chore: run generate scripts after dead code removal

refactor: remove dead code and clarify observations vs mental models
- Delete engine/mental_models/ module (stale Pydantic models with wrong schema, describing an old design where mental models were directives; had no importers outside itself) - Remove unused imports in api/http.py (acquire_with_retry, Observation) - Remove unused Pydantic models in api/http.py (BanksResponse, ObservationEvidenceResponse) - Add clarifying NOTE to consolidation/consolidator.py distinguishing observations (auto-generated bottom-up) from mental models (user-defined pinned reflections refreshed via reflect)
2026-03-06 14:25:16 +01:00 · 2026-03-06 14:00:26 +01:00
652 changed files with 3488 additions and 21039 deletions
@@ -2,7 +2,7 @@
 # Copy this file to .env and fill in your values

 # LLM Configuration (Required)
-# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai, minimax
+# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai
 HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
 HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
@@ -20,11 +20,6 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 # HINDSIGHT_API_LLM_VERTEXAI_REGION=us-central1
 # HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/path/to/service-account-key.json  # Optional, uses ADC if not set

-# Example: MiniMax configuration (204K context window)
-# HINDSIGHT_API_LLM_PROVIDER=minimax
-# HINDSIGHT_API_LLM_API_KEY=your-minimax-api-key
-# HINDSIGHT_API_LLM_MODEL=MiniMax-M2.5
-
 # Example: LM Studio local configuration (Qwen 2.5 32B recommended)
 # HINDSIGHT_API_LLM_PROVIDER=lmstudio
 # HINDSIGHT_API_LLM_API_KEY=lmstudio
@@ -1,6 +0,0 @@
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"
@@ -21,20 +21,20 @@ jobs:
  build:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v6
-      - uses: actions/setup-node@v6
+      - 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@v7
+      - 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
        env:
          UMAMI_URL: https://analytics.hindsight.vectorize.io
          UMAMI_WEBSITE_ID: ${{ secrets.UMAMI_WEBSITE_ID }}
-      - uses: actions/upload-pages-artifact@v4
+      - uses: actions/upload-pages-artifact@v3
        with:
          path: hindsight-docs/build
  deploy:
@@ -13,15 +13,15 @@ jobs:
      id-token: write

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -30,20 +30,12 @@ jobs:
      working-directory: ./hindsight-clients/python
      run: uv build --out-dir dist

-    - name: Build hindsight-api-slim
-      working-directory: ./hindsight-api-slim
-      run: uv build --out-dir dist
-
    - name: Build hindsight-api
      working-directory: ./hindsight-api
      run: uv build --out-dir dist

    - name: Build hindsight-all
-      working-directory: ./hindsight-all
-      run: uv build --out-dir dist
-
-    - name: Build hindsight-all-slim
-      working-directory: ./hindsight-all-slim
+      working-directory: ./hindsight
      run: uv build --out-dir dist

    - name: Build hindsight-litellm
@@ -62,19 +54,13 @@ jobs:
      working-directory: ./hindsight-integrations/pydantic-ai
      run: uv build --out-dir dist

-    # Publish in order (client and api-slim first, then api/all wrappers which depend on them)
+    # 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
      with:
        packages-dir: ./hindsight-clients/python/dist
        skip-existing: true

-    - name: Publish hindsight-api-slim to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-api-slim/dist
-        skip-existing: true
-
    - name: Publish hindsight-api to PyPI
      uses: pypa/gh-action-pypi-publish@release/v1
      with:
@@ -84,13 +70,7 @@ jobs:
    - name: Publish hindsight-all to PyPI
      uses: pypa/gh-action-pypi-publish@release/v1
      with:
-        packages-dir: ./hindsight-all/dist
-        skip-existing: true
-
-    - name: Publish hindsight-all-slim to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-all-slim/dist
+        packages-dir: ./hindsight/dist
        skip-existing: true

    - name: Publish hindsight-litellm to PyPI
@@ -119,15 +99,13 @@ jobs:

    # Upload artifacts for GitHub release
    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: python-packages
        path: |
          hindsight-clients/python/dist/*
-          hindsight-api-slim/dist/*
          hindsight-api/dist/*
-          hindsight-all/dist/*
-          hindsight-all-slim/dist/*
+          hindsight/dist/*
          hindsight-integrations/litellm/dist/*
          hindsight-embed/dist/*
          hindsight-integrations/crewai/dist/*
@@ -139,10 +117,10 @@ jobs:
    environment: npm

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'
        registry-url: 'https://registry.npmjs.org'
@@ -177,7 +155,7 @@ jobs:
      run: npm pack

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: typescript-client
        path: hindsight-clients/typescript/*.tgz
@@ -188,10 +166,10 @@ jobs:
    environment: npm

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'
        registry-url: 'https://registry.npmjs.org'
@@ -226,7 +204,7 @@ jobs:
      run: npm pack

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: openclaw-integration
        path: hindsight-integrations/openclaw/*.tgz
@@ -237,10 +215,10 @@ jobs:
    environment: npm

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'
        registry-url: 'https://registry.npmjs.org'
@@ -275,7 +253,7 @@ jobs:
      run: npm pack

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: ai-sdk-integration
        path: hindsight-integrations/ai-sdk/*.tgz
@@ -286,10 +264,10 @@ jobs:
    environment: npm

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'
        registry-url: 'https://registry.npmjs.org'
@@ -324,7 +302,7 @@ jobs:
      run: npm pack

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: chat-integration
        path: hindsight-integrations/chat/*.tgz
@@ -335,10 +313,10 @@ jobs:
    environment: npm

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'
        registry-url: 'https://registry.npmjs.org'
@@ -386,7 +364,7 @@ jobs:
      run: npm pack

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: control-plane
        path: hindsight-control-plane/*.tgz
@@ -415,7 +393,7 @@ jobs:
            asset_name: hindsight-linux-arm64

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install Rust
      uses: dtolnay/rust-toolchain@stable
@@ -433,7 +411,7 @@ jobs:
        chmod +x artifacts/${{ matrix.asset_name }}

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: rust-cli-${{ matrix.asset_name }}
        path: artifacts/${{ matrix.asset_name }}
@@ -474,7 +452,7 @@ jobs:
              PRELOAD_ML_MODELS=false

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
@@ -488,13 +466,13 @@ jobs:
        swap-storage: true

    - name: Set up QEMU
-      uses: docker/setup-qemu-action@v4
+      uses: docker/setup-qemu-action@v3

    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v4
+      uses: docker/setup-buildx-action@v3

    - name: Log in to GitHub Container Registry
-      uses: docker/login-action@v4
+      uses: docker/login-action@v3
      with:
        registry: ghcr.io
        username: ${{ github.actor }}
@@ -506,7 +484,7 @@ jobs:

    - name: Extract metadata for release tags
      id: meta
-      uses: docker/metadata-action@v6
+      uses: docker/metadata-action@v5
      with:
        images: ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}
        flavor: |
@@ -522,7 +500,7 @@ jobs:
    # # 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@v7
+    #   uses: docker/build-push-action@v6
    #   with:
    #     context: .
    #     file: docker/standalone/Dockerfile
@@ -541,7 +519,7 @@ jobs:

    # Build multi-platform and push to release tags
    - name: Build and push release images
-      uses: docker/build-push-action@v7
+      uses: docker/build-push-action@v6
      with:
        context: .
        file: docker/standalone/Dockerfile
@@ -559,7 +537,7 @@ jobs:
      packages: write

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install Helm
      uses: azure/setup-helm@v4
@@ -579,7 +557,7 @@ jobs:
      run: helm push helm-packages/*.tgz oci://ghcr.io/${{ github.repository_owner }}/charts

    - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: helm-chart
        path: helm-packages/*.tgz
@@ -592,68 +570,68 @@ jobs:
      contents: write

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Extract version from tag
      id: get_version
      run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT

    - name: Download Python packages
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: python-packages
        path: ./artifacts/python-packages

    - name: Download TypeScript client
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: typescript-client
        path: ./artifacts/typescript-client

    - name: Download OpenClaw Integration
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: openclaw-integration
        path: ./artifacts/openclaw-integration

    - name: Download AI SDK Integration
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: ai-sdk-integration
        path: ./artifacts/ai-sdk-integration

    - name: Download Chat Integration
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: chat-integration
        path: ./artifacts/chat-integration

    - name: Download Control Plane
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: control-plane
        path: ./artifacts/control-plane

    - name: Download Rust CLI (Linux)
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: rust-cli-hindsight-linux-amd64
        path: ./artifacts/rust-cli-linux

    - name: Download Rust CLI (macOS Intel)
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: rust-cli-hindsight-darwin-amd64
        path: ./artifacts/rust-cli-darwin-amd64

    - name: Download Rust CLI (macOS ARM)
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: rust-cli-hindsight-darwin-arm64
        path: ./artifacts/rust-cli-darwin-arm64

    - name: Download Helm chart
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
      with:
        name: helm-chart
        path: ./artifacts/helm-chart
@@ -663,10 +641,8 @@ jobs:
        mkdir -p release-assets
        # Python packages
        cp artifacts/python-packages/hindsight-clients/python/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-api-slim/dist/* release-assets/ || true
        cp artifacts/python-packages/hindsight-api/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-all/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-all-slim/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-integrations/pydantic-ai/dist/* release-assets/ || true
        cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
@@ -16,30 +16,30 @@ jobs:
        python-version: ['3.11', '3.12', '3.13', '3.14']

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true

    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version: ${{ matrix.python-version }}

    - name: Build hindsight-api
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

  build-typescript-client:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'
        cache: 'npm'
@@ -55,10 +55,10 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'

@@ -78,10 +78,10 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'

@@ -101,10 +101,10 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'

@@ -124,10 +124,10 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'
        cache: 'npm'
@@ -176,10 +176,10 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'
        cache: 'npm'
@@ -202,7 +202,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -214,7 +214,7 @@ jobs:
      uses: dtolnay/rust-toolchain@stable

    - name: Cache cargo
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: |
          ~/.cargo/registry
@@ -231,41 +231,41 @@ jobs:
      run: cargo build --release

    - name: Upload CLI artifact
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
      with:
        name: hindsight-cli
        path: hindsight-cli/target/release/hindsight
        retention-days: 1

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

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

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -316,7 +316,7 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install Helm
      uses: azure/setup-helm@v4
@@ -358,7 +358,7 @@ jobs:
              PRELOAD_ML_MODELS=false

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
@@ -372,10 +372,10 @@ jobs:
        swap-storage: true

    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v4
+      uses: docker/setup-buildx-action@v3

    - name: Build ${{ matrix.name }} image (${{ matrix.variant }})
-      uses: docker/build-push-action@v7
+      uses: docker/build-push-action@v6
      with:
        context: .
        file: docker/standalone/Dockerfile
@@ -424,7 +424,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -433,34 +433,34 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

    - name: Install dependencies
-      working-directory: ./hindsight-api-slim
-      run: uv sync --frozen --all-extras --index-strategy unsafe-best-match
+      working-directory: ./hindsight-api
+      run: uv sync --frozen --extra test --no-install-project --index-strategy unsafe-best-match

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -472,7 +472,7 @@ jobs:
        "

    - name: Run tests
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv run pytest tests -v

  test-python-client:
@@ -487,7 +487,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -496,18 +496,18 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

    - name: Build Python client
@@ -519,19 +519,19 @@ jobs:
      run: uv sync --frozen --extra test --index-strategy unsafe-best-match

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

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -590,7 +590,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -599,28 +599,28 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

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

    - name: Install TypeScript client dependencies
      working-directory: ./hindsight-clients/typescript
@@ -631,15 +631,15 @@ jobs:
      run: npm run build

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -690,7 +690,7 @@ jobs:
    runs-on: ubuntu-24.04-arm

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install Rust
      uses: dtolnay/rust-toolchain@stable
@@ -698,7 +698,7 @@ jobs:
        targets: aarch64-unknown-linux-gnu

    - name: Cache cargo
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: |
          ~/.cargo/registry
@@ -722,7 +722,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -731,13 +731,13 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -745,7 +745,7 @@ jobs:
      uses: dtolnay/rust-toolchain@stable

    - name: Cache cargo
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: |
          ~/.cargo/registry
@@ -754,23 +754,23 @@ jobs:
        key: ${{ runner.os }}-cargo-client-${{ hashFiles('hindsight-clients/rust/Cargo.lock') }}

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

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

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -829,7 +829,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -838,40 +838,40 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Set up Go
-      uses: actions/setup-go@v6
+      uses: actions/setup-go@v5
      with:
        go-version: '1.23'
        cache-dependency-path: hindsight-clients/go/go.sum

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

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

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -934,7 +934,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -943,43 +943,43 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '22'

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

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

-    - name: Install API dependencies
-      working-directory: ./hindsight-api-slim
-      run: uv sync --frozen --all-extras --index-strategy unsafe-best-match
-
    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -1041,7 +1041,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -1050,38 +1050,38 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Build API
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: uv build

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

-    - name: Install API dependencies
-      working-directory: ./hindsight-api-slim
-      run: uv sync --frozen --all-extras --index-strategy unsafe-best-match
-
    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -1132,16 +1132,16 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -1161,16 +1161,16 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -1190,16 +1190,16 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -1215,66 +1215,6 @@ jobs:
      working-directory: ./hindsight-integrations/pydantic-ai
      run: uv run pytest tests -v

-  test-pip-slim:
-    runs-on: ubuntu-latest
-    env:
-      HINDSIGHT_API_LLM_PROVIDER: vertexai
-      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
-      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
-      HINDSIGHT_API_EMBEDDINGS_PROVIDER: cohere
-      HINDSIGHT_API_RERANKER_PROVIDER: cohere
-      HINDSIGHT_API_COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
-
-    steps:
-    - uses: actions/checkout@v6
-
-    - name: Setup GCP credentials
-      run: |
-        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
-        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
-        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v7
-      with:
-        enable-cache: true
-        prune-cache: false
-
-    - name: Set up Python
-      uses: actions/setup-python@v6
-      with:
-        python-version-file: ".python-version"
-
-    - name: Install hindsight-api-slim (embedded-db only, no local ML)
-      working-directory: ./hindsight-api-slim
-      run: uv sync --frozen --extra embedded-db --index-strategy unsafe-best-match
-
-    - name: Start API server
-      working-directory: ./hindsight-api-slim
-      run: |
-        uv run hindsight-api --port 8888 > /tmp/slim-api-server.log 2>&1 &
-        for i in $(seq 1 60); do
-          if curl -s http://localhost:8888/health | grep -q "healthy"; 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/slim-api-server.log
-            exit 1
-          fi
-          sleep 1
-        done
-
-    - name: Smoke test - retain and recall
-      run: ./scripts/smoke-test-slim.sh http://localhost:8888
-
-    - name: Show API server logs
-      if: always()
-      run: |
-        echo "=== API Server Logs ==="
-        cat /tmp/slim-api-server.log 2>/dev/null || true
-
  test-embed:
    runs-on: ubuntu-latest
    env:
@@ -1285,7 +1225,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -1294,13 +1234,13 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -1308,12 +1248,8 @@ jobs:
      working-directory: ./hindsight-embed
      run: uv sync --frozen --index-strategy unsafe-best-match

-    - name: Install API dependencies (with local-ml and embedded-db for smoke test)
-      working-directory: ./hindsight-api-slim
-      run: uv sync --frozen --all-extras --index-strategy unsafe-best-match
-
    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
        key: ${{ runner.os }}-huggingface-embed-${{ hashFiles('hindsight-embed/pyproject.toml') }}
@@ -1343,7 +1279,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -1352,35 +1288,35 @@ jobs:
        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Build hindsight-all
-      working-directory: ./hindsight-all
+      working-directory: ./hindsight
      run: uv build

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

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-all-${{ hashFiles('hindsight-all/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-all-${{ hashFiles('hindsight/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-all-
          ${{ runner.os }}-huggingface-

    - name: Run unit tests
-      working-directory: ./hindsight-all
+      working-directory: ./hindsight
      run: uv run pytest tests/ -v

  test-doc-examples:
@@ -1399,7 +1335,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Setup GCP credentials
      run: |
@@ -1413,7 +1349,7 @@ jobs:

    - name: Cache cargo
      if: matrix.language == 'cli'
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: |
          ~/.cargo/registry
@@ -1429,35 +1365,35 @@ jobs:
        cp target/release/hindsight /usr/local/bin/hindsight

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Set up Node.js
      if: matrix.language == 'node'
-      uses: actions/setup-node@v6
+      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 --frozen --no-install-project --index-strategy unsafe-best-match
+
    - name: Install Python client dependencies
      if: matrix.language == 'python'
      working-directory: ./hindsight-clients/python
      run: uv sync --frozen --extra test --index-strategy unsafe-best-match

-    - name: Build and install API
-      working-directory: ./hindsight-api-slim
-      run: |
-        uv build
-        uv sync --frozen --all-extras --index-strategy unsafe-best-match
-
    - name: Install TypeScript client
      if: matrix.language == 'node'
      run: |
@@ -1465,15 +1401,15 @@ jobs:
        npm run build --workspace=hindsight-clients/typescript

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -1533,7 +1469,7 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
      with:
        fetch-depth: 0  # Full history needed for git clone of tags

@@ -1547,21 +1483,21 @@ jobs:
      run: git fetch --tags

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true
        prune-cache: false

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Cache HuggingFace models
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
        restore-keys: |
          ${{ runner.os }}-huggingface-

@@ -1570,11 +1506,11 @@ jobs:
      run: uv sync --frozen --extra test --index-strategy unsafe-best-match

    - name: Install current hindsight-api
-      working-directory: ./hindsight-api-slim
-      run: uv sync --frozen --all-extras --index-strategy unsafe-best-match
+      working-directory: ./hindsight-api
+      run: uv sync --frozen --index-strategy unsafe-best-match

    - name: Pre-download models
-      working-directory: ./hindsight-api-slim
+      working-directory: ./hindsight-api
      run: |
        uv run python -c "
        from sentence_transformers import SentenceTransformer, CrossEncoder
@@ -1607,20 +1543,20 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

    - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
      with:
        node-version: '20'
        cache: 'npm'
@@ -1630,7 +1566,7 @@ jobs:
      uses: dtolnay/rust-toolchain@stable

    - name: Cache cargo
-      uses: actions/cache@v5
+      uses: actions/cache@v4
      with:
        path: |
          ~/.cargo/registry
@@ -1679,17 +1615,17 @@ jobs:
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
      with:
        fetch-depth: 0  # Fetch full git history to access base branch

    - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
      with:
        enable-cache: true

    - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
      with:
        python-version-file: ".python-version"

@@ -17,20 +17,20 @@ Hindsight is an agent memory system that provides long-term memory for AI agents
 ./scripts/dev/start-api.sh

 # Run all tests (parallelized with pytest-xdist)
-cd hindsight-api-slim && uv run pytest tests/
+cd hindsight-api && uv run pytest tests/

 # Run specific test file
-cd hindsight-api-slim && uv run pytest tests/test_http_api_integration.py -v
+cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v

 # Run single test function
-cd hindsight-api-slim && uv run pytest tests/test_retain.py::test_retain_simple -v
+cd hindsight-api && uv run pytest tests/test_retain.py::test_retain_simple -v

 # Lint and format
-cd hindsight-api-slim && uv run ruff check .
-cd hindsight-api-slim && uv run ruff format .
+cd hindsight-api && uv run ruff check .
+cd hindsight-api && uv run ruff format .

 # Type checking (uses ty - extremely fast type checker from Astral)
-cd hindsight-api-slim && uv run ty check hindsight_api/
+cd hindsight-api && uv run ty check hindsight_api/
 ```

 ### Control Plane (Next.js)
@@ -72,7 +72,7 @@ cd hindsight-control-plane && npm run dev
 ## Architecture

 ### Monorepo Structure
- **hindsight-api-slim/**: Core FastAPI server with memory engine (Python, uv)
+- **hindsight-api/**: Core FastAPI server with memory engine (Python, uv)
 - **hindsight/**: Embedded Python bundle (hindsight-all package)
 - **hindsight-control-plane/**: Admin UI (Next.js, npm)
 - **hindsight-cli/**: CLI tool (Rust, cargo, uses progenitor for API client)
@@ -81,9 +81,9 @@ cd hindsight-control-plane && npm run dev
 - **hindsight-integrations/**: Framework integrations (LiteLLM, OpenAI)
 - **hindsight-dev/**: Development tools and benchmarks

-### Core Engine (hindsight-api-slim/hindsight_api/engine/)
+### Core Engine (hindsight-api/hindsight_api/engine/)
 - `memory_engine.py`: Main orchestrator (~170KB) for retain/recall/reflect operations
- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, MiniMax, Ollama, LM Studio
+- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio
 - `embeddings.py`: Embedding generation (local sentence-transformers or TEI)
 - `cross_encoder.py`: Reranking (local or TEI)
 - `entity_resolver.py`: Entity extraction and normalization
@@ -101,7 +101,7 @@ cd hindsight-control-plane && npm run dev
 - `fusion.py`: Reciprocal rank fusion for combining results
 - `reranking.py`: Cross-encoder reranking

-### API Layer (hindsight-api-slim/hindsight_api/api/)
+### API Layer (hindsight-api/hindsight_api/api/)
 - `http.py`: FastAPI HTTP routers (~80KB) for all REST endpoints
 - `mcp.py`: Model Context Protocol server implementation

@@ -111,13 +111,13 @@ Main operations:
 - **Reflect**: Disposition-aware reasoning using memories and mental models.

 ### Database
-PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api-slim/hindsight_api/alembic/`. Migrations run automatically on API startup.
+PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api/hindsight_api/alembic/`. Migrations run automatically on API startup.

 Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`

 ### Adding Database Migrations

-1. **Create a new migration file** in `hindsight-api-slim/hindsight_api/alembic/versions/`:
+1. **Create a new migration file** in `hindsight-api/hindsight_api/alembic/versions/`:
   - File name format: `<revision_id>_<description>.py` (e.g., `f1a2b3c4d5e6_add_new_index.py`)
   - Use a unique hex revision ID (12 chars)
   - Set `down_revision` to the previous migration's revision ID
@@ -154,7 +154,7 @@ Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`

 3. **Run migrations locally**:
   ```bash
-   # Set database URL and run migrations for the base schema plus all tenants
+   # Set database URL and run migrations
   uv run hindsight-admin run-db-migration

   # Run on a specific tenant schema
@@ -251,7 +251,7 @@ Fields must be categorized as either **hierarchical** (can be overridden per-ten

 #### Adding a New Configuration Field

-1. **config.py** (`hindsight-api-slim/hindsight_api/config.py`):
+1. **config.py** (`hindsight-api/hindsight_api/config.py`):
   - Add `ENV_*` constant for the environment variable name (e.g., `ENV_MY_SETTING = "HINDSIGHT_API_MY_SETTING"`)
   - Add `DEFAULT_*` constant for the default value
   - Add field to `HindsightConfig` dataclass with type annotation
@@ -268,7 +268,7 @@ Fields must be categorized as either **hierarchical** (can be overridden per-ten
   # Static field - just don't add to _HIERARCHICAL_FIELDS
   ```

-2. **main.py** (`hindsight-api-slim/hindsight_api/main.py`):
+2. **main.py** (`hindsight-api/hindsight_api/main.py`):
   - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")

 3. **Use hierarchical config in MemoryEngine**:
@@ -308,14 +308,14 @@ cp .env.example .env
 # Edit .env with LLM API key

 # Python deps
-uv sync --directory hindsight-api-slim/
+uv sync --directory hindsight-api/

 # Node deps (uses npm workspaces)
 npm install
 ```

 Required env vars:
- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, minimax, ollama, lmstudio
+- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, ollama, lmstudio
 - `HINDSIGHT_API_LLM_API_KEY`: Your API key
 - `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., gpt-4o-mini, claude-sonnet-4-20250514)

@@ -9,9 +9,8 @@
 [![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)
-<br/>

-<a href="https://trendshift.io/repositories/15603" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15603" alt="vectorize-io%2Fhindsight | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+
 </div>

 ---
@@ -70,7 +69,7 @@ docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
 >API: http://localhost:8888
 >UI: http://localhost:9999

-You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio`, and `minimax`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
+You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, and `lmstudio`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).



@@ -42,22 +42,25 @@ RUN apt-get update && apt-get install -y \
    && pip install --no-cache-dir uv

 # Copy dependency files and README (required by pyproject.toml)
-COPY hindsight-api-slim/pyproject.toml ./api/
-COPY hindsight-api-slim/README.md ./api/
+COPY hindsight-api/pyproject.toml ./api/
+COPY hindsight-api/README.md ./api/

 WORKDIR /app/api

-# Sync dependencies using appropriate extras based on INCLUDE_LOCAL_MODELS
-# local-ml: torch, sentence-transformers, transformers, einops, flashrank, mlx (optional)
-# embedded-db: pg0-embedded (always included for embedded PostgreSQL support)
-RUN if [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-        uv sync --extra local-ml --extra embedded-db; \
-    else \
-        uv sync --extra embedded-db; \
+# Remove local ML model dependencies if INCLUDE_LOCAL_MODELS=false
+# This creates a smaller image when using external providers (TEI, OpenAI, Cohere)
+RUN if [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then \
+        echo "Removing local-models dependencies (sentence-transformers, torch, transformers)..." && \
+        sed -i '/"sentence-transformers/d' pyproject.toml && \
+        sed -i '/"transformers/d' pyproject.toml && \
+        sed -i '/"torch/d' pyproject.toml; \
    fi

+# Sync dependencies (will create lock file if needed)
+RUN uv sync
+
 # Copy source code (alembic migrations are inside hindsight_api/)
-COPY hindsight-api-slim/hindsight_api ./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 .
@@ -77,32 +77,18 @@ PIDS=()
 # Start API if enabled
 if [ "$ENABLE_API" = "true" ]; then
    cd /app/api
-    API_HEALTH_URL="${HINDSIGHT_API_HEALTH_URL:-http://localhost:8888/health}"
-    API_STARTUP_WAIT_SECONDS="${HINDSIGHT_API_STARTUP_WAIT_SECONDS:-300}"
-
    # Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
    hindsight-api &
    API_PID=$!
    PIDS+=($API_PID)

    # Wait for API to be ready
-    api_ready=false
-    for ((i=1; i<=API_STARTUP_WAIT_SECONDS; i++)); do
-        if ! kill -0 "$API_PID" 2>/dev/null; then
-            wait "$API_PID"
-            exit $?
-        fi
-        if curl -sf "$API_HEALTH_URL" &>/dev/null; then
-            api_ready=true
+    for i in {1..60}; do
+        if curl -sf http://localhost:8888/health &>/dev/null; then
            break
        fi
        sleep 1
    done
-
-    if [ "$api_ready" != "true" ]; then
-        echo "❌ API did not become healthy within ${API_STARTUP_WAIT_SECONDS}s"
-        exit 1
-    fi
 else
    echo "API disabled (HINDSIGHT_ENABLE_API=false)"
 fi
@@ -111,7 +97,6 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
    echo "🎛️  Starting Control Plane..."
    cd /app/control-plane
-    export HOSTNAME="${HINDSIGHT_CP_HOSTNAME:-0.0.0.0}"
    PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
    CP_PID=$!
    PIDS+=($CP_PID)
@@ -49,9 +49,6 @@

