docs: fix leaderboard description to cover retain, reflect, and observations

* feat: entity labels

* feat: entity labels — optional, free_values, multi_value, UI polish

Completes the entity labels system:

**Schema & extraction**
- Dynamic Pydantic Labels model per fact: each group becomes a typed
  field (Literal | None, list[Literal], str | None, or list[str])
- `optional: bool` flag per group — non-optional enum fields appear in
  JSON schema required array so structured-output providers enforce them
- `free_values: bool` flag per group — accepts any LLM-generated string
  instead of a predefined enum; example values shown as hints in prompt
- New `is_label_entity()` helper for labels-only mode filtering that
  handles both enum lookup and free_values key-prefix matching
- Sentinel rejection: "None"/"null"/"n/a" strings dropped in post-processing

**BM25 / dense retrieval**
- `text_signals` column on memory_units: entity names + date tokens for
  enriched BM25 indexing without polluting stored fact text
- Dense embedding includes occurred_end when it differs from occurred_start
- Alembic migration z1u2v3w4x5y6 (merge revision fixing two heads)

**UI (bank-config-view)**
- Shadcn Switch replaces custom Toggle for both entity-labels and observations
- Shadcn Checkbox for multi/optional/free_values per group
- Input heights bumped to h-8 throughout the editor
- "Label Groups" → "Entity Labels", "Free-form entities" → "Entities"
- Free-text groups show "Example hints" banner in values section

**Tests (45 unit + 3 LLM integration)**
- build_labels_model: single, multi, mixed, free_values optional/required/multi
- is_label_entity: enum match, free_values prefix match, no false positives
- Post-processing: null/absent/string-None/free_values/sentinels/multi-value
- Schema: labels in required, structured object, no labels when unconfigured
- LLM integration: single-value enum, multi-value enum, free_values retain

**Docs**
- retain.md: new Entity Labels section covering groups, flags, examples
- configuration.md: retain_free_form_entities env var + entity_labels note

* fix(tests): update hierarchical fields count for entity_labels additions

entity_labels and retain_free_form_entities are hierarchical fields,
bumping the expected count from 11 to 13.

* fix(migration): rename text_signals revision to avoid collision with main

Main branch claimed z1u2v3w4x5y6 for observation_scopes. Rename our
text_signals migration to a2b3c4d5e6f7, chaining after z1u2v3w4x5y6.

* refactor(entity-labels): simplify free_values — always str|None, no multi

- free_values groups always produce str | None (multi_value and optional
  flags are ignored for free text groups — always optional, never multi)
- Prompt section for free_values groups shows only key + description,
  no values list (users put examples in the description instead)
- UI: section title "Entities", toggle "Free Form Entities", replace
  per-group checkboxes with a type dropdown (Enum / Free text); only
  show multi checkbox and values list when type is Enum
- Update tests to reflect new behaviour

* refactor(entity-labels): replace free_values/multi_value booleans with type field

- LabelGroup now uses type: "value" | "multi-values" | "text" instead of
  free_values/multi_value boolean pair
- Backward-compat migration converts legacy dicts automatically
- Rename retain_free_form_entities → entities_allow_free_form throughout
- Update UI dropdown to show Single value / Multi-values / Free text
- Remove separate multi checkbox (captured by type selection)
- Update docs examples and configuration.md
- Update all tests to use new field names

* fix(migration): backfill observation_scopes column for DBs with swapped z1u2v3w4x5y6

Local DBs that had z1u2v3w4x5y6 applied when it referred to the old
text_signals migration (before it was renamed to a2b3c4d5e6f7) won't have
observation_scopes in their memory_units table. This migration adds the
column with IF NOT EXISTS so it's a no-op on clean installs.

* feat(entity-labels): add tag field to auto-populate memory unit tags from labels

When a LabelGroup has tag=True, extracted key:value entities for that group
are automatically written to the memory unit's tags array. This lets entity
labels double as tags, enabling immediate filtering via the existing
tags/tags_match API params with no extra infrastructure.

- Add tag: bool = False to LabelGroup
- _inject_label_tags() helper called in both sync and batch extraction paths
- UI: add Tag checkbox per label group row
- Docs: document the new tag field
- Tests: 4 new unit tests covering all tag injection paths

* style: ruff format migration file

* fix(migration): fix multiple alembic heads after rebase — point text_signals after nullable_event_date

* fix(clients): update timestamp field to use Timestamp wrapper type after timestamp=unset feature

* style: ruff format agent.py

* fix(docs): update Go quickstart example to use NullableTimestamp for timestamp field

.env.example

@@ -5,7 +5,7 @@
 # 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=o3-mini
+HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
 HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 
 # Example: Anthropic Claude configuration
@@ -31,10 +31,22 @@ HINDSIGHT_API_HOST=0.0.0.0
 HINDSIGHT_API_PORT=8888
 HINDSIGHT_API_LOG_LEVEL=info
 
+# Base Path / Reverse Proxy Support (Optional)
+# Set these when deploying behind a reverse proxy with path-based routing
+# Example: To deploy at example.com/hindsight/, set both to "/hindsight"
+# HINDSIGHT_API_BASE_PATH=/hindsight
+# NEXT_PUBLIC_BASE_PATH=/hindsight
+
 # Database (Optional - uses embedded pg0 by default)
 # HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
 # HINDSIGHT_API_DATABASE_SCHEMA=public  # PostgreSQL schema name (default: public)
 
+# Vector Extension (Optional - uses pgvector by default)
+# Options: "pgvector" (default), "vchord", "pgvectorscale" (DiskANN)
+# HINDSIGHT_API_VECTOR_EXTENSION=pgvector
+# For Azure PostgreSQL with DiskANN:
+# HINDSIGHT_API_VECTOR_EXTENSION=pgvectorscale  # Auto-detects pg_diskann on Azure
+
 # Embeddings Configuration (Optional - uses local by default)
 # Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)
 # HINDSIGHT_API_EMBEDDINGS_PROVIDER=local
@@ -50,3 +62,18 @@ HINDSIGHT_API_LOG_LEVEL=info
 # HINDSIGHT_API_RERANKER_LOCAL_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
 # For TEI provider:
 # HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081
+
+# Observability & Tracing (Optional - disabled by default)
+# Enable OpenTelemetry tracing for LLM calls (GenAI semantic conventions)
+# HINDSIGHT_API_OTEL_TRACES_ENABLED=true
+#
+# Local development with Grafana LGTM stack (recommended - see scripts/dev/grafana/README.md)
+# HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
+#
+# Cloud backends (Grafana Cloud, Langfuse, DataDog, etc.)
+# HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT=https://your-backend-url
+# HINDSIGHT_API_OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer your-token"
+#
+# Custom service name and environment (optional, defaults: hindsight-api, development)
+# HINDSIGHT_API_OTEL_SERVICE_NAME=hindsight-production
+# HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT=production

.github/workflows/deploy-docs.yml

@@ -31,6 +31,9 @@ jobs:
       - 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@v3
         with:
           path: hindsight-docs/build

.github/workflows/release.yml

@@ -46,6 +46,10 @@ jobs:
       working-directory: ./hindsight-embed
       run: uv build --out-dir dist
 
+    - name: Build hindsight-crewai
+      working-directory: ./hindsight-integrations/crewai
+      run: uv build --out-dir dist
+
     # Publish in order (client and api first, then hindsight-all which depends on them)
     - name: Publish hindsight-client to PyPI
       uses: pypa/gh-action-pypi-publish@release/v1
@@ -77,6 +81,12 @@ jobs:
         packages-dir: ./hindsight-embed/dist
         skip-existing: true
 
+    - name: Publish hindsight-crewai to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        packages-dir: ./hindsight-integrations/crewai/dist
+        skip-existing: true
+
     # Upload artifacts for GitHub release
     - name: Upload artifacts
       uses: actions/upload-artifact@v4
@@ -88,6 +98,7 @@ jobs:
           hindsight/dist/*
           hindsight-integrations/litellm/dist/*
           hindsight-embed/dist/*
+          hindsight-integrations/crewai/dist/*
         retention-days: 1
 
   release-typescript-client:
@@ -237,6 +248,55 @@ jobs:
         path: hindsight-integrations/ai-sdk/*.tgz
         retention-days: 1
 
+  release-chat-integration:
+    runs-on: ubuntu-latest
+    environment: npm
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+        registry-url: 'https://registry.npmjs.org'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/chat
+      run: npm ci
+
+    - name: Build
+      working-directory: ./hindsight-integrations/chat
+      run: npm run build
+
+    - name: Publish to npm
+      working-directory: ./hindsight-integrations/chat
+      run: |
+        set +e
+        OUTPUT=$(npm publish --access public 2>&1)
+        EXIT_CODE=$?
+        echo "$OUTPUT"
+        if [ $EXIT_CODE -ne 0 ]; then
+          if echo "$OUTPUT" | grep -q "cannot publish over"; then
+            echo "Package version already published, skipping..."
+            exit 0
+          fi
+          exit $EXIT_CODE
+        fi
+      env:
+        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+    - name: Pack for GitHub release
+      working-directory: ./hindsight-integrations/chat
+      run: npm pack
+
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: chat-integration
+        path: hindsight-integrations/chat/*.tgz
+        retention-days: 1
+
   release-control-plane:
     runs-on: ubuntu-latest
     environment: npm
@@ -268,11 +328,14 @@ jobs:
     - name: Build
       run: npm run build --workspace=hindsight-control-plane
 
+    - name: Verify standalone build
+      run: test -f hindsight-control-plane/standalone/server.js || (echo 'standalone/server.js missing - build failed' && exit 1)
+
     - name: Publish to npm
       working-directory: ./hindsight-control-plane
       run: |
         set +e
-        OUTPUT=$(npm publish --access public 2>&1)
+        OUTPUT=$(npm publish --access public --ignore-scripts 2>&1)
         EXIT_CODE=$?
         echo "$OUTPUT"
         if [ $EXIT_CODE -ne 0 ]; then
@@ -487,7 +550,7 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-chat-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
     permissions:
       contents: write
 
@@ -522,6 +585,12 @@ jobs:
         name: ai-sdk-integration
         path: ./artifacts/ai-sdk-integration
 
+    - name: Download Chat Integration
+      uses: actions/download-artifact@v4
+      with:
+        name: chat-integration
+        path: ./artifacts/chat-integration
+
     - name: Download Control Plane
       uses: actions/download-artifact@v4
       with:
@@ -567,6 +636,8 @@ jobs:
         cp artifacts/openclaw-integration/*.tgz release-assets/ || true
         # AI SDK Integration
         cp artifacts/ai-sdk-integration/*.tgz release-assets/ || true
+        # Chat Integration
+        cp artifacts/chat-integration/*.tgz release-assets/ || true
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.github/workflows/test.yml

@@ -97,6 +97,29 @@ jobs:
       working-directory: ./hindsight-integrations/ai-sdk
       run: npm run build
 
+  build-chat-integration:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/chat
+      run: npm ci
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/chat
+      run: npm test
+
+    - name: Build
+      working-directory: ./hindsight-integrations/chat
+      run: npm run build
+
   build-control-plane:
     runs-on: ubuntu-latest
 
@@ -171,9 +194,9 @@ jobs:
   test-rust-cli:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_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_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -181,6 +204,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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 Rust
       uses: dtolnay/rust-toolchain@stable
 
@@ -227,25 +256,46 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
     - name: Create .env file
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
         HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         EOF
 
     - name: Start API server
       run: |
         ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
         echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
+        for i in {1..120}; do
           if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
             echo "API server is ready after ${i}s"
             break
           fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -340,12 +390,21 @@ jobs:
 
     # Only test slim variants to save disk space (they're much smaller)
     # Slim variants require external embedding providers
+    - name: Setup GCP credentials for smoke test
+      if: matrix.variant == 'slim'
+      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: Smoke test - verify container starts
       if: matrix.variant == 'slim'
       env:
-        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-        HINDSIGHT_API_EMBEDDINGS_PROVIDER: openai
-        HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        HINDSIGHT_API_LLM_PROVIDER: vertexai
+        HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID: ${{ env.HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID }}
+        HINDSIGHT_API_EMBEDDINGS_PROVIDER: cohere
         HINDSIGHT_API_RERANKER_PROVIDER: cohere
         HINDSIGHT_API_COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
       run: ./docker/test-image.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"
@@ -353,14 +412,13 @@ jobs:
   test-api:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      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
       GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
       COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
       HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -368,6 +426,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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@v5
       with:
@@ -414,9 +478,9 @@ jobs:
   test-python-client:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_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_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -425,6 +489,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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@v5
       with:
@@ -452,25 +522,46 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
     - name: Create .env file
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
         HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         EOF
 
     - name: Start API server
       run: |
         ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
         echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
+        for i in {1..120}; do
           if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
             echo "API server is ready after ${i}s"
             break
           fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -490,9 +581,9 @@ jobs:
   test-typescript-client:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_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_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -501,6 +592,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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@v5
       with:
@@ -533,25 +630,46 @@ jobs:
       working-directory: ./hindsight-clients/typescript
       run: npm run build
 
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
     - name: Create .env file
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
         HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         EOF
 
     - name: Start API server
       run: |
         ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
         echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
+        for i in {1..120}; do
           if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
             echo "API server is ready after ${i}s"
             break
           fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -571,9 +689,9 @@ jobs:
   test-rust-client:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_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_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -582,6 +700,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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@v5
       with:
@@ -613,25 +737,46 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
     - name: Create .env file
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
         HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         EOF
 
     - name: Start API server
       run: |
         ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
         echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
+        for i in {1..120}; do
           if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
             echo "API server is ready after ${i}s"
             break
           fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -648,12 +793,225 @@ jobs:
         echo "=== API Server Logs ==="
         cat /tmp/api-server.log || echo "No API server log found"
 
+  test-go-client:
+    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_URL: http://localhost:8888
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ 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@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Set up Go
+      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
+      run: uv build
+
+    - name: Install API dependencies
+      working-directory: ./hindsight-api
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
+
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
+    - name: Create .env file
+      run: |
+        cat > .env << EOF
+        HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
+        HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
+        EOF
+
+    - name: Start API server
+      run: |
+        ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
+        echo "Waiting for API server to be ready..."
+        for i in {1..120}; do
+          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
+            echo "API server is ready after ${i}s"
+            break
+          fi
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
+            cat /tmp/api-server.log
+            exit 1
+          fi
+          sleep 1
+        done
+
+    - name: Build Go client
+      working-directory: ./hindsight-clients/go
+      run: go build ./...
+
+    - name: Run Go client tests
+      working-directory: ./hindsight-clients/go
+      run: go test -v -tags=integration
+
+    - name: Show API server logs
+      if: always()
+      run: |
+        echo "=== API Server Logs ==="
+        cat /tmp/api-server.log || echo "No API server log found"
+
+  test-openclaw-integration:
+    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_URL: http://localhost:8888
+      HINDSIGHT_EMBED_PACKAGE_PATH: ${{ github.workspace }}/hindsight-embed
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ 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@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+
+    - name: Build API
+      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: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
+    - name: Install openclaw integration dependencies
+      working-directory: ./hindsight-integrations/openclaw
+      run: npm ci
+
+    - name: Create .env file
+      run: |
+        cat > .env << EOF
+        HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
+        HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
+        EOF
+
+    - name: Start API server
+      run: |
+        ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
+        echo "Waiting for API server to be ready..."
+        for i in {1..120}; do
+          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
+            echo "API server is ready after ${i}s"
+            break
+          fi
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
+            cat /tmp/api-server.log
+            exit 1
+          fi
+          sleep 1
+        done
+
+    - name: Run openclaw integration tests
+      working-directory: ./hindsight-integrations/openclaw
+      run: npm run test:integration
+
+    - name: Show API server logs
+      if: always()
+      run: |
+        echo "=== API Server Logs ==="
+        cat /tmp/api-server.log || echo "No API server log found"
+
   test-integration:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_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_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -661,6 +1019,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -708,21 +1072,22 @@ jobs:
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
         HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         EOF
 
     - name: Start API server
       run: |
         ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
         echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
+        for i in {1..120}; do
           if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
             echo "API server is ready after ${i}s"
             break
           fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -739,6 +1104,35 @@ jobs:
         echo "=== API Server Logs ==="
         cat /tmp/api-server.log || echo "No API server log found"
 
+  test-crewai-integration:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Build crewai integration
+      working-directory: ./hindsight-integrations/crewai
+      run: uv build
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/crewai
+      run: uv sync --frozen
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/crewai
+      run: uv run pytest tests -v
+
   test-litellm-integration:
     runs-on: ubuntu-latest
 
@@ -771,15 +1165,21 @@ jobs:
   test-embed:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       # Prefer CPU-only PyTorch in CI
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
     steps:
     - uses: actions/checkout@v4
 
+    - 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@v5
       with:
@@ -815,19 +1215,25 @@ jobs:
   test-hindsight-all:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       # For test_server_integration.py compatibility
-      HINDSIGHT_LLM_PROVIDER: groq
-      HINDSIGHT_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_LLM_PROVIDER: vertexai
+      HINDSIGHT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_LLM_MODEL: google/gemini-2.5-flash-lite
       # Prefer CPU-only PyTorch in CI
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
     steps:
     - uses: actions/checkout@v4
 
+    - 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@v5
       with:
@@ -864,9 +1270,9 @@ jobs:
     runs-on: ubuntu-latest
     needs: test-rust-cli
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_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_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -874,6 +1280,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - 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: Download CLI artifact
       uses: actions/download-artifact@v4
       with:
@@ -916,55 +1328,57 @@ jobs:
         npm ci --workspace=hindsight-clients/typescript
         npm run build --workspace=hindsight-clients/typescript
 
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading reranker model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
     - name: Create .env file
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
         HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         EOF
 
     - name: Start API server
       run: |
         ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
         echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
+        for i in {1..120}; do
           if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
             echo "API server is ready after ${i}s"
             break
           fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
           sleep 1
         done
 
-    - name: Run Python doc examples
-      working-directory: ./hindsight-clients/python
-      run: |
-        for f in ../../hindsight-docs/examples/api/*.py; do
-          echo "Running $f..."
-          uv run python "$f"
-        done
-
-    - name: Run Node.js doc examples
-      run: |
-        for f in hindsight-docs/examples/api/*.mjs; do
-          echo "Running $f..."
-          node "$f"
-        done
-
     - name: Configure CLI
       run: hindsight configure --api-url http://localhost:8888
 
-    - name: Run CLI doc examples
-      run: |
-        for f in hindsight-docs/examples/api/*.sh; do
-          echo "Running $f..."
-          bash "$f"
-        done
+    - name: Run all doc examples
+      run: ./scripts/test-doc-examples.sh
 
     - name: Show API server logs
       if: always()
@@ -975,9 +1389,9 @@ jobs:
   test-upgrade:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
@@ -986,6 +1400,12 @@ jobs:
       with:
         fetch-depth: 0  # Full history needed for git clone of tags
 
+    - 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: Fetch tags
       run: git fetch --tags
 

.gitignore

@@ -46,6 +46,7 @@ hindsight-docs/static/llms-full.txt
 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
 hindsight-dev/benchmarks/consolidation/results/
+hindsight-dev/benchmarks/perf/results/
 benchmarks/results/
 hindsight-cli/target
 hindsight-clients/rust/target
@@ -53,4 +54,6 @@ hindsight-clients/rust/target
 whats-next.md
 TASK.md
 # Changelog is now tracked in hindsight-docs/src/pages/changelog.md
-# CHANGELOG.md
+# CHANGELOG.md
+
+blog-post*

CLAUDE.md

@@ -45,6 +45,7 @@ cd hindsight-control-plane && npm run dev
 ./scripts/dev/start-docs.sh
 ```
 
+
 ### Generating Clients/OpenAPI
 ```bash
 # Regenerate OpenAPI spec after API changes (REQUIRED after changing endpoints)
@@ -56,8 +57,15 @@ cd hindsight-control-plane && npm run dev
 
 ### Benchmarks
 ```bash
+# Accuracy benchmarks
 ./scripts/benchmarks/run-longmemeval.sh
 ./scripts/benchmarks/run-locomo.sh
+
+# Performance benchmarks
+./scripts/benchmarks/run-consolidation.sh
+./scripts/benchmarks/run-retain-perf.sh --document <path>  # Requires API server running
+
+# Results viewer
 ./scripts/benchmarks/start-visualizer.sh  # View results at localhost:8001
 ```
 
@@ -237,26 +245,61 @@ def process(data: UserData) -> str:
 
 ### Adding New API Configuration Flags
 
-When adding a new environment variable configuration:
+Configuration follows a hierarchical system: **Global (env vars) → Tenant (via extension) → Bank (database)**.
+
+Fields must be categorized as either **hierarchical** (can be overridden per-tenant/bank) or **static** (server-level only).
+
+#### Adding a New Configuration Field
 
 1. **config.py** (`hindsight-api/hindsight_api/config.py`):
-   - Add `ENV_*` constant for the environment variable name
+   - 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
+   - Add field to `HindsightConfig` dataclass with type annotation
+   - **Mark as hierarchical or static** by adding to `_HIERARCHICAL_FIELDS` set (hierarchical) or leaving it out (static)
    - Add initialization in `from_env()` method
 
+   ```python
+   # Hierarchical field (can be overridden per-bank)
+   _HIERARCHICAL_FIELDS = {
+       ...,
+       "my_setting",  # Add here for hierarchical
+   }
+
+   # Static field - just don't add to _HIERARCHICAL_FIELDS
+   ```
+
 2. **main.py** (`hindsight-api/hindsight_api/main.py`):
    - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
 
-3. **Use the config** in code:
+3. **Use hierarchical config in MemoryEngine**:
+   ```python
+   # Config is resolved automatically per bank via ConfigResolver
+   config_dict = await self._config_resolver.get_bank_config(bank_id, context)
+   value = config_dict["my_setting"]
+   ```
+
+4. **Use static config** (non-hierarchical):
    ```python
    from ...config import get_config
    config = get_config()
-   value = config.your_new_field
+   value = config.my_static_field
    ```
 
-4. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
+5. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
    - Add to appropriate section table with Variable, Description, Default
+   - Mark if it's hierarchical (can be overridden per-bank)
+
+#### Hierarchical vs Static Guidelines
+
+**Hierarchical** (per-bank overridable):
+- LLM settings (provider, model, API key, base URL)
+- Operation-specific settings (retain mode, chunk size, etc.)
+- Feature flags that vary by customer/bank
+
+**Static** (server-level only):
+- Infrastructure settings (database URL, port, host)
+- Global limits (max concurrent operations)
+- System-wide feature flags
 
 ## Environment Setup
 
@@ -274,9 +317,10 @@ npm install
 Required env vars:
 - `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., o3-mini, claude-sonnet-4-20250514)
+- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., gpt-4o-mini, claude-sonnet-4-20250514)
 
 Optional (uses local models by default):
 - `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_RERANKER_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_DATABASE_URL`: External PostgreSQL (uses embedded pg0 by default)
+- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: true)

README.md

@@ -2,7 +2,7 @@
 
 ![Hindsight Banner](./hindsight-docs/static/img/hindsight-github-banner.png)
 
-[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://vectorize.io/hindsight/cloud)
+[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://ui.hindsight.vectorize.io/signup)
 
 [![CI](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml)
 [![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)
@@ -36,12 +36,22 @@ Hindsight is being used in production at Fortune 500 enterprises and by a growin
 
 ## Adding Hindsight to Your AI Agents
 
-The easiest way use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.
+The easiest way to use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.
 
 If you need more control over how and when your agent stores and recalls memories, there's also a simple API you can integrate with using the SDKs or directly via HTTP.
 
 ![Hindsight Banner](./hindsight-docs/static/img/migration-code.png)
 
+---
+
+> 🤖 **Using a coding agent?** Install the Hindsight documentation skill for instant access to docs while you code:
+> ```bash
+> npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docs
+> ```
+> Works with Claude Code, Cursor, and other AI coding assistants.
+
+---
+
 
 ## Quick Start
 
@@ -171,7 +181,7 @@ Satisfying these requirements in Hindsight is straightforward. When new user inp
 
 ![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
 
-Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
+Most agent memory implementations rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
 
 - **World:** Facts about the world ("The stove gets hot")
 - **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
@@ -297,3 +307,5 @@ MIT — see [LICENSE](./LICENSE)
 ---
 
 Built by [Vectorize.io](https://vectorize.io)
+
+<img src="https://umami-pixel.chris-latimer.workers.dev/?id=a8b043e6-6964-454d-80df-69b69d3f0d50&host=github.com&url=/vectorize-io/hindsight" width="1" height="1" alt="" />

docker/docker-compose/nginx/README.md

@@ -0,0 +1,96 @@
+# Nginx Reverse Proxy with Custom Base Path
+
+Deploy Hindsight API under `/hindsight` (or any custom path) using Nginx reverse proxy.
+
+## Quick Start (Published Image - API Only)
+
+```bash
+docker-compose up
+```
+
+- **API:** http://localhost:8080/hindsight/docs
+- **Control Plane:** http://localhost:9999 (direct access, not proxied)
+
+## Full Stack with Custom Base Path (Requires Build)
+
+**Important:** You cannot rebuild from the published image with build args. You must build from source.
+
+### Build from Source with Custom Base Path
+
+1. **Clone the repository** (if you haven't):
+```bash
+git clone https://github.com/vectorize-io/hindsight.git
+cd hindsight
+```
+
+2. **Build with base path**:
+```bash
+docker build \
+  --build-arg NEXT_PUBLIC_BASE_PATH=/hindsight \
+  -f docker/standalone/Dockerfile \
+  -t hindsight:custom \
+  .
+```
+
+3. **Update docker-compose.yml** to use your built image:
+```yaml
+services:
+  hindsight:
+    image: hindsight:custom  # ← Change this
+    environment:
+      HINDSIGHT_API_BASE_PATH: /hindsight
+      NEXT_PUBLIC_BASE_PATH: /hindsight
+```
+
+4. **Update nginx.conf** to handle Control Plane routes (see below)
+
+5. **Run**:
+```bash
+docker-compose up
+```
+
+### Required nginx.conf for Full Stack
+
+Replace the current `nginx.conf` with this to proxy both API and Control Plane:
+
+```nginx
+events { worker_connections 1024; }
+
+http {
+    include /etc/nginx/mime.types;
+    default_type application/octet-stream;
+
+    upstream hindsight_api { server hindsight:8888; }
+    upstream hindsight_cp { server hindsight:9999; }
+
+    server {
+        listen 80;
+
+        # API
+        location ~ ^/hindsight/(docs|openapi\.json|health|metrics|v1|mcp) {
+            proxy_pass http://hindsight_api;
+            proxy_set_header Host $http_host;
+        }
+
+        # Control Plane static files
+        location ~ ^/hindsight/_next/ {
+            proxy_pass http://hindsight_cp;
+            proxy_set_header Host $http_host;
+        }
+
+        # Control Plane UI
+        location /hindsight {
+            proxy_pass http://hindsight_cp;
+            proxy_set_header Host $http_host;
+        }
+
+        location = / { return 301 /hindsight; }
+    }
+}
+```
+
+### Why Build is Required
+
+Next.js requires `basePath` at **build time**. The published image was built without a custom base path, so you must rebuild from source with the `NEXT_PUBLIC_BASE_PATH` build arg to deploy the Control Plane under a subpath.
+
+The API works without rebuild because `HINDSIGHT_API_BASE_PATH` is a runtime environment variable.

docker/docker-compose/nginx/docker-compose.yml

@@ -0,0 +1,88 @@
+# Hindsight API deployment with Nginx reverse proxy (API-only)
+#
+# This example deploys Hindsight API under the path /hindsight with:
+# - Hindsight standalone image (API + Control Plane + embedded pg0)
+# - Nginx reverse proxy (API only)
+#
+# Quick Start:
+#   docker-compose -f docker/docker-compose/nginx/docker-compose.yml up
+#
+# Access:
+#   API (via nginx):         http://localhost:8080/hindsight/docs
+#   Control Plane (direct):  http://localhost:9999
+#
+# For full stack deployment (API + Control Plane both under /hindsight):
+#   See README.md in this directory for instructions on building with basePath.
+#
+# Note: This configuration uses the published image (no build required).
+#       Control Plane is served directly because Next.js basePath requires
+#       build-time configuration. See README.md for the full stack option.
+
+services:
+  # Hindsight (API + Control Plane + embedded pg0)
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:latest
+    ports:
+      - "9999:9999"  # Control Plane (direct access, not proxied)
+    environment:
+      # API base path for reverse proxy
+      HINDSIGHT_API_BASE_PATH: /hindsight
+
+      # LLM configuration
+      # Using mock provider for testing (no API key needed)
+      # For production, set OPENAI_API_KEY or ANTHROPIC_API_KEY and use a real provider
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-mock}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-not-needed-for-mock}
+      HINDSIGHT_API_LLM_MODEL: ${HINDSIGHT_API_LLM_MODEL:-mock-model}
+
+      # Production examples (uncomment and set appropriate API key):
+      # HINDSIGHT_API_LLM_PROVIDER: openai
+      # HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY}
+      # HINDSIGHT_API_LLM_MODEL: gpt-4o-mini
+
+      # HINDSIGHT_API_LLM_PROVIDER: anthropic
+      # HINDSIGHT_API_LLM_API_KEY: ${ANTHROPIC_API_KEY}
+      # HINDSIGHT_API_LLM_MODEL: claude-sonnet-4-20250514
+
+      # Server config
+      HINDSIGHT_API_HOST: 0.0.0.0
+      HINDSIGHT_API_PORT: 8888
+      HINDSIGHT_API_LOG_LEVEL: info
+
+      # Control Plane config
+      HINDSIGHT_CP_DATAPLANE_API_URL: http://localhost:8888
+    volumes:
+      # Persist embedded pg0 database
+      - hindsight_data:/app/data
+    # Note: Ports not exposed - access via Nginx at localhost:8080/hindsight/
+    # To debug directly, uncomment these ports:
+    # ports:
+    #   - "8888:8888"  # API
+    #   - "9999:9999"  # Control Plane
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/hindsight/health"]
+      interval: 10s
+      timeout: 5s
+      retries: 3
+      start_period: 30s
+    networks:
+      - hindsight
+
+  # Nginx reverse proxy
+  nginx:
+    image: nginx:alpine
+    ports:
+      - "8080:80"
+    volumes:
+      - ./nginx.conf:/etc/nginx/nginx.conf:ro
+    depends_on:
+      hindsight:
+        condition: service_healthy
+    networks:
+      - hindsight
+
+volumes:
+  hindsight_data:
+
+networks:
+  hindsight:

docker/docker-compose/nginx/nginx.conf

@@ -0,0 +1,40 @@
+# Nginx configuration for API-only reverse proxy
+# Control Plane accessed directly (not through nginx)
+
+events {
+    worker_connections 1024;
+}
+
+http {
+    include /etc/nginx/mime.types;
+    default_type application/octet-stream;
+
+    # Logging
+    access_log /var/log/nginx/access.log;
+    error_log /var/log/nginx/error.log;
+
+    # Upstream - Hindsight API
+    upstream hindsight_api {
+        server hindsight:8888;
+    }
+
+    server {
+        listen 80;
+        server_name _;
+
+        # API endpoints - forward with /hindsight prefix
+        location /hindsight/ {
+            proxy_pass http://hindsight_api;
+
+            proxy_set_header Host $http_host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+        }
+
+        # Redirect root to API docs
+        location = / {
+            return 301 /hindsight/docs;
+        }
+    }
+}

docker/docker-compose/pg_textsearch/Dockerfile

@@ -0,0 +1,32 @@
+# PostgreSQL with pgvector and pg_textsearch extensions
+# Note: pg_textsearch requires PostgreSQL 17+
+FROM postgres:17
+
+# Install build dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    git \
+    postgresql-server-dev-17 \
+    libpq-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install pgvector
+RUN cd /tmp && \
+    git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git && \
+    cd pgvector && \
+    make && \
+    make install
+
+# Install pg_textsearch
+RUN cd /tmp && \
+    git clone https://github.com/timescale/pg_textsearch.git && \
+    cd pg_textsearch && \
+    make && \
+    make install
+
+# Clean up source files and build dependencies
+RUN rm -rf /tmp/pgvector /tmp/pg_textsearch && \
+    apt-get purge -y --auto-remove build-essential git postgresql-server-dev-17
+
+# Ensure extensions are preloaded
+RUN echo "shared_preload_libraries = 'pg_textsearch'" >> /usr/share/postgresql/postgresql.conf.sample

docker/docker-compose/pg_textsearch/docker-compose.yaml

@@ -0,0 +1,91 @@
+name: hindsight
+# Docker Compose file for Hindsight with PostgreSQL and Timescale pg_textsearch
+# docker compose -f docker/docker-compose/pg_textsearch/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/pg_textsearch/docker-compose.yaml up -d
+# Make sure to set the required environment variables before running:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - Configure LLM provider variables as needed (see below in the hindsight service)
+#
+# Usage:
+# docker compose up -d
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+
+services:
+  db:
+    # Use custom PostgreSQL image with pgvector and pg_textsearch extensions
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: hindsight-db
+    restart: always
+    # Expose PostgreSQL port
+    ports:
+      - "5437:5432"
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/data
+    networks:
+      - hindsight-net
+
+  pg-textsearch-init:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    depends_on:
+      - db
+    environment:
+      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+    command: >
+      bash -c "
+        echo 'Waiting for PostgreSQL to be ready...';
+        until pg_isready -h hindsight-db -p 5432 -U hindsight_user; do
+          echo 'PostgreSQL is unavailable - sleeping';
+          sleep 2;
+        done;
+        echo 'PostgreSQL is ready - creating hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
+        echo 'Creating extensions in hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vector CASCADE;';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE;';
+        echo 'Database and extensions created successfully';
+      "
+    restart: "no"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      # LLM Configuration
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
+
+      # Database Configuration
+      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+
+      # Vector and Text Search Extensions
+      HINDSIGHT_API_VECTOR_EXTENSION: pgvector
+      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: pg_textsearch
+
+    depends_on:
+      - db
+    networks:
+      - hindsight-net
+
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:

docker/docker-compose/s3-file-storage/docker-compose.yaml

@@ -0,0 +1,83 @@
+# Docker Compose file for Hindsight with S3 file storage (SeaweedFS)
+#
+# SeaweedFS (Apache 2.0) provides an S3-compatible object storage backend
+# for storing uploaded files instead of PostgreSQL BYTEA storage.
+#
+# Make sure to set the required environment variables before running:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - Configure LLM provider variables as needed (see below in the hindsight service)
+#
+# Usage:
+# docker compose up -d
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+# - HINDSIGHT_DB_VERSION: PostgreSQL version (default: 18)
+# - SEAWEEDFS_S3_ACCESS_KEY: S3 access key (default: hindsight_s3_key)
+# - SEAWEEDFS_S3_SECRET_KEY: S3 secret key (default: hindsight_s3_secret)
+
+services:
+  db:
+    image: pgvector/pgvector:pg${HINDSIGHT_DB_VERSION:-18}
+    container_name: hindsight-db
+    restart: always
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:?Please set the HINDSIGHT_DB_PASSWORD env variable}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/${HINDSIGHT_DB_VERSION:-18}/docker
+    networks:
+      - hindsight-net
+
+  seaweedfs:
+    image: chrislusf/seaweedfs:latest
+    container_name: hindsight-seaweedfs
+    restart: always
+    # Single-node mode: master + volume + filer + S3 gateway all in one process
+    command: >
+      server
+      -s3
+      -s3.port=8333
+      -s3.config=/etc/seaweedfs/s3.json
+      -ip.bind=0.0.0.0
+    volumes:
+      - seaweedfs_data:/data
+      - ./s3.json:/etc/seaweedfs/s3.json:ro
+    # Expose S3 API port (uncomment to access from host)
+    # ports:
+    #   - "8333:8333"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      - HINDSIGHT_API_LLM_API_KEY=${OPENAI_API_KEY?Please set the OPENAI_API_KEY env variable}
+      - HINDSIGHT_API_DATABASE_URL=postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:?Please set the HINDSIGHT_DB_PASSWORD env variable}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+      # S3 file storage configuration (SeaweedFS)
+      - HINDSIGHT_API_FILE_STORAGE_TYPE=s3
+      - HINDSIGHT_API_FILE_STORAGE_S3_BUCKET=hindsight
+      - HINDSIGHT_API_FILE_STORAGE_S3_ENDPOINT=http://seaweedfs:8333
+      - HINDSIGHT_API_FILE_STORAGE_S3_REGION=us-east-1
+      - HINDSIGHT_API_FILE_STORAGE_S3_ACCESS_KEY_ID=${SEAWEEDFS_S3_ACCESS_KEY:-hindsight_s3_key}
+      - HINDSIGHT_API_FILE_STORAGE_S3_SECRET_ACCESS_KEY=${SEAWEEDFS_S3_SECRET_KEY:-hindsight_s3_secret}
+    depends_on:
+      - db
+      - seaweedfs
+    networks:
+      - hindsight-net
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:
+  seaweedfs_data:

docker/docker-compose/s3-file-storage/s3.json

@@ -0,0 +1,19 @@
+{
+  "identities": [
+    {
+      "name": "hindsight",
+      "credentials": [
+        {
+          "accessKey": "hindsight_s3_key",
+          "secretKey": "hindsight_s3_secret"
+        }
+      ],
+      "actions": [
+        "Admin",
+        "Read",
+        "Write",
+        "List"
+      ]
+    }
+  ]
+}

docker/docker-compose/timescale/.dockerignore

@@ -0,0 +1,16 @@
+# Git
+.git
+.gitignore
+.gitattributes
+
+# Docker
+docker-compose.yaml
+.dockerignore
+
+# Documentation
+README.md
+*.md
+
+# Environment
+.env
+.env.example

docker/docker-compose/timescale/.env.example

@@ -0,0 +1,25 @@
+# PostgreSQL Configuration
+HINDSIGHT_DB_USER=hindsight_user
+HINDSIGHT_DB_PASSWORD=change-me-to-secure-password
+HINDSIGHT_DB_NAME=hindsight_db
+
+# Hindsight Version
+HINDSIGHT_VERSION=latest
+
+# LLM Configuration
+HINDSIGHT_API_LLM_PROVIDER=openai
+OPENAI_API_KEY=your-openai-api-key-here
+
+# Alternative LLM providers (uncomment and configure as needed):
+# HINDSIGHT_API_LLM_PROVIDER=anthropic
+# ANTHROPIC_API_KEY=your-anthropic-api-key
+
+# HINDSIGHT_API_LLM_PROVIDER=gemini
+# GEMINI_API_KEY=your-gemini-api-key
+
+# HINDSIGHT_API_LLM_PROVIDER=groq
+# GROQ_API_KEY=your-groq-api-key
+
+# Vector and Text Search (already configured in docker-compose.yaml)
+# HINDSIGHT_API_VECTOR_EXTENSION=pgvectorscale
+# HINDSIGHT_API_TEXT_SEARCH_EXTENSION=pg_textsearch

docker/docker-compose/timescale/Dockerfile

@@ -0,0 +1,55 @@
+# PostgreSQL with pgvector, pgvectorscale, and pg_textsearch extensions
+# All three extensions from Timescale/pgvector for high-performance vector and text search
+# Note: Requires PostgreSQL 16+
+FROM postgres:17
+
+# Install build dependencies and Rust toolchain
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    git \
+    postgresql-server-dev-17 \
+    libpq-dev \
+    cmake \
+    curl \
+    pkg-config \
+    libssl-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Rust toolchain (required for pgvectorscale)
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+# Install pgvector (required by pgvectorscale)
+RUN cd /tmp && \
+    git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git && \
+    cd pgvector && \
+    make && \
+    make install && \
+    rm -rf /tmp/pgvector
+
+# Install cargo-pgrx (PostgreSQL extension framework for Rust)
+RUN cargo install cargo-pgrx --version 0.12.5 --locked && \
+    cargo pgrx init --pg17 /usr/bin/pg_config
+
+# Install pgvectorscale (DiskANN index support)
+RUN cd /tmp && \
+    git clone --branch 0.5.1 https://github.com/timescale/pgvectorscale.git && \
+    cd pgvectorscale/pgvectorscale && \
+    cargo pgrx install --release && \
+    rm -rf /tmp/pgvectorscale
+
+# Install pg_textsearch (BM25 text search)
+RUN cd /tmp && \
+    git clone https://github.com/timescale/pg_textsearch.git && \
+    cd pg_textsearch && \
+    make && \
+    make install && \
+    rm -rf /tmp/pg_textsearch
+
+# Clean up build dependencies (keep runtime dependencies)
+RUN apt-get purge -y --auto-remove git cmake curl && \
+    rm -rf /root/.cargo/registry /root/.cargo/git
+
+# Ensure extensions are preloaded (pg_textsearch requires preloading)
+RUN echo "shared_preload_libraries = 'pg_textsearch'" >> /usr/share/postgresql/postgresql.conf.sample
+

docker/docker-compose/timescale/README.md

@@ -0,0 +1,101 @@
+# Hindsight with Timescale Extensions
+
+This Docker Compose setup provides a complete Hindsight deployment with **Timescale extensions**:
+- **pgvectorscale** - DiskANN algorithm for disk-based scalable vector search  
+- **pg_textsearch** - High-performance BM25 text search
+
+Both extensions are from [Timescale](https://github.com/timescale) and provide production-grade performance.
+
+## Prerequisites
+
+- Docker and Docker Compose installed
+- OpenAI API key (or another LLM provider)
+
+## Quick Start
+
+```bash
+# Set environment variables
+export HINDSIGHT_DB_PASSWORD="your-secure-password"
+export OPENAI_API_KEY="your-openai-api-key"
+
+# Build and start
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml up -d --build
+
+# Check logs
+
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml logs -f
+```
+
+**Access:**
+- API: http://localhost:8888
+- Control Plane: http://localhost:9999
+
+## Stop and Clean Up
+
+```bash
+# Stop services
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml down
+
+# Remove volumes (deletes all data)
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml down -v
+```
+
+## Configuration
+
+### Environment Variables
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `HINDSIGHT_DB_PASSWORD` | PostgreSQL password | `hindsight_password` |
+| `HINDSIGHT_DB_USER` | PostgreSQL username | `hindsight_user` |
+| `HINDSIGHT_DB_NAME` | Database name | `hindsight_db` |
+| `HINDSIGHT_VERSION` | Hindsight Docker image version | `latest` |
+| `OPENAI_API_KEY` | OpenAI API key | (required) |
+| `HINDSIGHT_API_LLM_PROVIDER` | LLM provider | `openai` |
+
+### Why Timescale Extensions?
+
+**pgvectorscale (DiskANN):**
+- 28x lower p95 latency vs dedicated vector databases
+- 16x higher query throughput at 99% recall
+- 60-75% cost reduction (disk is cheaper than RAM)
+- Best for large datasets (10M+ vectors)
+
+**pg_textsearch (BM25):**
+- High-performance keyword retrieval
+- Native BM25 ranking algorithm
+- Optimized for full-text search
+
+## Troubleshooting
+
+### Extensions not installed
+
+Check if extensions are available:
+
+```bash
+docker exec -it hindsight-db-timescale psql -U hindsight_user -d hindsight_db -c "\dx"
+```
+
+You should see:
+- `vector` (pgvector)
+- `vectorscale` (pgvectorscale/DiskANN)
+- `pg_textsearch` (BM25 search)
+
+### Build fails
+
+If the Docker build fails during pgvectorscale compilation:
+
+1. Ensure you have sufficient memory (recommended: 4GB+)
+2. Check Docker build logs for Rust compilation errors
+3. Try building with more resources: `docker compose build --no-cache --memory 4g`
+
+### Port conflicts
+
+If port 5438 is already in use, modify the `ports` section in docker-compose.yaml.
+
+## Learn More
+
+- [pgvectorscale GitHub](https://github.com/timescale/pgvectorscale)
+- [pg_textsearch GitHub](https://github.com/timescale/pg_textsearch)
+- [HNSW vs DiskANN](https://www.tigerdata.com/learn/hnsw-vs-diskann)
+- [Hindsight Documentation](https://hindsight.dev)

docker/docker-compose/timescale/docker-compose.yaml

@@ -0,0 +1,108 @@
+name: hindsight
+# Docker Compose file for Hindsight with Timescale extensions
+# - pgvectorscale: DiskANN vector search (disk-based, scalable)
+# - pg_textsearch: BM25 text search (high-performance keyword retrieval)
+#
+# Quick start:
+# docker compose -f docker/docker-compose/timescale/docker-compose.yaml up -d --build
+#
+# Required environment variables:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - OPENAI_API_KEY (or configure another LLM provider)
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+
+services:
+  db:
+    # Custom PostgreSQL image with Timescale extensions (pgvectorscale + pg_textsearch)
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: hindsight-db-timescale
+    restart: always
+    # Expose PostgreSQL port (using 5438 to avoid conflicts with other setups)
+    ports:
+      - "5438:5432"
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/data
+    networks:
+      - hindsight-net
+    # Health check to ensure database is ready
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U hindsight_user"]
+      interval: 5s
+      timeout: 5s
+      retries: 5
+
+  timescale-init:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    depends_on:
+      db:
+        condition: service_healthy
+    environment:
+      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+    command: >
+      bash -c "
+        echo 'PostgreSQL is ready - creating hindsight_db database';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
+        echo 'Installing Timescale extensions...';
+        echo '1/3: Installing pgvector (required by pgvectorscale)...';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vector CASCADE;';
+        echo '2/3: Installing pgvectorscale (DiskANN vector search)...';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE;';
+        echo '3/3: Installing pg_textsearch (BM25 text search)...';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE;';
+        echo '';
+        echo '✅ Timescale extensions installed successfully';
+        echo '';
+        echo 'Installed extensions:';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c \"\\dx\" | grep -E '(vector|vectorscale|pg_textsearch)';
+      "
+    restart: "no"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app-timescale
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      # LLM Configuration
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
+
+      # Database Configuration
+      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+
+      # Timescale Extensions
+      # pgvectorscale: DiskANN algorithm for disk-based scalable vector search
+      HINDSIGHT_API_VECTOR_EXTENSION: pgvectorscale
+      # pg_textsearch: High-performance BM25 text search
+      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: pg_textsearch
+
+    depends_on:
+      db:
+        condition: service_healthy
+      timescale-init:
+        condition: service_completed_successfully
+    networks:
+      - hindsight-net
+
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:

docker/docker-compose/vchord/docker-compose.yaml

@@ -0,0 +1,93 @@
+name: hindsight
+# Docker Compose file for Hindsight with PostgreSQL and vectorchord
+# docker compose -f docker/docker-compose/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/docker-compose.yaml up -d
+# Make sure to set the required environment variables before running:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - Configure LLM provider variables as needed (see below in the hindsight service)
+#
+# Usage:
+# docker compose up -d
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+# - HINDSIGHT_DB_VERSION: PostgreSQL version (default: 18)
+
+services:
+  db:
+    # Use a PostgreSQL-Image with vectorchord extension pre-installed
+    image: tensorchord/vchord-suite:pg${HINDSIGHT_DB_VERSION:-18-latest}
+    container_name: hindsight-db
+    restart: always
+    # Expose PostgreSQL port
+    ports:
+      - "5436:5432"
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/${HINDSIGHT_DB_VERSION:-18}/docker
+    networks:
+      - hindsight-net
+
+  vectorchord-init:
+    image: tensorchord/vchord-suite:pg18-latest
+    #container_name: vectorchord-init
+    depends_on:
+      - db
+    environment:
+      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+    command: >
+      bash -c "
+        echo 'Waiting for PostgreSQL to be ready...';
+        until pg_isready -h hindsight-db -p 5432 -U hindsight_user; do
+          echo 'PostgreSQL is unavailable - sleeping';
+          sleep 2;
+        done;
+        echo 'PostgreSQL is ready - creating hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
+        echo 'Creating extensions in hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vchord CASCADE;';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_tokenizer CASCADE;';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE;';
+        echo 'Creating llmlingua2 tokenizer';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c \"SELECT create_tokenizer('llmlingua2', \\$\\$ model = \\\"llmlingua2\\\" \\$\\$);\" 2>/dev/null || echo 'Tokenizer already exists or creation skipped';
+        echo 'Database and extensions created successfully';
+      "
+    restart: "no"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      # LLM Configuration (uses OpenAI for testing vchord)
+      # LLM configuration
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
+
+      # Database Configuration
+      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+
+      # Vector and Text Search Extensions
+      HINDSIGHT_API_VECTOR_EXTENSION: vchord
+      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: vchord
+
+    depends_on:
+      - db
+    networks:
+      - hindsight-net
+
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:

docker/standalone/Dockerfile

@@ -8,6 +8,7 @@
 #                                     Set to false when using external providers (TEI, OpenAI, Cohere)
 #   PRELOAD_ML_MODELS=true/false   - Pre-download ML models during build (default: true)
 #                                     Only effective when INCLUDE_LOCAL_MODELS=true
+#                                     NOTE: tiktoken encodings are ALWAYS preloaded (required for air-gapped deployments)
 #
 # Examples:
 #   docker build -t hindsight .                                          # Both (standalone)
@@ -111,6 +112,10 @@ RUN rm -f package-lock.json && sed -i '/"@vectorize-io\/hindsight-client":/d' pa
 # Copy built SDK directly into node_modules (more reliable than npm link in Docker)
 COPY --from=sdk-builder /app/hindsight-clients/typescript ./node_modules/@vectorize-io/hindsight-client
 
