test: add client tests for ReflectResponse parsing

Added comprehensive tests in hindsight-clients/python/tests to verify:
- v0.4.0+ format with empty based_on object
- v0.4.0+ format with null based_on
- v0.4.0+ format with populated facts
- v0.3.0 format (list) correctly fails validation
- Missing based_on field handling

These tests document the v0.3.0 -> v0.4.0 breaking change where
based_on changed from list to object.

.claude-plugin/marketplace.json

@@ -1,15 +0,0 @@
-{
-  "$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
-  "name": "hindsight",
-  "description": "Official Hindsight integrations for Claude Code",
-  "owner": {
-    "name": "vectorize-io"
-  },
-  "plugins": [
-    {
-      "name": "hindsight-memory",
-      "description": "Automatic long-term memory for Claude Code via Hindsight",
-      "source": "./hindsight-integrations/claude-code"
-    }
-  ]
-}

.claude/skills/code-review/SKILL.md

@@ -1,196 +0,0 @@
----
-name: code-review
-description: Review changed code against project standards. Checks for missing tests, dead code, type safety, lint issues, and coding conventions. Run after completing any implementation work.
-user_invocable: true
----
-
-# Code Review
-
-Review all changed code against the project's quality standards and coding conventions.
-
-## Code Standards
-
-Read and internalize these standards before writing code. The review steps below verify compliance.
-
-### Python Style
-- Python 3.11+, type hints required
-- Async throughout (asyncpg, async FastAPI)
-- Pydantic models for request/response
-- Ruff for linting (line-length 120)
-- No Python files at project root - maintain clean directory structure
-- **Never use multi-item tuple return values** — not even for internal/private functions. Always use a dataclass or Pydantic model. No exceptions, no "it's just two values" shortcuts. If a function returns more than one value, define a named type for it.
-
-### Type Safety with Pydantic Models
-**NEVER use raw `dict` types for structured data** — this applies to all code, including internal helpers and private functions. If the dict has known keys, it must be a dataclass or Pydantic model:
-- Use Pydantic `BaseModel` for all data structures passed between functions
-- Use `@dataclass` for lightweight internal data containers when Pydantic validation isn't needed
-- Add `@field_validator` for type coercion (e.g., ensuring datetimes are timezone-aware)
-- Avoid `dict.get()` patterns - use typed model attributes instead
-- Parse external data (JSON, API responses) into Pydantic models at the boundary
-- This catches type errors at parse time, not deep in business logic
-- The only acceptable `dict` usage is for truly dynamic/unknown keys (e.g., arbitrary metadata, JSON blobs with no fixed schema)
-
-```python
-# BAD - error-prone dict access
-def process(data: dict) -> str:
-    return data.get("name", "")  # No validation, silent failures
-
-# GOOD - typed and validated
-class UserData(BaseModel):
-    name: str
-    created_at: datetime
-
-    @field_validator("created_at", mode="before")
-    @classmethod
-    def ensure_tz_aware(cls, v):
-        if isinstance(v, str):
-            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
-        if v.tzinfo is None:
-            return v.replace(tzinfo=timezone.utc)
-        return v
-
-def process(data: UserData) -> str:
-    return data.name  # Type-safe, validated at construction
-```
-
-### TypeScript Style
-- Next.js App Router for control plane
-- Tailwind CSS with shadcn/ui components
-
-### Code Comments
-- **Always comment non-trivial technical decisions** with the reasoning behind the choice. If someone would ask "why is it done this way?", there should be a comment.
-- **Keep comments up to date with history** — when changing an approach, update the comment to explain what was tried before and why it was changed. Comments serve as a tracker of previous implementations that likely had problems.
-- Don't comment obvious code — only where the "why" isn't self-evident from the code itself.
-
-```python
-# BAD - no context for future readers
-results = await asyncio.gather(*tasks, return_exceptions=True)
-
-# GOOD - explains the non-obvious choice
-# Use return_exceptions=True to avoid cancelling sibling tasks on failure.
-# Previously we used TaskGroup but it cancelled all tasks when one failed,
-# causing partial writes that left orphaned entity links (see #412).
-results = await asyncio.gather(*tasks, return_exceptions=True)
-```
-
-### Branch Hygiene
-- **Always start new feature branches from `origin/main`** — rebase to ensure a clean base.
-- **Only include commits relevant to the PR/branch/feature** — no unrelated changes. If the branch contains commits that don't belong, they must be removed before merging.
-
-### General Principles
-- Don't add features, refactor code, or make "improvements" beyond what was asked
-- Don't add unnecessary error handling for impossible scenarios
-- Don't create helpers or abstractions for one-time operations
-- No backwards-compatibility hacks (unused vars, re-exports, "removed" comments)
-- Three similar lines of code is better than a premature abstraction
-
-## Review Steps
-
-### 1. Check branch hygiene
-
-- Run `git log --oneline main..HEAD` to list all commits on the branch.
-- Verify every commit is relevant to the feature/PR. Flag any unrelated commits.
-- Check the branch is based on a recent `origin/main` (no stale base).
-
-### 2. Identify changed files
-
-Run `git diff --name-only HEAD` (unstaged) and `git diff --cached --name-only` (staged) to get all changed files. If there are no local changes, diff against the base branch using `git diff main...HEAD --name-only` and `git diff main...HEAD` to review all commits on the current branch.
-
-### 3. Run linters
-
-```bash
-./scripts/hooks/lint.sh
-```
-
-Report any failures. Do NOT fix them yourself — just report.
-
-### 4. Check for dead code
-
-For each changed Python file, check for:
-- Unused imports (Ruff should catch these, but verify)
-- Functions/methods/classes that were added but are never called from anywhere
-- Variables assigned but never read
-- Commented-out code blocks that should be removed
-
-For each changed TypeScript file, check for:
-- Unused imports
-- Unused variables or functions
-- Commented-out code
-
-### 5. Check type safety (Python)
-
-For each changed Python file, check for violations:
-- **No raw `dict` for structured data** — must use Pydantic model or dataclass, even for internal/private functions (only exception: truly dynamic/unknown keys)
-- **No multi-item tuple returns** — must use dataclass or Pydantic model, even for internal/private functions (no exceptions)
-- **Missing type hints** on function parameters and return types
-- **Missing `@field_validator`** for datetime fields that should be timezone-aware
-
-### 6. Check for missing tests
-
-For each new or significantly changed function/endpoint/class:
-- Check if there is a corresponding test addition or update
-- New API endpoints MUST have integration tests
-- New utility functions MUST have unit tests
-- Bug fixes SHOULD have a regression test
-
-Flag any new logic that lacks test coverage.
-
-### 7. Check API consistency
-
-If any files in `hindsight-api-slim/hindsight_api/api/` were changed:
-- Were the OpenAPI specs regenerated? (`./scripts/generate-openapi.sh`)
-- Were the client SDKs regenerated? (`./scripts/generate-clients.sh`)
-- Were the control plane proxy routes updated? (`hindsight-control-plane/src/app/api/`)
-
-### 8. Check code comments
-
-For each non-trivial change:
-- **New non-obvious logic** — is there a comment explaining the reasoning?
-- **Changed approach** — does the comment include what was done before and why it changed?
-- **Stale comments** — do existing comments near the changed code still accurately describe the behavior?
-
-### 9. Check integration completeness
-
-If any files in `hindsight-integrations/` were added or changed, verify:
-- **Tests exist** — the integration must have tests that simulate/exercise the external framework (not just pure unit tests of helpers). Check for a `tests/` directory with meaningful test files.
-- **CI job exists** — check `.github/workflows/test.yml` for a corresponding `test-<name>-integration` job. If missing, flag it.
-- **Release process** — check that the integration name is in the `VALID_INTEGRATIONS` array in `scripts/release-integration.sh`. If missing, flag it.
-- **Code standards** — the integration code must follow all Python style rules (type hints, no raw dicts, no tuple returns, etc.).
-
-### 10. Review against other coding standards
-
-Check the diff for violations of the standards listed above:
-- Python files at project root (not allowed)
-- Missing async patterns (should be async throughout)
-- Pydantic models for request/response
-- Line length > 120 chars
-- New features/code beyond what was asked (over-engineering)
-- Unnecessary error handling for impossible scenarios
-- Premature abstractions or speculative helpers
-- Backwards-compatibility hacks (unused vars, re-exports, "removed" comments)
-
-### 11. Report findings
-
-Present a clear summary organized by severity:
-
-**Must fix** — issues that will break CI or violate hard project rules:
-- Unrelated commits on the branch
-- Lint failures
-- Missing type hints on public functions
-- Raw dict usage for structured data (including internal code)
-- Multi-item tuple returns (including internal code)
-- Missing tests for new endpoints
-- New integration missing tests, CI job, or release-integration.sh entry
-
-**Should fix** — issues that hurt code quality:
-- Dead code / unused imports missed by linter
-- Missing tests for non-trivial utility functions
-- Over-engineering beyond the task scope
-
-**Note** — observations that may or may not need action:
-- API changes that might need client regeneration
-- Patterns that deviate from nearby code style
-
-For each finding, include the file path, line number, and a brief explanation.
-
-Do NOT auto-fix any issues. Report all findings and let the user decide what to address. If there are no findings, confirm the code looks good.

.env.example

@@ -2,10 +2,10 @@
 # Copy this file to .env and fill in your values
 
 # LLM Configuration (Required)
-# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai, minimax, volcano
+# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai
 HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
-HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
+HINDSIGHT_API_LLM_MODEL=o3-mini
 HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 
 # Example: Anthropic Claude configuration
@@ -20,11 +20,6 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 # HINDSIGHT_API_LLM_VERTEXAI_REGION=us-central1
 # HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/path/to/service-account-key.json  # Optional, uses ADC if not set
 
-# Example: MiniMax configuration (1M context window)
-# HINDSIGHT_API_LLM_PROVIDER=minimax
-# HINDSIGHT_API_LLM_API_KEY=your-minimax-api-key
-# HINDSIGHT_API_LLM_MODEL=MiniMax-M2.7
-
 # Example: LM Studio local configuration (Qwen 2.5 32B recommended)
 # HINDSIGHT_API_LLM_PROVIDER=lmstudio
 # HINDSIGHT_API_LLM_API_KEY=lmstudio
@@ -36,23 +31,10 @@ 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_MIGRATION_DATABASE_URL=  # Direct PostgreSQL URL for migrations (bypasses PgBouncer). Falls back to DATABASE_URL.
 # 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

.github/dependabot.yml

@@ -1,6 +0,0 @@
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"

.github/workflows/deploy-docs.yml

@@ -21,20 +21,17 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
-      - uses: actions/setup-node@v6
+      - uses: actions/checkout@v4
+      - uses: actions/setup-node@v4
         with:
           node-version: 20
           cache: npm
           cache-dependency-path: package-lock.json
-      - uses: astral-sh/setup-uv@v7
+      - uses: astral-sh/setup-uv@v4
       - run: npm ci --workspace=hindsight-docs
       - run: uv run generate-llms-full
       - run: npm run build --workspace=hindsight-docs
-        env:
-          UMAMI_URL: https://analytics.hindsight.vectorize.io
-          UMAMI_WEBSITE_ID: ${{ secrets.UMAMI_WEBSITE_ID }}
-      - uses: actions/upload-pages-artifact@v4
+      - uses: actions/upload-pages-artifact@v3
         with:
           path: hindsight-docs/build
   deploy:
@@ -44,5 +41,5 @@ jobs:
     runs-on: ubuntu-latest
     needs: build
     steps:
-      - uses: actions/deploy-pages@v5
+      - uses: actions/deploy-pages@v4
         id: deployment

.github/workflows/release-integration.yml

@@ -1,111 +0,0 @@
-name: Release Integration
-
-on:
-  push:
-    tags:
-      - 'integrations/**'
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    permissions:
-      id-token: write  # for PyPI trusted publishing
-
-    steps:
-    - uses: actions/checkout@v6
-
-    - name: Extract integration info
-      id: info
-      run: |
-        # refs/tags/integrations/litellm/v0.1.0 → integration=litellm, version=0.1.0
-        TAG="${GITHUB_REF#refs/tags/}"
-        INTEGRATION=$(echo "$TAG" | cut -d'/' -f2)
-        VERSION=$(echo "$TAG" | cut -d'/' -f3 | sed 's/^v//')
-        echo "integration=$INTEGRATION" >> $GITHUB_OUTPUT
-        echo "version=$VERSION" >> $GITHUB_OUTPUT
-        echo "tag=$TAG" >> $GITHUB_OUTPUT
-        echo "Integration: $INTEGRATION, Version: $VERSION"
-
-    - name: Detect integration type
-      id: type
-      run: |
-        if [ -f "hindsight-integrations/${{ steps.info.outputs.integration }}/pyproject.toml" ]; then
-          echo "type=python" >> $GITHUB_OUTPUT
-        elif [ -f "hindsight-integrations/${{ steps.info.outputs.integration }}/package.json" ]; then
-          echo "type=typescript" >> $GITHUB_OUTPUT
-        else
-          echo "type=plugin" >> $GITHUB_OUTPUT
-        fi
-
-    # ── Python integrations (litellm, pydantic-ai, crewai) ──────────────────
-
-    - name: Install uv
-      if: steps.type.outputs.type == 'python'
-      uses: astral-sh/setup-uv@v7
-      with:
-        enable-cache: true
-
-    - name: Set up Python
-      if: steps.type.outputs.type == 'python'
-      uses: actions/setup-python@v6
-      with:
-        python-version-file: ".python-version"
-
-    - name: Build Python package
-      if: steps.type.outputs.type == 'python'
-      working-directory: ./hindsight-integrations/${{ steps.info.outputs.integration }}
-      run: uv build --out-dir dist
-
-    - name: Publish Python package to PyPI
-      if: steps.type.outputs.type == 'python'
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-integrations/${{ steps.info.outputs.integration }}/dist
-        skip-existing: true
-
-    # ── TypeScript integrations (ai-sdk, chat, openclaw) ────────────────────
-
-    # ── Plugin integrations (claude-code) — no package to publish ───────────
-
-    - name: Plugin release
-      if: steps.type.outputs.type == 'plugin'
-      run: |
-        echo "Plugin integration ${{ steps.info.outputs.integration }} v${{ steps.info.outputs.version }} — no package to publish."
-        echo "Users install via: claude plugin marketplace add vectorize-io/hindsight --sparse hindsight-integrations"
-
-    # ── TypeScript integrations (ai-sdk, chat, openclaw) ────────────────────
-
-    - name: Set up Node.js
-      if: steps.type.outputs.type == 'typescript'
-      uses: actions/setup-node@v6
-      with:
-        node-version: '22'
-        registry-url: 'https://registry.npmjs.org'
-
-    - name: Install dependencies
-      if: steps.type.outputs.type == 'typescript'
-      working-directory: ./hindsight-integrations/${{ steps.info.outputs.integration }}
-      run: npm ci
-
-    - name: Build TypeScript package
-      if: steps.type.outputs.type == 'typescript'
-      working-directory: ./hindsight-integrations/${{ steps.info.outputs.integration }}
-      run: npm run build
-
-    - name: Publish TypeScript package to npm
-      if: steps.type.outputs.type == 'typescript'
-      working-directory: ./hindsight-integrations/${{ steps.info.outputs.integration }}
-      run: |
-        set +e
-        OUTPUT=$(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 }}

.github/workflows/release.yml

@@ -13,15 +13,15 @@ jobs:
       id-token: write
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Install uv
-      uses: astral-sh/setup-uv@v7
+      uses: astral-sh/setup-uv@v5
       with:
         enable-cache: true
 
     - name: Set up Python
-      uses: actions/setup-python@v6
+      uses: actions/setup-python@v5
       with:
         python-version-file: ".python-version"
 
@@ -30,39 +30,29 @@ jobs:
       working-directory: ./hindsight-clients/python
       run: uv build --out-dir dist
 
-    - name: Build hindsight-api-slim
-      working-directory: ./hindsight-api-slim
-      run: uv build --out-dir dist
-
     - name: Build hindsight-api
       working-directory: ./hindsight-api
       run: uv build --out-dir dist
 
     - name: Build hindsight-all
-      working-directory: ./hindsight-all
+      working-directory: ./hindsight
       run: uv build --out-dir dist
 
-    - name: Build hindsight-all-slim
-      working-directory: ./hindsight-all-slim
+    - name: Build hindsight-litellm
+      working-directory: ./hindsight-integrations/litellm
       run: uv build --out-dir dist
 
     - name: Build hindsight-embed
       working-directory: ./hindsight-embed
       run: uv build --out-dir dist
 
-    # Publish in order (client and api-slim first, then api/all wrappers which depend on them)
+    # Publish in order (client and api first, then hindsight-all which depends on them)
     - name: Publish hindsight-client to PyPI
       uses: pypa/gh-action-pypi-publish@release/v1
       with:
         packages-dir: ./hindsight-clients/python/dist
         skip-existing: true
 
-    - name: Publish hindsight-api-slim to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-api-slim/dist
-        skip-existing: true
-
     - name: Publish hindsight-api to PyPI
       uses: pypa/gh-action-pypi-publish@release/v1
       with:
@@ -72,13 +62,13 @@ jobs:
     - name: Publish hindsight-all to PyPI
       uses: pypa/gh-action-pypi-publish@release/v1
       with:
-        packages-dir: ./hindsight-all/dist
+        packages-dir: ./hindsight/dist
         skip-existing: true
 
-    - name: Publish hindsight-all-slim to PyPI
+    - name: Publish hindsight-litellm to PyPI
       uses: pypa/gh-action-pypi-publish@release/v1
       with:
-        packages-dir: ./hindsight-all-slim/dist
+        packages-dir: ./hindsight-integrations/litellm/dist
         skip-existing: true
 
     - name: Publish hindsight-embed to PyPI
@@ -89,15 +79,14 @@ jobs:
 
     # Upload artifacts for GitHub release
     - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
       with:
         name: python-packages
         path: |
           hindsight-clients/python/dist/*
-          hindsight-api-slim/dist/*
           hindsight-api/dist/*
-          hindsight-all/dist/*
-          hindsight-all-slim/dist/*
+          hindsight/dist/*
+          hindsight-integrations/litellm/dist/*
           hindsight-embed/dist/*
         retention-days: 1
 
@@ -106,10 +95,10 @@ jobs:
     environment: npm
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
       with:
         node-version: '20'
         registry-url: 'https://registry.npmjs.org'
@@ -144,21 +133,119 @@ jobs:
       run: npm pack
 
     - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
       with:
         name: typescript-client
         path: hindsight-clients/typescript/*.tgz
         retention-days: 1
 
+  release-openclaw-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/openclaw
+      run: npm ci
+
+    - name: Build
+      working-directory: ./hindsight-integrations/openclaw
+      run: npm run build
+
+    - name: Publish to npm
+      working-directory: ./hindsight-integrations/openclaw
+      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: ${{ secrets.NPM_TOKEN }}
+
+    - name: Pack for GitHub release
+      working-directory: ./hindsight-integrations/openclaw
+      run: npm pack
+
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: openclaw-integration
+        path: hindsight-integrations/openclaw/*.tgz
+        retention-days: 1
+
+  release-ai-sdk-integration:
+    runs-on: ubuntu-latest
+    environment: npm
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+        registry-url: 'https://registry.npmjs.org'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/ai-sdk
+      run: npm ci
+
+    - name: Build
+      working-directory: ./hindsight-integrations/ai-sdk
+      run: npm run build
+
+    - name: Publish to npm
+      working-directory: ./hindsight-integrations/ai-sdk
+      run: |
+        set +e
+        OUTPUT=$(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/ai-sdk
+      run: npm pack
+
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: ai-sdk-integration
+        path: hindsight-integrations/ai-sdk/*.tgz
+        retention-days: 1
+
   release-control-plane:
     runs-on: ubuntu-latest
     environment: npm
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Set up Node.js
-      uses: actions/setup-node@v6
+      uses: actions/setup-node@v4
       with:
         node-version: '20'
         registry-url: 'https://registry.npmjs.org'
@@ -181,14 +268,11 @@ 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=$(npm publish --access public --ignore-scripts 2>&1)
+        OUTPUT=$(npm publish --access public 2>&1)
         EXIT_CODE=$?
         echo "$OUTPUT"
         if [ $EXIT_CODE -ne 0 ]; then
@@ -206,7 +290,7 @@ jobs:
       run: npm pack
 
     - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
       with:
         name: control-plane
         path: hindsight-control-plane/*.tgz
@@ -229,13 +313,9 @@ jobs:
             target: aarch64-apple-darwin
             artifact_name: hindsight
             asset_name: hindsight-darwin-arm64
-          - os: ubuntu-24.04-arm
-            target: aarch64-unknown-linux-gnu
-            artifact_name: hindsight
-            asset_name: hindsight-linux-arm64
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Install Rust
       uses: dtolnay/rust-toolchain@stable
@@ -253,7 +333,7 @@ jobs:
         chmod +x artifacts/${{ matrix.asset_name }}
 
     - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
       with:
         name: rust-cli-${{ matrix.asset_name }}
         path: artifacts/${{ matrix.asset_name }}
@@ -294,7 +374,7 @@ jobs:
               PRELOAD_ML_MODELS=false
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Free Disk Space
       uses: jlumbroso/free-disk-space@main
@@ -308,13 +388,13 @@ jobs:
         swap-storage: true
 
     - name: Set up QEMU
-      uses: docker/setup-qemu-action@v4
+      uses: docker/setup-qemu-action@v3
 
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v4
+      uses: docker/setup-buildx-action@v3
 
     - name: Log in to GitHub Container Registry
-      uses: docker/login-action@v4
+      uses: docker/login-action@v3
       with:
         registry: ghcr.io
         username: ${{ github.actor }}
@@ -326,7 +406,7 @@ jobs:
 
     - name: Extract metadata for release tags
       id: meta
-      uses: docker/metadata-action@v6
+      uses: docker/metadata-action@v5
       with:
         images: ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}
         flavor: |
@@ -342,7 +422,7 @@ jobs:
     # # Step 1: Build for local testing (single platform, no push)
     # # This creates an identical image to what will be released, just for one platform
     # - name: Build image for testing
-    #   uses: docker/build-push-action@v7
+    #   uses: docker/build-push-action@v6
     #   with:
     #     context: .
     #     file: docker/standalone/Dockerfile
@@ -361,7 +441,7 @@ jobs:
 
     # Build multi-platform and push to release tags
     - name: Build and push release images
-      uses: docker/build-push-action@v7
+      uses: docker/build-push-action@v6
       with:
         context: .
         file: docker/standalone/Dockerfile
@@ -379,10 +459,10 @@ jobs:
       packages: write
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Install Helm
-      uses: azure/setup-helm@v5
+      uses: azure/setup-helm@v4
       with:
         version: 'latest'
 
@@ -399,7 +479,7 @@ jobs:
       run: helm push helm-packages/*.tgz oci://ghcr.io/${{ github.repository_owner }}/charts
 
     - name: Upload artifacts
-      uses: actions/upload-artifact@v7
+      uses: actions/upload-artifact@v4
       with:
         name: helm-chart
         path: helm-packages/*.tgz
@@ -407,55 +487,67 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
     permissions:
       contents: write
 
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v4
 
     - name: Extract version from tag
       id: get_version
       run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT
 
     - name: Download Python packages
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: python-packages
         path: ./artifacts/python-packages
 
     - name: Download TypeScript client
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: typescript-client
         path: ./artifacts/typescript-client
 
+    - name: Download OpenClaw Integration
+      uses: actions/download-artifact@v4
+      with:
+        name: openclaw-integration
+        path: ./artifacts/openclaw-integration
+
+    - name: Download AI SDK Integration
+      uses: actions/download-artifact@v4
+      with:
+        name: ai-sdk-integration
+        path: ./artifacts/ai-sdk-integration
+
     - name: Download Control Plane
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: control-plane
         path: ./artifacts/control-plane
 
     - name: Download Rust CLI (Linux)
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: rust-cli-hindsight-linux-amd64
         path: ./artifacts/rust-cli-linux
 
     - name: Download Rust CLI (macOS Intel)
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: rust-cli-hindsight-darwin-amd64
         path: ./artifacts/rust-cli-darwin-amd64
 
     - name: Download Rust CLI (macOS ARM)
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: rust-cli-hindsight-darwin-arm64
         path: ./artifacts/rust-cli-darwin-arm64
 
     - name: Download Helm chart
-      uses: actions/download-artifact@v8
+      uses: actions/download-artifact@v4
       with:
         name: helm-chart
         path: ./artifacts/helm-chart
@@ -465,13 +557,16 @@ jobs:
         mkdir -p release-assets
         # Python packages
         cp artifacts/python-packages/hindsight-clients/python/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-api-slim/dist/* release-assets/ || true
         cp artifacts/python-packages/hindsight-api/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-all/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-all-slim/dist/* release-assets/ || true
+        cp artifacts/python-packages/hindsight/dist/* release-assets/ || true
+        cp artifacts/python-packages/hindsight-integrations/litellm/dist/* release-assets/ || true
         cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
         # TypeScript client
         cp artifacts/typescript-client/*.tgz release-assets/ || true
+        # OpenClaw Integration
+        cp artifacts/openclaw-integration/*.tgz release-assets/ || true
+        # AI SDK Integration
+        cp artifacts/ai-sdk-integration/*.tgz release-assets/ || true
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.gitignore

@@ -46,12 +46,10 @@ 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
-.claude/*
-!.claude/skills/
+.claude
 whats-next.md
 TASK.md
 # Changelog is now tracked in hindsight-docs/src/pages/changelog.md

CLAUDE.md

@@ -11,32 +11,26 @@ Hindsight is an agent memory system that provides long-term memory for AI agents
 
 ## Development Commands
 
-### Local Development (API + UI)
-```bash
-# Start both API server and control plane UI
-./scripts/dev/start.sh
-```
-
 ### API Server (Python/FastAPI)
 ```bash
-# Start API server only (loads .env automatically)
+# Start API server (loads .env automatically)
 ./scripts/dev/start-api.sh
 
 # Run all tests (parallelized with pytest-xdist)
-cd hindsight-api-slim && uv run pytest tests/
+cd hindsight-api && uv run pytest tests/
 
 # Run specific test file
-cd hindsight-api-slim && uv run pytest tests/test_http_api_integration.py -v
+cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v
 
 # Run single test function
-cd hindsight-api-slim && uv run pytest tests/test_retain.py::test_retain_simple -v
+cd hindsight-api && uv run pytest tests/test_retain.py::test_retain_simple -v
 
 # Lint and format
-cd hindsight-api-slim && uv run ruff check .
-cd hindsight-api-slim && uv run ruff format .
+cd hindsight-api && uv run ruff check .
+cd hindsight-api && uv run ruff format .
 
 # Type checking (uses ty - extremely fast type checker from Astral)
-cd hindsight-api-slim && uv run ty check hindsight_api/
+cd hindsight-api && uv run ty check hindsight_api/
 ```
 
 ### Control Plane (Next.js)
@@ -63,32 +57,26 @@ 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
 ```
 
 ## Architecture
 
 ### Monorepo Structure
-- **hindsight-api-slim/**: Core FastAPI server with memory engine (Python, uv)
+- **hindsight-api/**: Core FastAPI server with memory engine (Python, uv)
+- **hindsight/**: Embedded Python bundle (hindsight-all package)
 - **hindsight-control-plane/**: Admin UI (Next.js, npm)
 - **hindsight-cli/**: CLI tool (Rust, cargo, uses progenitor for API client)
 - **hindsight-clients/**: Generated SDK clients (Python, TypeScript, Rust)
 - **hindsight-docs/**: Docusaurus documentation site
-- **hindsight-integrations/**: Framework integrations (LiteLLM, CrewAI, LangGraph, Pydantic AI, AG2, Claude Code, etc.)
+- **hindsight-integrations/**: Framework integrations (LiteLLM, OpenAI)
 - **hindsight-dev/**: Development tools and benchmarks
 
-### Core Engine (hindsight-api-slim/hindsight_api/engine/)
-- `memory_engine.py`: Main orchestrator for retain/recall/reflect operations
-- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, VertexAI, Groq, MiniMax, Ollama, LM Studio, LiteLLM, Claude Code
+### Core Engine (hindsight-api/hindsight_api/engine/)
+- `memory_engine.py`: Main orchestrator (~170KB) for retain/recall/reflect operations
+- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio
 - `embeddings.py`: Embedding generation (local sentence-transformers or TEI)
 - `cross_encoder.py`: Reranking (local or TEI)
 - `entity_resolver.py`: Entity extraction and normalization
@@ -106,8 +94,8 @@ cd hindsight-control-plane && npm run dev
 - `fusion.py`: Reciprocal rank fusion for combining results
 - `reranking.py`: Cross-encoder reranking
 
-### API Layer (hindsight-api-slim/hindsight_api/api/)
-- `http.py`: FastAPI HTTP routers for all REST endpoints
+### API Layer (hindsight-api/hindsight_api/api/)
+- `http.py`: FastAPI HTTP routers (~80KB) for all REST endpoints
 - `mcp.py`: Model Context Protocol server implementation
 
 Main operations:
@@ -116,13 +104,13 @@ Main operations:
 - **Reflect**: Disposition-aware reasoning using memories and mental models.
 
 ### Database
-PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api-slim/hindsight_api/alembic/`. Migrations run automatically on API startup.
+PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api/hindsight_api/alembic/`. Migrations run automatically on API startup.
 
 Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
 
 ### Adding Database Migrations
 
-1. **Create a new migration file** in `hindsight-api-slim/hindsight_api/alembic/versions/`:
+1. **Create a new migration file** in `hindsight-api/hindsight_api/alembic/versions/`:
    - File name format: `<revision_id>_<description>.py` (e.g., `f1a2b3c4d5e6_add_new_index.py`)
    - Use a unique hex revision ID (12 chars)
    - Set `down_revision` to the previous migration's revision ID
@@ -159,7 +147,7 @@ Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
 
 3. **Run migrations locally**:
    ```bash
-   # Set database URL and run migrations for the base schema plus all tenants
+   # Set database URL and run migrations
    uv run hindsight-admin run-db-migration
 
    # Run on a specific tenant schema
@@ -169,17 +157,11 @@ Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
 ## Key Conventions
 
 ### Code Quality
-
-**Before writing code, read `.claude/skills/code-review/SKILL.md`** for the full coding standards (Python style, type safety, TypeScript style, general principles).
-
 **Always run the lint script after making Python or TypeScript/Node changes:**
 ```bash
 ./scripts/hooks/lint.sh
 ```
-
-**After completing any implementation work, run `/code-review`** to verify your changes against project standards (missing tests, dead code, type safety, etc.). Fix any "must fix" issues before considering the task done.
-
-**MANDATORY: Run `/code-review` before pushing code or creating a pull request.** Do not push or create a PR until all "must fix" issues are resolved.
+This runs the same checks as the pre-commit hook (Ruff for Python, ESLint/Prettier for TypeScript).
 
 ### Memory Banks
 - Each bank is an isolated memory store (like a "brain" for one user/agent)
@@ -211,74 +193,71 @@ When adding or modifying parameters in the dataplane API (hindsight-api), you mu
    - Update the client type definition in `lib/api.ts`
    - Update any UI components that need to use the new parameter
 
-### Adding New Integrations
+### Python Style
+- Python 3.11+, type hints required
+- Async throughout (asyncpg, async FastAPI)
+- Pydantic models for request/response
+- Ruff for linting (line-length 120)
+- No Python files at project root - maintain clean directory structure
+- **Never use multi-item tuple return values** - prefer dataclass or Pydantic model for structured returns
 
-Every new integration in `hindsight-integrations/` must satisfy all of the following before it can be merged:
+### Type Safety with Pydantic Models
+**NEVER use raw `dict` types for structured data.** Always use Pydantic models:
+- Use Pydantic `BaseModel` for all data structures passed between functions
+- Add `@field_validator` for type coercion (e.g., ensuring datetimes are timezone-aware)
+- Avoid `dict.get()` patterns - use typed model attributes instead
+- Parse external data (JSON, API responses) into Pydantic models at the boundary
+- This catches type errors at parse time, not deep in business logic
 
-1. **Tests are required** — tests must simulate or exercise the external system (mock the framework's interfaces and verify the integration actually calls Hindsight correctly). Pure unit tests of helper functions are not sufficient.
-2. **CI job** — add a test job in `.github/workflows/test.yml` following the existing pattern (e.g., `test-crewai-integration`). The job must build, install deps, and run `uv run pytest tests -v`. Also add the integration to `detect-changes` outputs so it only runs when its files change.
-3. **Release process** — add the integration name to the `VALID_INTEGRATIONS` array in `scripts/release-integration.sh` so it can be released via the standard release workflow.
-4. **Follow project code standards** — Python style, type safety, no raw dicts for structured data, no multi-item tuple returns (see `.claude/skills/code-review/SKILL.md`).
+```python
+# BAD - error-prone dict access
+def process(data: dict) -> str:
+    return data.get("name", "")  # No validation, silent failures
 
-If any of these are missing, the integration is incomplete and must not be pushed or merged.
+# GOOD - typed and validated
+class UserData(BaseModel):
+    name: str
+    created_at: datetime
+
+    @field_validator("created_at", mode="before")
+    @classmethod
+    def ensure_tz_aware(cls, v):
+        if isinstance(v, str):
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if v.tzinfo is None:
+            return v.replace(tzinfo=timezone.utc)
+        return v
+
+def process(data: UserData) -> str:
+    return data.name  # Type-safe, validated at construction
+```
+
+### TypeScript Style
+- Next.js App Router for control plane
+- Tailwind CSS with shadcn/ui components
 
 ### Adding New API Configuration Flags
 
-Configuration follows a hierarchical system: **Global (env vars) → Tenant (via extension) → Bank (database)**.
+When adding a new environment variable configuration:
 
-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-slim/hindsight_api/config.py`):
-   - Add `ENV_*` constant for the environment variable name (e.g., `ENV_MY_SETTING = "HINDSIGHT_API_MY_SETTING"`)
+1. **config.py** (`hindsight-api/hindsight_api/config.py`):
+   - Add `ENV_*` constant for the environment variable name
    - Add `DEFAULT_*` constant for the default value
-   - Add field to `HindsightConfig` dataclass with type annotation
-   - **Mark as configurable** by adding to `_CONFIGURABLE_FIELDS` set if the field should be overridable per-tenant/bank via API
+   - Add field to `HindsightConfig` dataclass
    - Add initialization in `from_env()` method
 
-   ```python
-   # Configurable field (can be overridden per-tenant/bank via API)
-   _CONFIGURABLE_FIELDS = {
-       ...,
-       "my_setting",  # Add here for configurable
-   }
-
-   # Static field - just don't add to _CONFIGURABLE_FIELDS
-   ```
-
-2. **main.py** (`hindsight-api-slim/hindsight_api/main.py`):
+2. **main.py** (`hindsight-api/hindsight_api/main.py`):
    - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
 
-3. **Use hierarchical config in MemoryEngine**:
-   ```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):
+3. **Use the config** in code:
    ```python
    from ...config import get_config
    config = get_config()
-   value = config.my_static_field
+   value = config.your_new_field
    ```
 
-5. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
+4. **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
 