 set -euo pipefail

-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(dirname "$SCRIPT_DIR")"
-
 # Colors for output
 RED='\033[0;31m'
 GREEN='\033[0;32m'
@@ -181,21 +178,6 @@ for i in $(seq 1 "$TIMEOUT"); do
        echo "=== Health Response ==="
        curl -s "http://localhost:${HEALTH_PORT}${HEALTH_PATH}" | python3 -m json.tool 2>/dev/null || curl -s "http://localhost:${HEALTH_PORT}${HEALTH_PATH}"
        echo ""
-
-        # Run retain/recall smoke test for API targets
-        if [ "$TARGET" != "cp-only" ]; then
-            echo ""
-            echo "=== Retain/Recall Smoke Test ==="
-            if ! "$REPO_ROOT/scripts/smoke-test-slim.sh" "http://localhost:${HEALTH_PORT}"; then
-                echo ""
-                echo "=== Container Logs (last 50 lines) ==="
-                docker logs "$CONTAINER_NAME" 2>&1 | tail -50
-                echo ""
-                echo -e "${RED}Smoke test FAILED${NC}"
-                exit 1
-            fi
-        fi
-
        echo ""
        echo "=== Container Logs (last 50 lines) ==="
        docker logs "$CONTAINER_NAME" 2>&1 | tail -50
@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.18
-appVersion: "0.4.18"
+version: 0.4.16
+appVersion: "0.4.16"
 keywords:
  - ai
  - memory
@@ -1,33 +0,0 @@
-[build-system]
-requires = ["setuptools>=61"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "hindsight-all-slim"
-version = "0.4.18"
-description = "Hindsight: Agent Memory That Works Like Human Memory - Slim All-in-One Bundle"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    "hindsight-api-slim>=0.4.17",
-    "hindsight-client>=0.0.7",
-    "hindsight-embed>=0.1.0",
-]
-
-[tool.uv.sources]
-hindsight-api-slim = { workspace = true }
-hindsight-client = { workspace = true }
-hindsight-embed = { workspace = true }
-
-[project.optional-dependencies]
-test = [
-    "pytest>=7.0.0",
-    "pytest-asyncio>=0.21.0",
-]
-
-[tool.setuptools]
-packages = []
-
-[tool.pytest.ini_options]
-asyncio_mode = "auto"
-asyncio_default_fixture_loop_scope = "function"
@@ -1,48 +0,0 @@
-# hindsight-all
-
-All-in-one package for Hindsight - Agent Memory That Works Like Human Memory
-
-## Quick Start
-
-```python
-from hindsight import start_server, HindsightClient
-
-# Start server with embedded PostgreSQL
-server = start_server(
-    llm_provider="groq",
-    llm_api_key="your-api-key",
-    llm_model="openai/gpt-oss-120b"
-)
-
-# Create client
-client = HindsightClient(base_url=server.url)
-
-# Store memories
-client.put(agent_id="assistant", content="User prefers Python for data analysis")
-
-# Search memories
-results = client.search(agent_id="assistant", query="programming preferences")
-
-# Generate contextual response
-response = client.think(agent_id="assistant", query="What languages should I recommend?")
-
-# Stop server when done
-server.stop()
-```
-
-## Using Context Manager
-
-```python
-from hindsight import HindsightServer, HindsightClient
-
-with HindsightServer(llm_provider="groq", llm_api_key="...") as server:
-    client = HindsightClient(base_url=server.url)
-    # ... use client ...
-# Server automatically stops
-```
-
-## Installation
-
-```bash
-pip install hindsight-all
-```
@@ -1,423 +0,0 @@
-"""
-Wrapper for Hindsight client that adds API namespaces.
-
-Provides organized access to different parts of the Hindsight API through
-namespaces like .banks, .mental_models, etc.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from hindsight_client import Hindsight
-
-
-class BanksAPI:
-    """Namespace for bank-related operations.
-    
-    Provides methods to create, delete, and manage memory banks.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def create(
-        self,
-        bank_id: str,
-        name: str | None = None,
-        mission: str | None = None,
-        disposition: dict[str, Any] | None = None,
-    ) -> Any:
-        """Create a new bank.
-        
-        Args:
-            bank_id: Unique identifier for the bank.
-            name: Optional display name for the bank.
-            mission: Optional mission statement for the bank.
-            disposition: Optional disposition configuration dict.
-            
-        Returns:
-            Bank creation response from the API.
-        """
-        return self._client.create_bank(
-            bank_id=bank_id,
-            name=name,
-            mission=mission,
-            disposition=disposition,
-        )
-
-    def delete(self, bank_id: str) -> Any:
-        """Delete a bank.
-        
-        Args:
-            bank_id: The ID of the bank to delete.
-            
-        Returns:
-            Deletion response from the API.
-        """
-        return self._client.delete_bank(bank_id=bank_id)
-
-    def set_mission(self, bank_id: str, mission: str) -> Any:
-        """Set or update the mission for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mission: The mission statement to set.
-            
-        Returns:
-            API response confirming the update.
-        """
-        return self._client.set_mission(bank_id=bank_id, mission=mission)
-
-    def set_disposition(self, bank_id: str, disposition: dict[str, Any]) -> Any:
-        """Set or update the disposition for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            disposition: The disposition configuration dict.
-            
-        Returns:
-            API response confirming the update.
-        """
-        return self._client.set_disposition(bank_id=bank_id, disposition=disposition)
-
-    def list(self) -> Any:
-        """List all banks.
-        
-        Returns:
-            List of banks from the API.
-        """
-        from hindsight_client.hindsight_client import _run_async
-
-        return _run_async(self._client._banks_api.list_banks())
-
-
-class MentalModelsAPI:
-    """Namespace for mental model operations.
-    
-    Mental models are reusable knowledge structures that guide agent behavior.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def create(
-        self,
-        bank_id: str,
-        name: str,
-        content: str,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Create a new mental model.
-        
-        Args:
-            bank_id: The ID of the bank to add the model to.
-            name: Name for the mental model.
-            content: The content/instructions for the mental model.
-            tags: Optional list of tags for categorization.
-            
-        Returns:
-            Creation response from the API.
-        """
-        return self._client.create_mental_model(
-            bank_id=bank_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def list(self, bank_id: str, tags: list[str] | None = None) -> Any:
-        """List all mental models for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            tags: Optional filter by tags.
-            
-        Returns:
-            List of mental models.
-        """
-        return self._client.list_mental_models(bank_id=bank_id, tags=tags)
-
-    def get(self, bank_id: str, mental_model_id: str) -> Any:
-        """Get a specific mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model.
-            
-        Returns:
-            The mental model details.
-        """
-        return self._client.get_mental_model(bank_id=bank_id, mental_model_id=mental_model_id)
-
-    def refresh(self, bank_id: str, mental_model_id: str) -> Any:
-        """Refresh a mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model to refresh.
-            
-        Returns:
-            Refresh response from the API.
-        """
-        return self._client.refresh_mental_model(bank_id=bank_id, mental_model_id=mental_model_id)
-
-    def update(
-        self,
-        bank_id: str,
-        mental_model_id: str,
-        name: str | None = None,
-        content: str | None = None,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Update a mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model to update.
-            name: Optional new name.
-            content: Optional new content.
-            tags: Optional new tags list.
-            
-        Returns:
-            Update response from the API.
-        """
-        return self._client.update_mental_model(
-            bank_id=bank_id,
-            mental_model_id=mental_model_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def delete(self, bank_id: str, mental_model_id: str) -> Any:
-        """Delete a mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model to delete.
-            
-        Returns:
-            Deletion response from the API.
-        """
-        return self._client.delete_mental_model(bank_id=bank_id, mental_model_id=mental_model_id)
-
-
-class DirectivesAPI:
-    """Namespace for directive operations.
-    
-    Directives are explicit instructions that guide agent behavior.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def create(
-        self,
-        bank_id: str,
-        name: str,
-        content: str,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Create a new directive.
-        
-        Args:
-            bank_id: The ID of the bank to add the directive to.
-            name: Name for the directive.
-            content: The directive content/instructions.
-            tags: Optional list of tags for categorization.
-            
-        Returns:
-            Creation response from the API.
-        """
-        return self._client.create_directive(
-            bank_id=bank_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def list(self, bank_id: str, tags: list[str] | None = None) -> Any:
-        """List all directives for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            tags: Optional filter by tags.
-            
-        Returns:
-            List of directives.
-        """
-        return self._client.list_directives(bank_id=bank_id, tags=tags)
-
-    def get(self, bank_id: str, directive_id: str) -> Any:
-        """Get a specific directive.
-        
-        Args:
-            bank_id: The ID of the bank.
-            directive_id: The ID of the directive.
-            
-        Returns:
-            The directive details.
-        """
-        return self._client.get_directive(bank_id=bank_id, directive_id=directive_id)
-
-    def update(
-        self,
-        bank_id: str,
-        directive_id: str,
-        name: str | None = None,
-        content: str | None = None,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Update a directive.
-        
-        Args:
-            bank_id: The ID of the bank.
-            directive_id: The ID of the directive to update.
-            name: Optional new name.
-            content: Optional new content.
-            tags: Optional new tags list.
-            
-        Returns:
-            Update response from the API.
-        """
-        return self._client.update_directive(
-            bank_id=bank_id,
-            directive_id=directive_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def delete(self, bank_id: str, directive_id: str) -> Any:
-        """Delete a directive.
-        
-        Args:
-            bank_id: The ID of the bank.
-            directive_id: The ID of the directive to delete.
-            
-        Returns:
-            Deletion response from the API.
-        """
-        return self._client.delete_directive(bank_id=bank_id, directive_id=directive_id)
-
-
-class MemoriesAPI:
-    """Namespace for memory operations.
-    
-    Provides methods to query and retrieve stored memories.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def list(
-        self,
-        bank_id: str,
-        type: str | None = None,
-        search_query: str | None = None,
-        limit: int = 100,
-        offset: int = 0,
-    ) -> Any:
-        """List memories in a bank.
-        
-        Args:
-            bank_id: The ID of the bank to query.
-            type: Optional filter by memory type.
-            search_query: Optional search query for filtering.
-            limit: Maximum number of results to return (default: 100).
-            offset: Number of results to skip for pagination (default: 0).
-            
-        Returns:
-            List of memories matching the criteria.
-        """
-        return self._client.list_memories(
-            bank_id=bank_id,
-            type=type,
-            search_query=search_query,
-            limit=limit,
-            offset=offset,
-        )
-
-
-class HindsightClient(Hindsight):
-    """
-    Enhanced Hindsight client with organized API namespaces.
-
-    This wrapper extends the auto-generated Hindsight client with organized
-    access to different parts of the API through namespaces.
-
-    Example:
-        ```python
-        from hindsight import HindsightClient
-
-        client = HindsightClient(base_url="http://localhost:8888")
-
-        # Core operations (inherited from Hindsight)
-        client.retain(bank_id="test", content="Hello")
-        results = client.recall(bank_id="test", query="Hello")
-
-        # Organized API access through namespaces
-        client.banks.create(bank_id="test", name="Test Bank")
-        models = client.mental_models.list(bank_id="test")
-        directives = client.directives.list(bank_id="test")
-        memories = client.memories.list(bank_id="test")
-        ```
-        
-    Attributes:
-        banks: Namespace for bank management operations.
-        mental_models: Namespace for mental model operations.
-        directives: Namespace for directive operations.
-        memories: Namespace for memory listing operations.
-    """
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-        self._banks_namespace: BanksAPI | None = None
-        self._mental_models_namespace: MentalModelsAPI | None = None
-        self._directives_namespace: DirectivesAPI | None = None
-        self._memories_namespace: MemoriesAPI | None = None
-
-    @property
-    def banks(self) -> BanksAPI:
-        """Access bank management operations.
-        
-        Returns:
-            BanksAPI instance for bank operations.
-        """
-        if self._banks_namespace is None:
-            self._banks_namespace = BanksAPI(self)
-        return self._banks_namespace
-
-    @property
-    def mental_models(self) -> MentalModelsAPI:
-        """Access mental model operations.
-        
-        Returns:
-            MentalModelsAPI instance for mental model operations.
-        """
-        if self._mental_models_namespace is None:
-            self._mental_models_namespace = MentalModelsAPI(self)
-        return self._mental_models_namespace
-
-    @property
-    def directives(self) -> DirectivesAPI:
-        """Access directive operations.
-        
-        Returns:
-            DirectivesAPI instance for directive operations.
-        """
-        if self._directives_namespace is None:
-            self._directives_namespace = DirectivesAPI(self)
-        return self._directives_namespace
-
-    @property
-    def memories(self) -> MemoriesAPI:
-        """Access memory listing operations.
-        
-        Returns:
-            MemoriesAPI instance for memory operations.
-        """
-        if self._memories_namespace is None:
-            self._memories_namespace = MemoriesAPI(self)
-        return self._memories_namespace
@@ -1,137 +0,0 @@
-# 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`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio` | `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
@@ -1,30 +0,0 @@
-"""Add history column to mental_models
-
-Revision ID: c3d4e5f6g7h8
-Revises: a2b3c4d5e6f7, a2b3c4d5e6f8
-Create Date: 2026-03-06
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "c3d4e5f6g7h8"
-down_revision: str | Sequence[str] | None = ("a2b3c4d5e6f7", "a2b3c4d5e6f8")
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}mental_models ADD COLUMN IF NOT EXISTS history JSONB DEFAULT '[]'::jsonb")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS history")
@@ -1,53 +0,0 @@
-"""Recreate idx_memory_units_source_memory_ids GIN index with fastupdate=off
-
-GIN indexes use a "fastupdate" pending list by default: small writes are
-buffered there and flushed to the main GIN tree in bulk. Flushing requires
-AccessExclusiveLock on the index. Under high insert concurrency (e.g. 8
-parallel pytest-xdist workers all calling retain_async) two transactions can
-each trigger a flush simultaneously and deadlock.
-
-Disabling fastupdate makes every insert write directly to the GIN tree
-(slightly slower per insert, but no pending-list lock cycles).
-
-Revision ID: d4e5f6g7h8i9
-Revises: d5e6f7a8b9c0
-Create Date: 2026-03-11
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "d4e5f6g7h8i9"
-down_revision: str | Sequence[str] | None = "d5e6f7a8b9c0"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    # DROP + CREATE CONCURRENTLY must run outside a transaction block.
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
-        f"ON {schema}memory_units USING GIN (source_memory_ids) "
-        f"WITH (fastupdate=off) "
-        f"WHERE source_memory_ids IS NOT NULL"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
-        f"ON {schema}memory_units USING GIN (source_memory_ids) "
-        f"WHERE source_memory_ids IS NOT NULL"
-    )
@@ -1,131 +0,0 @@
-"""Add internal_id to banks and per-(bank, fact_type) partial HNSW indexes
-
-Revision ID: d5e6f7a8b9c0
-Revises: a3b4c5d6e7f8
-Create Date: 2026-03-11
-
-This migration:
-1. Adds internal_id UUID column to banks (stable identifier for index naming)
-2. Drops the global HNSW index (competes with per-bank partial indexes)
-3. Creates per-(bank_id, fact_type) partial HNSW indexes for all existing banks
-   (new banks get indexes created at bank-creation time via bank_utils.create_bank_hnsw_indexes)
-
-Why per-(bank, fact_type) indexes:
- fact_type-only partial indexes are never chosen by the planner when bank_id is in the WHERE
-  clause, because the idx_memory_units_bank_id B-tree index always wins at planning time.
- Per-(bank, fact_type) partial indexes have both predicates matching → planner selects them.
- The global HNSW index competes for larger partitions (world, observation) and must be dropped.
-
-For large deployments, create indexes CONCURRENTLY before running this migration:
-    SELECT internal_id, bank_id FROM banks;
-    -- for each bank and each fact_type in (world, experience, observation):
-    CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_mu_emb_{ft}_{uid16}
-        ON memory_units USING hnsw (embedding vector_cosine_ops)
-        WHERE fact_type = '{ft}' AND bank_id = '{bank_id}';
-    DROP INDEX CONCURRENTLY IF EXISTS idx_memory_units_embedding;
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-from sqlalchemy import text
-
-revision: str = "d5e6f7a8b9c0"
-down_revision: str | Sequence[str] | None = "c3d4e5f6g7h8"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-_HNSW_FACT_TYPES: dict[str, str] = {
-    "world": "worl",
-    "experience": "expr",
-    "observation": "obsv",
-}
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # 1. Add internal_id column to banks
-    op.execute(
-        f"ALTER TABLE {schema}banks ADD COLUMN IF NOT EXISTS internal_id UUID DEFAULT gen_random_uuid() NOT NULL"
-    )
-    op.execute(f"ALTER TABLE {schema}banks ADD CONSTRAINT banks_internal_id_unique UNIQUE (internal_id)")
-
-    # 2. Drop any fact_type-only partial HNSW indexes that may exist from prior migrations
-    #    (bank_id B-tree always wins over them when bank_id is in the WHERE clause)
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_world")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_observation")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_experience")
-
-    # 4. Drop global HNSW index (competes with per-bank partial indexes)
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_embedding")
-
-    # 5. Create per-(bank, fact_type) partial HNSW indexes for all existing banks
-    bind = op.get_bind()
-    schema_name = context.config.get_main_option("target_schema")
-    table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
-    banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-
-    rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
-    for row in rows:
-        bank_id = row[0]
-        internal_id = str(row[1]).replace("-", "")[:16]
-        escaped_bank_id = bank_id.replace("'", "''")
-        for ft, ft_short in _HNSW_FACT_TYPES.items():
-            idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
-            # Index name is schema-unqualified (indexes live in the schema of their table)
-            bind.execute(
-                text(
-                    f"CREATE INDEX IF NOT EXISTS {idx_name} "
-                    f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-                    f"WHERE fact_type = '{ft}' AND bank_id = '{escaped_bank_id}'"
-                )
-            )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # Drop per-bank HNSW indexes (iterate existing banks)
-    bind = op.get_bind()
-    schema_name = context.config.get_main_option("target_schema")
-    banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-
-    rows = bind.execute(text(f"SELECT internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
-    for row in rows:
-        internal_id = str(row[0]).replace("-", "")[:16]
-        for ft_short in _HNSW_FACT_TYPES.values():
-            idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
-            bind.execute(text(f"DROP INDEX IF EXISTS {schema}{idx_name}"))
-
-    # Restore the global HNSW index
-    table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_memory_units_embedding ON {table_ref} USING hnsw (embedding vector_cosine_ops)"
-    )
-
-    # Restore old fact_type-only partial indexes
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_mu_emb_world "
-        f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-        f"WHERE fact_type = 'world'"
-    )
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_mu_emb_observation "
-        f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-        f"WHERE fact_type = 'observation'"
-    )
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_mu_emb_experience "
-        f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-        f"WHERE fact_type = 'experience'"
-    )
-
-    # Drop internal_id column
-    op.execute(f"ALTER TABLE {schema}banks DROP CONSTRAINT IF EXISTS banks_internal_id_unique")
-    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS internal_id")
@@ -1,73 +0,0 @@
-"""Add CASCADE DELETE FK from async_operations and webhooks to banks.
-
-When a bank is deleted, all its async_operations and webhooks rows are
-automatically deleted by the database. This ensures that any in-flight
-worker tasks detect the deletion via _check_op_alive() and abort early.
-
-Revision ID: e5f6g7h8i9j0
-Revises: d4e5f6g7h8i9
-Create Date: 2026-03-11
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "e5f6g7h8i9j0"
-down_revision: str | Sequence[str] | None = "d4e5f6g7h8i9"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # Remove orphaned async_operations rows whose bank no longer exists
-    # (can happen because there was no FK before this migration).
-    op.execute(
-        f"""
-        DELETE FROM {schema}async_operations
-        WHERE bank_id IS NOT NULL
-          AND bank_id NOT IN (SELECT bank_id FROM {schema}banks)
-        """
-    )
-
-    # Remove orphaned webhooks rows whose bank no longer exists.
-    op.execute(
-        f"""
-        DELETE FROM {schema}webhooks
-        WHERE bank_id IS NOT NULL
-          AND bank_id NOT IN (SELECT bank_id FROM {schema}banks)
-        """
-    )
-
-    # Add FK with ON DELETE CASCADE so that deleting a bank automatically
-    # cleans up all its pending/processing operations and webhook configs.
-    op.execute(
-        f"""
-        ALTER TABLE {schema}async_operations
-            ADD CONSTRAINT fk_async_operations_bank_id
-            FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id)
-            ON DELETE CASCADE
-        """
-    )
-
-    op.execute(
-        f"""
-        ALTER TABLE {schema}webhooks
-            ADD CONSTRAINT fk_webhooks_bank_id
-            FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id)
-            ON DELETE CASCADE
-        """
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}async_operations DROP CONSTRAINT IF EXISTS fk_async_operations_bank_id")
-    op.execute(f"ALTER TABLE {schema}webhooks DROP CONSTRAINT IF EXISTS fk_webhooks_bank_id")
@@ -1,38 +0,0 @@
-"""chunk_fk_cascade_delete
-
-Revision ID: f6g7h8i9j0k1
-Revises: e5f6g7h8i9j0
-Create Date: 2026-03-16 00:00:00.000000
-
-"""
-
-from collections.abc import Sequence
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "f6g7h8i9j0k1"
-down_revision: str | Sequence[str] | None = "e5f6g7h8i9j0"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def upgrade() -> None:
-    """Change memory_units.chunk_id FK from SET NULL to CASCADE.
-
-    When a document is deleted the CASCADE reaches chunks first; with SET NULL
-    the memory_units rows survived with chunk_id = NULL, leaving ghost records.
-    Switching to CASCADE ensures they are removed together with their chunk.
-    """
-    op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
-    op.create_foreign_key(
-        "memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="CASCADE"
-    )
-
-
-def downgrade() -> None:
-    """Revert to SET NULL behaviour."""
-    op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
-    op.create_foreign_key(
-        "memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="SET NULL"
-    )
@@ -1,71 +0,0 @@
-"""backsweep_orphan_memory_units
-
-Two-pass cleanup of memory_units rows that were never removed by earlier bugs:
-
-Pass 1 — any fact_type, bank gone:
-  memory_units whose bank_id no longer exists in banks.  These accumulate when
-  a bank is deleted without a proper cascade (no FK from memory_units to banks
-  exists in the schema).
-
-Pass 2 — observations only, all sources gone:
-  observation rows whose bank still exists but every source_memory_id points
-  to a deleted memory unit.  These were left behind before PR #580 fixed the
-  chunk FK cascade and before delete_document() called
-  _delete_stale_observations_for_memories.
-
-Revision ID: g7h8i9j0k1l2
-Revises: f6g7h8i9j0k1
-Create Date: 2026-03-16
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "g7h8i9j0k1l2"
-down_revision: str | Sequence[str] | None = "f6g7h8i9j0k1"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    mu = f"{schema}memory_units"
-    banks = f"{schema}banks"
-
-    # Pass 1: delete all memory_units (any fact_type) whose bank no longer exists.
-    # There is no FK from memory_units to banks, so these never cascade away.
-    op.execute(
-        f"""
-        DELETE FROM {mu}
-        WHERE NOT EXISTS (
-            SELECT 1 FROM {banks} b WHERE b.bank_id = {mu}.bank_id
-        )
-        """
-    )
-
-    # Pass 2: delete orphaned observations whose bank still exists but every
-    # source_memory_id refers to a now-deleted memory unit (or the array is
-    # empty).  Observations with at least one surviving source are left alone.
-    op.execute(
-        f"""
-        DELETE FROM {mu} orphan
-        WHERE orphan.fact_type = 'observation'
-          AND NOT EXISTS (
-              SELECT 1
-              FROM {mu} src
-              WHERE src.id = ANY(orphan.source_memory_ids)
-                AND src.bank_id = orphan.bank_id
-          )
-        """
-    )
-
-
-def downgrade() -> None:
-    # Deleted rows cannot be restored.
-    pass
@@ -1,144 +0,0 @@
-"""
-MLX implementation of jina-reranker-v3 for Apple Silicon.
-
-This file is adapted from the official model repository:
-  https://huggingface.co/jinaai/jina-reranker-v3-mlx/blob/main/rerank.py
-
-License: CC BY-NC 4.0 (contact Jina AI for commercial usage)
-
-Changes from upstream:
- Removed the __main__ example block
- Type annotations added to public methods
- top_n parameter added to rerank() (upstream only exposed it implicitly)
-"""
-
-import numpy as np
-
-
-class _MLPProjector:
-    def __init__(self):
-        import mlx.nn as nn
-
-        self.linear1 = nn.Linear(1024, 512, bias=False)
-        self.linear2 = nn.Linear(512, 512, bias=False)
-
-    def __call__(self, x):
-        import mlx.nn as nn
-
-        x = self.linear1(x)
-        x = nn.relu(x)
-        x = self.linear2(x)
-        return x
-
-
-def _load_projector(projector_path: str) -> _MLPProjector:
-    import mlx.core as mx
-    from safetensors import safe_open
-
-    projector = _MLPProjector()
-    with safe_open(projector_path, framework="numpy") as f:
-        projector.linear1.weight = mx.array(f.get_tensor("linear1.weight"))
-        projector.linear2.weight = mx.array(f.get_tensor("linear2.weight"))
-    return projector
-
-
-def _sanitize(text: str, special_tokens: dict[str, str]) -> str:
-    for token in special_tokens.values():
-        text = text.replace(token, "")
-    return text
-
-
-def _format_prompt(query: str, docs: list[str], special_tokens: dict[str, str]) -> str:
-    query = _sanitize(query, special_tokens)
-    docs = [_sanitize(d, special_tokens) for d in docs]
-
-    doc_token = special_tokens["doc_embed_token"]
-    query_token = special_tokens["query_embed_token"]
-
-    prefix = (
-        "<|im_start|>system\n"
-        "You are a search relevance expert who can determine a ranking of the passages based on how relevant they are to the query. "
-        "If the query is a question, how relevant a passage is depends on how well it answers the question. "
-        "If not, try to analyze the intent of the query and assess how well each passage satisfies the intent. "
-        "If an instruction is provided, you should follow the instruction when determining the ranking."
-        "<|im_end|>\n<|im_start|>user\n"
-    )
-    suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
-
-    body = (
-        f"I will provide you with {len(docs)} passages, each indicated by a numerical identifier. "
-        f"Rank the passages based on their relevance to query: {query}\n"
-    )
-    body += "\n".join(f'<passage id="{i}">\n{doc}{doc_token}\n</passage>' for i, doc in enumerate(docs))
-    body += f"\n<query>\n{query}{query_token}\n</query>"
-    return prefix + body + suffix
-
-
-class MLXReranker:
-    """
-    MLX-accelerated jina-reranker-v3 for Apple Silicon.
-
-    Loads the model from a local directory (use huggingface_hub.snapshot_download
-    to fetch jinaai/jina-reranker-v3-mlx if you don't have it already).
-    """
-
-    _SPECIAL_TOKENS = {
-        "query_embed_token": "<|rerank_token|>",
-        "doc_embed_token": "<|embed_token|>",
-    }
-    _DOC_TOKEN_ID = 151670
-    _QUERY_TOKEN_ID = 151671
-
-    def __init__(self, model_path: str, projector_path: str):
-        from mlx_lm import load
-
-        self.model, self.tokenizer = load(model_path)
-        self.model.eval()
-        self.projector = _load_projector(projector_path)
-
-    def rerank(self, query: str, documents: list[str], top_n: int | None = None) -> list[dict]:
-        """
-        Rank documents by relevance to a query.
-
-        Returns a list of dicts with keys: document, relevance_score, index.
-        Sorted by descending relevance_score.
-        """
-        import mlx.core as mx
-
-        prompt = _format_prompt(query, documents, self._SPECIAL_TOKENS)
-        input_ids = self.tokenizer.encode(prompt)
-        hidden_states = self.model.model([input_ids])[0]  # [seq_len, hidden_size]
-
-        input_ids_np = np.array(input_ids)
-        query_positions = np.where(input_ids_np == self._QUERY_TOKEN_ID)[0]
-        doc_positions = np.where(input_ids_np == self._DOC_TOKEN_ID)[0]
-
-        if len(query_positions) == 0:
-            raise ValueError("Query embed token not found in prompt")
-        if len(doc_positions) == 0:
-            raise ValueError("Document embed tokens not found in prompt")
-
-        query_hidden = mx.expand_dims(hidden_states[int(query_positions[0])], axis=0)
-        doc_hidden = mx.stack([hidden_states[int(p)] for p in doc_positions])
-
-        query_emb = self.projector(query_hidden)  # [1, 512]
-        doc_emb = self.projector(doc_hidden)  # [num_docs, 512]
-
-        query_exp = mx.broadcast_to(mx.expand_dims(query_emb, 0), (1, len(documents), 512))
-        doc_exp = mx.expand_dims(doc_emb, 0)
-
-        scores = mx.sum(doc_exp * query_exp, axis=-1) / (
-            mx.sqrt(mx.sum(doc_exp * doc_exp, axis=-1)) * mx.sqrt(mx.sum(query_exp * query_exp, axis=-1))
-        )  # [1, num_docs]
-        scores_np = np.array(scores[0])
-
-        order = np.argsort(scores_np)[::-1]
-        n = min(top_n, len(documents)) if top_n is not None else len(documents)
-        return [
-            {
-                "document": documents[order[i]],
-                "relevance_score": float(scores_np[order[i]]),
-                "index": int(order[i]),
-            }
-            for i in range(n)
-        ]
@@ -1,128 +0,0 @@
-"""File parser implementations."""
-
-import logging
-from dataclasses import dataclass
-
-from .base import FileParser, UnsupportedFileTypeError
-from .iris import IrisParser
-from .markitdown import MarkitdownParser
-
-__all__ = [
-    "FileParser",
-    "UnsupportedFileTypeError",
-    "IrisParser",
-    "MarkitdownParser",
-    "FileParserRegistry",
-    "ConvertResult",
-]
-
-
-@dataclass
-class ConvertResult:
-    """Result of a successful file conversion."""
-
-    content: str
-    parser_name: str
-
-
-logger = logging.getLogger(__name__)
-
-
-class FileParserRegistry:
-    """Registry for file parsers with auto-detection."""
-
-    def __init__(self):
-        """Initialize empty parser registry."""
-        self._parsers: dict[str, FileParser] = {}
-
-    def register(self, parser: FileParser):
-        """
-        Register a parser.
-
-        Args:
-            parser: FileParser instance
-        """
-        self._parsers[parser.name()] = parser
-
-    def get_parser(
-        self,
-        name: str | None,
-        filename: str,
-        content_type: str | None = None,
-    ) -> FileParser:
-        """
-        Get parser by name or auto-detect.
-
-        Args:
-            name: Parser name (e.g., "markitdown") or None for auto-detect
-            filename: File name for auto-detection
-            content_type: MIME type (optional)
-
-        Returns:
-            FileParser instance
-
-        Raises:
-            ValueError: If no suitable parser found
-        """
-        if name:
-            # Explicit parser requested — return it directly, let the parser
-            # raise UnsupportedFileTypeError from convert() if needed
-            if name not in self._parsers:
-                raise ValueError(f"Parser '{name}' not found. Available: {list(self._parsers.keys())}")
-            return self._parsers[name]
-
-        # Auto-detect parser
-        for parser in self._parsers.values():
-            if parser.supports(filename, content_type):
-                return parser
-
-        raise ValueError(f"No parser found for {filename}. Available parsers: {list(self._parsers.keys())}")
-
-    async def convert_with_fallback(
-        self,
-        parsers: list[str],
-        file_data: bytes,
-        filename: str,
-        content_type: str | None = None,
-    ) -> ConvertResult:
-        """
-        Try each parser in order, falling back on failure or empty content.
-
-        Moves to the next parser if the current one raises UnsupportedFileTypeError
-        or returns empty content. Any other exception (RuntimeError, network error,
-        etc.) also triggers a fallback so the chain is exhausted before failing.
-
-        Args:
-            parsers: Ordered list of parser names to try
-            file_data: Raw file bytes
-            filename: Original filename
-            content_type: MIME type (optional)
-
-        Returns:
-            ConvertResult with the parsed content and the name of the parser that succeeded
-
-        Raises:
-            ValueError: If a parser name is not registered
-            RuntimeError: If all parsers fail or return empty content
-        """
-        last_error: Exception | None = None
-        for name in parsers:
-            parser = self.get_parser(name, filename, content_type)
-            try:
-                content = await parser.convert(file_data, filename)
-                if content and content.strip():
-                    return ConvertResult(content=content, parser_name=name)
-                logger.warning(f"Parser '{name}' returned empty content for '{filename}', trying next")
-                last_error = RuntimeError(f"Parser '{name}' returned no content for '{filename}'")
-            except UnsupportedFileTypeError as e:
-                logger.warning(f"Parser '{name}' does not support '{filename}', trying next: {e}")
-                last_error = e
-            except Exception as e:
-                logger.warning(f"Parser '{name}' failed for '{filename}', trying next: {e}")
-                last_error = e
-
-        raise last_error or RuntimeError(f"No parsers available for '{filename}'")
-
-    def list_parsers(self) -> list[str]:
-        """Get list of registered parser names."""
-        return list(self._parsers.keys())
@@ -1,544 +0,0 @@
-"""
-Main orchestrator for the retain pipeline.
-
-Coordinates all retain pipeline modules to store memories efficiently.
-"""
-
-import logging
-import time
-import uuid
-from collections.abc import Awaitable, Callable
-from datetime import UTC, datetime
-from typing import Any
-
-from ..db_utils import acquire_with_retry, retry_with_backoff
-from . import bank_utils
-
-
-def utcnow():
-    """Get current UTC time."""
-    return datetime.now(UTC)
-
-
-def parse_datetime_flexible(value: Any) -> datetime:
-    """
-    Parse a datetime value that could be either a datetime object or an ISO string.
-
-    This handles datetime values from both direct Python calls and deserialized JSON
-    (where datetime objects are serialized as ISO strings).
-
-    Args:
-        value: Either a datetime object or an ISO format string
-
-    Returns:
-        datetime object (timezone-aware)
-
-    Raises:
-        TypeError: If value is neither datetime nor string
-        ValueError: If string is not a valid ISO datetime
-    """
-    if isinstance(value, datetime):
-        # Ensure timezone-aware
-        if value.tzinfo is None:
-            return value.replace(tzinfo=UTC)
-        return value
-    elif isinstance(value, str):
-        # Parse ISO format string (handles both 'Z' and '+00:00' timezone formats)
-        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
-        # Ensure timezone-aware
-        if dt.tzinfo is None:
-            return dt.replace(tzinfo=UTC)
-        return dt
-    else:
-        raise TypeError(f"Expected datetime or string, got {type(value).__name__}")
-
-
-import asyncpg
-
-from ..response_models import TokenUsage
-from . import (
-    chunk_storage,
-    embedding_processing,
-    entity_processing,
-    fact_extraction,
-    fact_storage,
-    link_creation,
-)
-from .types import EntityLink, ExtractedFact, ProcessedFact, RetainContent, RetainContentDict
-
-logger = logging.getLogger(__name__)
-
-
-async def retain_batch(
-    pool,
-    embeddings_model,
-    llm_config,
-    entity_resolver,
-    format_date_fn,
-    bank_id: str,
-    contents_dicts: list[RetainContentDict],
-    config,
-    document_id: str | None = None,
-    is_first_batch: bool = True,
-    fact_type_override: str | None = None,
-    confidence_score: float | None = None,
-    document_tags: list[str] | None = None,
-    operation_id: str | None = None,
-    schema: str | None = None,
-    outbox_callback: Callable[["asyncpg.Connection"], Awaitable[None]] | None = None,
-) -> tuple[list[list[str]], TokenUsage]:
-    """
-    Process a batch of content through the retain pipeline.
-
-    Args:
-        pool: Database connection pool
-        embeddings_model: Embeddings model for generating embeddings
-        llm_config: LLM configuration for fact extraction
-        entity_resolver: Entity resolver for entity processing
-        format_date_fn: Function to format datetime to readable string
-        bank_id: Bank identifier
-        contents_dicts: List of content dictionaries
-        config: Resolved HindsightConfig for this bank
-        document_id: Optional document ID
-        is_first_batch: Whether this is the first batch
-        fact_type_override: Override fact type for all facts
-        confidence_score: Confidence score for opinions
-        document_tags: Tags applied to all items in this batch
-
-    Returns:
-        Tuple of (unit ID lists, token usage for fact extraction)
-    """
-    start_time = time.time()
-    total_chars = sum(len(item.get("content", "")) for item in contents_dicts)
-
-    # Buffer all logs
-    log_buffer = []
-    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}")
-
-    # Get bank profile
-    profile = await bank_utils.get_bank_profile(pool, bank_id)
-    agent_name = profile["name"]
-
-    # Convert dicts to RetainContent objects
-    contents = []
-    for item in contents_dicts:
-        # Merge item-level tags with document-level tags
-        item_tags = item.get("tags", []) or []
-        merged_tags = list(set(item_tags + (document_tags or [])))
-
-        # Handle event_date: distinguish "not provided" (default to now) from
-        # "explicitly None" (caller opted into no timestamp).
-        if "event_date" in item and item["event_date"] is None:
-            event_date_value = None  # Caller explicitly signalled "unknown date"
-        elif item.get("event_date"):
-            event_date_value = parse_datetime_flexible(item["event_date"])
-        else:
-            event_date_value = utcnow()  # Backward-compatible default
-
-        content = RetainContent(
-            content=item["content"],
-            context=item.get("context", ""),
-            event_date=event_date_value,
-            metadata=item.get("metadata", {}),
-            entities=item.get("entities", []),
-            tags=merged_tags,
-            observation_scopes=item.get("observation_scopes"),
-        )
-        contents.append(content)
-
-    # Step 1: Extract facts from all contents
-    step_start = time.time()
-
-    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
-        contents, llm_config, agent_name, config, pool, operation_id, schema
-    )
-    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:
-        # Still need to create document if document_id was provided or chunks exist
-        from collections import defaultdict
-
-        docs_tracked = 0
-        async with acquire_with_retry(pool) as conn:
-            async with conn.transaction():
-                # Group contents by document_id (consistent with normal path)
-                contents_by_doc_early = defaultdict(list)
-                for idx, content_dict in enumerate(contents_dicts):
-                    doc_id = content_dict.get("document_id")
-                    contents_by_doc_early[doc_id].append((idx, content_dict))
-
-                if document_id:
-                    # Legacy: single document_id parameter
