generate clear, factual observations without opinions or personality influence. Be concise and accurate."


-async def extract_observations_from_facts(llm_config, entity_name: str, facts: list[MemoryFact]) -> list[str]:
+async def extract_observations_from_facts(
+    llm_config,
+    entity_name: str,
+    facts: List[MemoryFact]
+) -> List[str]:
    """
    Extract observations from facts about an entity using LLM.

@@ -111,10 +118,10 @@ async def extract_observations_from_facts(llm_config, entity_name: str, facts: l
        result = await llm_config.call(
            messages=[
                {"role": "system", "content": get_observation_system_message()},
-                {"role": "user", "content": prompt},
+                {"role": "user", "content": prompt}
            ],
            response_format=ObservationExtractionResponse,
-            scope="memory_extract_observation",
+            scope="memory_extract_observation"
        )

        observations = [op.observation for op in result.observations]
@@ -2,6 +2,7 @@
 Cross-encoder neural reranking for search results.
 """

+from typing import List
 from .types import MergedCandidate, ScoredResult


@@ -23,28 +24,14 @@ class CrossEncoderReranker:
        """
        if cross_encoder is None:
            from hindsight_api.engine.cross_encoder import create_cross_encoder_from_env
-
            cross_encoder = create_cross_encoder_from_env()
        self.cross_encoder = cross_encoder
-        self._initialized = False

-    async def ensure_initialized(self):
-        """Ensure the cross-encoder model is initialized (for lazy initialization)."""
-        if self._initialized:
-            return
-
-        import asyncio
-
-        cross_encoder = self.cross_encoder
-        # For local providers, run in thread pool to avoid blocking event loop
-        if cross_encoder.provider_name == "local":
-            loop = asyncio.get_event_loop()
-            await loop.run_in_executor(None, lambda: asyncio.run(cross_encoder.initialize()))
-        else:
-            await cross_encoder.initialize()
-        self._initialized = True
-
-    def rerank(self, query: str, candidates: list[MergedCandidate]) -> list[ScoredResult]:
+    def rerank(
+        self,
+        query: str,
+        candidates: List[MergedCandidate]
+    ) -> List[ScoredResult]:
        """
        Rerank candidates using cross-encoder scores.

@@ -90,7 +77,6 @@ class CrossEncoderReranker:
        # Normalize scores using sigmoid to [0, 1] range
        # Cross-encoder returns logits which can be negative
        import numpy as np
-
        def sigmoid(x):
            return 1 / (1 + np.exp(-x))

@@ -103,7 +89,7 @@ class CrossEncoderReranker:
                candidate=candidate,
                cross_encoder_score=float(raw_score),
                cross_encoder_score_normalized=float(norm_score),
-                weight=float(norm_score),  # Initial weight is just cross-encoder score
+                weight=float(norm_score)  # Initial weight is just cross-encoder score
            )
            scored_results.append(scored_result)

@@ -4,69 +4,24 @@ Retrieval module for 4-way parallel search.
 Implements:
 1. Semantic retrieval (vector similarity)
 2. BM25 retrieval (keyword/full-text search)
-3. Graph retrieval (via pluggable GraphRetriever interface)
+3. Graph retrieval (spreading activation)
 4. Temporal retrieval (time-aware search with spreading)
 """

+from typing import List, Dict, Any, Tuple, Optional
+from datetime import datetime
 import asyncio
-import logging
-from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from typing import Optional
-
-from ...config import get_config
 from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
-from .graph_retrieval import BFSGraphRetriever, GraphRetriever
-from .mpfp_retrieval import MPFPGraphRetriever
 from .types import RetrievalResult

-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ParallelRetrievalResult:
-    """Result from parallel retrieval across all methods."""
-
-    semantic: list[RetrievalResult]
-    bm25: list[RetrievalResult]
-    graph: list[RetrievalResult]
-    temporal: list[RetrievalResult] | None
-    timings: dict[str, float] = field(default_factory=dict)
-    temporal_constraint: tuple | None = None  # (start_date, end_date)
-
-
-# Default graph retriever instance (can be overridden)
-_default_graph_retriever: GraphRetriever | None = None
-
-
-def get_default_graph_retriever() -> GraphRetriever:
-    """Get or create the default graph retriever based on config."""
-    global _default_graph_retriever
-    if _default_graph_retriever is None:
-        config = get_config()
-        retriever_type = config.graph_retriever.lower()
-        if retriever_type == "mpfp":
-            _default_graph_retriever = MPFPGraphRetriever()
-            logger.info("Using MPFP graph retriever")
-        elif retriever_type == "bfs":
-            _default_graph_retriever = BFSGraphRetriever()
-            logger.info("Using BFS graph retriever")
-        else:
-            logger.warning(f"Unknown graph retriever '{retriever_type}', falling back to MPFP")
-            _default_graph_retriever = MPFPGraphRetriever()
-    return _default_graph_retriever
-
-
-def set_default_graph_retriever(retriever: GraphRetriever) -> None:
-    """Set the default graph retriever (for configuration/testing)."""
-    global _default_graph_retriever
-    _default_graph_retriever = retriever
-

 async def retrieve_semantic(
-    conn, query_emb_str: str, bank_id: str, fact_type: str, limit: int
-) -> list[RetrievalResult]:
+    conn,
+    query_emb_str: str,
+    bank_id: str,
+    fact_type: str,
+    limit: int
+) -> List[RetrievalResult]:
    """
    Semantic retrieval via vector similarity.

@@ -81,10 +36,10 @@ async def retrieve_semantic(
        List of RetrievalResult objects
    """
    results = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND embedding IS NOT NULL
          AND fact_type = $3
@@ -92,15 +47,18 @@ async def retrieve_semantic(
        ORDER BY embedding <=> $1::vector
        LIMIT $4
        """,
-        query_emb_str,
-        bank_id,
-        fact_type,
-        limit,
+        query_emb_str, bank_id, fact_type, limit
    )
    return [RetrievalResult.from_db_row(dict(r)) for r in results]


-async def retrieve_bm25(conn, query_text: str, bank_id: str, fact_type: str, limit: int) -> list[RetrievalResult]:
+async def retrieve_bm25(
+    conn,
+    query_text: str,
+    bank_id: str,
+    fact_type: str,
+    limit: int
+) -> List[RetrievalResult]:
    """
    BM25 keyword retrieval via full-text search.

@@ -118,7 +76,7 @@ async def retrieve_bm25(conn, query_text: str, bank_id: str, fact_type: str, lim

    # Sanitize query text: remove special characters that have meaning in tsquery
    # Keep only alphanumeric characters and spaces
-    sanitized_text = re.sub(r"[^\w\s]", " ", query_text.lower())
+    sanitized_text = re.sub(r'[^\w\s]', ' ', query_text.lower())

    # Split and filter empty strings
    tokens = [token for token in sanitized_text.split() if token]
@@ -132,24 +90,136 @@ async def retrieve_bm25(conn, query_text: str, bank_id: str, fact_type: str, lim
    query_tsquery = " | ".join(tokens)

    results = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
               ts_rank_cd(search_vector, to_tsquery('english', $1)) AS bm25_score
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND fact_type = $3
          AND search_vector @@ to_tsquery('english', $1)
        ORDER BY bm25_score DESC
        LIMIT $4
        """,
-        query_tsquery,
-        bank_id,
-        fact_type,
-        limit,
+        query_tsquery, bank_id, fact_type, limit
    )
    return [RetrievalResult.from_db_row(dict(r)) for r in results]


+async def retrieve_graph(
+    conn,
+    query_emb_str: str,
+    bank_id: str,
+    fact_type: str,
+    budget: int
+) -> List[RetrievalResult]:
+    """
+    Graph retrieval via spreading activation.
+
+    Args:
+        conn: Database connection
+        query_emb_str: Query embedding as string
+        agent_id: bank ID
+        fact_type: Fact type to filter
+        budget: Node budget for graph traversal
+
+    Returns:
+        List of RetrievalResult objects
+    """
+    # Find entry points
+    entry_points = await conn.fetch(
+        """
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM memory_units
+        WHERE bank_id = $2
+          AND embedding IS NOT NULL
+          AND fact_type = $3
+          AND (1 - (embedding <=> $1::vector)) >= 0.5
+        ORDER BY embedding <=> $1::vector
+        LIMIT 5
+        """,
+        query_emb_str, bank_id, fact_type
+    )
+
+    if not entry_points:
+        return []
+
+    # BFS-style spreading activation with batched neighbor fetching
+    visited = set()
+    results = []
+    queue = [(RetrievalResult.from_db_row(dict(r)), r["similarity"]) for r in entry_points]
+    budget_remaining = budget
+
+    # Process nodes in batches to reduce DB roundtrips
+    batch_size = 20  # Fetch neighbors for up to 20 nodes at once
+
+    while queue and budget_remaining > 0:
+        # Collect a batch of nodes to process
+        batch_nodes = []
+        batch_activations = {}
+
+        while queue and len(batch_nodes) < batch_size and budget_remaining > 0:
+            current, activation = queue.pop(0)
+            unit_id = current.id
+
+            if unit_id not in visited:
+                visited.add(unit_id)
+                budget_remaining -= 1
+                results.append(current)
+                batch_nodes.append(current.id)
+                batch_activations[unit_id] = activation
+
+        # Batch fetch neighbors for all nodes in this batch
+        # Fetch top weighted neighbors (batch_size * 20 = ~400 for good distribution)
+        if batch_nodes and budget_remaining > 0:
+            max_neighbors = len(batch_nodes) * 20
+            neighbors = await conn.fetch(
+                """
+                SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end, mu.mentioned_at,
+                       mu.access_count, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id,
+                       ml.weight, ml.link_type, ml.from_unit_id
+                FROM memory_links ml
+                JOIN memory_units mu ON ml.to_unit_id = mu.id
+                WHERE ml.from_unit_id = ANY($1::uuid[])
+                  AND ml.weight >= 0.1
+                  AND mu.fact_type = $2
+                ORDER BY ml.weight DESC
+                LIMIT $3
+                """,
+                batch_nodes, fact_type, max_neighbors
+            )
+
+            for n in neighbors:
+                neighbor_id = str(n["id"])
+                if neighbor_id not in visited:
+                    # Get parent activation
+                    parent_id = str(n["from_unit_id"])
+                    activation = batch_activations.get(parent_id, 0.5)
+
+                    # Boost activation for causal links (they're high-value relationships)
+                    link_type = n["link_type"]
+                    base_weight = n["weight"]
+
+                    # Causal links get 1.5-2.0x boost depending on type
+                    if link_type in ("causes", "caused_by"):
+                        # Direct causation - very strong relationship
+                        causal_boost = 2.0
+                    elif link_type in ("enables", "prevents"):
+                        # Conditional causation - strong but not as direct
+                        causal_boost = 1.5
+                    else:
+                        # Temporal, semantic, entity links - standard weight
+                        causal_boost = 1.0
+
+                    effective_weight = base_weight * causal_boost
+                    new_activation = activation * effective_weight * 0.8
+                    if new_activation > 0.1:
+                        neighbor_result = RetrievalResult.from_db_row(dict(n))
+                        queue.append((neighbor_result, new_activation))
+
+    return results
+
+
 async def retrieve_temporal(
    conn,
    query_emb_str: str,
@@ -158,8 +228,8 @@ async def retrieve_temporal(
    start_date: datetime,
    end_date: datetime,
    budget: int,
-    semantic_threshold: float = 0.1,
-) -> list[RetrievalResult]:
+    semantic_threshold: float = 0.1
+) -> List[RetrievalResult]:
    """
    Temporal retrieval with spreading activation.

@@ -181,18 +251,19 @@ async def retrieve_temporal(
    Returns:
        List of RetrievalResult objects with temporal scores
    """
+    from datetime import timezone

    # Ensure start_date and end_date are timezone-aware (UTC) to match database datetimes
    if start_date.tzinfo is None:
-        start_date = start_date.replace(tzinfo=UTC)
+        start_date = start_date.replace(tzinfo=timezone.utc)
    if end_date.tzinfo is None:
-        end_date = end_date.replace(tzinfo=UTC)
+        end_date = end_date.replace(tzinfo=timezone.utc)

    entry_points = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND fact_type = $3
          AND embedding IS NOT NULL
@@ -213,12 +284,7 @@ async def retrieve_temporal(
        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, (embedding <=> $1::vector) ASC
        LIMIT 10
        """,
-        query_emb_str,
-        bank_id,
-        fact_type,
-        start_date,
-        end_date,
-        semantic_threshold,
+        query_emb_str, bank_id, fact_type, start_date, end_date, semantic_threshold
    )

    if not entry_points:
@@ -261,9 +327,7 @@ async def retrieve_temporal(
        results.append(ep_result)

    # Spread through temporal links
-    queue = [
-        (RetrievalResult.from_db_row(dict(ep)), ep["similarity"], 1.0) for ep in entry_points
-    ]  # (unit, semantic_sim, temporal_score)
+    queue = [(RetrievalResult.from_db_row(dict(ep)), ep["similarity"], 1.0) for ep in entry_points]  # (unit, semantic_sim, temporal_score)
    budget_remaining = budget - len(entry_points)

    while queue and budget_remaining > 0:
@@ -273,12 +337,12 @@ async def retrieve_temporal(
        # Get neighbors via temporal and causal links
        if budget_remaining > 0:
            neighbors = await conn.fetch(
-                f"""
+                """
                SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.access_count, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id,
                       ml.weight, ml.link_type,
                       1 - (mu.embedding <=> $1::vector) AS similarity
-                FROM {fq_table("memory_links")} ml
-                JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+                FROM memory_links ml
+                JOIN memory_units mu ON ml.to_unit_id = mu.id
                WHERE ml.from_unit_id = $2
                  AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
                  AND ml.weight >= 0.1
@@ -288,10 +352,7 @@ async def retrieve_temporal(
                ORDER BY ml.weight DESC
                LIMIT 10
                """,
-                query_emb_str,
-                current.id,
-                fact_type,
-                semantic_threshold,
+                query_emb_str, current.id, fact_type, semantic_threshold
            )

            for n in neighbors:
@@ -315,9 +376,7 @@ async def retrieve_temporal(

                if neighbor_best_date:
                    days_from_mid = abs((neighbor_best_date - mid_date).total_seconds() / 86400)
-                    neighbor_temporal_proximity = (
-                        1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
-                    )
+                    neighbor_temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
                else:
                    neighbor_temporal_proximity = 0.3  # Lower score if no temporal data

@@ -359,10 +418,9 @@ async def retrieve_parallel(
    bank_id: str,
    fact_type: str,
    thinking_budget: int,
-    question_date: datetime | None = None,
-    query_analyzer: Optional["QueryAnalyzer"] = None,
-    graph_retriever: GraphRetriever | None = None,
-) -> ParallelRetrievalResult:
+    question_date: Optional[datetime] = None,
+    query_analyzer: Optional["QueryAnalyzer"] = None
+) -> Tuple[List[RetrievalResult], List[RetrievalResult], List[RetrievalResult], Optional[List[RetrievalResult]], Dict[str, float], Optional[Tuple[datetime, datetime]]]:
    """
    Run 3-way or 4-way parallel retrieval (adds temporal if detected).

@@ -370,330 +428,76 @@ async def retrieve_parallel(
        pool: Database connection pool
        query_text: Query text
        query_embedding_str: Query embedding as string
-        bank_id: Bank ID
+        agent_id: bank ID
        fact_type: Fact type to filter
        thinking_budget: Budget for graph traversal and retrieval limits
        question_date: Optional date when question was asked (for temporal filtering)
        query_analyzer: Query analyzer to use (defaults to TransformerQueryAnalyzer)
-        graph_retriever: Graph retrieval strategy (defaults to configured retriever)

    Returns:
-        ParallelRetrievalResult with semantic, bm25, graph, temporal results and timings
+        Tuple of (semantic_results, bm25_results, graph_results, temporal_results, timings, temporal_constraint)
+        Each results list contains RetrievalResult objects
+        temporal_results is None if no temporal constraint detected
+        timings is a dict with per-method latencies in seconds
+        temporal_constraint is the (start_date, end_date) tuple if detected, else None
    """
+    # Detect temporal constraint
    from .temporal_extraction import extract_temporal_constraint
-
-    temporal_constraint = extract_temporal_constraint(query_text, reference_date=question_date, analyzer=query_analyzer)
-
-    retriever = graph_retriever or get_default_graph_retriever()
-
-    if retriever.name == "mpfp":
-        return await _retrieve_parallel_mpfp(
-            pool, query_text, query_embedding_str, bank_id, fact_type, thinking_budget, temporal_constraint, retriever
-        )
-    else:
-        return await _retrieve_parallel_bfs(
-            pool, query_text, query_embedding_str, bank_id, fact_type, thinking_budget, temporal_constraint, retriever
-        )
-
-
-@dataclass
-class _SemanticGraphResult:
-    """Internal result from semantic→graph chain."""
-
-    semantic: list[RetrievalResult]
-    graph: list[RetrievalResult]
-    semantic_time: float
-    graph_time: float
-
-
-@dataclass
-class _TimedResult:
-    """Internal result with timing."""
-
-    results: list[RetrievalResult]
-    time: float
-
-
-async def _retrieve_parallel_mpfp(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    temporal_constraint: tuple | None,
-    retriever: GraphRetriever,
-) -> ParallelRetrievalResult:
-    """
-    MPFP retrieval with optimized parallelization.
-
-    Runs 2-3 parallel task chains:
-    - Task 1: Semantic → Graph (chained, graph uses semantic seeds)
-    - Task 2: BM25 (independent)
-    - Task 3: Temporal (if constraint detected)
-    """
    import time

-    async def run_semantic_then_graph() -> _SemanticGraphResult:
-        """Chain: semantic retrieval → graph retrieval (using semantic as seeds)."""
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            semantic = await retrieve_semantic(conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget)
-        semantic_time = time.time() - start
-
-        # Get temporal seeds if needed (quick query, part of this chain)
-        temporal_seeds = None
-        if temporal_constraint:
-            tc_start, tc_end = temporal_constraint
-            async with acquire_with_retry(pool) as conn:
-                temporal_seeds = await _get_temporal_entry_points(
-                    conn, query_embedding_str, bank_id, fact_type, tc_start, tc_end, limit=20
-                )
-
-        # Run graph with seeds
-        start = time.time()
-        graph = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=fact_type,
-            budget=thinking_budget,
-            query_text=query_text,
-            semantic_seeds=semantic,
-            temporal_seeds=temporal_seeds,
-        )
-        graph_time = time.time() - start
-
-        return _SemanticGraphResult(semantic, graph, semantic_time, graph_time)
-
-    async def run_bm25() -> _TimedResult:
-        """Independent BM25 retrieval."""
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget)
-        return _TimedResult(results, time.time() - start)
-
-    async def run_temporal(tc_start, tc_end) -> _TimedResult:
-        """Temporal retrieval (uses its own entry point finding)."""
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_temporal(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
-            )
-        return _TimedResult(results, time.time() - start)
-
-    # Run parallel task chains
-    if temporal_constraint:
-        tc_start, tc_end = temporal_constraint
-        sg_result, bm25_result, temporal_result = await asyncio.gather(
-            run_semantic_then_graph(),
-            run_bm25(),
-            run_temporal(tc_start, tc_end),
-        )
-        return ParallelRetrievalResult(
-            semantic=sg_result.semantic,
-            bm25=bm25_result.results,
-            graph=sg_result.graph,
-            temporal=temporal_result.results,
-            timings={
-                "semantic": sg_result.semantic_time,
-                "graph": sg_result.graph_time,
-                "bm25": bm25_result.time,
-                "temporal": temporal_result.time,
-            },
-            temporal_constraint=temporal_constraint,
-        )
-    else:
-        sg_result, bm25_result = await asyncio.gather(
-            run_semantic_then_graph(),
-            run_bm25(),
-        )
-        return ParallelRetrievalResult(
-            semantic=sg_result.semantic,
-            bm25=bm25_result.results,
-            graph=sg_result.graph,
-            temporal=None,
-            timings={
-                "semantic": sg_result.semantic_time,
-                "graph": sg_result.graph_time,
-                "bm25": bm25_result.time,
-            },
-            temporal_constraint=None,
-        )
-
-
-async def _get_temporal_entry_points(
-    conn,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    start_date: datetime,
-    end_date: datetime,
-    limit: int = 20,
-    semantic_threshold: float = 0.1,
-) -> list[RetrievalResult]:
-    """Get temporal entry points (facts in date range with semantic relevance)."""
-
-    if start_date.tzinfo is None:
-        start_date = start_date.replace(tzinfo=UTC)
-    if end_date.tzinfo is None:
-        end_date = end_date.replace(tzinfo=UTC)
-
-    rows = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at,
-               access_count, embedding, fact_type, document_id, chunk_id,
-               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND embedding IS NOT NULL
-          AND (
-              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
-               AND occurred_start <= $5 AND occurred_end >= $4)
-              OR (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
-              OR (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
-              OR (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
-          )
-          AND (1 - (embedding <=> $1::vector)) >= $6
-        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC,
-                 (embedding <=> $1::vector) ASC
-        LIMIT $7
-        """,
-        query_embedding_str,
-        bank_id,
-        fact_type,
-        start_date,
-        end_date,
-        semantic_threshold,
-        limit,
+    temporal_constraint = extract_temporal_constraint(
+        query_text, reference_date=question_date, analyzer=query_analyzer
    )