@@ -287,19 +266,18 @@ cp .env.example .env
 # Edit .env with LLM API key
 
 # Python deps
-uv sync --directory hindsight-api-slim/
+uv sync --directory hindsight-api/
 
 # Node deps (uses npm workspaces)
 npm install
 ```
 
 Required env vars:
-- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, minimax, ollama, lmstudio
+- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, ollama, lmstudio
 - `HINDSIGHT_API_LLM_API_KEY`: Your API key
-- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., gpt-4o-mini, claude-sonnet-4-20250514)
+- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., o3-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

@@ -2,17 +2,15 @@
 
 ![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://ui.hindsight.vectorize.io/signup)
+[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)
 
 [![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)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![gitcgr](https://gitcgr.com/badge/vectorize-io/hindsight.svg)](https://gitcgr.com/vectorize-io/hindsight)
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/hindsight-api?label=PyPI)
 ![NPM Downloads](https://img.shields.io/npm/dm/%40vectorize-io%2Fhindsight-client?logoColor=orange&label=NPM&color=blue&link=https%3A%2F%2Fwww.npmjs.com%2Fpackage%2F%40vectorize-io%2Fhindsight-client)
-<br/>
 
-<a href="https://trendshift.io/repositories/15603" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15603" alt="vectorize-io%2Fhindsight | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+
 </div>
 
 ---
@@ -38,7 +36,7 @@ Hindsight is being used in production at Fortune 500 enterprises and by a growin
 
 ## Adding Hindsight to Your AI Agents
 
-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.
+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.
 
 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.
 
@@ -71,7 +69,7 @@ docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
 >API: http://localhost:8888
 >UI: http://localhost:9999
 
-You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio`, and `minimax`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
+You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, and `lmstudio`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
 
 
 
@@ -183,7 +181,7 @@ Satisfying these requirements in Hindsight is straightforward. When new user inp
 
 ![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
 
-Most agent memory 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:
+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:
 
 - **World:** Facts about the world ("The stove gets hot")
 - **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
@@ -309,5 +307,3 @@ 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="" />

deno.lock

@@ -1,139 +0,0 @@
-{
-  "version": "5",
-  "specifiers": {
-    "jsr:@std/assert@^1.0.17": "1.0.19",
-    "jsr:@std/assert@^1.0.19": "1.0.19",
-    "jsr:@std/expect@*": "1.0.18",
-    "jsr:@std/internal@^1.0.12": "1.0.12",
-    "jsr:@std/path@^1.1.4": "1.1.4",
-    "jsr:@std/testing@*": "1.0.17"
-  },
-  "jsr": {
-    "@std/[email protected]": {
-      "integrity": "eaada96ee120cb980bc47e040f82814d786fe8162ecc53c91d8df60b8755991e",
-      "dependencies": [
-        "jsr:@std/internal"
-      ]
-    },
-    "@std/[email protected]": {
-      "integrity": "8566eab35200466f8609eb7e7aed062ed0db314e9a258d5d201b1b8997ce801a",
-      "dependencies": [
-        "jsr:@std/assert@^1.0.19",
-        "jsr:@std/internal",
-        "jsr:@std/path"
-      ]
-    },
-    "@std/[email protected]": {
-      "integrity": "972a634fd5bc34b242024402972cd5143eac68d8dffaca5eaa4dba30ce17b027"
-    },
-    "@std/[email protected]": {
-      "integrity": "1d2d43f39efb1b42f0b1882a25486647cb851481862dc7313390b2bb044314b5",
-      "dependencies": [
-        "jsr:@std/internal"
-      ]
-    },
-    "@std/[email protected]": {
-      "integrity": "87bdc2700fa98249d48a17cd72413352d3d3680dcfbdb64947fd0982d6bbf681",
-      "dependencies": [
-        "jsr:@std/assert@^1.0.17",
-        "jsr:@std/internal"
-      ]
-    }
-  },
-  "workspace": {
-    "members": {
-      "hindsight-clients/typescript": {
-        "packageJson": {
-          "dependencies": [
-            "npm:@hey-api/[email protected]",
-            "npm:@types/jest@29",
-            "npm:@types/node@20",
-            "npm:jest@29",
-            "npm:ts-jest@29",
-            "npm:tsup@^8.5.1",
-            "npm:typescript@5"
-          ]
-        }
-      },
-      "hindsight-control-plane": {
-        "packageJson": {
-          "dependencies": [
-            "npm:@eslint/eslintrc@^3.3.3",
-            "npm:@eslint/js@^9.39.2",
-            "npm:@radix-ui/react-alert-dialog@^1.1.15",
-            "npm:@radix-ui/react-checkbox@^1.3.3",
-            "npm:@radix-ui/react-dialog@^1.1.15",
-            "npm:@radix-ui/react-dropdown-menu@^2.1.16",
-            "npm:@radix-ui/react-label@^2.1.8",
-            "npm:@radix-ui/react-popover@^1.1.15",
-            "npm:@radix-ui/react-radio-group@^1.3.8",
-            "npm:@radix-ui/react-select@^2.2.6",
-            "npm:@radix-ui/react-slider@^1.3.6",
-            "npm:@radix-ui/react-slot@^1.2.4",
-            "npm:@radix-ui/react-switch@^1.2.6",
-            "npm:@radix-ui/react-tabs@^1.1.13",
-            "npm:@radix-ui/react-tooltip@^1.2.8",
-            "npm:@tailwindcss/postcss@^4.1.17",
-            "npm:@tailwindcss/typography@~0.5.19",
-            "npm:@types/cytoscape@^3.21.9",
-            "npm:@types/node@^24.10.0",
-            "npm:@types/react-dom@^19.2.2",
-            "npm:@types/react@^19.2.2",
-            "npm:autoprefixer@^10.4.21",
-            "npm:class-variance-authority@~0.7.1",
-            "npm:clsx@^2.1.1",
-            "npm:cmdk@^1.1.1",
-            "npm:cytoscape-fcose@^2.2.0",
-            "npm:cytoscape@^3.33.1",
-            "npm:eslint-config-next@^16.0.1",
-            "npm:eslint-plugin-react-hooks@^7.0.1",
-            "npm:eslint-plugin-react@^7.37.5",
-            "npm:eslint@^9.39.1",
-            "npm:[email protected]",
-            "npm:next-themes@~0.4.6",
-            "npm:next@^16.1.6",
-            "npm:postcss@^8.5.6",
-            "npm:prettier@^3.7.4",
-            "npm:react-chrono@^2.9.1",
-            "npm:react-dom@^19.2.0",
-            "npm:react-markdown@^10.1.0",
-            "npm:react18-json-view@~0.2.9",
-            "npm:react@^19.2.0",
-            "npm:recharts@^3.5.1",
-            "npm:remark-gfm@^4.0.1",
-            "npm:sonner@^2.0.7",
-            "npm:tailwind-merge@^3.4.0",
-            "npm:tailwindcss-animate@^1.0.7",
-            "npm:tailwindcss@^4.1.17",
-            "npm:[email protected]",
-            "npm:typescript-eslint@^8.50.0",
-            "npm:typescript@^5.9.3"
-          ]
-        }
-      },
-      "hindsight-docs": {
-        "packageJson": {
-          "dependencies": [
-            "npm:@docusaurus/[email protected]",
-            "npm:@docusaurus/[email protected]",
-            "npm:@docusaurus/[email protected]",
-            "npm:@docusaurus/theme-common@^3.9.2",
-            "npm:@docusaurus/theme-mermaid@^3.9.2",
-            "npm:@docusaurus/[email protected]",
-            "npm:@docusaurus/[email protected]",
-            "npm:@easyops-cn/docusaurus-search-local@~0.52.2",
-            "npm:@mdx-js/react@3",
-            "npm:clsx@2",
-            "npm:prism-react-renderer@^2.3.0",
-            "npm:raw-loader@^4.0.2",
-            "npm:react-dom@19",
-            "npm:react-icons@^5.6.0",
-            "npm:react@19",
-            "npm:redocusaurus@^2.5.0",
-            "npm:typescript@~5.6.2"
-          ]
-        }
-      }
-    }
-  }
-}

docker/docker-compose/nginx/README.md

@@ -1,96 +0,0 @@
-# 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

@@ -1,88 +0,0 @@
-# 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

@@ -1,40 +0,0 @@
-# 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

@@ -1,32 +0,0 @@
-# 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

@@ -1,91 +0,0 @@
-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

@@ -1,83 +0,0 @@
-# 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

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

docker/docker-compose/timescale/.dockerignore

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

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

@@ -1,25 +0,0 @@
-# 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

@@ -1,55 +0,0 @@
-# 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

@@ -1,101 +0,0 @@
-# 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

@@ -1,108 +0,0 @@
-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

@@ -1,93 +0,0 @@
-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

@@ -42,22 +42,25 @@ RUN apt-get update && apt-get install -y \
     && pip install --no-cache-dir uv
 
 # Copy dependency files and README (required by pyproject.toml)
-COPY hindsight-api-slim/pyproject.toml ./api/
-COPY hindsight-api-slim/README.md ./api/
+COPY hindsight-api/pyproject.toml ./api/
+COPY hindsight-api/README.md ./api/
 
 WORKDIR /app/api
 
-# Sync dependencies using appropriate extras based on INCLUDE_LOCAL_MODELS
-# local-ml: torch, sentence-transformers, transformers, einops, flashrank, mlx (optional)
-# embedded-db: pg0-embedded (always included for embedded PostgreSQL support)
-RUN if [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-        uv sync --extra local-ml --extra embedded-db; \
-    else \
-        uv sync --extra embedded-db; \
+# Remove local ML model dependencies if INCLUDE_LOCAL_MODELS=false
+# This creates a smaller image when using external providers (TEI, OpenAI, Cohere)
+RUN if [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then \
+        echo "Removing local-models dependencies (sentence-transformers, torch, transformers)..." && \
+        sed -i '/"sentence-transformers/d' pyproject.toml && \
+        sed -i '/"transformers/d' pyproject.toml && \
+        sed -i '/"torch/d' pyproject.toml; \
     fi
 
+# Sync dependencies (will create lock file if needed)
+RUN uv sync
+
 # Copy source code (alembic migrations are inside hindsight_api/)
-COPY hindsight-api-slim/hindsight_api ./hindsight_api
+COPY hindsight-api/hindsight_api ./hindsight_api
 
 # Install the local package (uv sync only installed dependencies, not the package itself)
 RUN uv pip install -e .
@@ -109,10 +112,6 @@ 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
@@ -167,11 +166,6 @@ 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)
@@ -323,11 +317,6 @@ 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)

docker/standalone/start-all.sh

@@ -1,28 +1,6 @@
 #!/bin/bash
 set -e
 
-# =============================================================================
-# Embedded pg0 data integrity check (#675)
-#
-# When using embedded pg0, check if the data directory has existing PostgreSQL
-# data before starting. If the directory exists but appears empty/corrupt
-# (e.g., missing PG_VERSION file), log a warning. This helps diagnose data
-# loss scenarios where a container restart caused the data directory to be
-# wiped despite a volume mount being present.
-# =============================================================================
-PG0_DATA_DIR="${HOME}/.pg0"
-if [ -d "$PG0_DATA_DIR" ]; then
-    # Look for actual PostgreSQL data directories (pg0 creates subdirs per instance)
-    if compgen -G "$PG0_DATA_DIR"/*/PG_VERSION > /dev/null 2>&1; then
-        echo "✅ Existing pg0 data directory detected at $PG0_DATA_DIR"
-    elif [ "$(ls -A "$PG0_DATA_DIR" 2>/dev/null)" ]; then
-        echo "⚠️  WARNING: pg0 data directory exists at $PG0_DATA_DIR but no PG_VERSION found."
-        echo "   This may indicate data corruption or an incomplete previous shutdown."
-        echo "   If you see all migrations running from scratch after this, your data may have been lost."
-        echo "   See: https://github.com/vectorize-io/hindsight/issues/675"
-    fi
-fi
-
 # Service flags (default to true if not set)
 ENABLE_API="${HINDSIGHT_ENABLE_API:-true}"
 ENABLE_CP="${HINDSIGHT_ENABLE_CP:-true}"
@@ -93,95 +71,24 @@ if [ "${HINDSIGHT_WAIT_FOR_DEPS:-false}" = "true" ]; then
     done
 fi
 
-# =============================================================================
-# Graceful shutdown handler (#675)
-#
-# Docker sends SIGTERM on `docker stop`/`docker restart`. Without a trap, child
-# processes (hindsight-api + pg0, control-plane) are killed abruptly. For the
-# embedded pg0 database this can cause data loss when the data directory is on
-# a Docker volume that gets remounted after restart.
-#
-# The trap forwards SIGTERM to all tracked child PIDs so that:
-#   - hindsight-api receives the signal and can run its shutdown hooks
-#   - pg0 gets a clean PostgreSQL shutdown (checkpoint + WAL flush)
-#   - The control-plane Node.js process exits cleanly
-# =============================================================================
-# Guard against concurrent cleanup (e.g., child crash + SIGTERM arriving together)
-SHUTTING_DOWN=false
-
-cleanup() {
-    if $SHUTTING_DOWN; then return; fi
-    SHUTTING_DOWN=true
-
-    echo ""
-    echo "🛑 Received shutdown signal, stopping services gracefully..."
-    for pid in "${PIDS[@]}"; do
-        if kill -0 "$pid" 2>/dev/null; then
-            kill -TERM "$pid" 2>/dev/null
-        fi
-    done
-    # Give processes time to shut down cleanly (pg0 needs to flush WAL).
-    # NOTE: Docker's default stop_grace_period is 10s. If you use the default,
-    # either set stop_grace_period: 30s in your compose file / docker stop -t 30,
-    # or Docker will SIGKILL the container before this timeout expires.
-    local timeout=30
-    for ((i=1; i<=timeout; i++)); do
-        local all_stopped=true
-        for pid in "${PIDS[@]}"; do
-            if kill -0 "$pid" 2>/dev/null; then
-                all_stopped=false
-                break
-            fi
-        done
-        if $all_stopped; then
-            echo "✅ All services stopped cleanly"
-            exit 0
-        fi
-        sleep 1
-    done
-    # Force kill if still running after timeout
-    echo "⚠️  Timeout reached, forcing shutdown..."
-    for pid in "${PIDS[@]}"; do
-        if kill -0 "$pid" 2>/dev/null; then
-            kill -9 "$pid" 2>/dev/null
-        fi
-    done
-    exit 1
-}
-trap cleanup SIGTERM SIGINT
-
 # Track PIDs for wait
 PIDS=()
 
 # Start API if enabled
 if [ "$ENABLE_API" = "true" ]; then
     cd /app/api
-    API_HEALTH_URL="${HINDSIGHT_API_HEALTH_URL:-http://localhost:8888/health}"
-    API_STARTUP_WAIT_SECONDS="${HINDSIGHT_API_STARTUP_WAIT_SECONDS:-300}"
-
     # Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
     hindsight-api &
     API_PID=$!
     PIDS+=($API_PID)
 
     # Wait for API to be ready
-    api_ready=false
-    for ((i=1; i<=API_STARTUP_WAIT_SECONDS; i++)); do
-        if ! kill -0 "$API_PID" 2>/dev/null; then
-            wait "$API_PID"
-            exit $?
-        fi
-        if curl -sf "$API_HEALTH_URL" &>/dev/null; then
-            api_ready=true
+    for i in {1..60}; do
+        if curl -sf http://localhost:8888/health &>/dev/null; then
             break
         fi
         sleep 1
     done
-
-    if [ "$api_ready" != "true" ]; then
-        echo "❌ API did not become healthy within ${API_STARTUP_WAIT_SECONDS}s"
-        exit 1
-    fi
 else
     echo "API disabled (HINDSIGHT_ENABLE_API=false)"
 fi
@@ -190,8 +97,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
     echo "🎛️  Starting Control Plane..."
     cd /app/control-plane
-    export HOSTNAME="${HINDSIGHT_CP_HOSTNAME:-0.0.0.0}"
-    PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
+    PORT=9999 node server.js &
     CP_PID=$!
     PIDS+=($CP_PID)
 else
@@ -204,7 +110,7 @@ echo "✅ Hindsight is running!"
 echo ""
 echo "📍 Access:"
 if [ "$ENABLE_CP" = "true" ]; then
-    echo "   Control Plane: http://localhost:${HINDSIGHT_CP_PORT:-9999}"
+    echo "   Control Plane: http://localhost:9999"
 fi
 if [ "$ENABLE_API" = "true" ]; then
     echo "   API:           http://localhost:8888"
@@ -217,21 +123,8 @@ if [ ${#PIDS[@]} -eq 0 ]; then
     exit 1
 fi
 
-# Wait for any process to exit (use wait -n with trap-safe loop)
-while true; do
-    # wait -n returns when any child exits; it also returns on signal delivery
-    # (the trap handler will run and exit, so this loop is just for robustness).
-    # `&& true` prevents `set -e` from killing the script when wait -n returns
-    # non-zero (child exited with error or no backgrounded children remain).
-    wait -n && true
-    # Check if any tracked PID has exited
-    for pid in "${PIDS[@]}"; do
-        if ! kill -0 "$pid" 2>/dev/null; then
-            wait "$pid" 2>/dev/null
-            exit_code=$?
-            echo "⚠️  Service (PID $pid) exited with code $exit_code"
-            # Trigger cleanup for remaining services
-            cleanup
-        fi
-    done
-done
+# Wait for any process to exit
+wait -n
+
+# Exit with status of first exited process
+exit $?

docker/test-image.sh

@@ -13,9 +13,9 @@
 #   target  - Optional: 'cp-only' for control plane, otherwise assumes API image (default: api)
 #
 # Environment variables:
-#   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)
+#   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_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 HINDSIGHT_API_LLM_API_KEY=sk_xxx
+#   export GROQ_API_KEY=gsk_xxx
 #   export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 #   export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxx
 #   export HINDSIGHT_API_RERANKER_PROVIDER=cohere
@@ -49,9 +49,6 @@
 
 set -euo pipefail
 
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(dirname "$SCRIPT_DIR")"
-
 # Colors for output
 RED='\033[0;31m'
 GREEN='\033[0;32m'
@@ -63,8 +60,8 @@ IMAGE="${1:-}"
 TARGET="${2:-api}"
 TIMEOUT="${SMOKE_TEST_TIMEOUT:-120}"
 CONTAINER_NAME="${SMOKE_TEST_CONTAINER_NAME:-hindsight-smoke-test}"
-LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-openai}"
-LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-gpt-4o-mini}"
+LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-groq}"
+LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-llama-3.3-70b-versatile}"
 
 # Validate arguments
 if [ -z "$IMAGE" ]; then
@@ -91,9 +88,9 @@ else
 fi
 
 # Check for required environment variables
-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${NC}"
-    echo "Set it with: export HINDSIGHT_API_LLM_API_KEY=your-api-key"
+if [ "$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"
     exit 2
 fi
 
@@ -126,25 +123,9 @@ 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=$LLM_PROVIDER"
-    if [ -n "${HINDSIGHT_API_LLM_API_KEY:-}" ]; then
-        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${HINDSIGHT_API_LLM_API_KEY}"
-    fi
+    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${GROQ_API_KEY}"
     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}"
@@ -181,21 +162,6 @@ for i in $(seq 1 "$TIMEOUT"); do
         echo "=== Health Response ==="
         curl -s "http://localhost:${HEALTH_PORT}${HEALTH_PATH}" | python3 -m json.tool 2>/dev/null || curl -s "http://localhost:${HEALTH_PORT}${HEALTH_PATH}"
         echo ""
-
-        # Run retain/recall smoke test for API targets
-        if [ "$TARGET" != "cp-only" ]; then
-            echo ""
-            echo "=== Retain/Recall Smoke Test ==="
-            if ! "$REPO_ROOT/scripts/smoke-test-slim.sh" "http://localhost:${HEALTH_PORT}"; then
-                echo ""
-                echo "=== Container Logs (last 50 lines) ==="
-                docker logs "$CONTAINER_NAME" 2>&1 | tail -50
-                echo ""
-                echo -e "${RED}Smoke test FAILED${NC}"
-                exit 1
-            fi
-        fi
-
         echo ""
         echo "=== Container Logs (last 50 lines) ==="
         docker logs "$CONTAINER_NAME" 2>&1 | tail -50

docker/test-slim-local.sh

@@ -6,17 +6,24 @@
 # 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:
-#   OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
+#   GROQ_API_KEY=gsk_xxx 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"
@@ -34,10 +41,7 @@ IMAGE="${1:-hindsight-slim:test}"
 echo "Testing image: $IMAGE"
 echo ""
 
-# Set up LLM and external providers
-export HINDSIGHT_API_LLM_PROVIDER=openai
-export HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY
-export HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
+# Set up external providers
 export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$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: 0.4.22
-appVersion: "0.4.22"
+version: 0.4.10
+appVersion: "0.4.10"
 keywords:
   - ai
   - memory

hindsight-all-slim/pyproject.toml

@@ -1,33 +0,0 @@
-[build-system]
-requires = ["setuptools>=61"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "hindsight-all-slim"
-version = "0.4.22"
-description = "Hindsight: Agent Memory That Works Like Human Memory - Slim All-in-One Bundle"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    "hindsight-api-slim>=0.4.17",
-    "hindsight-client>=0.0.7",
-    "hindsight-embed>=0.1.0",
-]
-
-[tool.uv.sources]
-hindsight-api-slim = { workspace = true }
-hindsight-client = { workspace = true }
-hindsight-embed = { workspace = true }
-
-[project.optional-dependencies]
-test = [
-    "pytest>=7.0.0",
-    "pytest-asyncio>=0.21.0",
-]
-
-[tool.setuptools]
-packages = []
-
-[tool.pytest.ini_options]
-asyncio_mode = "auto"
-asyncio_default_fixture_loop_scope = "function"

hindsight-all/README.md

@@ -1,48 +0,0 @@
-# hindsight-all
-
-All-in-one package for Hindsight - Agent Memory That Works Like Human Memory
-
-## Quick Start
-
-```python
-from hindsight import start_server, HindsightClient
-
-# Start server with embedded PostgreSQL
-server = start_server(
-    llm_provider="groq",
-    llm_api_key="your-api-key",
-    llm_model="openai/gpt-oss-120b"
-)
-
-# Create client
-client = HindsightClient(base_url=server.url)
-
-# Store memories
-client.put(agent_id="assistant", content="User prefers Python for data analysis")
-
-# Search memories
-results = client.search(agent_id="assistant", query="programming preferences")
-
-# Generate contextual response
-response = client.think(agent_id="assistant", query="What languages should I recommend?")
-
-# Stop server when done
-server.stop()
-```
-
-## Using Context Manager
-
-```python
-from hindsight import HindsightServer, HindsightClient
-
-with HindsightServer(llm_provider="groq", llm_api_key="...") as server:
-    client = HindsightClient(base_url=server.url)
-    # ... use client ...
-# Server automatically stops
-```
-
-## Installation
-
-```bash
-pip install hindsight-all
-```

hindsight-all/hindsight/client_wrapper.py

@@ -1,423 +0,0 @@
-"""
-Wrapper for Hindsight client that adds API namespaces.