-                    combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                    # Collect tags from all content items and merge with document_tags
-                    all_tags = set(document_tags or [])
-                    for item in contents_dicts:
-                        item_tags = item.get("tags", []) or []
-                        all_tags.update(item_tags)
-                    merged_tags = list(all_tags)
-
-                    retain_params = {}
-                    if contents_dicts:
-                        first_item = contents_dicts[0]
-                        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"])
-                            )
-                        if first_item.get("metadata"):
-                            retain_params["metadata"] = first_item["metadata"]
-                    await fact_storage.handle_document_tracking(
-                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
-                    )
-                    docs_tracked += 1
-                else:
-                    # Handle per-item document_ids and/or chunks (mirrors normal path logic)
-                    has_any_doc_ids = any(item.get("document_id") for item in contents_dicts)
-
-                    if has_any_doc_ids or chunks:
-                        for original_doc_id, doc_contents in contents_by_doc_early.items():
-                            should_create_doc = (original_doc_id is not None) or chunks
-                            if not should_create_doc:
-                                continue
-
-                            actual_doc_id = original_doc_id
-                            if actual_doc_id is None:
-                                # No document_id but have chunks - generate one
-                                actual_doc_id = str(uuid.uuid4())
-
-                            combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-                            all_tags = set(document_tags or [])
-                            for _, item in doc_contents:
-                                item_tags = item.get("tags", []) or []
-                                all_tags.update(item_tags)
-                            merged_tags = list(all_tags)
-
-                            retain_params = {}
-                            if doc_contents:
-                                first_item = doc_contents[0][1]
-                                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"])
-                                    )
-                                if first_item.get("metadata"):
-                                    retain_params["metadata"] = first_item["metadata"]
-                            await fact_storage.handle_document_tracking(
-                                conn,
-                                bank_id,
-                                actual_doc_id,
-                                combined_content,
-                                is_first_batch,
-                                retain_params,
-                                merged_tags,
-                            )
-                            docs_tracked += 1
-
-        total_time = time.time() - start_time
-        doc_status = f"{docs_tracked} document(s) tracked" if docs_tracked > 0 else "no document tracked"
-        logger.info(
-            f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s ({doc_status}, no facts)"
-        )
-        return [[] for _ in contents], usage
-
-    # Apply fact_type_override if provided
-    if fact_type_override:
-        for fact in extracted_facts:
-            fact.fact_type = fact_type_override
-
-    # Step 2: Augment texts and generate embeddings
-    step_start = time.time()
-    augmented_texts = embedding_processing.augment_texts_with_dates(extracted_facts, format_date_fn)
-    embeddings = await embedding_processing.generate_embeddings_batch(embeddings_model, augmented_texts)
-    log_buffer.append(f"[2] Generate embeddings: {len(embeddings)} embeddings in {time.time() - step_start:.3f}s")
-
-    # Step 3: Convert to ProcessedFact objects (without chunk_ids yet)
-    processed_facts = [
-        ProcessedFact.from_extracted_fact(extracted_fact, embedding)
-        for extracted_fact, embedding in zip(extracted_facts, embeddings)
-    ]
-
-    # 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")
-        contents_by_doc[doc_id].append((idx, content_dict))
-
-    # Step 4: Database transaction (retried on deadlock)
-    result_unit_ids: list[list[str]] = []
-
-    log_buffer_pre_db = len(log_buffer)
-
-    async def _run_db_work() -> None:
-        nonlocal result_unit_ids
-
-        # Reset per-fact mutations and log buffer so each retry attempt starts clean
-        del log_buffer[log_buffer_pre_db:]
-        document_ids_added: list[str] = []
-        for pf in processed_facts:
-            pf.document_id = None
-            pf.chunk_id = None
-
-        async with acquire_with_retry(pool) as conn:
-            async with conn.transaction():
-                # Handle document tracking for all documents
-                step_start = time.time()
-                # Map None document_id to generated UUIDs
-                doc_id_mapping = {}  # Maps original doc_id (including None) to actual doc_id used
-
-                if document_id:
-                    # Legacy: single document_id parameter
-                    combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                    retain_params = {}
-                    # Collect tags from all content items and merge with document_tags
-                    all_tags = set(document_tags or [])
-                    for item in contents_dicts:
-                        item_tags = item.get("tags", []) or []
-                        all_tags.update(item_tags)
-                    merged_tags = list(all_tags)
-
-                    if contents_dicts:
-                        first_item = contents_dicts[0]
-                        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"])
-                            )
-                        if first_item.get("metadata"):
-                            retain_params["metadata"] = first_item["metadata"]
-
-                    await fact_storage.handle_document_tracking(
-                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
-                    )
-                    document_ids_added.append(document_id)
-                    doc_id_mapping[None] = document_id  # For backwards compatibility
-                else:
-                    # Handle per-item document_ids (create documents if any item has document_id or if chunks exist)
-                    has_any_doc_ids = any(item.get("document_id") for item in contents_dicts)
-
-                    if has_any_doc_ids or chunks:
-                        for original_doc_id, doc_contents in contents_by_doc.items():
-                            actual_doc_id = original_doc_id
-
-                            # Only create document record if:
-                            # 1. Item has explicit document_id, OR
-                            # 2. There are chunks (need document for chunk storage)
-                            should_create_doc = (original_doc_id is not None) or chunks
-
-                            if should_create_doc:
-                                if actual_doc_id is None:
-                                    # No document_id but have chunks - generate one
-                                    actual_doc_id = str(uuid.uuid4())
-
-                                # Store mapping for later use
-                                doc_id_mapping[original_doc_id] = actual_doc_id
-
-                                # Combine content for this document
-                                combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-
-                                # Collect tags from all content items for this document and merge with document_tags
-                                all_tags = set(document_tags or [])
-                                for _, item in doc_contents:
-                                    item_tags = item.get("tags", []) or []
-                                    all_tags.update(item_tags)
-                                merged_tags = list(all_tags)
-
-                                # Extract retain params from first content item
-                                retain_params = {}
-                                if doc_contents:
-                                    first_item = doc_contents[0][1]
-                                    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"])
-                                        )
-                                    if first_item.get("metadata"):
-                                        retain_params["metadata"] = first_item["metadata"]
-
-                                await fact_storage.handle_document_tracking(
-                                    conn,
-                                    bank_id,
-                                    actual_doc_id,
-                                    combined_content,
-                                    is_first_batch,
-                                    retain_params,
-                                    merged_tags,
-                                )
-                                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"
-                    )
-
-                # Store chunks and map to facts for all documents
-                step_start = time.time()
-                chunk_id_map_by_doc = {}  # Maps (doc_id, chunk_index) -> chunk_id
-
-                if chunks:
-                    # Group chunks by their source document
-                    chunks_by_doc = defaultdict(list)
-                    for chunk in chunks:
-                        # chunk.content_index tells us which content this chunk came from
-                        original_doc_id = contents_dicts[chunk.content_index].get("document_id")
-                        # Map to actual document_id (handles None -> generated UUID mapping)
-                        actual_doc_id = doc_id_mapping.get(original_doc_id, original_doc_id)
-                        if actual_doc_id is None and document_id:
-                            actual_doc_id = document_id
-                        chunks_by_doc[actual_doc_id].append(chunk)
-
-                    # Store chunks for each document
-                    for doc_id, doc_chunks in chunks_by_doc.items():
-                        chunk_id_map = await chunk_storage.store_chunks_batch(conn, bank_id, doc_id, doc_chunks)
-                        # Store mapping with document context
-                        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"
-                    )
-
-                    # Map chunk_ids and document_ids to facts
-                    for fact, processed_fact in zip(extracted_facts, processed_facts):
-                        # Get the original document_id for this fact's source content
-                        original_doc_id = contents_dicts[fact.content_index].get("document_id")
-                        # Map to actual document_id (handles None -> generated UUID mapping)
-                        actual_doc_id = doc_id_mapping.get(original_doc_id, original_doc_id)
-                        if actual_doc_id is None and document_id:
-                            actual_doc_id = document_id
-
-                        # Set document_id on the fact
-                        processed_fact.document_id = actual_doc_id
-
-                        # Map chunk_id if this fact came from a chunk
-                        if fact.chunk_index is not None:
-                            # Look up chunk_id using (doc_id, chunk_index)
-                            chunk_id = chunk_id_map_by_doc.get((actual_doc_id, fact.chunk_index))
-                            if chunk_id:
-                                processed_fact.chunk_id = chunk_id
-                else:
-                    # No chunks - still need to set document_id on facts
-                    for fact, processed_fact in zip(extracted_facts, processed_facts):
-                        original_doc_id = contents_dicts[fact.content_index].get("document_id")
-                        # Map to actual document_id (handles None -> generated UUID mapping)
-                        actual_doc_id = doc_id_mapping.get(original_doc_id, original_doc_id)
-                        if actual_doc_id is None and document_id:
-                            actual_doc_id = document_id
-                        processed_fact.document_id = actual_doc_id
-
-                non_duplicate_facts = processed_facts
-
-                # Insert facts (document_id is now stored per-fact)
-                step_start = time.time()
-                unit_ids = await fact_storage.insert_facts_batch(conn, bank_id, non_duplicate_facts)
-                log_buffer.append(f"[5] Insert facts: {len(unit_ids)} units in {time.time() - step_start:.3f}s")
-
-                # Process entities
-                step_start = time.time()
-                # Build map of content_index -> user entities for merging
-                user_entities_per_content = {
-                    idx: content.entities for idx, content in enumerate(contents) if content.entities
-                }
-                entity_links = await entity_processing.process_entities_batch(
-                    entity_resolver,
-                    conn,
-                    bank_id,
-                    unit_ids,
-                    non_duplicate_facts,
-                    log_buffer,
-                    user_entities_per_content=user_entities_per_content,
-                    entity_labels=getattr(config, "entity_labels", None),
-                )
-                log_buffer.append(f"[6] Process entities: {len(entity_links)} links in {time.time() - step_start:.3f}s")
-
-                # Create temporal links
-                step_start = time.time()
-                temporal_link_count = await link_creation.create_temporal_links_batch(conn, bank_id, unit_ids)
-                log_buffer.append(f"[7] Temporal links: {temporal_link_count} links in {time.time() - step_start:.3f}s")
-
-                # 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
-                )
-                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"
-                )
-
-                # Create causal links
-                step_start = time.time()
-                causal_link_count = await link_creation.create_causal_links_batch(conn, unit_ids, non_duplicate_facts)
-                log_buffer.append(f"[10] Causal links: {causal_link_count} links in {time.time() - step_start:.3f}s")
-
-                # Map results back to original content items
-                result_unit_ids = _map_results_to_contents(contents, extracted_facts, unit_ids)
-
-                # Transactional outbox: queue any side-effect tasks (e.g. webhook deliveries)
-                # inside the same transaction so they are atomically committed with the retain data.
-                if outbox_callback:
-                    await outbox_callback(conn)
-
-            # Flush entity stats (mention_count / last_seen) now that the transaction
-            # has committed.  Uses a fresh pool connection — no locks held.
-            await entity_resolver.flush_pending_stats()
-
-            # Log final summary
-            total_time = time.time() - start_time
-            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}")
-
-            logger.info("\n" + "\n".join(log_buffer) + "\n")
-
-    await retry_with_backoff(_run_db_work)
-    return result_unit_ids, usage
-
-
-def _map_results_to_contents(
-    contents: list[RetainContent],
-    extracted_facts: list[ExtractedFact],
-    unit_ids: list[str],
-) -> list[list[str]]:
-    """Map created unit IDs back to original content items."""
-    facts_by_content: dict[int, list[int]] = {i: [] for i in range(len(contents))}
-    for i, fact in enumerate(extracted_facts):
-        facts_by_content[fact.content_index].append(i)
-
-    result_unit_ids = []
-    unit_idx = 0
-    for content_index in range(len(contents)):
-        content_unit_ids = []
-        for _ in facts_by_content[content_index]:
-            content_unit_ids.append(unit_ids[unit_idx])
-            unit_idx += 1
-        result_unit_ids.append(content_unit_ids)
-
-    return result_unit_ids
@@ -1,390 +0,0 @@
-"""
-Tags filtering utilities for retrieval.
-
-Provides SQL building functions for filtering memories by tags.
-Supports four matching modes via TagsMatch enum:
- "any": OR matching, includes untagged memories (default, backward compatible)
- "all": AND matching, includes untagged memories
- "any_strict": OR matching, excludes untagged memories
- "all_strict": AND matching, excludes untagged memories
-
-OR matching (any/any_strict): Memory matches if ANY of its tags overlap with request tags
-AND matching (all/all_strict): Memory matches if ALL request tags are present in its tags
-"""
-
-from __future__ import annotations
-
-from typing import Annotated, Literal
-
-from pydantic import BaseModel, ConfigDict, Field
-
-TagsMatch = Literal["any", "all", "any_strict", "all_strict"]
-
-
-def _parse_tags_match(match: TagsMatch) -> tuple[str, bool]:
-    """
-    Parse TagsMatch into operator and include_untagged flag.
-
-    Returns:
-        Tuple of (operator, include_untagged)
-        - operator: "&&" for any/any_strict, "@>" for all/all_strict
-        - include_untagged: True for any/all, False for any_strict/all_strict
-    """
-    if match == "any":
-        return "&&", True
-    elif match == "all":
-        return "@>", True
-    elif match == "any_strict":
-        return "&&", False
-    elif match == "all_strict":
-        return "@>", False
-    else:
-        # Default to "any" behavior
-        return "&&", True
-
-
-def build_tags_where_clause(
-    tags: list[str] | None,
-    param_offset: int = 1,
-    table_alias: str = "",
-    match: TagsMatch = "any",
-) -> tuple[str, list, int]:
-    """
-    Build a SQL WHERE clause for filtering by tags.
-
-    Supports four matching modes:
-    - "any" (default): OR matching, includes untagged memories
-    - "all": AND matching, includes untagged memories
-    - "any_strict": OR matching, excludes untagged memories
-    - "all_strict": AND matching, excludes untagged memories
-
-    Args:
-        tags: List of tags to filter by. If None or empty, returns empty clause (no filtering).
-        param_offset: Starting parameter number for SQL placeholders (default 1).
-        table_alias: Optional table alias prefix (e.g., "mu." for "memory_units mu").
-        match: Matching mode. Defaults to "any".
-
-    Returns:
-        Tuple of (sql_clause, params, next_param_offset):
-        - sql_clause: SQL WHERE clause string
-        - params: List of parameter values to bind
-        - next_param_offset: Next available parameter number
-
-    Example:
-        >>> clause, params, next_offset = build_tags_where_clause(['user_a'], 3, 'mu.', 'any_strict')
-        >>> print(clause)  # "AND mu.tags IS NOT NULL AND mu.tags != '{}' AND mu.tags && $3"
-    """
-    if not tags:
-        return "", [], param_offset
-
-    column = f"{table_alias}tags" if table_alias else "tags"
-    operator, include_untagged = _parse_tags_match(match)
-
-    if include_untagged:
-        # Include untagged memories (NULL or empty array) OR matching tags
-        clause = f"AND ({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_offset})"
-    else:
-        # Strict: only memories with matching tags (exclude NULL and empty)
-        clause = f"AND {column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_offset}"
-
-    return clause, [tags], param_offset + 1
-
-
-def build_tags_where_clause_simple(
-    tags: list[str] | None,
-    param_num: int,
-    table_alias: str = "",
-    match: TagsMatch = "any",
-) -> str:
-    """
-    Build a simple SQL WHERE clause for tags filtering.
-
-    This is a convenience version that returns just the clause string,
-    assuming the caller will add the tags array to their params list.
-
-    Args:
-        tags: List of tags to filter by. If None or empty, returns empty string.
-        param_num: Parameter number to use in the clause.
-        table_alias: Optional table alias prefix.
-        match: Matching mode. Defaults to "any".
-
-    Returns:
-        SQL clause string or empty string.
-    """
-    if not tags:
-        return ""
-
-    column = f"{table_alias}tags" if table_alias else "tags"
-    operator, include_untagged = _parse_tags_match(match)
-
-    if include_untagged:
-        # Include untagged memories (NULL or empty array) OR matching tags
-        return f"AND ({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_num})"
-    else:
-        # Strict: only memories with matching tags (exclude NULL and empty)
-        return f"AND {column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_num}"
-
-
-def filter_results_by_tags(
-    results: list,
-    tags: list[str] | None,
-    match: TagsMatch = "any",
-) -> list:
-    """
-    Filter retrieval results by tags in Python (for post-processing).
-
-    Used when SQL filtering isn't possible (e.g., graph traversal results).
-
-    Args:
-        results: List of RetrievalResult objects with a 'tags' attribute.
-        tags: List of tags to filter by. If None or empty, returns all results.
-        match: Matching mode. Defaults to "any".
-
-    Returns:
-        Filtered list of results.
-    """
-    if not tags:
-        return results
-
-    _, include_untagged = _parse_tags_match(match)
-    is_any_match = match in ("any", "any_strict")
-
-    tags_set = set(tags)
-    filtered = []
-
-    for result in results:
-        result_tags = getattr(result, "tags", None)
-
-        # Check if untagged
-        is_untagged = result_tags is None or len(result_tags) == 0
-
-        if is_untagged:
-            if include_untagged:
-                filtered.append(result)
-            # else: skip untagged
-        else:
-            result_tags_set = set(result_tags)
-            if is_any_match:
-                # Any overlap
-                if result_tags_set & tags_set:
-                    filtered.append(result)
-            else:
-                # All tags must be present
-                if tags_set <= result_tags_set:
-                    filtered.append(result)
-
-    return filtered
-
-
-# =============================================================================
-# Compound tag group models (recursive boolean expressions)
-# =============================================================================
-
-
-class TagGroupLeaf(BaseModel):
-    """A leaf tag filter: matches memories by tag list and match mode."""
-
-    tags: list[str]
-    match: TagsMatch = "any_strict"
-
-
-class TagGroupAnd(BaseModel):
-    """Compound AND group: all child filters must match."""
-
-    model_config = ConfigDict(populate_by_name=True)
-    filters: list[TagGroup] = Field(alias="and")
-
-
-class TagGroupOr(BaseModel):
-    """Compound OR group: at least one child filter must match."""
-
-    model_config = ConfigDict(populate_by_name=True)
-    filters: list[TagGroup] = Field(alias="or")
-
-
-class TagGroupNot(BaseModel):
-    """Compound NOT group: child filter must NOT match."""
-
-    model_config = ConfigDict(populate_by_name=True)
-    filter: TagGroup = Field(alias="not")
-
-
-# TagGroup is a discriminated union; Pydantic will try left-to-right.
-# TagGroupLeaf is identified by the presence of 'tags'.
-# TagGroupAnd / TagGroupOr / TagGroupNot are compound (no 'tags' key).
-TagGroup = Annotated[
-    TagGroupLeaf | TagGroupAnd | TagGroupOr | TagGroupNot,
-    Field(union_mode="left_to_right"),
-]
-
-# Rebuild forward-reference models so recursive TagGroup is resolved.
-TagGroupAnd.model_rebuild()
-TagGroupOr.model_rebuild()
-TagGroupNot.model_rebuild()
-
-
-# =============================================================================
-# SQL builder for compound tag groups
-# =============================================================================
-
-
-def _build_group_clause(
-    group: TagGroup,
-    param_offset: int,
-    table_alias: str,
-) -> tuple[str, list, int]:
-    """
-    Recursively build an inner SQL clause (no leading AND/OR) for a single TagGroup.
-
-    Returns:
-        (inner_clause, params, next_param_offset)
-    """
-    if isinstance(group, TagGroupLeaf):
-        column = f"{table_alias}tags" if table_alias else "tags"
-        operator, include_untagged = _parse_tags_match(group.match)
-        if include_untagged:
-            clause = f"({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_offset})"
-        else:
-            clause = f"({column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_offset})"
-        return clause, [group.tags], param_offset + 1
-
-    elif isinstance(group, TagGroupAnd):
-        parts = []
-        params: list = []
-        offset = param_offset
-        for child in group.filters:
-            child_clause, child_params, offset = _build_group_clause(child, offset, table_alias)
-            parts.append(child_clause)
-            params.extend(child_params)
-        inner = " AND ".join(parts)
-        return f"({inner})", params, offset
-
-    elif isinstance(group, TagGroupOr):
-        parts = []
-        params = []
-        offset = param_offset
-        for child in group.filters:
-            child_clause, child_params, offset = _build_group_clause(child, offset, table_alias)
-            parts.append(child_clause)
-            params.extend(child_params)
-        inner = " OR ".join(parts)
-        return f"({inner})", params, offset
-
-    elif isinstance(group, TagGroupNot):
-        child_clause, child_params, next_offset = _build_group_clause(group.filter, param_offset, table_alias)
-        return f"NOT {child_clause}", child_params, next_offset
-
-    else:
-        # Should never happen with proper Pydantic validation
-        return "", [], param_offset
-
-
-def build_tag_groups_where_clause(
-    tag_groups: list[TagGroup] | None,
-    param_offset: int,
-    table_alias: str = "",
-) -> tuple[str, list, int]:
-    """
-    Build a SQL WHERE clause for compound tag group filtering.
-
-    Top-level groups are AND-ed together. Each group is a recursive boolean
-    expression (leaf, and, or, not).
-
-    Args:
-        tag_groups: List of TagGroup objects. If None or empty, returns empty clause.
-        param_offset: Starting parameter number for SQL placeholders.
-        table_alias: Optional table alias prefix (e.g., "mu." for "memory_units mu").
-
-    Returns:
-        Tuple of (sql_clause, params, next_param_offset):
-        - sql_clause: SQL WHERE clause string starting with "AND" (or empty string)
-        - params: List of parameter values to bind (one per leaf node)
-        - next_param_offset: Next available parameter number
-
-    Example:
-        >>> groups = [TagGroupLeaf(tags=["user:alice"], match="all_strict")]
-        >>> clause, params, next_offset = build_tag_groups_where_clause(groups, 3)
-        >>> print(clause)  # "AND (tags IS NOT NULL AND tags != '{}' AND tags @> $3)"
-    """
-    if not tag_groups:
-        return "", [], param_offset
-
-    all_params: list = []
-    all_clauses: list[str] = []
-    offset = param_offset
-
-    for group in tag_groups:
-        inner_clause, group_params, offset = _build_group_clause(group, offset, table_alias)
-        all_clauses.append(inner_clause)
-        all_params.extend(group_params)
-
-    combined = " AND ".join(all_clauses)
-    return f"AND {combined}", all_params, offset
-
-
-# =============================================================================
-# Python-side filter for compound tag groups (post-retrieval filtering)
-# =============================================================================
-
-
-def _match_group(result: object, group: TagGroup) -> bool:
-    """
-    Recursively evaluate a TagGroup against a retrieval result.
-
-    Args:
-        result: Any object with a 'tags' attribute (list[str] or None).
-        group: The TagGroup to evaluate.
-
-    Returns:
-        True if the result matches the group, False otherwise.
-    """
-    if isinstance(group, TagGroupLeaf):
-        result_tags = getattr(result, "tags", None)
-        is_untagged = result_tags is None or len(result_tags) == 0
-        _, include_untagged = _parse_tags_match(group.match)
-        is_any_match = group.match in ("any", "any_strict")
-        tags_set = set(group.tags)
-
-        if is_untagged:
-            return include_untagged
-        else:
-            result_tags_set = set(result_tags)
-            if is_any_match:
-                return bool(result_tags_set & tags_set)
-            else:
-                return tags_set <= result_tags_set
-
-    elif isinstance(group, TagGroupAnd):
-        return all(_match_group(result, child) for child in group.filters)
-
-    elif isinstance(group, TagGroupOr):
-        return any(_match_group(result, child) for child in group.filters)
-
-    elif isinstance(group, TagGroupNot):
-        return not _match_group(result, group.filter)
-
-    else:
-        return True
-
-
-def filter_results_by_tag_groups(
-    results: list,
-    tag_groups: list[TagGroup] | None,
-) -> list:
-    """
-    Filter retrieval results by compound tag groups in Python (for post-processing).
-
-    Used when SQL filtering isn't possible (e.g., graph traversal results).
-    Top-level groups are AND-ed together.
-
-    Args:
-        results: List of RetrievalResult objects with a 'tags' attribute.
-        tag_groups: List of TagGroup objects. If None or empty, returns all results.
-
-    Returns:
-        Filtered list of results where ALL top-level groups match.
-    """
-    if not tag_groups:
-        return results
-
-    return [r for r in results if all(_match_group(r, group) for group in tag_groups)]
@@ -1,105 +0,0 @@
-"""Google Cloud Storage backend using obstore."""
-
-import logging
-import os
-from datetime import datetime, timedelta, timezone
-
-import obstore as obs
-from obstore.store import GCSStore
-
-from .base import FileStorage
-
-logger = logging.getLogger(__name__)
-
-
-def _make_google_auth_credential_provider():
-    """Create a credential provider using google.auth (supports all credential types).
-
-    obstore's built-in credential parsing only supports service_account and
-    authorized_user JSON types.  This provider uses the google-auth library
-    which additionally handles external_account (Workload Identity Federation),
-    impersonated credentials, and metadata-server credentials.
-    """
-    import google.auth
-    import google.auth.transport.requests
-
-    credentials, _ = google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
-    request = google.auth.transport.requests.Request()
-
-    def _provide():
-        credentials.refresh(request)
-        expiry = credentials.expiry
-        if expiry and expiry.tzinfo is None:
-            expiry = expiry.replace(tzinfo=timezone.utc)
-        return {"token": credentials.token, "expires_at": expiry}
-
-    return _provide
-
-
-class GCSFileStorage(FileStorage):
-    """
-    Google Cloud Storage backend.
-
-    Uses obstore (Rust-backed) for high-throughput async access to GCS.
-    Supports Application Default Credentials, service account keys, and explicit credentials.
-    """
-
-    def __init__(
-        self,
-        bucket: str,
-        service_account_key: str | None = None,
-    ):
-        kwargs: dict = {}
-        if service_account_key:
-            kwargs["service_account_key"] = service_account_key
-        else:
-            # Use google.auth credential provider for broad credential type support
-            # (service_account, authorized_user, external_account, metadata server, etc.)
-            try:
-                kwargs["credential_provider"] = _make_google_auth_credential_provider()
-                logger.info("Using google.auth credential provider for GCS")
-            except Exception as e:
-                logger.warning(
-                    f"Failed to create google.auth credential provider, falling back to obstore defaults: {e}"
-                )
-
-        # Workaround for https://github.com/developmentseed/obstore/issues/605
-        # obstore's Rust layer doesn't support external_account credentials (Workload
-        # Identity Federation) and eagerly parses GOOGLE_APPLICATION_CREDENTIALS even
-        # when credential_provider is given. Per the obstore maintainer's guidance,
-        # remove env vars so the Rust code doesn't try to authenticate itself.
-        # google.auth (used by credential_provider above) has already loaded credentials.
-        gac = os.environ.pop("GOOGLE_APPLICATION_CREDENTIALS", None)
-        try:
-            self._store = GCSStore(bucket, **kwargs)
-        finally:
-            if gac is not None:
-                os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = gac
-        logger.info(f"Initialized GCS file storage: bucket={bucket}")
-
-    async def store(self, file_data: bytes, key: str, metadata: dict[str, str] | None = None) -> str:
-        await obs.put_async(self._store, key, file_data)
-        logger.debug(f"Stored file {key} ({len(file_data)} bytes) in GCS")
-        return key
-
-    async def retrieve(self, key: str) -> bytes:
-        try:
-            response = await obs.get_async(self._store, key)
-            return await response.bytes_async()
-        except Exception as e:
-            if "not found" in str(e).lower():
-                raise FileNotFoundError(f"File not found: {key}") from e
-            raise
-
-    async def delete(self, key: str) -> None:
-        await obs.delete_async(self._store, key)
-
-    async def exists(self, key: str) -> bool:
-        try:
-            await obs.head_async(self._store, key)
-            return True
-        except Exception:
-            return False
-
-    async def get_download_url(self, key: str, expires_in: int = 3600) -> str:
-        return await obs.sign_async(self._store, "GET", key, timedelta(seconds=expires_in))
@@ -1,198 +0,0 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[project]
-name = "hindsight-api-slim"
-version = "0.4.18"
-description = "Hindsight: Agent Memory That Works Like Human Memory"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    "asyncpg>=0.29.0",
-    "python-dotenv>=1.0.0",
-    "openai>=1.0.0",
-    "pydantic>=2.0.0",
-    "rich>=13.0.0",
-    "langchain-text-splitters>=0.3.0",
-    "fastapi[standard]>=0.120.3",
-    "uvicorn>=0.38.0",
-    "wsproto>=1.0.0",
-    "sqlalchemy>=2.0.44",
-    "alembic>=1.17.1",
-    "pgvector>=0.4.1",
-    "greenlet>=3.2.4",
-    "psycopg2-binary>=2.9.11",
-    "tiktoken>=0.12.0",
-    "httpx>=0.27.0",
-    "PyJWT[crypto]>=2.8.0",
-    "fastmcp>=2.14.0", # CVE-2025-66416
-    "python-dateutil>=2.8.0",
-    "opentelemetry-api>=1.20.0",
-    "opentelemetry-sdk>=1.20.0",
-    "opentelemetry-instrumentation-fastapi>=0.41b0",
-    "opentelemetry-exporter-prometheus>=0.41b0",
-    "opentelemetry-exporter-otlp-proto-http>=1.20.0",
-    "opentelemetry-semantic-conventions>=0.41b0",
-    "dateparser>=1.2.2",
-    "google-genai>=1.0.0",
-    "google-auth>=2.0.0",
-    "anthropic>=0.40.0",
-    "typer>=0.9.0",
-    "cohere>=5.0.0",
-    "litellm>=1.0.0",
-    "markitdown[pdf,docx,pptx,xlsx,xls]>=0.1.4", # File to markdown conversion
-    "obstore>=0.4.0", # S3/GCS/Azure object storage client (Rust-backed)
-    "uvloop>=0.22.1",
-    # Transitive dependency security fixes
-    "pyasn1>=0.6.2", # DoS vulnerability fix
-    "urllib3>=2.6.3", # Decompression-bomb safeguards bypass fix
-    "langchain-core>=1.2.11", # Serialization injection + SSRF vulnerability fix
-    "langsmith>=0.6.3", # SSRF via tracing header injection fix
-    "protobuf>=6.33.5", # JSON recursion depth bypass fix
-    "pillow>=12.1.1", # Out-of-bounds write in PSD image loading fix
-    "cryptography>=46.0.5", # Subgroup attack vulnerability fix
-    "filelock>=3.20.1", # TOCTOU race condition fix
-    "authlib>=1.6.6", # Account takeover vulnerability fix
-    "aiohttp>=3.13.3", # Multiple DoS vulnerabilities
-    "claude-agent-sdk>=0.1.27",
-]
-
-[project.optional-dependencies]
-local-ml = [
-    # Local ML models for embeddings/reranking
-    "sentence-transformers>=3.3.0",
-    "transformers>=4.53.0", # Security fixes for ReDoS vulnerabilities
-    "torch>=2.6.0", # CVE fix for remote code execution
-    "einops>=0.8.2",
-    "flashrank>=0.2.0",
-    # Apple Silicon local inference
-    "mlx>=0.31.0",
-    "mlx-lm>=0.31.1",
-    "safetensors>=0.6.2",
-]
-embedded-db = [
-    "pg0-embedded>=0.11.0",
-]
-all = [
-    "hindsight-api-slim[local-ml,embedded-db]",
-]
-test = [
-    "pytest>=7.0.0",
-    "pytest-asyncio>=0.21.0",
-    "pytest-timeout>=2.4.0",
-    "pytest-xdist>=3.0.0",
-    "filelock>=3.20.1",  # TOCTOU race condition fix
-    "testcontainers>=4.0.0",
-]
-
-[project.scripts]
-hindsight-api = "hindsight_api.main:main"
-hindsight-worker = "hindsight_api.worker.main:main"
-hindsight-local-mcp = "hindsight_api.mcp_local:main"
-hindsight-admin = "hindsight_api.admin.cli:main"
-
-[tool.hatch.build.targets.wheel]
-packages = ["hindsight_api"]
-
-[tool.hatch.build.targets.wheel.sources]
-"hindsight_api" = "hindsight_api"
-
-[tool.hatch.build.targets.sdist]
-include = [
-    "hindsight_api/**/*",
-]
-
-[tool.hatch.build]
-include = [
-    "hindsight_api/**/*.py",
-    "hindsight_api/alembic/**/*",
-]
-
-[tool.pytest.ini_options]
-log_cli = true
-log_cli_level = "INFO"
-log_cli_format = "%(asctime)s - %(levelname)s - %(name)s - %(message)s"
-log_cli_date_format = "%Y-%m-%d %H:%M:%S"
-addopts = "--timeout 300 -n 8 --dist loadgroup --durations=10 -v"
-asyncio_mode = "auto"
-asyncio_default_fixture_loop_scope = "function"
-log_auto_indent = true
-filterwarnings = [
-    "ignore:The @wait_container_is_ready decorator is deprecated:DeprecationWarning",
-    "ignore::RuntimeWarning:asyncio",
-]
-
-[dependency-groups]
-dev = [
-    "pytest>=9.0.0",
-    "pytest-asyncio>=1.3.0",
-    "pytest-timeout>=2.4.0",
-    "pytest-xdist>=3.8.0",
-    "python-dotenv>=1.2.1",
-    "filelock>=3.20.1",  # TOCTOU race condition fix
-    "ruff>=0.8.0",
-    "ty>=0.0.1",
-    "testcontainers>=4.0.0",
-]
-
-[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.lint.isort]
-known-third-party = ["alembic"]
-
-[tool.ruff.format]
-quote-style = "double"
-indent-style = "space"
-
-[tool.uv]
-# Allow uv to search all configured indexes for packages, not just the first one
-# This prevents dependency resolution failures when using pytorch index + PyPI
-index-strategy = "unsafe-best-match"
-
-[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
@@ -1,193 +0,0 @@
-"""
-Tests for per-bank HNSW index lifecycle and UNION ALL retrieval.
-
-Covers:
- _hnsw_index_name deterministic naming
- Per-bank HNSW indexes created on bank creation (retain_async / ensure_bank_exists)
- Per-bank HNSW indexes dropped on bank deletion
- retrieve_semantic_bm25_combined groups results correctly by fact_type and source
-"""
-import uuid
-from datetime import datetime, timezone
-
-import pytest
-
-from hindsight_api.engine.retain.bank_utils import _HNSW_FACT_TYPES, _hnsw_index_name
-
-
-# ---------------------------------------------------------------------------
-# Unit tests — no DB required
-# ---------------------------------------------------------------------------
-
-
-class TestHnswIndexName:
-    def test_deterministic(self):
-        uid = "550e8400-e29b-41d4-a716-446655440000"
-        assert _hnsw_index_name("world", uid) == _hnsw_index_name("world", uid)
-
-    def test_strips_dashes(self):
-        uid = "550e8400-e29b-41d4-a716-446655440000"
-        name = _hnsw_index_name("world", uid)
-        # uid16 should be hex chars only
-        assert "-" not in name
-
-    def test_uses_first_16_hex_chars(self):
-        uid = "550e8400-e29b-41d4-a716-446655440000"
-        uid16 = uid.replace("-", "")[:16]  # "550e8400e29b41d4"
-        assert name_ends_with(name=_hnsw_index_name("world", uid), suffix=uid16)
-
-    def test_suffix_per_fact_type(self):
-        uid = "550e8400-e29b-41d4-a716-446655440000"
-        names = {ft: _hnsw_index_name(ft, uid) for ft in _HNSW_FACT_TYPES}
-        # All three names must be distinct
-        assert len(set(names.values())) == 3
-
-    def test_all_fact_types_covered(self):
-        assert set(_HNSW_FACT_TYPES) == {"world", "experience", "observation"}
-
-    def test_fits_pg_identifier_limit(self):
-        # PostgreSQL max identifier length is 63 chars
-        uid = "f" * 32  # simulated UUID without dashes
-        for ft in _HNSW_FACT_TYPES:
-            assert len(_hnsw_index_name(ft, uid)) <= 63
-
-
-def name_ends_with(name: str, suffix: str) -> bool:
-    return name.endswith(suffix)
-
-
-# ---------------------------------------------------------------------------
-# Integration tests — require DB (memory fixture)
-# ---------------------------------------------------------------------------
-
-
-async def _get_bank_hnsw_indexes(pool, bank_id: str) -> list[str]:
-    """Return index names for memory_units that match the per-bank pattern."""
-    async with pool.acquire() as conn:
-        rows = await conn.fetch(
-            """
-            SELECT indexname
-            FROM pg_indexes
-            WHERE tablename = 'memory_units'
-              AND indexname LIKE 'idx_mu_emb_%'
-              AND indexdef LIKE $1
-            ORDER BY indexname
-            """,
-            f"%bank_id = '{bank_id}'%",
-        )
-    return [row["indexname"] for row in rows]
-
-
-@pytest.mark.asyncio
-async def test_retain_creates_per_bank_hnsw_indexes(memory, request_context):
-    """retain_async on a new bank must create 3 per-(bank, fact_type) HNSW indexes."""
-    bank_id = f"test_hnsw_create_{uuid.uuid4().hex[:8]}"
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice is a software engineer.",
-            request_context=request_context,
-        )
-        indexes = await _get_bank_hnsw_indexes(memory._pool, bank_id)
-        assert len(indexes) == 3, f"Expected 3 per-bank HNSW indexes, got: {indexes}"
-        for ft_short in _HNSW_FACT_TYPES.values():
-            assert any(ft_short in idx for idx in indexes), (
-                f"Missing index for fact_type short '{ft_short}' in {indexes}"
-            )
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delete_bank_drops_hnsw_indexes(memory, request_context):
-    """delete_bank must drop all per-bank HNSW indexes."""
-    bank_id = f"test_hnsw_drop_{uuid.uuid4().hex[:8]}"
-
-    await memory.retain_async(
-        bank_id=bank_id,
-        content="Bob is a data scientist.",
-        request_context=request_context,
-    )
-    # Verify indexes exist before deletion
-    indexes_before = await _get_bank_hnsw_indexes(memory._pool, bank_id)
-    assert len(indexes_before) == 3
-
-    await memory.delete_bank(bank_id, request_context=request_context)
-
-    indexes_after = await _get_bank_hnsw_indexes(memory._pool, bank_id)
-    assert indexes_after == [], f"Indexes should be dropped after bank deletion, got: {indexes_after}"
-
-
-@pytest.mark.asyncio