+# Accept base path as build argument for reverse proxy deployments
+# Usage: docker build --build-arg NEXT_PUBLIC_BASE_PATH=/hindsight ...
+ARG NEXT_PUBLIC_BASE_PATH=""
+
 # Build Control Plane - run next build first, then custom standalone copy
 # (The build:standalone script expects a specific path structure that differs in Docker)
 RUN npm exec -- next build
@@ -165,8 +170,35 @@ RUN chown -R hindsight:hindsight /app
 
 USER hindsight
 
+# Create pg0 data directory as hindsight user so that Docker seeds new named
+# volumes with correct ownership (UID 1000) on first use, avoiding the
+# "Permission denied" error when mounting a fresh root-owned volume.
+RUN mkdir -p /home/hindsight/.pg0
+
 ENV PATH="/app/api/.venv/bin:${PATH}"
 
+# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
+# Tiktoken is a core runtime dependency, not an optional ML model
+RUN MAX_RETRIES=3; \
+    RETRY_DELAY=5; \
+    for i in $(seq 1 $MAX_RETRIES); do \
+      echo "Attempt $i/$MAX_RETRIES: Downloading tiktoken encoding..."; \
+      /app/api/.venv/bin/python -c "\
+import tiktoken; \
+print('Downloading cl100k_base encoding...'); \
+tiktoken.get_encoding('cl100k_base'); \
+print('Tiktoken encoding cached successfully')" && break; \
+      if [ $i -lt $MAX_RETRIES ]; then \
+        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
+        sleep $RETRY_DELAY; \
+        RETRY_DELAY=$((RETRY_DELAY * 2)); \
+      fi; \
+    done; \
+    if [ $i -eq $MAX_RETRIES ]; then \
+      echo "ERROR: Failed to download tiktoken encoding after $MAX_RETRIES attempts"; \
+      exit 1; \
+    fi
+
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
 # Includes retry logic with exponential backoff for transient network failures
@@ -185,7 +217,6 @@ print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Downloading tiktoken encoding...'); import tiktoken; tiktoken.get_encoding('cl100k_base'); \
 print('Models cached successfully')" && break; \
       if [ $i -lt $MAX_RETRIES ]; then \
         echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
@@ -295,8 +326,35 @@ RUN chown -R hindsight:hindsight /app
 
 USER hindsight
 
+# Create pg0 data directory as hindsight user so that Docker seeds new named
+# volumes with correct ownership (UID 1000) on first use, avoiding the
+# "Permission denied" error when mounting a fresh root-owned volume.
+RUN mkdir -p /home/hindsight/.pg0
+
 ENV PATH="/app/api/.venv/bin:${PATH}"
 
+# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
+# Tiktoken is a core runtime dependency, not an optional ML model
+RUN MAX_RETRIES=3; \
+    RETRY_DELAY=5; \
+    for i in $(seq 1 $MAX_RETRIES); do \
+      echo "Attempt $i/$MAX_RETRIES: Downloading tiktoken encoding..."; \
+      /app/api/.venv/bin/python -c "\
+import tiktoken; \
+print('Downloading cl100k_base encoding...'); \
+tiktoken.get_encoding('cl100k_base'); \
+print('Tiktoken encoding cached successfully')" && break; \
+      if [ $i -lt $MAX_RETRIES ]; then \
+        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
+        sleep $RETRY_DELAY; \
+        RETRY_DELAY=$((RETRY_DELAY * 2)); \
+      fi; \
+    done; \
+    if [ $i -eq $MAX_RETRIES ]; then \
+      echo "ERROR: Failed to download tiktoken encoding after $MAX_RETRIES attempts"; \
+      exit 1; \
+    fi
+
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
 # Includes retry logic with exponential backoff for transient network failures
@@ -315,7 +373,6 @@ print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Downloading tiktoken encoding...'); import tiktoken; tiktoken.get_encoding('cl100k_base'); \
 print('Models cached successfully')" && break; \
       if [ $i -lt $MAX_RETRIES ]; then \
         echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \

docker/standalone/start-all.sh

@@ -97,7 +97,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
     echo "🎛️  Starting Control Plane..."
     cd /app/control-plane
-    PORT=9999 node server.js &
+    PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
     CP_PID=$!
     PIDS+=($CP_PID)
 else
@@ -110,7 +110,7 @@ echo "✅ Hindsight is running!"
 echo ""
 echo "📍 Access:"
 if [ "$ENABLE_CP" = "true" ]; then
-    echo "   Control Plane: http://localhost:9999"
+    echo "   Control Plane: http://localhost:${HINDSIGHT_CP_PORT:-9999}"
 fi
 if [ "$ENABLE_API" = "true" ]; then
     echo "   API:           http://localhost:8888"

docker/test-image.sh

@@ -13,9 +13,9 @@
 #   target  - Optional: 'cp-only' for control plane, otherwise assumes API image (default: api)
 #
 # Environment variables:
-#   GROQ_API_KEY                                - Required for API/standalone images (LLM verification)
-#   HINDSIGHT_API_LLM_PROVIDER                  - LLM provider (default: groq)
-#   HINDSIGHT_API_LLM_MODEL                     - LLM model (default: llama-3.3-70b-versatile)
+#   HINDSIGHT_API_LLM_API_KEY                   - Required for API/standalone images (LLM verification)
+#   HINDSIGHT_API_LLM_PROVIDER                  - LLM provider (default: openai)
+#   HINDSIGHT_API_LLM_MODEL                     - LLM model (default: gpt-4o-mini)
 #   HINDSIGHT_API_EMBEDDINGS_PROVIDER           - Embeddings provider (optional, for slim images: openai, cohere, tei)
 #   HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY     - OpenAI API key for embeddings (optional)
 #   HINDSIGHT_API_RERANKER_PROVIDER             - Reranker provider (optional, for slim images: cohere, tei)
@@ -34,7 +34,7 @@
 #   ./docker/test-image.sh hindsight-control-plane:test cp-only
 #
 #   # Test slim image with external providers
-#   export GROQ_API_KEY=gsk_xxx
+#   export HINDSIGHT_API_LLM_API_KEY=sk_xxx
 #   export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 #   export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxx
 #   export HINDSIGHT_API_RERANKER_PROVIDER=cohere
@@ -60,8 +60,8 @@ IMAGE="${1:-}"
 TARGET="${2:-api}"
 TIMEOUT="${SMOKE_TEST_TIMEOUT:-120}"
 CONTAINER_NAME="${SMOKE_TEST_CONTAINER_NAME:-hindsight-smoke-test}"
-LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-groq}"
-LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-llama-3.3-70b-versatile}"
+LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-openai}"
+LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-gpt-4o-mini}"
 
 # Validate arguments
 if [ -z "$IMAGE" ]; then
@@ -88,9 +88,9 @@ else
 fi
 
 # Check for required environment variables
-if [ "$NEEDS_LLM" = true ] && [ -z "${GROQ_API_KEY:-}" ]; then
-    echo -e "${RED}Error: GROQ_API_KEY environment variable is required for API/standalone images${NC}"
-    echo "Set it with: export GROQ_API_KEY=your-api-key"
+if [ "$NEEDS_LLM" = true ] && [ "$LLM_PROVIDER" != "vertexai" ] && [ -z "${HINDSIGHT_API_LLM_API_KEY:-}" ]; then
+    echo -e "${RED}Error: HINDSIGHT_API_LLM_API_KEY environment variable is required for API/standalone images${NC}"
+    echo "Set it with: export HINDSIGHT_API_LLM_API_KEY=your-api-key"
     exit 2
 fi
 
@@ -123,9 +123,25 @@ else
     # Build docker run command with required and optional env vars
     DOCKER_CMD="docker run -d --name $CONTAINER_NAME"
     DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_PROVIDER=$LLM_PROVIDER"
-    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${GROQ_API_KEY}"
+    if [ -n "${HINDSIGHT_API_LLM_API_KEY:-}" ]; then
+        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${HINDSIGHT_API_LLM_API_KEY}"
+    fi
     DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_MODEL=$LLM_MODEL"
 
+    # Add Vertex AI config if provider is vertexai
+    if [ "$LLM_PROVIDER" = "vertexai" ]; then
+        if [ -n "${HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY:-}" ]; then
+            DOCKER_CMD="$DOCKER_CMD -v ${HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY}:/tmp/gcp-credentials.json:ro"
+            DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json"
+        fi
+        if [ -n "${HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID:-}" ]; then
+            DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=${HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID}"
+        fi
+        if [ -n "${HINDSIGHT_API_LLM_VERTEXAI_REGION:-}" ]; then
+            DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_VERTEXAI_REGION=${HINDSIGHT_API_LLM_VERTEXAI_REGION}"
+        fi
+    fi
+
     # Add optional embeddings provider config
     if [ -n "${HINDSIGHT_API_EMBEDDINGS_PROVIDER:-}" ]; then
         DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_EMBEDDINGS_PROVIDER=${HINDSIGHT_API_EMBEDDINGS_PROVIDER}"

docker/test-slim-local.sh

@@ -6,24 +6,17 @@
 # It expects API keys to be set in environment variables.
 #
 # Usage:
-#   export GROQ_API_KEY=gsk_xxx
 #   export OPENAI_API_KEY=sk-xxx
 #   export COHERE_API_KEY=xxx
 #   ./docker/test-slim-local.sh
 #
 # Or inline:
-#   GROQ_API_KEY=gsk_xxx OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
+#   OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
 #
 
 set -euo pipefail
 
 # Check for required API keys
-if [ -z "${GROQ_API_KEY:-}" ]; then
-    echo "❌ Error: GROQ_API_KEY environment variable is required"
-    echo "Set it with: export GROQ_API_KEY=gsk_xxx"
-    exit 1
-fi
-
 if [ -z "${OPENAI_API_KEY:-}" ]; then
     echo "❌ Error: OPENAI_API_KEY environment variable is required"
     echo "Set it with: export OPENAI_API_KEY=sk-xxx"
@@ -41,7 +34,10 @@ IMAGE="${1:-hindsight-slim:test}"
 echo "Testing image: $IMAGE"
 echo ""
 
-# Set up external providers
+# Set up LLM and external providers
+export HINDSIGHT_API_LLM_PROVIDER=openai
+export HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY
+export HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
 export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$OPENAI_API_KEY
 export HINDSIGHT_API_RERANKER_PROVIDER=cohere

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.10
-appVersion: "0.4.10"
+version: 0.4.14
+appVersion: "0.4.14"
 keywords:
   - ai
   - memory

helm/hindsight/templates/_helpers.tpl

@@ -127,6 +127,38 @@ API URL for control plane
 {{- printf "http://%s-api:%d" (include "hindsight.fullname" .) (.Values.api.service.port | int) }}
 {{- end }}
 
+{{/*
+TEI reranker labels
+*/}}
+{{- define "hindsight.tei.reranker.labels" -}}
+{{ include "hindsight.labels" . }}
+app.kubernetes.io/component: tei-reranker
+{{- end }}
+
+{{/*
+TEI reranker selector labels
+*/}}
+{{- define "hindsight.tei.reranker.selectorLabels" -}}
+{{ include "hindsight.selectorLabels" . }}
+app.kubernetes.io/component: tei-reranker
+{{- end }}
+
+{{/*
+TEI embedding labels
+*/}}
+{{- define "hindsight.tei.embedding.labels" -}}
+{{ include "hindsight.labels" . }}
+app.kubernetes.io/component: tei-embedding
+{{- end }}
+
+{{/*
+TEI embedding selector labels
+*/}}
+{{- define "hindsight.tei.embedding.selectorLabels" -}}
+{{ include "hindsight.selectorLabels" . }}
+app.kubernetes.io/component: tei-embedding
+{{- end }}
+
 {{/*
 Get the name of the secret to use
 */}}

helm/hindsight/templates/api-deployment.yaml

@@ -67,6 +67,18 @@ spec:
         - name: {{ $key }}
           value: {{ $value | quote }}
         {{- end }}
+        {{- if .Values.tei.reranker.enabled }}
+        - name: HINDSIGHT_API_RERANKER_PROVIDER
+          value: "tei"
+        - name: HINDSIGHT_API_RERANKER_TEI_URL
+          value: "http://{{ include "hindsight.fullname" . }}-tei-reranker:{{ .Values.tei.reranker.port }}"
+        {{- end }}
+        {{- if .Values.tei.embedding.enabled }}
+        - name: HINDSIGHT_API_EMBEDDINGS_PROVIDER
+          value: "tei"
+        - name: HINDSIGHT_API_EMBEDDINGS_TEI_URL
+          value: "http://{{ include "hindsight.fullname" . }}-tei-embedding:{{ .Values.tei.embedding.port }}"
+        {{- end }}
         {{- /* Only use api.secrets when not using existingSecret (for chart-managed secrets) */}}
         {{- if not .Values.existingSecret }}
         {{- range $key, $value := .Values.api.secrets }}

helm/hindsight/templates/tei-embedding-deployment.yaml

@@ -0,0 +1,76 @@
+{{- if .Values.tei.embedding.enabled }}
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: {{ include "hindsight.fullname" . }}-tei-embedding
+  labels:
+    {{- include "hindsight.tei.embedding.labels" . | nindent 4 }}
+spec:
+  replicas: {{ .Values.tei.embedding.replicaCount }}
+  selector:
+    matchLabels:
+      {{- include "hindsight.tei.embedding.selectorLabels" . | nindent 6 }}
+  template:
+    metadata:
+      {{- with .Values.podAnnotations }}
+      annotations:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      labels:
+        {{- include "hindsight.tei.embedding.selectorLabels" . | nindent 8 }}
+    spec:
+      {{- if .Values.serviceAccount.create }}
+      serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
+      {{- end }}
+      securityContext:
+        {{- toYaml .Values.podSecurityContext | nindent 8 }}
+      containers:
+      - name: tei-embedding
+        securityContext:
+          {{- toYaml .Values.securityContext | nindent 10 }}
+        image: "{{ .Values.tei.embedding.image.repository }}:{{ .Values.tei.embedding.image.tag }}"
+        imagePullPolicy: {{ .Values.tei.embedding.image.pullPolicy }}
+        args:
+          - "--model-id"
+          - {{ .Values.tei.embedding.model | quote }}
+          - "--hostname"
+          - "0.0.0.0"
+          {{- range .Values.tei.embedding.args }}
+          - {{ . | quote }}
+          {{- end }}
+        ports:
+        - name: http
+          containerPort: {{ .Values.tei.embedding.port }}
+          protocol: TCP
+        env:
+        - name: PORT
+          value: {{ .Values.tei.embedding.port | quote }}
+        {{- range $key, $value := .Values.tei.embedding.env }}
+        - name: {{ $key }}
+          value: {{ $value | quote }}
+        {{- end }}
+        livenessProbe:
+          {{- toYaml .Values.tei.embedding.livenessProbe | nindent 10 }}
+        readinessProbe:
+          {{- toYaml .Values.tei.embedding.readinessProbe | nindent 10 }}
+        resources:
+          {{- toYaml .Values.tei.embedding.resources | nindent 10 }}
+        volumeMounts:
+        - name: model-cache
+          mountPath: /data
+      volumes:
+      - name: model-cache
+        emptyDir: {}
+      {{- with .Values.nodeSelector }}
+      nodeSelector:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      {{- with .Values.affinity }}
+      affinity:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      {{- with .Values.tolerations }}
+      tolerations:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+{{- end }}

helm/hindsight/templates/tei-embedding-service.yaml

@@ -0,0 +1,17 @@
+{{- if .Values.tei.embedding.enabled }}
+apiVersion: v1
+kind: Service
+metadata:
+  name: {{ include "hindsight.fullname" . }}-tei-embedding
+  labels:
+    {{- include "hindsight.tei.embedding.labels" . | nindent 4 }}
+spec:
+  type: ClusterIP
+  ports:
+  - port: {{ .Values.tei.embedding.port }}
+    targetPort: http
+    protocol: TCP
+    name: http
+  selector:
+    {{- include "hindsight.tei.embedding.selectorLabels" . | nindent 4 }}
+{{- end }}

helm/hindsight/templates/tei-reranker-deployment.yaml

@@ -0,0 +1,76 @@
+{{- if .Values.tei.reranker.enabled }}
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: {{ include "hindsight.fullname" . }}-tei-reranker
+  labels:
+    {{- include "hindsight.tei.reranker.labels" . | nindent 4 }}
+spec:
+  replicas: {{ .Values.tei.reranker.replicaCount }}
+  selector:
+    matchLabels:
+      {{- include "hindsight.tei.reranker.selectorLabels" . | nindent 6 }}
+  template:
+    metadata:
+      {{- with .Values.podAnnotations }}
+      annotations:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      labels:
+        {{- include "hindsight.tei.reranker.selectorLabels" . | nindent 8 }}
+    spec:
+      {{- if .Values.serviceAccount.create }}
+      serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
+      {{- end }}
+      securityContext:
+        {{- toYaml .Values.podSecurityContext | nindent 8 }}
+      containers:
+      - name: tei-reranker
+        securityContext:
+          {{- toYaml .Values.securityContext | nindent 10 }}
+        image: "{{ .Values.tei.reranker.image.repository }}:{{ .Values.tei.reranker.image.tag }}"
+        imagePullPolicy: {{ .Values.tei.reranker.image.pullPolicy }}
+        args:
+          - "--model-id"
+          - {{ .Values.tei.reranker.model | quote }}
+          - "--hostname"
+          - "0.0.0.0"
+          {{- range .Values.tei.reranker.args }}
+          - {{ . | quote }}
+          {{- end }}
+        ports:
+        - name: http
+          containerPort: {{ .Values.tei.reranker.port }}
+          protocol: TCP
+        env:
+        - name: PORT
+          value: {{ .Values.tei.reranker.port | quote }}
+        {{- range $key, $value := .Values.tei.reranker.env }}
+        - name: {{ $key }}
+          value: {{ $value | quote }}
+        {{- end }}
+        livenessProbe:
+          {{- toYaml .Values.tei.reranker.livenessProbe | nindent 10 }}
+        readinessProbe:
+          {{- toYaml .Values.tei.reranker.readinessProbe | nindent 10 }}
+        resources:
+          {{- toYaml .Values.tei.reranker.resources | nindent 10 }}
+        volumeMounts:
+        - name: model-cache
+          mountPath: /data
+      volumes:
+      - name: model-cache
+        emptyDir: {}
+      {{- with .Values.nodeSelector }}
+      nodeSelector:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      {{- with .Values.affinity }}
+      affinity:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      {{- with .Values.tolerations }}
+      tolerations:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+{{- end }}

helm/hindsight/templates/tei-reranker-service.yaml

@@ -0,0 +1,17 @@
+{{- if .Values.tei.reranker.enabled }}
+apiVersion: v1
+kind: Service
+metadata:
+  name: {{ include "hindsight.fullname" . }}-tei-reranker
+  labels:
+    {{- include "hindsight.tei.reranker.labels" . | nindent 4 }}
+spec:
+  type: ClusterIP
+  ports:
+  - port: {{ .Values.tei.reranker.port }}
+    targetPort: http
+    protocol: TCP
+    name: http
+  selector:
+    {{- include "hindsight.tei.reranker.selectorLabels" . | nindent 4 }}
+{{- end }}

helm/hindsight/values.yaml

@@ -293,6 +293,84 @@ tolerations: []
 # Affinity (applied to all components unless overridden per-component)
 affinity: {}
 
+# TEI (Text Embeddings Inference) - optional standalone deployments
+# for reranking and/or embedding models
+tei:
+  reranker:
+    enabled: false
+    replicaCount: 1
+    image:
+      repository: ghcr.io/huggingface/text-embeddings-inference
+      tag: cpu-1.8.3
+      pullPolicy: IfNotPresent
+    model: "cross-encoder/ms-marco-MiniLM-L-6-v2"
+    port: 8090
+    args:
+      - "--auto-truncate"
+    env:
+      PAYLOAD_LIMIT: "10000000"
+      MAX_CLIENT_BATCH_SIZE: "256"
+    resources:
+      limits:
+        cpu: 2000m
+        memory: 2Gi
+      requests:
+        cpu: 500m
+        memory: 1Gi
+    livenessProbe:
+      httpGet:
+        path: /health
+        port: 8090
+      initialDelaySeconds: 30
+      periodSeconds: 10
+      timeoutSeconds: 5
+      failureThreshold: 6
+    readinessProbe:
+      httpGet:
+        path: /health
+        port: 8090
+      initialDelaySeconds: 15
+      periodSeconds: 5
+      timeoutSeconds: 3
+      failureThreshold: 3
+
+  embedding:
+    enabled: false
+    replicaCount: 1
+    image:
+      repository: ghcr.io/huggingface/text-embeddings-inference
+      tag: cpu-1.8.3
+      pullPolicy: IfNotPresent
+    model: "sentence-transformers/all-MiniLM-L6-v2"
+    port: 8091
+    args: []
+    env:
+      PAYLOAD_LIMIT: "10000000"
+      MAX_CLIENT_BATCH_SIZE: "256"
+    resources:
+      limits:
+        cpu: 2000m
+        memory: 2Gi
+      requests:
+        cpu: 500m
+        memory: 1Gi
+    livenessProbe:
+      httpGet:
+        path: /health
+        port: 8091
+      initialDelaySeconds: 30
+      periodSeconds: 10
+      timeoutSeconds: 5
+      failureThreshold: 6
+    readinessProbe:
+      httpGet:
+        path: /health
+        port: 8091
+      initialDelaySeconds: 15
+      periodSeconds: 5
+      timeoutSeconds: 3
+      failureThreshold: 3
+
 # Autoscaling
 autoscaling:
   enabled: false

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.10"
+__version__ = "0.4.14"

hindsight-api/hindsight_api/alembic/versions/5a366d414dce_initial_schema.py

@@ -6,6 +6,7 @@ Create Date: 2025-11-27 11:54:19.228030
 
 """
 
+import os
 from collections.abc import Sequence
 
 import sqlalchemy as sa
@@ -21,6 +22,96 @@ branch_labels: str | Sequence[str] | None = None
 depends_on: str | Sequence[str] | None = None
 
 
+def _detect_vector_extension() -> str:
+    """
+    Detect or validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
+    Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
+    """
+    conn = op.get_bind()
+    vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
+
+    # Validate configured extension is installed
+    if vector_extension == "pgvectorscale":
+        # pgvectorscale/DiskANN requires pgvector
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "DiskANN requires pgvector. Install with: CREATE EXTENSION vector; then vectorscale or pg_diskann CASCADE;"
+            )
+        # Check for either vectorscale (open source) or pg_diskann (Azure)
+        vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
+        pg_diskann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_diskann'")).scalar()
+
+        if vectorscale_check:
+            return "pgvectorscale"
+        elif pg_diskann_check:
+            return "pg_diskann"
+        else:
+            raise RuntimeError(
+                "Configured vector extension 'pgvectorscale' not found. Install either:\n"
+                "  - pgvectorscale: CREATE EXTENSION vectorscale CASCADE;\n"
+                "  - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
+            )
+    elif vector_extension == "vchord":
+        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
+        if not vchord_check:
+            raise RuntimeError(
+                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
+            )
+        return "vchord"
+    elif vector_extension == "pgvector":
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
+            )
+        return "pgvector"
+    else:
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
+        )
+
+
+def _detect_text_search_extension() -> str:
+    """
+    Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
+    Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
+    Creates the extension if needed.
+    """
+    text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+    if text_search_extension == "vchord":
+        # Create vchord_bm25 extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord_bm25'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "vchord"
+    elif text_search_extension == "pg_textsearch":
+        # Create pg_textsearch extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_textsearch'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "pg_textsearch"
+    elif text_search_extension == "native":
+        return "native"
+    else:
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
+        )
+
+
 def upgrade() -> None:
     """Upgrade schema - create all tables from scratch."""
 
@@ -166,11 +257,29 @@ def upgrade() -> None:
     )
 
     # Add search_vector column for full-text search
-    op.execute("""
-        ALTER TABLE memory_units
-        ADD COLUMN search_vector tsvector
-        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
-    """)
+    # Type depends on configured text search backend
+    text_search_ext = _detect_text_search_extension()
+
+    if text_search_ext == "vchord":
+        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
+        # Note: vchord_bm25 extension creates types in bm25_catalog schema
+        op.execute("""
+            ALTER TABLE memory_units
+            ADD COLUMN search_vector bm25_catalog.bm25vector
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
+        op.execute("""
+            ALTER TABLE memory_units
+            ADD COLUMN search_vector TEXT
+        """)
+    else:  # native
+        # Native PostgreSQL: tsvector with automatic generation
+        op.execute("""
+            ALTER TABLE memory_units
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
+        """)
 
     op.create_index("idx_memory_units_bank_id", "memory_units", ["bank_id"])
     op.create_index("idx_memory_units_document_id", "memory_units", ["document_id"])
@@ -200,19 +309,61 @@ def upgrade() -> None:
         ["bank_id", sa.text("event_date DESC")],
         postgresql_where=sa.text("fact_type = 'observation'"),
     )
-    op.create_index(
-        "idx_memory_units_embedding",
-        "memory_units",
-        ["embedding"],
-        postgresql_using="hnsw",
-        postgresql_ops={"embedding": "vector_cosine_ops"},
-    )
+    # Create vector index - conditional based on available extension
+    vector_ext = _detect_vector_extension()
 
-    # Create BM25 full-text search index on search_vector
-    op.execute("""
-        CREATE INDEX idx_memory_units_text_search ON memory_units
-        USING gin(search_vector)
-    """)
+    if vector_ext == "pgvectorscale":
+        # Use DiskANN index for pgvectorscale (disk-based, scalable)
+        op.execute("""
+            CREATE INDEX idx_memory_units_embedding ON memory_units
+            USING diskann (embedding vector_cosine_ops)
+            WITH (num_neighbors = 50)
+        """)
+    elif vector_ext == "pg_diskann":
+        # Use DiskANN index for pg_diskann (Azure)
+        op.execute("""
+            CREATE INDEX idx_memory_units_embedding ON memory_units
+            USING diskann (embedding vector_cosine_ops)
+            WITH (max_neighbors = 50)
+        """)
+    elif vector_ext == "vchord":
+        # Use vchordrq index for vchord (supports high-dimensional embeddings)
+        op.execute("""
+            CREATE INDEX idx_memory_units_embedding ON memory_units
+            USING vchordrq (embedding vector_l2_ops)
+        """)
+    else:  # pgvector
+        # Use HNSW index for pgvector
+        op.create_index(
+            "idx_memory_units_embedding",
+            "memory_units",
+            ["embedding"],
+            postgresql_using="hnsw",
+            postgresql_ops={"embedding": "vector_cosine_ops"},
+        )
+
+    # Create full-text search index on search_vector
+    # Index type depends on text search backend
+    if text_search_ext == "vchord":
+        # VectorChord BM25 index
+        op.execute("""
+            CREATE INDEX idx_memory_units_text_search ON memory_units
+            USING bm25 (search_vector bm25_catalog.bm25_ops)
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch BM25 index on text column
+        # Note: pg_textsearch doesn't support expressions, so we index the main text column
+        op.execute("""
+            CREATE INDEX idx_memory_units_text_search ON memory_units
+            USING bm25(text)
+            WITH (text_config='english')
+        """)
+    else:  # native
+        # Native PostgreSQL GIN index
+        op.execute("""
+            CREATE INDEX idx_memory_units_text_search ON memory_units
+            USING gin(search_vector)
+        """)
 
     op.execute("""
         CREATE MATERIALIZED VIEW memory_units_bm25 AS

hindsight-api/hindsight_api/alembic/versions/a1b2c3d4e5f6_add_file_storage_table.py

@@ -0,0 +1,70 @@
+"""Add file_storage table for BYTEA-based file storage
+
+Revision ID: a1b2c3d4e5f6
+Revises: y0t1u2v3w4x5
+Create Date: 2026-02-16
+
+Creates a dedicated table for storing uploaded files using BYTEA.
+This provides zero-config file storage that "just works" for development
+and small deployments. For production/scale, use S3-compatible storage.
+
+Files are stored in a separate table to avoid bloating the documents table.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "a1b2c3d4e5f6"
+down_revision: str | Sequence[str] | None = "y0t1u2v3w4x5"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Create file_storage table for BYTEA storage."""
+    schema = _get_schema_prefix()
+
+    # Create file_storage table (minimal: just key + data)
+    op.execute(
+        f"""
+        CREATE TABLE {schema}file_storage (
+            storage_key TEXT PRIMARY KEY,
+            data BYTEA NOT NULL
+        )
+    """
+    )
+
+    # Add file tracking columns to documents table
+    op.execute(
+        f"""
+        ALTER TABLE {schema}documents
+        ADD COLUMN IF NOT EXISTS file_storage_key TEXT,
+        ADD COLUMN IF NOT EXISTS file_original_name TEXT,
+        ADD COLUMN IF NOT EXISTS file_content_type TEXT
+    """
+    )
+
+
+def downgrade() -> None:
+    """Remove file_storage table and related columns."""
+    schema = _get_schema_prefix()
+
+    # Drop columns from documents table
+    op.execute(
+        f"""
+        ALTER TABLE {schema}documents
+        DROP COLUMN IF EXISTS file_storage_key,
+        DROP COLUMN IF EXISTS file_original_name,
+        DROP COLUMN IF EXISTS file_content_type
+    """
+    )
+
+    # Drop file_storage table
+    op.execute(f"DROP TABLE IF EXISTS {schema}file_storage")

hindsight-api/hindsight_api/alembic/versions/a2b3c4d5e6f7_add_text_signals_column.py

@@ -0,0 +1,88 @@
+"""Add text_signals column to memory_units for enriched BM25 indexing.
+
+text_signals stores a denormalized space-separated string of entity names
+(and future signals) to improve full-text search recall without polluting
+the stored fact text.
+
+- vchord: text_signals included in tokenize() at insert time
+- native: search_vector GENERATED column regenerated to include text_signals
+- pg_textsearch: no change (index only supports a single base column)
+
+Revision ID: a2b3c4d5e6f7
+Revises: z1u2v3w4x5y6
+Create Date: 2026-02-28
+"""
+
+import os
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "a2b3c4d5e6f7"
+down_revision: str | Sequence[str] | None = "aa2b3c4d5e6f"
+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 _detect_text_search_extension() -> str:
+    return os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+    table = f"{schema}memory_units"
+    text_search_ext = _detect_text_search_extension()
+
+    # Add text_signals column (nullable TEXT, populated at retain time)
+    op.execute(f"ALTER TABLE {table} ADD COLUMN IF NOT EXISTS text_signals TEXT")
+
+    if text_search_ext == "native":
+        # Native PostgreSQL: drop and recreate the GENERATED tsvector column to include text_signals
+        op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS search_vector")
+        op.execute(f"""
+            ALTER TABLE {table}
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (
+                to_tsvector('english',
+                    COALESCE(text, '') || ' ' ||
+                    COALESCE(context, '') || ' ' ||
+                    COALESCE(text_signals, '')
+                )
+            ) STORED
+        """)
+        # Recreate GIN index (was dropped with the column)
+        op.execute(f"""
+            CREATE INDEX IF NOT EXISTS idx_memory_units_text_search
+            ON {table} USING gin(search_vector)
+        """)
+
+    # vchord: tokenize() call in fact_storage.py is updated to include text_signals at insert time
+    # pg_textsearch: no change — index operates on the base `text` column only
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    table = f"{schema}memory_units"
+    text_search_ext = _detect_text_search_extension()
+
+    if text_search_ext == "native":
+        op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_text_search")
+        op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS search_vector")
+        op.execute(f"""
+            ALTER TABLE {table}
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (
+                to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))
+            ) STORED
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_memory_units_text_search
+            ON {table} USING gin(search_vector)
+        """)
+
+    op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS text_signals")

hindsight-api/hindsight_api/alembic/versions/aa2b3c4d5e6f_nullable_event_date.py

@@ -0,0 +1,36 @@
+"""Make event_date nullable in memory_units to support timestamp-free content
+
+Revision ID: aa2b3c4d5e6f
+Revises: z1u2v3w4x5y6
+Create Date: 2026-03-02
+
+When callers retain content without a timestamp (e.g. fictional documents, static text),
+the event_date column should be allowed to be NULL rather than defaulting to utcnow().
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "aa2b3c4d5e6f"
+down_revision: str | Sequence[str] | None = "z1u2v3w4x5y6"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    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}memory_units ALTER COLUMN event_date DROP NOT NULL")
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    # Backfill NULLs with now() before restoring the NOT NULL constraint
+    op.execute(f"UPDATE {schema}memory_units SET event_date = now() WHERE event_date IS NULL")
+    op.execute(f"ALTER TABLE {schema}memory_units ALTER COLUMN event_date SET NOT NULL")

hindsight-api/hindsight_api/alembic/versions/b4c5d6e7f8a9_backfill_observation_scopes.py

@@ -0,0 +1,34 @@
+"""Backfill observation_scopes column if missing.
+
+This migration ensures observation_scopes exists even on databases that had
+revision z1u2v3w4x5y6 applied when it referred to the old text_signals migration
+(before it was renamed to a2b3c4d5e6f7). The ADD COLUMN IF NOT EXISTS makes this
+a no-op on databases that already have the column.
+
+Revision ID: b4c5d6e7f8a9
+Revises: a2b3c4d5e6f7
+Create Date: 2026-03-02
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "b4c5d6e7f8a9"
+down_revision: str | Sequence[str] | None = "a2b3c4d5e6f7"
+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}memory_units ADD COLUMN IF NOT EXISTS observation_scopes JSONB")
+
+
+def downgrade() -> None:
+    pass  # intentionally no-op — safe to leave the column in place

hindsight-api/hindsight_api/alembic/versions/n9i0j1k2l3m4_learnings_and_pinned_reflections.py

@@ -10,9 +10,11 @@ This migration:
 3. Adds consolidation tracking columns to the 'banks' table
 """
 
+import os
 from collections.abc import Sequence
 
 from alembic import context, op
+from sqlalchemy import text
 
 # revision identifiers, used by Alembic.
 revision: str = "n9i0j1k2l3m4"
@@ -27,10 +29,106 @@ def _get_schema_prefix() -> str:
     return f'"{schema}".' if schema else ""
 
 
+def _detect_vector_extension() -> str:
+    """
+    Detect or validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
+    Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
+    """
+    conn = op.get_bind()
+    vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
+
+    # Validate configured extension is installed
+    if vector_extension == "pgvectorscale":
+        # pgvectorscale/DiskANN requires pgvector
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "DiskANN requires pgvector. Install with: CREATE EXTENSION vector; then vectorscale or pg_diskann CASCADE;"
+            )
+        # Check for either vectorscale (open source) or pg_diskann (Azure)
+        vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
+        pg_diskann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_diskann'")).scalar()
+
+        if vectorscale_check:
+            return "pgvectorscale"
+        elif pg_diskann_check:
+            return "pg_diskann"
+        else:
+            raise RuntimeError(
+                "Configured vector extension 'pgvectorscale' not found. Install either:\n"
+                "  - pgvectorscale: CREATE EXTENSION vectorscale CASCADE;\n"
+                "  - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
+            )
+    elif vector_extension == "vchord":
+        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
+        if not vchord_check:
+            raise RuntimeError(
+                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
+            )
+        return "vchord"
+    elif vector_extension == "pgvector":
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
+            )
+        return "pgvector"
+    else:
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
+        )
+
+
+def _detect_text_search_extension() -> str:
+    """
+    Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
+    Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
+    Creates the extension if needed.
+    """
+    text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+    if text_search_extension == "vchord":
+        # Create vchord_bm25 extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord_bm25'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "vchord"
+    elif text_search_extension == "pg_textsearch":
+        # Create pg_textsearch extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_textsearch'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "pg_textsearch"
+    elif text_search_extension == "native":
+        return "native"
+    else:
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
+        )
+
+
 def upgrade() -> None:
     """Create learnings and pinned_reflections tables."""
     schema = _get_schema_prefix()
 
+    # Detect which vector extension is available
+    vector_ext = _detect_vector_extension()
+
+    # Detect which text search extension to use
+    text_search_ext = _detect_text_search_extension()
+
     # 1. Create learnings table
     op.execute(f"""
         CREATE TABLE {schema}learnings (
@@ -57,18 +155,60 @@ def upgrade() -> None:
 
     # Indexes for learnings
     op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
-    op.execute(f"""
-        CREATE INDEX idx_learnings_embedding ON {schema}learnings
-        USING hnsw (embedding vector_cosine_ops)
-    """)
+
+    # Create vector index based on detected extension
+    if vector_ext == "pgvectorscale":
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING diskann (embedding vector_cosine_ops)
+            WITH (num_neighbors = 50)
+        """)
+    elif vector_ext == "pg_diskann":
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING diskann (embedding vector_cosine_ops)
+            WITH (max_neighbors = 50)
+        """)
+    elif vector_ext == "vchord":
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING vchordrq (embedding vector_l2_ops)
+        """)
+    else:  # pgvector
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING hnsw (embedding vector_cosine_ops)
+        """)
+
     op.execute(f"CREATE INDEX idx_learnings_tags ON {schema}learnings USING GIN(tags)")
 
     # Full-text search for learnings
-    op.execute(f"""
-        ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
-        GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
-    """)
-    op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")
+    if text_search_ext == "vchord":
+        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
+        # Note: vchord_bm25 extension creates types in bm25_catalog schema
+        op.execute(f"""
+            ALTER TABLE {schema}learnings ADD COLUMN search_vector bm25_catalog.bm25vector
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_learnings_text_search ON {schema}learnings
+            USING bm25 (search_vector bm25_catalog.bm25_ops)
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
+        op.execute(f"""
+            ALTER TABLE {schema}learnings ADD COLUMN search_vector TEXT
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_learnings_text_search ON {schema}learnings
+            USING bm25(text) WITH (text_config='english')
+        """)
+    else:  # native
+        # Native PostgreSQL: tsvector with automatic generation
+        op.execute(f"""
+            ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
+        """)
+        op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")
 
     # 2. Create pinned_reflections table
     op.execute(f"""
@@ -94,21 +234,64 @@ def upgrade() -> None:
 
     # Indexes for pinned_reflections
     op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
-    op.execute(f"""
-        CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
-        USING hnsw (embedding vector_cosine_ops)
-    """)
+
+    # Create vector index based on detected extension
+    if vector_ext == "pgvectorscale":
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING diskann (embedding vector_cosine_ops)
+            WITH (num_neighbors = 50)
+        """)
+    elif vector_ext == "pg_diskann":
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING diskann (embedding vector_cosine_ops)
+            WITH (max_neighbors = 50)
+        """)
+    elif vector_ext == "vchord":
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING vchordrq (embedding vector_l2_ops)
+        """)
+    else:  # pgvector
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING hnsw (embedding vector_cosine_ops)
+        """)
+
     op.execute(f"CREATE INDEX idx_pinned_reflections_tags ON {schema}pinned_reflections USING GIN(tags)")
 