-
-Provides organized access to different parts of the Hindsight API through
-namespaces like .banks, .mental_models, etc.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from hindsight_client import Hindsight
-
-
-class BanksAPI:
-    """Namespace for bank-related operations.
-    
-    Provides methods to create, delete, and manage memory banks.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def create(
-        self,
-        bank_id: str,
-        name: str | None = None,
-        mission: str | None = None,
-        disposition: dict[str, Any] | None = None,
-    ) -> Any:
-        """Create a new bank.
-        
-        Args:
-            bank_id: Unique identifier for the bank.
-            name: Optional display name for the bank.
-            mission: Optional mission statement for the bank.
-            disposition: Optional disposition configuration dict.
-            
-        Returns:
-            Bank creation response from the API.
-        """
-        return self._client.create_bank(
-            bank_id=bank_id,
-            name=name,
-            mission=mission,
-            disposition=disposition,
-        )
-
-    def delete(self, bank_id: str) -> Any:
-        """Delete a bank.
-        
-        Args:
-            bank_id: The ID of the bank to delete.
-            
-        Returns:
-            Deletion response from the API.
-        """
-        return self._client.delete_bank(bank_id=bank_id)
-
-    def set_mission(self, bank_id: str, mission: str) -> Any:
-        """Set or update the mission for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mission: The mission statement to set.
-            
-        Returns:
-            API response confirming the update.
-        """
-        return self._client.set_mission(bank_id=bank_id, mission=mission)
-
-    def set_disposition(self, bank_id: str, disposition: dict[str, Any]) -> Any:
-        """Set or update the disposition for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            disposition: The disposition configuration dict.
-            
-        Returns:
-            API response confirming the update.
-        """
-        return self._client.set_disposition(bank_id=bank_id, disposition=disposition)
-
-    def list(self) -> Any:
-        """List all banks.
-        
-        Returns:
-            List of banks from the API.
-        """
-        from hindsight_client.hindsight_client import _run_async
-
-        return _run_async(self._client._banks_api.list_banks())
-
-
-class MentalModelsAPI:
-    """Namespace for mental model operations.
-    
-    Mental models are reusable knowledge structures that guide agent behavior.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def create(
-        self,
-        bank_id: str,
-        name: str,
-        content: str,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Create a new mental model.
-        
-        Args:
-            bank_id: The ID of the bank to add the model to.
-            name: Name for the mental model.
-            content: The content/instructions for the mental model.
-            tags: Optional list of tags for categorization.
-            
-        Returns:
-            Creation response from the API.
-        """
-        return self._client.create_mental_model(
-            bank_id=bank_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def list(self, bank_id: str, tags: list[str] | None = None) -> Any:
-        """List all mental models for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            tags: Optional filter by tags.
-            
-        Returns:
-            List of mental models.
-        """
-        return self._client.list_mental_models(bank_id=bank_id, tags=tags)
-
-    def get(self, bank_id: str, mental_model_id: str) -> Any:
-        """Get a specific mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model.
-            
-        Returns:
-            The mental model details.
-        """
-        return self._client.get_mental_model(bank_id=bank_id, mental_model_id=mental_model_id)
-
-    def refresh(self, bank_id: str, mental_model_id: str) -> Any:
-        """Refresh a mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model to refresh.
-            
-        Returns:
-            Refresh response from the API.
-        """
-        return self._client.refresh_mental_model(bank_id=bank_id, mental_model_id=mental_model_id)
-
-    def update(
-        self,
-        bank_id: str,
-        mental_model_id: str,
-        name: str | None = None,
-        content: str | None = None,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Update a mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model to update.
-            name: Optional new name.
-            content: Optional new content.
-            tags: Optional new tags list.
-            
-        Returns:
-            Update response from the API.
-        """
-        return self._client.update_mental_model(
-            bank_id=bank_id,
-            mental_model_id=mental_model_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def delete(self, bank_id: str, mental_model_id: str) -> Any:
-        """Delete a mental model.
-        
-        Args:
-            bank_id: The ID of the bank.
-            mental_model_id: The ID of the mental model to delete.
-            
-        Returns:
-            Deletion response from the API.
-        """
-        return self._client.delete_mental_model(bank_id=bank_id, mental_model_id=mental_model_id)
-
-
-class DirectivesAPI:
-    """Namespace for directive operations.
-    
-    Directives are explicit instructions that guide agent behavior.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def create(
-        self,
-        bank_id: str,
-        name: str,
-        content: str,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Create a new directive.
-        
-        Args:
-            bank_id: The ID of the bank to add the directive to.
-            name: Name for the directive.
-            content: The directive content/instructions.
-            tags: Optional list of tags for categorization.
-            
-        Returns:
-            Creation response from the API.
-        """
-        return self._client.create_directive(
-            bank_id=bank_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def list(self, bank_id: str, tags: list[str] | None = None) -> Any:
-        """List all directives for a bank.
-        
-        Args:
-            bank_id: The ID of the bank.
-            tags: Optional filter by tags.
-            
-        Returns:
-            List of directives.
-        """
-        return self._client.list_directives(bank_id=bank_id, tags=tags)
-
-    def get(self, bank_id: str, directive_id: str) -> Any:
-        """Get a specific directive.
-        
-        Args:
-            bank_id: The ID of the bank.
-            directive_id: The ID of the directive.
-            
-        Returns:
-            The directive details.
-        """
-        return self._client.get_directive(bank_id=bank_id, directive_id=directive_id)
-
-    def update(
-        self,
-        bank_id: str,
-        directive_id: str,
-        name: str | None = None,
-        content: str | None = None,
-        tags: list[str] | None = None,
-    ) -> Any:
-        """Update a directive.
-        
-        Args:
-            bank_id: The ID of the bank.
-            directive_id: The ID of the directive to update.
-            name: Optional new name.
-            content: Optional new content.
-            tags: Optional new tags list.
-            
-        Returns:
-            Update response from the API.
-        """
-        return self._client.update_directive(
-            bank_id=bank_id,
-            directive_id=directive_id,
-            name=name,
-            content=content,
-            tags=tags,
-        )
-
-    def delete(self, bank_id: str, directive_id: str) -> Any:
-        """Delete a directive.
-        
-        Args:
-            bank_id: The ID of the bank.
-            directive_id: The ID of the directive to delete.
-            
-        Returns:
-            Deletion response from the API.
-        """
-        return self._client.delete_directive(bank_id=bank_id, directive_id=directive_id)
-
-
-class MemoriesAPI:
-    """Namespace for memory operations.
-    
-    Provides methods to query and retrieve stored memories.
-    """
-
-    def __init__(self, client: Hindsight):
-        self._client = client
-
-    def list(
-        self,
-        bank_id: str,
-        type: str | None = None,
-        search_query: str | None = None,
-        limit: int = 100,
-        offset: int = 0,
-    ) -> Any:
-        """List memories in a bank.
-        
-        Args:
-            bank_id: The ID of the bank to query.
-            type: Optional filter by memory type.
-            search_query: Optional search query for filtering.
-            limit: Maximum number of results to return (default: 100).
-            offset: Number of results to skip for pagination (default: 0).
-            
-        Returns:
-            List of memories matching the criteria.
-        """
-        return self._client.list_memories(
-            bank_id=bank_id,
-            type=type,
-            search_query=search_query,
-            limit=limit,
-            offset=offset,
-        )
-
-
-class HindsightClient(Hindsight):
-    """
-    Enhanced Hindsight client with organized API namespaces.
-
-    This wrapper extends the auto-generated Hindsight client with organized
-    access to different parts of the API through namespaces.
-
-    Example:
-        ```python
-        from hindsight import HindsightClient
-
-        client = HindsightClient(base_url="http://localhost:8888")
-
-        # Core operations (inherited from Hindsight)
-        client.retain(bank_id="test", content="Hello")
-        results = client.recall(bank_id="test", query="Hello")
-
-        # Organized API access through namespaces
-        client.banks.create(bank_id="test", name="Test Bank")
-        models = client.mental_models.list(bank_id="test")
-        directives = client.directives.list(bank_id="test")
-        memories = client.memories.list(bank_id="test")
-        ```
-        
-    Attributes:
-        banks: Namespace for bank management operations.
-        mental_models: Namespace for mental model operations.
-        directives: Namespace for directive operations.
-        memories: Namespace for memory listing operations.
-    """
-
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-        self._banks_namespace: BanksAPI | None = None
-        self._mental_models_namespace: MentalModelsAPI | None = None
-        self._directives_namespace: DirectivesAPI | None = None
-        self._memories_namespace: MemoriesAPI | None = None
-
-    @property
-    def banks(self) -> BanksAPI:
-        """Access bank management operations.
-        
-        Returns:
-            BanksAPI instance for bank operations.
-        """
-        if self._banks_namespace is None:
-            self._banks_namespace = BanksAPI(self)
-        return self._banks_namespace
-
-    @property
-    def mental_models(self) -> MentalModelsAPI:
-        """Access mental model operations.
-        
-        Returns:
-            MentalModelsAPI instance for mental model operations.
-        """
-        if self._mental_models_namespace is None:
-            self._mental_models_namespace = MentalModelsAPI(self)
-        return self._mental_models_namespace
-
-    @property
-    def directives(self) -> DirectivesAPI:
-        """Access directive operations.
-        
-        Returns:
-            DirectivesAPI instance for directive operations.
-        """
-        if self._directives_namespace is None:
-            self._directives_namespace = DirectivesAPI(self)
-        return self._directives_namespace
-
-    @property
-    def memories(self) -> MemoriesAPI:
-        """Access memory listing operations.
-        
-        Returns:
-            MemoriesAPI instance for memory operations.
-        """
-        if self._memories_namespace is None:
-            self._memories_namespace = MemoriesAPI(self)
-        return self._memories_namespace

hindsight-api-slim/README.md

@@ -1,137 +0,0 @@
-# Hindsight API
-
-**Memory System for AI Agents** — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.
-
-Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.
-
-## Installation
-
-```bash
-pip install hindsight-api
-```
-
-## Quick Start
-
-### Run the Server
-
-```bash
-# Set your LLM provider
-export HINDSIGHT_API_LLM_PROVIDER=openai
-export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
-
-# Start the server (uses embedded PostgreSQL by default)
-hindsight-api
-```
-
-The server starts at http://localhost:8888 with:
-- REST API for memory operations
-- MCP server at `/mcp` for tool-use integration
-
-### Use the Python API
-
-```python
-from hindsight_api import MemoryEngine
-
-# Create and initialize the memory engine
-memory = MemoryEngine()
-await memory.initialize()
-
-# Create a memory bank for your agent
-bank = await memory.create_memory_bank(
-    name="my-assistant",
-    background="A helpful coding assistant"
-)
-
-# Store a memory
-await memory.retain(
-    memory_bank_id=bank.id,
-    content="The user prefers Python for data science projects"
-)
-
-# Recall memories
-results = await memory.recall(
-    memory_bank_id=bank.id,
-    query="What programming language does the user prefer?"
-)
-
-# Reflect with reasoning
-response = await memory.reflect(
-    memory_bank_id=bank.id,
-    query="Should I recommend Python or R for this ML project?"
-)
-```
-
-## CLI Options
-
-```bash
-hindsight-api --help
-
-# Common options
-hindsight-api --port 9000          # Custom port (default: 8888)
-hindsight-api --host 127.0.0.1     # Bind to localhost only
-hindsight-api --workers 4          # Multiple worker processes
-hindsight-api --log-level debug    # Verbose logging
-```
-
-## Configuration
-
-Configure via environment variables:
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `HINDSIGHT_API_DATABASE_URL` | PostgreSQL connection string | `pg0` (embedded) |
-| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio` | `openai` |
-| `HINDSIGHT_API_LLM_API_KEY` | API key for LLM provider | - |
-| `HINDSIGHT_API_LLM_MODEL` | Model name | `gpt-4o-mini` |
-| `HINDSIGHT_API_HOST` | Server bind address | `0.0.0.0` |
-| `HINDSIGHT_API_PORT` | Server port | `8888` |
-
-### Example with External PostgreSQL
-
-```bash
-export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
-export HINDSIGHT_API_LLM_PROVIDER=groq
-export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
-
-hindsight-api
-```
-
-## Docker
-
-```bash
-docker run --rm -it -p 8888:8888 \
-  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
-  ghcr.io/vectorize-io/hindsight:latest
-```
-
-## MCP Server
-
-For local MCP integration without running the full API server:
-
-```bash
-hindsight-local-mcp
-```
-
-This runs a stdio-based MCP server that can be used directly with MCP-compatible clients.
-
-## Key Features
-
-- **Multi-Strategy Retrieval (TEMPR)** — Semantic, keyword, graph, and temporal search combined with RRF fusion
-- **Entity Graph** — Automatic entity extraction and relationship tracking
-- **Temporal Reasoning** — Native support for time-based queries
-- **Disposition Traits** — Configurable skepticism, literalism, and empathy influence opinion formation
-- **Three Memory Types** — World facts, bank actions, and formed opinions with confidence scores
-
-## Documentation
-
-Full documentation: [https://hindsight.vectorize.io](https://hindsight.vectorize.io)
-
-- [Installation Guide](https://hindsight.vectorize.io/developer/installation)
-- [Configuration Reference](https://hindsight.vectorize.io/developer/configuration)
-- [API Reference](https://hindsight.vectorize.io/api-reference)
-- [Python SDK](https://hindsight.vectorize.io/sdks/python)
-
-## License
-
-Apache 2.0

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

@@ -1,70 +0,0 @@
-"""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 IF NOT EXISTS {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-slim/hindsight_api/alembic/versions/a2b3c4d5e6f7_add_text_signals_column.py

@@ -1,88 +0,0 @@
-"""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-slim/hindsight_api/alembic/versions/a2b3c4d5e6f8_add_gin_index_source_memory_ids.py

@@ -1,54 +0,0 @@
-"""Add GIN index on source_memory_ids for observation lookup performance
-
-Without this index, queries using the array overlap operator (&&) or array
-containment (@>) on source_memory_ids require a full sequential scan over all
-observation memory_units. At ~77k observations this was measured at 45ms per
-query, becoming a bottleneck during consolidation recall (57-64s timeouts) and
-user recall (18-27s average).
-
-The GIN index reduces these queries to index scans: 45ms → 0.049ms (927x
-speedup). Recall dropped from 18-27s to ~6s, and consolidation recall
-stabilised from timeout to ~15s.
-
-Created with CONCURRENTLY so the migration does not block reads or writes.
-CONCURRENTLY requires running outside a transaction block, so the migration
-emits an explicit COMMIT before the statement and uses IF NOT EXISTS for
-idempotency.
-
-Revision ID: a2b3c4d5e6f8
-Revises: f7g8h9i0j1k2
-Create Date: 2026-03-04
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "a2b3c4d5e6f8"
-down_revision: str | Sequence[str] | None = "f7g8h9i0j1k2"
-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()
-
-    # CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
-    # Commit the current Alembic transaction first.
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
-        f"ON {schema}memory_units USING GIN (source_memory_ids) "
-        f"WHERE source_memory_ids IS NOT NULL"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")

hindsight-api-slim/hindsight_api/alembic/versions/a3b4c5d6e7f8_add_consolidation_failed_at_to_memory_units.py

@@ -1,52 +0,0 @@
-"""Add consolidation_failed_at column to memory_units for tracking persistent LLM failures.
-
-When all LLM retries are exhausted on a single-memory batch, the memory is marked
-with consolidation_failed_at instead of consolidated_at, so it is not silently lost
-and can be retried later via the API.
-
-Revision ID: a3b4c5d6e7f8
-Revises: g7h8i9j0k1l2
-Create Date: 2026-03-17
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "a3b4c5d6e7f8"
-down_revision: str | Sequence[str] | None = "g7h8i9j0k1l2"
-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 consolidation_failed_at TIMESTAMPTZ DEFAULT NULL
-        """
-    )
-
-    # Index to efficiently query memories that failed consolidation for a given bank
-    op.execute(
-        f"""
-        CREATE INDEX IF NOT EXISTS idx_memory_units_consolidation_failed
-        ON {schema}memory_units (bank_id, consolidation_failed_at)
-        WHERE consolidation_failed_at IS NOT NULL AND fact_type IN ('experience', 'world')
-        """
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_consolidation_failed")
-    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS consolidation_failed_at")

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

@@ -1,36 +0,0 @@
-"""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-slim/hindsight_api/alembic/versions/b3c4d5e6f7a8_add_content_hash_to_chunks.py

@@ -1,32 +0,0 @@
-"""add content_hash to chunks table for delta retain
-
-Revision ID: b3c4d5e6f7a8
-Revises: a3b4c5d6e7f8
-Create Date: 2026-03-25
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "b3c4d5e6f7a8"
-down_revision: str | Sequence[str] | None = "a3b4c5d6e7f8"
-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()
-    # Add content_hash column to chunks table for delta comparison
-    op.execute(f"ALTER TABLE {schema}chunks ADD COLUMN IF NOT EXISTS content_hash TEXT")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}chunks DROP COLUMN IF EXISTS content_hash")

hindsight-api-slim/hindsight_api/alembic/versions/b3c4d5e6f7g8_add_temporal_date_indexes.py

@@ -1,68 +0,0 @@
-"""Add partial indexes on memory_units temporal date fields for fast temporal retrieval
-
-Revision ID: b3c4d5e6f7g8
-Revises: c1a2b3d4e5f6
-Create Date: 2026-03-02
-
-The temporal retrieval entry-point query filters memory_units by occurred_start,
-occurred_end, and mentioned_at using OR conditions. Without dedicated indexes the
-planner falls back to a sequential scan of all bank rows after applying the
-(bank_id, fact_type) index, then re-checks each date field.
-
-These three partial indexes give the planner bitmap-index scan options for the
-three most common date predicates, dramatically reducing the row set before any
-embedding computation is required.
-
-All indexes are created CONCURRENTLY so the migration does not block writes on
-memory_units during production deployments. CONCURRENTLY requires running outside
-a transaction block; see migrations.py for how this is handled safely.
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "b3c4d5e6f7g8"
-down_revision: str | Sequence[str] | None = "c1a2b3d4e5f6"
-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()
-    # Partial index on occurred_start (covers "occurred_start BETWEEN $4 AND $5")
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_start "
-        f"ON {schema}memory_units(bank_id, fact_type, occurred_start) "
-        f"WHERE occurred_start IS NOT NULL"
-    )
-    # Partial index on occurred_end (covers "occurred_end BETWEEN $4 AND $5")
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_end "
-        f"ON {schema}memory_units(bank_id, fact_type, occurred_end) "
-        f"WHERE occurred_end IS NOT NULL"
-    )
-    # Partial index on mentioned_at (covers "mentioned_at BETWEEN $4 AND $5")
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_mentioned_at "
-        f"ON {schema}memory_units(bank_id, fact_type, mentioned_at) "
-        f"WHERE mentioned_at IS NOT NULL"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_mentioned_at")
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_end")
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_start")

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

@@ -1,34 +0,0 @@
-"""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-slim/hindsight_api/alembic/versions/c1a2b3d4e5f6_enable_pg_trgm_and_entities_trgm_index.py

@@ -1,59 +0,0 @@
-"""Enable pg_trgm extension and add GIN trigram index on entities.canonical_name
-
-Revision ID: c1a2b3d4e5f6
-Revises: b4c5d6e7f8a9
-Create Date: 2026-03-02
-
-Index is created CONCURRENTLY so the migration does not block writes on entities
-during production deployments. CONCURRENTLY requires running outside a transaction