-async def test_retain_idempotent_bank_creation(memory, request_context):
-    """Retaining into the same bank twice must not error and still have exactly 3 indexes."""
-    bank_id = f"test_hnsw_idem_{uuid.uuid4().hex[:8]}"
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Carol is a product manager.",
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Carol joined the company in 2022.",
-            request_context=request_context,
-        )
-        indexes = await _get_bank_hnsw_indexes(memory._pool, bank_id)
-        assert len(indexes) == 3
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_retrieve_semantic_bm25_grouped_by_fact_type(memory, request_context):
-    """
-    retrieve_semantic_bm25_combined must return a dict keyed by fact_type with
-    (semantic_list, bm25_list) tuples.  All returned facts must belong to their
-    declared fact_type.
-    """
-    from hindsight_api.engine.search.retrieval import retrieve_semantic_bm25_combined
-
-    bank_id = f"test_retrieval_{uuid.uuid4().hex[:8]}"
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=(
-                "Alice is a software engineer at TechCorp. "
-                "She visited Paris in 2023 for a conference."
-            ),
-            context="background",
-            event_date=datetime(2023, 6, 1, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        query_emb = memory.embeddings.encode(["software engineer Alice"])
-        query_emb_str = str(query_emb[0])
-
-        fact_types = ["world", "experience"]
-        async with memory._pool.acquire() as conn:
-            results = await retrieve_semantic_bm25_combined(
-                conn=conn,
-                query_emb_str=query_emb_str,
-                query_text="software engineer Alice",
-                bank_id=bank_id,
-                fact_types=fact_types,
-                limit=5,
-            )
-
-        # Must return an entry for every requested fact_type
-        assert set(results.keys()) == set(fact_types)
-
-        for ft, (sem, bm25) in results.items():
-            # Semantic and BM25 lists must be lists
-            assert isinstance(sem, list)
-            assert isinstance(bm25, list)
-            # All semantic results must declare the correct fact_type
-            for r in sem:
-                assert r.fact_type == ft, f"Semantic result has wrong fact_type: {r.fact_type}"
-            # All BM25 results must declare the correct fact_type
-            for r in bm25:
-                assert r.fact_type == ft, f"BM25 result has wrong fact_type: {r.fact_type}"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
@@ -1,37 +0,0 @@
-import pytest
-
-from hindsight_api.engine.llm_wrapper import sanitize_llm_output
-
-
-@pytest.mark.parametrize(
-    "input_text, expected",
-    [
-        # Null bytes stripped
-        ("hello\x00world", "helloworld"),
-        ("FIRST\u0000PAGE", "FIRSTPAGE"),
-        # Multiple null bytes
-        ("\x00\x00text\x00", "text"),
-        # Other control characters stripped (non-whitespace)
-        ("text\x01\x02\x03end", "textend"),
-        ("text\x08end", "textend"),  # backspace
-        ("text\x0cend", "textend"),  # form feed
-        ("text\x0bend", "textend"),  # vertical tab
-        ("text\x1fend", "textend"),  # unit separator
-        ("text\x7fend", "textend"),  # DEL
-        # Whitespace preserved
-        ("hello\tworld", "hello\tworld"),
-        ("hello\nworld", "hello\nworld"),
-        ("hello\r\nworld", "hello\r\nworld"),
-        # Unicode surrogates stripped
-        ("text\ud800end", "textend"),
-        ("text\udfffend", "textend"),
-        # Clean text unchanged
-        ("normal text", "normal text"),
-        ("unicode: café naïve", "unicode: café naïve"),
-        # Edge cases
-        ("", ""),
-        (None, None),
-    ],
-)
-def test_sanitize_llm_output(input_text, expected):
-    assert sanitize_llm_output(input_text) == expected
@@ -1,321 +0,0 @@
-"""
-Reproduce issue #520: Reflect fails with LM Studio due to unsupported tool_choice format.
-
-The reflect agent forces tool selection via named tool_choice dicts on the first few iterations:
-  {"type": "function", "function": {"name": "search_mental_models"}}
-
-LM Studio (and Ollama) reject this format with HTTP 400:
-  "Tool choice of type 'function' is not supported. Use 'auto', 'none', or 'required'."
-
-The fix should convert named tool_choice to "required" and filter the tools list
-to only the requested tool for providers that don't support named tool_choice.
-"""
-
-import json
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from openai import APIStatusError
-
-from hindsight_api.engine.providers.openai_compatible_llm import OpenAICompatibleLLM
-
-# Reflect agent tools (subset matching what agent.py uses)
-REFLECT_TOOLS = [
-    {
-        "type": "function",
-        "function": {
-            "name": "search_mental_models",
-            "description": "Search consolidated mental models",
-            "parameters": {
-                "type": "object",
-                "properties": {"query": {"type": "string"}},
-                "required": ["query"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "search_observations",
-            "description": "Search raw observations",
-            "parameters": {
-                "type": "object",
-                "properties": {"query": {"type": "string"}},
-                "required": ["query"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "recall",
-            "description": "Recall semantic memories",
-            "parameters": {
-                "type": "object",
-                "properties": {"query": {"type": "string"}},
-                "required": ["query"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "done",
-            "description": "Finish and return the answer",
-            "parameters": {
-                "type": "object",
-                "properties": {"answer": {"type": "string"}},
-                "required": ["answer"],
-            },
-        },
-    },
-]
-
-
-def _make_lmstudio_llm() -> OpenAICompatibleLLM:
-    return OpenAICompatibleLLM(
-        provider="lmstudio",
-        api_key="local",
-        base_url="http://localhost:1234/v1",
-        model="openai/gpt-oss-20b",
-    )
-
-
-def _lmstudio_400_error(msg: str = "Tool choice of type 'function' is not supported. Use 'auto', 'none', or 'required'.") -> APIStatusError:
-    """Simulate the HTTP 400 LM Studio returns for unsupported tool_choice format."""
-    mock_response = MagicMock()
-    mock_response.status_code = 400
-    mock_response.headers = {}
-    return APIStatusError(
-        message=msg,
-        response=mock_response,
-        body={"error": {"message": msg, "type": "invalid_request_error"}},
-    )
-
-
-def _make_tool_call_response(tool_name: str, arguments: dict) -> MagicMock:
-    """Build a mock successful tool call response from the LLM API."""
-    mock_tc = MagicMock()
-    mock_tc.id = "call_abc123"
-    mock_tc.function.name = tool_name
-    mock_tc.function.arguments = json.dumps(arguments)
-
-    mock_response = MagicMock()
-    mock_response.usage.prompt_tokens = 120
-    mock_response.usage.completion_tokens = 40
-    mock_response.usage.total_tokens = 160
-    mock_response.choices[0].finish_reason = "tool_calls"
-    mock_response.choices[0].message.content = None
-    mock_response.choices[0].message.tool_calls = [mock_tc]
-    return mock_response
-
-
-class TestLMStudioNamedToolChoiceBug:
-    """
-    Reproduces issue #520.
-
-    The reflect agent (agent.py lines 546-555) sets tool_choice to a named dict
-    on the first iterations to force sequential retrieval:
-
-        iteration=0, has_mental_models=True  → {"type": "function", "function": {"name": "search_mental_models"}}
-        iteration=0, has_mental_models=False → {"type": "function", "function": {"name": "search_observations"}}
-        iteration=1, has_mental_models=True  → {"type": "function", "function": {"name": "search_observations"}}
-        iteration=1 or (2 with models)       → {"type": "function", "function": {"name": "recall"}}
-
-    LM Studio rejects these dict formats with HTTP 400.
-    """
-
-    @pytest.mark.asyncio
-    async def test_lmstudio_named_tool_choice_no_longer_causes_400(self):
-        """
-        Regression test for issue #520: named tool_choice dict is converted to
-        "required" + filtered tools before the API call, so LM Studio never
-        sees the unsupported format and the 400 error no longer occurs.
-        """
-        llm = _make_lmstudio_llm()
-        named_tool_choice = {"type": "function", "function": {"name": "search_mental_models"}}
-        success_response = _make_tool_call_response("search_mental_models", {"query": "user name"})
-
-        with patch.object(llm._client.chat.completions, "create", new_callable=AsyncMock) as mock_create:
-            mock_create.return_value = success_response
-
-            # Should succeed — no 400 because the dict is converted before sending
-            result = await llm.call_with_tools(
-                messages=[{"role": "user", "content": "What is the user's name?"}],
-                tools=REFLECT_TOOLS,
-                tool_choice=named_tool_choice,
-                max_retries=0,
-            )
-
-        assert len(result.tool_calls) == 1
-        assert result.tool_calls[0].name == "search_mental_models"
-
-        sent_kwargs = mock_create.call_args.kwargs
-        assert sent_kwargs["tool_choice"] == "required"
-        assert len(sent_kwargs["tools"]) == 1
-        assert sent_kwargs["tools"][0]["function"]["name"] == "search_mental_models"
-
-    @pytest.mark.asyncio
-    @pytest.mark.parametrize(
-        "forced_tool_name",
-        ["search_mental_models", "search_observations", "recall"],
-    )
-    async def test_all_reflect_forced_tools_fail_on_lmstudio(self, forced_tool_name: str):
-        """
-        Each named tool_choice the reflect agent uses on iterations 0-2 triggers
-        the same 400 error on LM Studio.
-        """
-        llm = _make_lmstudio_llm()
-        named_tool_choice = {"type": "function", "function": {"name": forced_tool_name}}
-
-        with patch.object(llm._client.chat.completions, "create", new_callable=AsyncMock) as mock_create:
-            mock_create.side_effect = _lmstudio_400_error()
-
-            with pytest.raises(APIStatusError) as exc_info:
-                await llm.call_with_tools(
-                    messages=[{"role": "user", "content": "Test query"}],
-                    tools=REFLECT_TOOLS,
-                    tool_choice=named_tool_choice,
-                    max_retries=0,
-                )
-
-            assert exc_info.value.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_lmstudio_string_tool_choice_works_fine(self):
-        """
-        String tool_choice values ("auto", "none", "required") ARE supported by LM Studio.
-        Only the dict format {"type": "function", "function": {"name": "..."}} fails.
-        This test confirms the control case works.
-        """
-        llm = _make_lmstudio_llm()
-        success_response = _make_tool_call_response("search_mental_models", {"query": "user name"})
-
-        with patch.object(llm._client.chat.completions, "create", new_callable=AsyncMock) as mock_create:
-            mock_create.return_value = success_response
-
-            result = await llm.call_with_tools(
-                messages=[{"role": "user", "content": "What is the user's name?"}],
-                tools=REFLECT_TOOLS,
-                tool_choice="required",  # string form — LM Studio accepts this
-                max_retries=0,
-            )
-
-        assert len(result.tool_calls) == 1
-        assert result.tool_calls[0].name == "search_mental_models"
-
-        # Confirm "required" was sent, not a dict
-        sent_kwargs = mock_create.call_args.kwargs
-        assert sent_kwargs["tool_choice"] == "required"
-
-
-class TestExpectedFixBehavior:
-    """
-    Tests that document the EXPECTED behavior after the fix is applied.
-
-    For lmstudio (and ollama) providers, when tool_choice is a named dict:
-      {"type": "function", "function": {"name": "search_mental_models"}}
-
-    The fix should:
-      1. Convert tool_choice to "required"
-      2. Filter tools to only the requested tool
-
-    These tests currently FAIL (because the fix is not yet implemented).
-    After the fix is applied, they should PASS.
-    """
-
-    @pytest.mark.asyncio
-    async def test_fix_converts_named_tool_choice_to_required(self):
-        """
-        After fix: named tool_choice dict is converted to "required" for lmstudio.
-        The API receives tool_choice="required" instead of the unsupported dict.
-        """
-        llm = _make_lmstudio_llm()
-        named_tool_choice = {"type": "function", "function": {"name": "search_mental_models"}}
-        success_response = _make_tool_call_response("search_mental_models", {"query": "user name"})
-
-        with patch.object(llm._client.chat.completions, "create", new_callable=AsyncMock) as mock_create:
-            mock_create.return_value = success_response
-
-            result = await llm.call_with_tools(
-                messages=[{"role": "user", "content": "What is the user's name?"}],
-                tools=REFLECT_TOOLS,
-                tool_choice=named_tool_choice,
-                max_retries=0,
-            )
-
-        assert len(result.tool_calls) == 1
-        assert result.tool_calls[0].name == "search_mental_models"
-
-        sent_kwargs = mock_create.call_args.kwargs
-        # Fix: dict was converted to "required"
-        assert sent_kwargs["tool_choice"] == "required", (
-            f"Expected tool_choice='required', got {sent_kwargs['tool_choice']!r}"
-        )
-        # Fix: tools filtered to just the requested one
-        assert len(sent_kwargs["tools"]) == 1
-        assert sent_kwargs["tools"][0]["function"]["name"] == "search_mental_models"
-
-    @pytest.mark.asyncio
-    @pytest.mark.parametrize(
-        "forced_tool_name",
-        ["search_mental_models", "search_observations", "recall"],
-    )
-    async def test_fix_filters_tools_to_requested_tool(self, forced_tool_name: str):
-        """
-        After fix: tools list is filtered to only the forced tool so the model
-        can only call that one tool (equivalent to the named tool_choice behavior).
-        """
-        llm = _make_lmstudio_llm()
-        named_tool_choice = {"type": "function", "function": {"name": forced_tool_name}}
-        success_response = _make_tool_call_response(forced_tool_name, {"query": "test"})
-
-        with patch.object(llm._client.chat.completions, "create", new_callable=AsyncMock) as mock_create:
-            mock_create.return_value = success_response
-
-            await llm.call_with_tools(
-                messages=[{"role": "user", "content": "Test query"}],
-                tools=REFLECT_TOOLS,
-                tool_choice=named_tool_choice,
-                max_retries=0,
-            )
-
-        sent_kwargs = mock_create.call_args.kwargs
-        assert sent_kwargs["tool_choice"] == "required"
-        assert len(sent_kwargs["tools"]) == 1
-        assert sent_kwargs["tools"][0]["function"]["name"] == forced_tool_name
-
-    @pytest.mark.asyncio
-    async def test_fix_also_applies_to_openai_provider(self):
-        """
-        The fix is generalized: all providers convert named tool_choice to
-        "required" + filtered tools.  OpenAI natively supports the dict format
-        too, so the behaviour is semantically identical either way.
-        """
-        from hindsight_api.engine.providers.openai_compatible_llm import OpenAICompatibleLLM
-
-        openai_llm = OpenAICompatibleLLM(
-            provider="openai",
-            api_key="sk-test",
-            base_url="",
-            model="gpt-4o-mini",
-        )
-
-        named_tool_choice = {"type": "function", "function": {"name": "search_mental_models"}}
-        success_response = _make_tool_call_response("search_mental_models", {"query": "test"})
-
-        with patch.object(openai_llm._client.chat.completions, "create", new_callable=AsyncMock) as mock_create:
-            mock_create.return_value = success_response
-
-            await openai_llm.call_with_tools(
-                messages=[{"role": "user", "content": "Test"}],
-                tools=REFLECT_TOOLS,
-                tool_choice=named_tool_choice,
-                max_retries=0,
-            )
-
-        sent_kwargs = mock_create.call_args.kwargs
-        # Generalized fix applies to OpenAI too
-        assert sent_kwargs["tool_choice"] == "required"
-        assert len(sent_kwargs["tools"]) == 1
-        assert sent_kwargs["tools"][0]["function"]["name"] == "search_mental_models"
@@ -1,154 +0,0 @@
-"""Tests for migration g7h8i9j0k1l2 (backsweep orphaned memory_units).
-
-Uses a dedicated pg0 instance (port 5562) so the test can control exactly
-which migrations have run before inserting the orphan seed data.
-"""
-
-import asyncio
-import uuid
-from pathlib import Path
-
-import pytest
-from alembic import command
-from alembic.config import Config
-from sqlalchemy import create_engine, text
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-_SCRIPT_LOCATION = str(Path(__file__).parent.parent / "hindsight_api" / "alembic")
-
-
-def _alembic_cfg(db_url: str) -> Config:
-    cfg = Config()
-    cfg.set_main_option("script_location", _SCRIPT_LOCATION)
-    cfg.set_main_option("sqlalchemy.url", db_url)
-    cfg.set_main_option("prepend_sys_path", ".")
-    cfg.set_main_option("path_separator", "os")
-    return cfg
-
-
-def _upgrade(db_url: str, revision: str) -> None:
-    command.upgrade(_alembic_cfg(db_url), revision)
-
-
-# ---------------------------------------------------------------------------
-# Fixture: fresh database at the revision just before the backsweep
-# ---------------------------------------------------------------------------
-
-@pytest.fixture(scope="module")
-def pre_backsweep_db_url():
-    """
-    Spin up a dedicated pg0 instance and run all migrations up to (but not
-    including) the backsweep revision so each test can seed orphan data and
-    then apply the backsweep itself.
-    """
-    from hindsight_api.pg0 import EmbeddedPostgres
-
-    pg0 = EmbeddedPostgres(name="hindsight-backsweep-test", port=5562)
-    loop = asyncio.new_event_loop()
-    try:
-        url = loop.run_until_complete(pg0.ensure_running())
-    finally:
-        loop.close()
-
-    # Migrate up to the revision just before the backsweep.
-    _upgrade(url, "f6g7h8i9j0k1")
-    return url
-
-
-# ---------------------------------------------------------------------------
-# The test
-# ---------------------------------------------------------------------------
-
-def test_backsweep_removes_orphans_and_preserves_legit_rows(pre_backsweep_db_url):
-    """
-    Seed four kinds of rows then apply the backsweep migration and verify:
-
-    Rows that MUST be deleted
-    ─────────────────────────
-    A. Any fact_type, bank_id missing from banks
-       → Pass 1 deletes these regardless of fact_type or source links.
-
-    B. observation, bank exists, but ALL source_memory_ids are gone
-       → Pass 2 deletes these.
-
-    Rows that MUST survive
-    ──────────────────────
-    C. observation, bank exists, at least ONE source_memory_id still live
-       → Pass 2 must not touch these.
-
-    D. Non-observation (world), bank exists, no sources (not relevant)
-       → Pass 1 must not touch these (bank exists).
-    """
-    db_url = pre_backsweep_db_url
-    engine = create_engine(db_url)
-
-    alive_bank = f"bank_{uuid.uuid4().hex[:8]}"
-    ghost_bank = f"bank_{uuid.uuid4().hex[:8]}"  # never inserted into banks
-
-    # UUIDs for memory units
-    id_pass1_world = uuid.uuid4()       # A: world unit, ghost bank
-    id_pass1_obs = uuid.uuid4()         # A: observation, ghost bank
-    id_pass2_obs = uuid.uuid4()         # B: observation, all sources gone
-    id_keep_obs = uuid.uuid4()          # C: observation with one live source
-    id_keep_world = uuid.uuid4()        # D: world unit, alive bank
-    id_live_source = uuid.uuid4()       # live source for C
-
-    with engine.connect() as conn:
-        # --- banks ---
-        conn.execute(text("INSERT INTO banks (bank_id) VALUES (:b)"), {"b": alive_bank})
-
-        # --- seed memory_units ---
-        def insert_mu(uid, bank, fact_type, sources=None):
-            src_arr = "{" + ",".join(str(s) for s in (sources or [])) + "}"
-            conn.execute(
-                text(
-                    """
-                    INSERT INTO memory_units
-                        (id, bank_id, text, fact_type, source_memory_ids)
-                    VALUES
-                        (:id, :bank, :text, :ft, CAST(:src AS uuid[]))
-                    """
-                ),
-                {"id": uid, "bank": bank, "text": "test", "ft": fact_type, "src": src_arr},
-            )
-
-        # A: ghost-bank rows (Pass 1 targets)
-        insert_mu(id_pass1_world, ghost_bank, "world")
-        insert_mu(id_pass1_obs, ghost_bank, "observation", sources=[uuid.uuid4()])
-
-        # B: observation with all-dead sources (Pass 2 target)
-        insert_mu(id_pass2_obs, alive_bank, "observation", sources=[uuid.uuid4(), uuid.uuid4()])
-
-        # C: observation with one live source (must survive)
-        insert_mu(id_live_source, alive_bank, "world")
-        insert_mu(id_keep_obs, alive_bank, "observation", sources=[id_live_source, uuid.uuid4()])
-
-        # D: world unit in alive bank (must survive)
-        insert_mu(id_keep_world, alive_bank, "world")
-
-        conn.commit()
-
-    # --- apply the backsweep ---
-    _upgrade(db_url, "g7h8i9j0k1l2")
-
-    # --- verify ---
-    with engine.connect() as conn:
-        def exists(uid):
-            return conn.execute(
-                text("SELECT 1 FROM memory_units WHERE id = :id"), {"id": uid}
-            ).fetchone() is not None
-
-        # Must be gone
-        assert not exists(id_pass1_world), "Pass 1: world unit with ghost bank should be deleted"
-        assert not exists(id_pass1_obs), "Pass 1: observation with ghost bank should be deleted"
-        assert not exists(id_pass2_obs), "Pass 2: observation with all-dead sources should be deleted"
-
-        # Must survive
-        assert exists(id_keep_obs), "observation with a live source must not be deleted"
-        assert exists(id_keep_world), "world unit in alive bank must not be deleted"
-        assert exists(id_live_source), "live source memory unit must not be deleted"
-
-    engine.dispose()
@@ -1,48 +0,0 @@
-import threading
-import time
-
-from hindsight_api import migrations
-
-
-def test_run_migrations_internal_serializes_alembic_upgrade(monkeypatch):
-    max_concurrent_upgrades = 0
-    active_upgrades = 0
-    active_lock = threading.Lock()
-    start_barrier = threading.Barrier(2)
-
-    def fake_upgrade(_cfg, _revision):
-        nonlocal max_concurrent_upgrades, active_upgrades
-        with active_lock:
-            active_upgrades += 1
-            max_concurrent_upgrades = max(max_concurrent_upgrades, active_upgrades)
-        time.sleep(0.05)
-        with active_lock:
-            active_upgrades -= 1
-
-    monkeypatch.setattr(migrations.command, "upgrade", fake_upgrade)
-
-    errors = []
-
-    def run_in_thread(schema):
-        try:
-            start_barrier.wait()
-            migrations._run_migrations_internal(
-                "postgresql://user:pass@localhost/db",
-                "/tmp/alembic",
-                schema=schema,
-            )
-        except Exception as exc:  # pragma: no cover - diagnostic path
-            errors.append(exc)
-
-    threads = [
-        threading.Thread(target=run_in_thread, args=("tenant_alpha",)),
-        threading.Thread(target=run_in_thread, args=("tenant_beta",)),
-    ]
-
-    for thread in threads:
-        thread.start()
-    for thread in threads:
-        thread.join()
-
-    assert not errors
-    assert max_concurrent_upgrades == 1
@@ -1,311 +0,0 @@
-"""Tests for operation cancellation when a bank is deleted.
-
-Covers:
- CASCADE DELETE: deleting a bank removes async_operations and webhooks rows
- _check_op_alive: returns True when op exists, False when deleted
- _mark_operation_completed / _mark_operation_failed: graceful no-op when row is gone
- Consolidation checkpoint: stops early after a batch commit if op was deleted
- Retain checkpoint: stops between sub-batches if op was deleted
-"""
-
-import uuid
-from unittest.mock import AsyncMock, patch
-
-import pytest
-import pytest_asyncio
-
-from hindsight_api.engine.memory_engine import MemoryEngine
-
-
-pytestmark = pytest.mark.xdist_group("op_cancellation_tests")
-
-_BANK_PREFIX = "test-op-cancel"
-
-
-@pytest_asyncio.fixture
-async def pool(pg0_db_url):
-    import asyncpg
-    from hindsight_api.pg0 import resolve_database_url
-
-    resolved_url = await resolve_database_url(pg0_db_url)
-    p = await asyncpg.create_pool(resolved_url, min_size=1, max_size=5, command_timeout=30)
-    yield p
-    await p.close()
-
-
-@pytest_asyncio.fixture(autouse=True)
-async def cleanup(pool):
-    """Remove test rows before and after each test."""
-    await pool.execute(f"DELETE FROM banks WHERE bank_id LIKE '{_BANK_PREFIX}%'")
-    yield
-    await pool.execute(f"DELETE FROM banks WHERE bank_id LIKE '{_BANK_PREFIX}%'")
-
-
-async def _insert_bank(pool, bank_id: str):
-    await pool.execute(
-        "INSERT INTO banks (bank_id, name) VALUES ($1, $2) ON CONFLICT DO NOTHING",
-        bank_id,
-        bank_id,
-    )
-
-
-async def _insert_op(pool, bank_id: str, op_id: uuid.UUID | None = None) -> uuid.UUID:
-    op_id = op_id or uuid.uuid4()
-    await pool.execute(
-        """
-        INSERT INTO async_operations (operation_id, bank_id, operation_type, status)
-        VALUES ($1, $2, 'consolidation', 'processing')
-        """,
-        op_id,
-        bank_id,
-    )
-    return op_id
-
-
-# ---------------------------------------------------------------------------
-# CASCADE DELETE tests
-# ---------------------------------------------------------------------------
-
-
-class TestCascadeDeleteOnBankDeletion:
-    @pytest.mark.asyncio
-    async def test_bank_deletion_cascades_to_async_operations(self, pool):
-        bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-        await _insert_bank(pool, bank_id)
-        op_id = await _insert_op(pool, bank_id)
-
-        # Verify op exists
-        row = await pool.fetchrow("SELECT operation_id FROM async_operations WHERE operation_id = $1", op_id)
-        assert row is not None
-
-        # Delete the bank — should cascade to async_operations
-        await pool.execute("DELETE FROM banks WHERE bank_id = $1", bank_id)
-
-        row = await pool.fetchrow("SELECT operation_id FROM async_operations WHERE operation_id = $1", op_id)
-        assert row is None, "async_operations row should be deleted by CASCADE"
-
-    @pytest.mark.asyncio
-    async def test_bank_deletion_cascades_to_webhooks(self, pool):
-        bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-        await _insert_bank(pool, bank_id)
-        webhook_id = uuid.uuid4()
-        await pool.execute(
-            """
-            INSERT INTO webhooks (id, bank_id, url, event_types)
-            VALUES ($1, $2, 'https://example.com/hook', '{}')
-            """,
-            webhook_id,
-            bank_id,
-        )
-
-        row = await pool.fetchrow("SELECT id FROM webhooks WHERE id = $1", webhook_id)
-        assert row is not None
-
-        await pool.execute("DELETE FROM banks WHERE bank_id = $1", bank_id)
-
-        row = await pool.fetchrow("SELECT id FROM webhooks WHERE id = $1", webhook_id)
-        assert row is None, "webhooks row should be deleted by CASCADE"
-
-
-# ---------------------------------------------------------------------------
-# _check_op_alive tests
-# ---------------------------------------------------------------------------
-
-
-class TestCheckOpAlive:
-    @pytest.mark.asyncio
-    async def test_returns_true_when_op_exists(self, memory: MemoryEngine, request_context):
-        bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-        op_id = uuid.uuid4()
-        async with memory._pool.acquire() as conn:
-            await conn.execute(
-                """
-                INSERT INTO async_operations (operation_id, bank_id, operation_type, status)
-                VALUES ($1, $2, 'consolidation', 'processing')
-                """,
-                op_id,
-                bank_id,
-            )
-
-        assert await memory._check_op_alive(str(op_id)) is True
-
-    @pytest.mark.asyncio
-    async def test_returns_false_when_op_deleted(self, memory: MemoryEngine, request_context):
-        bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-        op_id = uuid.uuid4()
-        async with memory._pool.acquire() as conn:
-            await conn.execute(
-                """
-                INSERT INTO async_operations (operation_id, bank_id, operation_type, status)
-                VALUES ($1, $2, 'consolidation', 'processing')
-                """,
-                op_id,
-                bank_id,
-            )
-            await conn.execute("DELETE FROM async_operations WHERE operation_id = $1", op_id)
-
-        assert await memory._check_op_alive(str(op_id)) is False
-
-    @pytest.mark.asyncio
-    async def test_returns_false_after_bank_cascade_delete(self, memory: MemoryEngine, request_context):
-        bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-        op_id = uuid.uuid4()
-        async with memory._pool.acquire() as conn:
-            await conn.execute(
-                """
-                INSERT INTO async_operations (operation_id, bank_id, operation_type, status)
-                VALUES ($1, $2, 'consolidation', 'processing')
-                """,
-                op_id,
-                bank_id,
-            )
-
-        # Delete the bank — cascades to the op row
-        await memory.delete_bank(bank_id=bank_id, request_context=request_context)
-
-        assert await memory._check_op_alive(str(op_id)) is False
-
-
-# ---------------------------------------------------------------------------
-# _mark_operation_completed / _mark_operation_failed graceful no-op
-# ---------------------------------------------------------------------------
-
-
-class TestMarkOperationGracefulOnMissingRow:
-    @pytest.mark.asyncio
-    async def test_mark_completed_does_not_raise_when_row_missing(self, memory: MemoryEngine):
-        # Row never existed — should log and return cleanly
-        missing_id = str(uuid.uuid4())
-        await memory._mark_operation_completed(missing_id)  # no exception
-
-    @pytest.mark.asyncio
-    async def test_mark_failed_does_not_raise_when_row_missing(self, memory: MemoryEngine):
-        missing_id = str(uuid.uuid4())
-        await memory._mark_operation_failed(missing_id, "some error", "traceback here")  # no exception
-
-    @pytest.mark.asyncio
-    async def test_mark_completed_and_fire_webhook_does_not_raise_when_row_missing(
-        self, memory: MemoryEngine
-    ):
-        missing_id = str(uuid.uuid4())
-        await memory._mark_operation_completed_and_fire_webhook(
-            operation_id=missing_id,
-            bank_id="nonexistent-bank",
-            status="completed",
-            result=None,
-        )  # no exception
-
-
-# ---------------------------------------------------------------------------
-# Consolidation checkpoint
-# ---------------------------------------------------------------------------
-
-
-class TestConsolidationCheckpoint:
-    @pytest.mark.asyncio
-    async def test_consolidation_stops_early_when_op_cancelled(self, memory: MemoryEngine, request_context):
-        """Consolidation returns 'cancelled' status after the first batch if _check_op_alive is False."""
-        from hindsight_api.config import _get_raw_config
-        from hindsight_api.engine.consolidation.consolidator import run_consolidation_job
-
-        config = _get_raw_config()
-        original = config.enable_observations
-        config.enable_observations = True
-
-        try:
-            bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-            await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-            # Insert a few unconsolidated memories directly so we control the batch without LLM
-            async with memory._pool.acquire() as conn:
-                for i in range(3):
-                    await conn.execute(
-                        """
-                        INSERT INTO memory_units
-                            (id, bank_id, text, fact_type, created_at, updated_at)
-                        VALUES (gen_random_uuid(), $1, $2, 'experience', NOW(), NOW())
-                        """,
-                        bank_id,
-                        f"Test memory {i} for cancellation test",
-                    )
-
-            op_id = str(uuid.uuid4())
-            call_count = 0
-
-            async def _fake_check(operation_id: str) -> bool:
-                nonlocal call_count
-                call_count += 1
-                # Return False on the very first checkpoint call
-                return False
-
-            with patch.object(memory, "_check_op_alive", side_effect=_fake_check):
-                result = await run_consolidation_job(
-                    memory_engine=memory,
-                    bank_id=bank_id,
-                    request_context=request_context,
-                    operation_id=op_id,
-                )
-
-            assert result["status"] == "cancelled"
-            assert call_count >= 1
-        finally:
-            config.enable_observations = original
-
-
-# ---------------------------------------------------------------------------
-# Retain checkpoint
-# ---------------------------------------------------------------------------
-
-
-class TestRetainCheckpoint:
-    @pytest.mark.asyncio
-    async def test_retain_stops_between_sub_batches_when_cancelled(
-        self, memory: MemoryEngine, request_context
-    ):
-        """retain_batch_async returns partial results if _check_op_alive is False between sub-batches."""
-        from hindsight_api.config import _get_raw_config
-
-        bank_id = f"{_BANK_PREFIX}-{uuid.uuid4().hex[:8]}"
-        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-        # Force sub-batch splitting by temporarily lowering the token threshold
-        config = _get_raw_config()
-        original_tokens = config.retain_batch_tokens
-        # Set threshold very low so each item becomes its own sub-batch
-        config.retain_batch_tokens = 1
-
-        try:
-            op_id = str(uuid.uuid4())
-            check_calls = 0
-
-            async def _fake_check(operation_id: str) -> bool:
-                nonlocal check_calls
-                check_calls += 1
-                # Cancel after the first sub-batch completes
-                return check_calls <= 1
-
-            contents = [
-                {"content": f"Memory item {i} about something interesting."} for i in range(4)
-            ]
-
-            with patch.object(memory, "_check_op_alive", side_effect=_fake_check):
-                result = await memory.retain_batch_async(
-                    bank_id=bank_id,
-                    contents=contents,
-                    request_context=request_context,
-                    operation_id=op_id,
-                )
-
-            # Should have stopped early: fewer results than total items
-            assert len(result) < len(contents), (
-                f"Expected early stop but got {len(result)}/{len(contents)} results"
-            )
-            assert check_calls >= 1
-        finally:
-            config.retain_batch_tokens = original_tokens
@@ -1,170 +0,0 @@
-"""Tests for source_facts token limiting in recall.
-
-Covers:
- max_source_facts_tokens: total token budget across all source facts
- max_source_facts_tokens_per_observation: per-observation cap
-
-Both parameters are tested at the recall_async level and verified to produce
-fewer source facts when the budget is tight vs. unlimited.
-"""
-
-import pytest
-
-from hindsight_api.config import _get_raw_config
-from hindsight_api.engine.memory_engine import Budget
-
-
-@pytest.fixture(autouse=True)
-def enable_observations():
-    config = _get_raw_config()
-    original = config.enable_observations
-    config.enable_observations = True
-    yield
-    config.enable_observations = original
-
-
-async def _setup_bank_with_observations(memory, bank_id, request_context):
-    """Retain several memories and trigger consolidation to produce observations with source facts."""
-    contents = [
-        "Alice is a software engineer who loves Python programming.",
-        "Alice has been working at TechCorp for 5 years.",
-        "Alice recently completed a machine learning certification course.",
-        "Alice mentors junior developers on the team.",
-        "Alice prefers functional programming patterns in her code.",
-    ]
-    for content in contents:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            request_context=request_context,
-        )
-    await memory.run_consolidation(bank_id=bank_id, request_context=request_context)
-
-
-class TestRecallSourceFactsPerObservationCap:
-    @pytest.mark.asyncio
-    async def test_per_observation_cap_reduces_source_facts(self, memory, request_context):
-        """A tight per-observation token cap should return fewer source facts than unlimited."""
-        bank_id = "test-sf-per-obs-cap"
-        try:
-            await _setup_bank_with_observations(memory, bank_id, request_context)
-
-            result_limited = await memory.recall_async(
-                bank_id=bank_id,
-                query="Alice engineer",
-                fact_type=["observation"],
-                max_tokens=4096,
-                include_source_facts=True,
-                max_source_facts_tokens_per_observation=1,  # Effectively cuts all source facts
-                budget=Budget.MID,
-                request_context=request_context,
-            )
-
-            result_unlimited = await memory.recall_async(
-                bank_id=bank_id,
-                query="Alice engineer",
-                fact_type=["observation"],
-                max_tokens=4096,
-                include_source_facts=True,
-                max_source_facts_tokens_per_observation=-1,
-                budget=Budget.MID,
-                request_context=request_context,
-            )
-
-            unlimited_count = len(result_unlimited.source_facts) if result_unlimited.source_facts else 0
-            limited_count = len(result_limited.source_facts) if result_limited.source_facts else 0
-
-            if unlimited_count > 0:
-                assert limited_count <= unlimited_count, (
-                    f"Per-observation cap should yield fewer source facts ({limited_count} <= {unlimited_count})"
-                )
-        finally:
-            await memory.delete_bank(bank_id, request_context=request_context)
-
-    @pytest.mark.asyncio
-    async def test_per_observation_cap_does_not_mix_between_observations(self, memory, request_context):
-        """Each observation's source facts are capped independently — not as a shared pool."""
-        bank_id = "test-sf-per-obs-independent"
-        try:
-            await _setup_bank_with_observations(memory, bank_id, request_context)
-
-            # With a generous per-observation limit each observation can have facts;
-            # with a global limit of 1 token the first observation would consume the whole budget.
-            result_per_obs = await memory.recall_async(
-                bank_id=bank_id,
-                query="Alice engineer",
-                fact_type=["observation"],
-                max_tokens=4096,
-                include_source_facts=True,
-                max_source_facts_tokens=4096,   # large global budget
-                max_source_facts_tokens_per_observation=512,  # reasonable per-obs limit
-                budget=Budget.MID,
-                request_context=request_context,
-            )
-
-            # Should not raise; source_facts may be populated for multiple observations
-            assert result_per_obs.source_facts is not None or len(result_per_obs.results) == 0
-        finally:
-            await memory.delete_bank(bank_id, request_context=request_context)
-
-
-class TestRecallSourceFactsTotalBudget:
-    @pytest.mark.asyncio
-    async def test_total_budget_limits_source_facts(self, memory, request_context):
-        """A tight total token budget should return fewer source facts than unlimited."""
-        bank_id = "test-sf-total-budget"
-        try:
-            await _setup_bank_with_observations(memory, bank_id, request_context)
-
-            result_tight = await memory.recall_async(
-                bank_id=bank_id,
-                query="Alice engineer",
-                fact_type=["observation"],
-                max_tokens=4096,
-                include_source_facts=True,
-                max_source_facts_tokens=1,  # Effectively cuts all source facts
-                budget=Budget.MID,
-                request_context=request_context,
-            )
-
-            result_unlimited = await memory.recall_async(
-                bank_id=bank_id,
-                query="Alice engineer",
-                fact_type=["observation"],
-                max_tokens=4096,
-                include_source_facts=True,
-                max_source_facts_tokens=-1,
-                budget=Budget.MID,
-                request_context=request_context,
-            )
-
-            unlimited_count = len(result_unlimited.source_facts) if result_unlimited.source_facts else 0
-            tight_count = len(result_tight.source_facts) if result_tight.source_facts else 0
-
-            if unlimited_count > 0:
-                assert tight_count <= unlimited_count, (
-                    f"Total budget should yield fewer source facts ({tight_count} <= {unlimited_count})"
-                )
-        finally:
-            await memory.delete_bank(bank_id, request_context=request_context)
-
-    @pytest.mark.asyncio
-    async def test_no_source_facts_without_flag(self, memory, request_context):
-        """source_facts should be None when include_source_facts is not set."""
-        bank_id = "test-sf-no-flag"
-        try:
-            await _setup_bank_with_observations(memory, bank_id, request_context)
-
-            result = await memory.recall_async(
-                bank_id=bank_id,
-                query="Alice engineer",
-                fact_type=["observation"],
-                max_tokens=4096,
-                include_source_facts=False,  # default
-                budget=Budget.MID,
-                request_context=request_context,
-            )
-
-            assert result.source_facts is None or len(result.source_facts) == 0
-        finally:
-            await memory.delete_bank(bank_id, request_context=request_context)
@@ -46,4 +46,4 @@ __all__ = [
    "RemoteTEICrossEncoder",
    "LLMConfig",
 ]
-__version__ = "0.4.18"
+__version__ = "0.4.16"
@@ -14,8 +14,7 @@ from typing import Any
 import asyncpg
 import typer