     # Full-text search for pinned_reflections
-    op.execute(f"""
-        ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
-        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
-    """)
-    op.execute(f"""
-        CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
-        USING gin(search_vector)
-    """)
+    if text_search_ext == "vchord":
+        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT/UPDATE)
+        # Note: vchord_bm25 extension creates types in bm25_catalog schema
+        op.execute(f"""
+            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector bm25_catalog.bm25vector
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+            USING bm25 (search_vector bm25_catalog.bm25_ops)
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
+        op.execute(f"""
+            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector TEXT
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+            USING bm25(content)
+            WITH (text_config='english')
+        """)
+    else:  # native
+        # Native PostgreSQL: tsvector with automatic generation
+        op.execute(f"""
+            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+            USING gin(search_vector)
+        """)
 
     # 3. Add consolidation tracking columns to banks table
     op.execute(f"""

hindsight-api/hindsight_api/alembic/versions/x9s0t1u2v3w4_add_bank_config_column.py

@@ -0,0 +1,64 @@
+"""Add config JSONB column to banks table for hierarchical configuration
+
+Revision ID: x9s0t1u2v3w4
+Revises: w8r9s0t1u2v3
+Create Date: 2026-02-09
+
+This migration adds a `config` JSONB column to the banks table to support
+per-bank configuration overrides. This enables hierarchical configuration where:
+- Global config is loaded from environment variables
+- Tenant config is provided via TenantExtension
+- Bank config overrides are stored in banks.config JSONB column
+
+The config column stores overrides for hierarchical fields (LLM settings,
+retention parameters, retrieval settings, etc.) in Python field name format.
+"""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import context, op
+from sqlalchemy.dialects.postgresql import JSONB
+
+revision: str = "x9s0t1u2v3w4"
+down_revision: str | Sequence[str] | None = "w8r9s0t1u2v3"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add config JSONB column to banks table with GIN index."""
+    schema = _get_schema_prefix()
+
+    # Add config column to banks table
+    op.execute(f"""
+        ALTER TABLE {schema}banks
+        ADD COLUMN config JSONB NOT NULL DEFAULT '{{}}'::jsonb
+    """)
+
+    # Add GIN index for efficient JSONB queries
+    op.execute(f"""
+        CREATE INDEX idx_banks_config
+        ON {schema}banks
+        USING gin(config)
+    """)
+
+
+def downgrade() -> None:
+    """Remove config column and index from banks table."""
+    schema = _get_schema_prefix()
+
+    # Drop index first
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_banks_config")
+
+    # Drop column
+    op.execute(f"""
+        ALTER TABLE {schema}banks
+        DROP COLUMN IF EXISTS config
+    """)

hindsight-api/hindsight_api/alembic/versions/y0t1u2v3w4x5_add_result_metadata_gin_index.py

@@ -0,0 +1,49 @@
+"""Add GIN index on async_operations.result_metadata for parent_operation_id queries
+
+Revision ID: y0t1u2v3w4x5
+Revises: x9s0t1u2v3w4
+Create Date: 2026-02-13
+
+This migration adds a GIN index on the result_metadata JSONB column in the
+async_operations table to support efficient queries for child operations by
+parent_operation_id.
+
+The index enables fast lookups when querying for child operations:
+  SELECT * FROM async_operations
+  WHERE result_metadata::jsonb @> '{"parent_operation_id": "uuid"}'::jsonb
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "y0t1u2v3w4x5"
+down_revision: str | Sequence[str] | None = "x9s0t1u2v3w4"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add GIN index on result_metadata for efficient parent_operation_id queries."""
+    schema = _get_schema_prefix()
+
+    # Add GIN index for JSONB containment queries (@> operator)
+    op.execute(f"""
+        CREATE INDEX idx_async_operations_result_metadata
+        ON {schema}async_operations
+        USING gin(result_metadata)
+    """)
+
+
+def downgrade() -> None:
+    """Remove GIN index on result_metadata."""
+    schema = _get_schema_prefix()
+
+    # Drop index
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_result_metadata")

hindsight-api/hindsight_api/alembic/versions/z1u2v3w4x5y6_add_observation_tags_to_memory_units.py

@@ -0,0 +1,35 @@
+"""Add observation_scopes column to memory_units table
+
+Revision ID: z1u2v3w4x5y6
+Revises: a1b2c3d4e5f6
+Create Date: 2026-02-25
+
+Adds observation_scopes JSONB column to memory_units to control how observations
+are scoped during consolidation. Accepts "per_tag", "combined", or an explicit
+list of tag-set lists for custom multi-pass consolidation.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "z1u2v3w4x5y6"
+down_revision: str | Sequence[str] | None = "a1b2c3d4e5f6"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    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}memory_units ADD COLUMN IF NOT EXISTS observation_scopes JSONB")
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS observation_scopes")

hindsight-api/hindsight_api/api/__init__.py

@@ -6,7 +6,6 @@ Provides both HTTP REST API and MCP (Model Context Protocol) server.
 
 import logging
 from contextlib import asynccontextmanager
-from typing import Optional
 
 from fastapi import FastAPI
 
@@ -46,14 +45,14 @@ def create_app(
         # Both HTTP and MCP
         app = create_app(memory, mcp_api_enabled=True)
     """
-    mcp_app = None
+    mcp_servers = None
 
-    # Create MCP app first if enabled (we need its lifespan for chaining)
+    # Create MCP servers first if enabled (we need their lifespans for chaining)
     if mcp_api_enabled:
         try:
-            from .mcp import create_mcp_app
+            from .mcp import MCPMiddleware, create_mcp_servers
 
-            mcp_app = create_mcp_app(memory=memory)
+            mcp_servers = create_mcp_servers(memory=memory)
         except ImportError as e:
             logger.error(f"MCP server requested but dependencies not available: {e}")
             logger.error("Install with: pip install hindsight-api[mcp]")
@@ -70,11 +69,9 @@ def create_app(
         app = FastAPI(title="Hindsight API", version="0.0.7")
         logger.info("HTTP REST API disabled")
 
-    # Mount MCP server and chain its lifespan if enabled
-    if mcp_app is not None:
-        # Get both MCP apps' underlying Starlette apps for lifespan access
-        multi_bank_starlette_app = mcp_app.multi_bank_app
-        single_bank_starlette_app = mcp_app.single_bank_app
+    # Add MCP middleware and chain its lifespan if enabled
+    if mcp_servers is not None:
+        multi_bank_server, single_bank_server, multi_bank_starlette_app, single_bank_starlette_app = mcp_servers
 
         # Store the original lifespan
         original_lifespan = app.router.lifespan_context
@@ -94,8 +91,19 @@ def create_app(
         # Replace the app's lifespan with the chained version
         app.router.lifespan_context = chained_lifespan
 
-        # Mount the MCP middleware
-        app.mount(mcp_mount_path, mcp_app)
+        # Add MCP as a wrapping middleware — intercepts /mcp* requests directly,
+        # passes everything else through to the FastAPI app. No Starlette Mount
+        # means no 307 redirect for /mcp (no trailing slash).
+        app.add_middleware(
+            MCPMiddleware,
+            memory=memory,
+            prefix=mcp_mount_path,
+            multi_bank_app=multi_bank_starlette_app,
+            single_bank_app=single_bank_starlette_app,
+            multi_bank_server=multi_bank_server,
+            single_bank_server=single_bank_server,
+        )
+
         logger.info(f"MCP server enabled at {mcp_mount_path}/")
 
     return app

hindsight-api/hindsight_api/api/mcp.py

@@ -8,12 +8,48 @@ from contextvars import ContextVar
 from fastmcp import FastMCP
 
 from hindsight_api import MemoryEngine
+from hindsight_api.config import _get_raw_config
 from hindsight_api.engine.memory_engine import _current_schema
 from hindsight_api.extensions import MCPExtension, load_extension
 from hindsight_api.extensions.tenant import AuthenticationError
 from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
 from hindsight_api.models import RequestContext
 
+# All tools available in the system (explicit list — no wildcards)
+_ALL_TOOLS: frozenset[str] = frozenset(
+    {
+        "retain",
+        "recall",
+        "reflect",
+        "list_banks",
+        "create_bank",
+        "list_mental_models",
+        "get_mental_model",
+        "create_mental_model",
+        "update_mental_model",
+        "delete_mental_model",
+        "refresh_mental_model",
+        "list_directives",
+        "create_directive",
+        "delete_directive",
+        "list_memories",
+        "get_memory",
+        "delete_memory",
+        "list_documents",
+        "get_document",
+        "delete_document",
+        "list_operations",
+        "get_operation",
+        "cancel_operation",
+        "list_tags",
+        "get_bank",
+        "get_bank_stats",
+        "update_bank",
+        "delete_bank",
+        "clear_memories",
+    }
+)
+
 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
 _log_level_map = {
@@ -43,6 +79,10 @@ _current_bank_id: ContextVar[str | None] = ContextVar("current_bank_id", default
 # Context variable to hold the current API key (for tenant auth propagation)
 _current_api_key: ContextVar[str | None] = ContextVar("current_api_key", default=None)
 
+# Context variables for tenant_id and api_key_id (set by authenticate, used by usage metering)
+_current_tenant_id: ContextVar[str | None] = ContextVar("current_tenant_id", default=None)
+_current_api_key_id: ContextVar[str | None] = ContextVar("current_api_key_id", default=None)
+
 
 def get_current_bank_id() -> str | None:
     """Get the current bank_id from context."""
@@ -54,6 +94,16 @@ def get_current_api_key() -> str | None:
     return _current_api_key.get()
 
 
+def get_current_tenant_id() -> str | None:
+    """Get the current tenant_id from context."""
+    return _current_tenant_id.get()
+
+
+def get_current_api_key_id() -> str | None:
+    """Get the current api_key_id from context."""
+    return _current_api_key_id.get()
+
+
 def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
     """
     Create and configure the Hindsight MCP server.
@@ -64,18 +114,58 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
                    If False, only expose bank-scoped tools without bank_id parameters.
 
     Returns:
-        Configured FastMCP server instance with stateless_http enabled
+        Configured FastMCP server instance
     """
-    # Use stateless_http=True for Claude Code compatibility
-    mcp = FastMCP("hindsight-mcp-server", stateless_http=True)
+    mcp = FastMCP("hindsight-mcp-server")
+
+    global_config = _get_raw_config()
+
+    # Tools available for this mode (multi-bank exposes all tools; single-bank excludes bank-management tools)
+    _SINGLE_BANK_TOOLS: frozenset[str] = frozenset(
+        {
+            "retain",
+            "recall",
+            "reflect",
+            "list_mental_models",
+            "get_mental_model",
+            "create_mental_model",
+            "update_mental_model",
+            "delete_mental_model",
+            "refresh_mental_model",
+            "list_directives",
+            "create_directive",
+            "delete_directive",
+            "list_memories",
+            "get_memory",
+            "delete_memory",
+            "list_documents",
+            "get_document",
+            "delete_document",
+            "list_operations",
+            "get_operation",
+            "cancel_operation",
+            "list_tags",
+            "get_bank",
+            "update_bank",
+            "delete_bank",
+            "clear_memories",
+        }
+    )
+    base_tools: frozenset[str] | None = None if multi_bank else _SINGLE_BANK_TOOLS
+
+    # Apply global mcp_enabled_tools filter (env-level allowlist)
+    if global_config.mcp_enabled_tools is not None:
+        allowed = frozenset(global_config.mcp_enabled_tools)
+        base_tools = (base_tools if base_tools is not None else _ALL_TOOLS) & allowed
 
     # Configure and register tools using shared module
     config = MCPToolsConfig(
         bank_id_resolver=get_current_bank_id,
         api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
+        tenant_id_resolver=get_current_tenant_id,  # Propagate tenant_id for usage metering
+        api_key_id_resolver=get_current_api_key_id,  # Propagate api_key_id for usage metering
         include_bank_id_param=multi_bank,
-        tools=None if multi_bank else {"retain", "recall", "reflect"},  # Scoped tools for single-bank mode
-        retain_fire_and_forget=False,  # HTTP MCP supports sync/async modes
+        tools=base_tools,
     )
 
     register_mcp_tools(mcp, memory, config)
@@ -86,11 +176,44 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
         logger.info(f"Loading MCP extension: {mcp_extension.__class__.__name__}")
         mcp_extension.register_tools(mcp, memory)
 
+    # Make all tools tolerant of extra arguments from LLMs (e.g., "explanation")
+    _make_tools_tolerant(mcp)
+
     return mcp
 
 
+def _make_tools_tolerant(mcp: FastMCP) -> None:
+    """Wrap all tool run methods to strip unknown arguments before validation.
+
+    LLMs frequently add extra fields like "explanation" or "reasoning" to tool calls.
+    FastMCP's Pydantic TypeAdapter rejects these with "Unexpected keyword argument".
+    This wraps each tool's run() to filter arguments to only known parameters.
+    """
+    try:
+        for name, tool in mcp._tool_manager._tools.items():
+            if hasattr(tool, "parameters") and tool.parameters:
+                allowed = set(tool.parameters.get("properties", {}).keys())
+                original_run = tool.run
+
+                async def _tolerant_run(arguments, _allowed=allowed, _orig=original_run):
+                    extra_keys = set(arguments.keys()) - _allowed
+                    if extra_keys:
+                        logger.debug(f"Stripping unknown arguments from tool call: {extra_keys}")
+                        arguments = {k: v for k, v in arguments.items() if k in _allowed}
+                    return await _orig(arguments)
+
+                # FunctionTool is a Pydantic model with extra='forbid', so use
+                # object.__setattr__ to bypass Pydantic's setter validation.
+                object.__setattr__(tool, "run", _tolerant_run)
+    except (AttributeError, KeyError) as e:
+        logger.warning(f"Could not make tools tolerant of extra arguments: {e}")
+
+
 class MCPMiddleware:
-    """ASGI middleware that handles authentication and routes to appropriate MCP server.
+    """ASGI middleware that intercepts MCP requests and routes to appropriate MCP server.
+
+    This middleware wraps the main FastAPI app and intercepts requests matching the
+    configured prefix (default: /mcp). Non-MCP requests pass through to the inner app.
 
     Authentication:
         1. If HINDSIGHT_API_MCP_AUTH_TOKEN is set (legacy), validates against that token
@@ -111,6 +234,11 @@ class MCPMiddleware:
        - No bank management tools (list_banks, create_bank)
        - Recommended for agent isolation
 
+    Bank ID resolution priority:
+        1. URL path (e.g., /mcp/{bank_id}/) → single-bank mode
+        2. X-Bank-Id header → multi-bank mode
+        3. HINDSIGHT_MCP_BANK_ID env var → multi-bank mode (default: "default")
+
     Examples:
         # Single-bank mode (recommended for agent isolation)
         claude mcp add --transport http my-agent http://localhost:8888/mcp/my-agent-bank/ \\
@@ -121,27 +249,33 @@ class MCPMiddleware:
             --header "X-Bank-Id: my-bank" --header "Authorization: Bearer <token>"
     """
 
-    def __init__(self, app, memory: MemoryEngine):
+    def __init__(
+        self,
+        app,
+        memory: MemoryEngine,
+        prefix: str = "/mcp",
+        multi_bank_app=None,
+        single_bank_app=None,
+        multi_bank_server=None,
+        single_bank_server=None,
+    ):
         self.app = app
+        self.prefix = prefix
         self.memory = memory
         self.tenant_extension = memory._tenant_extension
 
-        # Create two server instances:
-        # 1. Multi-bank server (for /mcp/ root endpoint)
-        self.multi_bank_server = create_mcp_server(memory, multi_bank=True)
-        self.multi_bank_app = self.multi_bank_server.http_app(path="/")
-
-        # 2. Single-bank server (for /mcp/{bank_id}/ endpoints)
-        self.single_bank_server = create_mcp_server(memory, multi_bank=False)
-        self.single_bank_app = self.single_bank_server.http_app(path="/")
-
-        # Backward compatibility: expose multi_bank_app as mcp_app
-        self.mcp_app = self.multi_bank_app
-
-        # Expose the lifespan for the parent app to chain (use multi-bank as default)
-        self.lifespan = (
-            self.multi_bank_app.lifespan_handler if hasattr(self.multi_bank_app, "lifespan_handler") else None
-        )
+        if multi_bank_app and single_bank_app:
+            # Pre-created servers (used when called via add_middleware from create_app)
+            self.multi_bank_app = multi_bank_app
+            self.single_bank_app = single_bank_app
+            self.multi_bank_server = multi_bank_server
+            self.single_bank_server = single_bank_server
+        else:
+            # Create servers internally (for direct construction / tests)
+            self.multi_bank_server = create_mcp_server(memory, multi_bank=True)
+            self.multi_bank_app = self.multi_bank_server.http_app(path="/", stateless_http=True)
+            self.single_bank_server = create_mcp_server(memory, multi_bank=False)
+            self.single_bank_app = self.single_bank_server.http_app(path="/", stateless_http=True)
 
     def _get_header(self, scope: dict, name: str) -> str | None:
         """Extract a header value from ASGI scope."""
@@ -153,9 +287,20 @@ class MCPMiddleware:
 
     async def __call__(self, scope, receive, send):
         if scope["type"] != "http":
-            await self.multi_bank_app(scope, receive, send)
+            await self.app(scope, receive, send)
             return
 
+        path = scope.get("path", "")
+
+        # Check if this is an MCP request (matches prefix)
+        if not (path == self.prefix or path.startswith(self.prefix + "/")):
+            # Not an MCP request — pass through to the inner app
+            await self.app(scope, receive, send)
+            return
+
+        # Strip prefix from path
+        path = path[len(self.prefix) :] or "/"
+
         # Extract auth token from header (for tenant auth propagation)
         auth_header = self._get_header(scope, "Authorization")
         auth_token: str | None = None
@@ -165,6 +310,8 @@ class MCPMiddleware:
 
         # Authenticate: check legacy MCP_AUTH_TOKEN first, then TenantExtension
         tenant_context = None
+        auth_tenant_id: str | None = None
+        auth_api_key_id: str | None = None
         if MCP_AUTH_TOKEN:
             # Legacy authentication mode - validate against static token
             if not auth_token:
@@ -178,7 +325,11 @@ class MCPMiddleware:
         else:
             # Use TenantExtension.authenticate_mcp() for auth
             try:
-                tenant_context = await self.tenant_extension.authenticate_mcp(RequestContext(api_key=auth_token))
+                auth_context = RequestContext(api_key=auth_token)
+                tenant_context = await self.tenant_extension.authenticate_mcp(auth_context)
+                # Capture tenant_id and api_key_id set by authenticate() for usage metering
+                auth_tenant_id = auth_context.tenant_id
+                auth_api_key_id = auth_context.api_key_id
             except AuthenticationError as e:
                 await self._send_error(send, 401, str(e))
                 return
@@ -188,41 +339,25 @@ class MCPMiddleware:
             _current_schema.set(tenant_context.schema_name) if tenant_context and tenant_context.schema_name else None
         )
 
-        path = scope.get("path", "")
-
-        # Strip any mount prefix (e.g., /mcp) that FastAPI might not have stripped
-        root_path = scope.get("root_path", "")
-        if root_path and path.startswith(root_path):
-            path = path[len(root_path) :] or "/"
-
-        # Also handle case where mount path wasn't stripped (e.g., /mcp/...)
-        if path.startswith("/mcp/"):
-            path = path[4:]  # Remove /mcp prefix
-        elif path == "/mcp":
-            path = "/"
-
-        # Ensure path has leading slash (needed after stripping mount path)
-        if path and not path.startswith("/"):
-            path = "/" + path
-
-        # Try to get bank_id from header first (for Claude Code compatibility)
-        bank_id = self._get_header(scope, "X-Bank-Id")
+        # Resolve bank_id: path takes priority over header.
+        # Path = user's explicit connection endpoint (e.g., /mcp/my-bank/).
+        # X-Bank-Id header = per-request override for multi-bank mode only.
+        bank_id = None
         bank_id_from_path = False
-
-        # MCP endpoint paths that should not be treated as bank_ids
-        MCP_ENDPOINTS = {"sse", "messages"}
-
-        # If no header, try to extract from path: /{bank_id}/...
         new_path = path
-        if not bank_id and path.startswith("/") and len(path) > 1:
+
+        # First, try to extract from path: /{bank_id}/...
+        if path.startswith("/") and len(path) > 1:
             parts = path[1:].split("/", 1)
-            # Don't treat MCP endpoints as bank_ids
-            if parts[0] and parts[0] not in MCP_ENDPOINTS:
-                # First segment looks like a bank_id
+            if parts[0]:
                 bank_id = parts[0]
                 bank_id_from_path = True
                 new_path = "/" + parts[1] if len(parts) > 1 else "/"
 
+        # If no path-based bank_id, try X-Bank-Id header (multi-bank mode)
+        if not bank_id:
+            bank_id = self._get_header(scope, "X-Bank-Id")
+
         # Fall back to default bank_id
         if not bank_id:
             bank_id = DEFAULT_BANK_ID
@@ -233,19 +368,32 @@ class MCPMiddleware:
         # - Header/env bank_id → multi-bank app (bank_id param, all tools)
         target_app = self.single_bank_app if bank_id_from_path else self.multi_bank_app
 
-        # Set bank_id and api_key context
+        # Set bank_id, api_key, tenant_id, and api_key_id context
         bank_id_token = _current_bank_id.set(bank_id)
         # Store the auth token for tenant extension to validate
         api_key_token = _current_api_key.set(auth_token) if auth_token else None
+        # Store tenant_id and api_key_id from authentication for usage metering
+        tenant_id_token = _current_tenant_id.set(auth_tenant_id) if auth_tenant_id else None
+        api_key_id_token = _current_api_key_id.set(auth_api_key_id) if auth_api_key_id else None
         try:
             new_scope = scope.copy()
             new_scope["path"] = new_path
             # Clear root_path since we're passing directly to the app
             new_scope["root_path"] = ""
 
-            # Wrap send to rewrite the SSE endpoint URL to include bank_id if using path-based routing
+            # 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".
+            is_sse_response = False
+
             async def send_wrapper(message):
-                if message["type"] == "http.response.body" and bank_id_from_path:
+                nonlocal is_sse_response
+                if message["type"] == "http.response.start":
+                    for header_name, header_value in message.get("headers", []):
+                        if header_name == b"content-type" and b"text/event-stream" in header_value:
+                            is_sse_response = True
+                            break
+                if message["type"] == "http.response.body" and bank_id_from_path and is_sse_response:
                     body = message.get("body", b"")
                     if body and b"/messages" in body:
                         # Rewrite /messages to /{bank_id}/messages in SSE endpoint event
@@ -258,6 +406,10 @@ class MCPMiddleware:
             _current_bank_id.reset(bank_id_token)
             if api_key_token is not None:
                 _current_api_key.reset(api_key_token)
+            if tenant_id_token is not None:
+                _current_tenant_id.reset(tenant_id_token)
+            if api_key_id_token is not None:
+                _current_api_key_id.reset(api_key_id_token)
             if schema_token is not None:
                 _current_schema.reset(schema_token)
 
@@ -279,30 +431,19 @@ class MCPMiddleware:
         )
 
 
-def create_mcp_app(memory: MemoryEngine):
-    """
-    Create an ASGI app that handles MCP requests with dynamic tool exposure.
+def create_mcp_servers(memory: MemoryEngine):
+    """Create multi-bank and single-bank MCP servers and their Starlette apps.
 
-    Authentication:
-        Uses the TenantExtension from the MemoryEngine (same auth as REST API).
-
-    Two modes based on URL structure:
-
-    1. Single-bank mode (recommended for agent isolation):
-       - URL: /mcp/{bank_id}/
-       - Tools: retain, recall, reflect (no bank_id parameter)
-       - Example: claude mcp add --transport http my-agent http://localhost:8888/mcp/my-agent-bank/
-
-    2. Multi-bank mode (for cross-bank operations):
-       - URL: /mcp/
-       - Tools: retain, recall, reflect, list_banks, create_bank (all with bank_id parameter)
-       - Bank ID from: X-Bank-Id header or HINDSIGHT_MCP_BANK_ID env var (default: "default")
-       - Example: claude mcp add --transport http hindsight http://localhost:8888/mcp --header "X-Bank-Id: my-bank"
-
-    Args:
-        memory: MemoryEngine instance
+    Returns the servers and apps separately so lifespans can be chained before
+    the middleware wraps the main app.
 
     Returns:
-        ASGI application
+        Tuple of (multi_bank_server, single_bank_server, multi_bank_app, single_bank_app)
     """
-    return MCPMiddleware(None, memory)
+    multi_bank_server = create_mcp_server(memory, multi_bank=True)
+    multi_bank_app = multi_bank_server.http_app(path="/", stateless_http=True)
+
+    single_bank_server = create_mcp_server(memory, multi_bank=False)
+    single_bank_app = single_bank_server.http_app(path="/", stateless_http=True)
+
+    return multi_bank_server, single_bank_server, multi_bank_app, single_bank_app

hindsight-api/hindsight_api/banner.py

@@ -86,6 +86,8 @@ def print_startup_info(
     reranker_provider: str,
     mcp_enabled: bool = False,
     version: str | None = None,
+    vector_extension: str | None = None,
+    text_search_extension: str | None = None,
 ):
     """Print styled startup information."""
     print(color_start("Starting Hindsight API..."))
@@ -96,6 +98,8 @@ def print_startup_info(
     print(f"  {dim('LLM:')} {color(f'{llm_provider} / {llm_model}', 0.6)}")
     print(f"  {dim('Embeddings:')} {color(embeddings_provider, 0.8)}")
     print(f"  {dim('Reranker:')} {color(reranker_provider, 1.0)}")
+    extensions = f"{vector_extension or 'default'} (vector) / {text_search_extension or 'default'} (text)"
+    print(f"  {dim('Extensions:')} {color(extensions, 0.4)}")
     if mcp_enabled:
         print(f"  {dim('MCP:')} {color_end('enabled at /mcp')}")
     print()

hindsight-api/hindsight_api/config.py

@@ -8,8 +8,9 @@ import json
 import logging
 import os
 import sys
-from dataclasses import dataclass
+from dataclasses import dataclass, field, fields
 from datetime import datetime, timezone
+from typing import Any
 
 from dotenv import find_dotenv, load_dotenv
 
@@ -18,6 +19,103 @@ load_dotenv(find_dotenv(usecwd=True), override=True)
 
 logger = logging.getLogger(__name__)
 
+
+class ConfigFieldAccessError(AttributeError):
+    """Raised when trying to access a bank-configurable field from global config."""
+
+    pass
+
+
+class StaticConfigProxy:
+    """
+    Proxy that wraps HindsightConfig and only allows access to static (non-configurable) fields.
+
+    Raises ConfigFieldAccessError when trying to access configurable fields that vary per-bank.
+    Forces developers to use get_resolved_config(bank_id, context) for bank-specific settings.
+    """
+
+    def __init__(self, config: "HindsightConfig"):
+        object.__setattr__(self, "_config", config)
+        object.__setattr__(self, "_configurable_fields", HindsightConfig.get_configurable_fields())
+
+    def __getattribute__(self, name: str):
+        if name.startswith("_"):
+            return object.__getattribute__(self, name)
+
+        configurable_fields = object.__getattribute__(self, "_configurable_fields")
+        if name in configurable_fields:
+            raise ConfigFieldAccessError(
+                f"Field '{name}' is bank-configurable and cannot be accessed from global config. "
+                f"Use ConfigResolver.resolve_full_config(bank_id, context) to get bank-specific config. "
+                f"This prevents accidentally using global defaults when bank-specific overrides exist."
+            )
+
+        config = object.__getattribute__(self, "_config")
+        return getattr(config, name)
+
+    def __setattr__(self, name: str, value):
+        raise AttributeError("Config is read-only. Modifications must go through ConfigResolver.")
+
+
+# Configuration field markers for hierarchical configuration
+def hierarchical(default_value):
+    """
+    Mark a config field as hierarchical (can be overridden per-tenant/bank).
+
+    Hierarchical fields can be customized at the tenant or bank level via database
+    configuration. Examples: LLM settings, retention parameters, retrieval settings.
+    """
+    return field(default=default_value, metadata={"hierarchical": True})
+
+
+def static(default_value):
+    """
+    Mark a config field as static (server-level only, cannot be overridden).
+
+    Static fields are infrastructure-level settings that affect the entire server
+    and cannot vary per tenant or bank. Examples: database URL, API port, worker settings.
+    """
+    return field(default=default_value, metadata={"hierarchical": False})
+
+
+# Configuration key normalization utilities
+def normalize_config_key(key: str) -> str:
+    """
+    Convert environment variable format to Python field name format.
+
+    Examples:
+        HINDSIGHT_API_LLM_PROVIDER -> llm_provider
+        LLM_MODEL -> llm_model
+        llm_model -> llm_model (already normalized)
+
+    Args:
+        key: Environment variable name or Python field name
+
+    Returns:
+        Normalized Python field name (lowercase snake_case)
+    """
+    if key.startswith("HINDSIGHT_API_"):
+        key = key[len("HINDSIGHT_API_") :]
+    return key.lower()
+
+
+def normalize_config_dict(config: dict[str, Any]) -> dict[str, Any]:
+    """
+    Normalize all keys in a config dict to Python field names.
+
+    Allows users to provide config overrides in either format:
+    - Python field format: {"llm_provider": "openai"}
+    - Env var format: {"HINDSIGHT_API_LLM_PROVIDER": "openai"}
+
+    Args:
+        config: Dict with env var or Python field names as keys
+
+    Returns:
+        Dict with all keys normalized to Python field names
+    """
+    return {normalize_config_key(k): v for k, v in config.items()}
+
+
 # Environment variable names
 ENV_DATABASE_URL = "HINDSIGHT_API_DATABASE_URL"
 ENV_DATABASE_SCHEMA = "HINDSIGHT_API_DATABASE_SCHEMA"
@@ -31,6 +129,11 @@ ENV_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_LLM_INITIAL_BACKOFF"
 ENV_LLM_MAX_BACKOFF = "HINDSIGHT_API_LLM_MAX_BACKOFF"
 ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
 ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"
+ENV_LLM_OPENAI_SERVICE_TIER = "HINDSIGHT_API_LLM_OPENAI_SERVICE_TIER"
+
+# Defaults for service tiers
+DEFAULT_LLM_GROQ_SERVICE_TIER = "auto"  # "on_demand", "flex", or "auto"
+DEFAULT_LLM_OPENAI_SERVICE_TIER = None  # None (default) or "flex" (50% cheaper)
 
 # Per-operation LLM configuration (optional, falls back to global LLM config)
 ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
@@ -66,27 +169,48 @@ ENV_CONSOLIDATION_LLM_TIMEOUT = "HINDSIGHT_API_CONSOLIDATION_LLM_TIMEOUT"
 ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
 ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
 ENV_EMBEDDINGS_LOCAL_FORCE_CPU = "HINDSIGHT_API_EMBEDDINGS_LOCAL_FORCE_CPU"
+ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE"
 ENV_EMBEDDINGS_TEI_URL = "HINDSIGHT_API_EMBEDDINGS_TEI_URL"
 ENV_EMBEDDINGS_OPENAI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"
 ENV_EMBEDDINGS_OPENAI_MODEL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL"
 ENV_EMBEDDINGS_OPENAI_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_BASE_URL"
 
-ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
+# Cohere configuration (separate for embeddings and reranker)
+ENV_EMBEDDINGS_COHERE_API_KEY = "HINDSIGHT_API_EMBEDDINGS_COHERE_API_KEY"
 ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
 ENV_EMBEDDINGS_COHERE_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_COHERE_BASE_URL"
+ENV_RERANKER_COHERE_API_KEY = "HINDSIGHT_API_RERANKER_COHERE_API_KEY"
 ENV_RERANKER_COHERE_MODEL = "HINDSIGHT_API_RERANKER_COHERE_MODEL"
 ENV_RERANKER_COHERE_BASE_URL = "HINDSIGHT_API_RERANKER_COHERE_BASE_URL"
 
-# LiteLLM gateway configuration (for embeddings and reranker via LiteLLM proxy)
+# Deprecated: Legacy shared Cohere API key (for backward compatibility)
+ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
+
+# LiteLLM configuration (separate for embeddings and reranker)
+ENV_EMBEDDINGS_LITELLM_API_BASE = "HINDSIGHT_API_EMBEDDINGS_LITELLM_API_BASE"
+ENV_EMBEDDINGS_LITELLM_API_KEY = "HINDSIGHT_API_EMBEDDINGS_LITELLM_API_KEY"
+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"
+
+# LiteLLM SDK configuration (direct API access, no proxy needed)
+ENV_EMBEDDINGS_LITELLM_SDK_API_KEY = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_KEY"
+ENV_EMBEDDINGS_LITELLM_SDK_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_MODEL"
+ENV_EMBEDDINGS_LITELLM_SDK_API_BASE = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_BASE"
+ENV_RERANKER_LITELLM_SDK_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY"
+ENV_RERANKER_LITELLM_SDK_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL"
+ENV_RERANKER_LITELLM_SDK_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_BASE"
+
+# Deprecated: Legacy shared LiteLLM config (for backward compatibility)
 ENV_LITELLM_API_BASE = "HINDSIGHT_API_LITELLM_API_BASE"
 ENV_LITELLM_API_KEY = "HINDSIGHT_API_LITELLM_API_KEY"
-ENV_EMBEDDINGS_LITELLM_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL"
-ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"
 
 ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
 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_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"
@@ -94,20 +218,35 @@ ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
 ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
 ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"
 
+# ZeroEntropy configuration (reranker only)
+ENV_RERANKER_ZEROENTROPY_API_KEY = "HINDSIGHT_API_RERANKER_ZEROENTROPY_API_KEY"
+ENV_RERANKER_ZEROENTROPY_MODEL = "HINDSIGHT_API_RERANKER_ZEROENTROPY_MODEL"
+
+ENV_VECTOR_EXTENSION = "HINDSIGHT_API_VECTOR_EXTENSION"
+ENV_TEXT_SEARCH_EXTENSION = "HINDSIGHT_API_TEXT_SEARCH_EXTENSION"
+
 ENV_HOST = "HINDSIGHT_API_HOST"
 ENV_PORT = "HINDSIGHT_API_PORT"
+ENV_BASE_PATH = "HINDSIGHT_API_BASE_PATH"
 ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
 ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
 ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
+ENV_MCP_ENABLED_TOOLS = "HINDSIGHT_API_MCP_ENABLED_TOOLS"
+ENV_ENABLE_BANK_CONFIG_API = "HINDSIGHT_API_ENABLE_BANK_CONFIG_API"
 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_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
-ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
 ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"
 
+# OpenTelemetry tracing configuration
+ENV_OTEL_TRACES_ENABLED = "HINDSIGHT_API_OTEL_TRACES_ENABLED"
+ENV_OTEL_EXPORTER_OTLP_ENDPOINT = "HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT"
+ENV_OTEL_EXPORTER_OTLP_HEADERS = "HINDSIGHT_API_OTEL_EXPORTER_OTLP_HEADERS"
+ENV_OTEL_SERVICE_NAME = "HINDSIGHT_API_OTEL_SERVICE_NAME"
+ENV_OTEL_DEPLOYMENT_ENVIRONMENT = "HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT"
+
 # Vertex AI configuration
 ENV_LLM_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"
 ENV_LLM_VERTEXAI_REGION = "HINDSIGHT_API_LLM_VERTEXAI_REGION"
@@ -118,12 +257,38 @@ ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
 ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
 ENV_RETAIN_EXTRACT_CAUSAL_LINKS = "HINDSIGHT_API_RETAIN_EXTRACT_CAUSAL_LINKS"
 ENV_RETAIN_EXTRACTION_MODE = "HINDSIGHT_API_RETAIN_EXTRACTION_MODE"
+ENV_RETAIN_MISSION = "HINDSIGHT_API_RETAIN_MISSION"
 ENV_RETAIN_CUSTOM_INSTRUCTIONS = "HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS"
+ENV_RETAIN_BATCH_TOKENS = "HINDSIGHT_API_RETAIN_BATCH_TOKENS"
+ENV_RETAIN_BATCH_ENABLED = "HINDSIGHT_API_RETAIN_BATCH_ENABLED"
+ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS = "HINDSIGHT_API_RETAIN_BATCH_POLL_INTERVAL_SECONDS"
+
+# File storage configuration
+ENV_FILE_STORAGE_TYPE = "HINDSIGHT_API_FILE_STORAGE_TYPE"
+ENV_FILE_STORAGE_S3_BUCKET = "HINDSIGHT_API_FILE_STORAGE_S3_BUCKET"
+ENV_FILE_STORAGE_S3_REGION = "HINDSIGHT_API_FILE_STORAGE_S3_REGION"
+ENV_FILE_STORAGE_S3_ENDPOINT = "HINDSIGHT_API_FILE_STORAGE_S3_ENDPOINT"
+ENV_FILE_STORAGE_S3_ACCESS_KEY_ID = "HINDSIGHT_API_FILE_STORAGE_S3_ACCESS_KEY_ID"
+ENV_FILE_STORAGE_S3_SECRET_ACCESS_KEY = "HINDSIGHT_API_FILE_STORAGE_S3_SECRET_ACCESS_KEY"
+ENV_FILE_STORAGE_GCS_BUCKET = "HINDSIGHT_API_FILE_STORAGE_GCS_BUCKET"
+ENV_FILE_STORAGE_GCS_SERVICE_ACCOUNT_KEY = "HINDSIGHT_API_FILE_STORAGE_GCS_SERVICE_ACCOUNT_KEY"
+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_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"
+ENV_FILE_CONVERSION_MAX_BATCH_SIZE = "HINDSIGHT_API_FILE_CONVERSION_MAX_BATCH_SIZE"
+ENV_ENABLE_FILE_UPLOAD_API = "HINDSIGHT_API_ENABLE_FILE_UPLOAD_API"
+ENV_FILE_DELETE_AFTER_RETAIN = "HINDSIGHT_API_FILE_DELETE_AFTER_RETAIN"
 
 # Observations settings (consolidated knowledge from facts)
 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_OBSERVATIONS_MISSION = "HINDSIGHT_API_OBSERVATIONS_MISSION"
 
 # Optimization flags
 ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
@@ -149,6 +314,13 @@ ENV_WORKER_CONSOLIDATION_MAX_SLOTS = "HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLO
 
 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
+ENV_REFLECT_MAX_CONTEXT_TOKENS = "HINDSIGHT_API_REFLECT_MAX_CONTEXT_TOKENS"
+ENV_REFLECT_MISSION = "HINDSIGHT_API_REFLECT_MISSION"
+
+# Disposition settings
+ENV_DISPOSITION_SKEPTICISM = "HINDSIGHT_API_DISPOSITION_SKEPTICISM"
+ENV_DISPOSITION_LITERALISM = "HINDSIGHT_API_DISPOSITION_LITERALISM"
+ENV_DISPOSITION_EMPATHY = "HINDSIGHT_API_DISPOSITION_EMPATHY"
 
 # Default values
 DEFAULT_DATABASE_URL = "pg0"
@@ -157,18 +329,18 @@ DEFAULT_LLM_PROVIDER = "openai"
 
 # Provider-specific default models
 PROVIDER_DEFAULT_MODELS = {
-    "openai": "o3-mini",
+    "openai": "gpt-4o-mini",
     "anthropic": "claude-haiku-4-5-20251001",
     "gemini": "gemini-2.5-flash",
     "groq": "openai/gpt-oss-120b",
     "ollama": "gemma3:12b",
     "lmstudio": "local-model",
-    "vertexai": "gemini-2.0-flash-001",
+    "vertexai": "google/gemini-2.5-flash-lite",
     "openai-codex": "gpt-5.2-codex",
     "claude-code": "claude-sonnet-4-5-20250929",
     "mock": "mock-model",
 }
-DEFAULT_LLM_MODEL = "o3-mini"  # Fallback if provider not in table
+DEFAULT_LLM_MODEL = "gpt-4o-mini"  # Fallback if provider not in table
 DEFAULT_LLM_MAX_CONCURRENT = 32
 DEFAULT_LLM_MAX_RETRIES = 10  # Max retry attempts for LLM API calls
 DEFAULT_LLM_INITIAL_BACKOFF = 1.0  # Initial backoff in seconds for retry exponential backoff
@@ -183,6 +355,7 @@ DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = None  # Optional, uses ADC if not set
 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
 DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU = False  # Force CPU mode for local embeddings (avoids MPS/XPC issues on macOS)
+DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = False  # Security: disabled by default, required for some models
 DEFAULT_EMBEDDINGS_OPENAI_MODEL = "text-embedding-3-small"
 DEFAULT_EMBEDDING_DIMENSION = 384
 
@@ -190,6 +363,9 @@ DEFAULT_RERANKER_PROVIDER = "local"
 DEFAULT_RERANKER_LOCAL_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
 DEFAULT_RERANKER_LOCAL_FORCE_CPU = False  # Force CPU mode for local reranker (avoids MPS/XPC issues on macOS)
 DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound reranking to prevent thrashing
+DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE = (
+    False  # Security: disabled by default, required for some models like jina-reranker-v2
+)
 DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
 DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
 DEFAULT_RERANKER_MAX_CANDIDATES = 300
@@ -199,22 +375,36 @@ DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None  # Use default cache directory
 DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
 DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
 
+DEFAULT_RERANKER_ZEROENTROPY_MODEL = "zerank-2"
+
+# Vector extension (pgvector, vchord, or pgvectorscale)
+DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord", "pgvectorscale"
+
+# Text search extension (native PostgreSQL, vchord BM25, or Timescale pg_textsearch)
+DEFAULT_TEXT_SEARCH_EXTENSION = "native"  # Options: "native", "vchord", "pg_textsearch"
+
 # LiteLLM defaults
 DEFAULT_LITELLM_API_BASE = "http://localhost:4000"
 DEFAULT_EMBEDDINGS_LITELLM_MODEL = "text-embedding-3-small"
 DEFAULT_RERANKER_LITELLM_MODEL = "cohere/rerank-english-v3.0"
 
+# LiteLLM SDK defaults
+DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL = "cohere/embed-english-v3.0"
+DEFAULT_RERANKER_LITELLM_SDK_MODEL = "cohere/rerank-english-v3.0"
+
 DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
+DEFAULT_BASE_PATH = ""  # Empty string = root path
 DEFAULT_LOG_LEVEL = "info"
 DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
 DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
+DEFAULT_MCP_ENABLED_TOOLS: list[str] | None = None  # None = all tools enabled
+DEFAULT_ENABLE_BANK_CONFIG_API = True
 DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp", "bfs"
 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_MCP_LOCAL_BANK_ID = "mcp"
 DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY = 8  # Max concurrent mental model refreshes
 
 # Retain settings
@@ -223,12 +413,26 @@ DEFAULT_RETAIN_CHUNK_SIZE = 3000  # Max chars per chunk for fact extraction
 DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True  # Extract causal links between facts
 DEFAULT_RETAIN_EXTRACTION_MODE = "concise"  # Extraction mode: "concise", "verbose", or "custom"
 RETAIN_EXTRACTION_MODES = ("concise", "verbose", "custom")  # Allowed extraction modes
+DEFAULT_RETAIN_MISSION = None  # Declarative spec of what to retain (injected into any extraction mode)
 DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS = None  # Custom extraction guidelines (only used when mode="custom")
+DEFAULT_RETAIN_BATCH_TOKENS = 10_000  # ~40KB of text  # Max chars per sub-batch for async retain auto-splitting
+DEFAULT_RETAIN_BATCH_ENABLED = False  # Use LLM Batch API for fact extraction (only when async=True)
+DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS = 60  # Batch API polling interval in seconds
+
+# File storage defaults
+DEFAULT_FILE_STORAGE_TYPE = "native"  # PostgreSQL BYTEA storage
+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
+DEFAULT_FILE_DELETE_AFTER_RETAIN = True  # Delete file bytes after retain (saves storage)
 
 # Observations defaults (consolidated knowledge from facts)
 DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
 DEFAULT_CONSOLIDATION_BATCH_SIZE = 50  # Memories to load per batch (internal memory optimization)
-DEFAULT_CONSOLIDATION_MAX_TOKENS = 1024  # Max tokens for recall when finding related observations
+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_OBSERVATIONS_MISSION = None  # Declarative spec of what observations are for this bank
 
 # Database migrations
 DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
@@ -250,6 +454,17 @@ DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS = 2  # Max concurrent consolidation tasks
 