-block; see migrations.py for how this is handled safely.
-"""
-
-from collections.abc import Sequence
-
-import sqlalchemy as sa
-from alembic import context, op
-
-revision: str = "c1a2b3d4e5f6"
-down_revision: str | Sequence[str] | None = "b4c5d6e7f8a9"
-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:
-    # pg_trgm ships with most PostgreSQL installations as a contrib module.
-    # It enables fast similarity lookups via GIN indexes, used for entity name matching.
-    # On managed services (e.g. Azure Flexible Server), the extension may not be
-    # available or may require manual enablement.  We gracefully skip the index
-    # creation if the extension cannot be loaded — the entity resolver will
-    # auto-detect and fall back to the "full" lookup strategy at runtime.  See #626.
-    conn = op.get_bind()
-    try:
-        conn.execute(sa.text("CREATE EXTENSION IF NOT EXISTS pg_trgm"))
-    except Exception:
-        # Extension not available (managed Postgres, insufficient privileges, etc.)
-        # Roll back the failed statement and skip index creation.
-        conn.execute(sa.text("ROLLBACK"))
-        conn.execute(sa.text("BEGIN"))
-        return
-
-    schema = _get_schema_prefix()
-    # GIN index on canonical_name enables sub-millisecond trigram similarity queries
-    # (% operator, similarity()) instead of full-table scans across all bank entities.
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS entities_canonical_name_trgm_idx "
-        f"ON {schema}entities USING GIN (canonical_name gin_trgm_ops)"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}entities_canonical_name_trgm_idx")
-    # Note: not dropping pg_trgm extension as other indexes may depend on it

hindsight-api-slim/hindsight_api/alembic/versions/c2d3e4f5g6h7_add_audit_log_table.py

@@ -1,61 +0,0 @@
-"""Add audit_log table for feature usage tracking.
-
-Merge migration that combines the two existing heads (a3b4c5d6e7f8 + c8e5f2a3b4d1).
-
-Stores raw request/response as JSONB for expandability without future migrations.
-The metadata JSONB column allows adding arbitrary fields in the future.
-
-Revision ID: c2d3e4f5g6h7
-Revises: a3b4c5d6e7f8, c8e5f2a3b4d1
-Create Date: 2026-03-26
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "c2d3e4f5g6h7"
-down_revision: str | Sequence[str] | None = ("a3b4c5d6e7f8", "c8e5f2a3b4d1")
-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"""
-        CREATE TABLE IF NOT EXISTS {schema}audit_log (
-            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-            action TEXT NOT NULL,
-            transport TEXT NOT NULL,
-            bank_id TEXT,
-            started_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-            ended_at TIMESTAMPTZ,
-            request JSONB,
-            response JSONB,
-            metadata JSONB DEFAULT '{{}}'::jsonb
-        )
-        """
-    )
-
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_audit_log_action_started ON {schema}audit_log (action, started_at DESC)"
-    )
-    op.execute(f"CREATE INDEX IF NOT EXISTS idx_audit_log_bank_started ON {schema}audit_log (bank_id, started_at DESC)")
-    op.execute(f"CREATE INDEX IF NOT EXISTS idx_audit_log_started ON {schema}audit_log (started_at DESC)")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_audit_log_started")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_audit_log_bank_started")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_audit_log_action_started")
-    op.execute(f"DROP TABLE IF EXISTS {schema}audit_log")

hindsight-api-slim/hindsight_api/alembic/versions/c3d4e5f6g7h8_add_history_to_mental_models.py

@@ -1,30 +0,0 @@
-"""Add history column to mental_models
-
-Revision ID: c3d4e5f6g7h8
-Revises: a2b3c4d5e6f7, a2b3c4d5e6f8
-Create Date: 2026-03-06
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "c3d4e5f6g7h8"
-down_revision: str | Sequence[str] | None = ("a2b3c4d5e6f7", "a2b3c4d5e6f8")
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}mental_models ADD COLUMN IF NOT EXISTS history JSONB DEFAULT '[]'::jsonb")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS history")

hindsight-api-slim/hindsight_api/alembic/versions/c5d6e7f8a9b0_add_bank_id_to_memory_links.py

@@ -1,48 +0,0 @@
-"""Add bank_id column to memory_links for direct filtering
-
-The stats endpoint JOINs memory_links to memory_units just to filter by
-bank_id.  With millions of links this takes 18+ seconds.  Adding bank_id
-directly to memory_links lets Postgres push the filter down before the JOIN.
-
-Revision ID: c5d6e7f8a9b0
-Revises: b3c4d5e6f7a8
-Create Date: 2026-03-26
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "c5d6e7f8a9b0"
-down_revision: str | Sequence[str] | None = "b3c4d5e6f7a8"
-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()
-
-    # 1. Add nullable column
-    op.execute(f"ALTER TABLE {schema}memory_links ADD COLUMN IF NOT EXISTS bank_id TEXT")
-
-    # 2. Backfill from memory_units
-    op.execute(f"""
-        UPDATE {schema}memory_links ml
-        SET bank_id = mu.bank_id
-        FROM {schema}memory_units mu
-        WHERE ml.from_unit_id = mu.id
-          AND ml.bank_id IS NULL
-    """)
-
-    # 3. Set NOT NULL
-    op.execute(f"ALTER TABLE {schema}memory_links ALTER COLUMN bank_id SET NOT NULL")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}memory_links DROP COLUMN IF EXISTS bank_id")

hindsight-api-slim/hindsight_api/alembic/versions/d2e3f4a5b6c7_add_memory_links_expansion_indexes.py

@@ -1,83 +0,0 @@
-"""Add covering and composite indexes to speed up link expansion graph retrieval.
-
-Two indexes target the two bottlenecks identified by EXPLAIN ANALYZE on a 17M-row
-memory_links table:
-
-1. idx_memory_links_to_type_weight  (to_unit_id, link_type, weight DESC)
-   The semantic incoming direction — finding facts that consider seeds as their
-   nearest neighbour — currently hits an expensive BitmapAnd of two separate
-   bitmap scans (to_unit_id bitmap ∩ link_type bitmap).  A composite index
-   on (to_unit_id, link_type) turns this into a single index scan and reduces
-   latency from ~36 ms to < 5 ms per query.
-
-2. idx_memory_links_entity_covering  (from_unit_id) INCLUDE (to_unit_id, entity_id)
-   WHERE link_type = 'entity'
-   The entity co-occurrence expansion uses COUNT(DISTINCT ml.entity_id) and
-   joins on ml.to_unit_id.  Without a covering index the planner must read
-   ~2 500 heap pages to fetch entity_id and to_unit_id after the bitmap index
-   scan, adding ~230 ms of random I/O.  INCLUDE adds those two columns to the
-   index leaf pages so the entire query can be served from the index (index-only
-   scan), eliminating the heap reads entirely.
-   Partial index (WHERE link_type = 'entity') keeps index size ~40 % smaller.
-
-Both indexes are created with CONCURRENTLY so the migration does not block
-concurrent reads or writes on memory_links.  CONCURRENTLY requires running
-outside a transaction block, so the migration emits an explicit COMMIT before
-each statement and uses IF NOT EXISTS for idempotency.
-
-Revision ID: d2e3f4a5b6c7
-Revises: b3c4d5e6f7g8
-Create Date: 2026-03-02
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "d2e3f4a5b6c7"
-down_revision: str | Sequence[str] | None = "b3c4d5e6f7g8"
-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()
-
-    # CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
-    # Commit the current Alembic transaction, then issue each CONCURRENTLY
-    # statement in its own implicit autocommit transaction.
-    # IF NOT EXISTS makes each statement idempotent if the migration is retried.
-
-    # Index for the semantic *incoming* direction in link_expansion_retrieval.py.
-    # Replaces the BitmapAnd of idx_memory_links_to_unit ∩ idx_memory_links_link_type
-    # with a single composite index scan.
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_to_type_weight "
-        f"ON {schema}memory_links(to_unit_id, link_type, weight DESC)"
-    )
-
-    # Covering index for entity co-occurrence expansion.
-    # Enables an index-only scan: entity_id and to_unit_id are read from the
-    # index leaf pages instead of the heap, eliminating ~2 500 random heap-page
-    # reads per expansion query.
-    op.execute("COMMIT")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_entity_covering "
-        f"ON {schema}memory_links(from_unit_id) "
-        f"INCLUDE (to_unit_id, entity_id) "
-        f"WHERE link_type = 'entity'"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_entity_covering")
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_to_type_weight")

hindsight-api-slim/hindsight_api/alembic/versions/d4e5f6g7h8i9_gin_source_memory_ids_fastupdate_off.py

@@ -1,53 +0,0 @@
-"""Recreate idx_memory_units_source_memory_ids GIN index with fastupdate=off
-
-GIN indexes use a "fastupdate" pending list by default: small writes are
-buffered there and flushed to the main GIN tree in bulk. Flushing requires
-AccessExclusiveLock on the index. Under high insert concurrency (e.g. 8
-parallel pytest-xdist workers all calling retain_async) two transactions can
-each trigger a flush simultaneously and deadlock.
-
-Disabling fastupdate makes every insert write directly to the GIN tree
-(slightly slower per insert, but no pending-list lock cycles).
-
-Revision ID: d4e5f6g7h8i9
-Revises: d5e6f7a8b9c0
-Create Date: 2026-03-11
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "d4e5f6g7h8i9"
-down_revision: str | Sequence[str] | None = "d5e6f7a8b9c0"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    # DROP + CREATE CONCURRENTLY must run outside a transaction block.
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
-        f"ON {schema}memory_units USING GIN (source_memory_ids) "
-        f"WITH (fastupdate=off) "
-        f"WHERE source_memory_ids IS NOT NULL"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute("COMMIT")
-    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
-    op.execute(
-        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
-        f"ON {schema}memory_units USING GIN (source_memory_ids) "
-        f"WHERE source_memory_ids IS NOT NULL"
-    )

hindsight-api-slim/hindsight_api/alembic/versions/d5e6f7a8b9c0_add_bank_internal_id_and_per_bank_hnsw.py

@@ -1,131 +0,0 @@
-"""Add internal_id to banks and per-(bank, fact_type) partial HNSW indexes
-
-Revision ID: d5e6f7a8b9c0
-Revises: a3b4c5d6e7f8
-Create Date: 2026-03-11
-
-This migration:
-1. Adds internal_id UUID column to banks (stable identifier for index naming)
-2. Drops the global HNSW index (competes with per-bank partial indexes)
-3. Creates per-(bank_id, fact_type) partial HNSW indexes for all existing banks
-   (new banks get indexes created at bank-creation time via bank_utils.create_bank_hnsw_indexes)
-
-Why per-(bank, fact_type) indexes:
-- fact_type-only partial indexes are never chosen by the planner when bank_id is in the WHERE
-  clause, because the idx_memory_units_bank_id B-tree index always wins at planning time.
-- Per-(bank, fact_type) partial indexes have both predicates matching → planner selects them.
-- The global HNSW index competes for larger partitions (world, observation) and must be dropped.
-
-For large deployments, create indexes CONCURRENTLY before running this migration:
-    SELECT internal_id, bank_id FROM banks;
-    -- for each bank and each fact_type in (world, experience, observation):
-    CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_mu_emb_{ft}_{uid16}
-        ON memory_units USING hnsw (embedding vector_cosine_ops)
-        WHERE fact_type = '{ft}' AND bank_id = '{bank_id}';
-    DROP INDEX CONCURRENTLY IF EXISTS idx_memory_units_embedding;
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-from sqlalchemy import text
-
-revision: str = "d5e6f7a8b9c0"
-down_revision: str | Sequence[str] | None = "c3d4e5f6g7h8"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-_HNSW_FACT_TYPES: dict[str, str] = {
-    "world": "worl",
-    "experience": "expr",
-    "observation": "obsv",
-}
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # 1. Add internal_id column to banks
-    op.execute(
-        f"ALTER TABLE {schema}banks ADD COLUMN IF NOT EXISTS internal_id UUID DEFAULT gen_random_uuid() NOT NULL"
-    )
-    op.execute(f"ALTER TABLE {schema}banks ADD CONSTRAINT banks_internal_id_unique UNIQUE (internal_id)")
-
-    # 2. Drop any fact_type-only partial HNSW indexes that may exist from prior migrations
-    #    (bank_id B-tree always wins over them when bank_id is in the WHERE clause)
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_world")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_observation")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_experience")
-
-    # 4. Drop global HNSW index (competes with per-bank partial indexes)
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_embedding")
-
-    # 5. Create per-(bank, fact_type) partial HNSW indexes for all existing banks
-    bind = op.get_bind()
-    schema_name = context.config.get_main_option("target_schema")
-    table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
-    banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-
-    rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
-    for row in rows:
-        bank_id = row[0]
-        internal_id = str(row[1]).replace("-", "")[:16]
-        escaped_bank_id = bank_id.replace("'", "''")
-        for ft, ft_short in _HNSW_FACT_TYPES.items():
-            idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
-            # Index name is schema-unqualified (indexes live in the schema of their table)
-            bind.execute(
-                text(
-                    f"CREATE INDEX IF NOT EXISTS {idx_name} "
-                    f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-                    f"WHERE fact_type = '{ft}' AND bank_id = '{escaped_bank_id}'"
-                )
-            )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # Drop per-bank HNSW indexes (iterate existing banks)
-    bind = op.get_bind()
-    schema_name = context.config.get_main_option("target_schema")
-    banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-
-    rows = bind.execute(text(f"SELECT internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
-    for row in rows:
-        internal_id = str(row[0]).replace("-", "")[:16]
-        for ft_short in _HNSW_FACT_TYPES.values():
-            idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
-            bind.execute(text(f"DROP INDEX IF EXISTS {schema}{idx_name}"))
-
-    # Restore the global HNSW index
-    table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_memory_units_embedding ON {table_ref} USING hnsw (embedding vector_cosine_ops)"
-    )
-
-    # Restore old fact_type-only partial indexes
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_mu_emb_world "
-        f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-        f"WHERE fact_type = 'world'"
-    )
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_mu_emb_observation "
-        f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-        f"WHERE fact_type = 'observation'"
-    )
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_mu_emb_experience "
-        f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-        f"WHERE fact_type = 'experience'"
-    )
-
-    # Drop internal_id column
-    op.execute(f"ALTER TABLE {schema}banks DROP CONSTRAINT IF EXISTS banks_internal_id_unique")
-    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS internal_id")

hindsight-api-slim/hindsight_api/alembic/versions/d6e7f8a9b0c1_drop_documents_metadata_column.py

@@ -1,34 +0,0 @@
-"""Drop unused metadata column from documents table
-
-Revision ID: d6e7f8a9b0c1
-Revises: c2d3e4f5g6h7, c5d6e7f8a9b0
-Create Date: 2026-03-30
-
-The metadata column on documents was always stored as an empty dict {}.
-Actual document metadata is stored inside retain_params.metadata.
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "d6e7f8a9b0c1"
-down_revision: str | Sequence[str] | None = ("c2d3e4f5g6h7", "c5d6e7f8a9b0")
-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}documents DROP COLUMN IF EXISTS metadata")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}documents ADD COLUMN IF NOT EXISTS metadata jsonb DEFAULT '{{}}'")

hindsight-api-slim/hindsight_api/alembic/versions/e4f5a6b7c8d9_add_webhooks_tables.py

@@ -1,62 +0,0 @@
-"""Add webhooks table and next_retry_at to async_operations.
-
-Webhook deliveries are handled as async_operations tasks (operation_type='webhook_delivery')
-rather than a dedicated webhook_deliveries table.
-
-Revision ID: e4f5a6b7c8d9
-Revises: d2e3f4a5b6c7
-Create Date: 2026-03-04
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "e4f5a6b7c8d9"
-down_revision: str | Sequence[str] | None = "d2e3f4a5b6c7"
-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"""
-        CREATE TABLE IF NOT EXISTS {schema}webhooks (
-            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-            bank_id TEXT,
-            url TEXT NOT NULL,
-            secret TEXT,
-            event_types TEXT[] NOT NULL DEFAULT '{{}}',
-            enabled BOOLEAN NOT NULL DEFAULT TRUE,
-            created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-            updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
-        )
-        """
-    )
-
-    # Index for bank-scoped webhook lookup
-    op.execute(f"CREATE INDEX IF NOT EXISTS idx_webhooks_bank_id ON {schema}webhooks(bank_id)")
-
-    # Add next_retry_at to async_operations for task-owned retry scheduling
-    op.execute(f"ALTER TABLE {schema}async_operations ADD COLUMN IF NOT EXISTS next_retry_at TIMESTAMPTZ NULL")
-
-    # Index for polling: status + next_retry_at
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_async_operations_status_retry "
-        f"ON {schema}async_operations(status, next_retry_at)"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_status_retry")
-    op.execute(f"ALTER TABLE {schema}async_operations DROP COLUMN IF EXISTS next_retry_at")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_webhooks_bank_id")
-    op.execute(f"DROP TABLE IF EXISTS {schema}webhooks")

hindsight-api-slim/hindsight_api/alembic/versions/e5f6g7h8i9j0_cascade_delete_ops_on_bank_delete.py

@@ -1,73 +0,0 @@
-"""Add CASCADE DELETE FK from async_operations and webhooks to banks.
-
-When a bank is deleted, all its async_operations and webhooks rows are
-automatically deleted by the database. This ensures that any in-flight
-worker tasks detect the deletion via _check_op_alive() and abort early.
-
-Revision ID: e5f6g7h8i9j0
-Revises: d4e5f6g7h8i9
-Create Date: 2026-03-11
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "e5f6g7h8i9j0"
-down_revision: str | Sequence[str] | None = "d4e5f6g7h8i9"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # Remove orphaned async_operations rows whose bank no longer exists
-    # (can happen because there was no FK before this migration).
-    op.execute(
-        f"""
-        DELETE FROM {schema}async_operations
-        WHERE bank_id IS NOT NULL
-          AND bank_id NOT IN (SELECT bank_id FROM {schema}banks)
-        """
-    )
-
-    # Remove orphaned webhooks rows whose bank no longer exists.
-    op.execute(
-        f"""
-        DELETE FROM {schema}webhooks
-        WHERE bank_id IS NOT NULL
-          AND bank_id NOT IN (SELECT bank_id FROM {schema}banks)
-        """
-    )
-
-    # Add FK with ON DELETE CASCADE so that deleting a bank automatically
-    # cleans up all its pending/processing operations and webhook configs.
-    op.execute(
-        f"""
-        ALTER TABLE {schema}async_operations
-            ADD CONSTRAINT fk_async_operations_bank_id
-            FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id)
-            ON DELETE CASCADE
-        """
-    )
-
-    op.execute(
-        f"""
-        ALTER TABLE {schema}webhooks
-            ADD CONSTRAINT fk_webhooks_bank_id
-            FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id)
-            ON DELETE CASCADE
-        """
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}async_operations DROP CONSTRAINT IF EXISTS fk_async_operations_bank_id")
-    op.execute(f"ALTER TABLE {schema}webhooks DROP CONSTRAINT IF EXISTS fk_webhooks_bank_id")

hindsight-api-slim/hindsight_api/alembic/versions/f6g7h8i9j0k1_chunk_fk_cascade_delete.py

@@ -1,57 +0,0 @@
-"""chunk_fk_cascade_delete
-
-Revision ID: f6g7h8i9j0k1
-Revises: e5f6g7h8i9j0
-Create Date: 2026-03-16 00:00:00.000000
-
-"""
-
-from collections.abc import Sequence
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "f6g7h8i9j0k1"
-down_revision: str | Sequence[str] | None = "e5f6g7h8i9j0"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def upgrade() -> None:
-    """Change memory_units.chunk_id FK from SET NULL to CASCADE.
-
-    When a document is deleted the CASCADE reaches chunks first; with SET NULL
-    the memory_units rows survived with chunk_id = NULL, leaving ghost records.
-    Switching to CASCADE ensures they are removed together with their chunk.
-    """
-    from alembic import context
-
-    schema = context.config.get_main_option("target_schema")
-    schema_prefix = f'"{schema}".' if schema else ""
-    # Use raw SQL with IF EXISTS so this is safe on schemas where the FK was
-    # already dropped or never existed under this name.
-    op.execute(f"ALTER TABLE {schema_prefix}memory_units DROP CONSTRAINT IF EXISTS memory_units_chunk_fkey")
-    # Use a DO block so the ADD is also idempotent: if the FK already exists (e.g.
-    # the schema was provisioned after the base migration already added it) the
-    # duplicate_object exception is swallowed rather than failing the migration.
-    op.execute(
-        f"""
-        DO $$ BEGIN
-            ALTER TABLE {schema_prefix}memory_units
-                ADD CONSTRAINT memory_units_chunk_fkey
-                FOREIGN KEY (chunk_id)
-                REFERENCES {schema_prefix}chunks (chunk_id)
-                ON DELETE CASCADE;
-        EXCEPTION
-            WHEN duplicate_object THEN NULL;
-        END $$;
-        """
-    )
-
-
-def downgrade() -> None:
-    """Revert to SET NULL behaviour."""
-    op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
-    op.create_foreign_key(
-        "memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="SET NULL"
-    )

hindsight-api-slim/hindsight_api/alembic/versions/f7g8h9i0j1k2_add_webhook_http_config.py

@@ -1,33 +0,0 @@
-"""Add http_config JSONB column to webhooks table.
-
-Stores HTTP delivery configuration (method, timeout, headers, params) as a
-single JSONB column rather than separate columns.
-
-Revision ID: f7g8h9i0j1k2
-Revises: e4f5a6b7c8d9
-Create Date: 2026-03-04
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "f7g8h9i0j1k2"