-from ..config import DEFAULT_DATABASE_SCHEMA, HindsightConfig
-from ..extensions import TenantExtension, load_extension
+from ..config import HindsightConfig
 from ..pg0 import parse_pg0_url, resolve_database_url


@@ -215,81 +214,20 @@ def restore(
    typer.echo("Restore complete")


-async def _run_migration(
-    db_url: str,
-    schema: str | None = None,
-    base_schema: str = DEFAULT_DATABASE_SCHEMA,
-    embedding_dimension: int | None = None,
-) -> list[str]:
-    """Resolve database URL and run migrations for one schema or all discovered schemas."""
-    from ..migrations import (
-        ensure_embedding_dimension,
-        ensure_text_search_extension,
-        ensure_vector_extension,
-        run_migrations,
-    )
+async def _run_migration(db_url: str, schema: str = "public") -> None:
+    """Resolve database URL and run migrations."""
+    from ..migrations import run_migrations

    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
-
-    config = HindsightConfig.from_env()
-    if schema:
-        schemas = [schema]
-    else:
-        tenant_extension = load_extension("TENANT", TenantExtension)
-
-        schemas = [base_schema or DEFAULT_DATABASE_SCHEMA]
-        if tenant_extension:
-            tenants = await tenant_extension.list_tenants()
-            schemas.extend(tenant.schema for tenant in tenants if tenant.schema)
-
-        # Preserve order while removing duplicates.
-        schemas = list(dict.fromkeys(schemas))
-
-    for schema in schemas:
-        run_migrations(resolved_url, schema=schema)
-
-    if embedding_dimension is not None:
-        for schema in schemas:
-            ensure_embedding_dimension(
-                resolved_url,
-                embedding_dimension,
-                schema=schema,
-                vector_extension=config.vector_extension,
-            )
-
-    for schema in schemas:
-        ensure_vector_extension(
-            resolved_url,
-            vector_extension=config.vector_extension,
-            schema=schema,
-        )
-
-    for schema in schemas:
-        ensure_text_search_extension(
-            resolved_url,
-            text_search_extension=config.text_search_extension,
-            schema=schema,
-        )
-
-    return schemas
+    run_migrations(resolved_url, schema=schema)


@app.command(name="run-db-migration")
 def run_db_migration(
-    schema: str | None = typer.Option(
-        None,
-        "--schema",
-        "-s",
-        help="Database schema to run migrations on. If omitted, migrate the base schema and all discovered tenant schemas.",
-    ),
-    embedding_dimension: int | None = typer.Option(
-        None,
-        "--embedding-dimension",
-        help="Expected embedding dimension to enforce after migrations. Omit to skip dimension sync.",
-    ),
+    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to run migrations on"),
 ):
    """Run database migrations to the latest version."""
    config = HindsightConfig.from_env()
@@ -299,21 +237,11 @@ def run_db_migration(
        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
        raise typer.Exit(1)

-    if schema:
-        typer.echo(f"Running database migrations for schema: {schema}...")
-    else:
-        typer.echo("Running database migrations for base schema and all discovered tenant schemas...")
+    typer.echo(f"Running database migrations (schema: {schema})...")

-    schemas = asyncio.run(
-        _run_migration(
-            config.database_url,
-            schema=schema,
-            base_schema=config.database_schema,
-            embedding_dimension=embedding_dimension,
-        )
-    )
+    asyncio.run(_run_migration(config.database_url, schema))

-    typer.echo(f"Database migrations completed successfully for {len(schemas)} schema(s)")
+    typer.echo("Database migrations completed successfully")


 async def _decommission_worker(db_url: str, worker_id: str, schema: str = "public") -> int:
@@ -34,7 +34,7 @@ def upgrade() -> None:
    # Create file_storage table (minimal: just key + data)
    op.execute(
        f"""
-        CREATE TABLE IF NOT EXISTS {schema}file_storage (
+        CREATE TABLE {schema}file_storage (
            storage_key TEXT PRIMARY KEY,
            data BYTEA NOT NULL
        )
@@ -35,7 +35,7 @@ def upgrade() -> None:

    # Add GIN index for JSONB containment queries (@> operator)
    op.execute(f"""
-        CREATE INDEX IF NOT EXISTS idx_async_operations_result_metadata
+        CREATE INDEX idx_async_operations_result_metadata
        ON {schema}async_operations
        USING gin(result_metadata)
    """)
@@ -34,7 +34,7 @@ def _parse_metadata(metadata: Any) -> dict[str, Any]:

 from typing import Callable

-from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator

 from hindsight_api import MemoryEngine

@@ -73,13 +73,15 @@ def FieldWithDefault(default_factory: Callable, **kwargs) -> Any:
 from hindsight_api.config import get_config
 from hindsight_api.engine.memory_engine import Budget, _current_schema, _get_tiktoken_encoding, fq_table
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, MemoryFact, TokenUsage
-from hindsight_api.engine.search.tags import TagGroup, TagsMatch
+from hindsight_api.engine.search.tags import TagsMatch
 from hindsight_api.extensions import HttpExtension, OperationValidationError, load_extension
 from hindsight_api.metrics import create_metrics_collector, get_metrics_collector, initialize_metrics
 from hindsight_api.models import RequestContext

 logger = logging.getLogger(__name__)

+MAX_QUERY_TOKENS = 500  # Maximum tokens allowed in recall query
+

 class EntityIncludeOptions(BaseModel):
    """Options for including entity observations in recall results."""
@@ -96,12 +98,7 @@ class ChunkIncludeOptions(BaseModel):
 class SourceFactsIncludeOptions(BaseModel):
    """Options for including source facts for observation-type results."""

-    max_tokens: int = Field(
-        default=4096, description="Maximum total tokens for source facts across all observations (-1 = unlimited)"
-    )
-    max_tokens_per_observation: int = Field(
-        default=-1, description="Maximum tokens of source facts per observation (-1 = unlimited)"
-    )
+    max_tokens: int = Field(default=4096, description="Maximum tokens for source facts")


 class IncludeOptions(BaseModel):
@@ -163,17 +160,6 @@ class RecallRequest(BaseModel):
        description="How to match tags: 'any' (OR, includes untagged), 'all' (AND, includes untagged), "
        "'any_strict' (OR, excludes untagged), 'all_strict' (AND, excludes untagged).",
    )
-    tag_groups: list[TagGroup] | None = Field(
-        default=None,
-        description="Compound tag filter using boolean groups. Groups in the list are AND-ed. "
-        "Each group is a leaf {tags, match} or compound {and: [...]}, {or: [...]}, {not: ...}.",
-    )
-
-    @model_validator(mode="after")
-    def validate_tags_exclusive(self) -> "RecallRequest":
-        if self.tags is not None and self.tag_groups is not None:
-            raise ValueError("'tags' and 'tag_groups' are mutually exclusive. Use 'tag_groups' for compound filtering.")
-        return self


 class RecallResult(BaseModel):
@@ -486,11 +472,6 @@ class FileRetainMetadata(BaseModel):
    metadata: dict[str, Any] | None = Field(default=None, description="Additional metadata")
    tags: list[str] | None = Field(default=None, description="Tags for this file")
    timestamp: str | None = Field(default=None, description="ISO timestamp")
-    parser: str | list[str] | None = Field(
-        default=None,
-        description="Parser or ordered fallback chain for this file (overrides request-level parser). "
-        "E.g. 'iris' or ['iris', 'markitdown'].",
-    )


 class FileRetainRequest(BaseModel):
@@ -499,21 +480,14 @@ class FileRetainRequest(BaseModel):
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
-                "parser": "iris",
                "files_metadata": [
                    {"document_id": "report_2024", "tags": ["quarterly"]},
-                    {"context": "meeting notes", "parser": ["iris", "markitdown"]},
+                    {"context": "meeting notes"},
                ],
            }
        }
    )

-    parser: str | list[str] | None = Field(
-        default=None,
-        description="Default parser or ordered fallback chain for all files in this request. "
-        "E.g. 'markitdown' or ['iris', 'markitdown']. Falls back to server default if not set. "
-        "Per-file 'parser' in files_metadata takes precedence over this value.",
-    )
    files_metadata: list[FileRetainMetadata] | None = Field(
        default=None,
        description="Metadata for each file (optional, must match number of files if provided)",
@@ -650,17 +624,6 @@ class ReflectRequest(BaseModel):
        description="How to match tags: 'any' (OR, includes untagged), 'all' (AND, includes untagged), "
        "'any_strict' (OR, excludes untagged), 'all_strict' (AND, excludes untagged).",
    )
-    tag_groups: list[TagGroup] | None = Field(
-        default=None,
-        description="Compound tag filter using boolean groups. Groups in the list are AND-ed. "
-        "Each group is a leaf {tags, match} or compound {and: [...]}, {or: [...]}, {not: ...}.",
-    )
-
-    @model_validator(mode="after")
-    def validate_tags_exclusive(self) -> "ReflectRequest":
-        if self.tags is not None and self.tag_groups is not None:
-            raise ValueError("'tags' and 'tag_groups' are mutually exclusive. Use 'tag_groups' for compound filtering.")
-        return self


 class ReflectFact(BaseModel):
@@ -1226,30 +1189,6 @@ class DocumentResponse(BaseModel):
    tags: list[str] = FieldWithDefault(list, description="Tags associated with this document")


-class UpdateDocumentRequest(BaseModel):
-    """Request model for updating a document's mutable fields."""
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "tags": ["team-a", "team-b"],
-            }
-        }
-    )
-
-    tags: list[str] | None = Field(
-        default=None,
-        description="New tags for the document and its memory units. "
-        "Triggers observation invalidation and re-consolidation.",
-    )
-
-
-class UpdateDocumentResponse(BaseModel):
-    """Response model for update document endpoint."""
-
-    success: bool = True
-
-
 class DeleteDocumentResponse(BaseModel):
    """Response model for delete document endpoint."""

@@ -1578,24 +1517,6 @@ class CancelOperationResponse(BaseModel):
    operation_id: str


-class RetryOperationResponse(BaseModel):
-    """Response model for retry operation endpoint."""
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "success": True,
-                "message": "Operation 550e8400-e29b-41d4-a716-446655440000 queued for retry",
-                "operation_id": "550e8400-e29b-41d4-a716-446655440000",
-            }
-        }
-    )
-
-    success: bool
-    message: str
-    operation_id: str
-
-
 class ChildOperationStatus(BaseModel):
    """Status of a child operation (for batch operations)."""