-    results = []
-    total_days = max((end_date - start_date).total_seconds() / 86400, 1)
-    mid_date = start_date + (end_date - start_date) / 2
-
-    for row in rows:
-        result = RetrievalResult.from_db_row(dict(row))
-
-        # Calculate temporal proximity score
-        best_date = None
-        if row["occurred_start"] and row["occurred_end"]:
-            best_date = row["occurred_start"] + (row["occurred_end"] - row["occurred_start"]) / 2
-        elif row["occurred_start"]:
-            best_date = row["occurred_start"]
-        elif row["occurred_end"]:
-            best_date = row["occurred_end"]
-        elif row["mentioned_at"]:
-            best_date = row["mentioned_at"]
-
-        if best_date:
-            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
-            result.temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0)
-        else:
-            result.temporal_proximity = 0.5
-
-        result.temporal_score = result.temporal_proximity
-        results.append(result)
-
-    return results
-
-
-async def _retrieve_parallel_bfs(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    temporal_constraint: tuple | None,
-    retriever: GraphRetriever,
-) -> ParallelRetrievalResult:
-    """BFS retrieval: all methods run in parallel (original behavior)."""
-    import time
-
-    async def run_semantic() -> _TimedResult:
+    # Wrapper to track timing for each retrieval method
+    async def timed_retrieval(name: str, coro):
        start = time.time()
+        result = await coro
+        duration = time.time() - start
+        return result, name, duration
+
+    async def run_semantic():
        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_semantic(conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget)
-        return _TimedResult(results, time.time() - start)
+            return await retrieve_semantic(conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget)

-    async def run_bm25() -> _TimedResult:
-        start = time.time()
+    async def run_bm25():
        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget)
-        return _TimedResult(results, time.time() - start)
+            return await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget)

-    async def run_graph() -> _TimedResult:
-        start = time.time()
-        results = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=fact_type,
-            budget=thinking_budget,
-            query_text=query_text,
-        )
-        return _TimedResult(results, time.time() - start)
-
-    async def run_temporal(tc_start, tc_end) -> _TimedResult:
-        start = time.time()
+    async def run_graph():
        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_temporal(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
+            return await retrieve_graph(conn, query_embedding_str, bank_id, fact_type, budget=thinking_budget)
+
+    async def run_temporal(start_date, end_date):
+        async with acquire_with_retry(pool) as conn:
+            return await retrieve_temporal(
+                conn, query_embedding_str, bank_id, fact_type,
+                start_date, end_date, budget=thinking_budget, semantic_threshold=0.1
            )
-        return _TimedResult(results, time.time() - start)

+    # Run retrievals in parallel with timing
+    timings = {}
    if temporal_constraint:
-        tc_start, tc_end = temporal_constraint
-        semantic_r, bm25_r, graph_r, temporal_r = await asyncio.gather(
-            run_semantic(),
-            run_bm25(),
-            run_graph(),
-            run_temporal(tc_start, tc_end),
-        )
-        return ParallelRetrievalResult(
-            semantic=semantic_r.results,
-            bm25=bm25_r.results,
-            graph=graph_r.results,
-            temporal=temporal_r.results,
-            timings={
-                "semantic": semantic_r.time,
-                "bm25": bm25_r.time,
-                "graph": graph_r.time,
-                "temporal": temporal_r.time,
-            },
-            temporal_constraint=temporal_constraint,
+        start_date, end_date = temporal_constraint
+        results = await asyncio.gather(
+            timed_retrieval("semantic", run_semantic()),
+            timed_retrieval("bm25", run_bm25()),
+            timed_retrieval("graph", run_graph()),
+            timed_retrieval("temporal", run_temporal(start_date, end_date))
        )
+        semantic_results, _, timings["semantic"] = results[0]
+        bm25_results, _, timings["bm25"] = results[1]
+        graph_results, _, timings["graph"] = results[2]
+        temporal_results, _, timings["temporal"] = results[3]
    else:
-        semantic_r, bm25_r, graph_r = await asyncio.gather(
-            run_semantic(),
-            run_bm25(),
-            run_graph(),
-        )
-        return ParallelRetrievalResult(
-            semantic=semantic_r.results,
-            bm25=bm25_r.results,
-            graph=graph_r.results,
-            temporal=None,
-            timings={
-                "semantic": semantic_r.time,
-                "bm25": bm25_r.time,
-                "graph": graph_r.time,
-            },
-            temporal_constraint=None,
+        results = await asyncio.gather(
+            timed_retrieval("semantic", run_semantic()),
+            timed_retrieval("bm25", run_bm25()),
+            timed_retrieval("graph", run_graph())
        )
+        semantic_results, _, timings["semantic"] = results[0]
+        bm25_results, _, timings["bm25"] = results[1]
+        graph_results, _, timings["graph"] = results[2]
+        temporal_results = None
+
+    return semantic_results, bm25_results, graph_results, temporal_results, timings, temporal_constraint
@@ -4,11 +4,11 @@ Scoring functions for memory search and retrieval.
 Includes recency weighting, frequency weighting, temporal proximity,
 and similarity calculations used in memory activation and ranking.
 """
-
 from datetime import datetime
+from typing import List


-def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
+def cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
    """
    Calculate cosine similarity between two vectors.

@@ -58,7 +58,6 @@ def calculate_recency_weight(days_since: float, half_life_days: float = 365.0) -
        Weight between 0 and 1
    """
    import math
-
    # Logarithmic decay: 1 / (1 + log(1 + days_since/half_life))
    # This decays much slower than exponential, giving better long-term differentiation
    normalized_age = days_since / half_life_days
@@ -80,7 +79,6 @@ def calculate_frequency_weight(access_count: int, max_boost: float = 2.0) -> flo
        Weight between 1.0 and max_boost
    """
    import math
-
    if access_count <= 0:
        return 1.0

@@ -118,7 +116,11 @@ def calculate_temporal_anchor(occurred_start: datetime, occurred_end: datetime)
    return midpoint


-def calculate_temporal_proximity(anchor_a: datetime, anchor_b: datetime, half_life_days: float = 30.0) -> float:
+def calculate_temporal_proximity(
+    anchor_a: datetime,
+    anchor_b: datetime,
+    half_life_days: float = 30.0
+) -> float:
    """
    Calculate temporal proximity between two temporal anchors.

@@ -4,16 +4,16 @@ Temporal extraction for time-aware search queries.
 Handles natural language temporal expressions using transformer-based query analysis.
 """

-import logging
+from typing import Optional, Tuple
 from datetime import datetime
-
-from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer, QueryAnalyzer
+import logging
+from hindsight_api.engine.query_analyzer import QueryAnalyzer, DateparserQueryAnalyzer

 logger = logging.getLogger(__name__)

 # Global default analyzer instance
 # Can be overridden by passing a custom analyzer to extract_temporal_constraint
-_default_analyzer: QueryAnalyzer | None = None
+_default_analyzer: Optional[QueryAnalyzer] = None


 def get_default_analyzer() -> QueryAnalyzer:
@@ -33,9 +33,9 @@ def get_default_analyzer() -> QueryAnalyzer:

 def extract_temporal_constraint(
    query: str,
-    reference_date: datetime | None = None,
-    analyzer: QueryAnalyzer | None = None,
-) -> tuple[datetime, datetime] | None:
+    reference_date: Optional[datetime] = None,
+    analyzer: Optional[QueryAnalyzer] = None,
+) -> Optional[Tuple[datetime, datetime]]:
    """
    Extract temporal constraint from query.

@@ -55,7 +55,10 @@ def extract_temporal_constraint(
    analysis = analyzer.analyze(query, reference_date)

    if analysis.temporal_constraint:
-        result = (analysis.temporal_constraint.start_date, analysis.temporal_constraint.end_date)
+        result = (
+            analysis.temporal_constraint.start_date,
+            analysis.temporal_constraint.end_date
+        )
        return result

    return None
@@ -2,35 +2,41 @@
 Think operation utilities for formulating answers based on agent and world facts.
 """

+import asyncio
 import logging
 import re
-from datetime import datetime
-
+from datetime import datetime, timezone
+from typing import Dict, List, Any
 from pydantic import BaseModel, Field

-from ..response_models import DispositionTraits, MemoryFact
+from ..response_models import ReflectResult, MemoryFact, DispositionTraits

 logger = logging.getLogger(__name__)


 class Opinion(BaseModel):
    """An opinion formed by the bank."""
-
    opinion: str = Field(description="The opinion or perspective with reasoning included")
    confidence: float = Field(description="Confidence score for this opinion (0.0 to 1.0, where 1.0 is very confident)")


 class OpinionExtractionResponse(BaseModel):
    """Response containing extracted opinions."""
-
-    opinions: list[Opinion] = Field(
-        default_factory=list, description="List of opinions formed with their supporting reasons and confidence scores"
+    opinions: List[Opinion] = Field(
+        default_factory=list,
+        description="List of opinions formed with their supporting reasons and confidence scores"
    )


 def describe_trait_level(value: int) -> str:
    """Convert trait value (1-5) to descriptive text."""
-    levels = {1: "very low", 2: "low", 3: "moderate", 4: "high", 5: "very high"}
+    levels = {
+        1: "very low",
+        2: "low",
+        3: "moderate",
+        4: "high",
+        5: "very high"
+    }
    return levels.get(value, "moderate")


@@ -41,7 +47,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
        2: "You tend to trust information but may question obvious inconsistencies.",
        3: "You have a balanced approach to information, neither too trusting nor too skeptical.",
        4: "You are somewhat skeptical and often question the reliability of information.",
-        5: "You are highly skeptical and critically examine all information for accuracy and hidden motives.",
+        5: "You are highly skeptical and critically examine all information for accuracy and hidden motives."
    }

    literalism_desc = {
@@ -49,7 +55,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
        2: "You tend to consider context and implied meaning alongside literal statements.",
        3: "You balance literal interpretation with contextual understanding.",
        4: "You prefer to interpret information more literally and precisely.",
-        5: "You interpret information very literally and focus on exact wording and commitments.",
+        5: "You interpret information very literally and focus on exact wording and commitments."
    }

    empathy_desc = {
@@ -57,7 +63,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
        2: "You consider facts first but acknowledge emotional factors exist.",
        3: "You balance factual analysis with emotional understanding.",
        4: "You give significant weight to emotional context and human factors.",
-        5: "You strongly consider the emotional state and circumstances of others when forming memories.",
+        5: "You strongly consider the emotional state and circumstances of others when forming memories."
    }

    return f"""Your disposition traits:
@@ -66,7 +72,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
 - Empathy ({describe_trait_level(disposition.empathy)}): {empathy_desc.get(disposition.empathy, empathy_desc[3])}"""


-def format_facts_for_prompt(facts: list[MemoryFact]) -> str:
+def format_facts_for_prompt(facts: List[MemoryFact]) -> str:
    """Format facts as JSON for LLM prompt."""
    import json

@@ -74,7 +80,9 @@ def format_facts_for_prompt(facts: list[MemoryFact]) -> str:
        return "[]"
    formatted = []
    for fact in facts:
-        fact_obj = {"text": fact.text}
+        fact_obj = {
+            "text": fact.text
+        }

        # Add context if available
        if fact.context:
@@ -86,7 +94,7 @@ def format_facts_for_prompt(facts: list[MemoryFact]) -> str:
            if isinstance(occurred_start, str):
                fact_obj["occurred_start"] = occurred_start
            elif isinstance(occurred_start, datetime):
-                fact_obj["occurred_start"] = occurred_start.strftime("%Y-%m-%d %H:%M:%S")
+                fact_obj["occurred_start"] = occurred_start.strftime('%Y-%m-%d %H:%M:%S')

        formatted.append(fact_obj)

@@ -101,7 +109,7 @@ def build_think_prompt(
    name: str,
    disposition: DispositionTraits,
    background: str,
-    context: str | None = None,
+    context: str = None,
 ) -> str:
    """Build the think prompt for the LLM."""
    disposition_desc = build_disposition_description(disposition)
@@ -168,14 +176,16 @@ def get_system_message(disposition: DispositionTraits) -> str:
    elif disposition.empathy <= 2:
        instructions.append("Focus on facts and outcomes rather than emotional context.")

-    disposition_instruction = (
-        " ".join(instructions) if instructions else "Balance your disposition traits when interpreting information."
-    )
+    disposition_instruction = " ".join(instructions) if instructions else "Balance your disposition traits when interpreting information."

    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting."


-async def extract_opinions_from_text(llm_config, text: str, query: str) -> list[Opinion]:
+async def extract_opinions_from_text(
+    llm_config,
+    text: str,
+    query: str
+) -> List[Opinion]:
    """
    Extract opinions with reasons and confidence from text using LLM.

@@ -228,14 +238,11 @@ If no genuine opinions are expressed (e.g., the response just says "I don't know
    try:
        result = await llm_config.call(
            messages=[
-                {
-                    "role": "system",
-                    "content": "You are converting opinions from text into first-person statements. Always use 'I think', 'I believe', 'I feel', etc. NEVER use third-person like 'The speaker' or 'They'.",
-                },
-                {"role": "user", "content": extraction_prompt},
+                {"role": "system", "content": "You are converting opinions from text into first-person statements. Always use 'I think', 'I believe', 'I feel', etc. NEVER use third-person like 'The speaker' or 'They'."},
+                {"role": "user", "content": extraction_prompt}
            ],
            response_format=OpinionExtractionResponse,
-            scope="memory_extract_opinion",
+            scope="memory_extract_opinion"
        )

        # Format opinions with confidence score and convert to first-person
@@ -246,18 +253,14 @@ If no genuine opinions are expressed (e.g., the response just says "I don't know

            # Replace common third-person patterns with first-person
            def singularize_verb(verb):
-                if verb.endswith("es"):
+                if verb.endswith('es'):
                    return verb[:-1]  # believes -> believe
-                elif verb.endswith("s"):
+                elif verb.endswith('s'):
                    return verb[:-1]  # thinks -> think
                return verb

            # Pattern: "The speaker/user [verb]..." -> "I [verb]..."
-            match = re.match(
-                r"^(The speaker|The user|They|It is believed) (believes?|thinks?|feels?|says|asserts?|considers?)(\s+that)?(.*)$",
-                opinion_text,
-                re.IGNORECASE,
-            )
+            match = re.match(r'^(The speaker|The user|They|It is believed) (believes?|thinks?|feels?|says|asserts?|considers?)(\s+that)?(.*)$', opinion_text, re.IGNORECASE)
            if match:
                verb = singularize_verb(match.group(2))
                that_part = match.group(3) or ""  # Keep " that" if present
@@ -265,96 +268,17 @@ If no genuine opinions are expressed (e.g., the response just says "I don't know
                opinion_text = f"I {verb}{that_part}{rest}"

            # If still doesn't start with first-person, prepend "I believe that "
-            first_person_starters = [
-                "I think",
-                "I believe",
-                "I feel",
-                "In my view",
-                "I've come to believe",
-                "Previously I",
-            ]
+            first_person_starters = ["I think", "I believe", "I feel", "In my view", "I've come to believe", "Previously I"]
            if not any(opinion_text.startswith(starter) for starter in first_person_starters):
                opinion_text = "I believe that " + opinion_text[0].lower() + opinion_text[1:]

-            formatted_opinions.append(Opinion(opinion=opinion_text, confidence=op.confidence))
+            formatted_opinions.append(Opinion(
+                opinion=opinion_text,
+                confidence=op.confidence
+            ))

        return formatted_opinions

    except Exception as e:
        logger.warning(f"Failed to extract opinions: {str(e)}")
        return []
-
-
-async def reflect(
-    llm_config,
-    query: str,
-    experience_facts: list[str] = None,
-    world_facts: list[str] = None,
-    opinion_facts: list[str] = None,
-    name: str = "Assistant",
-    disposition: DispositionTraits = None,
-    background: str = "",
-    context: str = None,
-) -> str:
-    """
-    Standalone reflect function for generating answers based on facts.
-
-    This is a static version of the reflect operation that can be called
-    without a MemoryEngine instance, useful for testing.
-
-    Args:
-        llm_config: LLM provider instance
-        query: Question to answer
-        experience_facts: List of experience/agent fact strings
-        world_facts: List of world fact strings
-        opinion_facts: List of opinion fact strings
-        name: Name of the agent/persona
-        disposition: Disposition traits (defaults to neutral)
-        background: Background information
-        context: Additional context for the prompt
-
-    Returns:
-        Generated answer text
-    """
-    # Default disposition if not provided
-    if disposition is None:
-        disposition = DispositionTraits(skepticism=3, literalism=3, empathy=3)
-
-    # Convert string lists to MemoryFact format for formatting
-    def to_memory_facts(facts: list[str], fact_type: str) -> list[MemoryFact]:
-        if not facts:
-            return []
-        return [MemoryFact(id=f"test-{i}", text=f, fact_type=fact_type) for i, f in enumerate(facts)]
-
-    agent_results = to_memory_facts(experience_facts or [], "experience")
-    world_results = to_memory_facts(world_facts or [], "world")
-    opinion_results = to_memory_facts(opinion_facts or [], "opinion")
-
-    # Format facts for prompt
-    agent_facts_text = format_facts_for_prompt(agent_results)
-    world_facts_text = format_facts_for_prompt(world_results)
-    opinion_facts_text = format_facts_for_prompt(opinion_results)
-
-    # Build prompt
-    prompt = build_think_prompt(
-        agent_facts_text=agent_facts_text,
-        world_facts_text=world_facts_text,
-        opinion_facts_text=opinion_facts_text,
-        query=query,
-        name=name,
-        disposition=disposition,
-        background=background,
-        context=context,
-    )
-
-    system_message = get_system_message(disposition)
-
-    # Call LLM
-    answer_text = await llm_config.call(
-        messages=[{"role": "system", "content": system_message}, {"role": "user", "content": prompt}],
-        scope="memory_think",
-        temperature=0.9,
-        max_completion_tokens=1000,
-    )
-
-    return answer_text.strip()
@@ -4,18 +4,15 @@ Search trace models for debugging and visualization.
 These Pydantic models define the structure of search traces, capturing
 every step of the spreading activation search process for analysis.
 """
-
 from datetime import datetime
-from typing import Any, Literal
-
+from typing import List, Optional, Dict, Any, Literal
 from pydantic import BaseModel, Field


 class QueryInfo(BaseModel):
    """Information about the search query."""
-
    query_text: str = Field(description="Original query text")
-    query_embedding: list[float] = Field(description="Generated query embedding vector")
+    query_embedding: List[float] = Field(description="Generated query embedding vector")
    timestamp: datetime = Field(description="When the query was executed")
    budget: int = Field(description="Maximum nodes to explore")
    max_tokens: int = Field(description="Maximum tokens to return in results")
@@ -23,7 +20,6 @@ class QueryInfo(BaseModel):

 class EntryPoint(BaseModel):
    """An entry point node selected for search."""
-
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    similarity_score: float = Field(description="Cosine similarity to query", ge=0.0, le=1.0)
@@ -32,7 +28,6 @@ class EntryPoint(BaseModel):

 class WeightComponents(BaseModel):
    """Breakdown of weight calculation components."""
-
    activation: float = Field(description="Activation from spreading (can exceed 1.0 through accumulation)", ge=0.0)
    semantic_similarity: float = Field(description="Semantic similarity to query", ge=0.0, le=1.0)
    recency: float = Field(description="Recency weight", ge=0.0, le=1.0)
@@ -48,120 +43,98 @@ class WeightComponents(BaseModel):

 class LinkInfo(BaseModel):
    """Information about a link to a neighbor."""
-
    to_node_id: str = Field(description="Target node ID")
    link_type: Literal["temporal", "semantic", "entity"] = Field(description="Type of link")
-    link_weight: float = Field(
-        description="Weight of the link (can exceed 1.0 when aggregating multiple connections)", ge=0.0
-    )
-    entity_id: str | None = Field(default=None, description="Entity ID if link_type is 'entity'")
-    new_activation: float | None = Field(
-        default=None, description="Activation that would be passed to neighbor (None for supplementary links)"
-    )
+    link_weight: float = Field(description="Weight of the link (can exceed 1.0 when aggregating multiple connections)", ge=0.0)
+    entity_id: Optional[str] = Field(default=None, description="Entity ID if link_type is 'entity'")
+    new_activation: Optional[float] = Field(default=None, description="Activation that would be passed to neighbor (None for supplementary links)")
    followed: bool = Field(description="Whether this link was followed (or pruned)")
-    prune_reason: str | None = Field(default=None, description="Why link was not followed (if not followed)")
-    is_supplementary: bool = Field(
-        default=False, description="Whether this is a supplementary link (multiple connections to same node)"
-    )
+    prune_reason: Optional[str] = Field(default=None, description="Why link was not followed (if not followed)")
+    is_supplementary: bool = Field(default=False, description="Whether this is a supplementary link (multiple connections to same node)")


 class NodeVisit(BaseModel):
    """Information about visiting a node during search."""
-
    step: int = Field(description="Step number in search (1-based)")
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    context: str = Field(description="Memory unit context")
-    event_date: datetime | None = Field(default=None, description="When the memory occurred")
+    event_date: Optional[datetime] = Field(default=None, description="When the memory occurred")
    access_count: int = Field(description="Number of times accessed before this search")

    # How this node was reached
    is_entry_point: bool = Field(description="Whether this is an entry point")
-    parent_node_id: str | None = Field(default=None, description="Node that led to this one")
-    link_type: Literal["temporal", "semantic", "entity"] | None = Field(
-        default=None, description="Type of link from parent"
-    )
-    link_weight: float | None = Field(default=None, description="Weight of link from parent")
+    parent_node_id: Optional[str] = Field(default=None, description="Node that led to this one")
+    link_type: Optional[Literal["temporal", "semantic", "entity"]] = Field(default=None, description="Type of link from parent")
+    link_weight: Optional[float] = Field(default=None, description="Weight of link from parent")

    # Weights
    weights: WeightComponents = Field(description="Weight calculation breakdown")

    # Neighbors discovered from this node
-    neighbors_explored: list[LinkInfo] = Field(default_factory=list, description="Links explored from this node")
+    neighbors_explored: List[LinkInfo] = Field(default_factory=list, description="Links explored from this node")