-down_revision: str | Sequence[str] | None = "e4f5a6b7c8d9"
-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}webhooks ADD COLUMN IF NOT EXISTS http_config JSONB NOT NULL DEFAULT '{{}}'")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}webhooks DROP COLUMN IF EXISTS http_config")

hindsight-api-slim/hindsight_api/alembic/versions/g7h8i9j0k1l2_backsweep_orphan_observations.py

@@ -1,71 +0,0 @@
-"""backsweep_orphan_memory_units
-
-Two-pass cleanup of memory_units rows that were never removed by earlier bugs:
-
-Pass 1 — any fact_type, bank gone:
-  memory_units whose bank_id no longer exists in banks.  These accumulate when
-  a bank is deleted without a proper cascade (no FK from memory_units to banks
-  exists in the schema).
-
-Pass 2 — observations only, all sources gone:
-  observation rows whose bank still exists but every source_memory_id points
-  to a deleted memory unit.  These were left behind before PR #580 fixed the
-  chunk FK cascade and before delete_document() called
-  _delete_stale_observations_for_memories.
-
-Revision ID: g7h8i9j0k1l2
-Revises: f6g7h8i9j0k1
-Create Date: 2026-03-16
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "g7h8i9j0k1l2"
-down_revision: str | Sequence[str] | None = "f6g7h8i9j0k1"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    mu = f"{schema}memory_units"
-    banks = f"{schema}banks"
-
-    # Pass 1: delete all memory_units (any fact_type) whose bank no longer exists.
-    # There is no FK from memory_units to banks, so these never cascade away.
-    op.execute(
-        f"""
-        DELETE FROM {mu}
-        WHERE NOT EXISTS (
-            SELECT 1 FROM {banks} b WHERE b.bank_id = {mu}.bank_id
-        )
-        """
-    )
-
-    # Pass 2: delete orphaned observations whose bank still exists but every
-    # source_memory_id refers to a now-deleted memory unit (or the array is
-    # empty).  Observations with at least one surviving source are left alone.
-    op.execute(
-        f"""
-        DELETE FROM {mu} orphan
-        WHERE orphan.fact_type = 'observation'
-          AND NOT EXISTS (
-              SELECT 1
-              FROM {mu} src
-              WHERE src.id = ANY(orphan.source_memory_ids)
-                AND src.bank_id = orphan.bank_id
-          )
-        """
-    )
-
-
-def downgrade() -> None:
-    # Deleted rows cannot be restored.
-    pass

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

@@ -1,317 +0,0 @@
-"""learnings_and_pinned_reflections
-
-Revision ID: n9i0j1k2l3m4
-Revises: m8h9i0j1k2l3
-Create Date: 2026-01-21 00:00:00.000000
-
-This migration:
-1. Creates the 'learnings' table for automatic bottom-up consolidation
-2. Creates the 'pinned_reflections' table for user-curated living documents
-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"
-down_revision: str | Sequence[str] | None = "m8h9i0j1k2l3"
-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 _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 (
-            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-            bank_id VARCHAR(64) NOT NULL,
-            text TEXT NOT NULL,
-            proof_count INT NOT NULL DEFAULT 1,
-            history JSONB DEFAULT '[]'::jsonb,
-            mission_context VARCHAR(64),
-            pre_mission_change BOOLEAN DEFAULT FALSE,
-            embedding vector(384),
-            tags VARCHAR[] DEFAULT ARRAY[]::VARCHAR[],
-            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
-            updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
-        )
-    """)
-
-    # Add foreign key constraint
-    op.execute(f"""
-        ALTER TABLE {schema}learnings
-        ADD CONSTRAINT fk_learnings_bank_id
-        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
-    """)
-
-    # Indexes for learnings
-    op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
-
-    # 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
-    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"""
-        CREATE TABLE {schema}pinned_reflections (
-            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-            bank_id VARCHAR(64) NOT NULL,
-            name VARCHAR(256) NOT NULL,
-            source_query TEXT NOT NULL,
-            content TEXT NOT NULL,
-            embedding vector(384),
-            tags VARCHAR[] DEFAULT ARRAY[]::VARCHAR[],
-            last_refreshed_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
-            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
-        )
-    """)
-
-    # Add foreign key constraint
-    op.execute(f"""
-        ALTER TABLE {schema}pinned_reflections
-        ADD CONSTRAINT fk_pinned_reflections_bank_id
-        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
-    """)
-
-    # Indexes for pinned_reflections
-    op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
-
-    # 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
-    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"""
-        ALTER TABLE {schema}banks
-        ADD COLUMN IF NOT EXISTS last_consolidated_at TIMESTAMP WITH TIME ZONE
-    """)
-    op.execute(f"""
-        ALTER TABLE {schema}banks
-        ADD COLUMN IF NOT EXISTS mission_changed_at TIMESTAMP WITH TIME ZONE
-    """)
-
-
-def downgrade() -> None:
-    """Drop learnings and pinned_reflections tables."""
-    schema = _get_schema_prefix()
-
-    # Drop tables
-    op.execute(f"DROP TABLE IF EXISTS {schema}learnings CASCADE")
-    op.execute(f"DROP TABLE IF EXISTS {schema}pinned_reflections CASCADE")
-
-    # Remove columns from banks
-    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS last_consolidated_at")
-    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS mission_changed_at")

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

@@ -1,64 +0,0 @@
-"""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-slim/hindsight_api/alembic/versions/y0t1u2v3w4x5_add_result_metadata_gin_index.py

@@ -1,49 +0,0 @@
-"""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 IF NOT EXISTS 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-slim/hindsight_api/alembic/versions/z1u2v3w4x5y6_add_observation_tags_to_memory_units.py

@@ -1,35 +0,0 @@
-"""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-slim/hindsight_api/config_resolver.py

@@ -1,315 +0,0 @@
-"""
-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, replace
-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)
-
-        # Validate retain_strategies: reject empty string keys
-        if "retain_strategies" in normalized_updates and normalized_updates["retain_strategies"]:
-            empty_keys = [k for k in normalized_updates["retain_strategies"] if not str(k).strip()]
-            if empty_keys:
-                raise ValueError(
-                    "Strategy names must not be empty strings. Remove entries with empty names before saving."
-                )
-
-        # 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")
-
-
-def apply_strategy(config: HindsightConfig, strategy_name: str) -> HindsightConfig:
-    """
-    Apply a named retain strategy's overrides on top of a resolved config.
-
-    A strategy is a named set of hierarchical field overrides stored in
-    config.retain_strategies. Any field in _HIERARCHICAL_FIELDS can be
-    overridden, including retain_extraction_mode, retain_chunk_size,
-    entity_labels, entities_allow_free_form, etc.
-
-    Unknown strategy names log a warning and return config unchanged.
-    Unknown or non-hierarchical fields in the strategy are silently ignored.
-    """
-    strategies = config.retain_strategies or {}
-    if strategy_name not in strategies:
-        logger.warning(f"Unknown retain strategy '{strategy_name}', using resolved config as-is")
-        return config
-
-    overrides = strategies[strategy_name]
-    if not isinstance(overrides, dict):
-        logger.warning(f"Retain strategy '{strategy_name}' is not a dict, skipping")
-        return config
-
-    configurable = HindsightConfig.get_configurable_fields()
-    filtered = {k: v for k, v in overrides.items() if k in configurable}
-
-    if not filtered:
-        return config
-
-    logger.debug(f"Applying retain strategy '{strategy_name}': {list(filtered.keys())}")
-    return replace(config, **filtered)

hindsight-api-slim/hindsight_api/engine/audit.py

@@ -1,209 +0,0 @@
-"""Audit logging for feature usage tracking.
-
-Provides fire-and-forget audit logging of all mutating and core operations
-(retain, recall, reflect, bank CRUD, etc.) across HTTP, MCP, and system transports.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import uuid
-from collections.abc import Callable
-from contextlib import asynccontextmanager
-from dataclasses import dataclass, field
-from datetime import datetime, timezone
-from typing import Any
-
-import asyncpg
-
-from ..engine.db_utils import acquire_with_retry
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class AuditEntry:
-    """A single audit log entry."""
-
-    action: str
-    transport: str  # "http", "mcp", "system"
-    bank_id: str | None = None
-    started_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
-    ended_at: datetime | None = None
-    request: dict[str, Any] | None = None
-    response: dict[str, Any] | None = None
-    metadata: dict[str, Any] = field(default_factory=dict)
-
-
-def _json_default(obj: Any) -> str:
-    """JSON serializer for objects not serializable by default."""
-    if isinstance(obj, datetime):
-        return obj.isoformat()
-    if isinstance(obj, uuid.UUID):
-        return str(obj)
-    if isinstance(obj, bytes):
-        return "<bytes>"
-    if isinstance(obj, set):
-        return list(obj)
-    return str(obj)
-
-
-def _safe_json(data: Any) -> str | None:
-    """Serialize data to JSON string, returning None on failure."""
-    if data is None:
-        return None
-    try:
-        return json.dumps(data, default=_json_default)
-    except Exception:
-        logger.debug("Failed to serialize audit data", exc_info=True)
-        return None
-
-
-_SWEEP_INTERVAL_SECONDS = 3600  # Run retention sweep every hour
-
-
-class AuditLogger:
-    """Fire-and-forget audit log writer with optional retention sweep."""
-
-    def __init__(
-        self,
-        pool_getter: Callable[[], asyncpg.Pool | None],
-        schema_getter: Callable[[], str],
-        enabled: bool,
-        allowed_actions: list[str],
-        retention_days: int = -1,
-    ) -> None:
-        self._pool_getter = pool_getter
-        self._schema_getter = schema_getter
-        self._enabled = enabled
-        self._allowed_actions: frozenset[str] | None = frozenset(allowed_actions) if allowed_actions else None
-        self._retention_days = retention_days
-        self._sweep_task: asyncio.Task | None = None
-
-    def is_enabled(self, action: str) -> bool:
-        """Check if audit logging is enabled for this action."""
-        if not self._enabled:
-            return False
-        if self._allowed_actions is not None:
-            return action in self._allowed_actions
-        return True
-
-    def log_fire_and_forget(self, entry: AuditEntry) -> None:
-        """Schedule an audit write as a background task."""
-        if not self.is_enabled(entry.action):
-            return
-        try:
-            asyncio.create_task(self._safe_log(entry))
-        except RuntimeError:
-            # No running event loop (e.g. during shutdown)
-            logger.debug("Cannot schedule audit log write: no running event loop")
-
-    async def _safe_log(self, entry: AuditEntry) -> None:
-        """Write audit entry to DB. Errors are logged, never raised."""
-        pool = self._pool_getter()
-        if pool is None:
-            logger.debug("Audit log skipped: pool not available")
-            return
-        try:
-            schema = self._schema_getter()
-            table = f"{schema}.audit_log"
-            async with acquire_with_retry(pool, max_retries=1) as conn:
-                await conn.execute(
-                    f"""
-                    INSERT INTO {table}
-                        (id, action, transport, bank_id, started_at, ended_at, request, response, metadata)
-                    VALUES
-                        ($1, $2, $3, $4, $5, $6, $7::jsonb, $8::jsonb, $9::jsonb)
-                    """,
-                    uuid.uuid4(),
-                    entry.action,
-                    entry.transport,
-                    entry.bank_id,
-                    entry.started_at,
-                    entry.ended_at,
-                    _safe_json(entry.request),
-                    _safe_json(entry.response),
-                    _safe_json(entry.metadata) or "{}",
-                )
-        except Exception as e:
-            logger.warning(f"Audit log write failed for action={entry.action}: {e}")
-
-    def start_retention_sweep(self) -> None:
-        """Start the periodic retention sweep if retention is configured."""
-        if self._retention_days <= 0 or not self._enabled:
-            return
-        try:
-            self._sweep_task = asyncio.create_task(self._sweep_loop())
-        except RuntimeError:
-            logger.debug("Cannot start retention sweep: no running event loop")
-
-    async def stop_retention_sweep(self) -> None:
-        """Stop the periodic retention sweep."""
-        if self._sweep_task and not self._sweep_task.done():
-            self._sweep_task.cancel()
-            try:
-                await self._sweep_task
-            except asyncio.CancelledError:
-                pass
-            self._sweep_task = None
-
-    async def _sweep_loop(self) -> None:
-        """Periodically delete audit log entries older than retention_days."""
-        while True:
-            await self._run_sweep()
-            await asyncio.sleep(_SWEEP_INTERVAL_SECONDS)
-
-    async def _run_sweep(self) -> None:
-        """Delete expired audit log entries. Concurrent-safe via row-level deletes."""
-        pool = self._pool_getter()
-        if pool is None:
-            return
-        try:
-            schema = self._schema_getter()
-            table = f"{schema}.audit_log"
-            async with acquire_with_retry(pool, max_retries=1) as conn:
-                result = await conn.execute(
-                    f"DELETE FROM {table} WHERE started_at < NOW() - INTERVAL '{self._retention_days} days'"
-                )
-                if result and result != "DELETE 0":
-                    logger.info(f"Audit log retention sweep: {result}")
-        except Exception as e:
-            logger.warning(f"Audit log retention sweep failed: {e}")
-
-
-@asynccontextmanager
-async def audit_context(
-    audit_logger: AuditLogger | None,
-    action: str,
-    transport: str,
-    bank_id: str | None = None,
-    request: dict[str, Any] | None = None,
-    metadata: dict[str, Any] | None = None,
-):
-    """Async context manager that times the operation and writes audit on exit.
-
-    Usage:
-        async with audit_context(logger, "retain", "http", bank_id, request_dict) as entry:
-            result = await do_work()
-            entry.response = result_dict
-    """
-    if audit_logger is None or not audit_logger.is_enabled(action):
-        entry = AuditEntry(action=action, transport=transport, bank_id=bank_id)
-        yield entry
-        return
-
-    entry = AuditEntry(
-        action=action,
-        transport=transport,
-        bank_id=bank_id,
-        started_at=datetime.now(timezone.utc),
-        request=request,
-        metadata=metadata or {},
-    )
-    try:
-        yield entry
-    finally:
-        entry.ended_at = datetime.now(timezone.utc)
-        audit_logger.log_fire_and_forget(entry)

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

@@ -1,90 +0,0 @@
-"""Prompts for the consolidation engine."""
-
-# 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."
-
-# 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."""
-
-# Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
-_BATCH_DATA_SECTION = """
-NEW FACTS:
-{facts_text}
-
-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
-- occurred_start/occurred_end: temporal range of source facts
-- source_memories: array of supporting facts with their text and dates
-
-Compare the facts against existing observations:
-- Same topic as an existing observation → UPDATE it (observation_id + source_fact_ids)
-- New topic with durable knowledge → CREATE a new observation (source_fact_ids)
-- Cross-reference facts within the batch: a later fact may resolve a vague reference in an earlier one
-- Purely ephemeral facts → omit them unless the MISSION above explicitly targets such data (e.g. timestamped events, session state, screen content)"""
-
-# Output format — JSON braces escaped as {{ }} so .format() leaves them literal
-_BATCH_OUTPUT_FORMAT = """
-Output a JSON object with three arrays.
-
-## EXAMPLE
-
-Input facts:
-[a1b2c3d4-e5f6-7890-abcd-ef1234567890] Alice mentioned she works long hours, often past midnight | Involving: Alice (occurred_start=2024-01-15, mentioned_at=2024-01-15)
-[b2c3d4e5-f6a7-8901-bcde-f12345678901] Alice said she's exhausted from the project deadlines | Involving: Alice (occurred_start=2024-01-20, mentioned_at=2024-01-20)
-
-Good observation text — clean prose, no metadata, each fact tracked distinctly:
-  "Alice works long hours, often past midnight."
-  "Alice feels exhausted from project deadlines."
-
-Bad observation text — NEVER do this (verbatim copy of fact text with metadata):
-  "Alice mentioned she works long hours, often past midnight | Involving: Alice (occurred_start=2024-01-15, mentioned_at=2024-01-15)"
-
-Observation text rules:
-- Write clean prose — NEVER copy raw fact lines or their metadata (temporal fields, "Involving:", "When:" labels, UUIDs).
-- Parenthesized metadata like (occurred_start=...) and pipe-separated labels like "| Involving: ..." are fact formatting — strip them entirely from observation text.
-- How many observations to create and how much to aggregate is driven by the MISSION above.
-
-{{"creates": [{{"text": "Alice works long hours, often past midnight.", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890"]}}, {{"text": "Alice feels exhausted from project deadlines.", "source_fact_ids": ["b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}],
-  "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"}}]}}
-
-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,
-    observation_capacity_note: 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
-
-    capacity_section = ""
-    if observation_capacity_note:
-        capacity_section = f"\n\n## CAPACITY CONSTRAINT\n{observation_capacity_note}"
-
-    return (
-        "You are a memory consolidation system. Synthesize facts into observations "
-        "and merge with existing observations when appropriate.\n\n"
-        f"## MISSION\n{mission}{capacity_section}\n\n"
-        f"{_PROCESSING_RULES}" + _BATCH_DATA_SECTION + _BATCH_OUTPUT_FORMAT
-    )

hindsight-api-slim/hindsight_api/engine/jina_mlx_reranker.py

@@ -1,144 +0,0 @@
-"""
-MLX implementation of jina-reranker-v3 for Apple Silicon.
-
-This file is adapted from the official model repository:
-  https://huggingface.co/jinaai/jina-reranker-v3-mlx/blob/main/rerank.py
-
-License: CC BY-NC 4.0 (contact Jina AI for commercial usage)
-
-Changes from upstream:
-- Removed the __main__ example block
-- Type annotations added to public methods
-- top_n parameter added to rerank() (upstream only exposed it implicitly)
-"""
-
-import numpy as np
-
-
-class _MLPProjector:
-    def __init__(self):
-        import mlx.nn as nn
-
-        self.linear1 = nn.Linear(1024, 512, bias=False)
-        self.linear2 = nn.Linear(512, 512, bias=False)
-
-    def __call__(self, x):
-        import mlx.nn as nn
-
-        x = self.linear1(x)
-        x = nn.relu(x)
-        x = self.linear2(x)
-        return x
-
-
-def _load_projector(projector_path: str) -> _MLPProjector:
-    import mlx.core as mx
-    from safetensors import safe_open
-
-    projector = _MLPProjector()
-    with safe_open(projector_path, framework="numpy") as f:
-        projector.linear1.weight = mx.array(f.get_tensor("linear1.weight"))
-        projector.linear2.weight = mx.array(f.get_tensor("linear2.weight"))
-    return projector
-
-
-def _sanitize(text: str, special_tokens: dict[str, str]) -> str:
-    for token in special_tokens.values():
-        text = text.replace(token, "")
-    return text
-
-
-def _format_prompt(query: str, docs: list[str], special_tokens: dict[str, str]) -> str:
-    query = _sanitize(query, special_tokens)
-    docs = [_sanitize(d, special_tokens) for d in docs]
-
-    doc_token = special_tokens["doc_embed_token"]
-    query_token = special_tokens["query_embed_token"]
-
-    prefix = (
-        "<|im_start|>system\n"
-        "You are a search relevance expert who can determine a ranking of the passages based on how relevant they are to the query. "
-        "If the query is a question, how relevant a passage is depends on how well it answers the question. "
-        "If not, try to analyze the intent of the query and assess how well each passage satisfies the intent. "
-        "If an instruction is provided, you should follow the instruction when determining the ranking."
-        "<|im_end|>\n<|im_start|>user\n"
-    )
-    suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
-
-    body = (
-        f"I will provide you with {len(docs)} passages, each indicated by a numerical identifier. "
-        f"Rank the passages based on their relevance to query: {query}\n"
-    )
-    body += "\n".join(f'<passage id="{i}">\n{doc}{doc_token}\n</passage>' for i, doc in enumerate(docs))
-    body += f"\n<query>\n{query}{query_token}\n</query>"
-    return prefix + body + suffix
-
-
-class MLXReranker:
-    """
-    MLX-accelerated jina-reranker-v3 for Apple Silicon.
-
-    Loads the model from a local directory (use huggingface_hub.snapshot_download
-    to fetch jinaai/jina-reranker-v3-mlx if you don't have it already).
-    """
-
-    _SPECIAL_TOKENS = {
-        "query_embed_token": "<|rerank_token|>",
-        "doc_embed_token": "<|embed_token|>",
-    }
-    _DOC_TOKEN_ID = 151670
-    _QUERY_TOKEN_ID = 151671
-
-    def __init__(self, model_path: str, projector_path: str):
-        from mlx_lm import load
-
-        self.model, self.tokenizer = load(model_path)
-        self.model.eval()
-        self.projector = _load_projector(projector_path)
-
-    def rerank(self, query: str, documents: list[str], top_n: int | None = None) -> list[dict]:
-        """
-        Rank documents by relevance to a query.
-
-        Returns a list of dicts with keys: document, relevance_score, index.