@@ -2199,7 +2120,7 @@ def _register_routes(app: FastAPI):
    @app.get(
        "/v1/default/banks/{bank_id}/memories/{memory_id}",
        summary="Get memory unit",
-        description="Get a single memory unit by ID with all its metadata including entities and tags. Note: the 'history' field is deprecated and always returns an empty list - use GET /memories/{memory_id}/history instead.",
+        description="Get a single memory unit by ID with all its metadata including entities and tags.",
        operation_id="get_memory",
        tags=["Memory"],
    )
@@ -2229,39 +2150,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in /v1/default/banks/{bank_id}/memories/{memory_id}: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.get(
-        "/v1/default/banks/{bank_id}/memories/{memory_id}/history",
-        summary="Get observation history",
-        description="Get the full history of an observation, with each change's source facts resolved to their text.",
-        operation_id="get_observation_history",
-        tags=["Memory"],
-    )
-    async def api_get_observation_history(
-        bank_id: str,
-        memory_id: str,
-        request_context: RequestContext = Depends(get_request_context),
-    ):
-        """Get the history of a single observation by ID."""
-        try:
-            data = await app.state.memory.get_observation_history(
-                bank_id=bank_id,
-                memory_id=memory_id,
-                request_context=request_context,
-            )
-            if data is None:
-                raise HTTPException(status_code=404, detail=f"Memory unit '{memory_id}' not found")
-            return data
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in /v1/default/banks/{bank_id}/memories/{memory_id}/history: {error_detail}")
-            raise HTTPException(status_code=500, detail=str(e))
-
    @app.post(
        "/v1/default/banks/{bank_id}/memories/recall",
        response_model=RecallResponse,
@@ -2283,13 +2171,12 @@ def _register_routes(app: FastAPI):
        metrics = get_metrics_collector()

        # Validate query length to prevent expensive operations on oversized queries
-        max_query_tokens = get_config().recall_max_query_tokens
        encoding = _get_tiktoken_encoding()
        query_tokens = len(encoding.encode(request.query))
-        if query_tokens > max_query_tokens:
+        if query_tokens > MAX_QUERY_TOKENS:
            raise HTTPException(
                status_code=400,
-                detail=f"Query too long: {query_tokens} tokens exceeds maximum of {max_query_tokens}. Please shorten your query.",
+                detail=f"Query too long: {query_tokens} tokens exceeds maximum of {MAX_QUERY_TOKENS}. Please shorten your query.",
            )

        try:
@@ -2318,9 +2205,6 @@ def _register_routes(app: FastAPI):
            # Determine source facts inclusion settings
            include_source_facts = request.include.source_facts is not None
            max_source_facts_tokens = request.include.source_facts.max_tokens if include_source_facts else 4096
-            max_source_facts_tokens_per_observation = (
-                request.include.source_facts.max_tokens_per_observation if include_source_facts else -1
-            )

            pre_recall = time.time() - handler_start
            # Run recall with tracing (record metrics)
@@ -2342,11 +2226,9 @@ def _register_routes(app: FastAPI):
                    max_chunk_tokens=max_chunk_tokens,
                    include_source_facts=include_source_facts,
                    max_source_facts_tokens=max_source_facts_tokens,
-                    max_source_facts_tokens_per_observation=max_source_facts_tokens_per_observation,
                    request_context=request_context,
                    tags=request.tags,
                    tags_match=request.tags_match,
-                    tag_groups=request.tag_groups,
                )

            # Convert core MemoryFact objects to API RecallResult objects (excluding internal metrics)
@@ -2482,7 +2364,6 @@ def _register_routes(app: FastAPI):
                    request_context=request_context,
                    tags=request.tags,
                    tags_match=request.tags_match,
-                    tag_groups=request.tag_groups,
                )

            # Build based_on (memories + mental_models + directives) if facts are requested
@@ -2813,41 +2694,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in GET /v1/default/banks/{bank_id}/mental-models/{mental_model_id}: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.get(
-        "/v1/default/banks/{bank_id}/mental-models/{mental_model_id}/history",
-        summary="Get mental model history",
-        description="Get the refresh history of a mental model, showing content changes over time.",
-        operation_id="get_mental_model_history",
-        tags=["Mental Models"],
-    )
-    async def api_get_mental_model_history(
-        bank_id: str,
-        mental_model_id: str,
-        request_context: RequestContext = Depends(get_request_context),
-    ):
-        """Get the refresh history of a mental model."""
-        try:
-            data = await app.state.memory.get_mental_model_history(
-                bank_id=bank_id,
-                mental_model_id=mental_model_id,
-                request_context=request_context,
-            )
-            if data is None:
-                raise HTTPException(status_code=404, detail=f"Mental model '{mental_model_id}' not found")
-            return data
-        except (AuthenticationError, HTTPException):
-            raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(
-                f"Error in GET /v1/default/banks/{bank_id}/mental-models/{mental_model_id}/history: {error_detail}"
-            )
-            raise HTTPException(status_code=500, detail=str(e))
-
    @app.post(
        "/v1/default/banks/{bank_id}/mental-models",
        response_model=CreateMentalModelResponse,
@@ -3369,55 +3215,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in /v1/default/chunks/{chunk_id}: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.patch(
-        "/v1/default/banks/{bank_id}/documents/{document_id:path}",
-        response_model=UpdateDocumentResponse,
-        summary="Update document",
-        description="Update mutable fields on a document without re-processing its content.\n\n"
-        "**Tags** (`tags`): Propagated to all associated memory units. Observations derived from "
-        "those units are invalidated and queued for re-consolidation under the new tags. "
-        "Co-source memories from other documents that shared those observations are also reset.\n\n"
-        "At least one field must be provided.",
-        operation_id="update_document",
-        tags=["Documents"],
-    )
-    async def api_update_document(
-        bank_id: str,
-        document_id: str,
-        body: UpdateDocumentRequest,
-        request_context: RequestContext = Depends(get_request_context),
-    ):
-        """
-        Update mutable fields on a document without re-processing its content.
-
-        Args:
-            bank_id: Memory Bank ID (from path)
-            document_id: Document ID (from path)
-            body: Fields to update (tags, metadata, context)
-        """
-        if body.tags is None:
-            raise HTTPException(status_code=422, detail="At least one field (tags) must be provided")
-        try:
-            result = await app.state.memory.update_document(
-                document_id,
-                bank_id,
-                tags=body.tags,
-                request_context=request_context,
-            )
-            if not result:
-                raise HTTPException(status_code=404, detail="Document not found")
-            return UpdateDocumentResponse(success=True)
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in PATCH /v1/default/banks/{bank_id}/documents/{document_id}: {error_detail}")
-            raise HTTPException(status_code=500, detail=str(e))
-
    @app.delete(
        "/v1/default/banks/{bank_id}/documents/{document_id:path}",
        response_model=DeleteDocumentResponse,
@@ -3468,17 +3265,13 @@ def _register_routes(app: FastAPI):
        "/v1/default/banks/{bank_id}/operations",
        response_model=OperationsListResponse,
        summary="List async operations",
-        description="Get a list of async operations for a specific agent, with optional filtering by status and operation type. Results are sorted by most recent first.",
+        description="Get a list of async operations for a specific agent, with optional filtering by status. Results are sorted by most recent first.",
        operation_id="list_operations",
        tags=["Operations"],
    )
    async def api_list_operations(
        bank_id: str,
        status: str | None = Query(default=None, description="Filter by status: pending, completed, or failed"),
-        type: str | None = Query(
-            default=None,
-            description="Filter by operation type: retain, consolidation, refresh_mental_model, file_convert_retain, webhook_delivery",
-        ),
        limit: int = Query(default=20, ge=1, le=100, description="Maximum number of operations to return"),
        offset: int = Query(default=0, ge=0, description="Number of operations to skip"),
        request_context: RequestContext = Depends(get_request_context),
@@ -3486,7 +3279,7 @@ def _register_routes(app: FastAPI):
        """List async operations for a memory bank with optional filtering and pagination."""
        try:
            result = await app.state.memory.list_operations(
-                bank_id, status=status, task_type=type, limit=limit, offset=offset, request_context=request_context
+                bank_id, status=status, limit=limit, offset=offset, request_context=request_context
            )
            return OperationsListResponse(
                bank_id=bank_id,
@@ -3573,39 +3366,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in /v1/default/banks/{bank_id}/operations/{operation_id}: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.post(
-        "/v1/default/banks/{bank_id}/operations/{operation_id}/retry",
-        response_model=RetryOperationResponse,
-        summary="Retry a failed async operation",
-        description="Re-queue a failed async operation so the worker picks it up again",
-        operation_id="retry_operation",
-        tags=["Operations"],
-    )
-    async def api_retry_operation(
-        bank_id: str, operation_id: str, request_context: RequestContext = Depends(get_request_context)
-    ):
-        """Retry a failed async operation."""
-        try:
-            try:
-                uuid.UUID(operation_id)
-            except ValueError:
-                raise HTTPException(status_code=400, detail=f"Invalid operation_id format: {operation_id}")
-
-            result = await app.state.memory.retry_operation(bank_id, operation_id, request_context=request_context)
-            return RetryOperationResponse(**result)
-        except ValueError as e:
-            raise HTTPException(status_code=404, detail=str(e))
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in POST /v1/default/banks/{bank_id}/operations/{operation_id}/retry: {error_detail}")
-            raise HTTPException(status_code=500, detail=str(e))
-
    @app.get(
        "/v1/default/banks/{bank_id}/profile",
        response_model=BankProfileResponse,
@@ -4115,10 +3875,6 @@ def _register_routes(app: FastAPI):
        try:
            pool = await app.state.memory._get_pool()
            from hindsight_api.engine.memory_engine import fq_table
-            from hindsight_api.engine.retain import bank_utils
-
-            # Ensure the bank row exists before inserting into webhooks (FK constraint).
-            await bank_utils.get_bank_profile(pool, bank_id)

            webhook_id = uuid.uuid4()
            now = datetime.utcnow().isoformat() + "Z"
@@ -4556,14 +4312,8 @@ def _register_routes(app: FastAPI):
        "Use the operations endpoint to monitor progress.\n\n"
        "**Request format:** multipart/form-data with:\n"
        "- `files`: One or more files to upload\n"
-        "- `request`: JSON string with FileRetainRequest model\n\n"
-        "**Parser selection:**\n"
-        "- Set `parser` in the request body to override the server default for all files.\n"
-        "- Set `parser` inside a `files_metadata` entry for per-file control.\n"
-        "- Pass a list (e.g. `['iris', 'markitdown']`) to define an ordered fallback chain — "
-        "each parser is tried in sequence until one succeeds.\n"
-        "- Falls back to the server default (`HINDSIGHT_API_FILE_PARSER`) if not specified.\n"
-        "- Only parsers enabled on the server may be requested; others return HTTP 400.",
+        "- `request`: JSON string with FileRetainRequest model (files_metadata)\n\n"
+        "**Note:** File parser is configured server-side via `HINDSIGHT_API_FILE_PARSER` (default: markitdown).",
        operation_id="file_retain",
        tags=["Files"],
    )
@@ -4609,39 +4359,20 @@ def _register_routes(app: FastAPI):
                    detail=f"files_metadata count ({len(request_data.files_metadata)}) must match files count ({len(files)})",
                )

-            # Resolve the registered parser names for allowlist validation
-            registered_parsers = app.state.memory._parser_registry.list_parsers()
-            allowlist = config.file_parser_allowlist if config.file_parser_allowlist is not None else registered_parsers
-
-            def _resolve_parser(raw: str | list[str] | None) -> list[str]:
-                """Normalize parser value to a non-empty list of names."""
-                if raw is None:
-                    return config.file_parser
-                return [raw] if isinstance(raw, str) else list(raw)
-
-            def _validate_parsers(parsers: list[str], context: str) -> None:
-                """Raise HTTP 400 if any parser name is not in the allowlist."""
-                disallowed = [p for p in parsers if p not in allowlist]
-                if disallowed:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=f"Parser(s) not available ({context}): {disallowed}. Available: {allowlist}",
-                    )
-
-            # Validate request-level parser early (before reading files)
-            if request_data.parser is not None:
-                _validate_parsers(_resolve_parser(request_data.parser), "request-level parser")
-
            # Prepare file items and calculate total batch size
-            import io
-
            file_items = []
            total_batch_size = 0

            for i, file in enumerate(files):
                # Read file content to check size
                file_content = await file.read()
-                total_batch_size += len(file_content)
+                size = len(file_content)
+                total_batch_size += size
+
+                # Create a temporary file-like object from the bytes
+                import io
+
+                file_obj = io.BytesIO(file_content)

                # Create a mock UploadFile with the necessary attributes
                class FileWrapper:
@@ -4649,6 +4380,7 @@ def _register_routes(app: FastAPI):
                        self._content = content
                        self.filename = filename
                        self.content_type = content_type
+                        self._buffer = io.BytesIO(content)

                    async def read(self):
                        return self._content
@@ -4659,12 +4391,6 @@ def _register_routes(app: FastAPI):
                file_meta = request_data.files_metadata[i] if request_data.files_metadata else FileRetainMetadata()
                doc_id = file_meta.document_id or f"file_{uuid.uuid4()}"

-                # Resolve and validate per-file parser chain
-                # Priority: per-file > request-level > server default
-                raw_parser = file_meta.parser if file_meta.parser is not None else request_data.parser
-                parser_chain = _resolve_parser(raw_parser)
-                _validate_parsers(parser_chain, f"file '{file.filename}'")
-
                item = {
                    "file": wrapped_file,
                    "document_id": doc_id,
@@ -4672,7 +4398,6 @@ def _register_routes(app: FastAPI):
                    "metadata": file_meta.metadata or {},
                    "tags": file_meta.tags or [],
                    "timestamp": file_meta.timestamp,
-                    "parser": parser_chain,
                }
                file_items.append(item)

@@ -4687,6 +4412,7 @@ def _register_routes(app: FastAPI):
            result = await app.state.memory.submit_async_file_retain(
                bank_id=bank_id,
                file_items=file_items,
+                parser=config.file_parser,
                document_tags=None,
                request_context=request_context,
            )
@@ -381,15 +381,6 @@ class MCPMiddleware:
            # Clear root_path since we're passing directly to the app
            new_scope["root_path"] = ""

-            # Ensure Accept header includes required MIME types for MCP SDK.
-            # Some clients (e.g., Claude Code) don't send Accept, causing
-            # the SDK to reject with 406 Not Acceptable.
-            accept_header = self._get_header(new_scope, "accept")
-            if not accept_header or "text/event-stream" not in accept_header:
-                headers = [(k, v) for k, v in new_scope.get("headers", []) if k.lower() != b"accept"]
-                headers.append((b"accept", b"application/json, text/event-stream"))
-                new_scope["headers"] = headers
-
            # Wrap send to rewrite the SSE endpoint URL to include bank_id if using path-based routing.
            # Only rewrite SSE (text/event-stream) responses to avoid corrupting tool results
            # that might contain the literal string "data: /messages".
@@ -193,7 +193,6 @@ ENV_EMBEDDINGS_LITELLM_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL"
 ENV_RERANKER_LITELLM_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_API_BASE"
 ENV_RERANKER_LITELLM_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_API_KEY"
 ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"
-ENV_RERANKER_LITELLM_MAX_TOKENS_PER_DOC = "HINDSIGHT_API_RERANKER_LITELLM_MAX_TOKENS_PER_DOC"

 # LiteLLM SDK configuration (direct API access, no proxy needed)
 ENV_EMBEDDINGS_LITELLM_SDK_API_KEY = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_KEY"
@@ -212,9 +211,6 @@ ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
 ENV_RERANKER_LOCAL_FORCE_CPU = "HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU"
 ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
 ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_RERANKER_LOCAL_TRUST_REMOTE_CODE"
-ENV_RERANKER_LOCAL_FP16 = "HINDSIGHT_API_RERANKER_LOCAL_FP16"
-ENV_RERANKER_LOCAL_BUCKET_BATCHING = "HINDSIGHT_API_RERANKER_LOCAL_BUCKET_BATCHING"
-ENV_RERANKER_LOCAL_BATCH_SIZE = "HINDSIGHT_API_RERANKER_LOCAL_BATCH_SIZE"
 ENV_RERANKER_TEI_URL = "HINDSIGHT_API_RERANKER_TEI_URL"
 ENV_RERANKER_TEI_BATCH_SIZE = "HINDSIGHT_API_RERANKER_TEI_BATCH_SIZE"
 ENV_RERANKER_TEI_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_TEI_MAX_CONCURRENT"
@@ -242,7 +238,6 @@ ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
 ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
 ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
 ENV_RECALL_CONNECTION_BUDGET = "HINDSIGHT_API_RECALL_CONNECTION_BUDGET"
-ENV_RECALL_MAX_QUERY_TOKENS = "HINDSIGHT_API_RECALL_MAX_QUERY_TOKENS"
 ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"

 # OpenTelemetry tracing configuration
@@ -285,7 +280,6 @@ ENV_FILE_STORAGE_AZURE_CONTAINER = "HINDSIGHT_API_FILE_STORAGE_AZURE_CONTAINER"
 ENV_FILE_STORAGE_AZURE_ACCOUNT_NAME = "HINDSIGHT_API_FILE_STORAGE_AZURE_ACCOUNT_NAME"
 ENV_FILE_STORAGE_AZURE_ACCOUNT_KEY = "HINDSIGHT_API_FILE_STORAGE_AZURE_ACCOUNT_KEY"
 ENV_FILE_PARSER = "HINDSIGHT_API_FILE_PARSER"
-ENV_FILE_PARSER_ALLOWLIST = "HINDSIGHT_API_FILE_PARSER_ALLOWLIST"
 ENV_FILE_PARSER_IRIS_TOKEN = "HINDSIGHT_API_FILE_PARSER_IRIS_TOKEN"
 ENV_FILE_PARSER_IRIS_ORG_ID = "HINDSIGHT_API_FILE_PARSER_IRIS_ORG_ID"
 ENV_FILE_CONVERSION_MAX_BATCH_SIZE_MB = "HINDSIGHT_API_FILE_CONVERSION_MAX_BATCH_SIZE_MB"
@@ -298,13 +292,7 @@ ENV_ENABLE_OBSERVATIONS = "HINDSIGHT_API_ENABLE_OBSERVATIONS"
 ENV_CONSOLIDATION_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_BATCH_SIZE"
 ENV_CONSOLIDATION_LLM_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_LLM_BATCH_SIZE"
 ENV_CONSOLIDATION_MAX_TOKENS = "HINDSIGHT_API_CONSOLIDATION_MAX_TOKENS"
-ENV_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS = "HINDSIGHT_API_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS"
-ENV_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION = (
-    "HINDSIGHT_API_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION"
-)
 ENV_OBSERVATIONS_MISSION = "HINDSIGHT_API_OBSERVATIONS_MISSION"
-ENV_ENABLE_OBSERVATION_HISTORY = "HINDSIGHT_API_ENABLE_OBSERVATION_HISTORY"
-ENV_ENABLE_MENTAL_MODEL_HISTORY = "HINDSIGHT_API_ENABLE_MENTAL_MODEL_HISTORY"

 # Webhook configuration (global, static - server-level only)
 ENV_WEBHOOK_URL = "HINDSIGHT_API_WEBHOOK_URL"
@@ -355,7 +343,6 @@ PROVIDER_DEFAULT_MODELS = {
    "anthropic": "claude-haiku-4-5-20251001",
    "gemini": "gemini-2.5-flash",
    "groq": "openai/gpt-oss-120b",
-    "minimax": "MiniMax-M2.5",
    "ollama": "gemma3:12b",
    "lmstudio": "local-model",
    "vertexai": "google/gemini-2.5-flash-lite",
@@ -392,9 +379,6 @@ DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound rerankin
 DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE = (
    False  # Security: disabled by default, required for some models like jina-reranker-v2
 )
-DEFAULT_RERANKER_LOCAL_FP16 = False  # FP16 inference: opt-in, faster on MPS/CUDA (not CPU)
-DEFAULT_RERANKER_LOCAL_BUCKET_BATCHING = False  # Length-sorted bucket batching: opt-in, 36-54% speedup
-DEFAULT_RERANKER_LOCAL_BATCH_SIZE = 32  # Batch size for local reranker predict() calls
 DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
 DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
 DEFAULT_RERANKER_MAX_CANDIDATES = 300
@@ -416,7 +400,6 @@ DEFAULT_TEXT_SEARCH_EXTENSION = "native"  # Options: "native", "vchord", "pg_tex
 DEFAULT_LITELLM_API_BASE = "http://localhost:4000"
 DEFAULT_EMBEDDINGS_LITELLM_MODEL = "text-embedding-3-small"
 DEFAULT_RERANKER_LITELLM_MODEL = "cohere/rerank-english-v3.0"
-DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC: int | None = None

 # LiteLLM SDK defaults
 DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL = "cohere/embed-english-v3.0"
@@ -435,7 +418,6 @@ DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp",
 DEFAULT_MPFP_TOP_K_NEIGHBORS = 20  # Fan-out limit per node in MPFP graph traversal
 DEFAULT_RECALL_MAX_CONCURRENT = 32  # Max concurrent recall operations per worker
 DEFAULT_RECALL_CONNECTION_BUDGET = 4  # Max concurrent DB connections per recall operation
-DEFAULT_RECALL_MAX_QUERY_TOKENS = 500  # Maximum tokens allowed in recall query
 DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY = 8  # Max concurrent mental model refreshes

 # Retain settings
@@ -453,8 +435,7 @@ DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS = 60  # Batch API polling interval in

 # File storage defaults
 DEFAULT_FILE_STORAGE_TYPE = "native"  # PostgreSQL BYTEA storage
-DEFAULT_FILE_PARSER = "markitdown"  # Default parser fallback chain (comma-separated, e.g. "iris,markitdown")
-DEFAULT_FILE_PARSER_ALLOWLIST = None  # Allowlist of parsers clients may request (None = all registered parsers)
+DEFAULT_FILE_PARSER = "markitdown"  # File parser to use (markitdown is the only supported parser)
 DEFAULT_FILE_CONVERSION_MAX_BATCH_SIZE_MB = 100  # Max total batch size in MB (all files combined)
 DEFAULT_FILE_CONVERSION_MAX_BATCH_SIZE = 10  # Max files per batch upload
 DEFAULT_ENABLE_FILE_UPLOAD_API = True  # Enable file upload endpoint
@@ -462,17 +443,9 @@ DEFAULT_FILE_DELETE_AFTER_RETAIN = True  # Delete file bytes after retain (saves

 # Observations defaults (consolidated knowledge from facts)
 DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
-DEFAULT_ENABLE_OBSERVATION_HISTORY = True  # Observation history tracking enabled by default
-DEFAULT_ENABLE_MENTAL_MODEL_HISTORY = True  # Mental model history tracking enabled by default
 DEFAULT_CONSOLIDATION_BATCH_SIZE = 50  # Memories to load per batch (internal memory optimization)
 DEFAULT_CONSOLIDATION_LLM_BATCH_SIZE = 8  # Facts per LLM call (1 = no batching; >1 = batch mode)
 DEFAULT_CONSOLIDATION_MAX_TOKENS = 512  # Max tokens for recall when finding related observations
-DEFAULT_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS = (
-    -1
-)  # Total token budget for source facts in consolidation recall (-1 = unlimited)
-DEFAULT_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION = (
-    256  # Max tokens of source facts per observation in consolidation prompt (-1 = unlimited)
-)
 DEFAULT_OBSERVATIONS_MISSION = None  # Declarative spec of what observations are for this bank

 # Database migrations
@@ -567,11 +540,6 @@ class JsonFormatter(logging.Formatter):
        return json.dumps(log_entry)


-def _parse_str_list(value: str) -> list[str]:
-    """Parse a comma-separated string into a non-empty list of stripped tokens."""
-    return [v.strip() for v in value.split(",") if v.strip()]
-
-
 def _validate_extraction_mode(mode: str) -> str:
    """Validate and normalize extraction mode."""
    mode_lower = mode.lower()
@@ -674,9 +642,6 @@ class HindsightConfig:
    reranker_local_force_cpu: bool
    reranker_local_max_concurrent: int
    reranker_local_trust_remote_code: bool
-    reranker_local_fp16: bool
-    reranker_local_bucket_batching: bool
-    reranker_local_batch_size: int
    reranker_tei_url: str | None
    reranker_tei_batch_size: int
    reranker_tei_max_concurrent: int
@@ -687,7 +652,6 @@ class HindsightConfig:
    reranker_litellm_api_base: str
    reranker_litellm_api_key: str | None
    reranker_litellm_model: str
-    reranker_litellm_max_tokens_per_doc: int | None
    reranker_litellm_sdk_api_key: str | None
    reranker_litellm_sdk_model: str
    reranker_litellm_sdk_api_base: str | None
@@ -709,7 +673,6 @@ class HindsightConfig:
    mpfp_top_k_neighbors: int
    recall_max_concurrent: int
    recall_connection_budget: int
-    recall_max_query_tokens: int
    mental_model_refresh_concurrency: int

    # Retain settings
@@ -736,8 +699,7 @@ class HindsightConfig:
    file_storage_azure_container: str | None  # Azure container name (required for azure storage)
    file_storage_azure_account_name: str | None  # Azure storage account name
    file_storage_azure_account_key: str | None  # Azure storage account key
-    file_parser: list[str]  # Ordered fallback chain of parsers (e.g. ["iris", "markitdown"])
-    file_parser_allowlist: list[str] | None  # Parsers clients may request (None = all registered)
+    file_parser: str  # File parser to use (e.g., "markitdown", "iris")
    file_parser_iris_token: str | None  # Vectorize API token for iris parser (VECTORIZE_TOKEN)
    file_parser_iris_org_id: str | None  # Vectorize org ID for iris parser (VECTORIZE_ORG_ID)
    file_conversion_max_batch_size_mb: int  # Max total batch size in MB (all files combined)
@@ -747,13 +709,9 @@ class HindsightConfig:

    # Observations settings (consolidated knowledge from facts)
    enable_observations: bool
-    enable_observation_history: bool
-    enable_mental_model_history: bool
    consolidation_batch_size: int
    consolidation_llm_batch_size: int
    consolidation_max_tokens: int
-    consolidation_source_facts_max_tokens: int
-    consolidation_source_facts_max_tokens_per_observation: int
    observations_mission: str | None

    # Entity labels (controlled vocabulary of key:value classification labels extracted at retain time)
@@ -854,9 +812,6 @@ class HindsightConfig:
        "entities_allow_free_form",
        # Consolidation settings
        "enable_observations",
-        "consolidation_llm_batch_size",
-        "consolidation_source_facts_max_tokens",
-        "consolidation_source_facts_max_tokens_per_observation",
        "observations_mission",
        # Reflect settings
        "reflect_mission",
@@ -1101,17 +1056,6 @@ class HindsightConfig:
                ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE)
            ).lower()
            in ("true", "1"),
-            reranker_local_fp16=os.getenv(
-                ENV_RERANKER_LOCAL_FP16, str(DEFAULT_RERANKER_LOCAL_FP16)
-            ).lower()
-            in ("true", "1"),
-            reranker_local_bucket_batching=os.getenv(
-                ENV_RERANKER_LOCAL_BUCKET_BATCHING, str(DEFAULT_RERANKER_LOCAL_BUCKET_BATCHING)
-            ).lower()
-            in ("true", "1"),
-            reranker_local_batch_size=int(
-                os.getenv(ENV_RERANKER_LOCAL_BATCH_SIZE, str(DEFAULT_RERANKER_LOCAL_BATCH_SIZE))
-            ),
            reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
            reranker_tei_batch_size=int(os.getenv(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE))),
            reranker_tei_max_concurrent=int(
@@ -1127,9 +1071,6 @@ class HindsightConfig:
            or os.getenv(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE),
            reranker_litellm_api_key=os.getenv(ENV_RERANKER_LITELLM_API_KEY) or os.getenv(ENV_LITELLM_API_KEY),
            reranker_litellm_model=os.getenv(ENV_RERANKER_LITELLM_MODEL, DEFAULT_RERANKER_LITELLM_MODEL),
-            reranker_litellm_max_tokens_per_doc=int(v)
-            if (v := os.getenv(ENV_RERANKER_LITELLM_MAX_TOKENS_PER_DOC))
-            else DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC,
            # LiteLLM SDK reranker (direct API access)
            reranker_litellm_sdk_api_key=os.getenv(ENV_RERANKER_LITELLM_SDK_API_KEY),
            reranker_litellm_sdk_model=os.getenv(ENV_RERANKER_LITELLM_SDK_MODEL, DEFAULT_RERANKER_LITELLM_SDK_MODEL),
@@ -1156,7 +1097,6 @@ class HindsightConfig:
            recall_connection_budget=int(
                os.getenv(ENV_RECALL_CONNECTION_BUDGET, str(DEFAULT_RECALL_CONNECTION_BUDGET))
            ),
-            recall_max_query_tokens=int(os.getenv(ENV_RECALL_MAX_QUERY_TOKENS, str(DEFAULT_RECALL_MAX_QUERY_TOKENS))),
            mental_model_refresh_concurrency=int(
                os.getenv(ENV_MENTAL_MODEL_REFRESH_CONCURRENCY, str(DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY))
            ),
@@ -1196,10 +1136,7 @@ class HindsightConfig:
            file_storage_azure_container=os.getenv(ENV_FILE_STORAGE_AZURE_CONTAINER) or None,
            file_storage_azure_account_name=os.getenv(ENV_FILE_STORAGE_AZURE_ACCOUNT_NAME) or None,
            file_storage_azure_account_key=os.getenv(ENV_FILE_STORAGE_AZURE_ACCOUNT_KEY) or None,
-            file_parser=_parse_str_list(os.getenv(ENV_FILE_PARSER, DEFAULT_FILE_PARSER)),
-            file_parser_allowlist=_parse_str_list(os.getenv(ENV_FILE_PARSER_ALLOWLIST))
-            if os.getenv(ENV_FILE_PARSER_ALLOWLIST)
-            else None,
+            file_parser=os.getenv(ENV_FILE_PARSER, DEFAULT_FILE_PARSER),
            file_parser_iris_token=os.getenv(ENV_FILE_PARSER_IRIS_TOKEN) or None,
            file_parser_iris_org_id=os.getenv(ENV_FILE_PARSER_IRIS_ORG_ID) or None,
            file_conversion_max_batch_size_mb=int(
@@ -1216,14 +1153,6 @@ class HindsightConfig:
            == "true",
            # Observations settings (consolidated knowledge from facts)
            enable_observations=os.getenv(ENV_ENABLE_OBSERVATIONS, str(DEFAULT_ENABLE_OBSERVATIONS)).lower() == "true",
-            enable_observation_history=os.getenv(
-                ENV_ENABLE_OBSERVATION_HISTORY, str(DEFAULT_ENABLE_OBSERVATION_HISTORY)
-            ).lower()
-            == "true",
-            enable_mental_model_history=os.getenv(
-                ENV_ENABLE_MENTAL_MODEL_HISTORY, str(DEFAULT_ENABLE_MENTAL_MODEL_HISTORY)
-            ).lower()
-            == "true",
            consolidation_batch_size=int(
                os.getenv(ENV_CONSOLIDATION_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_BATCH_SIZE))
            ),
@@ -1233,15 +1162,6 @@ class HindsightConfig:
            consolidation_max_tokens=int(
                os.getenv(ENV_CONSOLIDATION_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_MAX_TOKENS))
            ),
-            consolidation_source_facts_max_tokens=int(
-                os.getenv(ENV_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS))
-            ),
-            consolidation_source_facts_max_tokens_per_observation=int(
-                os.getenv(
-                    ENV_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION,
-                    str(DEFAULT_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION),
-                )
-            ),
            observations_mission=os.getenv(ENV_OBSERVATIONS_MISSION) or DEFAULT_OBSERVATIONS_MISSION,
            entity_labels=None,
            entities_allow_free_form=True,
@@ -24,10 +24,9 @@ from datetime import datetime, timezone
 from itertools import combinations
 from typing import TYPE_CHECKING, Any