 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response
+DEFAULT_REFLECT_MAX_CONTEXT_TOKENS = 100_000  # Max accumulated context tokens before forcing final prompt
+
+# Disposition defaults (None = not set, fall back to bank DB value or 3)
+DEFAULT_DISPOSITION_SKEPTICISM = None
+DEFAULT_DISPOSITION_LITERALISM = None
+DEFAULT_DISPOSITION_EMPATHY = None
+
+# OpenTelemetry tracing configuration
+DEFAULT_OTEL_TRACES_ENABLED = False  # Disabled by default for backward compatibility
+DEFAULT_OTEL_SERVICE_NAME = "hindsight-api"
+DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT = "development"
 
 # Default MCP tool descriptions (can be customized via env vars)
 DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.
@@ -329,6 +544,8 @@ class HindsightConfig:
     # Database
     database_url: str
     database_schema: str
+    vector_extension: str  # "pgvector" or "vchord"
+    text_search_extension: str  # "native" or "vchord"
 
     # LLM (default, used as fallback for per-operation config)
     llm_provider: str
@@ -340,6 +557,8 @@ class HindsightConfig:
     llm_initial_backoff: float
     llm_max_backoff: float
     llm_timeout: float
+    llm_groq_service_tier: str  # Groq: "on_demand", "flex", or "auto"
+    llm_openai_service_tier: str | None  # OpenAI: None (default) or "flex" (50% cheaper)
 
     # Vertex AI configuration
     llm_vertexai_project_id: str | None
@@ -381,27 +600,50 @@ class HindsightConfig:
     embeddings_provider: str
     embeddings_local_model: str
     embeddings_local_force_cpu: bool
+    embeddings_local_trust_remote_code: bool
     embeddings_tei_url: str | None
     embeddings_openai_base_url: str | None
+    embeddings_cohere_api_key: str | None
+    embeddings_cohere_model: str
     embeddings_cohere_base_url: str | None
+    embeddings_litellm_api_base: str
+    embeddings_litellm_api_key: str | None
+    embeddings_litellm_model: str
+    embeddings_litellm_sdk_api_key: str | None
+    embeddings_litellm_sdk_model: str
+    embeddings_litellm_sdk_api_base: str | None
 
     # Reranker
     reranker_provider: str
     reranker_local_model: str
     reranker_local_force_cpu: bool
     reranker_local_max_concurrent: int
+    reranker_local_trust_remote_code: bool
     reranker_tei_url: str | None
     reranker_tei_batch_size: int
     reranker_tei_max_concurrent: int
     reranker_max_candidates: int
+    reranker_cohere_api_key: str | None
+    reranker_cohere_model: str
     reranker_cohere_base_url: str | None
+    reranker_litellm_api_base: str
+    reranker_litellm_api_key: str | None
+    reranker_litellm_model: str
+    reranker_litellm_sdk_api_key: str | None
+    reranker_litellm_sdk_model: str
+    reranker_litellm_sdk_api_base: str | None
+    reranker_zeroentropy_api_key: str | None
+    reranker_zeroentropy_model: str
 
     # Server
     host: str
     port: int
+    base_path: str
     log_level: str
     log_format: str
     mcp_enabled: bool
+    mcp_enabled_tools: list[str] | None  # None = all tools; explicit list = allowlist
+    enable_bank_config_api: bool
 
     # Recall
     graph_retriever: str
@@ -415,12 +657,53 @@ class HindsightConfig:
     retain_chunk_size: int
     retain_extract_causal_links: bool
     retain_extraction_mode: str
+    retain_mission: str | None
     retain_custom_instructions: str | None
+    retain_batch_tokens: int
+    retain_batch_enabled: bool
+    retain_batch_poll_interval_seconds: int
+
+    # File storage (static - server-level only)
+    file_storage_type: str  # "native" (PostgreSQL) or "s3" (S3-compatible)
+    file_storage_s3_bucket: str | None  # S3 bucket name (required for s3 storage)
+    file_storage_s3_region: str | None  # S3 region (optional, uses SDK default)
+    file_storage_s3_endpoint: str | None  # S3 endpoint URL (for MinIO, R2, etc.)
+    file_storage_s3_access_key_id: str | None  # S3 access key (optional, uses env/IAM)
+    file_storage_s3_secret_access_key: str | None  # S3 secret key (optional, uses env/IAM)
+    file_storage_gcs_bucket: str | None  # GCS bucket name (required for gcs storage)
+    file_storage_gcs_service_account_key: str | None  # GCS service account key JSON (optional, uses ADC)
+    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: 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)
+    file_conversion_max_batch_size: int  # Max files per request
+    enable_file_upload_api: bool
+    file_delete_after_retain: bool
 
     # Observations settings (consolidated knowledge from facts)
     enable_observations: bool
     consolidation_batch_size: int
+    consolidation_llm_batch_size: int
     consolidation_max_tokens: int
+    observations_mission: str | None
+
+    # Entity labels (controlled vocabulary of key:value classification labels extracted at retain time)
+    # List of label group dicts: [{key, description, type, optional, values: [{value, description}]}]
+    entity_labels: list | None
+    # Whether to extract regular named entities alongside entity labels (default: True)
+    # When False: only label entities are extracted (or no entities at all if no labels configured)
+    entities_allow_free_form: bool
+
+    # Reflect agent settings
+    reflect_mission: str | None
+
+    # Disposition settings (hierarchical - can be overridden per bank; None = fall back to DB)
+    disposition_skepticism: int | None
+    disposition_literalism: int | None
+    disposition_empathy: int | None
 
     # Optimization flags
     skip_llm_verification: bool
@@ -446,9 +729,142 @@ class HindsightConfig:
 
     # Reflect agent settings
     reflect_max_iterations: int
+    reflect_max_context_tokens: int
+
+    # OpenTelemetry tracing configuration
+    otel_traces_enabled: bool
+    otel_exporter_otlp_endpoint: str | None
+    otel_exporter_otlp_headers: str | None
+    otel_service_name: str
+    otel_deployment_environment: str
+
+    # Class-level sets for configuration categorization
+
+    # CREDENTIAL_FIELDS: Never exposed via API, never configurable per-tenant/bank
+    _CREDENTIAL_FIELDS = {
+        # API Keys
+        "llm_api_key",
+        "retain_llm_api_key",
+        "reflect_llm_api_key",
+        "consolidation_llm_api_key",
+        # Base URLs (could expose infrastructure)
+        "llm_base_url",
+        "retain_llm_base_url",
+        "reflect_llm_base_url",
+        "consolidation_llm_base_url",
+        "embeddings_tei_base_url",
+        "reranker_tei_base_url",
+        "reranker_cohere_base_url",
+        # Service Account Keys
+        "llm_vertexai_service_account_key",
+        # File storage credentials
+        "file_storage_s3_access_key_id",
+        "file_storage_s3_secret_access_key",
+        "file_storage_gcs_service_account_key",
+        "file_storage_azure_account_key",
+        # File parser credentials
+        "file_parser_iris_token",
+    }
+
+    # CONFIGURABLE_FIELDS: Safe behavioral settings that can be customized per-tenant/bank
+    # These fields are manually tagged as safe to expose and modify.
+    # Excludes credentials, infrastructure config, provider/model selection, and performance tuning.
+    _CONFIGURABLE_FIELDS = {
+        # MCP tool access control
+        "mcp_enabled_tools",
+        # Retention settings (behavioral)
+        "retain_chunk_size",
+        "retain_extraction_mode",
+        "retain_mission",
+        "retain_custom_instructions",
+        # Entity labels (controlled vocabulary for entity classification)
+        "entity_labels",
+        "entities_allow_free_form",
+        # Consolidation settings
+        "enable_observations",
+        "observations_mission",
+        # Reflect settings
+        "reflect_mission",
+        # Disposition settings
+        "disposition_skepticism",
+        "disposition_literalism",
+        "disposition_empathy",
+    }
+
+    @property
+    def file_conversion_max_batch_size_bytes(self) -> int:
+        """Get maximum total batch size in bytes."""
+        return self.file_conversion_max_batch_size_mb * 1024 * 1024
+
+    @classmethod
+    def get_configurable_fields(cls) -> set[str]:
+        """
+        Get set of field names that are configurable per-tenant/bank via API.
+
+        Configurable fields are manually tagged behavioral settings that are safe
+        to expose and modify (e.g., retain_chunk_size, custom_instructions).
+        Excludes credentials, infrastructure config, and provider/model selection.
+
+        Returns:
+            Set of configurable field names
+        """
+        return cls._CONFIGURABLE_FIELDS.copy()
+
+    @classmethod
+    def get_credential_fields(cls) -> set[str]:
+        """
+        Get set of field names that are credentials (NEVER exposed via API).
+
+        Credential fields include API keys, base URLs, and service account keys.
+        These must never be returned in API responses or accepted in updates.
+
+        Returns:
+            Set of credential field names
+        """
+        return cls._CREDENTIAL_FIELDS.copy()
+
+    @classmethod
+    def get_hierarchical_fields(cls) -> set[str]:
+        """
+        DEPRECATED: Use get_configurable_fields() instead.
+
+        Kept for backward compatibility during migration.
+        """
+        return cls.get_configurable_fields()
+
+    @classmethod
+    def get_static_fields(cls) -> set[str]:
+        """
+        Get set of field names that are static (server-level only).
+
+        Static fields are infrastructure-level settings that cannot vary
+        per tenant or bank. These include database config, API port, worker settings, etc.
+        Also includes credential fields which are never configurable.
+
+        Returns:
+            Set of static field names
+        """
+        # Get all field names from dataclass
+        all_fields = {f.name for f in fields(cls)}
+        # Static fields = all fields - configurable fields
+        return all_fields - cls._CONFIGURABLE_FIELDS
 
     def validate(self) -> None:
         """Validate configuration values and raise errors for invalid combinations."""
+        # Validate vector_extension
+        valid_extensions = ("pgvector", "vchord", "pgvectorscale")
+        if self.vector_extension not in valid_extensions:
+            raise ValueError(
+                f"Invalid vector_extension: {self.vector_extension}. Must be one of: {', '.join(valid_extensions)}"
+            )
+
+        # Validate text_search_extension
+        valid_text_search = ("native", "vchord", "pg_textsearch")
+        if self.text_search_extension not in valid_text_search:
+            raise ValueError(
+                f"Invalid text_search_extension: {self.text_search_extension}. Must be one of: {', '.join(valid_text_search)}"
+            )
+
         # RETAIN_MAX_COMPLETION_TOKENS must be greater than RETAIN_CHUNK_SIZE
         # to ensure the LLM has enough output capacity to extract facts from chunks
         if self.retain_max_completion_tokens <= self.retain_chunk_size:
@@ -474,6 +890,8 @@ class HindsightConfig:
             # Database
             database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
             database_schema=os.getenv(ENV_DATABASE_SCHEMA, DEFAULT_DATABASE_SCHEMA),
+            vector_extension=os.getenv(ENV_VECTOR_EXTENSION, DEFAULT_VECTOR_EXTENSION).lower(),
+            text_search_extension=os.getenv(ENV_TEXT_SEARCH_EXTENSION, DEFAULT_TEXT_SEARCH_EXTENSION).lower(),
             # LLM
             llm_provider=llm_provider,
             llm_api_key=os.getenv(ENV_LLM_API_KEY),
@@ -484,6 +902,8 @@ class HindsightConfig:
             llm_initial_backoff=float(os.getenv(ENV_LLM_INITIAL_BACKOFF, str(DEFAULT_LLM_INITIAL_BACKOFF))),
             llm_max_backoff=float(os.getenv(ENV_LLM_MAX_BACKOFF, str(DEFAULT_LLM_MAX_BACKOFF))),
             llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
+            llm_groq_service_tier=os.getenv(ENV_LLM_GROQ_SERVICE_TIER, DEFAULT_LLM_GROQ_SERVICE_TIER),
+            llm_openai_service_tier=os.getenv(ENV_LLM_OPENAI_SERVICE_TIER, DEFAULT_LLM_OPENAI_SERVICE_TIER),
             # Vertex AI
             llm_vertexai_project_id=os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID) or DEFAULT_LLM_VERTEXAI_PROJECT_ID,
             llm_vertexai_region=os.getenv(ENV_LLM_VERTEXAI_REGION, DEFAULT_LLM_VERTEXAI_REGION),
@@ -567,9 +987,27 @@ class HindsightConfig:
                 ENV_EMBEDDINGS_LOCAL_FORCE_CPU, str(DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU)
             ).lower()
             in ("true", "1"),
+            embeddings_local_trust_remote_code=os.getenv(
+                ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE)
+            ).lower()
+            in ("true", "1"),
             embeddings_tei_url=os.getenv(ENV_EMBEDDINGS_TEI_URL),
             embeddings_openai_base_url=os.getenv(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None,
+            # Cohere embeddings (with backward-compatible fallback to shared API key)
+            embeddings_cohere_api_key=os.getenv(ENV_EMBEDDINGS_COHERE_API_KEY) or os.getenv(ENV_COHERE_API_KEY),
+            embeddings_cohere_model=os.getenv(ENV_EMBEDDINGS_COHERE_MODEL, DEFAULT_EMBEDDINGS_COHERE_MODEL),
             embeddings_cohere_base_url=os.getenv(ENV_EMBEDDINGS_COHERE_BASE_URL) or None,
+            # LiteLLM embeddings (with backward-compatible fallback to shared config)
+            embeddings_litellm_api_base=os.getenv(ENV_EMBEDDINGS_LITELLM_API_BASE)
+            or os.getenv(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE),
+            embeddings_litellm_api_key=os.getenv(ENV_EMBEDDINGS_LITELLM_API_KEY) or os.getenv(ENV_LITELLM_API_KEY),
+            embeddings_litellm_model=os.getenv(ENV_EMBEDDINGS_LITELLM_MODEL, DEFAULT_EMBEDDINGS_LITELLM_MODEL),
+            # LiteLLM SDK embeddings (direct API access)
+            embeddings_litellm_sdk_api_key=os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_API_KEY),
+            embeddings_litellm_sdk_model=os.getenv(
+                ENV_EMBEDDINGS_LITELLM_SDK_MODEL, DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL
+            ),
+            embeddings_litellm_sdk_api_base=os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_API_BASE) or None,
             # Reranker
             reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
             reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
@@ -580,19 +1018,44 @@ class HindsightConfig:
             reranker_local_max_concurrent=int(
                 os.getenv(ENV_RERANKER_LOCAL_MAX_CONCURRENT, str(DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT))
             ),
+            reranker_local_trust_remote_code=os.getenv(
+                ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE)
+            ).lower()
+            in ("true", "1"),
             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(
                 os.getenv(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT))
             ),
             reranker_max_candidates=int(os.getenv(ENV_RERANKER_MAX_CANDIDATES, str(DEFAULT_RERANKER_MAX_CANDIDATES))),
+            # Cohere reranker (with backward-compatible fallback to shared API key)
+            reranker_cohere_api_key=os.getenv(ENV_RERANKER_COHERE_API_KEY) or os.getenv(ENV_COHERE_API_KEY),
+            reranker_cohere_model=os.getenv(ENV_RERANKER_COHERE_MODEL, DEFAULT_RERANKER_COHERE_MODEL),
             reranker_cohere_base_url=os.getenv(ENV_RERANKER_COHERE_BASE_URL) or None,
+            # LiteLLM reranker (with backward-compatible fallback to shared config)
+            reranker_litellm_api_base=os.getenv(ENV_RERANKER_LITELLM_API_BASE)
+            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),
+            # 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),
+            reranker_litellm_sdk_api_base=os.getenv(ENV_RERANKER_LITELLM_SDK_API_BASE) or None,
+            # ZeroEntropy reranker
+            reranker_zeroentropy_api_key=os.getenv(ENV_RERANKER_ZEROENTROPY_API_KEY),
+            reranker_zeroentropy_model=os.getenv(ENV_RERANKER_ZEROENTROPY_MODEL, DEFAULT_RERANKER_ZEROENTROPY_MODEL),
             # Server
             host=os.getenv(ENV_HOST, DEFAULT_HOST),
             port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
+            base_path=os.getenv(ENV_BASE_PATH, DEFAULT_BASE_PATH),
             log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
             log_format=os.getenv(ENV_LOG_FORMAT, DEFAULT_LOG_FORMAT).lower(),
             mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
+            mcp_enabled_tools=[t.strip() for t in os.getenv(ENV_MCP_ENABLED_TOOLS).split(",") if t.strip()]
+            if os.getenv(ENV_MCP_ENABLED_TOOLS)
+            else DEFAULT_MCP_ENABLED_TOOLS,
+            enable_bank_config_api=os.getenv(ENV_ENABLE_BANK_CONFIG_API, str(DEFAULT_ENABLE_BANK_CONFIG_API)).lower()
+            == "true",
             # Recall
             graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
             mpfp_top_k_neighbors=int(os.getenv(ENV_MPFP_TOP_K_NEIGHBORS, str(DEFAULT_MPFP_TOP_K_NEIGHBORS))),
@@ -618,15 +1081,55 @@ class HindsightConfig:
             retain_extraction_mode=_validate_extraction_mode(
                 os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
             ),
+            retain_mission=os.getenv(ENV_RETAIN_MISSION) or DEFAULT_RETAIN_MISSION,
             retain_custom_instructions=os.getenv(ENV_RETAIN_CUSTOM_INSTRUCTIONS) or DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS,
+            retain_batch_tokens=int(os.getenv(ENV_RETAIN_BATCH_TOKENS, str(DEFAULT_RETAIN_BATCH_TOKENS))),
+            retain_batch_enabled=os.getenv(ENV_RETAIN_BATCH_ENABLED, str(DEFAULT_RETAIN_BATCH_ENABLED)).lower()
+            == "true",
+            retain_batch_poll_interval_seconds=int(
+                os.getenv(ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS, str(DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS))
+            ),
+            # File storage
+            file_storage_type=os.getenv(ENV_FILE_STORAGE_TYPE, DEFAULT_FILE_STORAGE_TYPE),
+            file_storage_s3_bucket=os.getenv(ENV_FILE_STORAGE_S3_BUCKET) or None,
+            file_storage_s3_region=os.getenv(ENV_FILE_STORAGE_S3_REGION) or None,
+            file_storage_s3_endpoint=os.getenv(ENV_FILE_STORAGE_S3_ENDPOINT) or None,
+            file_storage_s3_access_key_id=os.getenv(ENV_FILE_STORAGE_S3_ACCESS_KEY_ID) or None,
+            file_storage_s3_secret_access_key=os.getenv(ENV_FILE_STORAGE_S3_SECRET_ACCESS_KEY) or None,
+            file_storage_gcs_bucket=os.getenv(ENV_FILE_STORAGE_GCS_BUCKET) or None,
+            file_storage_gcs_service_account_key=os.getenv(ENV_FILE_STORAGE_GCS_SERVICE_ACCOUNT_KEY) or None,
+            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=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(
+                os.getenv(ENV_FILE_CONVERSION_MAX_BATCH_SIZE_MB, str(DEFAULT_FILE_CONVERSION_MAX_BATCH_SIZE_MB))
+            ),
+            file_conversion_max_batch_size=int(
+                os.getenv(ENV_FILE_CONVERSION_MAX_BATCH_SIZE, str(DEFAULT_FILE_CONVERSION_MAX_BATCH_SIZE))
+            ),
+            enable_file_upload_api=os.getenv(ENV_ENABLE_FILE_UPLOAD_API, str(DEFAULT_ENABLE_FILE_UPLOAD_API)).lower()
+            == "true",
+            file_delete_after_retain=os.getenv(
+                ENV_FILE_DELETE_AFTER_RETAIN, str(DEFAULT_FILE_DELETE_AFTER_RETAIN)
+            ).lower()
+            == "true",
             # Observations settings (consolidated knowledge from facts)
             enable_observations=os.getenv(ENV_ENABLE_OBSERVATIONS, str(DEFAULT_ENABLE_OBSERVATIONS)).lower() == "true",
             consolidation_batch_size=int(
                 os.getenv(ENV_CONSOLIDATION_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_BATCH_SIZE))
             ),
+            consolidation_llm_batch_size=int(
+                os.getenv(ENV_CONSOLIDATION_LLM_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_LLM_BATCH_SIZE))
+            ),
             consolidation_max_tokens=int(
                 os.getenv(ENV_CONSOLIDATION_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_MAX_TOKENS))
             ),
+            observations_mission=os.getenv(ENV_OBSERVATIONS_MISSION) or DEFAULT_OBSERVATIONS_MISSION,
+            entity_labels=None,
+            entities_allow_free_form=True,
             # Database migrations
             run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
             # Database connection pool
@@ -646,6 +1149,27 @@ class HindsightConfig:
             ),
             # Reflect agent settings
             reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
+            reflect_max_context_tokens=int(
+                os.getenv(ENV_REFLECT_MAX_CONTEXT_TOKENS, str(DEFAULT_REFLECT_MAX_CONTEXT_TOKENS))
+            ),
+            reflect_mission=os.getenv(ENV_REFLECT_MISSION) or None,
+            # Disposition settings (None = fall back to DB value)
+            disposition_skepticism=int(os.getenv(ENV_DISPOSITION_SKEPTICISM))
+            if os.getenv(ENV_DISPOSITION_SKEPTICISM)
+            else DEFAULT_DISPOSITION_SKEPTICISM,
+            disposition_literalism=int(os.getenv(ENV_DISPOSITION_LITERALISM))
+            if os.getenv(ENV_DISPOSITION_LITERALISM)
+            else DEFAULT_DISPOSITION_LITERALISM,
+            disposition_empathy=int(os.getenv(ENV_DISPOSITION_EMPATHY))
+            if os.getenv(ENV_DISPOSITION_EMPATHY)
+            else DEFAULT_DISPOSITION_EMPATHY,
+            # OpenTelemetry tracing configuration
+            otel_traces_enabled=os.getenv(ENV_OTEL_TRACES_ENABLED, str(DEFAULT_OTEL_TRACES_ENABLED)).lower()
+            in ("true", "1", "yes"),
+            otel_exporter_otlp_endpoint=os.getenv(ENV_OTEL_EXPORTER_OTLP_ENDPOINT) or None,
+            otel_exporter_otlp_headers=os.getenv(ENV_OTEL_EXPORTER_OTLP_HEADERS) or None,
+            otel_service_name=os.getenv(ENV_OTEL_SERVICE_NAME, DEFAULT_OTEL_SERVICE_NAME),
+            otel_deployment_environment=os.getenv(ENV_OTEL_DEPLOYMENT_ENVIRONMENT, DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT),
         )
         config.validate()
         return config
@@ -726,8 +1250,35 @@ class HindsightConfig:
 _config_cache: HindsightConfig | None = None
 
 
-def get_config() -> HindsightConfig:
-    """Get the cached configuration, loading from environment on first call."""
+def get_config() -> StaticConfigProxy:
+    """
+    Get global configuration with ONLY static (non-configurable) fields accessible.
+
+    This returns a proxy that prevents access to bank-configurable fields
+    (like enable_observations, retain_chunk_size, etc.).
+
+    For bank-specific configuration, use:
+        config_resolver.resolve_full_config(bank_id, context)
+
+    This design prevents accidentally using global defaults when bank-specific
+    overrides exist.
+
+    Returns:
+        StaticConfigProxy that only exposes static infrastructure fields
+
+    Raises:
+        ConfigFieldAccessError: If you try to access a bank-configurable field
+    """
+    return StaticConfigProxy(_get_raw_config())
+
+
+def _get_raw_config() -> HindsightConfig:
+    """
+    Get raw config (internal use only).
+
+    INTERNAL USE ONLY. Do not use this directly in application code.
+    Use get_config() for static fields or ConfigResolver.resolve_full_config() for bank-specific config.
+    """
     global _config_cache
     if _config_cache is None:
         _config_cache = HindsightConfig.from_env()

hindsight-api/hindsight_api/config_resolver.py

@@ -0,0 +1,275 @@
+"""
+Configuration resolution with hierarchical overrides.
+
+Resolves config values through the hierarchy:
+  Global (env vars) → Tenant config (via extension) → Bank config (database)
+
+Config values are resolved on every request to ensure consistency across
+multiple API servers.
+"""
+
+import json
+import logging
+from dataclasses import asdict
+from typing import Any
+
+import asyncpg
+
+from hindsight_api.config import HindsightConfig, _get_raw_config, normalize_config_dict
+from hindsight_api.engine.memory_engine import fq_table
+from hindsight_api.extensions.tenant import TenantExtension
+from hindsight_api.models import RequestContext
+
+logger = logging.getLogger(__name__)
+
+
+class ConfigResolver:
+    """Resolves hierarchical configuration with tenant/bank overrides."""
+
+    def __init__(self, pool: asyncpg.Pool, tenant_extension: TenantExtension | None = None):
+        """
+        Initialize config resolver.
+
+        Args:
+            pool: Database connection pool
+            tenant_extension: Optional tenant extension for tenant-level config and permissions
+        """
+        self.pool = pool
+        self.tenant_extension = tenant_extension
+        self._global_config = _get_raw_config()
+        self._configurable_fields = HindsightConfig.get_configurable_fields()
+        self._credential_fields = HindsightConfig.get_credential_fields()
+
+    async def resolve_full_config(self, bank_id: str, context: RequestContext | None = None) -> HindsightConfig:
+        """
+        Resolve full HindsightConfig for a bank with hierarchical overrides applied.
+
+        This is for INTERNAL USE ONLY. Returns the complete config object with all fields
+        including credentials and static fields. Use get_bank_config() for API responses.
+
+        Resolution order:
+        1. Global config (from environment variables)
+        2. Tenant config overrides (from TenantExtension.get_tenant_config())
+        3. Bank config overrides (from banks.config JSONB)
+
+        Args:
+            bank_id: Bank identifier
+            context: Request context for tenant config resolution
+
+        Returns:
+            Complete HindsightConfig with hierarchical overrides applied
+        """
+        # Start with global config (all fields)
+        config_dict = asdict(self._global_config)
+
+        # Load tenant config overrides (if tenant extension available)
+        if self.tenant_extension and context:
+            try:
+                tenant_overrides = await self.tenant_extension.get_tenant_config(context)
+                if tenant_overrides:
+                    # Normalize keys and filter to configurable fields only
+                    normalized_tenant = normalize_config_dict(tenant_overrides)
+                    configurable_tenant = {k: v for k, v in normalized_tenant.items() if k in self._configurable_fields}
+                    config_dict.update(configurable_tenant)
+                    logger.debug(
+                        f"Applied tenant config overrides for bank {bank_id}: {list(configurable_tenant.keys())}"
+                    )
+            except Exception as e:
+                logger.warning(f"Failed to load tenant config for bank {bank_id}: {e}")
+
+        # Load bank config overrides
+        bank_overrides = await self._load_bank_config(bank_id)
+        if bank_overrides:
+            config_dict.update(bank_overrides)
+            logger.debug(f"Applied bank config overrides for bank {bank_id}: {list(bank_overrides.keys())}")
+
+        # Return full config object (dataclass doesn't have __init__ that accepts kwargs, so we update the object)
+        # Create a new config instance by copying the global config and updating fields
+        resolved_config = HindsightConfig(**config_dict)
+        return resolved_config
+
+    async def get_bank_config(self, bank_id: str, context: RequestContext | None = None) -> dict[str, Any]:
+        """
+        Get fully resolved config for a bank (filtered by permissions).
+
+        Resolution order:
+        1. Global config (from environment variables)
+        2. Tenant config overrides (from TenantExtension.get_tenant_config())
+        3. Bank config overrides (from banks.config JSONB)
+
+        Note: Config is resolved on every call (not cached) to ensure consistency
+        across multiple API servers.
+
+        SECURITY:
+        - Only returns configurable fields (excludes static/infrastructure fields)
+        - Filters out ALL credential fields (API keys, base URLs, etc.)
+        - Further filtered by tenant/bank permissions if extension provides them
+
+        Args:
+            bank_id: Bank identifier
+            context: Request context for tenant config resolution and permissions
+
+        Returns:
+            Dict of allowed configurable fields only (never includes credentials or static fields)
+        """
+        # Resolve full config with all hierarchical overrides
+        resolved_config = await self.resolve_full_config(bank_id, context)
+        config_dict = asdict(resolved_config)
+
+        # SECURITY: Filter to only configurable fields (exclude static/infrastructure)
+        filtered = {k: v for k, v in config_dict.items() if k in self._configurable_fields}
+
+        # SECURITY: Remove ALL credential fields (API keys, base URLs, etc.)
+        filtered = {k: v for k, v in filtered.items() if k not in self._credential_fields}
+
+        # PERMISSIONS: Further filter based on tenant/bank permissions
+        if self.tenant_extension and context:
+            try:
+                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
+                if allowed_fields is not None:  # None means "allow all"
+                    filtered = {k: v for k, v in filtered.items() if k in allowed_fields}
+                    logger.debug(
+                        f"Applied permission filter for bank {bank_id}: allowed={len(allowed_fields)} fields, "
+                        f"returned={len(filtered)} fields"
+                    )
+            except Exception as e:
+                logger.warning(f"Failed to load permissions for bank {bank_id}: {e}")
+
+        return filtered
+
+    async def _load_bank_config(self, bank_id: str) -> dict[str, Any]:
+        """
+        Load bank config overrides from banks.config JSONB column.
+
+        Args:
+            bank_id: Bank identifier
+
+        Returns:
+            Dict of config overrides (only configurable fields, normalized keys)
+        """
+        try:
+            async with self.pool.acquire() as conn:
+                row = await conn.fetchrow(
+                    f"""
+                    SELECT config FROM {fq_table("banks")} WHERE bank_id = $1
+                    """,
+                    bank_id,
+                )
+
+                if row and row["config"]:
+                    config_data = row["config"]
+
+                    # Handle case where JSONB is returned as JSON string
+                    if isinstance(config_data, str):
+                        config_data = json.loads(config_data)
+
+                    # Normalize keys (handle both env var format and Python field format)
+                    normalized = normalize_config_dict(config_data)
+
+                    # Only return overrides for configurable fields
+                    return {k: v for k, v in normalized.items() if k in self._configurable_fields}
+        except Exception as e:
+            logger.error(f"Failed to load bank config for {bank_id}: {e}")
+
+        return {}
+
+    async def update_bank_config(
+        self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
+    ) -> None:
+        """
+        Update bank configuration overrides (with permission checking).
+
+        Args:
+            bank_id: Bank identifier
+            updates: Dict of config field names to new values.
+                    Keys can be in env var format (HINDSIGHT_API_LLM_PROVIDER)
+                    or Python field format (llm_provider).
+                    Only configurable fields are allowed.
+            context: Request context for permission checking
+
+        Raises:
+            ValueError: If attempting to override invalid/disallowed fields
+        """
+        # Normalize keys
+        normalized_updates = normalize_config_dict(updates)
+
+        # SECURITY: Reject credential fields explicitly
+        credential_attempts = set(normalized_updates.keys()) & self._credential_fields
+        if credential_attempts:
+            raise ValueError(
+                f"Cannot set credential fields via API: {sorted(credential_attempts)}. "
+                f"Credentials (API keys, base URLs) must be set at server level only."
+            )
+
+        # Validate all fields are configurable
+        invalid_fields = set(normalized_updates.keys()) - self._configurable_fields
+        if invalid_fields:
+            static_fields = HindsightConfig.get_static_fields()
+            invalid_static = invalid_fields & static_fields
+            if invalid_static:
+                raise ValueError(
+                    f"Cannot override static (server-level) fields: {sorted(invalid_static)}. "
+                    f"Only configurable fields can be overridden per-bank. "
+                    f"Configurable fields include: {sorted(list(self._configurable_fields)[:10])}... "
+                    f"(total: {len(self._configurable_fields)} fields)"
+                )
+            else:
+                raise ValueError(
+                    f"Unknown configuration fields: {sorted(invalid_fields)}. "
+                    f"Valid configurable fields: {sorted(list(self._configurable_fields)[:10])}..."
+                )
+
+        # PERMISSIONS: Check tenant/bank permissions
+        if self.tenant_extension and context:
+            try:
+                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
+                if allowed_fields is not None:  # None means "allow all"
+                    disallowed = set(normalized_updates.keys()) - allowed_fields
+                    if disallowed:
+                        raise ValueError(
+                            f"Not allowed to modify fields: {sorted(disallowed)}. "
+                            f"Your permissions allow: {sorted(list(allowed_fields)[:10])}..."
+                            if allowed_fields
+                            else "Not allowed to modify fields: {sorted(disallowed)}. "
+                            "Your permissions do not allow any config modifications."
+                        )
+            except ValueError:
+                raise  # Re-raise permission errors
+            except Exception as e:
+                logger.warning(f"Failed to check permissions for bank {bank_id}: {e}")
+                # Continue without permission check (fail open for backward compatibility)
+
+        # Merge with existing config (JSONB || operator)
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                f"""
+                UPDATE {fq_table("banks")}
+                SET config = config || $1::jsonb,
+                    updated_at = now()
+                WHERE bank_id = $2
+                """,
+                json.dumps(normalized_updates),
+                bank_id,
+            )
+
+        logger.info(f"Updated bank config for {bank_id}: {list(normalized_updates.keys())}")
+
+    async def reset_bank_config(self, bank_id: str) -> None:
+        """
+        Reset bank configuration to defaults (remove all overrides).
+
+        Args:
+            bank_id: Bank identifier
+        """
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                f"""
+                UPDATE {fq_table("banks")}
+                SET config = '{{}}'::jsonb,
+                    updated_at = now()
+                WHERE bank_id = $1
+                """,
+                bank_id,
+            )
+
+        logger.info(f"Reset bank config for {bank_id} to defaults")

hindsight-api/hindsight_api/engine/consolidation/prompts.py

@@ -1,85 +1,66 @@
 """Prompts for the consolidation engine."""
 
-CONSOLIDATION_SYSTEM_PROMPT = """You are a memory consolidation system. Your job is to convert facts into durable knowledge (observations) and merge with existing knowledge when appropriate.
+# Default mission when no bank-specific mission is set
+_DEFAULT_MISSION = "Track every detail: names, numbers, dates, places, and relationships. Prefer specifics over abstractions, never generalise."
 
-You must output ONLY valid JSON with no markdown code blocks or additional text. However, the "text" field within each observation should use markdown formatting (headers, lists, bold, etc.) for clarity and readability.
+# Processing rules — always present regardless of mission
+_PROCESSING_RULES = """Processing rules (always apply):
+- REDUNDANT: same info worded differently → UPDATE the existing observation.
+- CONTRADICTION/UPDATE: capture both states with temporal markers ("used to X, now Y").
+- RESOLVE REFERENCES: when a new fact provides a concrete value resolving a vague placeholder in an existing observation (e.g. "home country", "hometown", "birthplace", "native language", "her ex", "that city"), UPDATE the observation to embed the resolved value explicitly. Example: new fact says "grandma in Sweden" + existing observation says "moved from her home country" → update to "home country is Sweden".
+- NEVER merge observations about different people or unrelated topics."""
 
-## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
-Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.
+# Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
+_BATCH_DATA_SECTION = """
+NEW FACTS:
+{facts_text}
 
-Examples of extracting durable knowledge:
-- "User moved to Room 203" -> "Room 203 exists" (location exists, not where user is now)
-- "User visited Acme Corp at Room 105" -> "Acme Corp is located in Room 105"
-- "User took the elevator to floor 3" -> "Floor 3 is accessible by elevator"
-- "User met Sarah at the lobby" -> "Sarah can be found at the lobby"
-
-DO NOT track current user position/state as knowledge - that changes constantly.
-DO track permanent facts learned from the user's actions.
-
-## PRESERVE SPECIFIC DETAILS
-Keep names, locations, numbers, and other specifics. Do NOT:
-- Abstract into general principles
-- Generate business insights
-- Make knowledge generic
-
-GOOD examples:
-- Fact: "John likes pizza" -> "John likes pizza"
-- Fact: "Alice works at Google" -> "Alice works at Google"
-
-BAD examples:
-- "John likes pizza" -> "Understanding dietary preferences helps..." (TOO ABSTRACT)
-- "User is at Room 203" -> "User is currently at Room 203" (EPHEMERAL STATE)
-
-## MERGE RULES (when comparing to existing observations):
-1. REDUNDANT: Same information worded differently → update existing
-2. CONTRADICTION: Opposite information about same topic → update with temporal markers showing change
-   Example: "Alex used to love pizza but now hates it" OR "Alex's pizza preference changed from love to hate"
-3. UPDATE: New state replacing old state → update showing the transition with "used to", "now", "changed from X to Y"
-
-## CRITICAL RULES:
-- NEVER merge facts about DIFFERENT people
-- NEVER merge unrelated topics (food preferences vs work vs hobbies)
-- When merging contradictions, the "text" field MUST capture BOTH states with temporal markers:
-  * Use "used to X, now Y" OR "changed from X to Y" OR "X but now Y"
-  * DO NOT just state the new fact - you MUST show the change
-- Keep observations focused on ONE specific topic per person
-- The "text" field MUST contain durable knowledge, not ephemeral state
-- Do NOT include "tags" in output - tags are handled automatically"""
-
-CONSOLIDATION_USER_PROMPT = """Analyze this new fact and consolidate into knowledge.
-{mission_section}
-NEW FACT: {fact_text}
-
-EXISTING OBSERVATIONS (JSON array with source memories and dates):
+EXISTING OBSERVATIONS (JSON array, pooled from recalls across all facts above):
 {observations_text}
 
 Each observation includes:
 - id: unique identifier for updating
 - text: the observation content
 - proof_count: number of supporting memories
-- tags: visibility scope (handled automatically)
-- created_at/updated_at: when observation was created/modified
 - occurred_start/occurred_end: temporal range of source facts
 - source_memories: array of supporting facts with their text and dates
 
-Instructions:
-1. Extract DURABLE KNOWLEDGE from the new fact (not ephemeral state)
-2. Review source_memories in existing observations to understand evidence
-3. Check dates to detect contradictions or updates
-4. Compare with observations:
-   - Same topic → UPDATE with learning_id
-   - New topic → CREATE new observation
-   - Purely ephemeral → return []
+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 (no create/update needed)"""
 
-Output JSON array of actions (the "text" field should use markdown formatting for structure):
-[
-  {{"action": "update", "learning_id": "uuid-from-observations", "text": "## Updated Knowledge\n\n**Key point**: details here\n\n- Supporting detail 1\n- Supporting detail 2", "reason": "..."}},
-  {{"action": "create", "text": "## New Durable Knowledge\n\nDescription with **emphasis** and proper structure", "reason": "..."}}
-]
+# Output format — JSON braces escaped as {{ }} so .format() leaves them literal
+_BATCH_OUTPUT_FORMAT = """
+Output a JSON object with three arrays.
 
-Return [] if fact contains no durable knowledge.
+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"}}]}}
 
-IMPORTANT: Format the "text" field with markdown for better readability:
-- Use headers, lists, bold/italic, tables where appropriate
-- CRITICAL: Add blank lines before and after block elements (tables, code blocks, lists)
-- Ensure proper spacing for markdown to render correctly"""
+Rules:
+- "source_fact_ids": copy the EXACT UUID strings shown in brackets [uuid] from NEW FACTS — never use integers or positions.
+- "observation_id": copy the EXACT "id" UUID string from EXISTING OBSERVATIONS.
+- One create/update may reference multiple facts when they jointly support the observation.
+- "deletes": only when an observation is directly superseded or contradicted by new facts.
+- Do NOT include "tags" — handled automatically.
+- Return {{"creates": [], "updates": [], "deletes": []}} if nothing durable is found."""
+
+
+def build_batch_consolidation_prompt(observations_mission: str | None = None) -> str:
+    """
+    Build the consolidation prompt for batch mode (multiple facts per LLM call).
+
+    The mission defines *what* to track (customisable per bank).
+    Processing rules and output format are always present regardless of mission.
+    """
+    mission = observations_mission or _DEFAULT_MISSION
+
+    return (
+        "You are a memory consolidation system. Synthesize facts into observations "
+        "and merge with existing observations when appropriate.\n\n"
+        f"## MISSION\n{mission}\n\n"
+        f"{_PROCESSING_RULES}" + _BATCH_DATA_SECTION + _BATCH_OUTPUT_FORMAT
+    )

hindsight-api/hindsight_api/engine/cross_encoder.py

@@ -21,27 +21,29 @@ from ..config import (
     DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
     DEFAULT_RERANKER_FLASHRANK_MODEL,
     DEFAULT_RERANKER_LITELLM_MODEL,
+    DEFAULT_RERANKER_LITELLM_SDK_MODEL,
     DEFAULT_RERANKER_LOCAL_FORCE_CPU,
     DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
     DEFAULT_RERANKER_LOCAL_MODEL,
+    DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE,
     DEFAULT_RERANKER_PROVIDER,
     DEFAULT_RERANKER_TEI_BATCH_SIZE,
     DEFAULT_RERANKER_TEI_MAX_CONCURRENT,
-    ENV_COHERE_API_KEY,
-    ENV_LITELLM_API_BASE,
-    ENV_LITELLM_API_KEY,
-    ENV_RERANKER_COHERE_BASE_URL,
+    DEFAULT_RERANKER_ZEROENTROPY_MODEL,
+    ENV_RERANKER_COHERE_API_KEY,
     ENV_RERANKER_COHERE_MODEL,
     ENV_RERANKER_FLASHRANK_CACHE_DIR,
     ENV_RERANKER_FLASHRANK_MODEL,
-    ENV_RERANKER_LITELLM_MODEL,
+    ENV_RERANKER_LITELLM_SDK_API_KEY,
     ENV_RERANKER_LOCAL_FORCE_CPU,
     ENV_RERANKER_LOCAL_MAX_CONCURRENT,
     ENV_RERANKER_LOCAL_MODEL,
+    ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE,
     ENV_RERANKER_PROVIDER,
     ENV_RERANKER_TEI_BATCH_SIZE,
     ENV_RERANKER_TEI_MAX_CONCURRENT,
     ENV_RERANKER_TEI_URL,
+    ENV_RERANKER_ZEROENTROPY_API_KEY,
 )
 
 logger = logging.getLogger(__name__)
@@ -102,7 +104,13 @@ class LocalSTCrossEncoder(CrossEncoderModel):
     _executor: ThreadPoolExecutor | None = None
     _max_concurrent: int = 4  # Limit concurrent CPU-bound reranking calls
 
-    def __init__(self, model_name: str | None = None, max_concurrent: int = 4, force_cpu: bool = False):
+    def __init__(
+        self,
+        model_name: str | None = None,
+        max_concurrent: int = 4,
+        force_cpu: bool = False,
+        trust_remote_code: bool = False,
+    ):
         """
         Initialize local SentenceTransformers cross-encoder.
 