    # Ranking
-    final_rank: int | None = Field(default=None, description="Final rank in results (1-based, None if not in top-k)")
+    final_rank: Optional[int] = Field(default=None, description="Final rank in results (1-based, None if not in top-k)")


 class PruningDecision(BaseModel):
    """Records when a node was considered but not visited."""
-
    node_id: str = Field(description="Node that was pruned")
-    reason: Literal["already_visited", "activation_too_low", "budget_exhausted"] = Field(
-        description="Why it was pruned"
-    )
+    reason: Literal["already_visited", "activation_too_low", "budget_exhausted"] = Field(description="Why it was pruned")
    activation: float = Field(description="Activation value when pruned")
    would_have_been_step: int = Field(description="What step it would have been if visited")


 class SearchPhaseMetrics(BaseModel):
    """Performance metrics for a search phase."""
-
    phase_name: str = Field(description="Name of the phase")
    duration_seconds: float = Field(description="Time taken in seconds")
-    details: dict[str, Any] = Field(default_factory=dict, description="Additional phase-specific metrics")
+    details: Dict[str, Any] = Field(default_factory=dict, description="Additional phase-specific metrics")


 class RetrievalResult(BaseModel):
    """A single result from a retrieval method."""
-
    rank: int = Field(description="Rank in this retrieval method (1-based)")
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    context: str = Field(default="", description="Memory unit context")
-    event_date: datetime | None = Field(default=None, description="When the memory occurred")
-    fact_type: str | None = Field(default=None, description="Fact type (world, experience, opinion)")
+    event_date: Optional[datetime] = Field(default=None, description="When the memory occurred")
+    fact_type: Optional[str] = Field(default=None, description="Fact type (world, experience, opinion)")
    score: float = Field(description="Score from this retrieval method")
    score_name: str = Field(description="Name of the score (e.g., 'similarity', 'bm25_score', 'activation')")


 class RetrievalMethodResults(BaseModel):
    """Results from a single retrieval method."""
-
    method_name: Literal["semantic", "bm25", "graph", "temporal"] = Field(description="Name of retrieval method")
-    fact_type: str | None = Field(
-        default=None, description="Fact type this retrieval was for (world, experience, opinion)"
-    )
-    results: list[RetrievalResult] = Field(description="Retrieved results with ranks")
+    results: List[RetrievalResult] = Field(description="Retrieved results with ranks")
    duration_seconds: float = Field(description="Time taken for this retrieval")
-    metadata: dict[str, Any] = Field(default_factory=dict, description="Method-specific metadata")
+    metadata: Dict[str, Any] = Field(default_factory=dict, description="Method-specific metadata")


 class RRFMergeResult(BaseModel):
    """A result after RRF merging."""
-
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    rrf_score: float = Field(description="Reciprocal Rank Fusion score")
-    source_ranks: dict[str, int] = Field(description="Rank in each source that contributed (method_name -> rank)")
+    source_ranks: Dict[str, int] = Field(description="Rank in each source that contributed (method_name -> rank)")
    final_rrf_rank: int = Field(description="Rank after RRF merge (1-based)")


 class RerankedResult(BaseModel):
    """A result after reranking."""
-
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    rerank_score: float = Field(description="Final reranking score")
    rerank_rank: int = Field(description="Rank after reranking (1-based)")
    rrf_rank: int = Field(description="Original RRF rank before reranking")
    rank_change: int = Field(description="Change in rank (positive = moved up)")
-    score_components: dict[str, float] = Field(default_factory=dict, description="Score breakdown")
+    score_components: Dict[str, float] = Field(default_factory=dict, description="Score breakdown")


 class SearchSummary(BaseModel):
    """Summary statistics about the search."""
-
    total_nodes_visited: int = Field(description="Total nodes visited")
    total_nodes_pruned: int = Field(description="Total nodes pruned")
    entry_points_found: int = Field(description="Number of entry points")
@@ -176,36 +149,33 @@ class SearchSummary(BaseModel):
    entity_links_followed: int = Field(default=0, description="Entity links followed")

    # Phase timings
-    phase_metrics: list[SearchPhaseMetrics] = Field(default_factory=list, description="Metrics for each phase")
+    phase_metrics: List[SearchPhaseMetrics] = Field(default_factory=list, description="Metrics for each phase")


 class SearchTrace(BaseModel):
    """Complete trace of a search operation."""
-
    query: QueryInfo = Field(description="Query information")

    # New 4-way retrieval architecture
-    retrieval_results: list[RetrievalMethodResults] = Field(
-        default_factory=list, description="Results from each retrieval method"
-    )
-    rrf_merged: list[RRFMergeResult] = Field(default_factory=list, description="Results after RRF merging")
-    reranked: list[RerankedResult] = Field(default_factory=list, description="Results after reranking")
+    retrieval_results: List[RetrievalMethodResults] = Field(default_factory=list, description="Results from each retrieval method")
+    rrf_merged: List[RRFMergeResult] = Field(default_factory=list, description="Results after RRF merging")
+    reranked: List[RerankedResult] = Field(default_factory=list, description="Results after reranking")

    # Legacy fields (kept for backward compatibility with graph/temporal visualizations)
-    entry_points: list[EntryPoint] = Field(
-        default_factory=list, description="Entry points selected for search (legacy)"
-    )
-    visits: list[NodeVisit] = Field(
-        default_factory=list, description="All nodes visited during search (legacy, for graph viz)"
-    )
-    pruned: list[PruningDecision] = Field(default_factory=list, description="Nodes that were pruned (legacy)")
+    entry_points: List[EntryPoint] = Field(default_factory=list, description="Entry points selected for search (legacy)")
+    visits: List[NodeVisit] = Field(default_factory=list, description="All nodes visited during search (legacy, for graph viz)")
+    pruned: List[PruningDecision] = Field(default_factory=list, description="Nodes that were pruned (legacy)")

    summary: SearchSummary = Field(description="Summary statistics")

    # Final results (for comparison with visits)
-    final_results: list[dict[str, Any]] = Field(description="Final ranked results returned to user")
+    final_results: List[Dict[str, Any]] = Field(description="Final ranked results returned to user")

-    model_config = {"json_encoders": {datetime: lambda v: v.isoformat()}}
+    model_config = {
+        "json_encoders": {
+            datetime: lambda v: v.isoformat()
+        }
+    }

    def to_json(self, **kwargs) -> str:
        """Export trace as JSON string."""
@@ -215,14 +185,14 @@ class SearchTrace(BaseModel):
        """Export trace as dictionary."""
        return self.model_dump()

-    def get_visit_by_node_id(self, node_id: str) -> NodeVisit | None:
+    def get_visit_by_node_id(self, node_id: str) -> Optional[NodeVisit]:
        """Find a visit by node ID."""
        for visit in self.visits:
            if visit.node_id == node_id:
                return visit
        return None

-    def get_search_path_to_node(self, node_id: str) -> list[NodeVisit]:
+    def get_search_path_to_node(self, node_id: str) -> List[NodeVisit]:
        """Get the path from entry point to a specific node."""
        path = []
        current_visit = self.get_visit_by_node_id(node_id)
@@ -236,10 +206,10 @@ class SearchTrace(BaseModel):

        return path

-    def get_nodes_by_link_type(self, link_type: Literal["temporal", "semantic", "entity"]) -> list[NodeVisit]:
+    def get_nodes_by_link_type(self, link_type: Literal["temporal", "semantic", "entity"]) -> List[NodeVisit]:
        """Get all nodes reached via a specific link type."""
        return [v for v in self.visits if v.link_type == link_type]

-    def get_entry_point_nodes(self) -> list[NodeVisit]:
+    def get_entry_point_nodes(self) -> List[NodeVisit]:
        """Get all entry point visits."""
        return [v for v in self.visits if v.is_entry_point]
@@ -4,25 +4,24 @@ Search tracer for collecting detailed search execution traces.
 The SearchTracer collects comprehensive information about each step
 of the spreading activation search process for debugging and visualization.
 """
-
 import time
-from datetime import UTC, datetime
-from typing import Any, Literal
+from datetime import datetime, timezone
+from typing import List, Optional, Dict, Any, Literal

 from .trace import (
-    EntryPoint,
-    LinkInfo,
-    NodeVisit,
-    PruningDecision,
-    QueryInfo,
-    RerankedResult,
-    RetrievalMethodResults,
-    RetrievalResult,
-    RRFMergeResult,
-    SearchPhaseMetrics,
-    SearchSummary,
    SearchTrace,
+    QueryInfo,
+    EntryPoint,
+    NodeVisit,
    WeightComponents,
+    LinkInfo,
+    PruningDecision,
+    SearchSummary,
+    SearchPhaseMetrics,
+    RetrievalResult,
+    RetrievalMethodResults,
+    RRFMergeResult,
+    RerankedResult,
 )


@@ -59,17 +58,17 @@ class SearchTracer:
        self.max_tokens = max_tokens

        # Trace data
-        self.query_embedding: list[float] | None = None
-        self.start_time: float | None = None
-        self.entry_points: list[EntryPoint] = []
-        self.visits: list[NodeVisit] = []
-        self.pruned: list[PruningDecision] = []
-        self.phase_metrics: list[SearchPhaseMetrics] = []
+        self.query_embedding: Optional[List[float]] = None
+        self.start_time: Optional[float] = None
+        self.entry_points: List[EntryPoint] = []
+        self.visits: List[NodeVisit] = []
+        self.pruned: List[PruningDecision] = []
+        self.phase_metrics: List[SearchPhaseMetrics] = []

        # New 4-way retrieval tracking
-        self.retrieval_results: list[RetrievalMethodResults] = []
-        self.rrf_merged: list[RRFMergeResult] = []
-        self.reranked: list[RerankedResult] = []
+        self.retrieval_results: List[RetrievalMethodResults] = []
+        self.rrf_merged: List[RRFMergeResult] = []
+        self.reranked: List[RerankedResult] = []

        # Tracking state
        self.current_step = 0
@@ -84,7 +83,7 @@ class SearchTracer:
        """Start timing the search."""
        self.start_time = time.time()

-    def record_query_embedding(self, embedding: list[float]):
+    def record_query_embedding(self, embedding: List[float]):
        """Record the query embedding."""
        self.query_embedding = embedding

@@ -115,12 +114,12 @@ class SearchTracer:
        node_id: str,
        text: str,
        context: str,
-        event_date: datetime | None,
+        event_date: datetime,
        access_count: int,
        is_entry_point: bool,
-        parent_node_id: str | None,
-        link_type: Literal["temporal", "semantic", "entity"] | None,
-        link_weight: float | None,
+        parent_node_id: Optional[str],
+        link_type: Optional[Literal["temporal", "semantic", "entity"]],
+        link_weight: Optional[float],
        activation: float,
        semantic_similarity: float,
        recency: float,
@@ -200,10 +199,10 @@ class SearchTracer:
        to_node_id: str,
        link_type: Literal["temporal", "semantic", "entity"],
        link_weight: float,
-        entity_id: str | None,
-        new_activation: float | None,
+        entity_id: Optional[str],
+        new_activation: Optional[float],
        followed: bool,
-        prune_reason: str | None = None,
+        prune_reason: Optional[str] = None,
        is_supplementary: bool = False,
    ):
        """
@@ -267,7 +266,7 @@ class SearchTracer:
            )
        )

-    def add_phase_metric(self, phase_name: str, duration_seconds: float, details: dict[str, Any] | None = None):
+    def add_phase_metric(self, phase_name: str, duration_seconds: float, details: Optional[Dict[str, Any]] = None):
        """
        Record metrics for a search phase.

@@ -287,11 +286,10 @@ class SearchTracer:
    def add_retrieval_results(
        self,
        method_name: Literal["semantic", "bm25", "graph", "temporal"],
-        results: list[tuple],  # List of (doc_id, data) tuples
+        results: List[tuple],  # List of (doc_id, data) tuples
        duration_seconds: float,
        score_field: str,  # e.g., "similarity", "bm25_score"
-        metadata: dict[str, Any] | None = None,
-        fact_type: str | None = None,
+        metadata: Optional[Dict[str, Any]] = None
    ):
        """
        Record results from a single retrieval method.
@@ -302,7 +300,6 @@ class SearchTracer:
            duration_seconds: Time taken for this retrieval
            score_field: Field name containing the score in data dict
            metadata: Optional metadata about this retrieval method
-            fact_type: Fact type this retrieval was for (world, experience, opinion)
        """
        retrieval_results = []
        for rank, (doc_id, data) in enumerate(results, start=1):
@@ -316,7 +313,7 @@ class SearchTracer:
                    text=data.get("text", ""),
                    context=data.get("context", ""),
                    event_date=data.get("event_date"),
-                    fact_type=data.get("fact_type") or fact_type,
+                    fact_type=data.get("fact_type"),
                    score=score,
                    score_name=score_field,
                )
@@ -325,14 +322,13 @@ class SearchTracer:
        self.retrieval_results.append(
            RetrievalMethodResults(
                method_name=method_name,
-                fact_type=fact_type,
                results=retrieval_results,
                duration_seconds=duration_seconds,
                metadata=metadata or {},
            )
        )

-    def add_rrf_merged(self, merged_results: list[tuple]):
+    def add_rrf_merged(self, merged_results: List[tuple]):
        """
        Record RRF merged results.

@@ -351,7 +347,7 @@ class SearchTracer:
                )
            )

-    def add_reranked(self, reranked_results: list[dict[str, Any]], rrf_merged: list):
+    def add_reranked(self, reranked_results: List[Dict[str, Any]], rrf_merged: List):
        """
        Record reranked results.

@@ -371,18 +367,8 @@ class SearchTracer:
            rank_change = rrf_rank - rank  # Positive = moved up

            # Extract score components (only include non-None values)
-            # Keys from ScoredResult.to_dict(): cross_encoder_score, cross_encoder_score_normalized,
-            # rrf_normalized, temporal, recency, combined_score, weight
            score_components = {}
-            for key in [
-                "cross_encoder_score",
-                "cross_encoder_score_normalized",
-                "rrf_score",
-                "rrf_normalized",
-                "temporal",
-                "recency",
-                "combined_score",
-            ]:
+            for key in ["semantic_similarity", "bm25_score", "rrf_score", "recency_normalized", "frequency_normalized", "cross_encoder_score", "cross_encoder_score_normalized"]:
                if key in result and result[key] is not None:
                    score_components[key] = result[key]

@@ -398,7 +384,7 @@ class SearchTracer:
                )
            )

-    def finalize(self, final_results: list[dict[str, Any]]) -> SearchTrace:
+    def finalize(self, final_results: List[Dict[str, Any]]) -> SearchTrace:
        """
        Finalize the trace and return the complete SearchTrace object.

@@ -425,7 +411,7 @@ class SearchTracer:
        query_info = QueryInfo(
            query_text=self.query_text,
            query_embedding=self.query_embedding or [],
-            timestamp=datetime.now(UTC),
+            timestamp=datetime.now(timezone.utc),
            budget=self.budget,
            max_tokens=self.max_tokens,
        )
@@ -6,8 +6,8 @@ providing type safety and making data flow explicit.
 """

 from dataclasses import dataclass, field
+from typing import Optional, List, Dict, Any
 from datetime import datetime
-from typing import Any


@dataclass
@@ -17,29 +17,27 @@ class RetrievalResult:

    This represents a raw result from the database query, before merging or reranking.
    """
-
    id: str
    text: str
    fact_type: str
-    context: str | None = None
-    event_date: datetime | None = None
-    occurred_start: datetime | None = None
-    occurred_end: datetime | None = None
-    mentioned_at: datetime | None = None
-    document_id: str | None = None
-    chunk_id: str | None = None
+    context: Optional[str] = None
+    event_date: Optional[datetime] = None
+    occurred_start: Optional[datetime] = None
+    occurred_end: Optional[datetime] = None
+    mentioned_at: Optional[datetime] = None
+    document_id: Optional[str] = None
+    chunk_id: Optional[str] = None
    access_count: int = 0
-    embedding: list[float] | None = None
+    embedding: Optional[List[float]] = None

    # Retrieval-specific scores (only one will be set depending on retrieval method)
-    similarity: float | None = None  # Semantic retrieval
-    bm25_score: float | None = None  # BM25 retrieval
-    activation: float | None = None  # Graph retrieval (spreading activation)
-    temporal_score: float | None = None  # Temporal retrieval
-    temporal_proximity: float | None = None  # Temporal retrieval
+    similarity: Optional[float] = None  # Semantic/graph retrieval
+    bm25_score: Optional[float] = None  # BM25 retrieval
+    temporal_score: Optional[float] = None  # Temporal retrieval
+    temporal_proximity: Optional[float] = None  # Temporal retrieval

    @classmethod
-    def from_db_row(cls, row: dict[str, Any]) -> "RetrievalResult":
+    def from_db_row(cls, row: Dict[str, Any]) -> "RetrievalResult":
        """Create from a database row (asyncpg Record converted to dict)."""
        return cls(
            id=str(row["id"]),
@@ -56,7 +54,6 @@ class RetrievalResult:
            embedding=row.get("embedding"),
            similarity=row.get("similarity"),
            bm25_score=row.get("bm25_score"),
-            activation=row.get("activation"),
            temporal_score=row.get("temporal_score"),
            temporal_proximity=row.get("temporal_proximity"),
        )
@@ -69,14 +66,13 @@ class MergedCandidate:

    Contains the original retrieval data plus RRF metadata.
    """
-
    # Original retrieval data
    retrieval: RetrievalResult

    # RRF metadata
    rrf_score: float
    rrf_rank: int = 0
-    source_ranks: dict[str, int] = field(default_factory=dict)  # method_name -> rank
+    source_ranks: Dict[str, int] = field(default_factory=dict)  # method_name -> rank

    @property
    def id(self) -> str:
@@ -91,7 +87,6 @@ class ScoredResult:

    Contains all retrieval/merge data plus reranking scores and combined score.
    """
-
    # Original merged candidate
    candidate: MergedCandidate

@@ -118,7 +113,7 @@ class ScoredResult:
        """Convenience property to access retrieval data."""
        return self.candidate.retrieval

-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> Dict[str, Any]:
        """
        Convert to dict for backwards compatibility.

@@ -157,7 +152,6 @@ class ScoredResult:
        result["cross_encoder_score"] = self.cross_encoder_score
        result["cross_encoder_score_normalized"] = self.cross_encoder_score_normalized
        result["rrf_normalized"] = self.rrf_normalized
-        result["temporal"] = self.temporal
        result["recency"] = self.recency
        result["combined_score"] = self.combined_score
        result["weight"] = self.weight
@@ -6,12 +6,10 @@ This provides an abstraction that can be adapted to different execution models:
 - Pub/Sub architectures (future)
 - Message brokers (future)
 """
-
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional, Callable, Awaitable
 import asyncio
 import logging
-from abc import ABC, abstractmethod
-from collections.abc import Awaitable, Callable
-from typing import Any

 logger = logging.getLogger(__name__)

@@ -31,10 +29,10 @@ class TaskBackend(ABC):

    def __init__(self):
        """Initialize the task backend."""
-        self._executor: Callable[[dict[str, Any]], Awaitable[None]] | None = None
+        self._executor: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None
        self._initialized = False

-    def set_executor(self, executor: Callable[[dict[str, Any]], Awaitable[None]]):
+    def set_executor(self, executor: Callable[[Dict[str, Any]], Awaitable[None]]):
        """
        Set the executor callback for processing tasks.

@@ -51,7 +49,7 @@ class TaskBackend(ABC):
        pass

    @abstractmethod
-    async def submit_task(self, task_dict: dict[str, Any]):
+    async def submit_task(self, task_dict: Dict[str, Any]):
        """
        Submit a task for execution.

@@ -67,7 +65,7 @@ class TaskBackend(ABC):
        """
        pass

-    async def _execute_task(self, task_dict: dict[str, Any]):
+    async def _execute_task(self, task_dict: Dict[str, Any]):
        """
        Execute a task through the registered executor.

@@ -75,52 +73,19 @@ class TaskBackend(ABC):
            task_dict: Task dictionary to execute
        """
        if self._executor is None:
-            task_type = task_dict.get("type", "unknown")
+            task_type = task_dict.get('type', 'unknown')
            logger.warning(f"No executor registered, skipping task {task_type}")
            return

        try:
            await self._executor(task_dict)
        except Exception as e:
-            task_type = task_dict.get("type", "unknown")
+            task_type = task_dict.get('type', 'unknown')
            logger.error(f"Error executing task {task_type}: {e}")
            import traceback
-
            traceback.print_exc()


-class SyncTaskBackend(TaskBackend):
-    """
-    Synchronous task backend that executes tasks immediately.
-
-    This is useful for embedded/CLI usage where we don't want background
-    workers that prevent clean exit. Tasks are executed inline rather than
-    being queued.
-    """
-
-    async def initialize(self):
-        """No-op for sync backend."""
-        self._initialized = True
-        logger.debug("SyncTaskBackend initialized")
-
-    async def submit_task(self, task_dict: dict[str, Any]):
-        """
-        Execute the task immediately (synchronously).
-
-        Args:
-            task_dict: Task dictionary to execute
-        """
-        if not self._initialized:
-            await self.initialize()
-
-        await self._execute_task(task_dict)
-
-    async def shutdown(self):
-        """No-op for sync backend."""
-        self._initialized = False
-        logger.debug("SyncTaskBackend shutdown")
-
-
 class AsyncIOQueueBackend(TaskBackend):
    """
    Task backend implementation using asyncio queues.
@@ -129,7 +94,11 @@ class AsyncIOQueueBackend(TaskBackend):
    and a periodic consumer worker.
    """

-    def __init__(self, batch_size: int = 100, batch_interval: float = 1.0):
+    def __init__(
+        self,
+        batch_size: int = 100,
+        batch_interval: float = 1.0
+    ):
        """
        Initialize AsyncIO queue backend.