-        Sorted by descending relevance_score.
-        """
-        import mlx.core as mx
-
-        prompt = _format_prompt(query, documents, self._SPECIAL_TOKENS)
-        input_ids = self.tokenizer.encode(prompt)
-        hidden_states = self.model.model([input_ids])[0]  # [seq_len, hidden_size]
-
-        input_ids_np = np.array(input_ids)
-        query_positions = np.where(input_ids_np == self._QUERY_TOKEN_ID)[0]
-        doc_positions = np.where(input_ids_np == self._DOC_TOKEN_ID)[0]
-
-        if len(query_positions) == 0:
-            raise ValueError("Query embed token not found in prompt")
-        if len(doc_positions) == 0:
-            raise ValueError("Document embed tokens not found in prompt")
-
-        query_hidden = mx.expand_dims(hidden_states[int(query_positions[0])], axis=0)
-        doc_hidden = mx.stack([hidden_states[int(p)] for p in doc_positions])
-
-        query_emb = self.projector(query_hidden)  # [1, 512]
-        doc_emb = self.projector(doc_hidden)  # [num_docs, 512]
-
-        query_exp = mx.broadcast_to(mx.expand_dims(query_emb, 0), (1, len(documents), 512))
-        doc_exp = mx.expand_dims(doc_emb, 0)
-
-        scores = mx.sum(doc_exp * query_exp, axis=-1) / (
-            mx.sqrt(mx.sum(doc_exp * doc_exp, axis=-1)) * mx.sqrt(mx.sum(query_exp * query_exp, axis=-1))
-        )  # [1, num_docs]
-        scores_np = np.array(scores[0])
-
-        order = np.argsort(scores_np)[::-1]
-        n = min(top_n, len(documents)) if top_n is not None else len(documents)
-        return [
-            {
-                "document": documents[order[i]],
-                "relevance_score": float(scores_np[order[i]]),
-                "index": int(order[i]),
-            }
-            for i in range(n)
-        ]

hindsight-api-slim/hindsight_api/engine/operation_metadata.py

@@ -1,69 +0,0 @@
-"""
-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-slim/hindsight_api/engine/parsers/__init__.py

@@ -1,128 +0,0 @@
-"""File parser implementations."""
-
-import logging
-from dataclasses import dataclass
-
-from .base import FileParser, UnsupportedFileTypeError
-from .iris import IrisParser
-from .markitdown import MarkitdownParser
-
-__all__ = [
-    "FileParser",
-    "UnsupportedFileTypeError",
-    "IrisParser",
-    "MarkitdownParser",
-    "FileParserRegistry",
-    "ConvertResult",
-]
-
-
-@dataclass
-class ConvertResult:
-    """Result of a successful file conversion."""
-
-    content: str
-    parser_name: str
-
-
-logger = logging.getLogger(__name__)
-
-
-class FileParserRegistry:
-    """Registry for file parsers with auto-detection."""
-
-    def __init__(self):
-        """Initialize empty parser registry."""
-        self._parsers: dict[str, FileParser] = {}
-
-    def register(self, parser: FileParser):
-        """
-        Register a parser.
-
-        Args:
-            parser: FileParser instance
-        """
-        self._parsers[parser.name()] = parser
-
-    def get_parser(
-        self,
-        name: str | None,
-        filename: str,
-        content_type: str | None = None,
-    ) -> FileParser:
-        """
-        Get parser by name or auto-detect.
-
-        Args:
-            name: Parser name (e.g., "markitdown") or None for auto-detect
-            filename: File name for auto-detection
-            content_type: MIME type (optional)
-
-        Returns:
-            FileParser instance
-
-        Raises:
-            ValueError: If no suitable parser found
-        """
-        if name:
-            # Explicit parser requested — return it directly, let the parser
-            # raise UnsupportedFileTypeError from convert() if needed
-            if name not in self._parsers:
-                raise ValueError(f"Parser '{name}' not found. Available: {list(self._parsers.keys())}")
-            return self._parsers[name]
-
-        # Auto-detect parser
-        for parser in self._parsers.values():
-            if parser.supports(filename, content_type):
-                return parser
-
-        raise ValueError(f"No parser found for {filename}. Available parsers: {list(self._parsers.keys())}")
-
-    async def convert_with_fallback(
-        self,
-        parsers: list[str],
-        file_data: bytes,
-        filename: str,
-        content_type: str | None = None,
-    ) -> ConvertResult:
-        """
-        Try each parser in order, falling back on failure or empty content.
-
-        Moves to the next parser if the current one raises UnsupportedFileTypeError
-        or returns empty content. Any other exception (RuntimeError, network error,
-        etc.) also triggers a fallback so the chain is exhausted before failing.
-
-        Args:
-            parsers: Ordered list of parser names to try
-            file_data: Raw file bytes
-            filename: Original filename
-            content_type: MIME type (optional)
-
-        Returns:
-            ConvertResult with the parsed content and the name of the parser that succeeded
-
-        Raises:
-            ValueError: If a parser name is not registered
-            RuntimeError: If all parsers fail or return empty content
-        """
-        last_error: Exception | None = None
-        for name in parsers:
-            parser = self.get_parser(name, filename, content_type)
-            try:
-                content = await parser.convert(file_data, filename)
-                if content and content.strip():
-                    return ConvertResult(content=content, parser_name=name)
-                logger.warning(f"Parser '{name}' returned empty content for '{filename}', trying next")
-                last_error = RuntimeError(f"Parser '{name}' returned no content for '{filename}'")
-            except UnsupportedFileTypeError as e:
-                logger.warning(f"Parser '{name}' does not support '{filename}', trying next: {e}")
-                last_error = e
-            except Exception as e:
-                logger.warning(f"Parser '{name}' failed for '{filename}', trying next: {e}")
-                last_error = e
-
-        raise last_error or RuntimeError(f"No parsers available for '{filename}'")
-
-    def list_parsers(self) -> list[str]:
-        """Get list of registered parser names."""
-        return list(self._parsers.keys())

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

@@ -1,58 +0,0 @@
-"""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-slim/hindsight_api/engine/parsers/iris.py

@@ -1,138 +0,0 @@
-"""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(timeout=httpx.Timeout(30.0, read=120.0)) 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)
-            # Ensure file_data is plain bytes (GCS storage may return obstore.Bytes)
-            upload_resp = await client.put(
-                upload_url,
-                content=bytes(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-slim/hindsight_api/engine/parsers/markitdown.py

@@ -1,109 +0,0 @@
-"""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-slim/hindsight_api/engine/providers/litellm_llm.py

@@ -1,380 +0,0 @@
-"""
-LiteLLM LLM provider for universal model support.
-
-This provider enables using 100+ LLM providers via the LiteLLM SDK, including:
-- AWS Bedrock (bedrock/anthropic.claude-3-5-sonnet-...)
-- Azure OpenAI (azure/gpt-4o)
-- Together AI (together_ai/meta-llama/...)
-- Any other LiteLLM-supported provider
-
-Uses litellm.acompletion() for async chat completions.
-Authentication for cloud providers (e.g., AWS Bedrock via boto3 credential chain)
-is handled automatically by LiteLLM.
-"""
-
-import asyncio
-import json
-import logging
-import time
-from typing import Any
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class LiteLLMLLM(LLMInterface):
-    """
-    LLM provider using the LiteLLM SDK for universal model support.
-
-    Supports any model accessible via litellm.acompletion(), including AWS Bedrock,
-    Azure OpenAI, Together AI, Fireworks AI, and more.
-
-    Model names follow LiteLLM conventions with provider prefixes:
-    - bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
-    - azure/gpt-4o
-    - together_ai/meta-llama/Llama-3-70b-chat-hf
-    - fireworks_ai/accounts/fireworks/models/llama-v3p1-70b-instruct
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        timeout: float = 300.0,
-        **kwargs: Any,
-    ):
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-        self.timeout = timeout
-        self._litellm: Any = None
-
-        try:
-            import litellm
-
-            self._litellm = litellm
-            # Suppress LiteLLM's verbose logging
-            litellm.suppress_debug_info = True  # type: ignore[assignment]
-            # Drop unsupported params instead of raising errors (e.g. tool_choice on some Bedrock models)
-            litellm.drop_params = True  # type: ignore[assignment]
-            logging.getLogger("LiteLLM").setLevel(logging.WARNING)
-            logger.info(f"LiteLLM SDK initialized for model: {self.model}")
-        except ImportError as e:
-            raise RuntimeError("LiteLLM SDK not installed. Run: uv add litellm or pip install litellm") from e
-
-    async def verify_connection(self) -> None:
-        try:
-            test_messages = [{"role": "user", "content": "test"}]
-            await self.call(
-                messages=test_messages,
-                max_completion_tokens=50,
-                temperature=0.0,
-                scope="verification",
-                max_retries=0,
-            )
-            logger.info("LiteLLM connection verified successfully")
-        except OutputTooLongError:
-            # Truncation is fine for verification — it means the connection works
-            logger.info("LiteLLM connection verified successfully (response truncated)")
-        except Exception as e:
-            logger.error(f"LiteLLM connection verification failed: {e}")
-            raise RuntimeError(f"Failed to verify LiteLLM connection: {e}") from e
-
-    def _build_common_kwargs(
-        self,
-        messages: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-    ) -> dict[str, Any]:
-        """Build common kwargs for litellm calls."""
-        kwargs: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-            "timeout": self.timeout,
-        }
-
-        if self.api_key:
-            kwargs["api_key"] = self.api_key
-        if self.base_url:
-            kwargs["api_base"] = self.base_url
-        if max_completion_tokens is not None:
-            kwargs["max_completion_tokens"] = max_completion_tokens
-        if temperature is not None:
-            kwargs["temperature"] = temperature
-
-        return kwargs
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        start_time = time.time()
-
-        call_kwargs = self._build_common_kwargs(messages, max_completion_tokens, temperature)
-
-        # Add JSON schema response format if provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            call_kwargs["response_format"] = {
-                "type": "json_schema",
-                "json_schema": {
-                    "name": response_format.__name__ if hasattr(response_format, "__name__") else "response",
-                    "schema": schema,
-                    "strict": strict_schema,
-                },
-            }
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._litellm.acompletion(**call_kwargs)
-
-                content = response.choices[0].message.content or ""
-                finish_reason = response.choices[0].finish_reason
-
-                # Check for length-limited output
-                if finish_reason == "length":
-                    raise OutputTooLongError("LiteLLM response was truncated due to token limit")
-
-                if response_format is not None:
-                    # Strip markdown code fences if present
-                    clean_content = content
-                    if "```json" in content:
-                        clean_content = content.split("```json")[1].split("```")[0].strip()
-                    elif "```" in content:
-                        clean_content = content.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_content)
-                    except json.JSONDecodeError:
-                        json_data = json.loads(content)
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Extract usage
-                input_tokens = getattr(response.usage, "prompt_tokens", 0) or 0
-                output_tokens = getattr(response.usage, "completion_tokens", 0) or 0
-                total_tokens = input_tokens + output_tokens
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import _serialize_for_span, 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=_serialize_for_span(result),
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                )
-
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=total_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except OutputTooLongError:
-                raise
-
-            except json.JSONDecodeError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    logger.warning("LiteLLM returned invalid JSON, retrying...")
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"LiteLLM returned invalid JSON after {max_retries + 1} attempts")
-                    raise
-
-            except Exception as e:
-                error_str = str(e).lower()
-                # Fast fail on auth errors
-                if "401" in error_str or "403" in error_str or "unauthorized" in error_str:
-                    logger.error(f"LiteLLM auth error, not retrying: {e}")
-                    raise
-
-                last_exception = e
-                if attempt < max_retries:
-                    # Retry on rate limits, connection errors, server errors
-                    is_retryable = any(
-                        keyword in error_str
-                        for keyword in ("rate", "limit", "timeout", "connection", "500", "502", "503", "529")
-                    )
-                    if is_retryable:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                        await asyncio.sleep(backoff + jitter)
-                        continue
-
-                logger.error(f"LiteLLM API error after {attempt + 1} attempts: {e}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("LiteLLM call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        start_time = time.time()
-
-        call_kwargs = self._build_common_kwargs(messages, max_completion_tokens, temperature)
-        call_kwargs["tools"] = tools
-        call_kwargs["tool_choice"] = tool_choice
-
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._litellm.acompletion(**call_kwargs)
-
-                message = response.choices[0].message
-                content = message.content
-                finish_reason = response.choices[0].finish_reason
-
-                # Extract tool calls
-                tool_calls: list[LLMToolCall] = []
-                if message.tool_calls:
-                    for tc in message.tool_calls:
-                        arguments = tc.function.arguments
-                        if isinstance(arguments, str):
-                            arguments = json.loads(arguments)
-                        tool_calls.append(
-                            LLMToolCall(
-                                id=tc.id,
-                                name=tc.function.name,
-                                arguments=arguments,
-                            )
-                        )
-
-                # Extract usage
-                input_tokens = getattr(response.usage, "prompt_tokens", 0) or 0
-                output_tokens = getattr(response.usage, "completion_tokens", 0) or 0
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import get_span_recorder
-
-                span_recorder = get_span_recorder()
-                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,
-                    finish_reason=finish_reason or ("tool_calls" if tool_calls else "stop"),
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except Exception as e:
-                error_str = str(e).lower()
-                if "401" in error_str or "403" in error_str or "unauthorized" in error_str:
-                    raise
-
-                last_exception = e
-                if attempt < max_retries:
-                    is_retryable = any(
-                        keyword in error_str
-                        for keyword in ("rate", "limit", "timeout", "connection", "500", "502", "503", "529")
-                    )
-                    if is_retryable:
-                        await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                        continue
-
-                logger.error(f"LiteLLM tool call error after {attempt + 1} attempts: {e}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("LiteLLM tool call failed after all retries")
-
-    async def cleanup(self) -> None:
-        """Clean up resources."""
-        pass

hindsight-api-slim/hindsight_api/engine/providers/none_llm.py

@@ -1,78 +0,0 @@
-"""
-No-op LLM provider for chunk-only storage mode.
-
-When the LLM provider is set to "none", the system operates without any LLM dependency.
-Retain uses chunks mode (no fact extraction), and reflect/consolidation are disabled.
-This provider acts as a safety net — if any code path unexpectedly tries to call the LLM,
-it raises a clear error instead of a confusing connection failure.
-"""
-
-import logging
-from typing import Any
-
-from ..llm_interface import LLMInterface
-from ..response_models import LLMToolCallResult
-
-logger = logging.getLogger(__name__)
-
-
-class LLMNotAvailableError(Exception):
-    """Raised when an operation requires an LLM but the provider is set to 'none'."""
-
-    pass
-
-
-class NoneLLM(LLMInterface):
-    """
-    No-op LLM provider that rejects all LLM calls.
-
-    Used when HINDSIGHT_API_LLM_PROVIDER=none to run Hindsight as a chunk store
-    with semantic search but without LLM-based features (fact extraction, reflect,
-    consolidation).
-    """
-
-    async def verify_connection(self) -> None:
-        """No-op — no LLM connection to verify."""
-        logger.debug("NoneLLM: no LLM connection to verify (provider=none)")
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """Raise LLMNotAvailableError — no LLM is configured."""
-        raise LLMNotAvailableError(
-            "LLM provider is set to 'none'. This operation requires an LLM. "
-            "Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
-        )
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """Raise LLMNotAvailableError — no LLM is configured."""
-        raise LLMNotAvailableError(
-            "LLM provider is set to 'none'. This operation requires an LLM. "
-            "Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
-        )
-
-    async def cleanup(self) -> None:
-        """No-op — nothing to clean up."""
-        pass

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

@@ -1,194 +0,0 @@
-"""
-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-slim/hindsight_api/engine/retain/fact_storage.py

@@ -1,337 +0,0 @@
-"""
-Fact storage for retain pipeline.
-
-Handles insertion of facts into the database.
-"""
-
-import json
-import logging
-import uuid
-
-from ...config import get_config
-from ..memory_engine import fq_table
-from .bank_utils import DEFAULT_DISPOSITION, create_bank_vector_indexes
-from .fact_extraction import _sanitize_text
-from .types import ProcessedFact
-
-logger = logging.getLogger(__name__)
-
-
-async def insert_facts_batch(
-    conn, bank_id: str, facts: list[ProcessedFact], document_id: str | None = None
-) -> list[str]:
-    """
-    Insert facts into the database in batch.
-
-    Args:
-        conn: Database connection
-        bank_id: Bank identifier
-        facts: List of ProcessedFact objects to insert
-        document_id: Optional document ID to associate with facts
-
-    Returns:
-        List of unit IDs (UUIDs as strings) for the inserted facts
-    """
-    if not facts:
-        return []
-
-    # Prepare data for batch insert
-    fact_texts = []
-    embeddings = []
-    event_dates = []
-    occurred_starts = []
-    occurred_ends = []
-    mentioned_ats = []
-    contexts = []
-    fact_types = []
-    confidence_scores = []
-    metadata_jsons = []
-    chunk_ids = []
-    document_ids = []
-    tags_list = []
-    observation_scopes_list = []
-    text_signals_list = []
-
-    for fact in facts:
-        fact_texts.append(_sanitize_text(fact.fact_text))
-        # Convert embedding to string for asyncpg vector type
-        embeddings.append(str(fact.embedding))
-        # event_date: Use occurred_start if available, otherwise use mentioned_at
-        # This maintains backward compatibility while handling None occurred_start
-        event_dates.append(fact.occurred_start if fact.occurred_start is not None else fact.mentioned_at)
-        occurred_starts.append(fact.occurred_start)
-        occurred_ends.append(fact.occurred_end)
-        mentioned_ats.append(fact.mentioned_at)
-        contexts.append(_sanitize_text(fact.context))
-        fact_types.append(fact.fact_type)
-        # confidence_score is only for opinion facts
-        confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
-        metadata_jsons.append(json.dumps(fact.metadata))
-        chunk_ids.append(fact.chunk_id)
-        # Use per-fact document_id if available, otherwise fallback to batch-level document_id
-        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:
-            try:
-                signal_parts.append(fact.occurred_start.strftime("%B %d %Y").lstrip("0").replace(" 0", " "))
-            except (ValueError, AttributeError):
-                pass
-        if fact.occurred_end and fact.occurred_end != fact.occurred_start:
-            try:
-                signal_parts.append(fact.occurred_end.strftime("%B %d %Y").lstrip("0").replace(" 0", " "))
-            except (ValueError, AttributeError):
-                pass
-        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
-    # 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)
-            )
-            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,
-        event_dates,  # event_date: occurred_start if available, else mentioned_at
-        occurred_starts,
-        occurred_ends,
-        mentioned_ats,
-        contexts,
-        fact_types,
-        confidence_scores,
-        metadata_jsons,
-        chunk_ids,
-        document_ids,
-        tags_list,
-        observation_scopes_list,
-        text_signals_list,
-    )
-
-    unit_ids = [str(row["id"]) for row in results]
-    return unit_ids
-
-
-async def ensure_bank_exists(conn, bank_id: str) -> None:
-    """
-    Ensure bank exists in the database.