@@ -113,9 +121,13 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                            Higher values may cause CPU thrashing under load.
             force_cpu: Force CPU mode (avoids MPS/XPC issues on macOS in daemon mode).
                       Default: False
+            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)
         """
         self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
         self.force_cpu = force_cpu
+        self.trust_remote_code = trust_remote_code
         self._model = None
         LocalSTCrossEncoder._max_concurrent = max_concurrent
 
@@ -181,6 +193,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                     self.model_name,
                     device=device,
                     model_kwargs={"low_cpu_mem_usage": False},
+                    trust_remote_code=self.trust_remote_code,
                 )
             finally:
                 # Restore original logging level
@@ -545,6 +558,104 @@ class CohereCrossEncoder(CrossEncoderModel):
         return all_scores
 
 
+class ZeroEntropyCrossEncoder(CrossEncoderModel):
+    """
+    ZeroEntropy cross-encoder implementation using the ZeroEntropy Rerank API.
+
+    Supports zerank-2 (flagship) and zerank-2-small models.
+    See: https://docs.zeroentropy.dev/models
+    """
+
+    RERANK_URL = "https://api.zeroentropy.dev/v1/models/rerank"
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_RERANKER_ZEROENTROPY_MODEL,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize ZeroEntropy cross-encoder client.
+
+        Args:
+            api_key: ZeroEntropy API key
+            model: ZeroEntropy rerank model name (default: zerank-2)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self._async_client: httpx.AsyncClient | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "zeroentropy"
+
+    async def initialize(self) -> None:
+        """Initialize the async HTTP client."""
+        if self._async_client is not None:
+            return
+
+        logger.info(f"Reranker: initializing ZeroEntropy provider with model {self.model}")
+        self._async_client = httpx.AsyncClient(
+            timeout=self.timeout,
+            headers={
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json",
+            },
+        )
+        logger.info("Reranker: ZeroEntropy provider initialized")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using the ZeroEntropy Rerank API.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores
+        """
+        if self._async_client is None:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        if not pairs:
+            return []
+
+        # Group pairs by query for efficient batching
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        all_scores = [0.0] * len(pairs)
+
+        for query, indexed_texts in query_groups.items():
+            texts = [text for _, text in indexed_texts]
+            indices = [idx for idx, _ in indexed_texts]
+
+            response = await self._async_client.post(
+                self.RERANK_URL,
+                json={
+                    "model": self.model,
+                    "query": query,
+                    "documents": texts,
+                    "top_n": len(texts),
+                },
+            )
+            response.raise_for_status()
+            result = response.json()
+
+            # Map scores back to original positions
+            for item in result.get("results", []):
+                original_idx = item["index"]
+                score = item["relevance_score"]
+                all_scores[indices[original_idx]] = score
+
+        return all_scores
+
+
 class RRFPassthroughCrossEncoder(CrossEncoderModel):
     """
     Passthrough cross-encoder that preserves RRF scores without neural reranking.
@@ -819,6 +930,126 @@ class LiteLLMCrossEncoder(CrossEncoderModel):
         return all_scores
 
 
+class LiteLLMSDKCrossEncoder(CrossEncoderModel):
+    """
+    LiteLLM SDK cross-encoder for direct API integration.
+
+    Supports reranking via LiteLLM SDK without requiring a proxy server.
+    Supported providers: Cohere, DeepInfra, Together AI, HuggingFace, Jina AI, Voyage AI, AWS Bedrock.
+
+    Example model names:
+    - cohere/rerank-english-v3.0
+    - deepinfra/Qwen3-reranker-8B
+    - together_ai/Salesforce/Llama-Rank-V1
+    - huggingface/BAAI/bge-reranker-v2-m3
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_RERANKER_LITELLM_SDK_MODEL,
+        api_base: str | None = None,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize LiteLLM SDK cross-encoder client.
+
+        Args:
+            api_key: API key for the reranking provider
+            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)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.api_base = api_base
+        self.timeout = timeout
+        self._initialized = False
+        self._litellm = None  # Will be set during initialization
+
+    @property
+    def provider_name(self) -> str:
+        return "litellm-sdk"
+
+    async def initialize(self) -> None:
+        """Initialize the LiteLLM SDK client."""
+        if self._initialized:
+            return
+
+        try:
+            import litellm
+
+            self._litellm = litellm  # Store reference
+        except ImportError:
+            raise ImportError("litellm is required for LiteLLMSDKCrossEncoder. Install it with: pip install litellm")
+
+        api_base_msg = f" at {self.api_base}" if self.api_base else ""
+        logger.info(f"Reranker: initializing LiteLLM SDK provider with model {self.model}{api_base_msg}")
+
+        self._initialized = True
+        logger.info("Reranker: LiteLLM SDK provider initialized")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using the LiteLLM SDK.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores
+        """
+        if not self._initialized:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        if not pairs:
+            return []
+
+        # Group pairs by query for efficient batching
+        # LiteLLM rerank expects one query with multiple documents
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        all_scores = [0.0] * len(pairs)
+
+        for query, indexed_texts in query_groups.items():
+            texts = [text for _, text in indexed_texts]
+            indices = [idx for idx, _ in indexed_texts]
+
+            # Build kwargs for rerank call
+            rerank_kwargs = {
+                "model": self.model,
+                "query": query,
+                "documents": texts,
+                "api_key": self.api_key,
+            }
+            if self.api_base:
+                rerank_kwargs["api_base"] = self.api_base
+
+            response = await self._litellm.arerank(**rerank_kwargs)
+
+            # Map scores back to original positions
+            # Response format: RerankResponse with results list
+            # Each result is a TypedDict with "index" and "relevance_score"
+            if hasattr(response, "results") and response.results:
+                for result in response.results:
+                    # Results are TypedDicts, use dict-style access
+                    original_idx = result["index"]
+                    score = result.get("relevance_score", result.get("score", 0.0))
+                    all_scores[indices[original_idx]] = score
+            elif isinstance(response, list):
+                # Direct list of scores (unlikely but defensive)
+                for i, score in enumerate(response):
+                    all_scores[indices[i]] = score
+            else:
+                logger.warning(f"Unexpected response format from LiteLLM rerank: {type(response)}")
+
+        return all_scores
+
+
 def create_cross_encoder_from_env() -> CrossEncoderModel:
     """
     Create a CrossEncoderModel instance based on configuration.
@@ -847,26 +1078,51 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
             model_name=config.reranker_local_model,
             max_concurrent=config.reranker_local_max_concurrent,
             force_cpu=config.reranker_local_force_cpu,
+            trust_remote_code=config.reranker_local_trust_remote_code,
         )
     elif provider == "cohere":
-        api_key = os.environ.get(ENV_COHERE_API_KEY)
+        api_key = config.reranker_cohere_api_key
         if not api_key:
-            raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'cohere'")
-        model = os.environ.get(ENV_RERANKER_COHERE_MODEL, DEFAULT_RERANKER_COHERE_MODEL)
-        base_url = os.environ.get(ENV_RERANKER_COHERE_BASE_URL) or None
-        return CohereCrossEncoder(api_key=api_key, model=model, base_url=base_url)
+            raise ValueError(f"{ENV_RERANKER_COHERE_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'cohere'")
+        return CohereCrossEncoder(
+            api_key=api_key,
+            model=config.reranker_cohere_model,
+            base_url=config.reranker_cohere_base_url,
+        )
     elif provider == "flashrank":
         model = os.environ.get(ENV_RERANKER_FLASHRANK_MODEL, DEFAULT_RERANKER_FLASHRANK_MODEL)
         cache_dir = os.environ.get(ENV_RERANKER_FLASHRANK_CACHE_DIR, DEFAULT_RERANKER_FLASHRANK_CACHE_DIR)
         return FlashRankCrossEncoder(model_name=model, cache_dir=cache_dir)
     elif provider == "litellm":
-        api_base = os.environ.get(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE)
-        api_key = os.environ.get(ENV_LITELLM_API_KEY)
-        model = os.environ.get(ENV_RERANKER_LITELLM_MODEL, DEFAULT_RERANKER_LITELLM_MODEL)
-        return LiteLLMCrossEncoder(api_base=api_base, api_key=api_key, model=model)
+        return LiteLLMCrossEncoder(
+            api_base=config.reranker_litellm_api_base,
+            api_key=config.reranker_litellm_api_key,
+            model=config.reranker_litellm_model,
+        )
+    elif provider == "litellm-sdk":
+        api_key = config.reranker_litellm_sdk_api_key
+        if not api_key:
+            raise ValueError(
+                f"{ENV_RERANKER_LITELLM_SDK_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'litellm-sdk'"
+            )
+        return LiteLLMSDKCrossEncoder(
+            api_key=api_key,
+            model=config.reranker_litellm_sdk_model,
+            api_base=config.reranker_litellm_sdk_api_base,
+        )
+    elif provider == "zeroentropy":
+        api_key = config.reranker_zeroentropy_api_key
+        if not api_key:
+            raise ValueError(
+                f"{ENV_RERANKER_ZEROENTROPY_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'zeroentropy'"
+            )
+        return ZeroEntropyCrossEncoder(
+            api_key=api_key,
+            model=config.reranker_zeroentropy_model,
+        )
     elif provider == "rrf":
         return RRFPassthroughCrossEncoder()
     else:
         raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'rrf'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
         )

hindsight-api/hindsight_api/engine/embeddings.py

@@ -19,24 +19,23 @@ import httpx
 from ..config import (
     DEFAULT_EMBEDDINGS_COHERE_MODEL,
     DEFAULT_EMBEDDINGS_LITELLM_MODEL,
+    DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
     DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU,
     DEFAULT_EMBEDDINGS_LOCAL_MODEL,
+    DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE,
     DEFAULT_EMBEDDINGS_OPENAI_MODEL,
     DEFAULT_EMBEDDINGS_PROVIDER,
     DEFAULT_LITELLM_API_BASE,
-    ENV_COHERE_API_KEY,
-    ENV_EMBEDDINGS_COHERE_BASE_URL,
-    ENV_EMBEDDINGS_COHERE_MODEL,
-    ENV_EMBEDDINGS_LITELLM_MODEL,
+    ENV_EMBEDDINGS_COHERE_API_KEY,
+    ENV_EMBEDDINGS_LITELLM_SDK_API_KEY,
     ENV_EMBEDDINGS_LOCAL_FORCE_CPU,
     ENV_EMBEDDINGS_LOCAL_MODEL,
+    ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE,
     ENV_EMBEDDINGS_OPENAI_API_KEY,
     ENV_EMBEDDINGS_OPENAI_BASE_URL,
     ENV_EMBEDDINGS_OPENAI_MODEL,
     ENV_EMBEDDINGS_PROVIDER,
     ENV_EMBEDDINGS_TEI_URL,
-    ENV_LITELLM_API_BASE,
-    ENV_LITELLM_API_KEY,
     ENV_LLM_API_KEY,
 )
 
@@ -95,7 +94,7 @@ class LocalSTEmbeddings(Embeddings):
     The embedding dimension is auto-detected from the model.
     """
 
-    def __init__(self, model_name: str | None = None, force_cpu: bool = False):
+    def __init__(self, model_name: str | None = None, force_cpu: bool = False, trust_remote_code: bool = False):
         """
         Initialize local SentenceTransformers embeddings.
 
@@ -104,9 +103,13 @@ class LocalSTEmbeddings(Embeddings):
                        Default: BAAI/bge-small-en-v1.5
             force_cpu: Force CPU mode (avoids MPS/XPC issues on macOS in daemon mode).
                       Default: False
+            trust_remote_code: Allow loading models with custom code (security risk).
+                              Required for some models with custom architectures.
+                              Default: False (disabled for security)
         """
         self.model_name = model_name or DEFAULT_EMBEDDINGS_LOCAL_MODEL
         self.force_cpu = force_cpu
+        self.trust_remote_code = trust_remote_code
         self._model = None
         self._dimension: int | None = None
 
@@ -176,6 +179,7 @@ class LocalSTEmbeddings(Embeddings):
                     self.model_name,
                     device=device,
                     model_kwargs={"low_cpu_mem_usage": False},
+                    trust_remote_code=self.trust_remote_code,
                 )
             finally:
                 # Restore original logging level
@@ -718,6 +722,150 @@ class LiteLLMEmbeddings(Embeddings):
         return all_embeddings
 
 
+class LiteLLMSDKEmbeddings(Embeddings):
+    """
+    LiteLLM SDK embeddings for direct API integration.
+
+    Supports embeddings via LiteLLM SDK without requiring a proxy server.
+    Supported providers: Cohere, OpenAI, Azure OpenAI, HuggingFace, Voyage AI, Together AI, etc.
+
+    Example model names:
+    - cohere/embed-english-v3.0
+    - openai/text-embedding-3-small
+    - together_ai/togethercomputer/m2-bert-80M-8k-retrieval
+    - voyage/voyage-2
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
+        api_base: str | None = None,
+        batch_size: int = 100,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize LiteLLM SDK embeddings client.
+
+        Args:
+            api_key: API key for the embedding provider
+            model: Model name with provider prefix (e.g., "cohere/embed-english-v3.0")
+            api_base: Custom base URL for API (optional)
+            batch_size: Maximum batch size for embedding requests (default: 100)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.api_base = api_base
+        self.batch_size = batch_size
+        self.timeout = timeout
+        self._litellm = None  # Will be set during initialization
+        self._dimension: int | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "litellm-sdk"
+
+    @property
+    def dimension(self) -> int:
+        if self._dimension is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+        return self._dimension
+
+    async def initialize(self) -> None:
+        """Initialize the LiteLLM SDK client and detect dimension."""
+        if self._litellm is not None:
+            return
+
+        try:
+            import litellm
+
+            self._litellm = litellm  # Store reference
+        except ImportError:
+            raise ImportError("litellm is required for LiteLLMSDKEmbeddings. Install it with: pip install litellm")
+
+        api_base_msg = f" at {self.api_base}" if self.api_base else ""
+        logger.info(f"Embeddings: initializing LiteLLM SDK provider with model {self.model}{api_base_msg}")
+
+        # Do a test embedding to detect dimension
+        try:
+            # Build kwargs for embedding call
+            embed_kwargs = {
+                "model": self.model,
+                "input": ["test"],
+                "api_key": self.api_key,
+                "encoding_format": "float",
+            }
+            if self.api_base:
+                embed_kwargs["api_base"] = self.api_base
+
+            # Use async embedding method (standard in litellm)
+            response = await self._litellm.aembedding(**embed_kwargs)
+
+            # Extract dimension from response
+            if response.data and len(response.data) > 0:
+                self._dimension = len(response.data[0]["embedding"])
+            else:
+                raise RuntimeError(f"Unable to detect embedding dimension for model {self.model}")
+
+        except Exception as e:
+            raise RuntimeError(f"Failed to initialize LiteLLM SDK embeddings: {e}")
+
+        logger.info(f"Embeddings: LiteLLM SDK provider initialized (model: {self.model}, dim: {self._dimension})")
+
+    def encode(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings using the LiteLLM SDK.
+
+        Args:
+            texts: List of text strings to encode
+
+        Returns:
+            List of embedding vectors (one per input text)
+        """
+        if self._litellm is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+
+        if not texts:
+            return []
+
+        all_embeddings = []
+
+        # Process in batches
+        for i in range(0, len(texts), self.batch_size):
+            batch = texts[i : i + self.batch_size]
+
+            try:
+                # Build kwargs for embedding call
+                embed_kwargs = {
+                    "model": self.model,
+                    "input": batch,
+                    "api_key": self.api_key,
+                    "encoding_format": "float",
+                }
+                if self.api_base:
+                    embed_kwargs["api_base"] = self.api_base
+
+                # Use sync embedding (litellm doesn't have async in thread-safe way)
+                response = self._litellm.embedding(**embed_kwargs)
+
+                # Extract embeddings from response
+                # Sort by index to ensure correct order
+                batch_embeddings = sorted(response.data, key=lambda x: x.get("index", 0))
+                all_embeddings.extend([e["embedding"] for e in batch_embeddings])
+
+            except Exception as e:
+                import traceback
+
+                logger.error(
+                    f"Error in LiteLLM embedding for batch starting at index {i}: {e}\n"
+                    f"Traceback: {traceback.format_exc()}"
+                )
+                raise
+
+        return all_embeddings
+
+
 def create_embeddings_from_env() -> Embeddings:
     """
     Create an Embeddings instance based on configuration.
@@ -741,6 +889,7 @@ def create_embeddings_from_env() -> Embeddings:
         return LocalSTEmbeddings(
             model_name=config.embeddings_local_model,
             force_cpu=config.embeddings_local_force_cpu,
+            trust_remote_code=config.embeddings_local_trust_remote_code,
         )
     elif provider == "openai":
         # Use dedicated embeddings API key, or fall back to LLM API key
@@ -754,18 +903,33 @@ def create_embeddings_from_env() -> Embeddings:
         base_url = os.environ.get(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None
         return OpenAIEmbeddings(api_key=api_key, model=model, base_url=base_url)
     elif provider == "cohere":
-        api_key = os.environ.get(ENV_COHERE_API_KEY)
+        api_key = config.embeddings_cohere_api_key
         if not api_key:
-            raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'cohere'")
-        model = os.environ.get(ENV_EMBEDDINGS_COHERE_MODEL, DEFAULT_EMBEDDINGS_COHERE_MODEL)
-        base_url = os.environ.get(ENV_EMBEDDINGS_COHERE_BASE_URL) or None
-        return CohereEmbeddings(api_key=api_key, model=model, base_url=base_url)
+            raise ValueError(f"{ENV_EMBEDDINGS_COHERE_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'cohere'")
+        return CohereEmbeddings(
+            api_key=api_key,
+            model=config.embeddings_cohere_model,
+            base_url=config.embeddings_cohere_base_url,
+        )
     elif provider == "litellm":
-        api_base = os.environ.get(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE)
-        api_key = os.environ.get(ENV_LITELLM_API_KEY)
-        model = os.environ.get(ENV_EMBEDDINGS_LITELLM_MODEL, DEFAULT_EMBEDDINGS_LITELLM_MODEL)
-        return LiteLLMEmbeddings(api_base=api_base, api_key=api_key, model=model)
+        return LiteLLMEmbeddings(
+            api_base=config.embeddings_litellm_api_base,
+            api_key=config.embeddings_litellm_api_key,
+            model=config.embeddings_litellm_model,
+        )
+    elif provider == "litellm-sdk":
+        api_key = config.embeddings_litellm_sdk_api_key
+        if not api_key:
+            raise ValueError(
+                f"{ENV_EMBEDDINGS_LITELLM_SDK_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'litellm-sdk'"
+            )
+        return LiteLLMSDKEmbeddings(
+            api_key=api_key,
+            model=config.embeddings_litellm_sdk_model,
+            api_base=config.embeddings_litellm_sdk_api_base,
+        )
     else:
         raise ValueError(
-            f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere', 'litellm'"
+            f"Unknown embeddings provider: {provider}. "
+            f"Supported: 'local', 'tei', 'openai', 'cohere', 'litellm', 'litellm-sdk'"
         )

hindsight-api/hindsight_api/engine/entity_resolver.py

@@ -12,6 +12,7 @@ import asyncpg
 
 from .db_utils import acquire_with_retry
 from .memory_engine import fq_table
+from .retain.entity_labels import build_labels_lookup as _build_labels_lookup_from_config
 
 # Load spaCy model (singleton)
 _nlp = None
@@ -31,6 +32,11 @@ class EntityResolver:
         """
         self.pool = pool
 
+    @staticmethod
+    def _build_labels_lookup(entity_labels: list | None) -> set[str]:
+        """Build a set of valid 'key:value' entity label strings for fast lookup."""
+        return _build_labels_lookup_from_config(entity_labels)
+
     async def resolve_entities_batch(
         self,
         bank_id: str,
@@ -38,6 +44,7 @@ class EntityResolver:
         context: str,
         unit_event_date,
         conn=None,
+        entity_labels: list | None = None,
     ) -> list[str]:
         """
         Resolve multiple entities in batch (MUCH faster than sequential).
@@ -58,14 +65,25 @@ class EntityResolver:
         if not entities_data:
             return []
 
+        taxonomy_lookup = self._build_labels_lookup(entity_labels)
         if conn is None:
             async with acquire_with_retry(self.pool) as conn:
-                return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
+                return await self._resolve_entities_batch_impl(
+                    conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
+                )
         else:
-            return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
+            return await self._resolve_entities_batch_impl(
+                conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
+            )
 
     async def _resolve_entities_batch_impl(
-        self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
+        self,
+        conn,
+        bank_id: str,
+        entities_data: list[dict],
+        context: str,
+        unit_event_date,
+        taxonomy_lookup: set[str] | None = None,
     ) -> list[str]:
         # Query ALL candidates for this bank
         all_entities = await conn.fetch(
@@ -135,12 +153,19 @@ class EntityResolver:
         entities_to_update = []  # (entity_id, event_date)
         entities_to_create = []  # (idx, entity_data, event_date)
 
+        taxonomy_lookup = taxonomy_lookup or set()
+
         for idx, entity_data in enumerate(entities_data):
             entity_text = entity_data["text"]
             nearby_entities = entity_data.get("nearby_entities", [])
             # Use per-entity date if available, otherwise fall back to batch-level date
             entity_event_date = entity_data.get("event_date", unit_event_date)
 
+            # Taxonomy entities: skip fuzzy matching, use exact canonical name
+            if taxonomy_lookup and entity_text.lower() in taxonomy_lookup:
+                entities_to_create.append((idx, entity_data, entity_event_date))
+                continue
+
             candidates = all_candidates.get(entity_text, [])
 
             if not candidates:
@@ -237,7 +262,7 @@ class EntityResolver:
             rows = await conn.fetch(
                 f"""
                 INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
-                SELECT $1, name, event_date, event_date, cnt
+                SELECT $1, name, COALESCE(event_date, now()), COALESCE(event_date, now()), cnt
                 FROM unnest($2::text[], $3::timestamptz[], $4::int[]) AS t(name, event_date, cnt)
                 ON CONFLICT (bank_id, LOWER(canonical_name))
                 DO UPDATE SET
@@ -408,7 +433,7 @@ class EntityResolver:
         entity_id = await conn.fetchval(
             f"""
             INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
-            VALUES ($1, $2, $3, $4, 1)
+            VALUES ($1, $2, COALESCE($3, now()), COALESCE($4, now()), 1)
             ON CONFLICT (bank_id, LOWER(canonical_name))
             DO UPDATE SET
                 mention_count = {fq_table("entities")}.mention_count + 1,

hindsight-api/hindsight_api/engine/interface.py

@@ -48,6 +48,7 @@ class MemoryEngineInterface(ABC):
         contents: list[dict[str, Any]],
         *,
         request_context: "RequestContext",
+        document_tags: list[str] | None = None,
     ) -> dict[str, Any]:
         """
         Retain a batch of memory items.
@@ -55,8 +56,9 @@ class MemoryEngineInterface(ABC):
         Args:
             bank_id: The memory bank ID.
             contents: List of content dicts with 'content', optional 'event_date',
-                     'context', 'metadata', 'document_id'.
+                     'context', 'metadata', 'document_id', and per-item 'tags'.
             request_context: Request context for authentication.
+            document_tags: Optional tags applied to all items in the batch.
 
         Returns:
             Dict with processing results.
@@ -561,6 +563,7 @@ class MemoryEngineInterface(ABC):
         contents: list[dict[str, Any]],
         *,
         request_context: "RequestContext",
+        document_tags: list[str] | None = None,
     ) -> dict[str, Any]:
         """
         Submit a batch retain operation to run asynchronously.
@@ -569,6 +572,7 @@ class MemoryEngineInterface(ABC):
             bank_id: The memory bank ID.
             contents: List of content dicts to retain.
             request_context: Request context for authentication.
+            document_tags: Optional tags applied to all items in the async batch.
 
         Returns:
             Dict with operation_id and items_count.

hindsight-api/hindsight_api/engine/llm_interface.py

@@ -128,6 +128,67 @@ class LLMInterface(ABC):
         """
         pass
 
+    async def supports_batch_api(self) -> bool:
+        """
+        Check if this provider supports batch API operations.
+
+        Returns:
+            True if provider supports submit_batch/get_batch_status/retrieve_batch_results
+        """
+        return False
+
+    async def submit_batch(
+        self,
+        requests: list[dict[str, Any]],
+        endpoint: str = "/v1/chat/completions",
+        completion_window: str = "24h",
+    ) -> dict[str, Any]:
+        """
+        Submit a batch of requests to the provider's batch API.
+
+        Args:
+            requests: List of request dicts in JSONL format (custom_id, method, url, body)
+            endpoint: API endpoint for the batch (e.g., "/v1/chat/completions")
+            completion_window: Completion window (e.g., "24h")
+
+        Returns:
+            Dict with batch metadata: {"batch_id": str, "status": str, ...}
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+    async def get_batch_status(self, batch_id: str) -> dict[str, Any]:
+        """
+        Get the status of a batch job.
+
+        Args:
+            batch_id: Batch identifier returned from submit_batch
+
+        Returns:
+            Dict with status info: {"batch_id": str, "status": str, "completed_at": str, ...}
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+    async def retrieve_batch_results(self, batch_id: str) -> list[dict[str, Any]]:
+        """
+        Retrieve completed batch results.
+
+        Args:
+            batch_id: Batch identifier returned from submit_batch
+
+        Returns:
+            List of result dicts (one per request, matched by custom_id)
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
     @abstractmethod
     async def cleanup(self) -> None:
         """Clean up resources (close connections, etc.)."""

hindsight-api/hindsight_api/engine/llm_wrapper.py

@@ -60,6 +60,59 @@ class OutputTooLongError(Exception):
     pass
 
 
+def parse_llm_json(raw: str) -> Any:
+    """
+    Robustly parse JSON returned by an LLM.
+
+    Handles common LLM output quirks:
+    1. Markdown code fences (```json ... ```) — strip them before parsing.
+    2. Embedded control characters (\\x00-\\x1f, \\x7f) — replace with space
+       and retry if the initial parse fails.
+
+    Args:
+        raw: Raw text returned by the LLM.
+
+    Returns:
+        Parsed Python object (dict, list, etc.).
+
+    Raises:
+        json.JSONDecodeError: If the text cannot be parsed even after cleanup.
+    """
+    text = raw.strip()
+
+    # Strip markdown code fences (some models wrap JSON in ```json ... ```)
+    if text.startswith("```"):
+        text = text.split("\n", 1)[1] if "\n" in text else text[3:]
+        if text.endswith("```"):
+            text = text[:-3]
+        text = text.strip()
+
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        # Some models (e.g. Gemini) embed raw control characters inside JSON
+        # string values. Replacing them with a space usually produces valid JSON.
+        cleaned = re.sub(r"[\x00-\x1f\x7f]", " ", text)
+        return json.loads(cleaned)
+
+
+_PROVIDERS_WITHOUT_API_KEY = frozenset(
+    {
+        "ollama",
+        "lmstudio",
+        "openai-codex",
+        "claude-code",
+        "mock",
+        "vertexai",
+    }
+)
+
+
+def requires_api_key(provider: str) -> bool:
+    """Return True if the given provider requires an API key to operate."""
+    return provider.lower() not in _PROVIDERS_WITHOUT_API_KEY
+
+
 def create_llm_provider(
     provider: str,
     api_key: str,
@@ -67,6 +120,7 @@ def create_llm_provider(
     model: str,
     reasoning_effort: str,
     groq_service_tier: str | None = None,
+    openai_service_tier: str | None = None,
     vertexai_project_id: str | None = None,
     vertexai_region: str | None = None,
     vertexai_credentials: Any = None,
@@ -80,7 +134,8 @@ def create_llm_provider(
         base_url: Base URL for the API.
         model: Model name.
         reasoning_effort: Reasoning effort level for supported providers.
-        groq_service_tier: Groq service tier (for Groq provider).
+        groq_service_tier: Groq service tier (for Groq provider) - "on_demand", "flex", or "auto".
+        openai_service_tier: OpenAI service tier (for OpenAI provider) - None (default) or "flex" (50% cheaper).
         vertexai_project_id: Vertex AI project ID (for VertexAI provider).
         vertexai_region: Vertex AI region (for VertexAI provider).
         vertexai_credentials: Vertex AI credentials object (for VertexAI provider).
@@ -156,6 +211,7 @@ def create_llm_provider(
             model=model,
             reasoning_effort=reasoning_effort,
             groq_service_tier=groq_service_tier,
+            openai_service_tier=openai_service_tier,
         )
 
     else:
@@ -177,6 +233,7 @@ class LLMProvider:
         model: str,
         reasoning_effort: str = "low",
         groq_service_tier: str | None = None,
+        openai_service_tier: str | None = None,
     ):
         """
         Initialize LLM provider.
@@ -187,15 +244,17 @@ class LLMProvider:
             base_url: Base URL for the API.
             model: Model name.
             reasoning_effort: Reasoning effort level for supported providers.
-            groq_service_tier: Groq service tier ("on_demand", "flex", "auto"). Default: None (uses Groq's default).
+            groq_service_tier: Groq service tier ("on_demand", "flex", "auto") - from config.
+            openai_service_tier: OpenAI service tier (None or "flex") - from config.
         """
         self.provider = provider.lower()
         self.api_key = api_key
         self.base_url = base_url
         self.model = model
         self.reasoning_effort = reasoning_effort
-        # Default to 'auto' for best performance, users can override to 'on_demand' for free tier
-        self.groq_service_tier = groq_service_tier or os.getenv(ENV_LLM_GROQ_SERVICE_TIER, "auto")
+        # Service tiers from hierarchical config (not env vars)
+        self.groq_service_tier = groq_service_tier
+        self.openai_service_tier = openai_service_tier
 
         # Validate provider
         valid_providers = [
@@ -272,6 +331,7 @@ class LLMProvider:
             model=self.model,
             reasoning_effort=self.reasoning_effort,
             groq_service_tier=self.groq_service_tier,
+            openai_service_tier=self.openai_service_tier,
             vertexai_project_id=vertexai_project_id,
             vertexai_region=vertexai_region,
             vertexai_credentials=vertexai_credentials,
@@ -545,8 +605,9 @@ class LLMProvider:
         provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq")
         api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY", "")
 
-        # API key not needed for openai-codex (uses OAuth) or claude-code (uses Keychain OAuth)
-        if not api_key and provider not in ("openai-codex", "claude-code"):
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), or vertexai (uses GCP service account credentials)
+        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
             raise ValueError(
                 "HINDSIGHT_API_LLM_API_KEY environment variable is required (unless using openai-codex or claude-code)"
             )
@@ -562,8 +623,9 @@ class LLMProvider:
         provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
         api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))
 
-        # API key not needed for openai-codex (uses OAuth) or claude-code (uses Keychain OAuth)
-        if not api_key and provider not in ("openai-codex", "claude-code"):
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), or vertexai (uses GCP service account credentials)
+        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
             raise ValueError(
                 "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_ANSWER_LLM_API_KEY environment variable is required "
                 "(unless using openai-codex or claude-code)"
@@ -580,8 +642,9 @@ class LLMProvider:
         provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
         api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))
 
-        # API key not needed for openai-codex (uses OAuth) or claude-code (uses Keychain OAuth)
-        if not api_key and provider not in ("openai-codex", "claude-code"):
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), or vertexai (uses GCP service account credentials)
+        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
             raise ValueError(
                 "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_JUDGE_LLM_API_KEY environment variable is required "
                 "(unless using openai-codex or claude-code)"

hindsight-api/hindsight_api/engine/operation_metadata.py

@@ -0,0 +1,69 @@
+"""
+Typed metadata models for async operations.
+
+These dataclasses define the structure of result_metadata for different operation types.
+The metadata is exposed in the API for debugging purposes and may change without notice.
+"""
+
+from dataclasses import asdict, dataclass
+from typing import Any
+
+
+@dataclass
+class BatchRetainParentMetadata:
+    """Metadata for parent batch_retain operations (when split into sub-batches)."""
+
+    items_count: int
+    total_tokens: int
+    num_sub_batches: int
+    is_parent: bool = True
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class BatchRetainChildMetadata:
+    """Metadata for child batch_retain operations (individual sub-batches)."""
+
+    items_count: int
+    parent_operation_id: str
+    sub_batch_index: int
+    total_sub_batches: int
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class RetainMetadata:
+    """Metadata for regular retain operations (non-batched, deprecated async path)."""
+
+    items_count: int
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class ConsolidationMetadata:
+    """Metadata for consolidation operations."""
+
+    # Currently empty, but structure for future fields
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class RefreshMentalModelMetadata:
+    """Metadata for mental model refresh operations."""
+
+    mental_model_id: str
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)

hindsight-api/hindsight_api/engine/parsers/__init__.py

@@ -0,0 +1,62 @@
+"""File parser implementations."""
+
+from .base import FileParser, UnsupportedFileTypeError
+from .iris import IrisParser
+from .markitdown import MarkitdownParser
+
+__all__ = ["FileParser", "UnsupportedFileTypeError", "IrisParser", "MarkitdownParser", "FileParserRegistry"]
+
+
+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())}")
+
+    def list_parsers(self) -> list[str]:
+        """Get list of registered parser names."""
+        return list(self._parsers.keys())

hindsight-api/hindsight_api/engine/parsers/base.py

@@ -0,0 +1,58 @@
+"""Abstract base class for file parsers."""
+
+from abc import ABC, abstractmethod
+
+
+class UnsupportedFileTypeError(Exception):
+    """Raised by a parser when it does not support the given file type."""
+
+    pass
+
+
+class FileParser(ABC):
+    """Abstract base for file to markdown parsers."""
+
+    @abstractmethod
+    async def convert(self, file_data: bytes, filename: str) -> str:
+        """
+        Parse file to markdown.
+
+        Args:
+            file_data: Raw file bytes
+            filename: Original filename (used for format detection)
+
+        Returns:
+            Markdown content as string
+
+        Raises:
+            UnsupportedFileTypeError: If the file type is not supported by this parser
+            RuntimeError: If parsing fails for another reason
+        """
+        pass
+
+    def supports(self, filename: str, content_type: str | None = None) -> bool:
+        """
+        Check if parser supports this file type.
+
+        Override this for local/static extension-based filtering.
+        Parsers that delegate to a remote service should leave this as True
+        and raise UnsupportedFileTypeError from convert() instead.
+
+        Args:
+            filename: File name (used for extension check)
+            content_type: MIME type (optional)
+
+        Returns:
+            True if this parser can handle the file (default: True)
+        """
+        return True
+
+    @abstractmethod
+    def name(self) -> str:
+        """
+        Get parser name.
+
+        Returns:
+            Parser name (e.g., "markitdown")
+        """
+        pass

hindsight-api/hindsight_api/engine/parsers/iris.py

@@ -0,0 +1,137 @@
+"""Iris parser implementation using the Vectorize Iris HTTP API."""
+
+import asyncio
+import logging
+import mimetypes
+import time
+
+import httpx
+
+from .base import FileParser, UnsupportedFileTypeError
+
+logger = logging.getLogger(__name__)
+
+_IRIS_BASE_URL = "https://api.vectorize.io/v1"
+_DEFAULT_POLL_INTERVAL = 2.0  # seconds
+_DEFAULT_TIMEOUT = 300.0  # seconds
+
+
+class IrisParser(FileParser):
+    """
+    Iris file parser using the Vectorize Iris cloud extraction service.
+
+    Uploads files to the Vectorize Iris API, starts an extraction job,
+    and polls until the text is ready. The API determines which file types
+    are supported — UnsupportedFileTypeError is raised if the file is rejected.
+
+    Authentication:
+        Requires HINDSIGHT_API_FILE_PARSER_IRIS_TOKEN and
+        HINDSIGHT_API_FILE_PARSER_IRIS_ORG_ID environment variables,
+        or pass them explicitly via the constructor.
+    """
+
+    def __init__(
+        self,
+        token: str,
+        org_id: str,
+        poll_interval: float = _DEFAULT_POLL_INTERVAL,
+        timeout: float = _DEFAULT_TIMEOUT,
+    ):
+        """
+        Initialize iris parser.
+
+        Args:
+            token: Vectorize API token
+            org_id: Vectorize organization ID
+            poll_interval: Seconds between status poll requests (default: 2)
+            timeout: Maximum seconds to wait for extraction (default: 300)
+        """
+        self._token = token
+        self._org_id = org_id
+        self._poll_interval = poll_interval
+        self._timeout = timeout
+        self._auth_headers = {"Authorization": f"Bearer {token}"}
+
+    async def convert(self, file_data: bytes, filename: str) -> str:
+        """
+        Parse file to text using the Vectorize Iris API.
+
+        Raises:
+            UnsupportedFileTypeError: If the Iris API rejects the file type (4xx)
+            RuntimeError: If extraction fails for another reason
+        """
+        content_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
+
+        async with httpx.AsyncClient() as client:
+            # Step 1: Request a presigned upload URL
+            init_resp = await client.post(
+                f"{_IRIS_BASE_URL}/org/{self._org_id}/files",
+                headers=self._auth_headers,
+                json={"name": filename, "contentType": content_type},
+            )
+            _raise_for_status(init_resp, filename, "file upload init")
+            init_data = init_resp.json()
+            file_id: str = init_data["fileId"]
+            upload_url: str = init_data["uploadUrl"]
+
+            # Step 2: Upload the file bytes to the presigned URL (no auth header)
+            upload_resp = await client.put(
+                upload_url,
+                content=file_data,
+                headers={"Content-Type": content_type},
+            )
+            _raise_for_status(upload_resp, filename, "file upload")
+
+            # Step 3: Start extraction
+            extract_resp = await client.post(
+                f"{_IRIS_BASE_URL}/org/{self._org_id}/extraction",
+                headers=self._auth_headers,
+                json={"fileId": file_id},
+            )
+            _raise_for_status(extract_resp, filename, "start extraction")
+            extraction_id: str = extract_resp.json()["extractionId"]
+
+            # Step 4: Poll until ready or timeout
+            deadline = time.monotonic() + self._timeout
+            while True:
+                status_resp = await client.get(
+                    f"{_IRIS_BASE_URL}/org/{self._org_id}/extraction/{extraction_id}",
+                    headers=self._auth_headers,
+                )
+                _raise_for_status(status_resp, filename, "poll extraction status")
+                status_data = status_resp.json()
+
+                if status_data.get("ready"):
+                    data = status_data.get("data", {})
+                    if not data.get("success"):
+                        error = data.get("error", "unknown error")
+                        raise RuntimeError(f"Iris extraction failed for '{filename}': {error}")
+                    text = data.get("text")
+                    if not text:
+                        raise RuntimeError(f"No content extracted from '{filename}'")
+                    return text
+
+                if time.monotonic() >= deadline:
+                    raise RuntimeError(f"Iris extraction timed out after {self._timeout}s for '{filename}'")
+
+                await asyncio.sleep(self._poll_interval)
+
+    def name(self) -> str:
+        """Get parser name."""
+        return "iris"
+
+
+def _raise_for_status(response: httpx.Response, filename: str, step: str) -> None:
+    """
+    Raise an appropriate error including the response body on HTTP errors.
+
+    Raises UnsupportedFileTypeError for 4xx responses (file rejected by the API),
+    RuntimeError for other HTTP errors.
+    """
+    if not response.is_error:
+        return
+    body = response.text or "<empty>"
+    msg = f"Iris API error during {step} for '{filename}': {response.status_code} {response.reason_phrase} — {body}"
+    if response.is_client_error:
+        raise UnsupportedFileTypeError(msg)
+    raise RuntimeError(msg)

hindsight-api/hindsight_api/engine/parsers/markitdown.py

@@ -0,0 +1,109 @@
+"""Markitdown parser implementation."""
+
+import asyncio
+import logging
+import tempfile
+from pathlib import Path
+
+from .base import FileParser
+
+logger = logging.getLogger(__name__)
+
+
+class MarkitdownParser(FileParser):
+    """
+    Markitdown file parser.
+
+    Uses Microsoft's markitdown library to convert various file formats
+    to markdown including PDF, Office docs, images (via OCR), audio, HTML.
+
+    Supported formats:
+    - PDF (.pdf)
+    - Word (.docx, .doc)
+    - PowerPoint (.pptx, .ppt)
+    - Excel (.xlsx, .xls)
+    - Images (.jpg, .jpeg, .png) - with OCR
+    - HTML (.html, .htm)
+    - Text (.txt, .md)
+    - Audio (.mp3, .wav) - with transcription
+    """
+
+    def __init__(self):
+        """Initialize markitdown parser."""
+        # Lazy import to avoid requiring markitdown for all users
+        try:
+            from markitdown import MarkItDown
+
+            self._markitdown = MarkItDown()
+        except ImportError as e:
+            raise ImportError(
+                "markitdown package is required for file parsing. Install with: pip install markitdown"
+            ) from e
+
+    async def convert(self, file_data: bytes, filename: str) -> str:
+        """Parse file to markdown using markitdown."""
+        # markitdown is synchronous, so we run it in executor to avoid blocking
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(None, self._convert_sync, file_data, filename)
+
+    def _convert_sync(self, file_data: bytes, filename: str) -> str:
+        """Synchronous parsing (runs in thread pool)."""
+        # Write to temp file (markitdown requires file path)
+        with tempfile.NamedTemporaryFile(suffix=Path(filename).suffix, delete=False) as tmp:
+            tmp.write(file_data)
+            tmp_path = tmp.name
+
+        try:
+            # Parse using markitdown
+            result = self._markitdown.convert(tmp_path)
+
+            if not result or not result.text_content:
+                raise RuntimeError(f"No content extracted from '{filename}'")
+
+            return result.text_content
+
+        except Exception as e:
+            logger.error(f"Markitdown parsing failed for {filename}: {e}")
+            raise RuntimeError(f"Failed to parse '{filename}': {e}") from e
+
+        finally:
+            # Clean up temp file
+            try:
+                Path(tmp_path).unlink()
+            except Exception:
+                pass
+
+    def supports(self, filename: str, content_type: str | None = None) -> bool:
+        """Check if markitdown supports this file type."""
+        # Supported extensions (from markitdown docs)
+        supported_extensions = {
+            # Documents
+            ".pdf",
+            ".docx",
+            ".doc",
+            ".pptx",
+            ".ppt",
+            ".xlsx",
+            ".xls",
+            # Images (with OCR)
+            ".jpg",
+            ".jpeg",
+            ".png",
+            # Web
+            ".html",
+            ".htm",
+            # Text
+            ".txt",
+            ".md",
+            ".csv",
+            # Audio (with transcription)
+            ".mp3",
+            ".wav",
+        }
+
+        ext = Path(filename).suffix.lower()
+        return ext in supported_extensions
+
+    def name(self) -> str:
+        """Get parser name."""
+        return "markitdown"

hindsight-api/hindsight_api/engine/providers/anthropic_llm.py

@@ -84,7 +84,7 @@ class AnthropicLLM(LLMInterface):
                 messages=test_messages,
                 max_completion_tokens=10,
                 temperature=0.0,
-                scope="test",
+                scope="verification",
                 max_retries=0,
             )
             logger.info("Anthropic connection verified successfully")
@@ -223,6 +223,24 @@ class AnthropicLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record trace span
+                from hindsight_api.tracing import _serialize_for_span, get_span_recorder
+
+                finish_reason = response.stop_reason if hasattr(response, "stop_reason") else None
+                span_recorder = get_span_recorder()
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=_serialize_for_span(result),
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                )
+
                 # Log slow calls
                 if duration > 10.0:
                     logger.info(
@@ -397,16 +415,41 @@ class AnthropicLLM(LLMInterface):
 
                 # Record metrics
                 metrics = get_metrics_collector()
+                duration = time.time() - start_time
                 metrics.record_llm_call(
                     provider=self.provider,
                     model=self.model,
                     scope=scope,
-                    duration=time.time() - start_time,
+                    duration=duration,
                     input_tokens=input_tokens,
                     output_tokens=output_tokens,
                     success=True,
                 )
 
+                # Record OpenTelemetry span
+                from hindsight_api.tracing import get_span_recorder
+
+                span_recorder = get_span_recorder()
+                # Convert LLMToolCall objects to dicts for span recording
+                tool_calls_dict = (
+                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
+                    if tool_calls
+                    else None
+                )
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=content,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                    tool_calls=tool_calls_dict,
+                )
+
                 return LLMToolCallResult(
                     content=content,
                     tool_calls=tool_calls,

hindsight-api/hindsight_api/engine/providers/claude_code_llm.py

@@ -95,7 +95,7 @@ class ClaudeCodeLLM(LLMInterface):
                 messages=test_messages,
                 max_completion_tokens=10,
                 temperature=0.0,
-                scope="test",
+                scope="verification",
                 max_retries=0,
             )
             logger.info("Claude Code connection verified successfully")
@@ -237,6 +237,26 @@ class ClaudeCodeLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record trace span
+                try:
+                    from hindsight_api.tracing import get_span_recorder
+
+                    span_recorder = get_span_recorder()
+                    span_recorder.record_llm_call(
+                        provider=self.provider,
+                        model=self.model,
+                        scope=scope,
+                        messages=messages,
+                        response_content=result if isinstance(result, str) else result.model_dump_json(),
+                        input_tokens=estimated_input,
+                        output_tokens=estimated_output,
+                        duration=duration,
+                        finish_reason=None,
+                        error=None,
+                    )
+                except Exception:
+                    pass  # logging failure must never affect the operation
+
                 # Log slow calls
                 if duration > 10.0:
                     logger.info(

hindsight-api/hindsight_api/engine/providers/codex_llm.py

@@ -136,6 +136,7 @@ class CodexLLM(LLMInterface):
                 max_retries=2,
                 initial_backoff=0.5,
                 max_backoff=2.0,
+                scope="verification",
             )
             logger.info(f"Codex LLM verified: {self.model}")
         except Exception as e:
@@ -261,6 +262,26 @@ class CodexLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record trace span
+                from hindsight_api.tracing import get_span_recorder
+
+                # Estimate tokens for tracing
+                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
+                estimated_output = len(content) // 4
+                span_recorder = get_span_recorder()
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=result if isinstance(result, str) else json.dumps(result),
+                    input_tokens=estimated_input,
+                    output_tokens=estimated_output,
+                    duration=duration,
+                    finish_reason=None,
+                    error=None,
+                )
+
                 if return_usage:
                     # Codex doesn't provide token counts, estimate based on content
                     estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
@@ -504,6 +525,28 @@ class CodexLLM(LLMInterface):
                 success=True,
             )
 