-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel

 from ...config import get_config
-from ..llm_wrapper import sanitize_llm_output
 from ..memory_engine import fq_table
 from ..retain import embedding_utils
 from .prompts import build_batch_consolidation_prompt
@@ -46,22 +45,12 @@ class _CreateAction(BaseModel):
    text: str
    source_fact_ids: list[str]  # memory UUIDs from the NEW FACTS list

-    @field_validator("text", mode="before")
-    @classmethod
-    def sanitize_text(cls, v: str) -> str:
-        return sanitize_llm_output(v) or ""
-

 class _UpdateAction(BaseModel):
    text: str
    observation_id: str  # UUID of the existing observation to update
    source_fact_ids: list[str]  # memory UUIDs from the NEW FACTS list

-    @field_validator("text", mode="before")
-    @classmethod
-    def sanitize_text(cls, v: str) -> str:
-        return sanitize_llm_output(v) or ""
-

 class _DeleteAction(BaseModel):
    observation_id: str  # UUID of the observation to remove
@@ -161,7 +150,6 @@ async def run_consolidation_job(
    memory_engine: "MemoryEngine",
    bank_id: str,
    request_context: "RequestContext",
-    operation_id: str | None = None,
 ) -> dict[str, Any]:
    """
    Run consolidation job for a bank.
@@ -387,13 +375,6 @@ async def run_consolidation_job(
                    [(m["id"],) for m in llm_batch],
                )

-            # Checkpoint: abort if the operation (and thus the bank) was deleted mid-run.
-            if operation_id and not await memory_engine._check_op_alive(operation_id):
-                logger.info(
-                    f"[CONSOLIDATION] bank={bank_id} operation {operation_id} cancelled (bank deleted), stopping early"
-                )
-                return {"status": "cancelled", "bank_id": bank_id, **stats}
-
            for result in results:
                stats["memories_processed"] += 1
                action = result.get("action")
@@ -785,17 +766,13 @@ async def _execute_update_action(
        logger.debug(f"Update skipped: observation {observation_id} not found in recall results")
        return

-    from ...config import get_config
-
-    history_entry = {
-        "previous_text": model.text,
-        "previous_tags": list(model.tags or []),
-        "previous_occurred_start": model.occurred_start,
-        "previous_occurred_end": model.occurred_end,
-        "previous_mentioned_at": model.mentioned_at,
-        "changed_at": datetime.now(timezone.utc).isoformat(),
-        "new_source_memory_ids": [str(mid) for mid in source_memory_ids],
-    }
+    history = [
+        {
+            "previous_text": model.text,
+            "changed_at": datetime.now(timezone.utc).isoformat(),
+            "source_memory_ids": [str(mid) for mid in source_memory_ids],
+        }
+    ]

    source_ids = list(model.source_fact_ids or []) + source_memory_ids

@@ -810,18 +787,13 @@ async def _execute_update_action(
    if perf:
        perf.record_timing("embedding", time.time() - t0)

-    config = get_config()
-    history_clause = (
-        "history = COALESCE(history, '[]'::jsonb) || $3::jsonb," if config.enable_observation_history else ""
-    )
-
    t0 = time.time()
    await conn.execute(
        f"""
        UPDATE {fq_table("memory_units")}
        SET text = $1,
            embedding = $2::vector,
-            {history_clause}
+            history = $3,
            source_memory_ids = $4,
            proof_count = $5,
            tags = $10,
@@ -833,7 +805,7 @@ async def _execute_update_action(
        """,
        new_text,
        embedding_str,
-        json.dumps([history_entry]),
+        json.dumps(history),
        source_ids,
        len(source_ids),
        uuid.UUID(observation_id),
@@ -945,9 +917,10 @@ async def _find_related_observations(
    """
    # Use recall to find related observations with token budget
    # max_tokens naturally limits how many observations are returned
+    from ...config import get_config
    from ...tracing import get_tracer, is_tracing_enabled

-    config = await memory_engine._config_resolver.resolve_full_config(bank_id, request_context)
+    config = get_config()

    # SECURITY: Use all_strict matching if tags provided to prevent cross-scope consolidation
    tags_match = "all_strict" if tags else "any"
@@ -972,8 +945,7 @@ async def _find_related_observations(
            tags=tags,  # Filter by source memory's tags
            tags_match=tags_match,  # Use strict matching for security
            include_source_facts=True,  # Embed source facts so we avoid a separate DB fetch
-            max_source_facts_tokens=config.consolidation_source_facts_max_tokens,
-            max_source_facts_tokens_per_observation=config.consolidation_source_facts_max_tokens_per_observation,
+            max_source_facts_tokens=-1,  # No token limit — we need all source facts for consolidation
            _quiet=True,  # Suppress logging
        )
    finally:
@@ -1037,17 +1009,14 @@ async def _consolidate_batch_with_llm(
        observations_text = "[]"

    def _fact_line(m: dict[str, Any]) -> str:
-        text = f"[{m['id']}] {m['text']}"
-        temporal_parts = []
+        parts = [f"[{m['id']}] {m['text']}"]
        if m.get("occurred_start"):
-            temporal_parts.append(f"occurred_start={m['occurred_start']}")
+            parts.append(f"occurred_start={m['occurred_start']}")
        if m.get("occurred_end"):
-            temporal_parts.append(f"occurred_end={m['occurred_end']}")
+            parts.append(f"occurred_end={m['occurred_end']}")
        if m.get("mentioned_at"):
-            temporal_parts.append(f"mentioned_at={m['mentioned_at']}")
-        if temporal_parts:
-            text += f" ({', '.join(temporal_parts)})"
-        return text
+            parts.append(f"mentioned_at={m['mentioned_at']}")
+        return " | ".join(parts)

    facts_lines = "\n".join(_fact_line(m) for m in memories)

@@ -29,31 +29,14 @@ Compare the facts against existing observations:
 - Same topic as an existing observation → UPDATE it (observation_id + source_fact_ids)
 - New topic with durable knowledge → CREATE a new observation (source_fact_ids)
 - Cross-reference facts within the batch: a later fact may resolve a vague reference in an earlier one
- Purely ephemeral facts → omit them unless the MISSION above explicitly targets such data (e.g. timestamped events, session state, screen content)"""
+- Purely ephemeral facts → omit them (no create/update needed)"""

 # Output format — JSON braces escaped as {{ }} so .format() leaves them literal
 _BATCH_OUTPUT_FORMAT = """
 Output a JSON object with three arrays.

-## EXAMPLE
-
-Input facts:
-[a1b2c3d4-e5f6-7890-abcd-ef1234567890] Alice mentioned she works long hours, often past midnight | Involving: Alice (occurred_start=2024-01-15, mentioned_at=2024-01-15)
-[b2c3d4e5-f6a7-8901-bcde-f12345678901] Alice said she's exhausted from the project deadlines | Involving: Alice (occurred_start=2024-01-20, mentioned_at=2024-01-20)
-
-Good observation text — clean prose, no metadata, each fact tracked distinctly:
-  "Alice works long hours, often past midnight."
-  "Alice feels exhausted from project deadlines."
-
-Bad observation text — NEVER do this (verbatim copy of fact text with metadata):
-  "Alice mentioned she works long hours, often past midnight | Involving: Alice (occurred_start=2024-01-15, mentioned_at=2024-01-15)"
-
-Observation text rules:
- Write clean prose — NEVER copy raw fact lines or their metadata (temporal fields, "Involving:", "When:" labels, UUIDs).
- Parenthesized metadata like (occurred_start=...) and pipe-separated labels like "| Involving: ..." are fact formatting — strip them entirely from observation text.
- How many observations to create and how much to aggregate is driven by the MISSION above.
-
-{{"creates": [{{"text": "Alice works long hours, often past midnight.", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890"]}}, {{"text": "Alice feels exhausted from project deadlines.", "source_fact_ids": ["b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}],
+Example (showing the required UUID format for all IDs):
+{{"creates": [{{"text": "Alice lives in Berlin", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890", "b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}],
  "updates": [{{"text": "Alice works at Acme Corp as a senior engineer", "observation_id": "c3d4e5f6-a7b8-9012-cdef-123456789012", "source_fact_ids": ["d4e5f6a7-b8c9-0123-defa-234567890123"]}}],
  "deletes": [{{"observation_id": "e5f6a7b8-c9d0-1234-efab-345678901234"}}]}}

@@ -20,10 +20,8 @@ from ..config import (
    DEFAULT_RERANKER_COHERE_MODEL,
    DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
    DEFAULT_RERANKER_FLASHRANK_MODEL,
-    DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC,
    DEFAULT_RERANKER_LITELLM_MODEL,
    DEFAULT_RERANKER_LITELLM_SDK_MODEL,
-    DEFAULT_RERANKER_LOCAL_BATCH_SIZE,
    DEFAULT_RERANKER_LOCAL_FORCE_CPU,
    DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
    DEFAULT_RERANKER_LOCAL_MODEL,
@@ -112,9 +110,6 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        max_concurrent: int = 4,
        force_cpu: bool = False,
        trust_remote_code: bool = False,
-        fp16: bool = False,
-        bucket_batching: bool = False,
-        batch_size: int = DEFAULT_RERANKER_LOCAL_BATCH_SIZE,
    ):
        """
        Initialize local SentenceTransformers cross-encoder.
@@ -129,20 +124,10 @@ class LocalSTCrossEncoder(CrossEncoderModel):
            trust_remote_code: Allow loading models with custom code (security risk).
                              Required for some models like jina-reranker-v2-base-multilingual.
                              Default: False (disabled for security)
-            fp16: Use FP16 (half precision) inference. Faster on MPS and CUDA,
-                  may be slower on CPU. Default: False (opt-in via env var).
-            bucket_batching: Sort pairs by token length before batching to reduce
-                            padding waste. 36-54% speedup, quality-identical.
-                            Default: False (opt-in via env var).
-            batch_size: Batch size for predict() calls. Optimal values vary by
-                       hardware and model (MPS: 32, CUDA: 128+). Default: 32.
        """
        self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
        self.force_cpu = force_cpu
        self.trust_remote_code = trust_remote_code
-        self.fp16 = fp16
-        self.bucket_batching = bucket_batching
-        self.batch_size = batch_size
        self._model = None
        LocalSTCrossEncoder._max_concurrent = max_concurrent

@@ -190,24 +175,6 @@ class LocalSTCrossEncoder(CrossEncoderModel):
            except Exception as e:
                logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")

-        # Patch transformers 5.x compatibility for models using XLM-RoBERTa
-        # (e.g., jina-reranker-v2-base-multilingual). transformers 5.x removed
-        # create_position_ids_from_input_ids as a module-level function; the custom
-        # code in these models still references it. This monkey-patch restores it.
-        try:
-            import transformers.models.xlm_roberta.modeling_xlm_roberta as xlm_module
-            from transformers.models.xlm_roberta.modeling_xlm_roberta import XLMRobertaEmbeddings
-
-            if not hasattr(xlm_module, "create_position_ids_from_input_ids"):
-                setattr(
-                    xlm_module,
-                    "create_position_ids_from_input_ids",
-                    XLMRobertaEmbeddings.create_position_ids_from_input_ids,
-                )
-                logger.info("Reranker: applied transformers 5.x compatibility patch for XLM-RoBERTa")
-        except Exception:
-            pass
-
        # Suppress verbose transformers warnings during model loading
        # This suppresses the "UNEXPECTED" warnings from CrossEncoder which are harmless
        # but look alarming to users (e.g., "embeddings.position_ids | UNEXPECTED")
@@ -232,12 +199,6 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                # Restore original logging level
                transformers_logger.setLevel(original_level)

-        # FP16 inference: convert model weights to half precision.
-        # Empirically validated: 27-36% faster on MPS, quality-identical (20/20 overlap).
-        if self.fp16 and device != "cpu":
-            self._model.model.half()
-            logger.info("Reranker: FP16 inference enabled")
-
        # Initialize shared executor (limited workers naturally limits concurrency)
        if LocalSTCrossEncoder._executor is None:
            LocalSTCrossEncoder._executor = ThreadPoolExecutor(
@@ -249,32 +210,8 @@ class LocalSTCrossEncoder(CrossEncoderModel):
            logger.info("Reranker: local provider initialized (using existing executor)")

    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """Synchronous prediction wrapper for thread pool execution.
-
-        Supports two optimizations (controlled via .env):
-        - bucket_batching: sort pairs by token length to reduce padding waste (36-54% speedup)
-        - batch_size: explicit batch size for predict() calls (MPS optimal: 32)
-        """
-        import numpy as np
-
-        if self.bucket_batching and len(pairs) > 1:
-            # Sort pairs by approximate token length to create homogeneous batches.
-            # This eliminates padding waste — short pairs aren't padded to the length
-            # of the longest pair in the batch. Quality-identical by construction.
-            lengths = [len(pairs[i][0]) + len(pairs[i][1]) for i in range(len(pairs))]
-            sorted_indices = sorted(range(len(pairs)), key=lambda i: lengths[i])
-            sorted_pairs = [pairs[i] for i in sorted_indices]
-
-            sorted_scores = self._model.predict(sorted_pairs, batch_size=self.batch_size, show_progress_bar=False)
-            sorted_scores = sorted_scores.tolist() if hasattr(sorted_scores, "tolist") else list(sorted_scores)
-
-            # Restore original order
-            scores = [0.0] * len(pairs)
-            for new_pos, orig_idx in enumerate(sorted_indices):
-                scores[orig_idx] = sorted_scores[new_pos]
-            return scores
-
-        scores = self._model.predict(pairs, batch_size=self.batch_size, show_progress_bar=False)
+        """Synchronous prediction wrapper for thread pool execution."""
+        scores = self._model.predict(pairs, show_progress_bar=False)
        return scores.tolist() if hasattr(scores, "tolist") else list(scores)

    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
@@ -883,17 +820,6 @@ class FlashRankCrossEncoder(CrossEncoderModel):
        return await loop.run_in_executor(FlashRankCrossEncoder._executor, self._predict_sync, pairs)


-def _truncate_to_tokens(text: str, max_tokens: int) -> str:
-    """Truncate text to at most max_tokens using the shared tiktoken encoder."""
-    from .memory_engine import _get_tiktoken_encoding
-
-    enc = _get_tiktoken_encoding()
-    tokens = enc.encode(text)
-    if len(tokens) <= max_tokens:
-        return text
-    return enc.decode(tokens[:max_tokens])
-
-
 class LiteLLMCrossEncoder(CrossEncoderModel):
    """
    LiteLLM cross-encoder implementation using LiteLLM proxy's /rerank endpoint.
@@ -917,7 +843,6 @@ class LiteLLMCrossEncoder(CrossEncoderModel):
        api_key: str | None = None,
        model: str = DEFAULT_RERANKER_LITELLM_MODEL,
        timeout: float = 60.0,
-        max_tokens_per_doc: int | None = DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC,
    ):
        """
        Initialize LiteLLM cross-encoder client.
@@ -928,15 +853,11 @@ class LiteLLMCrossEncoder(CrossEncoderModel):
            model: Reranking model name (default: cohere/rerank-english-v3.0)
                   Use provider prefix (e.g., cohere/, together_ai/, voyage/)
            timeout: Request timeout in seconds (default: 60.0)
-            max_tokens_per_doc: If set, truncate each document to this many tokens before
-                                sending to the reranker (uses tiktoken cl100k_base encoding).
-                                Useful for models with small context windows (e.g. 1024 tokens).
        """
        self.api_base = api_base.rstrip("/")
        self.api_key = api_key
        self.model = model
        self.timeout = timeout
-        self.max_tokens_per_doc = max_tokens_per_doc
        self._async_client: httpx.AsyncClient | None = None

    @property
@@ -984,8 +905,6 @@ class LiteLLMCrossEncoder(CrossEncoderModel):

        for query, indexed_texts in query_groups.items():
            texts = [text for _, text in indexed_texts]
-            if self.max_tokens_per_doc is not None:
-                texts = [_truncate_to_tokens(t, self.max_tokens_per_doc) for t in texts]
            indices = [idx for idx, _ in indexed_texts]

            # LiteLLM /rerank follows Cohere API format
@@ -1031,7 +950,6 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):
        model: str = DEFAULT_RERANKER_LITELLM_SDK_MODEL,
        api_base: str | None = None,
        timeout: float = 60.0,
-        max_tokens_per_doc: int | None = DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC,
    ):
        """
        Initialize LiteLLM SDK cross-encoder client.
@@ -1041,15 +959,11 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):
            model: Model name with provider prefix (e.g., "deepinfra/Qwen3-reranker-8B")
            api_base: Custom base URL for API (optional)
            timeout: Request timeout in seconds (default: 60.0)
-            max_tokens_per_doc: If set, truncate each document to this many tokens before
-                                sending to the reranker (uses tiktoken cl100k_base encoding).
-                                Useful for models with small context windows (e.g. 1024 tokens).
        """
        self.api_key = api_key
        self.model = model
        self.api_base = api_base
        self.timeout = timeout
-        self.max_tokens_per_doc = max_tokens_per_doc
        self._initialized = False
        self._litellm = None  # Will be set during initialization

@@ -1103,8 +1017,6 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):

        for query, indexed_texts in query_groups.items():
            texts = [text for _, text in indexed_texts]
-            if self.max_tokens_per_doc is not None:
-                texts = [_truncate_to_tokens(t, self.max_tokens_per_doc) for t in texts]
            indices = [idx for idx, _ in indexed_texts]

            # Build kwargs for rerank call
@@ -1138,97 +1050,6 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):
        return all_scores


-class JinaMLXCrossEncoder(CrossEncoderModel):
-    """
-    Jina Reranker v3 MLX implementation for Apple Silicon.
-
-    Uses jinaai/jina-reranker-v3-mlx — a 0.6B parameter multilingual listwise reranker
-    optimized for Apple Silicon via the MLX framework. No transformers/PyTorch dependency.
-
-    The model is downloaded automatically from HuggingFace Hub on first use.
-    Requires: mlx>=0.31.0, mlx-lm>=0.31.1, safetensors>=0.6.2
-    """
-
-    HF_REPO_ID = "jinaai/jina-reranker-v3-mlx"
-
-    def __init__(self, model_path: str | None = None):
-        """
-        Args:
-            model_path: Local path to the downloaded model directory.
-                        If None, the model is downloaded from HuggingFace Hub.
-        """
-        self.model_path = model_path
-        self._reranker = None
-
-    @property
-    def provider_name(self) -> str:
-        return "jina-mlx"
-
-    async def initialize(self) -> None:
-        if self._reranker is not None:
-            return
-
-        try:
-            import mlx.core  # noqa: F401
-            import mlx_lm  # noqa: F401
-        except ImportError:
-            raise ImportError(
-                "mlx and mlx-lm are required for JinaMLXCrossEncoder. "
-                "Install with: pip install mlx>=0.31.0 mlx-lm>=0.31.1 safetensors>=0.6.2"
-            )
-
-        loop = asyncio.get_event_loop()
-        await loop.run_in_executor(None, self._load_model)
-
-    def _load_model(self) -> None:
-        """Download (if needed) and load the MLX reranker. Runs in a thread."""
-        import os
-
-        from huggingface_hub import snapshot_download
-
-        from .jina_mlx_reranker import MLXReranker
-
-        model_path = self.model_path
-        if model_path is None:
-            logger.info(f"Reranker: downloading {self.HF_REPO_ID} from HuggingFace Hub...")
-            model_path = snapshot_download(repo_id=self.HF_REPO_ID)
-
-        logger.info(f"Reranker: loading jina-reranker-v3-mlx from {model_path}")
-        self._reranker = MLXReranker(
-            model_path=model_path,
-            projector_path=os.path.join(model_path, "projector.safetensors"),
-        )
-        logger.info("Reranker: jina-mlx provider initialized")
-
-    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """Score pairs grouped by query. Runs in a thread."""
-        if not pairs:
-            return []
-
-        query_groups: dict[str, list[tuple[int, str]]] = {}
-        for idx, (query, doc) in enumerate(pairs):
-            query_groups.setdefault(query, []).append((idx, doc))
-
-        all_scores = [0.0] * len(pairs)
-
-        for query, indexed_docs in query_groups.items():
-            docs = [doc for _, doc in indexed_docs]
-            indices = [idx for idx, _ in indexed_docs]
-            results = self._reranker.rerank(query, docs)
-            for result in results:
-                original_idx = result["index"]
-                all_scores[indices[original_idx]] = result["relevance_score"]
-
-        return all_scores
-
-    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
-        if self._reranker is None:
-            raise RuntimeError("Reranker not initialized. Call initialize() first.")
-
-        loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(None, self._predict_sync, pairs)
-
-
 def create_cross_encoder_from_env() -> CrossEncoderModel:
    """
    Create a CrossEncoderModel instance based on configuration.
@@ -1258,9 +1079,6 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
            max_concurrent=config.reranker_local_max_concurrent,
            force_cpu=config.reranker_local_force_cpu,
            trust_remote_code=config.reranker_local_trust_remote_code,
-            fp16=config.reranker_local_fp16,
-            bucket_batching=config.reranker_local_bucket_batching,
-            batch_size=config.reranker_local_batch_size,
        )
    elif provider == "cohere":
        api_key = config.reranker_cohere_api_key
@@ -1280,7 +1098,6 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
            api_base=config.reranker_litellm_api_base,
            api_key=config.reranker_litellm_api_key,
            model=config.reranker_litellm_model,
-            max_tokens_per_doc=config.reranker_litellm_max_tokens_per_doc,
        )
    elif provider == "litellm-sdk":
        api_key = config.reranker_litellm_sdk_api_key
@@ -1292,7 +1109,6 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
            api_key=api_key,
            model=config.reranker_litellm_sdk_model,
            api_base=config.reranker_litellm_sdk_api_base,
-            max_tokens_per_doc=config.reranker_litellm_max_tokens_per_doc,
        )
    elif provider == "zeroentropy":
        api_key = config.reranker_zeroentropy_api_key
@@ -1306,9 +1122,7 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
        )
    elif provider == "rrf":
        return RRFPassthroughCrossEncoder()
-    elif provider == "jina-mlx":
-        return JinaMLXCrossEncoder()
    else:
        raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf', 'jina-mlx'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
        )
@@ -58,16 +58,10 @@ async def retry_with_backoff(
            last_exception = e
            if attempt < max_retries:
                delay = min(base_delay * (2**attempt), max_delay)
-                if isinstance(e, asyncpg.exceptions.DeadlockDetectedError):
-                    logger.warning(
-                        f"Deadlock detected during parallel document processing — this is expected and will resolve automatically "
-                        f"(attempt {attempt + 1}/{max_retries + 1}, retrying in {delay:.1f}s)"
-                    )
-                else:
-                    logger.warning(
-                        f"Database operation failed (attempt {attempt + 1}/{max_retries + 1}): {e}. "
-                        f"Retrying in {delay:.1f}s..."
-                    )
+                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}")
@@ -459,12 +459,10 @@ class EntityResolver:
            entity_dates = [g.event_date for _, g in sorted_groups]

            # INSERT ... ON CONFLICT DO NOTHING — no row lock on already-existing entities.
-            # mention_count starts at 0 here; flush_pending_stats() is the sole source of
-            # truth for mention counting (one stat per original mention in the batch).
            inserted_rows = await conn.fetch(
                f"""
                INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
-                SELECT $1, name, COALESCE(event_date, now()), COALESCE(event_date, now()), 0
+                SELECT $1, name, COALESCE(event_date, now()), COALESCE(event_date, now()), 1
                FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
                ON CONFLICT (bank_id, LOWER(canonical_name))
                DO NOTHING
@@ -491,15 +489,13 @@ class EntityResolver:
                for row in existing_rows:
                    id_by_name[row["name_lower"]] = row["id"]

-            # Assign entity IDs back and queue one stat per original mention so that
-            # flush_pending_stats() increments mention_count by the true mention count,
-            # not just 1 per unique name.
+            # Assign entity IDs back and queue for post-txn stats flush.
            for name_lower, g in sorted_groups:
                entity_id = id_by_name.get(name_lower)
                if entity_id:
                    for original_idx in g.indices:
                        entity_ids[original_idx] = entity_id
-                        pending.append(_EntityStat(entity_id=entity_id, event_date=g.event_date))
+                    pending.append(_EntityStat(entity_id=entity_id, event_date=g.event_date))

        # Accumulate into the resolver's pending list; the orchestrator flushes
        # these with await entity_resolver.flush_pending_stats() after the txn.
@@ -48,28 +48,6 @@ _llm_max_concurrent = int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_
 _global_llm_semaphore = asyncio.Semaphore(_llm_max_concurrent)


-def sanitize_llm_output(text: str | None) -> str | None:
-    """
-    Sanitize text by removing characters that break downstream systems.
-
-    Removes:
-    - ASCII control characters (0x00-0x08, 0x0B-0x0C, 0x0E-0x1F, 0x7F): break
-      json.loads and PostgreSQL UTF-8 encoding; tab (0x09), newline (0x0A), and
-      carriage return (0x0D) are preserved as they are valid in text and JSON.
-    - Unicode surrogates (U+D800-U+DFFF): Invalid in UTF-8, break LLM APIs
-
-    Surrogate characters are used in UTF-16 encoding but cannot be encoded
-    in UTF-8. They can appear in Python strings from improperly decoded data
-    (e.g., from JavaScript or broken files). Control characters commonly appear
-    in LLM output embedded inside JSON string values.
-    """
-    if text is None:
-        return None
-    if not text:
-        return text
-    return re.sub(r"[\x00-\x08\x0b\x0c\x0e-\x1f\x7f\ud800-\udfff]", "", text)
-
-
 class OutputTooLongError(Exception):
    """
    Bridge exception raised when LLM output exceeds token limits.
@@ -227,7 +205,7 @@ def create_llm_provider(
            reasoning_effort=reasoning_effort,
        )

-    elif provider_lower in ("openai", "groq", "ollama", "lmstudio", "minimax"):
+    elif provider_lower in ("openai", "groq", "ollama", "lmstudio"):
        return OpenAICompatibleLLM(
            provider=provider,
            api_key=api_key,
@@ -296,7 +274,6 @@ class LLMProvider:
            "openai-codex",
            "claude-code",
            "mock",
-            "minimax",
        ]
        if self.provider not in valid_providers:
            raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")
@@ -309,8 +286,6 @@ class LLMProvider:
                self.base_url = "http://localhost:11434/v1"
            elif self.provider == "lmstudio":
                self.base_url = "http://localhost:1234/v1"
-            elif self.provider == "minimax":
-                self.base_url = "https://api.minimax.io/v1"

        # Prepare Vertex AI config (if applicable)
        vertexai_project_id = None
@@ -16,7 +16,7 @@ import logging
 import time
 import uuid
 from collections.abc import Awaitable, Callable
-from datetime import UTC, datetime, timedelta, timezone
+from datetime import UTC, datetime, timedelta
 from typing import TYPE_CHECKING, Any

 import asyncpg
@@ -51,9 +51,13 @@ def get_current_schema() -> str:
    return schema


+# Initialize tiktoken encoder once at module level for efficiency
+_tiktoken_encoder = tiktoken.get_encoding("cl100k_base")  # GPT-4/GPT-3.5-turbo encoding
+
+
 def count_tokens(text: str) -> int:
    """Count tokens in text using tiktoken (cl100k_base encoding for GPT-4/3.5)."""
-    return len(_get_tiktoken_encoding().encode(text))
+    return len(_tiktoken_encoder.encode(text))


 def fq_table(table_name: str) -> str:
@@ -164,7 +168,7 @@ from enum import Enum
 from ..metrics import get_metrics_collector
 from ..pg0 import EmbeddedPostgres, parse_pg0_url
 from .entity_resolver import EntityResolver
-from .llm_wrapper import LLMConfig, requires_api_key, sanitize_llm_output
+from .llm_wrapper import LLMConfig, requires_api_key
 from .query_analyzer import QueryAnalyzer
 from .reflect import run_reflect_agent
 from .reflect.tools import tool_expand, tool_recall, tool_search_mental_models, tool_search_observations
@@ -184,7 +188,7 @@ from .retain import bank_utils, embedding_utils
 from .retain.types import RetainContentDict
 from .search import think_utils
 from .search.reranking import CrossEncoderReranker, apply_combined_scoring
-from .search.tags import TagGroup, TagsMatch, build_tags_where_clause
+from .search.tags import TagsMatch, build_tags_where_clause
 from .task_backend import BrokerTaskBackend, SyncTaskBackend, TaskBackend


@@ -204,6 +208,8 @@ def utcnow():
 # Logger for memory system
 logger = logging.getLogger(__name__)

+import tiktoken
+
 from .db_utils import acquire_with_retry

 # Cache tiktoken encoding for token budget filtering (module-level singleton)
@@ -647,19 +653,13 @@ class MemoryEngine(MemoryEngineInterface):
            # Retrieve file from storage
            file_data = await self._file_storage.retrieve(storage_key)

-            # Convert to markdown using the ordered fallback chain stored in the task payload.
-            # task_dict["parser"] is always a list[str] set at submission time.
-            parser_chain: list[str] = task_dict.get("parser") or []
-            if not parser_chain:
-                raise ValueError("No parser chain defined for file_convert_retain task")
-            convert_result = await self._parser_registry.convert_with_fallback(
-                parsers=parser_chain,
-                file_data=file_data,
+            # Convert to markdown
+            parser = self._parser_registry.get_parser(
+                name=task_dict.get("parser"),
                filename=filename,
                content_type=task_dict.get("content_type"),
            )
-            markdown_content = sanitize_llm_output(convert_result.content) or ""
-            winning_parser = convert_result.parser_name
+            markdown_content = await parser.convert(file_data, filename)
        except Exception as e:
            # Re-raise with filename context for better error reporting
            error_msg = f"Failed to parse file '{filename}': {str(e)}"
@@ -671,31 +671,6 @@ class MemoryEngine(MemoryEngineInterface):
            f"document_id={document_id}, {len(markdown_content)} chars. Submitting retain task."
        )

-        # Fire file conversion hook (e.g., for Iris billing)
-        if self._operation_validator:
-            try:
-                from hindsight_api.extensions.operation_validator import FileConvertResult
-                from hindsight_api.models import RequestContext
-
-                convert_context = RequestContext(
-                    internal=True,
-                    user_initiated=True,
-                    tenant_id=task_dict.get("_tenant_id"),
-                    api_key_id=task_dict.get("_api_key_id"),
-                )
-                await self._operation_validator.on_file_convert_complete(
-                    FileConvertResult(
-                        bank_id=bank_id,
-                        parser_name=winning_parser,
-                        filename=filename,
-                        output_chars=len(markdown_content),
-                        output_text=markdown_content,
-                        request_context=convert_context,
-                    )
-                )
-            except Exception as e:
-                logger.warning(f"[FILE_CONVERT_RETAIN] on_file_convert_complete hook failed: {e}")
-
        # Build retain task payload
        retain_contents = [
            {
@@ -814,7 +789,6 @@ class MemoryEngine(MemoryEngineInterface):
            memory_engine=self,
            bank_id=bank_id,
            request_context=internal_context,
-            operation_id=task_dict.get("operation_id"),
        )

        logger.info(f"[CONSOLIDATION] bank={bank_id} completed: {result.get('memories_processed', 0)} processed")
@@ -1134,13 +1108,8 @@ class MemoryEngine(MemoryEngineInterface):
            )

        async def _callback(conn: asyncpg.Connection) -> None:
-            # Resolve schema at call time (not at callback creation time) because
-            # _current_schema contextvar may not yet be set when the callback is built
-            # from the HTTP path (http.py calls _build_retain_outbox_callback before
-            # retain_batch_async which is where _authenticate_tenant sets the schema).
-            resolved_schema = schema or _current_schema.get()
            for event in events:
-                await webhook_manager.fire_event_with_conn(event, conn, schema=resolved_schema)
+                await webhook_manager.fire_event_with_conn(event, conn, schema=schema)

        return _callback

@@ -1244,24 +1213,6 @@ class MemoryEngine(MemoryEngineInterface):
        except Exception as e:
            logger.error(f"Failed to delete async operation record {operation_id}: {e}")

-    async def _check_op_alive(self, operation_id: str) -> bool:
-        """Return False if the operation row no longer exists (e.g. bank was deleted via CASCADE).
-
-        Long-running operations should call this at natural checkpoints (e.g. after each
-        committed batch) to detect bank deletion early and abort cleanly.
-        """
-        try:
-            pool = await self._get_pool()
-            async with acquire_with_retry(pool) as conn:
-                row = await conn.fetchrow(
-                    f"SELECT operation_id FROM {fq_table('async_operations')} WHERE operation_id = $1",
-                    uuid.UUID(operation_id),
-                )
-                return row is not None
-        except Exception as e:
-            logger.error(f"Failed to check operation liveness {operation_id}: {e}")
-            return True  # Assume alive on DB error to avoid false-positive aborts
-
    async def _mark_operation_failed(self, operation_id: str, error_message: str, error_traceback: str):
        """Helper to mark an operation as failed in the database.