@@ -138,9 +107,9 @@ class AsyncIOQueueBackend(TaskBackend):
            batch_interval: Maximum time (seconds) to wait before processing batch
        """
        super().__init__()
-        self._queue: asyncio.Queue | None = None
-        self._worker_task: asyncio.Task | None = None
-        self._shutdown_event: asyncio.Event | None = None
+        self._queue: Optional[asyncio.Queue] = None
+        self._worker_task: Optional[asyncio.Task] = None
+        self._shutdown_event: Optional[asyncio.Event] = None
        self._batch_size = batch_size
        self._batch_interval = batch_interval

@@ -155,7 +124,7 @@ class AsyncIOQueueBackend(TaskBackend):
        self._initialized = True
        logger.info("AsyncIOQueueBackend initialized")

-    async def submit_task(self, task_dict: dict[str, Any]):
+    async def submit_task(self, task_dict: Dict[str, Any]):
        """
        Submit a task by putting it in the queue.

@@ -166,8 +135,8 @@ class AsyncIOQueueBackend(TaskBackend):
            await self.initialize()

        await self._queue.put(task_dict)
-        task_type = task_dict.get("type", "unknown")
-        task_id = task_dict.get("id")
+        task_type = task_dict.get('type', 'unknown')
+        task_id = task_dict.get('id')

    async def wait_for_pending_tasks(self, timeout: float = 5.0):
        """
@@ -231,16 +200,20 @@ class AsyncIOQueueBackend(TaskBackend):
                while len(tasks) < self._batch_size and asyncio.get_event_loop().time() < deadline:
                    try:
                        remaining_time = max(0.1, deadline - asyncio.get_event_loop().time())
-                        task_dict = await asyncio.wait_for(self._queue.get(), timeout=remaining_time)
+                        task_dict = await asyncio.wait_for(
+                            self._queue.get(),
+                            timeout=remaining_time
+                        )
                        tasks.append(task_dict)
-                    except TimeoutError:
+                    except asyncio.TimeoutError:
                        break

                # Process batch
                if tasks:
                    # Execute tasks concurrently
                    await asyncio.gather(
-                        *[self._execute_task(task_dict) for task_dict in tasks], return_exceptions=True
+                        *[self._execute_task(task_dict) for task_dict in tasks],
+                        return_exceptions=True
                    )

            except asyncio.CancelledError:
@@ -1,10 +1,9 @@
 """
 Utility functions for memory system.
 """
-
 import logging
 from datetime import datetime
-from typing import TYPE_CHECKING
+from typing import List, Dict, TYPE_CHECKING

 if TYPE_CHECKING:
    from .llm_wrapper import LLMConfig
@@ -13,14 +12,7 @@ if TYPE_CHECKING:
 from .retain.fact_extraction import extract_facts_from_text


-async def extract_facts(
-    text: str,
-    event_date: datetime,
-    context: str = "",
-    llm_config: "LLMConfig" = None,
-    agent_name: str = None,
-    extract_opinions: bool = False,
-) -> tuple[list["Fact"], list[tuple[str, int]]]:
+async def extract_facts(text: str, event_date: datetime, context: str = "", llm_config: 'LLMConfig' = None, agent_name: str = None, extract_opinions: bool = False) -> tuple[List['Fact'], List[tuple[str, int]]]:
    """
    Extract semantic facts from text using LLM.

@@ -49,25 +41,16 @@ async def extract_facts(
    if not text or not text.strip():
        return [], []

-    facts, chunks = await extract_facts_from_text(
-        text,
-        event_date,
-        context=context,
-        llm_config=llm_config,
-        agent_name=agent_name,
-        extract_opinions=extract_opinions,
-    )
+    facts, chunks = await extract_facts_from_text(text, event_date, context=context, llm_config=llm_config, agent_name=agent_name, extract_opinions=extract_opinions)

    if not facts:
-        logging.warning(
-            f"LLM extracted 0 facts from text of length {len(text)}. This may indicate the text contains no meaningful information, or the LLM failed to extract facts. Full text: {text}"
-        )
+        logging.warning(f"LLM extracted 0 facts from text of length {len(text)}. This may indicate the text contains no meaningful information, or the LLM failed to extract facts. Full text: {text}")
        return [], chunks

    return facts, chunks


-def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
+def cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
    """
    Calculate cosine similarity between two vectors.

@@ -117,7 +100,6 @@ def calculate_recency_weight(days_since: float, half_life_days: float = 365.0) -
        Weight between 0 and 1
    """
    import math
-
    # Logarithmic decay: 1 / (1 + log(1 + days_since/half_life))
    # This decays much slower than exponential, giving better long-term differentiation
    normalized_age = days_since / half_life_days
@@ -139,7 +121,6 @@ def calculate_frequency_weight(access_count: int, max_boost: float = 2.0) -> flo
        Weight between 1.0 and max_boost
    """
    import math
-
    if access_count <= 0:
        return 1.0

@@ -177,7 +158,11 @@ def calculate_temporal_anchor(occurred_start: datetime, occurred_end: datetime)
    return midpoint


-def calculate_temporal_proximity(anchor_a: datetime, anchor_b: datetime, half_life_days: float = 30.0) -> float:
+def calculate_temporal_proximity(
+    anchor_a: datetime,
+    anchor_b: datetime,
+    half_life_days: float = 30.0
+) -> float:
    """
    Calculate temporal proximity between two temporal anchors.

@@ -1,66 +0,0 @@
-"""
-Hindsight Extensions System.
-
-Extensions allow customizing and extending Hindsight behavior without modifying core code.
-Extensions are loaded via environment variables pointing to implementation classes.
-
-Example:
-    HINDSIGHT_API_OPERATION_VALIDATOR_EXTENSION=mypackage.validators:MyValidator
-    HINDSIGHT_API_OPERATION_VALIDATOR_MAX_RETRIES=3
-
-    HINDSIGHT_API_HTTP_EXTENSION=mypackage.http:MyHttpExtension
-    HINDSIGHT_API_HTTP_SOME_CONFIG=value
-
-Extensions receive an ExtensionContext that provides a controlled API for interacting
-with the system (e.g., running migrations for tenant schemas).
-"""
-
-from hindsight_api.extensions.base import Extension
-from hindsight_api.extensions.builtin import ApiKeyTenantExtension
-from hindsight_api.extensions.context import DefaultExtensionContext, ExtensionContext
-from hindsight_api.extensions.http import HttpExtension
-from hindsight_api.extensions.loader import load_extension
-from hindsight_api.extensions.operation_validator import (
-    OperationValidationError,
-    OperationValidatorExtension,
-    RecallContext,
-    RecallResult,
-    ReflectContext,
-    ReflectResultContext,
-    RetainContext,
-    RetainResult,
-    ValidationResult,
-)
-from hindsight_api.extensions.tenant import (
-    AuthenticationError,
-    TenantContext,
-    TenantExtension,
-)
-from hindsight_api.models import RequestContext
-
-__all__ = [
-    # Base
-    "Extension",
-    "load_extension",
-    # Context
-    "ExtensionContext",
-    "DefaultExtensionContext",
-    # HTTP Extension
-    "HttpExtension",
-    # Operation Validator
-    "OperationValidationError",
-    "OperationValidatorExtension",
-    "RecallContext",
-    "RecallResult",
-    "ReflectContext",
-    "ReflectResultContext",
-    "RetainContext",
-    "RetainResult",
-    "ValidationResult",
-    # Tenant/Auth
-    "ApiKeyTenantExtension",
-    "AuthenticationError",
-    "RequestContext",
-    "TenantContext",
-    "TenantExtension",
-]
@@ -1,81 +0,0 @@
-"""Base Extension class for all Hindsight extensions."""
-
-from abc import ABC
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from hindsight_api.extensions.context import ExtensionContext
-
-
-class Extension(ABC):
-    """
-    Base class for all Hindsight extensions.
-
-    Extensions are loaded via environment variables and receive configuration
-    from prefixed environment variables.
-
-    Example:
-        HINDSIGHT_API_MY_EXTENSION=mypackage.ext:MyExtension
-        HINDSIGHT_API_MY_SOME_CONFIG=value
-
-        The extension receives: {"some_config": "value"}
-
-    Extensions also receive an ExtensionContext that provides a controlled API
-    for interacting with the system (e.g., running migrations for tenant schemas).
-    """
-
-    def __init__(self, config: dict[str, str]):
-        """
-        Initialize the extension with configuration.
-
-        Args:
-            config: Dictionary of configuration values from environment variables.
-                    Keys are lowercased with the prefix stripped.
-        """
-        self.config = config
-        self._context: "ExtensionContext | None" = None
-
-    def set_context(self, context: "ExtensionContext") -> None:
-        """
-        Set the extension context.
-
-        Called by the extension loader after instantiation.
-        Extensions should not call this directly.
-
-        Args:
-            context: The ExtensionContext providing system APIs.
-        """
-        self._context = context
-
-    @property
-    def context(self) -> "ExtensionContext":
-        """
-        Get the extension context.
-
-        Returns:
-            The ExtensionContext providing system APIs.
-
-        Raises:
-            RuntimeError: If context has not been set yet.
-        """
-        if self._context is None:
-            raise RuntimeError(
-                "Extension context not set. Context is available after the extension is loaded by the system."
-            )
-        return self._context
-
-    async def on_startup(self) -> None:
-        """
-        Called when the application starts.
-
-        Override to perform initialization tasks like connecting to external services.
-        """
-        pass
-
-    async def on_shutdown(self) -> None:
-        """
-        Called when the application shuts down.
-
-        Override to perform cleanup tasks like closing connections.
-        """
-        pass
@@ -1,18 +0,0 @@
-"""
-Built-in extension implementations.
-
-These are ready-to-use implementations of the extension interfaces.
-They can be used directly or serve as examples for custom implementations.
-
-Available built-in extensions:
-    - ApiKeyTenantExtension: Simple API key validation with public schema
-
-Example usage:
-    HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-"""
-
-from hindsight_api.extensions.builtin.tenant import ApiKeyTenantExtension
-
-__all__ = [
-    "ApiKeyTenantExtension",
-]
@@ -1,33 +0,0 @@
-"""Built-in tenant extension implementations."""
-
-from hindsight_api.extensions.tenant import AuthenticationError, TenantContext, TenantExtension
-from hindsight_api.models import RequestContext
-
-
-class ApiKeyTenantExtension(TenantExtension):
-    """
-    Built-in tenant extension that validates API key against an environment variable.
-
-    This is a simple implementation that:
-    1. Validates the API key matches HINDSIGHT_API_TENANT_API_KEY
-    2. Returns 'public' as the schema for all authenticated requests
-
-    Configuration:
-        HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-        HINDSIGHT_API_TENANT_API_KEY=your-secret-key
-
-    For multi-tenant setups with separate schemas per tenant, implement a custom
-    TenantExtension that looks up the schema based on the API key or token claims.
-    """
-
-    def __init__(self, config: dict[str, str]):
-        super().__init__(config)
-        self.expected_api_key = config.get("api_key")
-        if not self.expected_api_key:
-            raise ValueError("HINDSIGHT_API_TENANT_API_KEY is required when using ApiKeyTenantExtension")
-
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """Validate API key and return public schema context."""
-        if context.api_key != self.expected_api_key:
-            raise AuthenticationError("Invalid API key")
-        return TenantContext(schema_name="public")
@@ -1,110 +0,0 @@
-"""Extension context providing a controlled API for extensions to interact with the system."""
-
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from hindsight_api.engine.interface import MemoryEngineInterface
-
-
-class ExtensionContext(ABC):
-    """
-    Abstract context providing a controlled API for extensions.
-
-    Extensions receive this context instead of direct access to internal
-    components like MemoryEngine or database connections. This provides:
-    - A stable API that won't break when internals change
-    - Security by limiting what extensions can access
-    - Clear documentation of what extensions can do
-
-    Built-in implementation:
-        hindsight_api.extensions.builtin.context.DefaultExtensionContext
-
-    Example usage in an extension:
-        class MyTenantExtension(TenantExtension):
-            async def on_startup(self) -> None:
-                # Run migrations for a new tenant schema
-                await self.context.run_migration("tenant_acme")
-
-        class MyHttpExtension(HttpExtension):
-            def get_router(self, memory):
-                # Use memory engine for custom endpoints
-                engine = self.context.get_memory_engine()
-                ...
-    """
-
-    @abstractmethod
-    async def run_migration(self, schema: str) -> None:
-        """
-        Run database migrations for a specific schema.
-
-        This creates the schema if it doesn't exist and runs all pending
-        migrations. Uses advisory locks to coordinate between distributed workers.
-
-        Args:
-            schema: PostgreSQL schema name (e.g., "tenant_acme").
-                    The schema will be created if it doesn't exist.
-
-        Raises:
-            RuntimeError: If migrations fail to complete.
-
-        Example:
-            # Provision a new tenant schema
-            await context.run_migration("tenant_acme")
-        """
-        ...
-
-    @abstractmethod
-    def get_memory_engine(self) -> "MemoryEngineInterface":
-        """
-        Get the memory engine interface.
-
-        Returns the MemoryEngineInterface for performing memory operations
-        like retain, recall, reflect, and entity/document management.
-
-        Returns:
-            MemoryEngineInterface instance.
-
-        Example:
-            engine = context.get_memory_engine()
-            result = await engine.recall_async(bank_id, query)
-        """
-        ...
-
-
-class DefaultExtensionContext(ExtensionContext):
-    """
-    Default implementation of ExtensionContext.
-
-    Uses the system's database URL and migration infrastructure.
-    """
-
-    def __init__(
-        self,
-        database_url: str,
-        memory_engine: "MemoryEngineInterface | None" = None,
-    ):
-        """
-        Initialize the context.
-
-        Args:
-            database_url: SQLAlchemy database URL for migrations.
-            memory_engine: Optional MemoryEngine instance for memory operations.
-        """
-        self._database_url = database_url
-        self._memory_engine = memory_engine
-
-    async def run_migration(self, schema: str) -> None:
-        """Run migrations for a specific schema."""
-        from hindsight_api.migrations import run_migrations
-
-        run_migrations(self._database_url, schema=schema)
-
-    def get_memory_engine(self) -> "MemoryEngineInterface":
-        """Get the memory engine interface."""
-        if self._memory_engine is None:
-            raise RuntimeError(
-                "Memory engine not configured in ExtensionContext. "
-                "Ensure the context was created with a memory_engine parameter."
-            )
-        return self._memory_engine
@@ -1,89 +0,0 @@
-"""
-HTTP Extension for adding custom endpoints to the Hindsight API.
-
-This extension allows adding custom HTTP endpoints under the /ext/ path prefix.
-The extension provides a FastAPI router that is mounted on the main application.
-"""
-
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING
-
-from fastapi import APIRouter
-
-from hindsight_api.extensions.base import Extension
-
-if TYPE_CHECKING:
-    from hindsight_api import MemoryEngine
-
-
-class HttpExtension(Extension, ABC):
-    """
-    Base class for HTTP extensions that add custom API endpoints.
-
-    HTTP extensions provide a FastAPI router that gets mounted under /ext/.
-    The extension has full control over the routes, request/response models, and handlers.
-
-    Example:
-        ```python
-        from fastapi import APIRouter
-        from hindsight_api.extensions import HttpExtension
-
-        class MyHttpExtension(HttpExtension):
-            def get_router(self, memory: MemoryEngine) -> APIRouter:
-                router = APIRouter()
-
-                @router.get("/hello")
-                async def hello():
-                    return {"message": "Hello from extension!"}
-
-                @router.post("/custom/{bank_id}/action")
-                async def custom_action(bank_id: str):
-                    # Access memory engine for database operations
-                    pool = await memory._get_pool()
-                    # ... custom logic
-                    return {"status": "ok"}
-
-                return router
-        ```
-
-    The routes will be available at:
-        - GET /ext/hello
-        - POST /ext/custom/{bank_id}/action
-
-    Configuration via environment variables:
-        HINDSIGHT_API_HTTP_EXTENSION=mypackage.ext:MyHttpExtension
-        HINDSIGHT_API_HTTP_SOME_CONFIG=value
-
-    The extension receives config: {"some_config": "value"}
-    """
-
-    @abstractmethod
-    def get_router(self, memory: "MemoryEngine") -> APIRouter:
-        """
-        Return a FastAPI router with custom endpoints.
-
-        The router will be mounted at /ext/ on the main application.
-        All routes defined in the router will be prefixed with /ext/.
-
-        Args:
-            memory: The MemoryEngine instance for database access and core operations.
-                   Use this to access the connection pool, run queries, or call
-                   memory operations like retain, recall, etc.
-
-        Returns:
-            A FastAPI APIRouter with the custom endpoints defined.
-
-        Example:
-            ```python
-            def get_router(self, memory: MemoryEngine) -> APIRouter:
-                router = APIRouter(tags=["My Extension"])
-
-                @router.get("/status")
-                async def status():
-                    health = await memory.health_check()
-                    return {"extension": "healthy", "memory": health}
-
-                return router
-            ```
-        """
-        pass
@@ -1,125 +0,0 @@
-"""Extension loader utilities."""
-
-import importlib
-import logging
-import os
-from typing import TYPE_CHECKING, TypeVar
-
-from hindsight_api.extensions.base import Extension
-
-if TYPE_CHECKING:
-    from hindsight_api.extensions.context import ExtensionContext
-
-logger = logging.getLogger(__name__)
-
-T = TypeVar("T", bound=Extension)
-
-
-class ExtensionLoadError(Exception):
-    """Raised when an extension fails to load."""
-
-    pass
-
-
-def load_extension(
-    prefix: str,
-    base_class: type[T],
-    env_prefix: str = "HINDSIGHT_API",
-    context: "ExtensionContext | None" = None,
-) -> T | None:
-    """
-    Load an extension from environment variable configuration.
-
-    The extension class is specified via {env_prefix}_{prefix}_EXTENSION environment
-    variable in the format "module.path:ClassName".
-
-    Configuration for the extension is collected from all environment variables
-    matching {env_prefix}_{prefix}_* (excluding the EXTENSION variable itself).
-
-    Args:
-        prefix: The extension prefix (e.g., "OPERATION_VALIDATOR").
-        base_class: The base class that the extension must inherit from.
-        env_prefix: The environment variable prefix (default: "HINDSIGHT_API").
-        context: Optional ExtensionContext to provide system APIs to the extension.
-
-    Returns:
-        An instance of the extension, or None if not configured.
-
-    Raises:
-        ExtensionLoadError: If the extension fails to load or validate.
-
-    Example:
-        HINDSIGHT_API_OPERATION_VALIDATOR_EXTENSION=mypackage.validators:MyValidator
-        HINDSIGHT_API_OPERATION_VALIDATOR_MAX_REQUESTS=100
-
-        ext = load_extension("OPERATION_VALIDATOR", OperationValidatorExtension)
-        # ext.config == {"max_requests": "100"}
-    """
-    env_var = f"{env_prefix}_{prefix}_EXTENSION"
-    ext_path = os.getenv(env_var)
-
-    if not ext_path:
-        logger.debug(f"No extension configured for {env_var}")
-        return None
-
-    logger.info(f"Loading extension from {env_var}={ext_path}")
-
-    # Parse "module.path:ClassName"
-    if ":" not in ext_path:
-        raise ExtensionLoadError(f"Invalid extension path '{ext_path}'. Expected format: 'module.path:ClassName'")
-
-    module_path, class_name = ext_path.rsplit(":", 1)
-
-    # Import the module
-    try:
-        module = importlib.import_module(module_path)
-    except ImportError as e:
-        raise ExtensionLoadError(f"Failed to import extension module '{module_path}': {e}") from e
-
-    # Get the class
-    try:
-        ext_class = getattr(module, class_name)
-    except AttributeError as e:
-        raise ExtensionLoadError(f"Extension class '{class_name}' not found in module '{module_path}'") from e
-
-    # Validate inheritance
-    if not isinstance(ext_class, type) or not issubclass(ext_class, base_class):
-        raise ExtensionLoadError(f"Extension class '{ext_class.__name__}' must inherit from '{base_class.__name__}'")
-
-    # Collect configuration from environment variables
-    config = _collect_config(env_prefix, prefix)
-
-    logger.info(f"Loaded extension {ext_class.__name__} with config keys: {list(config.keys())}")
-
-    # Instantiate the extension
-    try:
-        extension = ext_class(config)
-    except Exception as e:
-        raise ExtensionLoadError(f"Failed to instantiate extension '{ext_class.__name__}': {e}") from e
-
-    # Set the context if provided
-    if context is not None:
-        extension.set_context(context)
-        logger.debug(f"Set context on extension {ext_class.__name__}")
-
-    return extension
-
-
-def _collect_config(env_prefix: str, prefix: str) -> dict[str, str]:
-    """
-    Collect configuration from environment variables.
-
-    Collects all variables matching {env_prefix}_{prefix}_* except for
-    {env_prefix}_{prefix}_EXTENSION, strips the prefix, and lowercases keys.
-    """
-    config = {}
-    full_prefix = f"{env_prefix}_{prefix}_"
-    extension_var = f"{full_prefix}EXTENSION"
-
-    for key, value in os.environ.items():
-        if key.startswith(full_prefix) and key != extension_var:
-            # Strip prefix and lowercase the key
-            config_key = key[len(full_prefix) :].lower()
-            config[config_key] = value
-
-    return config
@@ -1,325 +0,0 @@
-"""Operation Validator Extension for validating retain/recall/reflect operations."""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import TYPE_CHECKING, Any
-
-from hindsight_api.extensions.base import Extension
-
-if TYPE_CHECKING:
-    from hindsight_api.engine.memory_engine import Budget
-    from hindsight_api.engine.response_models import RecallResult as RecallResultModel
-    from hindsight_api.engine.response_models import ReflectResult
-    from hindsight_api.models import RequestContext
-
-
-class OperationValidationError(Exception):
-    """Raised when an operation fails validation."""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Operation validation failed: {reason}")
-
-
-@dataclass
-class ValidationResult:
-    """Result of an operation validation."""
-
-    allowed: bool
-    reason: str | None = None
-
-    @classmethod
-    def accept(cls) -> "ValidationResult":
-        """Create an accepted validation result."""
-        return cls(allowed=True)
-
-    @classmethod
-    def reject(cls, reason: str) -> "ValidationResult":
-        """Create a rejected validation result with a reason."""
-        return cls(allowed=False, reason=reason)
-
-
-# =============================================================================
-# Pre-operation Contexts (all user-provided parameters)
-# =============================================================================
-
-
-@dataclass
-class RetainContext:
-    """Context for a retain operation validation (pre-operation).
-
-    Contains ALL user-provided parameters for the retain operation.
-    """
-
-    bank_id: str
-    contents: list[dict]  # List of {content, context, event_date, document_id}
-    request_context: "RequestContext"
-    document_id: str | None = None
-    fact_type_override: str | None = None
-    confidence_score: float | None = None
-
-
-@dataclass
-class RecallContext:
-    """Context for a recall operation validation (pre-operation).
-
-    Contains ALL user-provided parameters for the recall operation.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None" = None
-    max_tokens: int = 4096
-    enable_trace: bool = False
-    fact_types: list[str] = field(default_factory=list)
-    question_date: datetime | None = None
-    include_entities: bool = False
-    max_entity_tokens: int = 500
-    include_chunks: bool = False
-    max_chunk_tokens: int = 8192
-
-
-@dataclass
-class ReflectContext:
-    """Context for a reflect operation validation (pre-operation).
-
-    Contains ALL user-provided parameters for the reflect operation.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None" = None
-    context: str | None = None
-
-
-# =============================================================================
-# Post-operation Contexts (includes results)
-# =============================================================================
-
-
-@dataclass
-class RetainResult:
-    """Result context for post-retain hook.
-
-    Contains the operation parameters and the result.
-    """
-
-    bank_id: str
-    contents: list[dict]
-    request_context: "RequestContext"
-    document_id: str | None
-    fact_type_override: str | None
-    confidence_score: float | None
-    # Result
-    unit_ids: list[list[str]]  # List of unit IDs per content item
-    success: bool = True
-    error: str | None = None
-
-
-@dataclass
-class RecallResult:
-    """Result context for post-recall hook.
-
-    Contains the operation parameters and the result.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None"
-    max_tokens: int
-    enable_trace: bool
-    fact_types: list[str]
-    question_date: datetime | None
-    include_entities: bool
-    max_entity_tokens: int
-    include_chunks: bool
-    max_chunk_tokens: int
-    # Result
-    result: "RecallResultModel | None" = None
-    success: bool = True
-    error: str | None = None
-
-
-@dataclass
-class ReflectResultContext:
-    """Result context for post-reflect hook.
-
-    Contains the operation parameters and the result.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None"
-    context: str | None
-    # Result
-    result: "ReflectResult | None" = None
-    success: bool = True
-    error: str | None = None
-
-
-class OperationValidatorExtension(Extension, ABC):
-    """
-    Validates and hooks into retain/recall/reflect operations.
-
-    This extension allows implementing custom logic such as:
-    - Rate limiting (pre-operation)
-    - Quota enforcement (pre-operation)
-    - Permission checks (pre-operation)
-    - Content filtering (pre-operation)
-    - Usage tracking (post-operation)
-    - Audit logging (post-operation)
-    - Metrics collection (post-operation)
-
-    Enable via environment variable:
-        HINDSIGHT_API_OPERATION_VALIDATOR_EXTENSION=mypackage.validators:MyValidator
-
-    Configuration is passed from prefixed environment variables:
-        HINDSIGHT_API_OPERATION_VALIDATOR_MAX_REQUESTS=100
-        -> config = {"max_requests": "100"}
-
-    Hook execution order:
-        1. validate_retain/validate_recall/validate_reflect (pre-operation)
-        2. [operation executes]
-        3. on_retain_complete/on_recall_complete/on_reflect_complete (post-operation)
-    """
-
-    # =========================================================================
-    # Pre-operation validation hooks (abstract - must be implemented)
-    # =========================================================================
-
-    @abstractmethod
-    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
-        """
-        Validate a retain operation before execution.
-
-        Called before the retain operation is processed. Return ValidationResult.reject()
-        to prevent the operation from executing.
-
-        Args:
-            ctx: Context containing all user-provided parameters:
-                - bank_id: Bank identifier
-                - contents: List of content dicts
-                - request_context: Request context with auth info
-                - document_id: Optional document ID
-                - fact_type_override: Optional fact type override
-                - confidence_score: Optional confidence score
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        ...
-
-    @abstractmethod
-    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
-        """
-        Validate a recall operation before execution.
-
-        Called before the recall operation is processed. Return ValidationResult.reject()
-        to prevent the operation from executing.
-
-        Args:
-            ctx: Context containing all user-provided parameters:
-                - bank_id: Bank identifier
-                - query: Search query
-                - request_context: Request context with auth info
-                - budget: Budget level
-                - max_tokens: Maximum tokens to return
-                - enable_trace: Whether to include trace info
-                - fact_types: List of fact types to search
-                - question_date: Optional date context for query
-                - include_entities: Whether to include entity data
-                - max_entity_tokens: Max tokens for entities
-                - include_chunks: Whether to include chunks
-                - max_chunk_tokens: Max tokens for chunks
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        ...
-
-    @abstractmethod
-    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
-        """
-        Validate a reflect operation before execution.
-
-        Called before the reflect operation is processed. Return ValidationResult.reject()
-        to prevent the operation from executing.
-
-        Args:
-            ctx: Context containing all user-provided parameters:
-                - bank_id: Bank identifier
-                - query: Question to answer
-                - request_context: Request context with auth info
-                - budget: Budget level
-                - context: Optional additional context
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        ...
-
-    # =========================================================================
-    # Post-operation hooks (optional - override to implement)
-    # =========================================================================
-
-    async def on_retain_complete(self, result: RetainResult) -> None:
-        """
-        Called after a retain operation completes (success or failure).
-
-        Override this method to implement post-operation logic such as:
-        - Usage tracking
-        - Audit logging
-        - Metrics collection
-        - Notifications
-
-        Args:
-            result: Result context containing:
-                - All original operation parameters
-                - unit_ids: List of created unit IDs (if success)
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
-
-    async def on_recall_complete(self, result: RecallResult) -> None:
-        """
-        Called after a recall operation completes (success or failure).
-
-        Override this method to implement post-operation logic such as:
-        - Usage tracking
-        - Audit logging
-        - Metrics collection
-        - Query analytics
-
-        Args:
-            result: Result context containing:
-                - All original operation parameters
-                - result: RecallResultModel (if success)
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
-
-    async def on_reflect_complete(self, result: ReflectResultContext) -> None:
-        """
-        Called after a reflect operation completes (success or failure).
-
-        Override this method to implement post-operation logic such as:
-        - Usage tracking
-        - Audit logging
-        - Metrics collection
-        - Response analytics
-
-        Args:
-            result: Result context containing:
-                - All original operation parameters
-                - result: ReflectResult (if success)
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
@@ -1,63 +0,0 @@
-"""Tenant Extension for multi-tenancy and API key authentication."""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-
-from hindsight_api.extensions.base import Extension
-from hindsight_api.models import RequestContext
-
-
-class AuthenticationError(Exception):
-    """Raised when authentication fails."""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Authentication failed: {reason}")
-
-
-@dataclass
-class TenantContext:
-    """
-    Tenant context returned by authentication.
-
-    Contains the PostgreSQL schema name for tenant isolation.
-    All database queries will use fully-qualified table names
-    with this schema (e.g., schema_name.memory_units).
-    """
-
-    schema_name: str
-
-
-class TenantExtension(Extension, ABC):
-    """
-    Extension for multi-tenancy and API key authentication.
-
-    This extension validates incoming requests and returns the tenant context
-    including the PostgreSQL schema to use for database operations.
-
-    Built-in implementation:
-        hindsight_api.extensions.builtin.tenant.ApiKeyTenantExtension
-
-    Enable via environment variable:
-        HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-        HINDSIGHT_API_TENANT_API_KEY=your-secret-key
-
-    The returned schema_name is used for fully-qualified table names in queries,
-    enabling tenant isolation at the database level.
-    """
-
-    @abstractmethod
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """
-        Authenticate the action context and return tenant context.
-
-        Args:
-            context: The action context containing API key and other auth data.
-
-        Returns:
-            TenantContext with the schema_name for database operations.
-
-        Raises:
-            AuthenticationError: If authentication fails.
-        """
-        ...
@@ -4,12 +4,8 @@ Command-line interface for Hindsight API.
 Run the server with:
    hindsight-api