+            # Record OpenTelemetry span
+            from hindsight_api.tracing import get_span_recorder
+
+            span_recorder = get_span_recorder()
+            # Convert LLMToolCall objects to dicts for span recording
+            tool_calls_dict = (
+                [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls] if tool_calls else None
+            )
+            span_recorder.record_llm_call(
+                provider=self.provider,
+                model=self.model,
+                scope=scope,
+                messages=messages,
+                response_content=content,
+                input_tokens=0,  # Codex doesn't provide token counts
+                output_tokens=0,
+                duration=duration,
+                finish_reason="tool_calls" if tool_calls else "stop",
+                error=None,
+                tool_calls=tool_calls_dict,
+            )
+
             return LLMToolCallResult(
                 content=content,
                 tool_calls=tool_calls,

hindsight-api/hindsight_api/engine/providers/gemini_llm.py

@@ -18,6 +18,7 @@ from google.genai import errors as genai_errors
 from google.genai import types as genai_types
 
 from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
+from hindsight_api.engine.llm_wrapper import parse_llm_json
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
 from hindsight_api.metrics import get_metrics_collector
 
@@ -136,6 +137,7 @@ class GeminiLLM(LLMInterface):
                 max_retries=2,
                 initial_backoff=0.5,
                 max_backoff=2.0,
+                scope="verification",
             )
             logger.info(f"{self.provider.upper()} connection verified successfully")
         except Exception as e:
@@ -220,10 +222,13 @@ class GeminiLLM(LLMInterface):
 
         for attempt in range(max_retries + 1):
             try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=generation_config,
+                response = await asyncio.wait_for(
+                    self._client.aio.models.generate_content(
+                        model=self.model,
+                        contents=gemini_contents,
+                        config=generation_config,
+                    ),
+                    timeout=90.0,  # Safety net for network hangs; valid slow responses are <90s
                 )
 
                 content = response.text
@@ -246,7 +251,7 @@ class GeminiLLM(LLMInterface):
 
                 # Parse structured output if requested
                 if response_format is not None:
-                    json_data = json.loads(content)
+                    json_data = parse_llm_json(content)
                     if skip_validation:
                         result = json_data
                     else:
@@ -275,6 +280,29 @@ class GeminiLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record trace span
+                from hindsight_api.tracing import get_span_recorder
+
+                finish_reason = None
+                if hasattr(response, "candidates") and response.candidates:
+                    if hasattr(response.candidates[0], "finish_reason"):
+                        finish_reason = str(response.candidates[0].finish_reason)
+                span_recorder = get_span_recorder()
+                from hindsight_api.tracing import _serialize_for_span
+
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=_serialize_for_span(result),
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                )
+
                 # Log slow calls
                 if duration > 10.0 and input_tokens > 0:
                     logger.info(
@@ -381,31 +409,57 @@ class GeminiLLM(LLMInterface):
         # Convert messages
         system_instruction = None
         gemini_contents = []
-        for msg in messages:
+        msg_list = list(messages)
+        i = 0
+        while i < len(msg_list):
+            msg = msg_list[i]
             role = msg.get("role", "user")
             content = msg.get("content", "")
 
             if role == "system":
                 system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
+                i += 1
             elif role == "tool":
-                # Gemini uses function_response
-                gemini_contents.append(
-                    genai_types.Content(
-                        role="user",
-                        parts=[
-                            genai_types.Part(
-                                function_response=genai_types.FunctionResponse(
-                                    name=msg.get("name", ""),
-                                    response={"result": content},
-                                )
+                # Gemini requires ALL tool responses for a given model turn to be grouped
+                # into a single Content with multiple FunctionResponse parts.
+                # Consecutive role="tool" messages correspond to one model turn's tool calls.
+                parts = []
+                while i < len(msg_list) and msg_list[i].get("role") == "tool":
+                    tool_msg = msg_list[i]
+                    tool_content = tool_msg.get("content", "")
+                    parts.append(
+                        genai_types.Part(
+                            function_response=genai_types.FunctionResponse(
+                                name=tool_msg.get("name", ""),
+                                response={"result": tool_content},
                             )
-                        ],
+                        )
                     )
-                )
+                    i += 1
+                gemini_contents.append(genai_types.Content(role="user", parts=parts))
             elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
+                tool_calls_in_msg = msg.get("tool_calls", [])
+                if tool_calls_in_msg:
+                    # Convert OpenAI-style tool_calls to Gemini function_call parts
+                    # This is required for proper multi-turn conversation history
+                    parts = []
+                    if content:
+                        parts.append(genai_types.Part(text=content))
+                    for tc in tool_calls_in_msg:
+                        fn = tc.get("function", {})
+                        fn_name = fn.get("name", "")
+                        fn_args_str = fn.get("arguments", "{}")
+                        fn_args = parse_llm_json(fn_args_str)
+                        parts.append(
+                            genai_types.Part(function_call=genai_types.FunctionCall(name=fn_name, args=fn_args))
+                        )
+                    gemini_contents.append(genai_types.Content(role="model", parts=parts))
+                else:
+                    gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
+                i += 1
             else:
                 gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
+                i += 1
 
         config_kwargs: dict[str, Any] = {"tools": gemini_tools}
         if system_instruction:
@@ -413,15 +467,40 @@ class GeminiLLM(LLMInterface):
         if temperature is not None:
             config_kwargs["temperature"] = temperature
 
+        # Map OpenAI-style tool_choice to Gemini FunctionCallingConfig
+        if tool_choice == "required":
+            config_kwargs["tool_config"] = genai_types.ToolConfig(
+                function_calling_config=genai_types.FunctionCallingConfig(
+                    mode="ANY",
+                )
+            )
+        elif isinstance(tool_choice, dict) and tool_choice.get("type") == "function":
+            fn_name = tool_choice.get("function", {}).get("name")
+            if fn_name:
+                config_kwargs["tool_config"] = genai_types.ToolConfig(
+                    function_calling_config=genai_types.FunctionCallingConfig(
+                        mode="ANY",
+                        allowed_function_names=[fn_name],
+                    )
+                )
+        elif tool_choice == "none":
+            config_kwargs["tool_config"] = genai_types.ToolConfig(
+                function_calling_config=genai_types.FunctionCallingConfig(mode="NONE")
+            )
+        # "auto" is the default (no tool_config needed)
+
         config = genai_types.GenerateContentConfig(**config_kwargs)
 
         last_exception = None
         for attempt in range(max_retries + 1):
             try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=config,
+                response = await asyncio.wait_for(
+                    self._client.aio.models.generate_content(
+                        model=self.model,
+                        contents=gemini_contents,
+                        config=config,
+                    ),
+                    timeout=90.0,  # Safety net for network hangs; valid slow responses are <90s
                 )
 
                 # Extract content and tool calls
@@ -466,6 +545,30 @@ class GeminiLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record OpenTelemetry span
+                from hindsight_api.tracing import get_span_recorder
+
+                span_recorder = get_span_recorder()
+                # Convert LLMToolCall objects to dicts for span recording
+                tool_calls_dict = (
+                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
+                    if tool_calls
+                    else None
+                )
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=content,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                    tool_calls=tool_calls_dict,
+                )
+
                 return LLMToolCallResult(
                     content=content,
                     tool_calls=tool_calls,

hindsight-api/hindsight_api/engine/providers/mock_llm.py

@@ -129,6 +129,23 @@ class MockLLM(LLMInterface):
         if self._mock_exception is not None:
             raise self._mock_exception
 
+        # Record trace span (minimal for mock provider)
+        from hindsight_api.tracing import get_span_recorder
+
+        span_recorder = get_span_recorder()
+        span_recorder.record_llm_call(
+            provider=self.provider,
+            model=self.model,
+            scope=scope,
+            messages=messages,
+            response_content="mock response",
+            input_tokens=10,
+            output_tokens=5,
+            duration=0.001,  # Mock calls are instant
+            finish_reason="stop",
+            error=None,
+        )
+
         # Return mock response
         if self._mock_response is not None:
             result = self._mock_response
@@ -192,20 +209,50 @@ class MockLLM(LLMInterface):
         if self._mock_exception is not None:
             raise self._mock_exception
 
+        # Record OpenTelemetry span
+        from hindsight_api.tracing import get_span_recorder
+
+        span_recorder = get_span_recorder()
+
         if self._mock_response is not None:
             if isinstance(self._mock_response, LLMToolCallResult):
-                return self._mock_response
-            # Allow setting just tool calls as a list
-            if isinstance(self._mock_response, list):
-                return LLMToolCallResult(
+                result = self._mock_response
+            elif isinstance(self._mock_response, list):
+                # Allow setting just tool calls as a list
+                result = LLMToolCallResult(
                     tool_calls=[
                         LLMToolCall(id=f"mock_{i}", name=tc["name"], arguments=tc.get("arguments", {}))
                         for i, tc in enumerate(self._mock_response)
                     ],
                     finish_reason="tool_calls",
                 )
+            else:
+                result = LLMToolCallResult(content="mock response", finish_reason="stop")
+        else:
+            result = LLMToolCallResult(content="mock response", finish_reason="stop")
 
-        return LLMToolCallResult(content="mock response", finish_reason="stop")
+        # Record span with mock values
+        # Convert LLMToolCall objects to dicts for span recording
+        tool_calls_dict = (
+            [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in result.tool_calls]
+            if result.tool_calls
+            else None
+        )
+        span_recorder.record_llm_call(
+            provider=self.provider,
+            model=self.model,
+            scope=scope,
+            messages=messages,
+            response_content=result.content,
+            input_tokens=10,  # Mock value
+            output_tokens=5,  # Mock value
+            duration=0.1,  # Mock value
+            finish_reason=result.finish_reason,
+            error=None,
+            tool_calls=tool_calls_dict,
+        )
+
+        return result
 
     async def cleanup(self) -> None:
         """Clean up resources (no-op for mock provider)."""

hindsight-api/hindsight_api/engine/providers/openai_compatible_llm.py

@@ -16,6 +16,7 @@ Features:
 """
 
 import asyncio
+import io
 import json
 import logging
 import os
@@ -96,8 +97,9 @@ class OpenAICompatibleLLM(LLMInterface):
         if self.provider in ("openai", "groq") and not self.api_key:
             raise ValueError(f"API key is required for {self.provider}")
 
-        # Groq service tier configuration
-        self.groq_service_tier = groq_service_tier or os.getenv("HINDSIGHT_API_LLM_GROQ_SERVICE_TIER", "auto")
+        # Service tier configuration (from config, not env vars)
+        self.groq_service_tier = groq_service_tier
+        self.openai_service_tier = kwargs.get("openai_service_tier")
 
         # Get timeout config
         self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
@@ -130,6 +132,7 @@ class OpenAICompatibleLLM(LLMInterface):
                 max_retries=2,
                 initial_backoff=0.5,
                 max_backoff=2.0,
+                scope="verification",
             )
             logger.info(f"Connection verified: {self.provider}/{self.model}")
         except Exception as e:
@@ -368,6 +371,24 @@ class OpenAICompatibleLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record trace span
+                from hindsight_api.tracing import _serialize_for_span, get_span_recorder
+
+                finish_reason = response.choices[0].finish_reason if response.choices else None
+                span_recorder = get_span_recorder()
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=_serialize_for_span(result),
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                )
+
                 # Log slow calls
                 if duration > 10.0 and usage:
                     ratio = max(1, output_tokens) / max(1, input_tokens)
@@ -556,6 +577,30 @@ class OpenAICompatibleLLM(LLMInterface):
                     success=True,
                 )
 
+                # Record OpenTelemetry span
+                from hindsight_api.tracing import get_span_recorder
+
+                span_recorder = get_span_recorder()
+                # Convert LLMToolCall objects to dicts for span recording
+                tool_calls_dict = (
+                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
+                    if tool_calls
+                    else None
+                )
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=content,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                    tool_calls=tool_calls_dict,
+                )
+
                 return LLMToolCallResult(
                     content=content,
                     tool_calls=tool_calls,
@@ -739,6 +784,140 @@ class OpenAICompatibleLLM(LLMInterface):
             raise last_exception
         raise RuntimeError("Ollama call failed after all retries")
 
+    async def supports_batch_api(self) -> bool:
+        """Check if this provider supports batch API operations."""
+        # Only OpenAI and Groq support batch API
+        return self.provider in ("openai", "groq")
+
+    async def submit_batch(
+        self,
+        requests: list[dict[str, Any]],
+        endpoint: str = "/v1/chat/completions",
+        completion_window: str = "24h",
+    ) -> dict[str, Any]:
+        """
+        Submit a batch of requests to OpenAI/Groq Batch API.
+
+        Args:
+            requests: List of request dicts with custom_id, method, url, body
+            endpoint: API endpoint (e.g., "/v1/chat/completions")
+            completion_window: Completion window (e.g., "24h")
+
+        Returns:
+            Dict with batch metadata including batch_id
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        if not await self.supports_batch_api():
+            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+        logger.info(f"Submitting batch with {len(requests)} requests to {self.provider}")
+
+        # Format requests as JSONL
+        jsonl_content = "\n".join(json.dumps(req) for req in requests)
+
+        # Upload file to provider (wrap in BytesIO with filename)
+        file_bytes = io.BytesIO(jsonl_content.encode("utf-8"))
+        file_bytes.name = "batch_input.jsonl"  # OpenAI SDK needs a filename
+
+        file_response = await self._client.files.create(
+            file=file_bytes,
+            purpose="batch",
+        )
+
+        logger.debug(f"Uploaded batch file: {file_response.id}")
+
+        # Create batch
+        batch_response = await self._client.batches.create(
+            input_file_id=file_response.id,
+            endpoint=endpoint,
+            completion_window=completion_window,
+        )
+
+        logger.info(f"Batch submitted: {batch_response.id}, status={batch_response.status}")
+
+        return {
+            "batch_id": batch_response.id,
+            "status": batch_response.status,
+            "input_file_id": file_response.id,
+            "created_at": batch_response.created_at,
+            "request_count": len(requests),
+        }
+
+    async def get_batch_status(self, batch_id: str) -> dict[str, Any]:
+        """
+        Get the status of a batch job.
+
+        Args:
+            batch_id: Batch identifier
+
+        Returns:
+            Dict with status info (batch_id, status, completed_at, etc.)
+        """
+        if not await self.supports_batch_api():
+            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+        batch = await self._client.batches.retrieve(batch_id)
+
+        result = {
+            "batch_id": batch.id,
+            "status": batch.status,
+            "created_at": batch.created_at,
+            "request_counts": {
+                "total": batch.request_counts.total if batch.request_counts else 0,
+                "completed": batch.request_counts.completed if batch.request_counts else 0,
+                "failed": batch.request_counts.failed if batch.request_counts else 0,
+            },
+        }
+
+        if batch.completed_at:
+            result["completed_at"] = batch.completed_at
+        if batch.output_file_id:
+            result["output_file_id"] = batch.output_file_id
+        if batch.error_file_id:
+            result["error_file_id"] = batch.error_file_id
+        if batch.errors:
+            result["errors"] = batch.errors
+
+        return result
+
+    async def retrieve_batch_results(self, batch_id: str) -> list[dict[str, Any]]:
+        """
+        Retrieve completed batch results.
+
+        Args:
+            batch_id: Batch identifier
+
+        Returns:
+            List of result dicts (one per request, matched by custom_id)
+        """
+        if not await self.supports_batch_api():
+            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+        # Get batch status
+        batch = await self._client.batches.retrieve(batch_id)
+
+        if batch.status != "completed":
+            raise ValueError(f"Batch {batch_id} is not completed yet (status: {batch.status})")
+
+        if not batch.output_file_id:
+            raise ValueError(f"Batch {batch_id} has no output file")
+
+        # Download results file
+        logger.debug(f"Downloading results for batch {batch_id} from file {batch.output_file_id}")
+        file_content = await self._client.files.content(batch.output_file_id)
+
+        # Parse JSONL results
+        results = []
+        for line in file_content.text.strip().split("\n"):
+            if line:
+                results.append(json.loads(line))
+
+        logger.info(f"Retrieved {len(results)} results for batch {batch_id}")
+
+        return results
+
     async def cleanup(self) -> None:
         """Clean up resources (close OpenAI client connections)."""
         if hasattr(self, "_client") and self._client:

hindsight-api/hindsight_api/engine/reflect/agent.py

@@ -14,32 +14,26 @@ import re
 import time
 from typing import TYPE_CHECKING, Any, Awaitable, Callable
 
+import tiktoken
+
 from .models import DirectiveInfo, LLMCall, ReflectAgentResult, TokenUsageSummary, ToolCall
 from .prompts import FINAL_SYSTEM_PROMPT, _extract_directive_rules, build_final_prompt, build_system_prompt_for_tools
 from .tools_schema import get_reflect_tools
 
 
 def _build_directives_applied(directives: list[dict[str, Any]] | None) -> list[DirectiveInfo]:
-    """Build list of DirectiveInfo from directive mental models.
-
-    Handles multiple directive formats:
-    1. New format: directives have direct 'content' field
-    2. Fallback: directives have 'description' field
-    """
+    """Build list of DirectiveInfo from directives."""
     if not directives:
         return []
 
-    result = []
-    for directive in directives:
-        directive_id = directive.get("id", "")
-        directive_name = directive.get("name", "")
-
-        # Get content from 'content' field or fallback to 'description'
-        content = directive.get("content", "") or directive.get("description", "")
-
-        result.append(DirectiveInfo(id=directive_id, name=directive_name, content=content))
-
-    return result
+    return [
+        DirectiveInfo(
+            id=directive.get("id", ""),
+            name=directive.get("name", ""),
+            content=directive.get("content", ""),
+        )
+        for directive in directives
+    ]
 
 
 if TYPE_CHECKING:
@@ -267,6 +261,46 @@ OUTPUT:"""
         return None, 0, 0
 
 
+_TIKTOKEN_ENCODING = tiktoken.get_encoding("cl100k_base")
+
+
+def _count_messages_tokens(messages: list[dict[str, Any]]) -> int:
+    """Estimate the token count of the messages list using cl100k_base encoding."""
+    total = 0
+    for msg in messages:
+        content = msg.get("content") or ""
+        if isinstance(content, str):
+            total += len(_TIKTOKEN_ENCODING.encode(content))
+        elif isinstance(content, list):
+            for part in content:
+                if isinstance(part, dict) and isinstance(part.get("text"), str):
+                    total += len(_TIKTOKEN_ENCODING.encode(part["text"]))
+        # Tool call arguments and results also count
+        for tc in msg.get("tool_calls") or []:
+            if isinstance(tc, dict):
+                func = tc.get("function", {})
+                total += len(_TIKTOKEN_ENCODING.encode(func.get("arguments", "")))
+    return total
+
+
+def _is_context_overflow_error(exc: Exception) -> bool:
+    """Return True if the exception signals the LLM context window was exceeded."""
+    msg = str(exc).lower()
+    return any(
+        phrase in msg
+        for phrase in (
+            "context_length_exceeded",
+            "context length exceeded",
+            "maximum context length",
+            "prompt_too_long",
+            "prompt is too long",
+            "resource_exhausted",
+            "input is too long",
+            "too many tokens",
+        )
+    )
+
+
 async def run_reflect_agent(
     llm_config: "LLMProvider",
     bank_id: str,
@@ -274,7 +308,7 @@ async def run_reflect_agent(
     bank_profile: dict[str, Any],
     search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
     search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
     expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
     context: str | None = None,
     max_iterations: int = DEFAULT_MAX_ITERATIONS,
@@ -283,6 +317,7 @@ async def run_reflect_agent(
     directives: list[dict[str, Any]] | None = None,
     has_mental_models: bool = False,
     budget: str | None = None,
+    max_context_tokens: int = 100_000,
 ) -> ReflectAgentResult:
     """
     Execute the reflect agent loop using native tool calling.
@@ -390,19 +425,22 @@ async def run_reflect_agent(
             f"total={elapsed_ms}ms"
         )
 
+    consecutive_errors = 0
     for iteration in range(max_iterations):
         is_last = iteration == max_iterations - 1
 
         if is_last:
             # Force text response on last iteration - no tools
-            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
                     {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                     {"role": "user", "content": prompt},
                 ],
-                scope="reflect_agent_final",
+                scope="reflect",
                 max_completion_tokens=max_tokens,
                 return_usage=True,
             )
@@ -440,17 +478,91 @@ async def run_reflect_agent(
                 directives_applied=directives_applied,
             )
 
+        # Proactive context-window guard: if accumulated messages would exceed the
+        # configured token budget, bail out early and synthesize from what we have.
+        estimated_tokens = _count_messages_tokens(messages)
+        if estimated_tokens >= max_context_tokens and (
+            bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
+        ):
+            logger.warning(
+                f"[REFLECT {reflect_id}] Context budget exceeded on iteration {iteration + 1}: "
+                f"~{estimated_tokens} tokens >= {max_context_tokens} limit. Forcing final synthesis."
+            )
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
+            llm_start = time.time()
+            response, usage = await llm_config.call(
+                messages=[
+                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                scope="reflect",
+                max_completion_tokens=max_tokens,
+                return_usage=True,
+            )
+            llm_duration = int((time.time() - llm_start) * 1000)
+            total_input_tokens += usage.input_tokens
+            total_output_tokens += usage.output_tokens
+            llm_trace.append(
+                {
+                    "scope": "final",
+                    "duration_ms": llm_duration,
+                    "input_tokens": usage.input_tokens,
+                    "output_tokens": usage.output_tokens,
+                }
+            )
+            answer = _clean_answer_text(response.strip())
+
+            structured_output = None
+            if response_schema and answer:
+                structured_output, struct_in, struct_out = await _generate_structured_output(
+                    answer, response_schema, llm_config, reflect_id
+                )
+                total_input_tokens += struct_in
+                total_output_tokens += struct_out
+
+            _log_completion(answer, iteration + 1, forced=True)
+            return ReflectAgentResult(
+                text=answer,
+                structured_output=structured_output,
+                iterations=iteration + 1,
+                tools_called=total_tools_called,
+                tool_trace=tool_trace,
+                llm_trace=_get_llm_trace(),
+                usage=_get_usage(),
+                directives_applied=directives_applied,
+            )
+
         # Call LLM with tools
         llm_start = time.time()
 
+        # Determine tool_choice for this iteration.
+        # Force the full hierarchical retrieval path before allowing auto:
+        # With mental models:
+        #   0 → search_mental_models, 1 → search_observations, 2 → recall, 3+ → auto
+        # Without mental models:
+        #   0 → search_observations, 1 → recall, 2+ → auto
+        if iteration == 0 and has_mental_models:
+            iter_tool_choice: str | dict = {"type": "function", "function": {"name": "search_mental_models"}}
+        elif iteration == 0:
+            iter_tool_choice = {"type": "function", "function": {"name": "search_observations"}}
+        elif iteration == 1 and has_mental_models:
+            iter_tool_choice = {"type": "function", "function": {"name": "search_observations"}}
+        elif iteration == 1 or (iteration == 2 and has_mental_models):
+            iter_tool_choice = {"type": "function", "function": {"name": "recall"}}
+        else:
+            iter_tool_choice = "auto"
+
         try:
             result = await llm_config.call_with_tools(
                 messages=messages,
                 tools=tools,
-                scope="reflect_agent",
-                tool_choice="required" if iteration == 0 else "auto",  # Force tool use on first iteration
+                scope="reflect_tool_call",
+                tool_choice=iter_tool_choice,
             )
             llm_duration = int((time.time() - llm_start) * 1000)
+            consecutive_errors = 0
             total_input_tokens += result.input_tokens
             total_output_tokens += result.output_tokens
             llm_trace.append(
@@ -464,22 +576,32 @@ async def run_reflect_agent(
 
         except Exception as e:
             err_duration = int((time.time() - llm_start) * 1000)
+            consecutive_errors += 1
             logger.warning(f"[REFLECT {reflect_id}] LLM error on iteration {iteration + 1}: {e} ({err_duration}ms)")
             llm_trace.append({"scope": f"agent_{iteration + 1}_err", "duration_ms": err_duration})
-            # Guardrail: If no evidence gathered yet, retry
             has_gathered_evidence = (
                 bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
             )
-            if not has_gathered_evidence and iteration < max_iterations - 1:
+            # Context overflow errors must never be retried — retrying would only make them worse.
+            # Skip straight to final synthesis with whatever evidence we have.
+            if _is_context_overflow_error(e):
+                logger.warning(
+                    f"[REFLECT {reflect_id}] Context window exceeded on iteration {iteration + 1}, "
+                    "forcing final synthesis from gathered evidence."
+                )
+            # For other errors: retry if no evidence yet (but cap consecutive errors to avoid long hangs)
+            elif not has_gathered_evidence and iteration < max_iterations - 1 and consecutive_errors < 2:
                 continue
-            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
                     {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                     {"role": "user", "content": prompt},
                 ],
-                scope="reflect_agent_final",
+                scope="reflect",
                 max_completion_tokens=max_tokens,
                 return_usage=True,
             )
@@ -543,14 +665,16 @@ async def run_reflect_agent(
                     directives_applied=directives_applied,
                 )
             # Empty response, force final
-            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
                     {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                     {"role": "user", "content": prompt},
                 ],
-                scope="reflect_agent_final",
+                scope="reflect",
                 max_completion_tokens=max_tokens,
                 return_usage=True,
             )
@@ -617,23 +741,30 @@ async def run_reflect_agent(
                 )
                 continue
 
-            # Process done tool
-            return await _process_done_tool(
-                done_call,
-                available_memory_ids,
-                available_mental_model_ids,
-                available_observation_ids,
-                iteration + 1,
-                total_tools_called,
-                tool_trace,
-                _get_llm_trace(),
-                _get_usage(),
-                _log_completion,
-                reflect_id,
-                directives_applied=directives_applied,
-                llm_config=llm_config,
-                response_schema=response_schema,
-            )
+            # Process done tool - wrap with tool call span
+            from hindsight_api.tracing import get_tracer
+
+            tracer = get_tracer()
+            span_name = "hindsight.reflect_tool_call"
+            with tracer.start_as_current_span(span_name) as span:
+                span.set_attribute("hindsight.scope", "reflect_tool_call")
+                span.set_attribute("hindsight.operation", "reflect_tool_call")
+                return await _process_done_tool(
+                    done_call,
+                    available_memory_ids,
+                    available_mental_model_ids,
+                    available_observation_ids,
+                    iteration + 1,
+                    total_tools_called,
+                    tool_trace,
+                    _get_llm_trace(),
+                    _get_usage(),
+                    _log_completion,
+                    reflect_id,
+                    directives_applied=directives_applied,
+                    llm_config=llm_config,
+                    response_schema=response_schema,
+                )
 
         # Execute other tools in parallel (exclude done tool in all its format variants)
         other_tools = [tc for tc in result.tool_calls if not _is_done_tool(tc.name)]
@@ -800,9 +931,9 @@ async def _process_done_tool(
         answer = "No answer provided."
 
     # Validate IDs (only include IDs that were actually retrieved)
-    used_memory_ids = [mid for mid in args.get("memory_ids", []) if mid in available_memory_ids]
-    used_mental_model_ids = [mid for mid in args.get("mental_model_ids", []) if mid in available_mental_model_ids]
-    used_observation_ids = [oid for oid in args.get("observation_ids", []) if oid in available_observation_ids]
+    used_memory_ids = [mid for mid in (args.get("memory_ids") or []) if mid in available_memory_ids]
+    used_mental_model_ids = [mid for mid in (args.get("mental_model_ids") or []) if mid in available_mental_model_ids]
+    used_observation_ids = [oid for oid in (args.get("observation_ids") or []) if oid in available_observation_ids]
 
     # Generate structured output if schema provided
     structured_output = None
@@ -838,21 +969,71 @@ async def _execute_tool_with_timing(
     tc: "LLMToolCall",
     search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
     search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
     expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
 ) -> tuple[dict[str, Any], int]:
     """Execute a tool call and return result with timing."""
-    start = time.time()
-    result = await _execute_tool(
-        tc.name,
-        tc.arguments,
-        search_mental_models_fn,
-        search_observations_fn,
-        recall_fn,
-        expand_fn,
-    )
-    duration_ms = int((time.time() - start) * 1000)
-    return result, duration_ms
+    from hindsight_api.tracing import get_tracer
+
+    start_time = time.time()
+
+    # Create span for tool execution
+    tracer = get_tracer()
+    # Normalize tool name for span
+    normalized_name = _normalize_tool_name(tc.name)
+    span_name = f"hindsight.reflect_tool_exec.{normalized_name}"
+
+    # Calculate timestamps
+    start_time_ns = time.time_ns()
+
+    with tracer.start_as_current_span(
+        span_name,
+        start_time=start_time_ns,
+        end_on_exit=False,
+    ) as span:
+        # Set attributes
+        span.set_attribute("hindsight.tool.name", normalized_name)
+        span.set_attribute("hindsight.tool.id", tc.id)
+        span.set_attribute("hindsight.tool.arguments", json.dumps(tc.arguments))
+
+        try:
+            result = await _execute_tool(
+                tc.name,
+                tc.arguments,
+                search_mental_models_fn,
+                search_observations_fn,
+                recall_fn,
+                expand_fn,
+            )
+
+            # Set success attributes
+            if isinstance(result, dict) and "error" in result:
+                from opentelemetry.trace import Status, StatusCode
+
+                span.set_status(Status(StatusCode.ERROR, result["error"]))
+            else:
+                from opentelemetry.trace import Status, StatusCode
+
+                span.set_status(Status(StatusCode.OK))
+
+            duration_ms = int((time.time() - start_time) * 1000)
+            span.set_attribute("hindsight.tool.duration_ms", duration_ms)
+
+            # End span with correct timestamp
+            end_time_ns = time.time_ns()
+            span.end(end_time=end_time_ns)
+
+            return result, duration_ms
+        except Exception as e:
+            from opentelemetry.trace import Status, StatusCode
+
+            span.set_status(Status(StatusCode.ERROR, str(e)))
+            span.record_exception(e)
+            duration_ms = int((time.time() - start_time) * 1000)
+            span.set_attribute("hindsight.tool.duration_ms", duration_ms)
+            end_time_ns = time.time_ns()
+            span.end(end_time=end_time_ns)
+            raise
 
 
 async def _execute_tool(
@@ -860,7 +1041,7 @@ async def _execute_tool(
     args: dict[str, Any],
     search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
     search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
     expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
 ) -> dict[str, Any]:
     """Execute a single tool by name."""
@@ -886,7 +1067,8 @@ async def _execute_tool(
         if not query:
             return {"error": "recall requires a query parameter"}
         max_tokens = max(int(args.get("max_tokens") or 2048), 1000)  # Default 2048, min 1000
-        return await recall_fn(query, max_tokens)
+        max_chunk_tokens = max(int(args.get("max_chunk_tokens") or 1000), 1000)  # Always enabled, min 1000
+        return await recall_fn(query, max_tokens, max_chunk_tokens)
 
     elif tool_name == "expand":
         memory_ids = args.get("memory_ids", [])
@@ -914,9 +1096,9 @@ def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
     elif tool_name == "recall":
         query = args.get("query", "")
         query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-        # Show actual value used (default 2048, min 1000)
         max_tokens = max(int(args.get("max_tokens") or 2048), 1000)
-        return f"(query={query_preview}, max_tokens={max_tokens})"
+        max_chunk_tokens = max(int(args.get("max_chunk_tokens") or 1000), 1000)
+        return f"(query={query_preview}, max_tokens={max_tokens}, max_chunk_tokens={max_chunk_tokens})"
     elif tool_name == "expand":
         memory_ids = args.get("memory_ids", [])
         depth = args.get("depth", "chunk")

hindsight-api/hindsight_api/engine/reflect/prompts.py

@@ -10,59 +10,30 @@ The reflect agent uses hierarchical retrieval:
 import json
 from typing import Any
 
+import tiktoken
+
+_TIKTOKEN_ENCODING = tiktoken.get_encoding("cl100k_base")
+
+# Fraction of max_context_tokens reserved for tool results in the final synthesis prompt.
+# The remainder covers the system prompt, question, bank context, and output tokens.
+_FINAL_PROMPT_CONTEXT_FRACTION = 0.8
+
 
 def _extract_directive_rules(directives: list[dict[str, Any]]) -> list[str]:
-    """
-    Extract directive rules as a list of strings.
-
-    Args:
-        directives: List of directives with name and content
-
-    Returns:
-        List of directive rule strings
-    """
+    """Extract directive rules as a list of strings."""
     rules = []
     for directive in directives:
-        directive_name = directive.get("name", "")
-        # New format: directives have direct content field
+        name = directive.get("name", "")
         content = directive.get("content", "")
         if content:
-            if directive_name:
-                rules.append(f"**{directive_name}**: {content}")
-            else:
-                rules.append(content)
-        else:
-            # Legacy format: check for observations
-            observations = directive.get("observations", [])
-            if observations:
-                for obs in observations:
-                    # Support both Pydantic Observation objects and dicts
-                    if hasattr(obs, "title"):
-                        title = obs.title
-                        obs_content = obs.content
-                    else:
-                        title = obs.get("title", "")
-                        obs_content = obs.get("content", "")
-                    if title and obs_content:
-                        rules.append(f"**{title}**: {obs_content}")
-                    elif obs_content:
-                        rules.append(obs_content)
-            elif directive_name:
-                # Fallback to description
-                desc = directive.get("description", "")
-                if desc:
-                    rules.append(f"**{directive_name}**: {desc}")
+            rules.append(f"**{name}**: {content}" if name else content)
     return rules
 
 
 def build_directives_section(directives: list[dict[str, Any]]) -> str:
-    """
-    Build the directives section for the system prompt.
+    """Build the directives section for the system prompt.
 
     Directives are hard rules that MUST be followed in all responses.
-
-    Args:
-        directives: List of directive mental models with observations
     """
     if not directives:
         return ""
@@ -169,6 +140,12 @@ def build_system_prompt_for_tools(
 
     parts.extend(
         [
+            "## LANGUAGE RULE (default - directives take precedence)",
+            "- By default, detect the language of the user's question and respond in that SAME language.",
+            "- If the question is in Chinese, respond in Chinese. If in Japanese, respond in Japanese.",
+            "- IMPORTANT: The DIRECTIVES section above has HIGHER PRIORITY than this rule.",
+            "  If a directive specifies a language (e.g. 'Always respond in French'), follow the directive.",
+            "",
             "## CRITICAL RULES",
             "- ONLY use information from tool results - no external knowledge or guessing",
             "- You SHOULD synthesize, infer, and reason from the retrieved memories",
@@ -205,6 +182,7 @@ def build_system_prompt_for_tools(
                 "### 3. RAW FACTS (recall) - Ground Truth",
                 "- Individual memories (world facts and experiences)",
                 "- Use when: no mental models/observations exist, they're stale, or you need specific details",
+                "- MANDATORY: If search_mental_models and search_observations both return 0 results, you MUST call recall() before giving up",
                 "- This is the source of truth that other levels are built from",
                 "",
             ]
@@ -222,6 +200,7 @@ def build_system_prompt_for_tools(
                 "### 2. RAW FACTS (recall) - Ground Truth",
                 "- Individual memories (world facts and experiences)",
                 "- Use when: no observations exist, they're stale, or you need specific details",
+                "- MANDATORY: If search_observations returns 0 results or count=0, you MUST call recall() before giving up",
                 "- This is the source of truth that observations are built from",
                 "",
             ]
@@ -299,7 +278,7 @@ def build_system_prompt_for_tools(
         parts.extend(
             [
                 "1. First, try search_observations() - check for consolidated knowledge",
-                "2. If observations are stale OR you need specific details, use recall() for raw facts",
+                "2. If search_observations returns 0 results OR observations are stale, you MUST call recall() for raw facts",
                 "3. Use expand() if you need more context on specific memories",
                 "4. When ready, call done() with your answer and supporting IDs",
             ]
@@ -315,6 +294,7 @@ def build_system_prompt_for_tools(
             "- Format for clarity and readability with proper spacing and hierarchy",
             "- NEVER include memory IDs, UUIDs, or 'Memory references' in the answer text",
             "- Put IDs ONLY in the memory_ids/mental_model_ids/observation_ids arrays, not in the answer",
+            "- CRITICAL: This is a NON-CONVERSATIONAL system. NEVER ask follow-up questions, offer further assistance, or suggest next steps. Your answer must be complete and self-contained. The user cannot reply.",
         ]
     )
 
@@ -422,6 +402,7 @@ def build_final_prompt(
     context_history: list[dict],
     bank_profile: dict,
     additional_context: str | None = None,
+    max_context_tokens: int = 100_000,
 ) -> str:
     """Build the final prompt when forcing a text response (no tools)."""
     parts = []
@@ -451,18 +432,32 @@ def build_final_prompt(
     if additional_context:
         parts.append(f"\n## Additional Context\n{additional_context}")
 
-    # Tool call history
+    # Tool call history — include as many entries as fit within the token budget,
+    # preferring the most recent calls (they tend to be the most targeted).
     if context_history:
         parts.append("\n## Retrieved Data (synthesize and reason from this data)")
-        for entry in context_history:
+        token_budget = int(max_context_tokens * _FINAL_PROMPT_CONTEXT_FRACTION)
+        # Render entries newest-first, then reverse so the prompt reads chronologically.
+        rendered: list[str] = []
+        truncated = False
+        for entry in reversed(context_history):
             tool = entry["tool"]
             output = entry["output"]
-            # Format as proper JSON for LLM readability
             try:
                 output_str = json.dumps(output, indent=2, default=str)
             except (TypeError, ValueError):
                 output_str = str(output)
-            parts.append(f"\n### From {tool}:\n```json\n{output_str}\n```")
+            block = f"\n### From {tool}:\n```json\n{output_str}\n```"
+            block_tokens = len(_TIKTOKEN_ENCODING.encode(block))
+            if block_tokens > token_budget:
+                truncated = True
+                break
+            rendered.append(block)
+            token_budget -= block_tokens
+        for block in reversed(rendered):
+            parts.append(block)
+        if truncated:
+            parts.append("\n*Note: Some earlier tool results were omitted to stay within the context window.*")
     else:
         parts.append("\n## Retrieved Data\nNo data was retrieved.")
 
@@ -510,4 +505,6 @@ CRITICAL: Output ONLY the final synthesized answer. Do NOT include:
 - Meta-commentary about what you're doing ("I'll search...", "Let me analyze...")
 - Explanations of your reasoning process
 - Descriptions of your approach
-Just provide the direct answer with proper markdown formatting."""
+Just provide the direct answer with proper markdown formatting.
+
+CRITICAL: This is a NON-CONVERSATIONAL system. NEVER ask follow-up questions, offer to search again, suggest alternatives, or end with anything like "Would you like me to..." or "Let me know if...". The user cannot reply. Your answer must be complete and self-contained."""

hindsight-api/hindsight_api/engine/reflect/tools.py

@@ -9,7 +9,7 @@ Implements hierarchical retrieval:
 
 import logging
 import uuid
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
@@ -20,9 +20,6 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
-# Observation is considered stale if not updated in this many days
-STALE_THRESHOLD_DAYS = 7
-
 
 async def tool_search_mental_models(
     conn: "Connection",
@@ -33,6 +30,7 @@ async def tool_search_mental_models(
     tags: list[str] | None = None,
     tags_match: str = "any",
     exclude_ids: list[str] | None = None,
+    pending_consolidation: int = 0,
 ) -> dict[str, Any]:
     """
     Search user-curated mental models by semantic similarity.
@@ -87,7 +85,6 @@ async def tool_search_mental_models(
         *params,
     )
 
-    now = datetime.now(timezone.utc)
     mental_models = []
 
     for row in rows:
@@ -95,11 +92,10 @@ async def tool_search_mental_models(
         if last_refreshed_at and last_refreshed_at.tzinfo is None:
             last_refreshed_at = last_refreshed_at.replace(tzinfo=timezone.utc)
 
-        # Calculate freshness
-        is_stale = False
-        if last_refreshed_at:
-            age = now - last_refreshed_at
-            is_stale = age > timedelta(days=STALE_THRESHOLD_DAYS)
+        # A mental model is stale when there are memories that haven't been consolidated yet —
+        # the same signal used for observations staleness.
+        is_stale = pending_consolidation > 0
+        staleness_reason = f"{pending_consolidation} memories pending consolidation" if is_stale else None
 
         mental_models.append(
             {
@@ -110,6 +106,7 @@ async def tool_search_mental_models(
                 "relevance": round(row["relevance"], 4),
                 "updated_at": last_refreshed_at.isoformat() if last_refreshed_at else None,
                 "is_stale": is_stale,
+                "staleness_reason": staleness_reason,
             }
         )
 
@@ -132,7 +129,7 @@ async def tool_search_observations(
     pending_consolidation: int = 0,
 ) -> dict[str, Any]:
     """
-    Search consolidated observations using recall with include_observations.
+    Search consolidated observations using recall with include_source_facts.
 