@@ -1277,19 +1228,15 @@ class MemoryEngine(MemoryEngineInterface):
            async with acquire_with_retry(pool) as conn:
                async with conn.transaction():
                    # Mark this operation as failed
-                    row = await conn.fetchrow(
+                    await conn.execute(
                        f"""
                        UPDATE {fq_table("async_operations")}
                        SET status = 'failed', error_message = $2, updated_at = NOW()
                        WHERE operation_id = $1
-                        RETURNING operation_id
                        """,
                        uuid.UUID(operation_id),
                        truncated_error,
                    )
-                    if row is None:
-                        logger.info(f"Operation {operation_id} no longer exists (bank deleted), skipping mark-failed")
-                        return
                    logger.info(f"Marked async operation as failed: {operation_id}")

                    # Check if this is a child operation and update parent if all siblings are done
@@ -1309,20 +1256,14 @@ class MemoryEngine(MemoryEngineInterface):
            async with acquire_with_retry(pool) as conn:
                async with conn.transaction():
                    # Mark this operation as completed
-                    row = await conn.fetchrow(
+                    await conn.execute(
                        f"""
                        UPDATE {fq_table("async_operations")}
                        SET status = 'completed', updated_at = NOW(), completed_at = NOW()
                        WHERE operation_id = $1
-                        RETURNING operation_id
                        """,
                        uuid.UUID(operation_id),
                    )
-                    if row is None:
-                        logger.info(
-                            f"Operation {operation_id} no longer exists (bank deleted), skipping mark-completed"
-                        )
-                        return
                    logger.info(f"Marked async operation as completed: {operation_id}")

                    # Check if this is a child operation and update parent if all siblings are done
@@ -1352,20 +1293,14 @@ class MemoryEngine(MemoryEngineInterface):
            pool = await self._get_pool()
            async with acquire_with_retry(pool) as conn:
                async with conn.transaction():
-                    row = await conn.fetchrow(
+                    await conn.execute(
                        f"""
                        UPDATE {fq_table("async_operations")}
                        SET status = 'completed', updated_at = NOW(), completed_at = NOW()
                        WHERE operation_id = $1
-                        RETURNING operation_id
                        """,
                        uuid.UUID(operation_id),
                    )
-                    if row is None:
-                        logger.info(
-                            f"Operation {operation_id} no longer exists (bank deleted), skipping mark-completed"
-                        )
-                        return
                    logger.info(f"Marked async operation as completed: {operation_id}")
                    await self._maybe_update_parent_operation(operation_id, conn)

@@ -1663,15 +1598,6 @@ class MemoryEngine(MemoryEngineInterface):
        # Create connection pool
        # For read-heavy workloads with many parallel think/search operations,
        # we need a larger pool. Read operations don't need strong isolation.
-        async def _init_connection(conn: asyncpg.Connection) -> None:
-            # SET (not SET LOCAL) so it persists for the connection lifetime.
-            # ef_search=200 improves HNSW recall quality for the per-fact_type
-            # semantic queries in retrieve_semantic_bm25_combined().
-            try:
-                await conn.execute("SET hnsw.ef_search = 200")
-            except Exception:
-                logger.debug("Could not set hnsw.ef_search — extension may not support it")
-
        self._pool = await asyncpg.create_pool(
            self.db_url,
            min_size=self._pool_min_size,
@@ -1679,7 +1605,6 @@ class MemoryEngine(MemoryEngineInterface):
            command_timeout=self._db_command_timeout,
            statement_cache_size=0,  # Disable prepared statement cache
            timeout=self._db_acquire_timeout,  # Connection acquisition timeout (seconds)
-            init=_init_connection,
        )

        # Initialize entity resolver with pool and configured lookup strategy
@@ -2086,15 +2011,6 @@ class MemoryEngine(MemoryEngineInterface):
            # Process each sub-batch
            all_results = []
            for i, sub_batch in enumerate(sub_batches, 1):
-                # Checkpoint: abort if the operation was deleted (bank was deleted) between sub-batches.
-                if operation_id and not await self._check_op_alive(operation_id):
-                    logger.info(
-                        f"[BATCH_RETAIN] bank={bank_id} operation {operation_id} cancelled (bank deleted), stopping after {i - 1}/{len(sub_batches)} sub-batches"
-                    )
-                    if return_usage:
-                        return all_results, total_usage
-                    return all_results
-
                sub_batch_tokens = sum(count_tokens(item.get("content", "")) for item in sub_batch)
                logger.info(
                    f"Processing sub-batch {i}/{len(sub_batches)}: {len(sub_batch)} items, {sub_batch_tokens:,} tokens"
@@ -2235,7 +2151,7 @@ class MemoryEngine(MemoryEngineInterface):
                    document_tags=document_tags,
                    config=resolved_config,
                    operation_id=operation_id,
-                    schema=_current_schema.get(),
+                    schema=request_context.tenant_id if request_context else None,
                    outbox_callback=outbox_callback,
                )

@@ -2296,11 +2212,9 @@ class MemoryEngine(MemoryEngineInterface):
        max_chunk_tokens: int = 8192,
        include_source_facts: bool = False,
        max_source_facts_tokens: int = 4096,
-        max_source_facts_tokens_per_observation: int = -1,
        request_context: "RequestContext",
        tags: list[str] | None = None,
        tags_match: TagsMatch = "any",
-        tag_groups: list[TagGroup] | None = None,
        _connection_budget: int | None = None,
        _quiet: bool = False,
    ) -> RecallResultModel:
@@ -2435,12 +2349,10 @@ class MemoryEngine(MemoryEngineInterface):
                            semaphore_wait=semaphore_wait,
                            tags=tags,
                            tags_match=tags_match,
-                            tag_groups=tag_groups,
                            connection_budget=_connection_budget,
                            quiet=_quiet,
                            include_source_facts=include_source_facts,
                            max_source_facts_tokens=max_source_facts_tokens,
-                            max_source_facts_tokens_per_observation=max_source_facts_tokens_per_observation,
                        )
                        break  # Success - exit retry loop
                    except Exception as e:
@@ -2563,12 +2475,10 @@ class MemoryEngine(MemoryEngineInterface):
        semaphore_wait: float = 0.0,
        tags: list[str] | None = None,
        tags_match: TagsMatch = "any",
-        tag_groups: list[TagGroup] | None = None,
        connection_budget: int | None = None,
        quiet: bool = False,
        include_source_facts: bool = False,
        max_source_facts_tokens: int = 4096,
-        max_source_facts_tokens_per_observation: int = -1,
    ) -> RecallResultModel:
        """
        Search implementation with modular retrieval and reranking.
@@ -2683,7 +2593,6 @@ class MemoryEngine(MemoryEngineInterface):
                        self.query_analyzer,
                        tags=tags,
                        tags_match=tags_match,
-                        tag_groups=tag_groups,
                    )
                    parallel_duration = time.time() - parallel_start
            finally:
@@ -3191,9 +3100,18 @@ class MemoryEngine(MemoryEngineInterface):

                            encoding = _get_tiktoken_encoding()
                            source_facts_dict = {}
-
-                            def _make_source_fact(sid: str, r: Any) -> MemoryFact:
-                                return MemoryFact(
+                            total_source_tokens = 0
+                            for sid in source_ids_ordered:
+                                if sid not in source_row_by_id:
+                                    continue
+                                r = source_row_by_id[sid]
+                                fact_tokens = len(encoding.encode(r["text"]))
+                                if (
+                                    max_source_facts_tokens >= 0
+                                    and total_source_tokens + fact_tokens > max_source_facts_tokens
+                                ):
+                                    break
+                                source_facts_dict[sid] = MemoryFact(
                                    id=sid,
                                    text=r["text"],
                                    fact_type=r["fact_type"],
@@ -3205,37 +3123,7 @@ class MemoryEngine(MemoryEngineInterface):
                                    chunk_id=str(r["chunk_id"]) if r["chunk_id"] else None,
                                    tags=r["tags"] or None,
                                )
-
-                            if max_source_facts_tokens_per_observation >= 0:
-                                # Per-observation capping: each observation independently selects
-                                # source facts up to its token budget.
-                                for obs_id, sids in source_fact_ids_by_obs.items():
-                                    obs_tokens = 0
-                                    for sid in sids:
-                                        if sid not in source_row_by_id:
-                                            continue
-                                        r = source_row_by_id[sid]
-                                        fact_tokens = len(encoding.encode(r["text"]))
-                                        if obs_tokens + fact_tokens > max_source_facts_tokens_per_observation:
-                                            break
-                                        obs_tokens += fact_tokens
-                                        if sid not in source_facts_dict:
-                                            source_facts_dict[sid] = _make_source_fact(sid, r)
-                            else:
-                                # Global budget: fill in order of first appearance until exhausted.
-                                total_source_tokens = 0
-                                for sid in source_ids_ordered:
-                                    if sid not in source_row_by_id:
-                                        continue
-                                    r = source_row_by_id[sid]
-                                    fact_tokens = len(encoding.encode(r["text"]))
-                                    if (
-                                        max_source_facts_tokens >= 0
-                                        and total_source_tokens + fact_tokens > max_source_facts_tokens
-                                    ):
-                                        break
-                                    source_facts_dict[sid] = _make_source_fact(sid, r)
-                                    total_source_tokens += fact_tokens
+                                total_source_tokens += fact_tokens

            # Get entities for each fact if include_entities is requested
            fact_entity_map = {}  # unit_id -> list of (entity_id, entity_name)
@@ -3497,140 +3385,6 @@ class MemoryEngine(MemoryEngineInterface):

        return result

-    async def update_document(
-        self,
-        document_id: str,
-        bank_id: str,
-        *,
-        tags: list[str] | None = None,
-        request_context: "RequestContext",
-    ) -> bool:
-        """
-        Update mutable fields on a document without re-processing its content.
-
-        Tag changes propagate to all associated memory units and trigger observation
-        invalidation + re-consolidation (same semantics as delete_document):
-        - Observations referencing the document's memory units are deleted.
-        - The document's own units and any co-source memories from other documents
-          have consolidated_at reset so they are re-consolidated under the new tags.
-
-        Args:
-            document_id: Document ID to update
-            bank_id: Bank ID that owns the document
-            tags: New tags to apply to the document and all its memory units (optional)
-            request_context: Request context for authentication.
-
-        Returns:
-            True if the document was found and updated, False if not found
-        """
-        await self._authenticate_tenant(request_context)
-        if self._operation_validator:
-            from hindsight_api.extensions import BankWriteContext
-
-            ctx = BankWriteContext(bank_id=bank_id, operation="update_document", request_context=request_context)
-            await self._validate_operation(self._operation_validator.validate_bank_write(ctx))
-        pool = await self._get_pool()
-        invalidated_obs = 0
-        async with acquire_with_retry(pool) as conn:
-            async with conn.transaction():
-                set_parts: list[str] = ["updated_at = now()"]
-                params: list[Any] = []
-                p = 1
-
-                if tags is not None:
-                    set_parts.append(f"tags = ${p}")
-                    params.append(tags)
-                    p += 1
-
-                params.extend([document_id, bank_id])
-                doc_id_found = await conn.fetchval(
-                    f"""
-                    UPDATE {fq_table("documents")}
-                    SET {", ".join(set_parts)}
-                    WHERE id = ${p} AND bank_id = ${p + 1}
-                    RETURNING id
-                    """,
-                    *params,
-                )
-                if not doc_id_found:
-                    return False
-
-                if tags is not None:
-                    unit_rows = await conn.fetch(
-                        f"SELECT id FROM {fq_table('memory_units')} WHERE document_id = $1 AND fact_type IN ('experience', 'world')",
-                        document_id,
-                    )
-                    unit_ids = [str(row["id"]) for row in unit_rows]
-
-                    await conn.execute(
-                        f"UPDATE {fq_table('memory_units')} SET tags = $1 WHERE document_id = $2",
-                        tags,
-                        document_id,
-                    )
-
-                    if unit_ids:
-                        import uuid as uuid_module
-
-                        unit_uuids = [uuid_module.UUID(uid) for uid in unit_ids]
-                        unit_uuid_set = {str(u) for u in unit_uuids}
-                        affected_obs = await conn.fetch(
-                            f"""
-                            SELECT id, source_memory_ids FROM {fq_table("memory_units")}
-                            WHERE bank_id = $1
-                              AND fact_type = 'observation'
-                              AND source_memory_ids && $2::uuid[]
-                            """,
-                            bank_id,
-                            unit_uuids,
-                        )
-                        if affected_obs:
-                            obs_ids = [obs["id"] for obs in affected_obs]
-
-                            seen: set[str] = set()
-                            other_source_uuids: list[uuid_module.UUID] = []
-                            for obs in affected_obs:
-                                for src_id in obs["source_memory_ids"] or []:
-                                    src_str = str(src_id)
-                                    if src_str not in unit_uuid_set and src_str not in seen:
-                                        other_source_uuids.append(src_id)
-                                        seen.add(src_str)
-
-                            await conn.execute(
-                                f"DELETE FROM {fq_table('memory_units')} WHERE id = ANY($1::uuid[])",
-                                obs_ids,
-                            )
-                            await conn.execute(
-                                f"""
-                                UPDATE {fq_table("memory_units")}
-                                SET consolidated_at = NULL
-                                WHERE id = ANY($1::uuid[])
-                                  AND fact_type IN ('experience', 'world')
-                                """,
-                                unit_uuids,
-                            )
-                            if other_source_uuids:
-                                await conn.execute(
-                                    f"""
-                                    UPDATE {fq_table("memory_units")}
-                                    SET consolidated_at = NULL
-                                    WHERE id = ANY($1::uuid[])
-                                      AND fact_type IN ('experience', 'world')
-                                    """,
-                                    other_source_uuids,
-                                )
-                            invalidated_obs = len(obs_ids)
-                            logger.info(
-                                f"[OBSERVATIONS] Deleted {invalidated_obs} observations, reset "
-                                f"{len(unit_ids)} document source memories and "
-                                f"{len(other_source_uuids)} co-source memories for re-consolidation "
-                                f"after document update on '{document_id}' in bank {bank_id}"
-                            )
-
-        if invalidated_obs > 0:
-            await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
-
-        return True
-
    async def delete_memory_unit(
        self,
        unit_id: str,
@@ -3728,7 +3482,6 @@ class MemoryEngine(MemoryEngineInterface):
        pool = await self._get_pool()
        invalidated_obs = 0
        result: dict[str, int] = {}
-        bank_internal_id: str | None = None
        async with acquire_with_retry(pool) as conn:
            # Ensure connection is not in read-only mode (can happen with connection poolers)
            await conn.execute("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE")
@@ -3784,12 +3537,8 @@ class MemoryEngine(MemoryEngineInterface):
                        # Delete entities (cascades to unit_entities, entity_cooccurrences, memory_links with entity_id)
                        await conn.execute(f"DELETE FROM {fq_table('entities')} WHERE bank_id = $1", bank_id)

-                        # Delete the bank profile and retrieve internal_id for HNSW index cleanup
-                        internal_id = await conn.fetchval(
-                            f"DELETE FROM {fq_table('banks')} WHERE bank_id = $1 RETURNING internal_id", bank_id
-                        )
-                        if internal_id:
-                            bank_internal_id = str(internal_id)
+                        # Delete the bank profile itself
+                        await conn.execute(f"DELETE FROM {fq_table('banks')} WHERE bank_id = $1", bank_id)

                        result = {
                            "memory_units_deleted": units_count,
@@ -3801,12 +3550,6 @@ class MemoryEngine(MemoryEngineInterface):
                except Exception as e:
                    raise Exception(f"Failed to delete agent data: {str(e)}")

-            # Drop per-bank HNSW indexes AFTER the transaction commits to avoid
-            # AccessExclusiveLock deadlocks with concurrent bank deletions.
-            # (DROP INDEX on memory_units conflicts with RowExclusiveLock from DELETE inside tx)
-            if bank_internal_id:
-                await bank_utils.drop_bank_hnsw_indexes(conn, bank_internal_id)
-
        if invalidated_obs > 0:
            await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)

@@ -4547,11 +4290,7 @@ class MemoryEngine(MemoryEngineInterface):
                "observation_scopes": row["observation_scopes"] if row["observation_scopes"] else None,
            }

-            # For observations, include source_memory_ids
-            # history is deprecated here - use GET /memories/{id}/history instead
-            if row["fact_type"] == "observation":
-                result["history"] = []
-
+            # For observations, include source_memory_ids and fetch source_memories
            if row["fact_type"] == "observation" and row["source_memory_ids"]:
                source_ids = row["source_memory_ids"]
                result["source_memory_ids"] = [str(sid) for sid in source_ids]
@@ -4580,95 +4319,6 @@ class MemoryEngine(MemoryEngineInterface):

            return result

-    async def get_observation_history(
-        self,
-        bank_id: str,
-        memory_id: str,
-        request_context: "RequestContext",
-    ) -> list[dict] | None:
-        """
-        Get the history of an observation, with source facts resolved to their text.
-
-        Returns None if the memory is not found or is not an observation.
-        Returns a list of history entries (most recent first), each with source_facts resolved.
-        """
-        await self._authenticate_tenant(request_context)
-        if self._operation_validator:
-            from hindsight_api.extensions import BankReadContext
-
-            ctx = BankReadContext(bank_id=bank_id, operation="get_observation_history", request_context=request_context)
-            await self._validate_operation(self._operation_validator.validate_bank_read(ctx))
-        pool = await self._get_pool()
-        async with acquire_with_retry(pool) as conn:
-            row = await conn.fetchrow(
-                f"""
-                SELECT fact_type, history, source_memory_ids
-                FROM {fq_table("memory_units")}
-                WHERE id = $1 AND bank_id = $2
-                """,
-                uuid.UUID(memory_id),
-                bank_id,
-            )
-            if not row:
-                return None
-            if row["fact_type"] != "observation":
-                return []
-
-            raw_history = row["history"]
-            if isinstance(raw_history, str):
-                raw_history = json.loads(raw_history)
-            if not raw_history:
-                return []
-
-            # Collect all source memory IDs (current full set + all historical new ones)
-            current_source_ids: list[str] = [str(sid) for sid in (row["source_memory_ids"] or [])]
-            all_source_ids: set[uuid.UUID] = set(uuid.UUID(sid) for sid in current_source_ids)
-            for entry in raw_history:
-                for sid in entry.get("new_source_memory_ids", []):
-                    try:
-                        all_source_ids.add(uuid.UUID(sid))
-                    except (ValueError, AttributeError):
-                        pass
-
-            # Resolve all source memories in one query
-            source_map: dict[str, dict] = {}
-            if all_source_ids:
-                source_rows = await conn.fetch(
-                    f"""
-                    SELECT id, text, fact_type, context
-                    FROM {fq_table("memory_units")}
-                    WHERE id = ANY($1::uuid[])
-                    """,
-                    list(all_source_ids),
-                )
-                for r in source_rows:
-                    source_map[str(r["id"])] = {
-                        "id": str(r["id"]),
-                        "text": r["text"],
-                        "type": r["fact_type"],
-                        "context": r["context"] or None,
-                    }
-
-            # Reconstruct cumulative source IDs per change by working backwards from current state.
-            # Source IDs are only ever accumulated (never removed), so:
-            #   after_change_N = before_change_N + new_source_memory_ids_N
-            cumulative_ids: list[str] = list(current_source_ids)
-            enriched: list[dict] = []
-            for entry in reversed(raw_history):
-                new_ids_in_entry: set[str] = set(entry.get("new_source_memory_ids", []))
-                source_facts = []
-                for sid in cumulative_ids:
-                    fact = source_map.get(sid, {"id": sid, "text": None, "type": None, "context": None})
-                    source_facts.append({**fact, "is_new": sid in new_ids_in_entry})
-                enriched_entry = dict(entry)
-                enriched_entry["source_facts"] = source_facts
-                enriched.append(enriched_entry)
-                # Step back: remove the new IDs added by this change to get the prior state
-                cumulative_ids = [sid for sid in cumulative_ids if sid not in new_ids_in_entry]
-
-            enriched.reverse()
-            return enriched
-
    async def list_documents(
        self,
        bank_id: str,
@@ -5044,7 +4694,6 @@ class MemoryEngine(MemoryEngineInterface):
        request_context: "RequestContext",
        tags: list[str] | None = None,
        tags_match: TagsMatch = "any",
-        tag_groups: list[TagGroup] | None = None,
        exclude_mental_model_ids: list[str] | None = None,
        _skip_span: bool = False,
    ) -> ReflectResult:
@@ -5147,7 +4796,6 @@ class MemoryEngine(MemoryEngineInterface):
                    max_results=max_results,
                    tags=tags,
                    tags_match=tags_match,
-                    tag_groups=tag_groups,
                    exclude_ids=exclude_mental_model_ids,
                    pending_consolidation=pending_consolidation,
                )
@@ -5161,7 +4809,6 @@ class MemoryEngine(MemoryEngineInterface):
                max_tokens=max_tokens,
                tags=tags,
                tags_match=tags_match,
-                tag_groups=tag_groups,
                last_consolidated_at=last_consolidated_at,
                pending_consolidation=pending_consolidation,
            )
@@ -5175,7 +4822,6 @@ class MemoryEngine(MemoryEngineInterface):
                max_tokens=max_tokens,
                tags=tags,
                tags_match=tags_match,
-                tag_groups=tag_groups,
                max_chunk_tokens=max_chunk_tokens,
            )

@@ -6205,39 +5851,6 @@ class MemoryEngine(MemoryEngineInterface):

        return result

-    async def get_mental_model_history(
-        self,
-        bank_id: str,
-        mental_model_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> list[dict] | None:
-        """Get the refresh history of a mental model.
-
-        Returns None if the mental model is not found.
-        Returns a list of history entries (most recent first), each with previous_content and changed_at.
-        """
-        await self._authenticate_tenant(request_context)
-        pool = await self._get_pool()
-        async with acquire_with_retry(pool) as conn:
-            row = await conn.fetchrow(
-                f"""
-                SELECT history
-                FROM {fq_table("mental_models")}
-                WHERE bank_id = $1 AND id = $2
-                """,
-                bank_id,
-                mental_model_id,
-            )
-            if row is None:
-                return None
-            raw_history = row["history"]
-            if isinstance(raw_history, str):
-                raw_history = json.loads(raw_history)
-            if not raw_history:
-                return []
-            return list(reversed(raw_history))
-
    async def create_mental_model(
        self,
        bank_id: str,
@@ -6458,17 +6071,6 @@ class MemoryEngine(MemoryEngineInterface):
        pool = await self._get_pool()

        async with acquire_with_retry(pool) as conn:
-            # If content is changing, fetch current content first to record history
-            previous_content: str | None = None
-            if content is not None:
-                current_row = await conn.fetchrow(
-                    f"SELECT content FROM {fq_table('mental_models')} WHERE bank_id = $1 AND id = $2",
-                    bank_id,
-                    mental_model_id,
-                )
-                if current_row:
-                    previous_content = current_row["content"]
-
            # Build dynamic update
            updates = []
            params: list[Any] = [bank_id, mental_model_id]
@@ -6484,14 +6086,6 @@ class MemoryEngine(MemoryEngineInterface):
                params.append(content)
                param_idx += 1
                updates.append("last_refreshed_at = NOW()")
-                # Record history entry with the previous content
-                if get_config().enable_mental_model_history:
-                    history_entry = json.dumps(
-                        [{"previous_content": previous_content, "changed_at": datetime.now(timezone.utc).isoformat()}]
-                    )
-                    updates.append(f"history = COALESCE(history, '[]'::jsonb) || ${param_idx}::jsonb")
-                    params.append(history_entry)
-                    param_idx += 1
                # Also update embedding (convert to string for asyncpg vector type)
                embedding_text = f"{name or ''} {content}"
                embedding = await embedding_utils.generate_embeddings_batch(self.embeddings, [embedding_text])
@@ -6912,7 +6506,6 @@ class MemoryEngine(MemoryEngineInterface):
        bank_id: str,
        *,
        status: str | None = None,
-        task_type: str | None = None,
        limit: int = 20,
        offset: int = 0,
        request_context: "RequestContext",
@@ -6922,7 +6515,6 @@ class MemoryEngine(MemoryEngineInterface):
        Args:
            bank_id: Bank identifier
            status: Optional status filter (pending, completed, failed)
-            task_type: Optional operation type filter (retain, consolidation, etc.)
            limit: Maximum number of operations to return (default 20)
            offset: Number of operations to skip (default 0)
            request_context: Request context for authentication
@@ -6951,10 +6543,6 @@ class MemoryEngine(MemoryEngineInterface):
                    where_conditions.append(f"status = ${len(params) + 1}")
                    params.append(status)

-            if task_type:
-                where_conditions.append(f"operation_type = ${len(params) + 1}")
-                params.append(task_type)
-
            where_clause = " AND ".join(where_conditions)

            # Get total count (with filter)
@@ -7185,64 +6773,6 @@ class MemoryEngine(MemoryEngineInterface):
                "bank_id": bank_id,
            }

-    async def retry_operation(
-        self,
-        bank_id: str,
-        operation_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """Re-queue a failed async operation."""
-        await self._authenticate_tenant(request_context)
-        from hindsight_api.extensions import OperationValidationError
-
-        if self._operation_validator:
-            from hindsight_api.extensions import BankWriteContext
-
-            ctx = BankWriteContext(bank_id=bank_id, operation="retry_operation", request_context=request_context)
-            await self._validate_operation(self._operation_validator.validate_bank_write(ctx))
-        pool = await self._get_pool()
-
-        op_uuid = uuid.UUID(operation_id)
-
-        async with acquire_with_retry(pool) as conn:
-            row = await conn.fetchrow(
-                f"SELECT bank_id, status FROM {fq_table('async_operations')} WHERE operation_id = $1 AND bank_id = $2",
-                op_uuid,
-                bank_id,
-            )
-
-            if not row:
-                raise ValueError(f"Operation {operation_id} not found for bank {bank_id}")
-
-            if row["status"] != "failed":
-                raise OperationValidationError(
-                    f"Operation {operation_id} cannot be retried: status is '{row['status']}', expected 'failed'",
-                    409,
-                )
-
-            await conn.execute(
-                f"""
-                UPDATE {fq_table("async_operations")}
-                SET status = 'pending',
-                    error_message = NULL,
-                    completed_at = NULL,
-                    next_retry_at = NULL,
-                    worker_id = NULL,
-                    claimed_at = NULL,
-                    retry_count = 0,
-                    updated_at = NOW()
-                WHERE operation_id = $1
-                """,
-                op_uuid,
-            )
-
-            return {
-                "success": True,
-                "message": f"Operation {operation_id} queued for retry",
-                "operation_id": operation_id,
-            }
-
    async def update_bank(
        self,
        bank_id: str,
@@ -7448,10 +6978,6 @@ class MemoryEngine(MemoryEngineInterface):
        parent_operation_id = uuid.uuid4()
        pool = await self._get_pool()

-        # Ensure the bank row exists before inserting async_operations (which now has a FK).
-        # Banks are created lazily on first retain, but the FK requires the row to exist first.
-        await bank_utils.get_bank_profile(pool, bank_id)
-
        # Create typed metadata for parent operation
        parent_metadata = BatchRetainParentMetadata(
            items_count=len(contents),
@@ -7518,6 +7044,7 @@ class MemoryEngine(MemoryEngineInterface):
        self,
        bank_id: str,
        file_items: list[dict[str, Any]],
+        parser: str,
        document_tags: list[str] | None,
        request_context: "RequestContext",
    ) -> dict[str, Any]:
@@ -7536,7 +7063,7 @@ class MemoryEngine(MemoryEngineInterface):
                - metadata: Optional metadata dict
                - tags: Optional tags list
                - timestamp: Optional timestamp
-                - parser: Ordered list of parser names to try (fallback chain)
+            parser: Parser name (e.g., "markitdown")
            document_tags: Tags applied to all documents
            request_context: Request context for authentication

@@ -7592,7 +7119,7 @@ class MemoryEngine(MemoryEngineInterface):
                "storage_key": storage_key,
                "original_filename": file.filename,
                "content_type": file.content_type or "application/octet-stream",
-                "parser": item["parser"],
+                "parser": parser,
                "context": item.get("context"),
                "metadata": item.get("metadata", {}),
                "tags": item.get("tags", []),
--- a/Show More
+++ b/Show More