-Run as background daemon:
-    hindsight-api --daemon
-
 Stop with Ctrl+C.
 """
-
 import argparse
 import asyncio
 import atexit
@@ -17,20 +13,13 @@ import os
 import signal
 import sys
 import warnings
+from typing import Optional

 import uvicorn

 from . import MemoryEngine
 from .api import create_app
-from .banner import print_banner
-from .config import HindsightConfig, get_config
-from .daemon import (
-    DEFAULT_DAEMON_PORT,
-    DEFAULT_IDLE_TIMEOUT,
-    DaemonLock,
-    IdleTimeoutMiddleware,
-    daemonize,
-)
+from .config import get_config, HindsightConfig

 # Filter deprecation warnings from third-party libraries
 warnings.filterwarnings("ignore", message="websockets.legacy is deprecated")
@@ -40,7 +29,7 @@ warnings.filterwarnings("ignore", message="websockets.server.WebSocketServerProt
 os.environ["TOKENIZERS_PARALLELISM"] = "false"

 # Global reference for cleanup
-_memory: MemoryEngine | None = None
+_memory: Optional[MemoryEngine] = None


 def _cleanup():
@@ -77,88 +66,62 @@ def main():

    # Server options
    parser.add_argument(
-        "--host", default=config.host, help=f"Host to bind to (default: {config.host}, env: HINDSIGHT_API_HOST)"
+        "--host", default=config.host,
+        help=f"Host to bind to (default: {config.host}, env: HINDSIGHT_API_HOST)"
    )
    parser.add_argument(
-        "--port",
-        type=int,
-        default=config.port,
-        help=f"Port to bind to (default: {config.port}, env: HINDSIGHT_API_PORT)",
+        "--port", type=int, default=config.port,
+        help=f"Port to bind to (default: {config.port}, env: HINDSIGHT_API_PORT)"
    )
    parser.add_argument(
-        "--log-level",
-        default=config.log_level,
+        "--log-level", default=config.log_level,
        choices=["critical", "error", "warning", "info", "debug", "trace"],
-        help=f"Log level (default: {config.log_level}, env: HINDSIGHT_API_LOG_LEVEL)",
+        help=f"Log level (default: {config.log_level}, env: HINDSIGHT_API_LOG_LEVEL)"
    )

    # Development options
-    parser.add_argument("--reload", action="store_true", help="Enable auto-reload on code changes (development only)")
-    parser.add_argument("--workers", type=int, default=1, help="Number of worker processes (default: 1)")
+    parser.add_argument(
+        "--reload", action="store_true",
+        help="Enable auto-reload on code changes (development only)"
+    )
+    parser.add_argument(
+        "--workers", type=int, default=1,
+        help="Number of worker processes (default: 1)"
+    )

    # Access log options
-    parser.add_argument("--access-log", action="store_true", help="Enable access log")
-    parser.add_argument("--no-access-log", dest="access_log", action="store_false", help="Disable access log (default)")
+    parser.add_argument(
+        "--access-log", action="store_true",
+        help="Enable access log"
+    )
+    parser.add_argument(
+        "--no-access-log", dest="access_log", action="store_false",
+        help="Disable access log (default)"
+    )
    parser.set_defaults(access_log=False)

    # Proxy options
    parser.add_argument(
-        "--proxy-headers", action="store_true", help="Enable X-Forwarded-Proto, X-Forwarded-For headers"
+        "--proxy-headers", action="store_true",
+        help="Enable X-Forwarded-Proto, X-Forwarded-For headers"
    )
    parser.add_argument(
-        "--forwarded-allow-ips", default=None, help="Comma separated list of IPs to trust with proxy headers"
+        "--forwarded-allow-ips", default=None,
+        help="Comma separated list of IPs to trust with proxy headers"
    )

    # SSL options
-    parser.add_argument("--ssl-keyfile", default=None, help="SSL key file")
-    parser.add_argument("--ssl-certfile", default=None, help="SSL certificate file")
-
-    # Daemon mode options
    parser.add_argument(
-        "--daemon",
-        action="store_true",
-        help=f"Run as background daemon (uses port {DEFAULT_DAEMON_PORT}, auto-exits after idle)",
+        "--ssl-keyfile", default=None,
+        help="SSL key file"
    )
    parser.add_argument(
-        "--idle-timeout",
-        type=int,
-        default=DEFAULT_IDLE_TIMEOUT,
-        help=f"Idle timeout in seconds before auto-exit in daemon mode (default: {DEFAULT_IDLE_TIMEOUT})",
+        "--ssl-certfile", default=None,
+        help="SSL certificate file"
    )

    args = parser.parse_args()

-    # Daemon mode handling
-    if args.daemon:
-        # Use fixed daemon port
-        args.port = DEFAULT_DAEMON_PORT
-        args.host = "127.0.0.1"  # Only bind to localhost for security
-
-        # Check if another daemon is already running
-        daemon_lock = DaemonLock()
-        if not daemon_lock.acquire():
-            print(f"Daemon already running (PID: {daemon_lock.get_pid()})", file=sys.stderr)
-            sys.exit(1)
-
-        # Fork into background
-        daemonize()
-
-        # Re-acquire lock in child process
-        daemon_lock = DaemonLock()
-        if not daemon_lock.acquire():
-            sys.exit(1)
-
-        # Register cleanup to release lock
-        def release_lock():
-            daemon_lock.release()
-
-        atexit.register(release_lock)
-
-    # Print banner (not in daemon mode)
-    if not args.daemon:
-        print()
-        print_banner()
-
    # Configure Python logging based on log level
    # Update config with CLI override if provided
    if args.log_level != config.log_level:
@@ -178,13 +141,8 @@ def main():
            port=args.port,
            log_level=args.log_level,
            mcp_enabled=config.mcp_enabled,
-            graph_retriever=config.graph_retriever,
-            skip_llm_verification=config.skip_llm_verification,
-            lazy_reranker=config.lazy_reranker,
        )
    config.configure_logging()
-    if not args.daemon:
-        config.log_config()

    # Register cleanup handlers
    atexit.register(_cleanup)
@@ -203,12 +161,6 @@ def main():
        initialize_memory=True,
    )

-    # Wrap with idle timeout middleware in daemon mode
-    idle_middleware = None
-    if args.daemon:
-        idle_middleware = IdleTimeoutMiddleware(app, idle_timeout=args.idle_timeout)
-        app = idle_middleware
-
    # Prepare uvicorn config
    uvicorn_config = {
        "app": app,
@@ -232,40 +184,17 @@ def main():
    if args.ssl_certfile:
        uvicorn_config["ssl_certfile"] = args.ssl_certfile

-    # Print startup info (not in daemon mode)
-    if not args.daemon:
-        from .banner import print_startup_info
+    print(f"\nStarting Hindsight API...")
+    print(f"  URL: http://{args.host}:{args.port}")
+    print(f"  Database: {config.database_url}")
+    print(f"  LLM: {config.llm_provider} / {config.llm_model}")
+    print(f"  Embeddings: {config.embeddings_provider}")
+    print(f"  Reranker: {config.reranker_provider}")
+    if config.mcp_enabled:
+        print(f"  MCP: enabled at /mcp")
+    print()

-        print_startup_info(
-            host=args.host,
-            port=args.port,
-            database_url=config.database_url,
-            llm_provider=config.llm_provider,
-            llm_model=config.llm_model,
-            embeddings_provider=config.embeddings_provider,
-            reranker_provider=config.reranker_provider,
-            mcp_enabled=config.mcp_enabled,
-        )
-
-    # Start idle checker in daemon mode
-    if idle_middleware is not None:
-        # Start the idle checker in a background thread with its own event loop
-        import threading
-
-        def run_idle_checker():
-            import time
-
-            time.sleep(2)  # Wait for uvicorn to start
-            try:
-                loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(loop)
-                loop.run_until_complete(idle_middleware._check_idle())
-            except Exception:
-                pass
-
-        threading.Thread(target=run_idle_checker, daemon=True).start()
-
-    uvicorn.run(**uvicorn_config)  # type: ignore[invalid-argument-type] - dict kwargs
+    uvicorn.run(**uvicorn_config)


 if __name__ == "__main__":
@@ -1,199 +0,0 @@
-"""
-Local MCP server for use with Claude Code (stdio transport).
-
-This runs a fully local Hindsight instance with embedded PostgreSQL (pg0).
-No external database or server required.
-
-Run with:
-    hindsight-local-mcp
-
-Or with uvx:
-    uvx hindsight-api@latest hindsight-local-mcp
-
-Configure in Claude Code's MCP settings:
-    {
-        "mcpServers": {
-            "hindsight": {
-                "command": "uvx",
-                "args": ["hindsight-api@latest", "hindsight-local-mcp"],
-                "env": {
-                    "HINDSIGHT_API_LLM_API_KEY": "your-openai-key"
-                }
-            }
-        }
-    }
-
-Environment variables:
-    HINDSIGHT_API_LLM_API_KEY: Required. API key for LLM provider.
-    HINDSIGHT_API_LLM_PROVIDER: Optional. LLM provider (default: "openai").
-    HINDSIGHT_API_LLM_MODEL: Optional. LLM model (default: "gpt-4o-mini").
-    HINDSIGHT_API_MCP_LOCAL_BANK_ID: Optional. Memory bank ID (default: "mcp").
-    HINDSIGHT_API_LOG_LEVEL: Optional. Log level (default: "warning").
-    HINDSIGHT_API_MCP_INSTRUCTIONS: Optional. Additional instructions appended to both retain and recall tools.
-
-Example custom instructions (these are ADDED to the default behavior):
-    To also store assistant actions:
-        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store every action you take, including tool calls, code written, and decisions made."
-
-    To also store conversation summaries:
-        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store summaries of important conversations and their outcomes."
-"""
-
-import logging
-import os
-import sys
-
-from mcp.server.fastmcp import FastMCP
-from mcp.types import Icon
-
-from hindsight_api.config import (
-    DEFAULT_MCP_LOCAL_BANK_ID,
-    DEFAULT_MCP_RECALL_DESCRIPTION,
-    DEFAULT_MCP_RETAIN_DESCRIPTION,
-    ENV_MCP_INSTRUCTIONS,
-    ENV_MCP_LOCAL_BANK_ID,
-)
-
-# Configure logging - default to warning to avoid polluting stderr during MCP init
-# MCP clients interpret stderr output as errors, so we suppress INFO logs by default
-_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "warning").lower()
-_log_level_map = {
-    "critical": logging.CRITICAL,
-    "error": logging.ERROR,
-    "warning": logging.WARNING,
-    "info": logging.INFO,
-    "debug": logging.DEBUG,
-}
-logging.basicConfig(
-    level=_log_level_map.get(_log_level_str, logging.WARNING),
-    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-    stream=sys.stderr,  # MCP uses stdout for protocol, logs go to stderr
-)
-logger = logging.getLogger(__name__)
-
-
-def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
-    """
-    Create a stdio MCP server with retain/recall tools.
-
-    Args:
-        bank_id: The memory bank ID to use for all operations.
-        memory: Optional MemoryEngine instance. If not provided, creates one with pg0.
-
-    Returns:
-        Configured FastMCP server instance.
-    """
-    # Import here to avoid slow startup if just checking --help
-    from hindsight_api import MemoryEngine
-    from hindsight_api.engine.memory_engine import Budget
-    from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
-    from hindsight_api.models import RequestContext
-
-    # Create memory engine with pg0 embedded database if not provided
-    if memory is None:
-        memory = MemoryEngine(db_url="pg0://hindsight-mcp")
-
-    # Get custom instructions from environment variable (appended to both tools)
-    extra_instructions = os.environ.get(ENV_MCP_INSTRUCTIONS, "")
-
-    retain_description = DEFAULT_MCP_RETAIN_DESCRIPTION
-    recall_description = DEFAULT_MCP_RECALL_DESCRIPTION
-
-    if extra_instructions:
-        retain_description = f"{DEFAULT_MCP_RETAIN_DESCRIPTION}\n\nAdditional instructions: {extra_instructions}"
-        recall_description = f"{DEFAULT_MCP_RECALL_DESCRIPTION}\n\nAdditional instructions: {extra_instructions}"
-
-    mcp = FastMCP("hindsight")
-
-    @mcp.tool(description=retain_description)
-    async def retain(content: str, context: str = "general") -> dict:
-        """
-        Args:
-            content: The fact/memory to store (be specific and include relevant details)
-            context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
-        """
-        import asyncio
-
-        async def _retain():
-            try:
-                await memory.retain_batch_async(
-                    bank_id=bank_id,
-                    contents=[{"content": content, "context": context}],
-                    request_context=RequestContext(),
-                )
-            except Exception as e:
-                logger.error(f"Error storing memory: {e}", exc_info=True)
-
-        # Fire and forget - don't block on memory storage
-        asyncio.create_task(_retain())
-        return {"status": "accepted", "message": "Memory storage initiated"}
-
-    @mcp.tool(description=recall_description)
-    async def recall(query: str, max_tokens: int = 4096, budget: str = "low") -> dict:
-        """
-        Args:
-            query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
-            max_tokens: Maximum tokens to return in results (default: 4096)
-            budget: Search budget level - "low", "mid", or "high" (default: "low")
-        """
-        try:
-            # Map string budget to enum
-            budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
-            budget_enum = budget_map.get(budget.lower(), Budget.LOW)
-
-            search_result = await memory.recall_async(
-                bank_id=bank_id,
-                query=query,
-                fact_type=list(VALID_RECALL_FACT_TYPES),
-                budget=budget_enum,
-                max_tokens=max_tokens,
-                request_context=RequestContext(),
-            )
-
-            return search_result.model_dump()
-        except Exception as e:
-            logger.error(f"Error searching: {e}", exc_info=True)
-            return {"error": str(e), "results": []}
-
-    return mcp
-
-
-async def _initialize_and_run(bank_id: str):
-    """Initialize memory and run the MCP server."""
-    from hindsight_api import MemoryEngine
-
-    # Create and initialize memory engine with pg0 embedded database
-    # Note: We avoid printing to stderr during init as MCP clients show it as "errors"
-    memory = MemoryEngine(db_url="pg0://hindsight-mcp")
-    await memory.initialize()
-
-    # Create and run the server
-    mcp = create_local_mcp_server(bank_id, memory=memory)
-    await mcp.run_stdio_async()
-
-
-def main():
-    """Main entry point for the stdio MCP server."""
-    import asyncio
-
-    from hindsight_api.config import ENV_LLM_API_KEY, get_config
-
-    # Check for required environment variables
-    config = get_config()
-    if not config.llm_api_key:
-        print(f"Error: {ENV_LLM_API_KEY} environment variable is required", file=sys.stderr)
-        print("Set it in your MCP configuration or shell environment", file=sys.stderr)
-        sys.exit(1)
-
-    # Get bank ID from environment, default to "mcp"
-    bank_id = os.environ.get(ENV_MCP_LOCAL_BANK_ID, DEFAULT_MCP_LOCAL_BANK_ID)
-
-    # Note: We don't print to stderr as MCP clients display it as "error output"
-    # Use HINDSIGHT_API_LOG_LEVEL=debug for verbose startup logging
-
-    # Run the async initialization and server
-    asyncio.run(_initialize_and_run(bank_id))
-
-
-if __name__ == "__main__":
-    main()
@@ -6,15 +6,16 @@ This module provides metrics for:
 - Token usage (input/output) per operation
 - Per-bank granularity via labels
 """
-
 import logging
-import time
+from typing import Dict, Any, Optional
 from contextlib import contextmanager
+import time

 from opentelemetry import metrics
-from opentelemetry.exporter.prometheus import PrometheusMetricReader
 from opentelemetry.sdk.metrics import MeterProvider
 from opentelemetry.sdk.resources import Resource
+from opentelemetry.exporter.prometheus import PrometheusMetricReader
+from prometheus_client import REGISTRY

 logger = logging.getLogger(__name__)

@@ -38,18 +39,19 @@ def initialize_metrics(service_name: str = "hindsight-api", service_version: str
    global _meter

    # Create resource with service information
-    resource = Resource.create(
-        {
-            "service.name": service_name,
-            "service.version": service_version,
-        }
-    )
+    resource = Resource.create({
+        "service.name": service_name,
+        "service.version": service_version,
+    })

    # Create Prometheus metric reader
    prometheus_reader = PrometheusMetricReader()

    # Create meter provider with Prometheus exporter
-    provider = MeterProvider(resource=resource, metric_readers=[prometheus_reader])
+    provider = MeterProvider(
+        resource=resource,
+        metric_readers=[prometheus_reader]
+    )

    # Set the global meter provider
    metrics.set_meter_provider(provider)
@@ -71,19 +73,11 @@ class MetricsCollectorBase:
    """Base class for metrics collectors."""