     Observations are auto-generated from memories. Returns freshness info
     so the agent knows if it should also verify with recall().
@@ -149,72 +146,24 @@ async def tool_search_observations(
         pending_consolidation: Number of memories waiting to be consolidated
 
     Returns:
-        Dict with matching observations including freshness info
+        Dict with matching observations including freshness info and source memories
     """
-    from ..memory_engine import fq_table
-
-    # Use recall to search observations (they come back in results field when fact_type=["observation"])
     result = await memory_engine.recall_async(
         bank_id=bank_id,
         query=query,
-        fact_type=["observation"],  # Only retrieve observations
-        max_tokens=max_tokens,  # Token budget controls how many observations are returned
+        fact_type=["observation"],
+        max_tokens=max_tokens,
         enable_trace=False,
         request_context=request_context,
         tags=tags,
         tags_match=tags_match,
+        include_source_facts=True,
+        max_source_facts_tokens=-1,  # No token limit — include all source facts
         _connection_budget=1,
         _quiet=True,
     )
 
-    observations = []
-
-    # When fact_type=["observation"], results come back in `results` field as MemoryFact objects
-    # We need to fetch additional fields (proof_count, source_memory_ids) from the database
-    if result.results:
-        obs_ids = [m.id for m in result.results]
-
-        # Fetch proof_count and source_memory_ids for these observations
-        pool = await memory_engine._get_pool()
-        async with pool.acquire() as conn:
-            obs_rows = await conn.fetch(
-                f"""
-                SELECT id, proof_count, source_memory_ids
-                FROM {fq_table("memory_units")}
-                WHERE id = ANY($1::uuid[])
-                """,
-                obs_ids,
-            )
-            obs_data = {str(row["id"]): row for row in obs_rows}
-
-        for m in result.results:
-            # Get additional data from DB lookup
-            extra = obs_data.get(m.id, {})
-            proof_count = extra.get("proof_count", 1) if extra else 1
-            source_ids = extra.get("source_memory_ids", []) if extra else []
-            # Convert UUIDs to strings
-            source_memory_ids = [str(sid) for sid in (source_ids or [])]
-
-            # Determine staleness
-            is_stale = False
-            staleness_reason = None
-            if pending_consolidation > 0:
-                is_stale = True
-                staleness_reason = f"{pending_consolidation} memories pending consolidation"
-
-            observations.append(
-                {
-                    "id": str(m.id),
-                    "text": m.text,
-                    "proof_count": proof_count,
-                    "source_memory_ids": source_memory_ids,
-                    "tags": m.tags or [],
-                    "is_stale": is_stale,
-                    "staleness_reason": staleness_reason,
-                }
-            )
-
-    # Return freshness info (more understandable than raw pending_consolidation count)
+    is_stale = pending_consolidation > 0
     if pending_consolidation == 0:
         freshness = "up_to_date"
     elif pending_consolidation < 10:
@@ -224,8 +173,10 @@ async def tool_search_observations(
 
     return {
         "query": query,
-        "count": len(observations),
-        "observations": observations,
+        "count": len(result.results),
+        "observations": [m.model_dump() for m in result.results],
+        "source_facts": {k: v.model_dump() for k, v in (result.source_facts or {}).items()},
+        "is_stale": is_stale,
         "freshness": freshness,
     }
 
@@ -236,10 +187,10 @@ async def tool_recall(
     query: str,
     request_context: "RequestContext",
     max_tokens: int = 2048,
-    max_results: int = 50,
     tags: list[str] | None = None,
     tags_match: str = "any",
     connection_budget: int = 1,
+    max_chunk_tokens: int = 1000,
 ) -> dict[str, Any]:
     """
     Search memories using TEMPR retrieval.
@@ -253,18 +204,19 @@ async def tool_recall(
         query: Search query
         request_context: Request context for authentication
         max_tokens: Maximum tokens for results (default 2048)
-        max_results: Maximum number of results
         tags: Filter by tags (includes untagged memories)
         tags_match: How to match tags - "any" (OR), "all" (AND), or "exact"
         connection_budget: Max DB connections for this recall (default 1 for internal ops)
+        max_chunk_tokens: Maximum tokens for raw source chunk text (default 1000, always included)
 
     Returns:
-        Dict with list of matching memories
+        Dict with list of matching memories including raw chunk text
     """
+    include_chunks = True
     result = await memory_engine.recall_async(
         bank_id=bank_id,
         query=query,
-        fact_type=["experience", "world"],  # Exclude opinions and observations
+        fact_type=["experience", "world"],
         max_tokens=max_tokens,
         enable_trace=False,
         request_context=request_context,
@@ -272,24 +224,14 @@ async def tool_recall(
         tags_match=tags_match,
         _connection_budget=connection_budget,
         _quiet=True,  # Suppress logging for internal operations
+        include_chunks=include_chunks,
+        max_chunk_tokens=max_chunk_tokens,
     )
 
-    memories = []
-    for m in result.results[:max_results]:
-        memories.append(
-            {
-                "id": str(m.id),
-                "text": m.text,
-                "type": m.fact_type,
-                "entities": m.entities or [],
-                "occurred": m.occurred_start,  # Already ISO format string
-            }
-        )
-
     return {
         "query": query,
-        "count": len(memories),
-        "memories": memories,
+        "memories": [m.model_dump() for m in result.results],
+        "chunks": {k: v.model_dump() for k, v in (result.chunks or {}).items()},
     }
 
 

hindsight-api/hindsight_api/engine/reflect/tools_schema.py

@@ -47,7 +47,8 @@ TOOL_SEARCH_OBSERVATIONS = {
         "description": (
             "Search consolidated observations (auto-generated knowledge). These are automatically "
             "synthesized from memories. Returns observations with freshness info (updated_at, is_stale). "
-            "If an observation is STALE, you should ALSO use recall() to verify with current facts."
+            "If an observation is STALE, you should ALSO use recall() to verify with current facts. "
+            "IMPORTANT: If search_mental_models is available, you MUST call it FIRST before using this tool."
         ),
         "parameters": {
             "type": "object",
@@ -95,6 +96,10 @@ TOOL_RECALL = {
                     "type": "integer",
                     "description": "Optional limit on result size (default 2048). Use higher values for broader searches.",
                 },
+                "max_chunk_tokens": {
+                    "type": "integer",
+                    "description": "Maximum tokens for raw source chunk text included alongside each memory fact (default 1000, min 1000). Chunks provide the surrounding context the fact was extracted from. Increase for broader context.",
+                },
             },
             "required": ["reason", "query"],
         },
@@ -139,7 +144,7 @@ TOOL_DONE_ANSWER = {
             "properties": {
                 "answer": {
                     "type": "string",
-                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array. LANGUAGE: By default, write in the SAME language as the user's question. However, if a language directive in the system prompt specifies a different language, follow that directive instead.",
                 },
                 "memory_ids": {
                     "type": "array",
@@ -190,7 +195,11 @@ def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
                 "properties": {
                     "answer": {
                         "type": "string",
-                        "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                        "description": (
+                            "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. "
+                            "NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array. "
+                            f"MANDATORY: Your answer MUST comply with ALL directives:\n{rules_list}"
+                        ),
                     },
                     "memory_ids": {
                         "type": "array",

hindsight-api/hindsight_api/engine/response_models.py

@@ -159,6 +159,10 @@ class MemoryFact(BaseModel):
         None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)"
     )
     tags: list[str] | None = Field(None, description="Visibility scope tags associated with this fact")
+    source_fact_ids: list[str] | None = Field(
+        None,
+        description="IDs of source facts this observation was derived from (observation type only, when source_facts is enabled)",
+    )
 
 
 class ChunkInfo(BaseModel):
@@ -226,6 +230,9 @@ class RecallResult(BaseModel):
     chunks: dict[str, ChunkInfo] | None = Field(
         None, description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
     )
+    source_facts: dict[str, MemoryFact] | None = Field(
+        None, description="Source facts for observation-type results, keyed by fact ID"
+    )
 
 
 class ReflectResult(BaseModel):

hindsight-api/hindsight_api/engine/retain/embedding_processing.py

@@ -27,11 +27,21 @@ def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list
     """
     augmented_texts = []
     for fact in facts:
-        # Use occurred_start as the representative date
+        # Use occurred_start as the representative date, fall back to mentioned_at
         fact_date = fact.occurred_start or fact.mentioned_at
-        readable_date = format_date_fn(fact_date)
-        # Augment text with date for embedding (but store original text in DB)
-        augmented_text = f"{fact.fact_text} (happened in {readable_date})"
+        # Augment text with date and entity names for embedding (but store original text in DB)
+        # Entity names (including key:value labels) improve retrieval without polluting stored content
+        if fact_date is not None:
+            readable_date = format_date_fn(fact_date)
+            if fact.occurred_end and fact.occurred_end != fact.occurred_start:
+                readable_end = format_date_fn(fact.occurred_end)
+                augmented_text = f"{fact.fact_text} (happened from {readable_date} to {readable_end})"
+            else:
+                augmented_text = f"{fact.fact_text} (happened in {readable_date})"
+        else:
+            augmented_text = fact.fact_text
+        if fact.entities:
+            augmented_text = f"{augmented_text} [{', '.join(fact.entities)}]"
         augmented_texts.append(augmented_text)
     return augmented_texts
 

hindsight-api/hindsight_api/engine/retain/entity_labels.py

@@ -0,0 +1,194 @@
+"""
+Entity labels models and helpers for retain pipeline.
+
+Defines a controlled vocabulary of key:value classification labels
+(e.g., 'pedagogy:scaffolding', 'interest:active') that are extracted
+at retain time and stored as entities.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field, create_model
+
+
+class LabelValue(BaseModel):
+    """A single allowed value for a label group."""
+
+    value: str
+    description: str = ""
+
+
+class LabelGroup(BaseModel):
+    """A label group (dimension) with its type and allowed values."""
+
+    key: str
+    description: str = ""
+    type: Literal["value", "multi-values", "text"] = "value"
+    optional: bool = True
+    tag: bool = False
+    values: list[LabelValue] = []
+
+
+class EntityLabelsConfig(BaseModel):
+    """Entity labels configuration for a bank (controlled vocabulary)."""
+
+    attributes: list[LabelGroup] = []
+
+
+def parse_entity_labels(raw: dict | list | None) -> EntityLabelsConfig | None:
+    """
+    Parse raw entity labels config into EntityLabelsConfig.
+
+    Accepts:
+    - None → returns None
+    - list → list of attribute dicts (each may use legacy free_values/multi_value or new type field)
+    - dict → {attributes: [...]}
+
+    Legacy migration (backward-compat):
+    - free_values=True          → type="text"
+    - multi_value=True          → type="multi-values"
+    - neither / free_values=False → type="value"
+
+    Args:
+        raw: Raw entity labels config from bank config
+
+    Returns:
+        EntityLabelsConfig or None if raw is None/empty
+    """
+    if raw is None:
+        return None
+
+    if isinstance(raw, list):
+        if not raw:
+            return None
+        attributes = [LabelGroup.model_validate(_migrate_label_group(a)) for a in raw]
+        return EntityLabelsConfig(attributes=attributes)
+
+    if isinstance(raw, dict):
+        attrs_raw = raw.get("attributes", [])
+        if not attrs_raw:
+            return None
+        attributes = [LabelGroup.model_validate(_migrate_label_group(a)) for a in attrs_raw]
+        return EntityLabelsConfig(attributes=attributes)
+
+    return None
+
+
+def _migrate_label_group(raw: dict) -> dict:
+    """Migrate legacy free_values/multi_value fields to the new type field."""
+    if not isinstance(raw, dict) or "type" in raw:
+        return raw
+    patched = dict(raw)
+    if patched.get("free_values"):
+        patched["type"] = "text"
+    elif patched.get("multi_value"):
+        patched["type"] = "multi-values"
+    else:
+        patched["type"] = "value"
+    # Remove legacy keys so Pydantic doesn't error on unknown fields
+    patched.pop("free_values", None)
+    patched.pop("multi_value", None)
+    return patched
+
+
+def build_labels_model(labels_cfg: EntityLabelsConfig) -> type[BaseModel] | None:
+    """
+    Build a dynamic Pydantic model for structured label extraction.
+
+    Each LabelGroup becomes a typed field based on its type:
+    - type="text"                        → str | None  (always optional)
+    - type="value",   optional=True      → Literal["v1","v2"] | None
+    - type="value",   optional=False     → Literal["v1","v2"]  (required)
+    - type="multi-values"                → list[Literal["v1","v2"]]
+
+    Args:
+        labels_cfg: Parsed EntityLabelsConfig
+
+    Returns:
+        Dynamic Pydantic model class, or None if no groups defined
+    """
+    fields: dict = {}
+    for group in labels_cfg.attributes:
+        if not group.key:
+            continue
+        description = group.description or group.key
+
+        if group.type == "text":
+            # Free-form: any string value accepted, always optional
+            fields[group.key] = (str | None, Field(default=None, description=description))
+        else:
+            # Enum-constrained: must have defined values
+            if not group.values:
+                continue
+            values = tuple(v.value for v in group.values if v.value)
+            if not values:
+                continue
+            # Literal[("v1", "v2")] is equivalent to Literal["v1", "v2"] in Python 3.11+
+            literal_type = Literal[values]  # type: ignore[valid-type]
+            if group.type == "multi-values":
+                fields[group.key] = (
+                    list[literal_type],  # type: ignore[valid-type]
+                    Field(default_factory=list, description=description),
+                )
+            elif group.optional:
+                fields[group.key] = (
+                    literal_type | None,  # type: ignore[valid-type]
+                    Field(default=None, description=description),
+                )
+            else:
+                fields[group.key] = (
+                    literal_type,  # type: ignore[valid-type]
+                    Field(description=description),
+                )
+
+    if not fields:
+        return None
+
+    return create_model("Labels", **fields)
+
+
+def is_label_entity(text: str, labels_cfg: EntityLabelsConfig, labels_lookup: set[str]) -> bool:
+    """
+    Return True if entity text belongs to any configured label group.
+
+    For enum groups: checks the pre-built lookup set.
+    For text groups: checks that the text starts with a known key prefix.
+    """
+    if text.lower() in labels_lookup:
+        return True
+    for group in labels_cfg.attributes:
+        if group.type == "text" and group.key and text.lower().startswith(f"{group.key.lower()}:"):
+            return True
+    return False
+
+
+def build_labels_lookup(labels_cfg: EntityLabelsConfig | list | None) -> set[str]:
+    """
+    Build a set of valid 'key:value' label strings (lowercase) for fast lookup.
+
+    Accepts either EntityLabelsConfig or raw list/None for backwards compatibility.
+
+    Args:
+        labels_cfg: EntityLabelsConfig, raw list of attribute dicts, or None
+
+    Returns:
+        Set of lowercase 'key:value' strings
+    """
+    if labels_cfg is None:
+        return set()
+
+    # Accept raw list/dict for backwards compatibility
+    if not isinstance(labels_cfg, EntityLabelsConfig):
+        parsed = parse_entity_labels(labels_cfg)
+        if parsed is None:
+            return set()
+        labels_cfg = parsed
+
+    valid = set()
+    for group in labels_cfg.attributes:
+        if group.type == "text":
+            continue  # No fixed vocabulary — all values accepted in post-processing
+        for v in group.values:
+            if group.key and v.value:
+                valid.add(f"{group.key}:{v.value}".lower())
+    return valid

hindsight-api/hindsight_api/engine/retain/entity_processing.py

@@ -20,6 +20,7 @@ async def process_entities_batch(
     facts: list[ProcessedFact],
     log_buffer: list[str] = None,
     user_entities_per_content: dict[int, list[dict]] = None,
+    entity_labels: list | None = None,
 ) -> list[EntityLink]:
     """
     Process entities for all facts and create entity links.
@@ -90,6 +91,7 @@ async def process_entities_batch(
         fact_dates,
         entities_per_fact,
         log_buffer,  # Pass log_buffer for detailed logging
+        entity_labels=entity_labels,
     )
 
     return entity_links

hindsight-api/hindsight_api/engine/retain/fact_storage.py

@@ -7,6 +7,7 @@ Handles insertion of facts into the database.
 import json
 import logging
 
+from ...config import get_config
 from ..memory_engine import fq_table
 from .fact_extraction import _sanitize_text
 from .types import ProcessedFact
@@ -46,6 +47,8 @@ async def insert_facts_batch(
     chunk_ids = []
     document_ids = []
     tags_list = []
+    observation_scopes_list = []
+    text_signals_list = []
 
     for fact in facts:
         fact_texts.append(_sanitize_text(fact.fact_text))
@@ -67,31 +70,87 @@ async def insert_facts_batch(
         document_ids.append(fact.document_id if fact.document_id else document_id)
         # Convert tags to JSON string for proper batch insertion (PostgreSQL unnest doesn't handle 2D arrays well)
         tags_list.append(json.dumps(fact.tags if fact.tags else []))
+        # observation_scopes: stored as JSONB (string or 2D array), None if not provided
+        observation_scopes_list.append(
+            json.dumps(fact.observation_scopes) if fact.observation_scopes is not None else None
+        )
+        # Build text_signals: entity names + date tokens for enriched BM25 indexing
+        signal_parts = []
+        if fact.entities:
+            signal_parts.extend(e.name for e in fact.entities)
+        if fact.occurred_start:
+            signal_parts.append(fact.occurred_start.strftime("%B %-d %Y"))
+        if fact.occurred_end and fact.occurred_end != fact.occurred_start:
+            signal_parts.append(fact.occurred_end.strftime("%B %-d %Y"))
+        text_signals_list.append(" ".join(signal_parts) if signal_parts else None)
 
     # Batch insert all facts
     # Note: tags are passed as JSON strings and converted back to varchar[] via jsonb_array_elements_text + array_agg
-    results = await conn.fetch(
-        f"""
-        WITH input_data AS (
-            SELECT * FROM unnest(
-                $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
-            ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                   context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
-        )
-        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                 context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
-        SELECT
-            $1,
-            text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-            context, fact_type, confidence_score, metadata, chunk_id, document_id,
-            COALESCE(
-                (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
-                '{{}}'::varchar[]
+    # Query varies based on text search backend
+    config = get_config()
+    if config.text_search_extension == "vchord":
+        # VectorChord: manually tokenize and insert search_vector
+        # text_signals (entity names etc.) are included in the tokenize input for enriched BM25
+        query = f"""
+            WITH input_data AS (
+                SELECT * FROM unnest(
+                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
+                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
+                       observation_scopes_json, text_signals)
             )
-        FROM input_data
-        RETURNING id
-        """,
+            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals, search_vector)
+            SELECT
+                $1,
+                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                context, fact_type, confidence_score, metadata, chunk_id, document_id,
+                COALESCE(
+                    (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                    '{{}}'::varchar[]
+                ),
+                observation_scopes_json,
+                text_signals,
+                tokenize(
+                    COALESCE(text, '') || ' ' || COALESCE(context, '') || ' ' || COALESCE(text_signals, ''),
+                    'llmlingua2'
+                )::bm25_catalog.bm25vector
+            FROM input_data
+            RETURNING id
+        """
+    else:  # native or pg_textsearch
+        # Native PostgreSQL: search_vector is GENERATED ALWAYS (expression includes text_signals), don't include it
+        # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
+        query = f"""
+            WITH input_data AS (
+                SELECT * FROM unnest(
+                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
+                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
+                       observation_scopes_json, text_signals)
+            )
+            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals)
+            SELECT
+                $1,
+                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                context, fact_type, confidence_score, metadata, chunk_id, document_id,
+                COALESCE(
+                    (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                    '{{}}'::varchar[]
+                ),
+                observation_scopes_json,
+                text_signals
+            FROM input_data
+            RETURNING id
+        """
+
+    results = await conn.fetch(
+        query,
         bank_id,
         fact_texts,
         embeddings,
@@ -106,6 +165,8 @@ async def insert_facts_batch(
         chunk_ids,
         document_ids,
         tags_list,
+        observation_scopes_list,
+        text_signals_list,
     )
 
     unit_ids = [str(row["id"]) for row in results]

hindsight-api/hindsight_api/engine/retain/link_utils.py

@@ -47,6 +47,9 @@ def compute_temporal_links(
 
     links = []
     for unit_id, unit_event_date in new_units.items():
+        # Units without event_date can't form temporal links
+        if unit_event_date is None:
+            continue
         # Normalize unit_event_date for consistent comparison
         unit_event_date_norm = _normalize_datetime(unit_event_date)
 
@@ -96,7 +99,11 @@ def compute_temporal_query_bounds(
         return None, None
 
     # Normalize all dates to be timezone-aware to avoid comparison issues
-    all_dates = [_normalize_datetime(d) for d in new_units.values()]
+    # Filter out None values — units without event_date can't form temporal links
+    all_dates = [_normalize_datetime(d) for d in new_units.values() if d is not None]
+
+    if not all_dates:
+        return None, None
 
     try:
         min_date = min(all_dates) - timedelta(hours=time_window_hours)
@@ -143,6 +150,7 @@ async def extract_entities_batch_optimized(
     fact_dates: list,
     llm_entities: list[list[dict]],
     log_buffer: list[str] = None,
+    entity_labels: list | None = None,
 ) -> list[tuple]:
     """
     Process LLM-extracted entities for ALL facts in batch.
@@ -232,6 +240,7 @@ async def extract_entities_batch_optimized(
                 context=context,
                 unit_event_date=None,  # Not used when per-entity dates provided
                 conn=conn,  # Use main transaction connection
+                entity_labels=entity_labels,
             )
 
             _log(
@@ -432,20 +441,23 @@ async def create_temporal_links_batch_per_fact(
         min_date, max_date = compute_temporal_query_bounds(new_units, time_window_hours)
 
         fetch_neighbors_start = time_mod.time()
-        all_candidates = await conn.fetch(
-            f"""
-            SELECT id, event_date
-            FROM {fq_table("memory_units")}
-            WHERE bank_id = $1
-              AND event_date BETWEEN $2 AND $3
-              AND id::text != ALL($4)
-            ORDER BY event_date DESC
-            """,
-            bank_id,
-            min_date,
-            max_date,
-            unit_ids,
-        )
+        if min_date is not None and max_date is not None:
+            all_candidates = await conn.fetch(
+                f"""
+                SELECT id, event_date
+                FROM {fq_table("memory_units")}
+                WHERE bank_id = $1
+                  AND event_date BETWEEN $2 AND $3
+                  AND id::text != ALL($4)
+                ORDER BY event_date DESC
+                """,
+                bank_id,
+                min_date,
+                max_date,
+                unit_ids,
+            )
+        else:
+            all_candidates = []
         _log(
             log_buffer,
             f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s",
@@ -460,11 +472,15 @@ async def create_temporal_links_batch_per_fact(
             # Convert new_units dict to candidate format for within-batch linking
             new_unit_items = list(new_units.items())
             for i, (unit_id, event_date) in enumerate(new_unit_items):
+                if event_date is None:
+                    continue  # Skip units without event_date for temporal linking
                 unit_event_date_norm = _normalize_datetime(event_date)
 
                 # Compare with other new units (only those after this one to avoid duplicates)
                 for j in range(i + 1, len(new_unit_items)):
                     other_id, other_event_date = new_unit_items[j]
+                    if other_event_date is None:
+                        continue  # Skip units without event_date
                     other_event_date_norm = _normalize_datetime(other_event_date)
 
                     # Check if within time window

hindsight-api/hindsight_api/engine/retain/orchestrator.py

@@ -76,11 +76,14 @@ async def retain_batch(
     duplicate_checker_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,
 ) -> tuple[list[list[str]], TokenUsage]:
     """
     Process a batch of content through the retain pipeline.
@@ -94,6 +97,7 @@ async def retain_batch(
         duplicate_checker_fn: Function to check for duplicate facts
         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
@@ -124,12 +128,14 @@ async def retain_batch(
         item_tags = item.get("tags", []) or []
         merged_tags = list(set(item_tags + (document_tags or [])))
 
-        # Handle event_date: parse flexibly (handles both datetime objects and ISO strings)
-        event_date_value = item.get("event_date")
-        if event_date_value:
-            event_date_value = parse_datetime_flexible(event_date_value)
+        # 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()
+            event_date_value = utcnow()  # Backward-compatible default
 
         content = RetainContent(
             content=item["content"],
@@ -138,25 +144,37 @@ async def retain_batch(
             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)
+    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
+        # 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():
                 await fact_storage.ensure_bank_exists(conn, bank_id)
 
-                # Handle document tracking even with no facts
+                # 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 [])
@@ -181,45 +199,57 @@ async def retain_batch(
                     await fact_storage.handle_document_tracking(
                         conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
                     )
+                    docs_tracked += 1
                 else:
-                    # Check for per-item document_ids
-                    from collections import defaultdict
+                    # 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)
 
-                    contents_by_doc = defaultdict(list)
-                    for idx, content_dict in enumerate(contents_dicts):
-                        doc_id = content_dict.get("document_id")
-                        if doc_id:
-                            contents_by_doc[doc_id].append((idx, content_dict))
+                    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
 
-                    for doc_id, doc_contents in contents_by_doc.items():
-                        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)
+                            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())
 
-                        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, doc_id, combined_content, is_first_batch, retain_params, merged_tags
-                        )
+                            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 (document tracked, no facts)"
+            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
 
@@ -442,6 +472,7 @@ async def retain_batch(
                 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")
 

hindsight-api/hindsight_api/engine/retain/types.py

@@ -6,8 +6,8 @@ from content input to fact storage.
 """
 
 from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from typing import TypedDict
+from datetime import datetime
+from typing import Literal, TypedDict
 from uuid import UUID
 
 
@@ -22,20 +22,21 @@ class RetainContentDict(TypedDict, total=False):
         document_id: Document ID for this content item (optional)
         entities: User-provided entities to merge with extracted entities (optional)
         tags: Visibility scope tags for this content item (optional)
+        observation_scopes: How to scope observations for consolidation (optional).
+            "per_tag" runs one pass per individual tag; "combined" (default) runs a
+            single pass with all tags; a list[list[str]] specifies exact passes.
     """
 
     content: str  # Required
     context: str
-    event_date: datetime
+    event_date: datetime | None
     metadata: dict[str, str]
     document_id: str
     entities: list[dict[str, str]]  # [{"text": "...", "type": "..."}]
     tags: list[str]  # Visibility scope tags
-
-
-def _now_utc() -> datetime:
-    """Factory function for default event_date."""
-    return datetime.now(UTC)
+    observation_scopes: (
+        Literal["per_tag", "combined", "all_combinations"] | list[list[str]]
+    )  # Observation scopes for consolidation
 
 
 @dataclass
@@ -48,10 +49,13 @@ class RetainContent:
 
     content: str
     context: str = ""
-    event_date: datetime = field(default_factory=_now_utc)
+    event_date: datetime | None = None
     metadata: dict[str, str] = field(default_factory=dict)
     entities: list[dict[str, str]] = field(default_factory=list)  # User-provided entities
     tags: list[str] = field(default_factory=list)  # Visibility scope tags
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = (
+        None  # Observation scopes
+    )
 
 
 @dataclass
@@ -117,6 +121,9 @@ class ExtractedFact:
     mentioned_at: datetime | None = None
     metadata: dict[str, str] = field(default_factory=dict)
     tags: list[str] = field(default_factory=list)  # Visibility scope tags
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = (
+        None  # Observation scopes
+    )
 
 
 @dataclass
@@ -135,7 +142,7 @@ class ProcessedFact:
     # Temporal data
     occurred_start: datetime | None
     occurred_end: datetime | None
-    mentioned_at: datetime
+    mentioned_at: datetime | None
 
     # Context and metadata
     context: str
@@ -165,6 +172,9 @@ class ProcessedFact:
     # Visibility scope tags
     tags: list[str] = field(default_factory=list)
 
+    # Observation scopes for consolidation
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = None
+
     @property
     def is_duplicate(self) -> bool:
         """Check if this fact was marked as a duplicate."""
@@ -185,12 +195,10 @@ class ProcessedFact:
         Returns:
             ProcessedFact ready for storage
         """
-        from datetime import datetime
-
         # Use occurred dates only if explicitly provided by LLM
         occurred_start = extracted_fact.occurred_start
         occurred_end = extracted_fact.occurred_end
-        mentioned_at = extracted_fact.mentioned_at or datetime.now(UTC)
+        mentioned_at = extracted_fact.mentioned_at  # May be None when caller opted into no timestamp
 
         # Convert entity strings to EntityRef objects
         entities = [EntityRef(name=name) for name in extracted_fact.entities]
@@ -209,6 +217,7 @@ class ProcessedFact:
             chunk_id=chunk_id,
             content_index=extracted_fact.content_index,
             tags=extracted_fact.tags,
+            observation_scopes=extracted_fact.observation_scopes,
         )
 
 

hindsight-api/hindsight_api/engine/search/__init__.py

@@ -13,12 +13,10 @@ from .reranking import CrossEncoderReranker
 from .retrieval import (
     ParallelRetrievalResult,
     get_default_graph_retriever,
-    retrieve_parallel,
     set_default_graph_retriever,
 )
 
 __all__ = [
-    "retrieve_parallel",
     "get_default_graph_retriever",
     "set_default_graph_retriever",
     "ParallelRetrievalResult",

hindsight-api/hindsight_api/engine/search/graph_retrieval.py

@@ -162,7 +162,7 @@ class BFSGraphRetriever(GraphRetriever):
         entry_points = await conn.fetch(
             f"""
             SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+                   mentioned_at, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity
             FROM {fq_table("memory_units")}
             WHERE bank_id = $2
@@ -216,7 +216,7 @@ class BFSGraphRetriever(GraphRetriever):
                 neighbors = await conn.fetch(
                     f"""
                     SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
-                           mu.mentioned_at, mu.embedding, mu.fact_type,
+                           mu.mentioned_at, mu.fact_type,
                            mu.document_id, mu.chunk_id, mu.tags,
                            ml.weight, ml.link_type, ml.from_unit_id
                     FROM {fq_table("memory_links")} ml

hindsight-api/hindsight_api/engine/search/link_expansion_retrieval.py

@@ -45,7 +45,7 @@ async def _find_semantic_seeds(
     rows = await conn.fetch(
         f"""
         SELECT id, text, context, event_date, occurred_start, occurred_end,
-               mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+               mentioned_at, fact_type, document_id, chunk_id, tags,
                1 - (embedding <=> $1::vector) AS similarity
         FROM {fq_table("memory_units")}
         WHERE bank_id = $2
@@ -216,7 +216,7 @@ class LinkExpansionRetriever(GraphRetriever):
                     -- Only exclude the actual seed observations
                     SELECT
                         mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                        mu.occurred_end, mu.mentioned_at, mu.embedding,
+                        mu.occurred_end, mu.mentioned_at,
                         mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                         COUNT(DISTINCT cs.source_id)::float AS score
                     FROM all_connected_sources cs
@@ -239,7 +239,7 @@ class LinkExpansionRetriever(GraphRetriever):
                     f"""
                     SELECT
                         mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                        mu.occurred_end, mu.mentioned_at, mu.embedding,
+                        mu.occurred_end, mu.mentioned_at,
                         mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                         COUNT(*)::float AS score
                     FROM {fq_table("unit_entities")} seed_ue
@@ -264,7 +264,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 f"""
                 SELECT DISTINCT ON (mu.id)
                     mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                    mu.occurred_end, mu.mentioned_at, mu.embedding,
+                    mu.occurred_end, mu.mentioned_at,
                     mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                     ml.weight + 1.0 AS score
                 FROM {fq_table("memory_links")} ml
@@ -291,7 +291,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 WITH outgoing AS (
                     -- Links FROM seeds TO other facts
                     SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                           mu.occurred_end, mu.mentioned_at, mu.embedding,
+                           mu.occurred_end, mu.mentioned_at,
                            mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                            ml.weight
                     FROM {fq_table("memory_links")} ml
@@ -305,7 +305,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 incoming AS (
                     -- Links FROM other facts TO seeds (reverse direction)
                     SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                           mu.occurred_end, mu.mentioned_at, mu.embedding,
+                           mu.occurred_end, mu.mentioned_at,
                            mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                            ml.weight
                     FROM {fq_table("memory_links")} ml
@@ -323,12 +323,12 @@ class LinkExpansionRetriever(GraphRetriever):
                 )
                 SELECT DISTINCT ON (id)
                     id, text, context, event_date, occurred_start,
-                    occurred_end, mentioned_at, embedding,
+                    occurred_end, mentioned_at,
                     fact_type, document_id, chunk_id, tags,
                     (MAX(weight) * 0.5) AS score
                 FROM combined
                 GROUP BY id, text, context, event_date, occurred_start,
-                         occurred_end, mentioned_at, embedding,
+                         occurred_end, mentioned_at,
                          fact_type, document_id, chunk_id, tags
                 ORDER BY id, score DESC
                 LIMIT $4

hindsight-api/hindsight_api/engine/search/mpfp_retrieval.py

@@ -449,7 +449,7 @@ async def fetch_memory_units_by_ids(
         rows = await conn.fetch(
             f"""
             SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags
+                   mentioned_at, fact_type, document_id, chunk_id, tags
             FROM {fq_table("memory_units")}
             WHERE id = ANY($1::uuid[])
               AND fact_type = $2

hindsight-api/hindsight_api/engine/search/retrieval.py

@@ -85,116 +85,6 @@ def set_default_graph_retriever(retriever: GraphRetriever) -> None:
     _default_graph_retriever = retriever
 
 
-async def retrieve_semantic(
-    conn,
-    query_emb_str: str,
-    bank_id: str,
-    fact_type: str,
-    limit: int,
-    tags: list[str] | None = None,
-) -> list[RetrievalResult]:
-    """
-    Semantic retrieval via vector similarity.
-
-    Args:
-        conn: Database connection
-        query_emb_str: Query embedding as string
-        agent_id: bank ID
-        fact_type: Fact type to filter
-        limit: Maximum results to return
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        List of RetrievalResult objects
-    """
-    from .tags import TagsMatch, build_tags_where_clause_simple
-
-    tags_clause = build_tags_where_clause_simple(tags, 5)
-    params = [query_emb_str, bank_id, fact_type, limit]
-    if tags:
-        params.append(tags)
-
-    results = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
-               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND embedding IS NOT NULL
-          AND fact_type = $3
-          AND (1 - (embedding <=> $1::vector)) >= 0.3
-          {tags_clause}
-        ORDER BY embedding <=> $1::vector
-        LIMIT $4
-        """,
-        *params,
-    )
-    return [RetrievalResult.from_db_row(dict(r)) for r in results]
-
-
-async def retrieve_bm25(
-    conn,
-    query_text: str,
-    bank_id: str,
-    fact_type: str,
-    limit: int,
-    tags: list[str] | None = None,
-) -> list[RetrievalResult]:
-    """
-    BM25 keyword retrieval via full-text search.
-
-    Args:
-        conn: Database connection
-        query_text: Query text
-        agent_id: bank ID
-        fact_type: Fact type to filter
-        limit: Maximum results to return
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        List of RetrievalResult objects
-    """
-    import re
-
-    from .tags import TagsMatch, build_tags_where_clause_simple
-
-    # Sanitize query text: remove special characters that have meaning in tsquery
-    # Keep only alphanumeric characters and spaces
-    sanitized_text = re.sub(r"[^\w\s]", " ", query_text.lower())
-
-    # Split and filter empty strings
-    tokens = [token for token in sanitized_text.split() if token]
-
-    if not tokens:
-        # If no valid tokens, return empty results
-        return []
-
-    # Convert query to tsquery using OR for more flexible matching
-    # This prevents empty results when some terms are missing
-    query_tsquery = " | ".join(tokens)
-
-    tags_clause = build_tags_where_clause_simple(tags, 5)
-    params = [query_tsquery, bank_id, fact_type, limit]
-    if tags:
-        params.append(tags)
-
-    results = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
-               ts_rank_cd(search_vector, to_tsquery('english', $1)) AS bm25_score
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND search_vector @@ to_tsquery('english', $1)
-          {tags_clause}
-        ORDER BY bm25_score DESC
-        LIMIT $4
-        """,
-        *params,
-    )
-    return [RetrievalResult.from_db_row(dict(r)) for r in results]
-
-
 async def retrieve_semantic_bm25_combined(
     conn,
     query_emb_str: str,
@@ -237,7 +127,7 @@ async def retrieve_semantic_bm25_combined(
         results = await conn.fetch(
             f"""
             WITH semantic_ranked AS (
-                SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+                SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                        1 - (embedding <=> $1::vector) AS similarity,
                        NULL::float AS bm25_score,
                        'semantic' AS source,
@@ -249,7 +139,7 @@ async def retrieve_semantic_bm25_combined(
                   AND (1 - (embedding <=> $1::vector)) >= 0.3
                   {tags_clause}
             )
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    similarity, bm25_score, source
             FROM semantic_ranked
             WHERE rn <= $4
@@ -268,20 +158,43 @@ async def retrieve_semantic_bm25_combined(
                 result_dict[ft][0].append(RetrievalResult.from_db_row(row))
         return result_dict
 
-    query_tsquery = " | ".join(tokens)
+    # Build BM25 query based on text search backend
+    config = get_config()
 
     # Build tags clause - param 6 if tags provided
     tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
-    params = [query_emb_str, bank_id, fact_types, limit, query_tsquery]
+
+    # Build backend-specific BM25 parts
+    if config.text_search_extension == "vchord":
+        # VectorChord BM25: use <&> operator with to_bm25query and tokenize
+        # Note: VectorChord scores are negative (higher = better, so -1 > -10)
+        bm25_score_expr = "search_vector <&> to_bm25query('idx_memory_units_text_search', tokenize($5, 'llmlingua2'))"
+        bm25_order_by = f"{bm25_score_expr} DESC"
+        bm25_where_filter = ""  # No additional WHERE filter for vchord
+        params = [query_emb_str, bank_id, fact_types, limit, query_text]  # Pass raw query_text for tokenization
+    elif config.text_search_extension == "pg_textsearch":
+        # Timescale pg_textsearch: use <@> operator with to_bm25query
+        # Note: pg_textsearch scores are negative (lower/more negative = better, so -10 > -1)
+        # We negate the score to maintain API consistency (higher = better)
+        bm25_score_expr = "-(text <@> to_bm25query($5, 'idx_memory_units_text_search'))"
+        bm25_order_by = "text <@> to_bm25query($5, 'idx_memory_units_text_search') ASC"
+        bm25_where_filter = ""  # No additional WHERE filter for pg_textsearch
+        params = [query_emb_str, bank_id, fact_types, limit, query_text]
+    else:  # native
+        # Native PostgreSQL: use ts_rank_cd with to_tsquery
+        query_tsquery = " | ".join(tokens)
+        bm25_score_expr = "ts_rank_cd(search_vector, to_tsquery('english', $5))"
+        bm25_order_by = f"{bm25_score_expr} DESC"
+        bm25_where_filter = "AND search_vector @@ to_tsquery('english', $5)"
+        params = [query_emb_str, bank_id, fact_types, limit, query_tsquery]
+
     if tags:
         params.append(tags)
 
-    # Combined CTE query for both semantic and BM25 across all fact types
-    # Uses window functions to limit per fact_type per method
-    results = await conn.fetch(
-        f"""
+    # Single query template with backend-specific parts injected
+    query = f"""
         WITH semantic_ranked AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity,
                    NULL::float AS bm25_score,
                    'semantic' AS source,
@@ -294,33 +207,35 @@ async def retrieve_semantic_bm25_combined(
               {tags_clause}
         ),
         bm25_ranked AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    NULL::float AS similarity,
-                   ts_rank_cd(search_vector, to_tsquery('english', $5)) AS bm25_score,
+                   {bm25_score_expr} AS bm25_score,
                    'bm25' AS source,
-                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY ts_rank_cd(search_vector, to_tsquery('english', $5)) DESC) AS rn
+                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY {bm25_order_by}) AS rn
             FROM {fq_table("memory_units")}
             WHERE bank_id = $2
               AND fact_type = ANY($3)
-              AND search_vector @@ to_tsquery('english', $5)
+              {bm25_where_filter}
               {tags_clause}
         ),
         semantic AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    similarity, bm25_score, source
             FROM semantic_ranked WHERE rn <= $4
         ),
         bm25 AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    similarity, bm25_score, source
             FROM bm25_ranked WHERE rn <= $4
         )
         SELECT * FROM semantic
         UNION ALL
         SELECT * FROM bm25
-        """,
-        *params,
-    )
+    """
+
+    # Combined CTE query for both semantic and BM25 across all fact types
+    # Uses window functions to limit per fact_type per method
+    results = await conn.fetch(query, *params)
 
     # Group results by fact_type and source
     result_dict: dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]] = {ft: ([], []) for ft in fact_types}
@@ -386,7 +301,7 @@ async def retrieve_temporal_combined(
     entry_points = await conn.fetch(
         f"""
         WITH ranked_entries AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity,
                    ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, embedding <=> $1::vector) AS rn
             FROM {fq_table("memory_units")}
@@ -406,7 +321,7 @@ async def retrieve_temporal_combined(
               AND (1 - (embedding <=> $1::vector)) >= $6
               {tags_clause}
         )
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags, similarity
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, similarity
         FROM ranked_entries
         WHERE rn <= 10
         """,
@@ -486,7 +401,7 @@ async def retrieve_temporal_combined(
 
             neighbors = await conn.fetch(
                 f"""
-                SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                        ml.weight, ml.link_type, ml.from_unit_id,
                        1 - (mu.embedding <=> $1::vector) AS similarity
                 FROM {fq_table("memory_links")} ml
@@ -561,623 +476,6 @@ async def retrieve_temporal_combined(
     return results_by_ft
 