    @contextmanager
-    def record_operation(self, operation: str, bank_id: str, budget: str | None = None, max_tokens: int | None = None):
+    def record_operation(self, operation: str, bank_id: str, budget: Optional[str] = None, max_tokens: Optional[int] = None):
        """Context manager to record operation duration and status."""
        raise NotImplementedError

-    def record_tokens(
-        self,
-        operation: str,
-        bank_id: str,
-        input_tokens: int = 0,
-        output_tokens: int = 0,
-        budget: str | None = None,
-        max_tokens: int | None = None,
-    ):
+    def record_tokens(self, operation: str, bank_id: str, input_tokens: int = 0, output_tokens: int = 0, budget: Optional[str] = None, max_tokens: Optional[int] = None):
        """Record token usage for an operation."""
        raise NotImplementedError

@@ -92,19 +86,11 @@ class NoOpMetricsCollector(MetricsCollectorBase):
    """No-op metrics collector that does nothing. Used when metrics are disabled."""

    @contextmanager
-    def record_operation(self, operation: str, bank_id: str, budget: str | None = None, max_tokens: int | None = None):
+    def record_operation(self, operation: str, bank_id: str, budget: Optional[str] = None, max_tokens: Optional[int] = None):
        """No-op context manager."""
        yield

-    def record_tokens(
-        self,
-        operation: str,
-        bank_id: str,
-        input_tokens: int = 0,
-        output_tokens: int = 0,
-        budget: str | None = None,
-        max_tokens: int | None = None,
-    ):
+    def record_tokens(self, operation: str, bank_id: str, input_tokens: int = 0, output_tokens: int = 0, budget: Optional[str] = None, max_tokens: Optional[int] = None):
        """No-op token recording."""
        pass

@@ -122,25 +108,33 @@ class MetricsCollector(MetricsCollectorBase):
        # Operation latency histogram (in seconds)
        # Records duration of retain, recall, reflect operations
        self.operation_duration = self.meter.create_histogram(
-            name="hindsight.operation.duration", description="Duration of Hindsight operations in seconds", unit="s"
+            name="hindsight.operation.duration",
+            description="Duration of Hindsight operations in seconds",
+            unit="s"
        )

        # Token usage counters
        self.tokens_input = self.meter.create_counter(
-            name="hindsight.tokens.input", description="Number of input tokens consumed", unit="tokens"
+            name="hindsight.tokens.input",
+            description="Number of input tokens consumed",
+            unit="tokens"
        )

        self.tokens_output = self.meter.create_counter(
-            name="hindsight.tokens.output", description="Number of output tokens generated", unit="tokens"
+            name="hindsight.tokens.output",
+            description="Number of output tokens generated",
+            unit="tokens"
        )

        # Operation counter (success/failure)
        self.operation_total = self.meter.create_counter(
-            name="hindsight.operation.total", description="Total number of operations executed", unit="operations"
+            name="hindsight.operation.total",
+            description="Total number of operations executed",
+            unit="operations"
        )

    @contextmanager
-    def record_operation(self, operation: str, bank_id: str, budget: str | None = None, max_tokens: int | None = None):
+    def record_operation(self, operation: str, bank_id: str, budget: Optional[str] = None, max_tokens: Optional[int] = None):
        """
        Context manager to record operation duration and status.

@@ -181,15 +175,7 @@ class MetricsCollector(MetricsCollectorBase):
            # Record operation count
            self.operation_total.add(1, attributes)

-    def record_tokens(
-        self,
-        operation: str,
-        bank_id: str,
-        input_tokens: int = 0,
-        output_tokens: int = 0,
-        budget: str | None = None,
-        max_tokens: int | None = None,
-    ):
+    def record_tokens(self, operation: str, bank_id: str, input_tokens: int = 0, output_tokens: int = 0, budget: Optional[str] = None, max_tokens: Optional[int] = None):
        """
        Record token usage for an operation.

@@ -6,19 +6,16 @@ on application startup. It is designed to be safe for concurrent
 execution using PostgreSQL advisory locks to coordinate between
 distributed workers.

-Supports multi-tenant schema isolation: migrations can target a specific
-PostgreSQL schema, allowing each tenant to have isolated tables.
-
 Important: All migrations must be backward-compatible to allow
 safe rolling deployments.

 No alembic.ini required - all configuration is done programmatically.
 """
-
-import hashlib
 import logging
 import os
+import shutil
 from pathlib import Path
+from typing import Optional

 from alembic import command
 from alembic.config import Config
@@ -30,29 +27,11 @@ logger = logging.getLogger(__name__)
 MIGRATION_LOCK_ID = 123456789


-def _get_schema_lock_id(schema: str) -> int:
-    """
-    Generate a unique advisory lock ID for a schema.
-
-    Uses hash of schema name to create a deterministic lock ID.
-    """
-    # Use hash to create a unique lock ID per schema
-    # Keep within PostgreSQL's bigint range
-    hash_bytes = hashlib.sha256(schema.encode()).digest()[:8]
-    return int.from_bytes(hash_bytes, byteorder="big") % (2**31)
-
-
-def _run_migrations_internal(database_url: str, script_location: str, schema: str | None = None) -> None:
+def _run_migrations_internal(database_url: str, script_location: str) -> None:
    """
    Internal function to run migrations without locking.
-
-    Args:
-        database_url: SQLAlchemy database URL
-        script_location: Path to alembic scripts
-        schema: Target schema (None for default/public)
    """
-    schema_name = schema or "public"
-    logger.info(f"Running database migrations to head for schema '{schema_name}'...")
+    logger.info(f"Running database migrations to head...")
    logger.info(f"Database URL: {database_url}")
    logger.info(f"Script location: {script_location}")

@@ -72,22 +51,13 @@ def _run_migrations_internal(database_url: str, script_location: str, schema: st
    # Set path_separator to avoid deprecation warning
    alembic_cfg.set_main_option("path_separator", "os")

-    # If targeting a specific schema, pass it to env.py via config
-    # env.py will handle setting search_path and version_table_schema
-    if schema:
-        alembic_cfg.set_main_option("target_schema", schema)
-
-    # Run migrations
+    # Run migrations to head (latest version)
    command.upgrade(alembic_cfg, "head")

-    logger.info(f"Database migrations completed successfully for schema '{schema_name}'")
+    logger.info("Database migrations completed successfully")


-def run_migrations(
-    database_url: str,
-    script_location: str | None = None,
-    schema: str | None = None,
-) -> None:
+def run_migrations(database_url: str, script_location: Optional[str] = None) -> None:
    """
    Run database migrations to the latest version using programmatic Alembic configuration.

@@ -96,28 +66,19 @@ def run_migrations(
    - Other workers wait for the lock, then verify migrations are complete
    - If schema is already up-to-date, this is a fast no-op

-    Supports multi-tenant schema isolation: when a schema is specified, migrations
-    run in that schema instead of public. This allows tenant extensions to provision
-    new tenant schemas with their own isolated tables.
-
    Args:
        database_url: SQLAlchemy database URL (e.g., "postgresql://user:pass@host/db")
        script_location: Path to alembic migrations directory (e.g., "/path/to/alembic").
                        If None, defaults to hindsight-api/alembic directory.
-        schema: Target PostgreSQL schema name. If None, uses default (public).
-                When specified, creates the schema if needed and runs migrations there.

    Raises:
        RuntimeError: If migrations fail to complete
        FileNotFoundError: If script_location doesn't exist

    Example:
-        # Using default location and public schema
+        # Using default location (hindsight_api package)
        run_migrations("postgresql://user:pass@host/db")

-        # Run migrations for a specific tenant schema
-        run_migrations("postgresql://user:pass@host/db", schema="tenant_acme")
-
        # Using custom location (when importing from another project)
        run_migrations(
            "postgresql://user:pass@host/db",
@@ -136,28 +97,25 @@ def run_migrations(
        script_path = Path(script_location)
        if not script_path.exists():
            raise FileNotFoundError(
-                f"Alembic script location not found at {script_location}. Database migrations cannot be run."
+                f"Alembic script location not found at {script_location}. "
+                "Database migrations cannot be run."
            )

-        # Use schema-specific lock ID for multi-tenant isolation
-        lock_id = _get_schema_lock_id(schema) if schema else MIGRATION_LOCK_ID
-        schema_name = schema or "public"
-
        # Use PostgreSQL advisory lock to coordinate between distributed workers
        engine = create_engine(database_url)
        with engine.connect() as conn:
            # pg_advisory_lock blocks until the lock is acquired
            # The lock is automatically released when the connection closes
-            logger.debug(f"Acquiring migration advisory lock for schema '{schema_name}' (id={lock_id})...")
-            conn.execute(text(f"SELECT pg_advisory_lock({lock_id})"))
+            logger.debug(f"Acquiring migration advisory lock (id={MIGRATION_LOCK_ID})...")
+            conn.execute(text(f"SELECT pg_advisory_lock({MIGRATION_LOCK_ID})"))
            logger.debug("Migration advisory lock acquired")

            try:
                # Run migrations while holding the lock
-                _run_migrations_internal(database_url, script_location, schema=schema)
+                _run_migrations_internal(database_url, script_location)
            finally:
                # Explicitly release the lock (also released on connection close)
-                conn.execute(text(f"SELECT pg_advisory_unlock({lock_id})"))
+                conn.execute(text(f"SELECT pg_advisory_unlock({MIGRATION_LOCK_ID})"))
                logger.debug("Migration advisory lock released")

    except FileNotFoundError:
@@ -172,9 +130,7 @@ def run_migrations(
        raise RuntimeError("Database migration failed") from e


-def check_migration_status(
-    database_url: str | None = None, script_location: str | None = None
-) -> tuple[str | None, str | None]:
+def check_migration_status(database_url: Optional[str] = None, script_location: Optional[str] = None) -> tuple[str | None, str | None]:
    """
    Check current database schema version and latest available version.

@@ -195,9 +151,7 @@ def check_migration_status(
        if database_url is None:
            database_url = os.getenv("HINDSIGHT_API_DATABASE_URL")
        if not database_url:
-            logger.warning(
-                "Database URL not provided and HINDSIGHT_API_DATABASE_URL not set, cannot check migration status"
-            )
+            logger.warning("Database URL not provided and HINDSIGHT_API_DATABASE_URL not set, cannot check migration status")
            return None, None

        # Get current revision from database
@@ -1,62 +1,49 @@
 """
 SQLAlchemy models for the memory system.
 """
-
-from dataclasses import dataclass
 from datetime import datetime
-from uuid import UUID as PyUUID
+from typing import Optional
+from uuid import UUID as PyUUID, uuid4

-
-@dataclass
-class RequestContext:
-    """
-    Context for request authentication and authorization.
-
-    This dataclass carries authentication data from HTTP requests to the
-    memory engine operations. It can be extended to include additional
-    context like headers, tokens, user info, etc.
-    """
-
-    api_key: str | None = None
-
-
-from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
    CheckConstraint,
+    Column,
    Float,
    ForeignKey,
    ForeignKeyConstraint,
    Index,
    Integer,
+    PrimaryKeyConstraint,
    Text,
    func,
-)
-from sqlalchemy import (
    text as sql_text,
 )
 from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP, UUID
 from sqlalchemy.ext.asyncio import AsyncAttrs
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
+from pgvector.sqlalchemy import Vector


 class Base(AsyncAttrs, DeclarativeBase):
    """Base class for all models."""
-
    pass


 class Document(Base):
    """Source documents for memory units."""
-
    __tablename__ = "documents"

    id: Mapped[str] = mapped_column(Text, primary_key=True)
    bank_id: Mapped[str] = mapped_column(Text, primary_key=True)
-    original_text: Mapped[str | None] = mapped_column(Text)
-    content_hash: Mapped[str | None] = mapped_column(Text)
+    original_text: Mapped[Optional[str]] = mapped_column(Text)
+    content_hash: Mapped[Optional[str]] = mapped_column(Text)
    doc_metadata: Mapped[dict] = mapped_column("metadata", JSONB, server_default=sql_text("'{}'::jsonb"))
-    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
-    updated_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    created_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )

    # Relationships
    memory_units = relationship("MemoryUnit", back_populates="document", cascade="all, delete-orphan")
@@ -69,42 +56,45 @@ class Document(Base):

 class MemoryUnit(Base):
    """Individual sentence-level memories."""
-
    __tablename__ = "memory_units"

    id: Mapped[PyUUID] = mapped_column(
        UUID(as_uuid=True), primary_key=True, server_default=sql_text("gen_random_uuid()")
    )
    bank_id: Mapped[str] = mapped_column(Text, nullable=False)
-    document_id: Mapped[str | None] = mapped_column(Text)
+    document_id: Mapped[Optional[str]] = mapped_column(Text)
    text: Mapped[str] = mapped_column(Text, nullable=False)
    embedding = mapped_column(Vector(384))  # pgvector type
-    context: Mapped[str | None] = mapped_column(Text)
-    event_date: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), nullable=False
-    )  # Kept for backward compatibility
-    occurred_start: Mapped[datetime | None] = mapped_column(
-        TIMESTAMP(timezone=True)
-    )  # When fact occurred (range start)
-    occurred_end: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact occurred (range end)
-    mentioned_at: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact was mentioned
+    context: Mapped[Optional[str]] = mapped_column(Text)
+    event_date: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), nullable=False)  # Kept for backward compatibility
+    occurred_start: Mapped[Optional[datetime]] = mapped_column(TIMESTAMP(timezone=True))  # When fact occurred (range start)
+    occurred_end: Mapped[Optional[datetime]] = mapped_column(TIMESTAMP(timezone=True))    # When fact occurred (range end)
+    mentioned_at: Mapped[Optional[datetime]] = mapped_column(TIMESTAMP(timezone=True))    # When fact was mentioned
    fact_type: Mapped[str] = mapped_column(Text, nullable=False, server_default="world")
-    confidence_score: Mapped[float | None] = mapped_column(Float)
+    confidence_score: Mapped[Optional[float]] = mapped_column(Float)
    access_count: Mapped[int] = mapped_column(Integer, server_default="0")
-    unit_metadata: Mapped[dict] = mapped_column(
-        "metadata", JSONB, server_default=sql_text("'{}'::jsonb")
-    )  # User-defined metadata (str->str)
-    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
-    updated_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    unit_metadata: Mapped[dict] = mapped_column("metadata", JSONB, server_default=sql_text("'{}'::jsonb"))  # User-defined metadata (str->str)
+    created_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )

    # Relationships
    document = relationship("Document", back_populates="memory_units")
    unit_entities = relationship("UnitEntity", back_populates="memory_unit", cascade="all, delete-orphan")
    outgoing_links = relationship(
-        "MemoryLink", foreign_keys="MemoryLink.from_unit_id", back_populates="from_unit", cascade="all, delete-orphan"
+        "MemoryLink",
+        foreign_keys="MemoryLink.from_unit_id",
+        back_populates="from_unit",
+        cascade="all, delete-orphan"
    )
    incoming_links = relationship(
-        "MemoryLink", foreign_keys="MemoryLink.to_unit_id", back_populates="to_unit", cascade="all, delete-orphan"
+        "MemoryLink",
+        foreign_keys="MemoryLink.to_unit_id",
+        back_populates="to_unit",
+        cascade="all, delete-orphan"
    )

    __table_args__ = (
@@ -120,7 +110,7 @@ class MemoryUnit(Base):
            "(fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
            "(fact_type = 'observation') OR "
            "(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL)",
-            name="confidence_score_fact_type_check",
+            name="confidence_score_fact_type_check"
        ),
        Index("idx_memory_units_bank_id", "bank_id"),
        Index("idx_memory_units_document_id", "document_id"),
@@ -129,46 +119,39 @@ class MemoryUnit(Base):
        Index("idx_memory_units_access_count", "access_count", postgresql_ops={"access_count": "DESC"}),
        Index("idx_memory_units_fact_type", "fact_type"),
        Index("idx_memory_units_bank_fact_type", "bank_id", "fact_type"),
-        Index(
-            "idx_memory_units_bank_type_date",
-            "bank_id",
-            "fact_type",
-            "event_date",
-            postgresql_ops={"event_date": "DESC"},
-        ),
+        Index("idx_memory_units_bank_type_date", "bank_id", "fact_type", "event_date", postgresql_ops={"event_date": "DESC"}),
        Index(
            "idx_memory_units_opinion_confidence",
            "bank_id",
            "confidence_score",
            postgresql_where=sql_text("fact_type = 'opinion'"),
-            postgresql_ops={"confidence_score": "DESC"},
+            postgresql_ops={"confidence_score": "DESC"}
        ),
        Index(
            "idx_memory_units_opinion_date",
            "bank_id",
            "event_date",
            postgresql_where=sql_text("fact_type = 'opinion'"),
-            postgresql_ops={"event_date": "DESC"},
+            postgresql_ops={"event_date": "DESC"}
        ),
        Index(
            "idx_memory_units_observation_date",
            "bank_id",
            "event_date",
            postgresql_where=sql_text("fact_type = 'observation'"),
-            postgresql_ops={"event_date": "DESC"},
+            postgresql_ops={"event_date": "DESC"}
        ),
        Index(
            "idx_memory_units_embedding",
            "embedding",
            postgresql_using="hnsw",
-            postgresql_ops={"embedding": "vector_cosine_ops"},
+            postgresql_ops={"embedding": "vector_cosine_ops"}
        ),
    )


 class Entity(Base):
    """Resolved entities (people, organizations, locations, etc.)."""
-
    __tablename__ = "entities"

    id: Mapped[PyUUID] = mapped_column(
@@ -177,8 +160,12 @@ class Entity(Base):
    canonical_name: Mapped[str] = mapped_column(Text, nullable=False)
    bank_id: Mapped[str] = mapped_column(Text, nullable=False)
    entity_metadata: Mapped[dict] = mapped_column("metadata", JSONB, server_default=sql_text("'{}'::jsonb"))
-    first_seen: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
-    last_seen: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    first_seen: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )
+    last_seen: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )
    mention_count: Mapped[int] = mapped_column(Integer, server_default="1")

    # Relationships
@@ -188,13 +175,13 @@ class Entity(Base):
        "EntityCooccurrence",
        foreign_keys="EntityCooccurrence.entity_id_1",
        back_populates="entity_1",
-        cascade="all, delete-orphan",
+        cascade="all, delete-orphan"
    )
    cooccurrences_2 = relationship(
        "EntityCooccurrence",
        foreign_keys="EntityCooccurrence.entity_id_2",
        back_populates="entity_2",
-        cascade="all, delete-orphan",
+        cascade="all, delete-orphan"
    )

    __table_args__ = (
@@ -206,7 +193,6 @@ class Entity(Base):

 class UnitEntity(Base):
    """Association between memory units and entities."""
-
    __tablename__ = "unit_entities"

    unit_id: Mapped[PyUUID] = mapped_column(
@@ -228,7 +214,6 @@ class UnitEntity(Base):

 class EntityCooccurrence(Base):
    """Materialized cache of entity co-occurrences."""
-
    __tablename__ = "entity_cooccurrences"

    entity_id_1: Mapped[PyUUID] = mapped_column(
@@ -238,7 +223,9 @@ class EntityCooccurrence(Base):
        UUID(as_uuid=True), ForeignKey("entities.id", ondelete="CASCADE"), primary_key=True
    )
    cooccurrence_count: Mapped[int] = mapped_column(Integer, server_default="1")
-    last_cooccurred: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    last_cooccurred: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )

    # Relationships
    entity_1 = relationship("Entity", foreign_keys=[entity_id_1], back_populates="cooccurrences_1")
@@ -254,7 +241,6 @@ class EntityCooccurrence(Base):

 class MemoryLink(Base):
    """Links between memory units (temporal, semantic, entity)."""
-
    __tablename__ = "memory_links"

    from_unit_id: Mapped[PyUUID] = mapped_column(
@@ -264,11 +250,13 @@ class MemoryLink(Base):
        UUID(as_uuid=True), ForeignKey("memory_units.id", ondelete="CASCADE"), primary_key=True
    )
    link_type: Mapped[str] = mapped_column(Text, primary_key=True)
-    entity_id: Mapped[PyUUID | None] = mapped_column(
+    entity_id: Mapped[Optional[PyUUID]] = mapped_column(
        UUID(as_uuid=True), ForeignKey("entities.id", ondelete="CASCADE"), primary_key=True
    )
    weight: Mapped[float] = mapped_column(Float, nullable=False, server_default="1.0")
-    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    created_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )

    # Relationships
    from_unit = relationship("MemoryUnit", foreign_keys=[from_unit_id], back_populates="outgoing_links")
@@ -278,7 +266,7 @@ class MemoryLink(Base):
    __table_args__ = (
        CheckConstraint(
            "link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')",
-            name="memory_links_link_type_check",
+            name="memory_links_link_type_check"
        ),
        CheckConstraint("weight >= 0.0 AND weight <= 1.0", name="memory_links_weight_check"),
        Index("idx_memory_links_from", "from_unit_id"),
@@ -290,22 +278,31 @@ class MemoryLink(Base):
            "from_unit_id",
            "weight",
            postgresql_where=sql_text("weight >= 0.1"),
-            postgresql_ops={"weight": "DESC"},
+            postgresql_ops={"weight": "DESC"}
        ),
    )


 class Bank(Base):
    """Memory bank profiles with disposition traits and background."""
-
    __tablename__ = "banks"

    bank_id: Mapped[str] = mapped_column(Text, primary_key=True)
    disposition: Mapped[dict] = mapped_column(
-        JSONB, nullable=False, server_default=sql_text('\'{"skepticism": 3, "literalism": 3, "empathy": 3}\'::jsonb')
+        JSONB,
+        nullable=False,
+        server_default=sql_text(
+            '\'{"skepticism": 3, "literalism": 3, "empathy": 3}\'::jsonb'
+        )
    )
    background: Mapped[str] = mapped_column(Text, nullable=False, server_default="")
-    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
-    updated_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    created_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        TIMESTAMP(timezone=True), server_default=func.now()
+    )

-    __table_args__ = (Index("idx_banks_bank_id", "bank_id"),)
+    __table_args__ = (
+        Index("idx_banks_bank_id", "bank_id"),
+    )
@@ -1,134 +1,406 @@
 import asyncio
+import json
 import logging
+import os
+import platform
+import re
+import shutil
+import stat
+import subprocess
+from pathlib import Path
+from typing import Optional

-from pg0 import Pg0
+import httpx

 logger = logging.getLogger(__name__)

+# pg0 configuration
+BINARY_NAME = "pg0"
+DEFAULT_PORT = 5555
 DEFAULT_USERNAME = "hindsight"
 DEFAULT_PASSWORD = "hindsight"
 DEFAULT_DATABASE = "hindsight"


+def get_platform_binary_name() -> str:
+    """Get the appropriate binary name for the current platform.
+
+    Supported platforms:
+    - macOS ARM64 (darwin-aarch64)
+    - Linux x86_64 (gnu)
+    - Linux ARM64 (gnu)
+    - Windows x86_64
+    """
+    system = platform.system().lower()
+    machine = platform.machine().lower()
+
+    # Normalize architecture names
+    if machine in ("x86_64", "amd64"):
+        arch = "x86_64"
+    elif machine in ("arm64", "aarch64"):
+        arch = "aarch64"
+    else:
+        raise RuntimeError(
+            f"Embedded PostgreSQL is not supported on architecture: {machine}. "
+            f"Supported architectures: x86_64/amd64 (Linux, Windows), aarch64/arm64 (macOS, Linux)"
+        )
+
+    if system == "darwin" and arch == "aarch64":
+        return "pg0-darwin-aarch64"
+    elif system == "linux" and arch == "x86_64":
+        return "pg0-linux-x86_64-gnu"
+    elif system == "linux" and arch == "aarch64":
+        return "pg0-linux-aarch64-gnu"
+    elif system == "windows" and arch == "x86_64":
+        return "pg0-windows-x86_64.exe"
+    else:
+        raise RuntimeError(
+            f"Embedded PostgreSQL is not supported on {system}-{arch}. "
+            f"Supported platforms: darwin-aarch64 (macOS ARM), linux-x86_64-gnu, linux-aarch64-gnu, windows-x86_64"
+        )
+
+
+def get_download_url(
+    version: str = "latest",
+    repo: str = "vectorize-io/pg0",
+) -> str:
+    """Get the download URL for pg0 binary."""
+    binary_name = get_platform_binary_name()
+
+    if version == "latest":
+        return f"https://github.com/{repo}/releases/latest/download/{binary_name}"
+    else:
+        return f"https://github.com/{repo}/releases/download/{version}/{binary_name}"
+
+
+def _find_pg0_binary() -> Optional[Path]:
+    """Find pg0 binary in PATH or default install location."""
+    # First check PATH
+    pg0_in_path = shutil.which("pg0")
+    if pg0_in_path:
+        return Path(pg0_in_path)
+
+    # Fall back to default install location
+    default_path = Path.home() / ".hindsight" / "bin" / "pg0"
+    if default_path.exists() and os.access(default_path, os.X_OK):
+        return default_path
+
+    return None
+
+
 class EmbeddedPostgres:
-    """Manages an embedded PostgreSQL server instance using pg0-embedded."""
+    """
+    Manages an embedded PostgreSQL server instance using pg0.
+
+    This class handles:
+    - Finding or downloading the pg0 CLI
+    - Starting/stopping the PostgreSQL server
+    - Getting the connection URI
+
+    Example:
+        pg = EmbeddedPostgres()
+        await pg.ensure_installed()
+        await pg.start()
+        uri = await pg.get_uri()
+        # ... use uri with asyncpg ...
+        await pg.stop()
+    """

    def __init__(
        self,
-        port: int | None = None,
+        version: str = "latest",
+        port: int = DEFAULT_PORT,
        username: str = DEFAULT_USERNAME,
        password: str = DEFAULT_PASSWORD,
        database: str = DEFAULT_DATABASE,
        name: str = "hindsight",
-        **kwargs,
    ):
-        self.port = port  # None means pg0 will auto-assign
+        """
+        Initialize the embedded PostgreSQL manager.
+
+        Args:
+            version: Version of pg0 to download if not found. Defaults to "latest"
+            port: Port to listen on. Defaults to 5555
+            username: Username for the database. Defaults to "hindsight"
+            password: Password for the database. Defaults to "hindsight"
+            database: Database name to create. Defaults to "hindsight"
+            name: Instance name for pg0. Defaults to "hindsight"
+        """
+        self.version = version
+        self.port = port
        self.username = username
        self.password = password
        self.database = database
        self.name = name
-        self._pg0: Pg0 | None = None

-    def _get_pg0(self) -> Pg0:
-        if self._pg0 is None:
-            kwargs = {
-                "name": self.name,
-                "username": self.username,
-                "password": self.password,
-                "database": self.database,
-            }
-            # Only set port if explicitly specified
-            if self.port is not None:
-                kwargs["port"] = self.port
-            self._pg0 = Pg0(**kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
-        return self._pg0
+        # Will be set when binary is found/installed
+        self._binary_path: Optional[Path] = _find_pg0_binary()

-    async def start(self, max_retries: int = 5, retry_delay: float = 4.0) -> str:
-        """Start the PostgreSQL server with retry logic."""
-        port_info = f"port={self.port}" if self.port else "port=auto"
-        logger.info(f"Starting embedded PostgreSQL (name={self.name}, {port_info})...")
+    @property
+    def binary_path(self) -> Path:
+        """Get the path to the pg0 binary."""
+        if self._binary_path is None:
+            # Default install location
+            return Path.home() / ".hindsight" / "bin" / "pg0"
+        return self._binary_path

-        pg0 = self._get_pg0()
-        last_error = None
+    def is_installed(self) -> bool:
+        """Check if pg0 is available (in PATH or installed)."""
+        self._binary_path = _find_pg0_binary()
+        return self._binary_path is not None

-        for attempt in range(1, max_retries + 1):
-            try:
-                loop = asyncio.get_event_loop()
-                info = await loop.run_in_executor(None, pg0.start)
-                # Get URI from pg0 (includes auto-assigned port)
-                uri = info.uri
-                logger.info(f"PostgreSQL started: {uri}")
-                return uri
-            except Exception as e:
-                last_error = str(e)
-                if attempt < max_retries:
-                    delay = retry_delay * (2 ** (attempt - 1))
-                    logger.debug(f"pg0 start attempt {attempt}/{max_retries} failed: {last_error}")
-                    logger.debug(f"Retrying in {delay:.1f}s...")
-                    await asyncio.sleep(delay)
-                else:
-                    logger.debug(f"pg0 start attempt {attempt}/{max_retries} failed: {last_error}")
+    async def ensure_installed(self) -> None:
+        """
+        Ensure pg0 is available.
+
+        Checks PATH and default location. If not found, raises an error
+        instructing the user to install pg0 manually.
+        """
+        if self.is_installed():
+            logger.debug(f"pg0 found at {self._binary_path}")
+            return

        raise RuntimeError(
-            f"Failed to start embedded PostgreSQL after {max_retries} attempts. Last error: {last_error}"
+            "pg0 is not installed. Please install it manually:\n"
+            "  curl -fsSL https://github.com/vectorize-io/pg0/releases/latest/download/pg0-linux-amd64 -o ~/.local/bin/pg0 && chmod +x ~/.local/bin/pg0\n"
+            "Or visit: https://github.com/vectorize-io/pg0/releases"
        )

+    def _run_command(self, *args: str, capture_output: bool = True) -> subprocess.CompletedProcess:
+        """Run a pg0 command synchronously."""
+        cmd = [str(self.binary_path), *args]
+        return subprocess.run(cmd, capture_output=capture_output, text=True)
+
+    async def _run_command_async(self, *args: str, timeout: int = 120) -> tuple[int, str, str]:
+        """Run a pg0 command asynchronously."""
+        cmd = [str(self.binary_path), *args]
+
+        def run_sync():
+            try:
+                result = subprocess.run(
+                    cmd,
+                    stdin=subprocess.DEVNULL,
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.PIPE,
+                    text=True,
+                    timeout=timeout,
+                )
+                return result.returncode, result.stdout, result.stderr
+            except subprocess.TimeoutExpired:
+                return 1, "", "Command timed out"
+
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(None, run_sync)
+
+    def _extract_uri_from_output(self, output: str) -> Optional[str]:
+        """Extract the PostgreSQL URI from pg0 start output."""
+        match = re.search(r"Connection URI:\s*(postgresql://[^\s]+)", output)
+        if match:
+            return match.group(1)
+        return None
+
+    async def _get_version(self) -> str:
+        """Get the pg0 version."""
+        returncode, stdout, stderr = await self._run_command_async("--version", timeout=10)
+        if returncode == 0 and stdout:
+            return stdout.strip()
+        return "unknown"
+
+    async def start(self, max_retries: int = 3, retry_delay: float = 2.0) -> str:
+        """
+        Start the PostgreSQL server with retry logic.
+
+        Args:
+            max_retries: Maximum number of start attempts (default: 3)
+            retry_delay: Initial delay between retries in seconds (default: 2.0)
+
+        Returns:
+            The connection URI for the started server.
+
+        Raises:
+            RuntimeError: If the server fails to start after all retries.
+        """
+        if not self.is_installed():
+            raise RuntimeError("pg0 is not installed. Call ensure_installed() first.")
+
+        # Log pg0 version
+        version = await self._get_version()
+        logger.info(f"Starting embedded PostgreSQL with pg0 {version} (name: {self.name}, port: {self.port})...")
+
+        last_error = None
+        for attempt in range(1, max_retries + 1):
+            returncode, stdout, stderr = await self._run_command_async(
+                "start",
+                "--name", self.name,
+                "--port", str(self.port),
+                "--username", self.username,
+                "--password", self.password,
+                "--database", self.database,
+                timeout=300,
+            )
+
+            # Try to extract URI from output
+            uri = self._extract_uri_from_output(stdout)
+            if uri:
+                logger.info(f"PostgreSQL started on port {self.port}")
+                return uri
+
+            # Check if pg0 info can find the running instance
+            try:
+                uri = await self.get_uri()
+                logger.info(f"PostgreSQL started on port {self.port}")
+                return uri
+            except RuntimeError:
+                pass
+
+            # Start failed, log and retry
+            last_error = stderr or f"pg0 start returned exit code {returncode}"
+            if attempt < max_retries:
+                delay = retry_delay * (2 ** (attempt - 1))
+                logger.warning(f"pg0 start attempt {attempt}/{max_retries} failed: {last_error.strip()}")
+                logger.info(f"Retrying in {delay:.1f}s...")
+                await asyncio.sleep(delay)
+            else:
+                logger.warning(f"pg0 start attempt {attempt}/{max_retries} failed: {last_error.strip()}")
+
+        # All retries exhausted - use constructed URI as fallback
+        uri = f"postgresql://{self.username}:{self.password}@localhost:{self.port}/{self.database}"
+        logger.warning(f"All pg0 start attempts failed, using constructed URI: {uri}")
+        return uri
+
    async def stop(self) -> None:
        """Stop the PostgreSQL server."""
-        pg0 = self._get_pg0()
+        if not self.is_installed():
+            return
+
        logger.info(f"Stopping embedded PostgreSQL (name: {self.name})...")

-        try:
-            loop = asyncio.get_event_loop()
-            await loop.run_in_executor(None, pg0.stop)
-            logger.info("Embedded PostgreSQL stopped")
-        except Exception as e:
-            if "not running" in str(e).lower():
+        returncode, stdout, stderr = await self._run_command_async("stop", "--name", self.name)
+
+        if returncode != 0:
+            if "not running" in stderr.lower():
                return
-            raise RuntimeError(f"Failed to stop PostgreSQL: {e}")
+            raise RuntimeError(f"Failed to stop PostgreSQL: {stderr}")
+
+        logger.info("Embedded PostgreSQL stopped")
+
+    async def _get_info(self) -> dict:
+        """Get info from pg0 using the `info -o json` command."""
+        if not self.is_installed():
+            raise RuntimeError("pg0 is not installed.")
+
+        returncode, stdout, stderr = await self._run_command_async(
+            "info", "--name", self.name, "-o", "json"
+        )
+
+        if returncode != 0:
+            raise RuntimeError(f"Failed to get PostgreSQL info: {stderr}")
+
+        try:
+            return json.loads(stdout.strip())
+        except json.JSONDecodeError as e:
+            raise RuntimeError(f"Failed to parse pg0 info output: {e}")

    async def get_uri(self) -> str:
        """Get the connection URI for the PostgreSQL server."""
-        pg0 = self._get_pg0()
-        loop = asyncio.get_event_loop()
-        info = await loop.run_in_executor(None, pg0.info)
-        return info.uri
+        info = await self._get_info()
+        uri = info.get("uri")
+        if not uri:
+            raise RuntimeError("PostgreSQL server is not running or URI not available")
+        return uri
+
+    async def status(self) -> dict:
+        """Get the status of the PostgreSQL server."""
+        if not self.is_installed():
+            return {"installed": False, "running": False}
+
+        try:
+            info = await self._get_info()
+            return {
+                "installed": True,
+                "running": info.get("running", False),
+                "uri": info.get("uri"),
+            }
+        except RuntimeError:
+            return {"installed": True, "running": False}

    async def is_running(self) -> bool:
        """Check if the PostgreSQL server is currently running."""
+        if not self.is_installed():
+            return False
        try:
-            pg0 = self._get_pg0()
-            loop = asyncio.get_event_loop()
-            info = await loop.run_in_executor(None, pg0.info)
-            return info is not None and info.running
-        except Exception:
+            info = await self._get_info()
+            return info.get("running", False)
+        except RuntimeError:
            return False

    async def ensure_running(self) -> str:
-        """Ensure the PostgreSQL server is running, starting it if needed."""
+        """
+        Ensure the PostgreSQL server is running.
+
+        Installs if needed, starts if not running.
+
+        Returns:
+            The connection URI.
+        """
+        await self.ensure_installed()
+
        if await self.is_running():
            return await self.get_uri()
+
        return await self.start()

+    def uninstall(self) -> None:
+        """Remove the pg0 binary (only if we installed it)."""
+        default_path = Path.home() / ".hindsight" / "bin" / "pg0"
+        if default_path.exists():
+            default_path.unlink()
+            logger.info(f"Removed {default_path}")

-_default_instance: EmbeddedPostgres | None = None
+    def clear_data(self) -> None:
+        """Remove all PostgreSQL data (destructive!)."""
+        result = self._run_command("drop", "--name", self.name, "--force")
+        if result.returncode == 0:
+            logger.info(f"Dropped pg0 instance {self.name}")
+        else:
+            logger.warning(f"Failed to drop pg0 instance {self.name}: {result.stderr}")
+
+
+# Convenience functions
+
+_default_instance: Optional[EmbeddedPostgres] = None


 def get_embedded_postgres() -> EmbeddedPostgres:
    """Get or create the default EmbeddedPostgres instance."""
    global _default_instance
+
    if _default_instance is None:
        _default_instance = EmbeddedPostgres()
+
    return _default_instance


 async def start_embedded_postgres() -> str:
-    """Quick start function for embedded PostgreSQL."""
-    return await get_embedded_postgres().ensure_running()
+    """
+    Quick start function for embedded PostgreSQL.
+
+    Downloads, installs, and starts PostgreSQL in one call.
+
+    Returns:
+        Connection URI string
+
+    Example:
+        db_url = await start_embedded_postgres()
+        conn = await asyncpg.connect(db_url)
+    """
+    pg = get_embedded_postgres()
+    return await pg.ensure_running()


 async def stop_embedded_postgres() -> None:
    """Stop the default embedded PostgreSQL instance."""
    global _default_instance
+
    if _default_instance:
        await _default_instance.stop()
@@ -6,7 +6,6 @@ This module provides the ASGI app for uvicorn import string usage:

 For CLI usage, use the hindsight-api command instead.
 """
-
 import os
 import warnings

@@ -30,11 +29,15 @@ config.configure_logging()
 _memory = MemoryEngine()

 # Create unified app with both HTTP and optionally MCP
-app = create_app(memory=_memory, http_api_enabled=True, mcp_api_enabled=config.mcp_enabled, mcp_mount_path="/mcp")
+app = create_app(
+    memory=_memory,
+    http_api_enabled=True,
+    mcp_api_enabled=config.mcp_enabled,
+    mcp_mount_path="/mcp"
+)


 if __name__ == "__main__":
    # When run directly, delegate to the CLI
    from hindsight_api.main import main
-
    main()
@@ -4,8 +4,8 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-api"
-version = "0.1.12"
-description = "Hindsight: Agent Memory That Works Like Human Memory"
+version = "0.1.0"
+description = "Temporal + Semantic + Entity Memory System for AI agents using PostgreSQL"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
@@ -14,7 +14,7 @@ dependencies = [
    "openai>=1.0.0",
    "pydantic>=2.0.0",
    "rich>=13.0.0",
-    "sentence-transformers>=3.0.0,<3.3.0",
+    "sentence-transformers>=3.0.0",
    "langchain-text-splitters>=0.3.0",
    "fastapi[standard]>=0.120.3",
    "uvicorn>=0.38.0",
@@ -24,12 +24,11 @@ dependencies = [
    "pgvector>=0.4.1",
    "greenlet>=3.2.4",
    "psycopg2-binary>=2.9.11",
-    "transformers>=4.30.0,<4.46.0",
+    "transformers>=4.30.0",
    "torch>=2.0.0",
    "tiktoken>=0.12.0",
    "httpx>=0.27.0",
-    "fastmcp>=2.3.0",
-    "pg0-embedded>=0.11.0",
+    "fastmcp>=2.0.0",
    "python-dateutil>=2.8.0",
    "opentelemetry-api>=1.20.0",
    "opentelemetry-sdk>=1.20.0",
@@ -50,7 +49,6 @@ test = [

 [project.scripts]
 hindsight-api = "hindsight_api.main:main"
-hindsight-local-mcp = "hindsight_api.mcp_local:main"

 [tool.hatch.build.targets.wheel]
 packages = ["hindsight_api"]
@@ -91,59 +89,4 @@ dev = [
    "pytest-xdist>=3.8.0",
    "python-dotenv>=1.2.1",
    "filelock>=3.0.0",
-    "ruff>=0.8.0",
-    "ty>=0.0.1",
 ]
-
-[tool.ruff]
-line-length = 120
-target-version = "py311"
-exclude = [
-    "tests/",
-    "**/tests/",
-]
-
-[tool.ruff.lint]
-select = [
-    "E",      # pycodestyle errors
-    "W",      # pycodestyle warnings
-    "F",      # Pyflakes
-    "I",      # isort
-]
-ignore = [
-    "E501",   # line too long (handled by formatter)
-    "E402",   # module import not at top of file
-    "F401",   # unused import (too noisy during development)
-    "F841",   # unused variable (too noisy during development)
-    "F811",   # redefined while unused
-    "F821",   # undefined name (forward references in type hints)
-]
-
-[tool.ruff.format]
-quote-style = "double"
-indent-style = "space"
-
-[tool.ty]
-# Type checking configuration
-# ty is an extremely fast Python type checker from Astral (same team as ruff/uv)
-
-[tool.ty.environment]
-python-version = "3.11"
-
-[tool.ty.src]
-exclude = [
-    "tests/",
-    "hindsight_api/alembic/",
-]
-
-[tool.ty.rules]
-# Disable noisy rules while keeping important ones
-invalid-argument-type = "ignore"    # False positives with **kwargs patterns
-invalid-return-type = "ignore"      # Often intentional in async code
-invalid-parameter-default = "ignore" # Optional params with None default
-possibly-missing-attribute = "ignore" # Common with Optional types
-invalid-raise = "ignore"            # False positives with exception tracking
-call-non-callable = "ignore"        # False positives with Optional types
-invalid-key = "ignore"              # Pydantic ConfigDict not understood
-invalid-method-override = "ignore"  # Intentional signature differences
-unresolved-reference = "ignore"     # Forward references not always resolved
@@ -8,7 +8,7 @@ import os
 import filelock
 from pathlib import Path
 from dotenv import load_dotenv
-from hindsight_api import MemoryEngine, LLMConfig, LocalSTEmbeddings, RequestContext
+from hindsight_api import MemoryEngine, LLMConfig, LocalSTEmbeddings

 from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
 from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
@@ -99,12 +99,6 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
    return url


-@pytest.fixture(scope="function")
-def request_context():
-    """Provide a default RequestContext for tests."""
-    return RequestContext()
-
-
@pytest.fixture(scope="session")
 def llm_config():
    """
@@ -3,7 +3,7 @@ Tests for agent management API (profile, disposition, background).
 """
 import pytest
 import uuid
-from hindsight_api import MemoryEngine, RequestContext
+from hindsight_api import MemoryEngine
 from hindsight_api.api import CreateBankRequest, DispositionTraits
 from hindsight_api.engine.memory_engine import Budget

@@ -17,11 +17,11 @@ class TestAgentProfile:
    """Tests for agent profile management."""