 
-async def retrieve_temporal(
-    conn,
-    query_emb_str: str,
-    bank_id: str,
-    fact_type: str,
-    start_date: datetime,
-    end_date: datetime,
-    budget: int,
-    semantic_threshold: float = 0.1,
-    tags: list[str] | None = None,
-) -> list[RetrievalResult]:
-    """
-    Temporal retrieval with spreading activation.
-
-    Strategy:
-    1. Find entry points (facts in date range with semantic relevance)
-    2. Spread through temporal links to related facts
-    3. Score by temporal proximity + semantic similarity + link weight
-
-    Args:
-        conn: Database connection
-        query_emb_str: Query embedding as string
-        agent_id: bank ID
-        fact_type: Fact type to filter
-        start_date: Start of time range
-        end_date: End of time range
-        budget: Node budget for spreading
-        semantic_threshold: Minimum semantic similarity to include
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        List of RetrievalResult objects with temporal scores
-    """
-
-    # Ensure start_date and end_date are timezone-aware (UTC) to match database datetimes
-    if start_date.tzinfo is None:
-        start_date = start_date.replace(tzinfo=UTC)
-    if end_date.tzinfo is None:
-        end_date = end_date.replace(tzinfo=UTC)
-
-    from .tags import TagsMatch, build_tags_where_clause_simple
-
-    tags_clause = build_tags_where_clause_simple(tags, 7)
-    params = [query_emb_str, bank_id, fact_type, start_date, end_date, semantic_threshold]
-    if tags:
-        params.append(tags)
-
-    entry_points = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
-               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND embedding IS NOT NULL
-          AND (
-              -- Match if occurred range overlaps with query range
-              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
-               AND occurred_start <= $5 AND occurred_end >= $4)
-              OR
-              -- Match if mentioned_at falls within query range
-              (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
-              OR
-              -- Match if any occurred date is set and overlaps (even if only start or end is set)
-              (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
-              OR
-              (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
-          )
-          AND (1 - (embedding <=> $1::vector)) >= $6
-          {tags_clause}
-        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, (embedding <=> $1::vector) ASC
-        LIMIT 10
-        """,
-        *params,
-    )
-
-    if not entry_points:
-        return []
-
-    # Calculate temporal scores for entry points
-    total_days = (end_date - start_date).total_seconds() / 86400
-    mid_date = start_date + (end_date - start_date) / 2  # Calculate once for all comparisons
-    results = []
-    visited = set()
-
-    for ep in entry_points:
-        unit_id = str(ep["id"])
-        visited.add(unit_id)
-
-        # Calculate temporal proximity using the most relevant date
-        # Priority: occurred_start/end (event time) > mentioned_at (mention time)
-        best_date = None
-        if ep["occurred_start"] is not None and ep["occurred_end"] is not None:
-            # Use midpoint of occurred range
-            best_date = ep["occurred_start"] + (ep["occurred_end"] - ep["occurred_start"]) / 2
-        elif ep["occurred_start"] is not None:
-            best_date = ep["occurred_start"]
-        elif ep["occurred_end"] is not None:
-            best_date = ep["occurred_end"]
-        elif ep["mentioned_at"] is not None:
-            best_date = ep["mentioned_at"]
-
-        # Temporal proximity score (closer to range center = higher score)
-        if best_date:
-            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
-            temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
-        else:
-            temporal_proximity = 0.5  # Fallback if no dates (shouldn't happen due to WHERE clause)
-
-        # Create RetrievalResult with temporal scores
-        ep_result = RetrievalResult.from_db_row(dict(ep))
-        ep_result.temporal_score = temporal_proximity
-        ep_result.temporal_proximity = temporal_proximity
-        results.append(ep_result)
-
-    # Spread through temporal links using BATCHED neighbor fetching
-    # Map node_id -> (semantic_sim, temporal_score) for propagation
-    node_scores = {str(ep["id"]): (ep["similarity"], 1.0) for ep in entry_points}
-    frontier = list(node_scores.keys())  # Current batch of nodes to expand
-    budget_remaining = budget - len(entry_points)
-    batch_size = 20  # Process this many nodes per DB query
-
-    while frontier and budget_remaining > 0:
-        # Take a batch from frontier
-        batch_ids = frontier[:batch_size]
-        frontier = frontier[batch_size:]
-
-        # Batch fetch all neighbors for this batch of nodes
-        neighbors = await conn.fetch(
-            f"""
-            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id,
-                   ml.weight, ml.link_type, ml.from_unit_id,
-                   1 - (mu.embedding <=> $1::vector) AS similarity
-            FROM {fq_table("memory_links")} ml
-            JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
-            WHERE ml.from_unit_id = ANY($2::uuid[])
-              AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
-              AND ml.weight >= 0.1
-              AND mu.fact_type = $3
-              AND mu.embedding IS NOT NULL
-              AND (1 - (mu.embedding <=> $1::vector)) >= $4
-            ORDER BY ml.weight DESC
-            LIMIT $5
-            """,
-            query_emb_str,
-            batch_ids,
-            fact_type,
-            semantic_threshold,
-            batch_size * 10,  # Allow up to 10 neighbors per node in batch
-        )
-
-        for n in neighbors:
-            neighbor_id = str(n["id"])
-            if neighbor_id in visited:
-                continue
-
-            visited.add(neighbor_id)
-            budget_remaining -= 1
-
-            # Get parent's scores for propagation
-            parent_id = str(n["from_unit_id"])
-            _, parent_temporal_score = node_scores.get(parent_id, (0.5, 0.5))
-
-            # Calculate temporal score for neighbor using best available date
-            neighbor_best_date = None
-            if n["occurred_start"] is not None and n["occurred_end"] is not None:
-                neighbor_best_date = n["occurred_start"] + (n["occurred_end"] - n["occurred_start"]) / 2
-            elif n["occurred_start"] is not None:
-                neighbor_best_date = n["occurred_start"]
-            elif n["occurred_end"] is not None:
-                neighbor_best_date = n["occurred_end"]
-            elif n["mentioned_at"] is not None:
-                neighbor_best_date = n["mentioned_at"]
-
-            if neighbor_best_date:
-                days_from_mid = abs((neighbor_best_date - mid_date).total_seconds() / 86400)
-                neighbor_temporal_proximity = (
-                    1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
-                )
-            else:
-                neighbor_temporal_proximity = 0.3  # Lower score if no temporal data
-
-            # Boost causal links (same as graph retrieval)
-            link_type = n["link_type"]
-            if link_type in ("causes", "caused_by"):
-                causal_boost = 2.0
-            elif link_type in ("enables", "prevents"):
-                causal_boost = 1.5
-            else:
-                causal_boost = 1.0
-
-            # Propagate temporal score through links (decay, with causal boost)
-            propagated_temporal = parent_temporal_score * n["weight"] * causal_boost * 0.7
-
-            # Combined temporal score
-            combined_temporal = max(neighbor_temporal_proximity, propagated_temporal)
-
-            # Create RetrievalResult with temporal scores
-            neighbor_result = RetrievalResult.from_db_row(dict(n))
-            neighbor_result.temporal_score = combined_temporal
-            neighbor_result.temporal_proximity = neighbor_temporal_proximity
-            results.append(neighbor_result)
-
-            # Track scores for propagation and add to frontier
-            if budget_remaining > 0 and combined_temporal > 0.2:
-                node_scores[neighbor_id] = (n["similarity"], combined_temporal)
-                frontier.append(neighbor_id)
-
-            if budget_remaining <= 0:
-                break
-
-    return results
-
-
-async def retrieve_parallel(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    question_date: datetime | None = None,
-    query_analyzer: Optional["QueryAnalyzer"] = None,
-    graph_retriever: GraphRetriever | None = None,
-    temporal_constraint: tuple | None = None,  # Pre-extracted temporal constraint
-    tags: list[str] | None = None,  # Visibility scope tags for filtering
-) -> ParallelRetrievalResult:
-    """
-    Run 3-way or 4-way parallel retrieval (adds temporal if detected).
-
-    Args:
-        pool: Database connection pool
-        query_text: Query text
-        query_embedding_str: Query embedding as string
-        bank_id: Bank ID
-        fact_type: Fact type to filter
-        thinking_budget: Budget for graph traversal and retrieval limits
-        question_date: Optional date when question was asked (for temporal filtering)
-        query_analyzer: Query analyzer to use (defaults to TransformerQueryAnalyzer)
-        graph_retriever: Graph retrieval strategy (defaults to configured retriever)
-        temporal_constraint: Pre-extracted temporal constraint (optional)
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        ParallelRetrievalResult with semantic, bm25, graph, temporal results and timings
-    """
-    retriever = graph_retriever or get_default_graph_retriever()
-
-    # Use optimized parallel path for MPFP and LinkExpansion (runs all methods truly in parallel)
-    # BFS uses legacy path that extracts temporal constraint upfront
-    if retriever.name in ("mpfp", "link_expansion"):
-        return await _retrieve_parallel_mpfp(
-            pool,
-            query_text,
-            query_embedding_str,
-            bank_id,
-            fact_type,
-            thinking_budget,
-            temporal_constraint,
-            retriever,
-            question_date,
-            query_analyzer,
-            tags=tags,
-        )
-    else:
-        # For BFS, extract temporal constraint upfront (legacy path)
-        if temporal_constraint is None:
-            from .temporal_extraction import extract_temporal_constraint
-
-            temporal_constraint = extract_temporal_constraint(
-                query_text, reference_date=question_date, analyzer=query_analyzer
-            )
-        return await _retrieve_parallel_bfs(
-            pool,
-            query_text,
-            query_embedding_str,
-            bank_id,
-            fact_type,
-            thinking_budget,
-            temporal_constraint,
-            retriever,
-            tags=tags,
-        )
-
-
-@dataclass
-class _TimedResult:
-    """Internal result with timing."""
-
-    results: list[RetrievalResult]
-    time: float
-    conn_wait: float = 0.0  # Connection acquisition wait time
-
-
-async def _retrieve_parallel_mpfp(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    temporal_constraint: tuple | None,
-    retriever: GraphRetriever,
-    question_date: datetime | None = None,
-    query_analyzer=None,
-    tags: list[str] | None = None,
-) -> ParallelRetrievalResult:
-    """
-    MPFP retrieval with true parallelization.
-
-    All methods run independently in parallel:
-    - Semantic: vector similarity search
-    - BM25: keyword search
-    - Graph: MPFP traversal (does its own semantic seeds internally)
-    - Temporal: date extraction (if needed) + date-range search
-
-    Temporal extraction runs IN PARALLEL with other retrievals, so even if
-    dateparser is slow, it doesn't block semantic/BM25/graph.
-    """
-    import time
-
-    async def run_semantic() -> _TimedResult:
-        """Independent semantic retrieval."""
-        start = time.time()
-        acquire_start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            conn_wait = time.time() - acquire_start
-            results = await retrieve_semantic(
-                conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget, tags=tags
-            )
-        return _TimedResult(results, time.time() - start, conn_wait)
-
-    async def run_bm25() -> _TimedResult:
-        """Independent BM25 retrieval."""
-        start = time.time()
-        acquire_start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            conn_wait = time.time() - acquire_start
-            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget, tags=tags)
-        return _TimedResult(results, time.time() - start, conn_wait)
-
-    async def run_graph() -> tuple[list[RetrievalResult], float, MPFPTimings | None]:
-        """Independent graph retrieval - does its own semantic seeds."""
-        start = time.time()
-
-        # MPFP does its own semantic seeds via _find_semantic_seeds
-        # Note: temporal_seeds not used here to avoid dependency on temporal extraction
-        results, mpfp_timing = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=fact_type,
-            budget=thinking_budget,
-            query_text=query_text,
-            semantic_seeds=None,  # Let MPFP find its own seeds
-            temporal_seeds=None,  # Don't wait for temporal extraction
-            tags=tags,
-        )
-        return results, time.time() - start, mpfp_timing
-
-    @dataclass
-    class _TemporalWithConstraint:
-        """Temporal results with the extracted constraint."""
-
-        results: list[RetrievalResult]
-        time: float
-        constraint: tuple | None
-        extraction_time: float  # Time spent in query analyzer (dateparser)
-        conn_wait: float = 0.0  # Connection acquisition wait time
-
-    async def run_temporal_with_extraction() -> _TemporalWithConstraint:
-        """
-        Extract temporal constraint AND run temporal retrieval.
-
-        This runs in parallel with semantic/BM25/graph, so dateparser
-        latency doesn't block other retrievals.
-        """
-        start = time.time()
-
-        # Use pre-provided constraint if available
-        tc = temporal_constraint
-        extraction_time = 0.0
-
-        # Otherwise extract from query (this is the potentially slow dateparser call)
-        if tc is None:
-            from .temporal_extraction import extract_temporal_constraint
-
-            extraction_start = time.time()
-            tc = extract_temporal_constraint(query_text, reference_date=question_date, analyzer=query_analyzer)
-            extraction_time = time.time() - extraction_start
-
-        # If no temporal constraint found, return empty (but still report extraction time)
-        if tc is None:
-            return _TemporalWithConstraint([], time.time() - start, None, extraction_time, 0.0)
-
-        # Run temporal retrieval with the extracted constraint
-        tc_start, tc_end = tc
-        acquire_start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            conn_wait = time.time() - acquire_start
-            results = await retrieve_temporal(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
-            )
-        return _TemporalWithConstraint(results, time.time() - start, tc, extraction_time, conn_wait)
-
-    # Run ALL methods in parallel (including temporal extraction!)
-    semantic_result, bm25_result, graph_result, temporal_result = await asyncio.gather(
-        run_semantic(),
-        run_bm25(),
-        run_graph(),
-        run_temporal_with_extraction(),
-    )
-    graph_results, graph_time, mpfp_timing = graph_result
-
-    # Compute max connection wait across all methods (graph handles its own connections)
-    max_conn_wait = max(semantic_result.conn_wait, bm25_result.conn_wait, temporal_result.conn_wait)
-
-    return ParallelRetrievalResult(
-        semantic=semantic_result.results,
-        bm25=bm25_result.results,
-        graph=graph_results,
-        temporal=temporal_result.results if temporal_result.results else None,
-        timings={
-            "semantic": semantic_result.time,
-            "bm25": bm25_result.time,
-            "graph": graph_time,
-            "temporal": temporal_result.time,
-            "temporal_extraction": temporal_result.extraction_time,
-        },
-        temporal_constraint=temporal_result.constraint,
-        mpfp_timings=[mpfp_timing] if mpfp_timing else [],
-        max_conn_wait=max_conn_wait,
-    )
-
-
-async def _get_temporal_entry_points(
-    conn,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    start_date: datetime,
-    end_date: datetime,
-    limit: int = 20,
-    semantic_threshold: float = 0.1,
-) -> list[RetrievalResult]:
-    """Get temporal entry points (facts in date range with semantic relevance)."""
-
-    if start_date.tzinfo is None:
-        start_date = start_date.replace(tzinfo=UTC)
-    if end_date.tzinfo is None:
-        end_date = end_date.replace(tzinfo=UTC)
-
-    rows = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at,
-               embedding, fact_type, document_id, chunk_id,
-               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND embedding IS NOT NULL
-          AND (
-              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
-               AND occurred_start <= $5 AND occurred_end >= $4)
-              OR (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
-              OR (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
-              OR (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
-          )
-          AND (1 - (embedding <=> $1::vector)) >= $6
-        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC,
-                 (embedding <=> $1::vector) ASC
-        LIMIT $7
-        """,
-        query_embedding_str,
-        bank_id,
-        fact_type,
-        start_date,
-        end_date,
-        semantic_threshold,
-        limit,
-    )
-
-    results = []
-    total_days = max((end_date - start_date).total_seconds() / 86400, 1)
-    mid_date = start_date + (end_date - start_date) / 2
-
-    for row in rows:
-        result = RetrievalResult.from_db_row(dict(row))
-
-        # Calculate temporal proximity score
-        best_date = None
-        if row["occurred_start"] and row["occurred_end"]:
-            best_date = row["occurred_start"] + (row["occurred_end"] - row["occurred_start"]) / 2
-        elif row["occurred_start"]:
-            best_date = row["occurred_start"]
-        elif row["occurred_end"]:
-            best_date = row["occurred_end"]
-        elif row["mentioned_at"]:
-            best_date = row["mentioned_at"]
-
-        if best_date:
-            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
-            result.temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0)
-        else:
-            result.temporal_proximity = 0.5
-
-        result.temporal_score = result.temporal_proximity
-        results.append(result)
-
-    return results
-
-
-async def _retrieve_parallel_bfs(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    temporal_constraint: tuple | None,
-    retriever: GraphRetriever,
-    tags: list[str] | None = None,
-) -> ParallelRetrievalResult:
-    """BFS retrieval: all methods run in parallel (original behavior)."""
-    import time
-
-    async def run_semantic() -> _TimedResult:
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_semantic(
-                conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget, tags=tags
-            )
-        return _TimedResult(results, time.time() - start)
-
-    async def run_bm25() -> _TimedResult:
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget, tags=tags)
-        return _TimedResult(results, time.time() - start)
-
-    async def run_graph() -> _TimedResult:
-        start = time.time()
-        results, _ = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=fact_type,
-            budget=thinking_budget,
-            query_text=query_text,
-            tags=tags,
-        )
-        return _TimedResult(results, time.time() - start)
-
-    async def run_temporal(tc_start, tc_end) -> _TimedResult:
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_temporal(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
-                tags=tags,
-            )
-        return _TimedResult(results, time.time() - start)
-
-    if temporal_constraint:
-        tc_start, tc_end = temporal_constraint
-        semantic_r, bm25_r, graph_r, temporal_r = await asyncio.gather(
-            run_semantic(),
-            run_bm25(),
-            run_graph(),
-            run_temporal(tc_start, tc_end),
-        )
-        return ParallelRetrievalResult(
-            semantic=semantic_r.results,
-            bm25=bm25_r.results,
-            graph=graph_r.results,
-            temporal=temporal_r.results,
-            timings={
-                "semantic": semantic_r.time,
-                "bm25": bm25_r.time,
-                "graph": graph_r.time,
-                "temporal": temporal_r.time,
-            },
-            temporal_constraint=temporal_constraint,
-        )
-    else:
-        semantic_r, bm25_r, graph_r = await asyncio.gather(
-            run_semantic(),
-            run_bm25(),
-            run_graph(),
-        )
-        return ParallelRetrievalResult(
-            semantic=semantic_r.results,
-            bm25=bm25_r.results,
-            graph=graph_r.results,
-            temporal=None,
-            timings={
-                "semantic": semantic_r.time,
-                "bm25": bm25_r.time,
-                "graph": graph_r.time,
-            },
-            temporal_constraint=None,
-        )
-
-
 async def retrieve_all_fact_types_parallel(
     pool,
     query_text: str,

hindsight-api/hindsight_api/engine/search/types.py

@@ -46,7 +46,6 @@ class RetrievalResult:
     mentioned_at: datetime | None = None
     document_id: str | None = None
     chunk_id: str | None = None
-    embedding: list[float] | None = None
     tags: list[str] | None = None  # Visibility scope tags
 
     # Retrieval-specific scores (only one will be set depending on retrieval method)
@@ -70,7 +69,6 @@ class RetrievalResult:
             mentioned_at=row.get("mentioned_at"),
             document_id=row.get("document_id"),
             chunk_id=row.get("chunk_id"),
-            embedding=row.get("embedding"),
             tags=row.get("tags"),
             similarity=row.get("similarity"),
             bm25_score=row.get("bm25_score"),
@@ -154,7 +152,6 @@ class ScoredResult:
             "mentioned_at": self.retrieval.mentioned_at,
             "document_id": self.retrieval.document_id,
             "chunk_id": self.retrieval.chunk_id,
-            "embedding": self.retrieval.embedding,
             "tags": self.retrieval.tags,
             "semantic_similarity": self.retrieval.similarity,
             "bm25_score": self.retrieval.bm25_score,

hindsight-api/hindsight_api/engine/storage/__init__.py

@@ -0,0 +1,79 @@
+"""File storage backends for uploaded files."""
+
+from collections.abc import Callable
+
+from .base import FileStorage
+from .postgresql import PostgreSQLFileStorage
+
+__all__ = ["FileStorage", "PostgreSQLFileStorage", "create_file_storage"]
+
+
+def create_file_storage(
+    storage_type: str,
+    pool_getter: Callable | None = None,
+    schema: str | None = None,
+    schema_getter: Callable | None = None,
+    **kwargs,
+) -> FileStorage:
+    """
+    Create file storage backend based on configuration.
+
+    Args:
+        storage_type: "native" (PostgreSQL BYTEA) or "s3" (S3-compatible object storage)
+        pool_getter: Database pool getter (required for native)
+        schema: Static database schema (for native single-tenant)
+        schema_getter: Callable returning current schema at query time (for native multi-tenant)
+        **kwargs: Additional args passed to storage backend
+
+    Returns:
+        FileStorage instance
+
+    Raises:
+        ValueError: If storage_type is unknown or required args are missing
+    """
+    if storage_type == "native":
+        if not pool_getter:
+            raise ValueError("pool_getter required for native (PostgreSQL) storage")
+        return PostgreSQLFileStorage(pool_getter=pool_getter, schema=schema, schema_getter=schema_getter)
+    elif storage_type == "s3":
+        from ...config import get_config
+        from .s3 import S3FileStorage
+
+        config = get_config()
+        bucket = config.file_storage_s3_bucket
+        if not bucket:
+            raise ValueError("HINDSIGHT_API_FILE_STORAGE_S3_BUCKET is required for S3 storage")
+        return S3FileStorage(
+            bucket=bucket,
+            region=config.file_storage_s3_region,
+            endpoint=config.file_storage_s3_endpoint,
+            access_key_id=config.file_storage_s3_access_key_id,
+            secret_access_key=config.file_storage_s3_secret_access_key,
+        )
+    elif storage_type == "gcs":
+        from ...config import get_config
+        from .gcs import GCSFileStorage
+
+        config = get_config()
+        bucket = config.file_storage_gcs_bucket
+        if not bucket:
+            raise ValueError("HINDSIGHT_API_FILE_STORAGE_GCS_BUCKET is required for GCS storage")
+        return GCSFileStorage(
+            bucket=bucket,
+            service_account_key=config.file_storage_gcs_service_account_key,
+        )
+    elif storage_type == "azure":
+        from ...config import get_config
+        from .azure import AzureFileStorage
+
+        config = get_config()
+        container = config.file_storage_azure_container
+        if not container:
+            raise ValueError("HINDSIGHT_API_FILE_STORAGE_AZURE_CONTAINER is required for Azure storage")
+        return AzureFileStorage(
+            container_name=container,
+            account_name=config.file_storage_azure_account_name,
+            account_key=config.file_storage_azure_account_key,
+        )
+    else:
+        raise ValueError(f"Unknown storage type: {storage_type}. Supported: 'native', 's3', 'gcs', 'azure'.")

hindsight-api/hindsight_api/engine/storage/azure.py

@@ -0,0 +1,62 @@
+"""Azure Blob Storage backend using obstore."""
+
+import logging
+from datetime import timedelta
+
+import obstore as obs
+from obstore.store import AzureStore
+
+from .base import FileStorage
+
+logger = logging.getLogger(__name__)
+
+
+class AzureFileStorage(FileStorage):
+    """
+    Azure Blob Storage backend.
+
+    Uses obstore (Rust-backed) for high-throughput async access to Azure Blob Storage.
+    Supports account key, SAS token, and default Azure credentials.
+    """
+
+    def __init__(
+        self,
+        container_name: str,
+        account_name: str | None = None,
+        account_key: str | None = None,
+    ):
+        kwargs: dict = {}
+        if account_name:
+            kwargs["account_name"] = account_name
+        if account_key:
+            kwargs["account_key"] = account_key
+
+        self._store = AzureStore(container_name, **kwargs)
+        logger.info(f"Initialized Azure file storage: container={container_name}, account={account_name}")
+
+    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 Azure")
+        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() or "BlobNotFound" in str(e):
+                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))

hindsight-api/hindsight_api/engine/storage/base.py

@@ -0,0 +1,83 @@
+"""Abstract base class for file storage backends."""
+
+from abc import ABC, abstractmethod
+
+
+class FileStorage(ABC):
+    """Abstract base for file storage backends."""
+
+    @abstractmethod
+    async def store(
+        self,
+        file_data: bytes,
+        key: str,
+        metadata: dict[str, str] | None = None,
+    ) -> str:
+        """
+        Store file and return storage key.
+
+        Args:
+            file_data: Raw file bytes
+            key: Storage key (e.g., "banks/{bank_id}/files/{file_id}.pdf")
+            metadata: Optional metadata to store with file
+
+        Returns:
+            Storage key that can be used to retrieve the file
+        """
+        pass
+
+    @abstractmethod
+    async def retrieve(self, key: str) -> bytes:
+        """
+        Retrieve file by storage key.
+
+        Args:
+            key: Storage key
+
+        Returns:
+            File data as bytes
+
+        Raises:
+            FileNotFoundError: If file does not exist
+        """
+        pass
+
+    @abstractmethod
+    async def delete(self, key: str) -> None:
+        """
+        Delete file by storage key.
+
+        Args:
+            key: Storage key
+        """
+        pass
+
+    @abstractmethod
+    async def exists(self, key: str) -> bool:
+        """
+        Check if file exists.
+
+        Args:
+            key: Storage key
+
+        Returns:
+            True if file exists, False otherwise
+        """
+        pass
+
+    @abstractmethod
+    async def get_download_url(self, key: str, expires_in: int = 3600) -> str:
+        """
+        Get a URL for downloading the file.
+
+        For PostgreSQL storage, this might be a relative API path.
+        For S3, this would be a pre-signed URL.
+
+        Args:
+            key: Storage key
+            expires_in: Expiration time in seconds (may be ignored for some backends)
+
+        Returns:
+            Download URL or path
+        """
+        pass

hindsight-api/hindsight_api/engine/storage/gcs.py

@@ -0,0 +1,59 @@
+"""Google Cloud Storage backend using obstore."""
+
+import logging
+from datetime import timedelta
+
+import obstore as obs
+from obstore.store import GCSStore
+
+from .base import FileStorage
+
+logger = logging.getLogger(__name__)
+
+
+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
+
+        self._store = GCSStore(bucket, **kwargs)
+        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))

hindsight-api/hindsight_api/engine/storage/postgresql.py

@@ -0,0 +1,153 @@
+"""PostgreSQL BYTEA-based file storage (default, zero-config)."""
+
+import logging
+from collections.abc import Callable
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import asyncpg
+
+from .base import FileStorage
+
+logger = logging.getLogger(__name__)
+
+
+def fq_table(table: str, schema: str | None = None) -> str:
+    """Get fully-qualified table name with optional schema prefix."""
+    if schema:
+        return f'"{schema}".{table}'
+    return table
+
+
+class PostgreSQLFileStorage(FileStorage):
+    """
+    PostgreSQL BYTEA-based file storage.
+
+    Stores files directly in PostgreSQL using BYTEA columns.
+    This is the default storage backend - zero configuration required!
+
+    Pros:
+    - Works out of the box (no external dependencies)
+    - Transactional consistency with database
+    - Simple backups (included in pg_dump)
+    - Good performance for <10MB files
+
+    Cons:
+    - Database bloat for large/many files
+    - Not ideal for distributed deployments
+    - Higher cost than object storage at scale
+
+    For production/scale, consider S3FileStorage instead.
+    """
+
+    def __init__(
+        self,
+        pool_getter: Callable[[], "asyncpg.Pool"],
+        schema: str | None = None,
+        schema_getter: Callable[[], str] | None = None,
+    ):
+        """
+        Initialize PostgreSQL file storage.
+
+        Args:
+            pool_getter: Function that returns asyncpg connection pool
+            schema: Static database schema (fallback for single-tenant / tests)
+            schema_getter: Callable returning current schema at query time (for multi-tenant)
+        """
+        self._pool_getter = pool_getter
+        self._static_schema = schema
+        self._schema_getter = schema_getter
+
+    @property
+    def _schema(self) -> str | None:
+        """Resolve schema dynamically per-request when schema_getter is provided."""
+        if self._schema_getter:
+            return self._schema_getter()
+        return self._static_schema
+
+    async def store(
+        self,
+        file_data: bytes,
+        key: str,
+        metadata: dict[str, str] | None = None,
+    ) -> str:
+        """Store file in PostgreSQL."""
+        pool = self._pool_getter()
+
+        async with pool.acquire() as conn:
+            await conn.execute(
+                f"""
+                INSERT INTO {fq_table("file_storage", self._schema)}
+                (storage_key, data)
+                VALUES ($1, $2)
+                ON CONFLICT (storage_key) DO UPDATE SET
+                    data = EXCLUDED.data
+                """,
+                key,
+                file_data,
+            )
+
+        logger.debug(f"Stored file {key} ({len(file_data)} bytes) in PostgreSQL")
+        return key
+
+    async def retrieve(self, key: str) -> bytes:
+        """Retrieve file from PostgreSQL."""
+        pool = self._pool_getter()
+
+        async with pool.acquire() as conn:
+            row = await conn.fetchrow(
+                f"""
+                SELECT data FROM {fq_table("file_storage", self._schema)}
+                WHERE storage_key = $1
+                """,
+                key,
+            )
+
+            if not row:
+                raise FileNotFoundError(f"File not found: {key}")
+
+            return bytes(row["data"])
+
+    async def delete(self, key: str) -> None:
+        """Delete file from PostgreSQL."""
+        pool = self._pool_getter()
+
+        async with pool.acquire() as conn:
+            result = await conn.execute(
+                f"""
+                DELETE FROM {fq_table("file_storage", self._schema)}
+                WHERE storage_key = $1
+                """,
+                key,
+            )
+
+            # Check if anything was deleted
+            if result == "DELETE 0":
+                logger.warning(f"Attempted to delete non-existent file: {key}")
+
+    async def exists(self, key: str) -> bool:
+        """Check if file exists in PostgreSQL."""
+        pool = self._pool_getter()
+
+        async with pool.acquire() as conn:
+            row = await conn.fetchrow(
+                f"""
+                SELECT 1 FROM {fq_table("file_storage", self._schema)}
+                WHERE storage_key = $1
+                """,
+                key,
+            )
+
+            return row is not None
+
+    async def get_download_url(self, key: str, expires_in: int = 3600) -> str:
+        """
+        Get download URL for PostgreSQL-stored file.
+
+        Returns an API endpoint path (not a pre-signed URL since the file
+        is stored in the database). The expires_in parameter is ignored
+        for PostgreSQL storage.
+        """
+        # Return API path for download endpoint
+        # (expires_in ignored for database storage - auth handled at API level)
+        return f"/v1/default/files/download/{key}"

hindsight-api/hindsight_api/engine/storage/s3.py

@@ -0,0 +1,71 @@
+"""S3 object storage backend using obstore."""
+
+import logging
+from datetime import timedelta
+
+import obstore as obs
+from obstore.store import S3Store
+
+from .base import FileStorage
+
+logger = logging.getLogger(__name__)
+
+
+class S3FileStorage(FileStorage):
+    """
+    S3-compatible object storage backend.
+
+    Uses obstore (Rust-backed) for high-throughput async access to
+    Amazon S3, MinIO, Cloudflare R2, and other S3-compliant APIs.
+    """
+
+    def __init__(
+        self,
+        bucket: str,
+        region: str | None = None,
+        endpoint: str | None = None,
+        access_key_id: str | None = None,
+        secret_access_key: str | None = None,
+    ):
+        kwargs: dict = {}
+        if region:
+            kwargs["region"] = region
+        if endpoint:
+            kwargs["endpoint"] = endpoint
+            # Allow plain HTTP for local S3-compatible services (MinIO, LocalStack, etc.)
+            if endpoint.startswith("http://"):
+                kwargs["allow_http"] = True
+        if access_key_id:
+            kwargs["access_key_id"] = access_key_id
+        if secret_access_key:
+            kwargs["secret_access_key"] = secret_access_key
+
+        self._store = S3Store(bucket, **kwargs)
+        logger.info(f"Initialized S3 file storage: bucket={bucket}, region={region}, endpoint={endpoint}")
+
+    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 S3")
+        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() or "NoSuchKey" in str(e):
+                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))

hindsight-api/hindsight_api/engine/utils.py

@@ -19,6 +19,7 @@ async def extract_facts(
     context: str = "",
     llm_config: "LLMConfig" = None,
     agent_name: str = None,
+    config=None,
 ) -> tuple[list["Fact"], list[tuple[str, int]]]:
     """
     Extract semantic facts from text using LLM.
@@ -35,6 +36,7 @@ async def extract_facts(
         context: Context about the conversation/document
         llm_config: LLM configuration to use
         agent_name: Optional agent name to help identify agent-related facts
+        config: HindsightConfig to use (defaults to global config if not provided)
 
     Returns:
         Tuple of (facts, chunks) where:
@@ -47,12 +49,19 @@ async def extract_facts(
     if not text or not text.strip():
         return [], []
 
+    # Use provided config or fall back to global config
+    if config is None:
+        from ..config import _get_raw_config
+
+        config = _get_raw_config()
+
     facts, chunks, _ = await extract_facts_from_text(
         text,
         event_date,
-        context=context,
         llm_config=llm_config,
         agent_name=agent_name,
+        config=config,
+        context=context,
     )
 
     if not facts:

hindsight-api/hindsight_api/extensions/__init__.py

@@ -22,6 +22,11 @@ from hindsight_api.extensions.http import HttpExtension
 from hindsight_api.extensions.loader import load_extension
 from hindsight_api.extensions.mcp import MCPExtension
 from hindsight_api.extensions.operation_validator import (
+    # Bank Management operations
+    BankListContext,
+    BankListResult,
+    BankReadContext,
+    BankWriteContext,
     # Consolidation operation
     ConsolidateContext,
     ConsolidateResult,
@@ -70,6 +75,11 @@ __all__ = [
     "RetainContext",
     "RetainResult",
     "ValidationResult",
+    # Operation Validator - Bank Management
+    "BankListContext",
+    "BankListResult",
+    "BankReadContext",
+    "BankWriteContext",
     # Operation Validator - Consolidation
     "ConsolidateContext",
     "ConsolidateResult",

hindsight-api/hindsight_api/extensions/context.py

@@ -96,7 +96,13 @@ class DefaultExtensionContext(ExtensionContext):
 
     async def run_migration(self, schema: str) -> None:
         """Run migrations for a specific schema."""
-        from hindsight_api.migrations import ensure_embedding_dimension, run_migrations
+        from hindsight_api.config import get_config
+        from hindsight_api.migrations import (
+            ensure_embedding_dimension,
+            ensure_text_search_extension,
+            ensure_vector_extension,
+            run_migrations,
+        )
 
         # Prefer getting URL from memory engine (handles pg0 case where URL is set after init)
         db_url = self._database_url
@@ -107,6 +113,9 @@ class DefaultExtensionContext(ExtensionContext):
 
         run_migrations(db_url, schema=schema)
 
+        # Get config for vector extension setting
+        config = get_config()
+
         # Ensure embedding column dimension matches the model's dimension
         # This is needed because migrations create columns with default dimension
         if self._memory_engine is not None:
@@ -114,7 +123,15 @@ class DefaultExtensionContext(ExtensionContext):
             if embeddings is not None:
                 dimension = getattr(embeddings, "dimension", None)
                 if dimension is not None:
-                    ensure_embedding_dimension(db_url, dimension, schema=schema)
+                    ensure_embedding_dimension(
+                        db_url, dimension, schema=schema, vector_extension=config.vector_extension
+                    )
+
+        # Ensure vector indexes match the configured extension
+        ensure_vector_extension(db_url, vector_extension=config.vector_extension, schema=schema)
+
+        # Ensure text search columns/indexes match the configured extension
+        ensure_text_search_extension(db_url, text_search_extension=config.text_search_extension, schema=schema)
 
     def get_memory_engine(self) -> "MemoryEngineInterface":
         """Get the memory engine interface."""

hindsight-api/hindsight_api/extensions/operation_validator.py

@@ -132,6 +132,10 @@ class RetainResult:
     unit_ids: list[list[str]]  # List of unit IDs per content item
     success: bool = True
     error: str | None = None
+    # Actual LLM token usage (populated by engine when available)
+    llm_input_tokens: int | None = None
+    llm_output_tokens: int | None = None
+    llm_total_tokens: int | None = None
 
 
 @dataclass
@@ -196,6 +200,44 @@ class ConsolidateResult:
     error: str | None = None
 
 
+# =============================================================================
+# Bank Management Contexts
+# =============================================================================
+
+
+@dataclass
+class BankReadContext:
+    """Context for a bank read operation validation (pre-operation)."""
+
+    bank_id: str
+    operation: str  # "get_bank_profile", "get_bank_stats"
+    request_context: "RequestContext"
+
+
+@dataclass
+class BankWriteContext:
+    """Context for a bank write operation validation (pre-operation)."""
+
+    bank_id: str
+    operation: str  # "delete_bank", "update_bank", "update_bank_disposition", "set_bank_mission", "merge_bank_mission", "clear_observations", "clear_observations_for_memory"
+    request_context: "RequestContext"
+
+
+@dataclass
+class BankListContext:
+    """Context for filtering the bank list (post-query)."""
+
+    banks: list[dict]
+    request_context: "RequestContext"
+
+
+@dataclass
+class BankListResult:
+    """Result of filtering the bank list."""
+
+    banks: list[dict]
+
+
 # =============================================================================
 # Mental Model Contexts
 # =============================================================================
@@ -531,3 +573,63 @@ class OperationValidatorExtension(Extension, ABC):
                 - error: Error message (if failed)
         """
         pass
+
+    # =========================================================================
+    # Bank Management - Validation hooks (optional - override to implement)
+    # =========================================================================
+
+    async def validate_bank_read(self, ctx: BankReadContext) -> ValidationResult:
+        """
+        Validate a bank read operation before execution.
+
+        Override to implement custom validation logic for bank reads
+        (get_bank_profile, get_bank_stats).
+
+        Args:
+            ctx: Context containing:
+                - bank_id: Bank identifier
+                - operation: Operation name
+                - request_context: Request context with auth info
+
+        Returns:
+            ValidationResult indicating whether the operation is allowed.
+        """
+        return ValidationResult.accept()
+
+    async def validate_bank_write(self, ctx: BankWriteContext) -> ValidationResult:
+        """
+        Validate a bank write operation before execution.
+
+        Override to implement custom validation logic for bank writes
+        (delete_bank, update_bank, update_bank_disposition, set_bank_mission,
+        merge_bank_mission, clear_observations, clear_observations_for_memory).
+
+        Args:
+            ctx: Context containing:
+                - bank_id: Bank identifier
+                - operation: Operation name
+                - request_context: Request context with auth info
+
+        Returns:
+            ValidationResult indicating whether the operation is allowed.
+        """
+        return ValidationResult.accept()
+
+    async def filter_bank_list(self, ctx: BankListContext) -> BankListResult:
+        """
+        Filter the bank list after querying.
+
+        Unlike validate_* methods, this is a post-query filter that narrows results
+        rather than a gate that blocks the operation.
+
+        Override to implement custom filtering (e.g., restrict to allowed banks).
+
+        Args:
+            ctx: Context containing:
+                - banks: List of bank dicts from the database
+                - request_context: Request context with auth info
+
+        Returns:
+            BankListResult with the filtered list of banks.
+        """
+        return BankListResult(banks=ctx.banks)

hindsight-api/hindsight_api/extensions/tenant.py

@@ -2,6 +2,7 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from typing import Any
 
 from hindsight_api.extensions.base import Extension
 from hindsight_api.models import RequestContext
@@ -88,6 +89,54 @@ class TenantExtension(Extension, ABC):
         """
         ...
 
+    async def get_tenant_config(self, context: RequestContext) -> dict[str, Any]:
+        """
+        Get tenant-specific configuration overrides.
+
+        This method is called during hierarchical configuration resolution to get
+        tenant-level config overrides. The returned dict should contain Python field
+        names (lowercase snake_case) as keys, not environment variable names.
+
+        Example:
+            {"llm_model": "gpt-4", "retain_extraction_mode": "verbose"}
+
+        The default implementation returns an empty dict (no tenant-specific config).
+        Override this method in custom extensions to provide tenant-specific configuration.
+
+        Args:
+            context: The request context containing tenant information.
+
+        Returns:
+            Dict of config field names to values (only configurable fields).
+            Empty dict if no tenant-specific config.
+        """
+        return {}
+
+    async def get_allowed_config_fields(self, context: RequestContext, bank_id: str) -> set[str] | None:
+        """
+        Get set of config fields that this tenant/bank is allowed to modify.
+
+        This method controls which configurable fields can be modified via the bank config API.
+        It enables fine-grained permission control per tenant or per bank.
+
+        Examples:
+            - Return None: Allow all configurable fields (default)
+            - Return {"retain_chunk_size", "retain_custom_instructions"}: Allow only these fields
+            - Return set(): Allow no modifications (read-only)
+
+        The default implementation returns None (all configurable fields allowed).
+        Override this method in custom extensions to implement custom permission logic.
+
+        Args:
+            context: The request context containing tenant information.
+            bank_id: The bank identifier for per-bank permissions.
+
+        Returns:
+            Set of allowed field names, or None to allow all configurable fields.
+            Returned fields must be a subset of HindsightConfig.get_configurable_fields().
+        """
+        return None
+
     async def authenticate_mcp(self, context: RequestContext) -> TenantContext:
         """
         Authenticate MCP requests.

hindsight-api/hindsight_api/main.py

@@ -23,7 +23,7 @@ import uvicorn
 from . import MemoryEngine, __version__
 from .api import create_app
 from .banner import print_banner
-from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, get_config
+from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, _get_raw_config
 from .daemon import (
     DEFAULT_DAEMON_PORT,
     DEFAULT_IDLE_TIMEOUT,
@@ -68,7 +68,7 @@ def main():
     global _memory
 
     # Load configuration from environment (for CLI args defaults)
-    config = get_config()
+    config = _get_raw_config()
 
     parser = argparse.ArgumentParser(
         prog="hindsight-api",
@@ -155,6 +155,8 @@ def main():
         config = HindsightConfig(
             database_url=config.database_url,
             database_schema=config.database_schema,
+            vector_extension=config.vector_extension,
+            text_search_extension=config.text_search_extension,
             llm_provider=config.llm_provider,
             llm_api_key=config.llm_api_key,
             llm_model=config.llm_model,
@@ -164,6 +166,8 @@ def main():
             llm_initial_backoff=config.llm_initial_backoff,
             llm_max_backoff=config.llm_max_backoff,
             llm_timeout=config.llm_timeout,
+            llm_groq_service_tier=config.llm_groq_service_tier,
+            llm_openai_service_tier=config.llm_openai_service_tier,
             llm_vertexai_project_id=config.llm_vertexai_project_id,
             llm_vertexai_region=config.llm_vertexai_region,
             llm_vertexai_service_account_key=config.llm_vertexai_service_account_key,
@@ -197,23 +201,46 @@ def main():
             embeddings_provider=config.embeddings_provider,
             embeddings_local_model=config.embeddings_local_model,
             embeddings_local_force_cpu=config.embeddings_local_force_cpu,
+            embeddings_local_trust_remote_code=config.embeddings_local_trust_remote_code,
             embeddings_tei_url=config.embeddings_tei_url,
             embeddings_openai_base_url=config.embeddings_openai_base_url,
+            embeddings_cohere_api_key=config.embeddings_cohere_api_key,
+            embeddings_cohere_model=config.embeddings_cohere_model,
             embeddings_cohere_base_url=config.embeddings_cohere_base_url,
+            embeddings_litellm_api_base=config.embeddings_litellm_api_base,
+            embeddings_litellm_api_key=config.embeddings_litellm_api_key,
+            embeddings_litellm_model=config.embeddings_litellm_model,
+            embeddings_litellm_sdk_api_key=config.embeddings_litellm_sdk_api_key,
+            embeddings_litellm_sdk_model=config.embeddings_litellm_sdk_model,
+            embeddings_litellm_sdk_api_base=config.embeddings_litellm_sdk_api_base,
             reranker_provider=config.reranker_provider,
             reranker_local_model=config.reranker_local_model,
             reranker_local_force_cpu=config.reranker_local_force_cpu,
             reranker_local_max_concurrent=config.reranker_local_max_concurrent,
+            reranker_local_trust_remote_code=config.reranker_local_trust_remote_code,
             reranker_tei_url=config.reranker_tei_url,
             reranker_tei_batch_size=config.reranker_tei_batch_size,
             reranker_tei_max_concurrent=config.reranker_tei_max_concurrent,
             reranker_max_candidates=config.reranker_max_candidates,
+            reranker_cohere_api_key=config.reranker_cohere_api_key,
+            reranker_cohere_model=config.reranker_cohere_model,
             reranker_cohere_base_url=config.reranker_cohere_base_url,
+            reranker_litellm_api_base=config.reranker_litellm_api_base,
+            reranker_litellm_api_key=config.reranker_litellm_api_key,
+            reranker_litellm_model=config.reranker_litellm_model,
+            reranker_litellm_sdk_api_key=config.reranker_litellm_sdk_api_key,
+            reranker_litellm_sdk_model=config.reranker_litellm_sdk_model,
+            reranker_litellm_sdk_api_base=config.reranker_litellm_sdk_api_base,
+            reranker_zeroentropy_api_key=config.reranker_zeroentropy_api_key,
+            reranker_zeroentropy_model=config.reranker_zeroentropy_model,
             host=args.host,
             port=args.port,
+            base_path=config.base_path,
             log_level=args.log_level,
             log_format=config.log_format,
             mcp_enabled=config.mcp_enabled,
+            mcp_enabled_tools=config.mcp_enabled_tools,
+            enable_bank_config_api=config.enable_bank_config_api,
             graph_retriever=config.graph_retriever,
             mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
             recall_max_concurrent=config.recall_max_concurrent,
@@ -222,10 +249,36 @@ def main():
             retain_chunk_size=config.retain_chunk_size,
             retain_extract_causal_links=config.retain_extract_causal_links,
             retain_extraction_mode=config.retain_extraction_mode,
+            retain_mission=config.retain_mission,
             retain_custom_instructions=config.retain_custom_instructions,
+            retain_batch_tokens=config.retain_batch_tokens,
+            retain_batch_enabled=config.retain_batch_enabled,
+            retain_batch_poll_interval_seconds=config.retain_batch_poll_interval_seconds,
+            file_storage_type=config.file_storage_type,
+            file_storage_s3_bucket=config.file_storage_s3_bucket,
+            file_storage_s3_region=config.file_storage_s3_region,
+            file_storage_s3_endpoint=config.file_storage_s3_endpoint,
+            file_storage_s3_access_key_id=config.file_storage_s3_access_key_id,
+            file_storage_s3_secret_access_key=config.file_storage_s3_secret_access_key,
+            file_storage_gcs_bucket=config.file_storage_gcs_bucket,
+            file_storage_gcs_service_account_key=config.file_storage_gcs_service_account_key,
+            file_storage_azure_container=config.file_storage_azure_container,
+            file_storage_azure_account_name=config.file_storage_azure_account_name,
+            file_storage_azure_account_key=config.file_storage_azure_account_key,
+            file_parser=config.file_parser,
+            file_parser_iris_token=config.file_parser_iris_token,
+            file_parser_iris_org_id=config.file_parser_iris_org_id,
+            file_conversion_max_batch_size_mb=config.file_conversion_max_batch_size_mb,
+            file_conversion_max_batch_size=config.file_conversion_max_batch_size,
+            enable_file_upload_api=config.enable_file_upload_api,
+            file_delete_after_retain=config.file_delete_after_retain,
             enable_observations=config.enable_observations,
             consolidation_batch_size=config.consolidation_batch_size,
+            consolidation_llm_batch_size=config.consolidation_llm_batch_size,
             consolidation_max_tokens=config.consolidation_max_tokens,
+            observations_mission=config.observations_mission,
+            entity_labels=config.entity_labels,
+            entities_allow_free_form=config.entities_allow_free_form,
             skip_llm_verification=config.skip_llm_verification,
             lazy_reranker=config.lazy_reranker,
             run_migrations_on_startup=config.run_migrations_on_startup,
@@ -241,7 +294,17 @@ def main():
             worker_max_slots=config.worker_max_slots,
             worker_consolidation_max_slots=config.worker_consolidation_max_slots,
             reflect_max_iterations=config.reflect_max_iterations,
+            reflect_max_context_tokens=config.reflect_max_context_tokens,
+            reflect_mission=config.reflect_mission,
+            disposition_skepticism=config.disposition_skepticism,
+            disposition_literalism=config.disposition_literalism,
+            disposition_empathy=config.disposition_empathy,
             mental_model_refresh_concurrency=config.mental_model_refresh_concurrency,
+            otel_traces_enabled=config.otel_traces_enabled,
+            otel_exporter_otlp_endpoint=config.otel_exporter_otlp_endpoint,
+            otel_exporter_otlp_headers=config.otel_exporter_otlp_headers,
+            otel_service_name=config.otel_service_name,
+            otel_deployment_environment=config.otel_deployment_environment,
         )
     config.configure_logging()
     if not args.daemon:
@@ -319,6 +382,7 @@ def main():
         "proxy_headers": args.proxy_headers,
         "ws": "wsproto",  # Use wsproto instead of websockets to avoid deprecation warnings
         "loop": loop_impl,  # Explicitly set event loop implementation
+        "timeout_keep_alive": 30,  # Exceed aiohttp's 15s client timeout so the client always closes first
     }
 
     # Add optional parameters if provided
@@ -347,6 +411,8 @@ def main():
             reranker_provider=config.reranker_provider,
             mcp_enabled=config.mcp_enabled,
             version=__version__,
+            vector_extension=config.vector_extension,
+            text_search_extension=config.text_search_extension,
         )
 
     # Start idle checker in daemon mode