    @pytest.mark.asyncio
-    async def test_get_agent_profile_creates_default(self, memory: MemoryEngine, request_context):
+    async def test_get_agent_profile_creates_default(self, memory: MemoryEngine):
        """Test that getting a profile for a new agent creates default disposition."""
        bank_id = unique_agent_id("test_profile_default")

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)

        assert profile is not None
        assert "disposition" in profile
@@ -35,11 +35,11 @@ class TestAgentProfile:
        assert profile["background"] == ""

    @pytest.mark.asyncio
-    async def test_update_agent_disposition(self, memory: MemoryEngine, request_context):
+    async def test_update_agent_disposition(self, memory: MemoryEngine):
        """Test updating agent disposition traits."""
        bank_id = unique_agent_id("test_profile_update")

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)
        assert profile["disposition"].skepticism == 3

        new_disposition = {
@@ -47,26 +47,26 @@ class TestAgentProfile:
            "literalism": 4,
            "empathy": 2,
        }
-        await memory.update_bank_disposition(bank_id, new_disposition, request_context=request_context)
+        await memory.update_bank_disposition(bank_id, new_disposition)

-        updated_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        updated_profile = await memory.get_bank_profile(bank_id)
        disposition = updated_profile["disposition"]
        assert disposition.skepticism == new_disposition["skepticism"]
        assert disposition.literalism == new_disposition["literalism"]
        assert disposition.empathy == new_disposition["empathy"]

    @pytest.mark.asyncio
-    async def test_list_agents(self, memory: MemoryEngine, request_context):
+    async def test_list_agents(self, memory: MemoryEngine):
        """Test listing all agents."""
        agent_id_1 = unique_agent_id("test_list")
        agent_id_2 = unique_agent_id("test_list")
        agent_id_3 = unique_agent_id("test_list")

-        await memory.get_bank_profile(agent_id_1, request_context=request_context)
-        await memory.get_bank_profile(agent_id_2, request_context=request_context)
-        await memory.get_bank_profile(agent_id_3, request_context=request_context)
+        await memory.get_bank_profile(agent_id_1)
+        await memory.get_bank_profile(agent_id_2)
+        await memory.get_bank_profile(agent_id_3)

-        agents = await memory.list_banks(request_context=request_context)
+        agents = await memory.list_banks()

        agent_ids = [a["bank_id"] for a in agents]
        assert agent_id_1 in agent_ids
@@ -85,50 +85,46 @@ class TestAgentBackground:
    """Tests for agent background management."""

    @pytest.mark.asyncio
-    async def test_merge_agent_background(self, memory: MemoryEngine, request_context):
+    async def test_merge_agent_background(self, memory: MemoryEngine):
        """Test merging agent background information."""
        bank_id = unique_agent_id("test_profile_merge")

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)
        assert profile["background"] == ""

        result1 = await memory.merge_bank_background(
            bank_id,
            "I was born in Texas",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Texas" in result1["background"]

        result2 = await memory.merge_bank_background(
            bank_id,
            "I have 10 years of startup experience",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Texas" in result2["background"] or "startup" in result2["background"]

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)
        assert final_profile["background"] != ""

    @pytest.mark.asyncio
-    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine, request_context):
+    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine):
        """Test that merging background handles conflicts (new overwrites old)."""
        bank_id = unique_agent_id("test_profile_conflict")

        result1 = await memory.merge_bank_background(
            bank_id,
            "I was born in Colorado",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Colorado" in result1["background"]

        result2 = await memory.merge_bank_background(
            bank_id,
            "You were born in Texas",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Texas" in result2["background"]

@@ -137,7 +133,7 @@ class TestAgentEndpoint:
    """Tests for agent PUT endpoint logic."""

    @pytest.mark.asyncio
-    async def test_put_agent_create(self, memory: MemoryEngine, request_context):
+    async def test_put_agent_create(self, memory: MemoryEngine):
        """Test creating an agent via PUT endpoint."""
        bank_id = unique_agent_id("test_put_create")

@@ -150,13 +146,12 @@ class TestAgentEndpoint:
            background="I am a creative software engineer"
        )

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)

        if request.disposition is not None:
            await memory.update_bank_disposition(
                bank_id,
-                request.disposition.model_dump(),
-                request_context=request_context,
+                request.disposition.model_dump()
            )

        if request.background is not None:
@@ -173,14 +168,14 @@ class TestAgentEndpoint:
                    request.background
                )

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)

        assert final_profile["disposition"].skepticism == 4
        assert final_profile["disposition"].literalism == 5
        assert final_profile["background"] == "I am a creative software engineer"

    @pytest.mark.asyncio
-    async def test_put_agent_partial_update(self, memory: MemoryEngine, request_context):
+    async def test_put_agent_partial_update(self, memory: MemoryEngine):
        """Test updating only background."""
        bank_id = unique_agent_id("test_put_partial")

@@ -188,7 +183,7 @@ class TestAgentEndpoint:
            background="I am a data scientist"
        )

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)

        if request.background is not None:
            pool = await memory._get_pool()
@@ -204,7 +199,7 @@ class TestAgentEndpoint:
                    request.background
                )

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)

        assert final_profile["disposition"].skepticism == 3  # Default
        assert final_profile["background"] == "I am a data scientist"
@@ -214,7 +209,7 @@ class TestAgentDispositionIntegration:
    """Tests for disposition integration with other features."""

    @pytest.mark.asyncio
-    async def test_think_uses_disposition(self, memory: MemoryEngine, request_context):
+    async def test_think_uses_disposition(self, memory: MemoryEngine):
        """Test that THINK operation uses agent disposition."""
        bank_id = unique_agent_id("test_think")

@@ -223,13 +218,12 @@ class TestAgentDispositionIntegration:
            "literalism": 4,  # High literalism
            "empathy": 2,     # Low empathy
        }
-        await memory.update_bank_disposition(bank_id, disposition, request_context=request_context)
+        await memory.update_bank_disposition(bank_id, disposition)

        await memory.merge_bank_background(
            bank_id,
            "I am a creative artist who values innovation over tradition",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )

        await memory.retain_batch_async(
@@ -238,14 +232,13 @@ class TestAgentDispositionIntegration:
                {"content": "Traditional painting techniques have been used for centuries"},
                {"content": "Modern digital art is changing the art world"}
            ],
-            request_context=request_context,
+            document_id="art_facts"
        )

        result = await memory.reflect_async(
            bank_id=bank_id,
            query="What do you think about traditional vs modern art?",
-            budget=Budget.LOW,
-            request_context=request_context,
+            budget=Budget.LOW
        )

        assert result.text is not None
@@ -6,7 +6,7 @@ import os


@pytest.mark.asyncio
-async def test_large_batch_auto_chunks(memory, request_context):
+async def test_large_batch_auto_chunks(memory):
    bank_id = "test_chunking_agent"
    # Create a large batch that should trigger chunking
    # Each item is ~2000 chars, so 30 items = 60k chars (exceeds 50k threshold)
@@ -24,8 +24,7 @@ async def test_large_batch_auto_chunks(memory, request_context):
    # Ingest the large batch (should auto-chunk)
    result = await memory.retain_batch_async(
        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
+        contents=contents
    )

    # Verify we got results back
@@ -34,7 +33,7 @@ async def test_large_batch_auto_chunks(memory, request_context):


@pytest.mark.asyncio
-async def test_small_batch_no_chunking(memory, request_context):
+async def test_small_batch_no_chunking(memory):
    bank_id = "test_no_chunking_agent"

    # Create a small batch that should NOT trigger chunking
@@ -51,8 +50,7 @@ async def test_small_batch_no_chunking(memory, request_context):
    # Ingest the small batch (should NOT auto-chunk)
    result = await memory.retain_batch_async(
        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
+        contents=contents
    )

    # Verify we got results back
@@ -1,334 +0,0 @@
-"""
-Tests for combined scoring functionality.
-
-Verifies that:
-1. RRF scores are properly normalized to [0, 1] range
-2. Combined scoring formula is applied correctly
-3. Tracer captures normalized values (not raw values)
-"""
-import pytest
-from datetime import datetime, timezone
-from hindsight_api.engine.search.types import RetrievalResult, MergedCandidate, ScoredResult
-from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext
-
-
-class TestRRFNormalization:
-    """Test that RRF scores are properly normalized."""
-
-    def test_rrf_normalized_range(self):
-        """RRF normalized values should be in [0, 1] range, not raw [0.04, 0.06]."""
-        # Simulate RRF scores like what we get from actual retrieval
-        raw_rrf_scores = [0.0607, 0.0550, 0.0480, 0.0390]
-
-        max_rrf = max(raw_rrf_scores)
-        min_rrf = min(raw_rrf_scores)
-        rrf_range = max_rrf - min_rrf
-
-        normalized = []
-        for score in raw_rrf_scores:
-            if rrf_range > 0:
-                norm = (score - min_rrf) / rrf_range
-            else:
-                norm = 0.5
-            normalized.append(norm)
-
-        # Verify normalized values are in [0, 1]
-        for i, norm in enumerate(normalized):
-            assert 0.0 <= norm <= 1.0, f"Normalized RRF {norm} not in [0, 1] for raw {raw_rrf_scores[i]}"
-
-        # Highest raw should be 1.0
-        assert normalized[0] == 1.0, f"Highest RRF should normalize to 1.0, got {normalized[0]}"
-
-        # Lowest raw should be 0.0
-        assert normalized[-1] == 0.0, f"Lowest RRF should normalize to 0.0, got {normalized[-1]}"
-
-    def test_rrf_all_same_scores(self):
-        """When all RRF scores are the same, normalized should be 0.5 (neutral)."""
-        raw_rrf_scores = [0.0500, 0.0500, 0.0500]
-
-        max_rrf = max(raw_rrf_scores)
-        min_rrf = min(raw_rrf_scores)
-        rrf_range = max_rrf - min_rrf
-
-        normalized = []
-        for score in raw_rrf_scores:
-            if rrf_range > 0:
-                norm = (score - min_rrf) / rrf_range
-            else:
-                norm = 0.5  # Neutral value when all same
-            normalized.append(norm)
-
-        # All should be 0.5 when scores are identical
-        for norm in normalized:
-            assert norm == 0.5, f"Expected 0.5 for identical scores, got {norm}"
-
-
-class TestCombinedScoringFormula:
-    """Test that the combined scoring formula is applied correctly."""
-
-    def test_combined_score_calculation(self):
-        """Verify the weighted combination: 0.6*CE + 0.2*RRF + 0.1*temporal + 0.1*recency."""
-        # Test case 1: All components at 1.0
-        ce_norm = 1.0
-        rrf_norm = 1.0
-        temporal = 1.0
-        recency = 1.0
-
-        expected = 0.6 * ce_norm + 0.2 * rrf_norm + 0.1 * temporal + 0.1 * recency
-        assert expected == 1.0, f"All 1.0 should give 1.0, got {expected}"
-
-        # Test case 2: All components at 0.0
-        ce_norm = 0.0
-        rrf_norm = 0.0
-        temporal = 0.0
-        recency = 0.0
-
-        expected = 0.6 * ce_norm + 0.2 * rrf_norm + 0.1 * temporal + 0.1 * recency
-        assert expected == 0.0, f"All 0.0 should give 0.0, got {expected}"
-
-        # Test case 3: High CE, low RRF (cross-encoder finds something retrieval missed)
-        ce_norm = 0.999
-        rrf_norm = 0.0  # Lowest in set
-        temporal = 0.5
-        recency = 0.5
-
-        expected = 0.6 * ce_norm + 0.2 * rrf_norm + 0.1 * temporal + 0.1 * recency
-        # 0.5994 + 0.0 + 0.05 + 0.05 = 0.6994
-        assert abs(expected - 0.6994) < 0.001, f"Expected ~0.6994, got {expected}"
-
-        # Test case 4: Medium CE, high RRF (retrieval consensus)
-        ce_norm = 0.8
-        rrf_norm = 1.0  # Highest in set
-        temporal = 0.5
-        recency = 0.5
-
-        expected = 0.6 * ce_norm + 0.2 * rrf_norm + 0.1 * temporal + 0.1 * recency
-        # 0.48 + 0.2 + 0.05 + 0.05 = 0.78
-        assert abs(expected - 0.78) < 0.001, f"Expected ~0.78, got {expected}"
-
-    def test_rrf_contribution_is_significant(self):
-        """Verify RRF actually contributes to the final score (not negligible)."""
-        # Same CE, different RRF
-        ce_norm = 0.8
-        temporal = 0.5
-        recency = 0.5
-
-        # Low RRF
-        score_low_rrf = 0.6 * ce_norm + 0.2 * 0.0 + 0.1 * temporal + 0.1 * recency
-
-        # High RRF
-        score_high_rrf = 0.6 * ce_norm + 0.2 * 1.0 + 0.1 * temporal + 0.1 * recency
-
-        # Difference should be 0.2 (20% contribution)
-        diff = score_high_rrf - score_low_rrf
-        assert abs(diff - 0.2) < 0.001, f"RRF should contribute 0.2 difference, got {diff}"
-
-
-@pytest.mark.asyncio
-async def test_trace_has_normalized_rrf(memory, request_context):
-    """Integration test: verify trace contains normalized RRF values, not raw."""
-    bank_id = f"test_scoring_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store multiple memories to ensure different RRF scores
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Python is a programming language created by Guido van Rossum",
-            context="tech facts",
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="JavaScript was created by Brendan Eich at Netscape",
-            context="tech facts",
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="The Eiffel Tower is located in Paris, France",
-            context="geography facts",
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Mount Everest is the tallest mountain on Earth",
-            context="geography facts",
-            request_context=request_context,
-        )
-
-        # Search with tracing
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="programming languages",
-            fact_type=["world"],
-            budget=Budget.LOW,
-            max_tokens=1024,
-            enable_trace=True,
-            request_context=request_context,
-        )
-
-        assert result.trace is not None, "Trace should be present"
-        trace = result.trace
-
-        # Check reranked results have proper score_components
-        assert "reranked" in trace, "Trace should have reranked results"
-        assert len(trace["reranked"]) > 0, "Should have reranked results"
-
-        has_valid_rrf = False
-        has_valid_temporal = False
-        has_valid_recency = False
-
-        for r in trace["reranked"]:
-            sc = r.get("score_components", {})
-
-            # Check RRF normalized is present and in valid range
-            if "rrf_normalized" in sc:
-                rrf_norm = sc["rrf_normalized"]
-                assert 0.0 <= rrf_norm <= 1.0, f"rrf_normalized {rrf_norm} should be in [0, 1]"
-                # Should NOT be raw RRF score (which would be ~0.04-0.06)
-                # A normalized value of exactly 0.0 or 1.0 is valid (min/max of set)
-                # But raw scores like 0.0607 should never appear as normalized
-                if rrf_norm > 0.1:  # Any value > 0.1 is likely properly normalized
-                    has_valid_rrf = True
-
-            # Check temporal is present and in valid range
-            if "temporal" in sc:
-                temporal = sc["temporal"]
-                assert 0.0 <= temporal <= 1.0, f"temporal {temporal} should be in [0, 1]"
-                has_valid_temporal = True
-
-            # Check recency is present and in valid range
-            if "recency" in sc:
-                recency = sc["recency"]
-                assert 0.0 <= recency <= 1.0, f"recency {recency} should be in [0, 1]"
-                has_valid_recency = True
-
-        # At least some results should have these components
-        # (might not have rrf > 0.1 if all scores are same, which is fine)
-        assert has_valid_temporal, "Should have temporal scores in trace"
-        assert has_valid_recency, "Should have recency scores in trace"
-
-        print("\n✓ Combined scoring trace test passed!")
-        print(f"  - Reranked results: {len(trace['reranked'])}")
-        if trace["reranked"]:
-            sc = trace["reranked"][0].get("score_components", {})
-            print(f"  - First result score components: {sc}")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_rrf_normalized_not_raw_in_trace(memory, request_context):
-    """Verify that raw RRF scores (0.04-0.06 range) don't appear as normalized values."""
-    bank_id = f"test_rrf_raw_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store enough memories to get varied RRF scores
-        for i in range(5):
-            await memory.retain_async(
-                bank_id=bank_id,
-                content=f"Test fact number {i} about various topics",
-                context="test context",
-                request_context=request_context,
-            )
-
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="test fact",
-            fact_type=["world"],
-            budget=Budget.LOW,
-            max_tokens=512,
-            enable_trace=True,
-            request_context=request_context,
-        )
-
-        trace = result.trace
-        assert trace is not None
-
-        # Check that rrf_normalized values are NOT in the raw range
-        raw_rrf_range = (0.01, 0.08)  # Raw RRF scores are typically in this range
-
-        for r in trace.get("reranked", []):
-            sc = r.get("score_components", {})
-
-            if "rrf_normalized" in sc and "rrf_score" in sc:
-                rrf_norm = sc["rrf_normalized"]
-                rrf_raw = sc["rrf_score"]
-
-                # Raw should be in the typical range
-                assert raw_rrf_range[0] <= rrf_raw <= raw_rrf_range[1], \
-                    f"Raw RRF {rrf_raw} should be in typical range {raw_rrf_range}"
-
-                # Normalized should either be:
-                # - 0.0 (min in set)
-                # - 1.0 (max in set)
-                # - 0.5 (all same)
-                # - Something in between (0.0 to 1.0)
-                # But NOT the same as raw (which would indicate no normalization)
-                if len(trace["reranked"]) > 1:
-                    # If we have multiple results, normalized should differ from raw
-                    # (unless by coincidence, which is very unlikely)
-                    assert rrf_norm != rrf_raw, \
-                        f"Normalized RRF ({rrf_norm}) should differ from raw ({rrf_raw})"
-
-        print("\n✓ RRF raw vs normalized test passed!")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_combined_score_matches_components(memory, request_context):
-    """Verify the final score actually equals the weighted sum of components."""
-    bank_id = f"test_combined_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="The quick brown fox jumps over the lazy dog",
-            context="test",
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="A quick test of the emergency broadcast system",
-            context="test",
-            request_context=request_context,
-        )
-
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="quick test",
-            fact_type=["world"],
-            budget=Budget.LOW,
-            max_tokens=512,
-            enable_trace=True,
-            request_context=request_context,
-        )
-
-        trace = result.trace
-        assert trace is not None
-
-        for r in trace.get("reranked", []):
-            sc = r.get("score_components", {})
-            final_score = r.get("rerank_score", 0)
-
-            # Get components (use defaults if missing)
-            ce = sc.get("cross_encoder_score_normalized", 0)
-            rrf = sc.get("rrf_normalized", 0.5)
-            tmp = sc.get("temporal", 0.5)
-            rec = sc.get("recency", 0.5)
-
-            # Calculate expected score
-            expected = 0.6 * ce + 0.2 * rrf + 0.1 * tmp + 0.1 * rec
-
-            # Allow small floating point difference
-            assert abs(final_score - expected) < 0.01, \
-                f"Final score {final_score} doesn't match expected {expected} from components"
-
-        print("\n✓ Combined score verification test passed!")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Nicolò Boschi	d8f2e3a6db	fix	2025-12-10 16:00:31 +01:00
Nicolò Boschi	ae5da7bcaa	fix: make sure openai provider works	2025-12-10 15:52:21 +01:00
Nicolò Boschi	30eed1fbed	fix: make sure openai provider works	2025-12-10 15:52:17 +01:00