-
-    Creates bank with default values if it doesn't exist.
-
-    Args:
-        conn: Database connection
-        bank_id: Bank identifier
-    """
-    # Generate internal_id here so we control the value and can use it
-    # immediately for HNSW index creation without a RETURNING round-trip.
-    internal_id = uuid.uuid4()
-    inserted = await conn.fetchval(
-        f"""
-        INSERT INTO {fq_table("banks")} (bank_id, disposition, mission, internal_id)
-        VALUES ($1, $2::jsonb, $3, $4)
-        ON CONFLICT (bank_id) DO NOTHING
-        RETURNING bank_id
-        """,
-        bank_id,
-        json.dumps(DEFAULT_DISPOSITION),
-        "",
-        internal_id,
-    )
-    if inserted:
-        # Fresh insert — create per-bank vector indexes
-        await create_bank_vector_indexes(conn, bank_id, str(internal_id))
-
-
-async def handle_document_tracking(
-    conn,
-    bank_id: str,
-    document_id: str,
-    combined_content: str,
-    is_first_batch: bool,
-    retain_params: dict | None = None,
-    document_tags: list[str] | None = None,
-) -> None:
-    """
-    Handle document tracking in the database (full-replace mode).
-
-    Deletes the existing document (cascading to all units and links) on the
-    first batch, then inserts the new document record.
-
-    Args:
-        conn: Database connection
-        bank_id: Bank identifier
-        document_id: Document identifier
-        combined_content: Combined content text from all content items
-        is_first_batch: Whether this is the first batch (for chunked operations)
-        retain_params: Optional parameters passed during retain (context, event_date, etc.)
-        document_tags: Optional list of tags to associate with the document
-    """
-    import hashlib
-
-    # Sanitize and calculate content hash
-    combined_content = _sanitize_text(combined_content) or ""
-    content_hash = hashlib.sha256(combined_content.encode()).hexdigest()
-
-    # Delete old document first (cascades to units and links)
-    # Only delete on the first batch to avoid deleting data we just inserted
-    if is_first_batch:
-        await conn.fetchval(
-            f"DELETE FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2 RETURNING id",
-            document_id,
-            bank_id,
-        )
-
-    # Insert document (or update if exists from concurrent operations)
-    await _upsert_document_row(conn, bank_id, document_id, combined_content, content_hash, retain_params, document_tags)
-
-
-async def upsert_document_metadata(
-    conn,
-    bank_id: str,
-    document_id: str,
-    combined_content: str,
-    retain_params: dict | None = None,
-    document_tags: list[str] | None = None,
-) -> None:
-    """
-    Update document metadata without deleting existing facts/chunks.
-
-    Used by delta retain: the document row is upserted but chunks and
-    memory_units are managed separately at the chunk level.
-    """
-    import hashlib
-
-    combined_content = _sanitize_text(combined_content) or ""
-    content_hash = hashlib.sha256(combined_content.encode()).hexdigest()
-
-    await _upsert_document_row(conn, bank_id, document_id, combined_content, content_hash, retain_params, document_tags)
-
-
-async def _upsert_document_row(
-    conn,
-    bank_id: str,
-    document_id: str,
-    combined_content: str,
-    content_hash: str,
-    retain_params: dict | None = None,
-    document_tags: list[str] | None = None,
-) -> None:
-    """Insert or update a document row."""
-    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, retain_params, tags)
-        VALUES ($1, $2, $3, $4, $5, $6)
-        ON CONFLICT (id, bank_id) DO UPDATE
-        SET original_text = EXCLUDED.original_text,
-            content_hash = EXCLUDED.content_hash,
-            retain_params = EXCLUDED.retain_params,
-            tags = EXCLUDED.tags,
-            updated_at = NOW()
-        """,
-        document_id,
-        bank_id,
-        combined_content,
-        content_hash,
-        json.dumps(retain_params) if retain_params else None,
-        document_tags or [],
-    )
-
-
-async def update_memory_units_tags(
-    conn,
-    bank_id: str,
-    document_id: str,
-    tags: list[str],
-) -> int:
-    """
-    Update tags on all memory_units belonging to a document.
-
-    Used during delta retain to propagate tag changes to unchanged facts.
-
-    Returns:
-        Number of memory units updated.
-    """
-    result = await conn.execute(
-        f"""
-        UPDATE {fq_table("memory_units")}
-        SET tags = $3, updated_at = NOW()
-        WHERE bank_id = $1 AND document_id = $2
-        """,
-        bank_id,
-        document_id,
-        tags or [],
-    )
-    # result is a status string like "UPDATE 5"
-    try:
-        return int(result.split()[-1])
-    except (ValueError, IndexError):
-        return 0

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

@@ -1,883 +0,0 @@
-"""
-Main orchestrator for the retain pipeline.
-
-Coordinates all retain pipeline modules to store memories efficiently.
-"""
-
-import logging
-import time
-import uuid
-from collections import defaultdict
-from collections.abc import Awaitable, Callable
-from datetime import UTC, datetime
-from typing import Any
-
-from ..db_utils import acquire_with_retry, retry_with_backoff
-from . import bank_utils
-
-
-def utcnow():
-    """Get current UTC time."""
-    return datetime.now(UTC)
-
-
-def parse_datetime_flexible(value: Any) -> datetime:
-    """
-    Parse a datetime value that could be either a datetime object or an ISO string.
-
-    This handles datetime values from both direct Python calls and deserialized JSON
-    (where datetime objects are serialized as ISO strings).
-
-    Args:
-        value: Either a datetime object or an ISO format string
-
-    Returns:
-        datetime object (timezone-aware)
-
-    Raises:
-        TypeError: If value is neither datetime nor string
-        ValueError: If string is not a valid ISO datetime
-    """
-    if isinstance(value, datetime):
-        # Ensure timezone-aware
-        if value.tzinfo is None:
-            return value.replace(tzinfo=UTC)
-        return value
-    elif isinstance(value, str):
-        # Parse ISO format string (handles both 'Z' and '+00:00' timezone formats)
-        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
-        # Ensure timezone-aware
-        if dt.tzinfo is None:
-            return dt.replace(tzinfo=UTC)
-        return dt
-    else:
-        raise TypeError(f"Expected datetime or string, got {type(value).__name__}")
-
-
-import asyncpg
-
-from ..response_models import TokenUsage
-from . import (
-    chunk_storage,
-    embedding_processing,
-    entity_processing,
-    fact_extraction,
-    fact_storage,
-    link_creation,
-)
-from .types import ChunkMetadata, EntityLink, ExtractedFact, ProcessedFact, RetainContent, RetainContentDict
-
-logger = logging.getLogger(__name__)
-
-
-def _build_retain_params(contents_dicts, document_tags=None, doc_contents=None):
-    """Build retain_params and merged_tags from content dicts."""
-    if doc_contents is not None:
-        # Per-document mode: doc_contents is list of (idx, content_dict)
-        items = [item for _, item in doc_contents]
-    else:
-        items = contents_dicts
-
-    all_tags = set(document_tags or [])
-    for item in items:
-        item_tags = item.get("tags", []) or []
-        all_tags.update(item_tags)
-    merged_tags = list(all_tags)
-
-    retain_params = {}
-    if items:
-        first_item = items[0]
-        if first_item.get("context"):
-            retain_params["context"] = first_item["context"]
-        if first_item.get("event_date"):
-            retain_params["event_date"] = (
-                first_item["event_date"].isoformat()
-                if hasattr(first_item["event_date"], "isoformat")
-                else str(first_item["event_date"])
-            )
-        if first_item.get("metadata"):
-            retain_params["metadata"] = first_item["metadata"]
-
-    return retain_params, merged_tags
-
-
-async def _insert_facts_and_links(
-    conn,
-    entity_resolver,
-    bank_id: str,
-    contents: list[RetainContent],
-    extracted_facts: list,
-    processed_facts: list[ProcessedFact],
-    config,
-    log_buffer: list[str],
-    outbox_callback=None,
-) -> list[list[str]]:
-    """
-    Shared pipeline: insert facts, process entities, create all link types.
-
-    Used by both the full retain and delta retain paths.
-
-    Returns:
-        List of unit ID lists mapped back to original content items.
-    """
-    unit_ids = await fact_storage.insert_facts_batch(conn, bank_id, processed_facts)
-    step_start = time.time()
-    log_buffer.append(f"  Insert facts: {len(unit_ids)} units in {time.time() - step_start:.3f}s")
-
-    if unit_ids:
-        # Process entities
-        step_start = time.time()
-        user_entities_per_content = {idx: content.entities for idx, content in enumerate(contents) if content.entities}
-        entity_links = await entity_processing.process_entities_batch(
-            entity_resolver,
-            conn,
-            bank_id,
-            unit_ids,
-            processed_facts,
-            log_buffer,
-            user_entities_per_content=user_entities_per_content,
-            entity_labels=getattr(config, "entity_labels", None),
-        )
-        log_buffer.append(f"  Process entities: {len(entity_links)} links in {time.time() - step_start:.3f}s")
-
-        # Create temporal links
-        step_start = time.time()
-        temporal_link_count = await link_creation.create_temporal_links_batch(conn, bank_id, unit_ids)
-        log_buffer.append(f"  Temporal links: {temporal_link_count} links in {time.time() - step_start:.3f}s")
-
-        # Create semantic links
-        step_start = time.time()
-        embeddings_for_links = [fact.embedding for fact in processed_facts]
-        semantic_link_count = await link_creation.create_semantic_links_batch(
-            conn, bank_id, unit_ids, embeddings_for_links
-        )
-        log_buffer.append(f"  Semantic links: {semantic_link_count} links in {time.time() - step_start:.3f}s")
-
-        # Insert entity links
-        step_start = time.time()
-        if entity_links:
-            await entity_processing.insert_entity_links_batch(conn, entity_links, bank_id)
-        log_buffer.append(
-            f"  Entity links: {len(entity_links) if entity_links else 0} links in {time.time() - step_start:.3f}s"
-        )
-
-        # Create causal links
-        step_start = time.time()
-        causal_link_count = await link_creation.create_causal_links_batch(conn, bank_id, unit_ids, processed_facts)
-        log_buffer.append(f"  Causal links: {causal_link_count} links in {time.time() - step_start:.3f}s")
-
-    # Map results back to original content items
-    result_unit_ids = _map_results_to_contents(contents, extracted_facts, unit_ids if unit_ids else [])
-
-    if outbox_callback:
-        await outbox_callback(conn)
-
-    return result_unit_ids
-
-
-async def _extract_and_embed(
-    contents: list[RetainContent],
-    llm_config,
-    agent_name: str,
-    config,
-    embeddings_model,
-    format_date_fn,
-    fact_type_override: str | None,
-    log_buffer: list[str],
-    pool=None,
-    operation_id: str | None = None,
-    schema: str | None = None,
-) -> tuple[list, list[ProcessedFact], list[ChunkMetadata], TokenUsage]:
-    """
-    Shared pipeline: extract facts from contents and generate embeddings.
-
-    Returns:
-        Tuple of (extracted_facts, processed_facts, chunks_metadata, usage)
-    """
-    step_start = time.time()
-    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
-        contents, llm_config, agent_name, config, pool, operation_id, schema
-    )
-    log_buffer.append(
-        f"  Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks "
-        f"from {len(contents)} contents in {time.time() - step_start:.3f}s"
-    )
-
-    if not extracted_facts:
-        return extracted_facts, [], chunks, usage
-
-    if fact_type_override:
-        for fact in extracted_facts:
-            fact.fact_type = fact_type_override
-
-    step_start = time.time()
-    augmented_texts = embedding_processing.augment_texts_with_dates(extracted_facts, format_date_fn)
-    embeddings = await embedding_processing.generate_embeddings_batch(embeddings_model, augmented_texts)
-    log_buffer.append(f"  Generate embeddings: {len(embeddings)} embeddings in {time.time() - step_start:.3f}s")
-
-    processed_facts = [ProcessedFact.from_extracted_fact(ef, emb) for ef, emb in zip(extracted_facts, embeddings)]
-
-    return extracted_facts, processed_facts, chunks, usage
-
-
-async def retain_batch(
-    pool,
-    embeddings_model,
-    llm_config,
-    entity_resolver,
-    format_date_fn,
-    bank_id: str,
-    contents_dicts: list[RetainContentDict],
-    config,
-    document_id: str | None = None,
-    is_first_batch: bool = True,
-    fact_type_override: str | None = None,
-    confidence_score: float | None = None,
-    document_tags: list[str] | None = None,
-    operation_id: str | None = None,
-    schema: str | None = None,
-    outbox_callback: Callable[["asyncpg.Connection"], Awaitable[None]] | None = None,
-) -> tuple[list[list[str]], TokenUsage]:
-    """
-    Process a batch of content through the retain pipeline.
-
-    Supports delta retain: when upserting a document that already has chunks,
-    only re-processes chunks whose content has changed. Unchanged chunks keep
-    their existing facts, entities, and links.
-    """
-    start_time = time.time()
-    total_chars = sum(len(item.get("content", "")) for item in contents_dicts)
-
-    log_buffer = []
-    log_buffer.append(f"{'=' * 60}")
-    log_buffer.append(f"RETAIN_BATCH START: {bank_id}")
-    log_buffer.append(f"Batch size: {len(contents_dicts)} content items, {total_chars:,} chars")
-    log_buffer.append(f"{'=' * 60}")
-
-    # Get bank profile
-    profile = await bank_utils.get_bank_profile(pool, bank_id)
-    agent_name = profile["name"]
-
-    # Convert dicts to RetainContent objects
-    contents = _build_contents(contents_dicts, document_tags)
-
-    # --- Delta retain: check if we can skip unchanged chunks ---
-    if is_first_batch:
-        delta_result = await _try_delta_retain(
-            pool,
-            embeddings_model,
-            llm_config,
-            entity_resolver,
-            format_date_fn,
-            bank_id,
-            contents_dicts,
-            contents,
-            config,
-            document_id,
-            fact_type_override,
-            document_tags,
-            agent_name,
-            log_buffer,
-            start_time,
-            operation_id,
-            schema,
-            outbox_callback,
-        )
-        if delta_result is not None:
-            return delta_result
-
-    # --- Full retain path ---
-    extracted_facts, processed_facts, chunks, usage = await _extract_and_embed(
-        contents,
-        llm_config,
-        agent_name,
-        config,
-        embeddings_model,
-        format_date_fn,
-        fact_type_override,
-        log_buffer,
-        pool,
-        operation_id,
-        schema,
-    )
-
-    if not extracted_facts:
-        await _handle_zero_facts_documents(
-            pool,
-            bank_id,
-            contents_dicts,
-            contents,
-            config,
-            document_id,
-            is_first_batch,
-            document_tags,
-            chunks,
-            log_buffer,
-            start_time,
-        )
-        return [[] for _ in contents], usage
-
-    # Group contents by document_id
-    contents_by_doc = defaultdict(list)
-    for idx, content_dict in enumerate(contents_dicts):
-        doc_id = content_dict.get("document_id")
-        contents_by_doc[doc_id].append((idx, content_dict))
-
-    # Database transaction (retried on deadlock)
-    result_unit_ids: list[list[str]] = []
-    log_buffer_pre_db = len(log_buffer)
-
-    async def _run_db_work() -> None:
-        nonlocal result_unit_ids
-        del log_buffer[log_buffer_pre_db:]
-        document_ids_added: list[str] = []
-        for pf in processed_facts:
-            pf.document_id = None
-            pf.chunk_id = None
-        entity_resolver.discard_pending_stats()
-
-        async with acquire_with_retry(pool) as conn:
-            async with conn.transaction():
-                # Handle document tracking
-                step_start = time.time()
-                doc_id_mapping = {}
-
-                if document_id:
-                    combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                    retain_params, merged_tags = _build_retain_params(contents_dicts, document_tags)
-                    await fact_storage.handle_document_tracking(
-                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
-                    )
-                    document_ids_added.append(document_id)
-                    doc_id_mapping[None] = document_id
-                else:
-                    has_any_doc_ids = any(item.get("document_id") for item in contents_dicts)
-                    if has_any_doc_ids or chunks:
-                        for original_doc_id, doc_contents in contents_by_doc.items():
-                            actual_doc_id = original_doc_id
-                            should_create_doc = (original_doc_id is not None) or chunks
-                            if should_create_doc:
-                                if actual_doc_id is None:
-                                    actual_doc_id = str(uuid.uuid4())
-                                doc_id_mapping[original_doc_id] = actual_doc_id
-                                combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-                                retain_params, merged_tags = _build_retain_params(
-                                    contents_dicts, document_tags, doc_contents=doc_contents
-                                )
-                                await fact_storage.handle_document_tracking(
-                                    conn,
-                                    bank_id,
-                                    actual_doc_id,
-                                    combined_content,
-                                    is_first_batch,
-                                    retain_params,
-                                    merged_tags,
-                                )
-                                document_ids_added.append(actual_doc_id)
-
-                if document_ids_added:
-                    log_buffer.append(
-                        f"  Document tracking: {len(document_ids_added)} documents in {time.time() - step_start:.3f}s"
-                    )
-
-                # Store chunks and map to facts
-                step_start = time.time()
-                chunk_id_map_by_doc = {}
-                if chunks:
-                    chunks_by_doc = defaultdict(list)
-                    for chunk in chunks:
-                        original_doc_id = contents_dicts[chunk.content_index].get("document_id")
-                        actual_doc_id = doc_id_mapping.get(original_doc_id, original_doc_id)
-                        if actual_doc_id is None and document_id:
-                            actual_doc_id = document_id
-                        chunks_by_doc[actual_doc_id].append(chunk)
-
-                    for doc_id, doc_chunks in chunks_by_doc.items():
-                        chunk_id_map = await chunk_storage.store_chunks_batch(conn, bank_id, doc_id, doc_chunks)
-                        for chunk_idx, chunk_id in chunk_id_map.items():
-                            chunk_id_map_by_doc[(doc_id, chunk_idx)] = chunk_id
-
-                    log_buffer.append(
-                        f"  Store chunks: {len(chunks)} chunks for {len(chunks_by_doc)} documents "
-                        f"in {time.time() - step_start:.3f}s"
-                    )
-
-                # Map chunk_ids and document_ids to facts
-                for fact, processed_fact in zip(extracted_facts, processed_facts):
-                    original_doc_id = contents_dicts[fact.content_index].get("document_id")
-                    actual_doc_id = doc_id_mapping.get(original_doc_id, original_doc_id)
-                    if actual_doc_id is None and document_id:
-                        actual_doc_id = document_id
-                    processed_fact.document_id = actual_doc_id
-                    if chunks and fact.chunk_index is not None:
-                        chunk_id = chunk_id_map_by_doc.get((actual_doc_id, fact.chunk_index))
-                        if chunk_id:
-                            processed_fact.chunk_id = chunk_id
-
-                # Insert facts and create all links (shared pipeline)
-                result_unit_ids = await _insert_facts_and_links(
-                    conn,
-                    entity_resolver,
-                    bank_id,
-                    contents,
-                    extracted_facts,
-                    processed_facts,
-                    config,
-                    log_buffer,
-                    outbox_callback,
-                )
-
-            await entity_resolver.flush_pending_stats()
-
-            total_time = time.time() - start_time
-            log_buffer.append(f"{'=' * 60}")
-            log_buffer.append(f"RETAIN_BATCH COMPLETE: {len(processed_facts)} units in {total_time:.3f}s")
-            if document_ids_added:
-                log_buffer.append(f"Documents: {', '.join(document_ids_added)}")
-            log_buffer.append(f"{'=' * 60}")
-            logger.info("\n" + "\n".join(log_buffer) + "\n")
-
-    await retry_with_backoff(_run_db_work)
-    return result_unit_ids, usage
-
-
-# ---------------------------------------------------------------------------
-# Delta retain
-# ---------------------------------------------------------------------------
-
-
-async def _try_delta_retain(
-    pool,
-    embeddings_model,
-    llm_config,
-    entity_resolver,
-    format_date_fn,
-    bank_id,
-    contents_dicts,
-    contents,
-    config,
-    document_id,
-    fact_type_override,
-    document_tags,
-    agent_name,
-    log_buffer,
-    start_time,
-    operation_id,
-    schema,
-    outbox_callback,
-):
-    """
-    Attempt delta retain for a document upsert. Returns result tuple if delta
-    was performed, or None to fall back to full retain.
-    """
-    # Need a single document_id
-    effective_doc_id = document_id
-    if not effective_doc_id:
-        doc_ids = {item.get("document_id") for item in contents_dicts if item.get("document_id")}
-        if len(doc_ids) != 1:
-            return None
-        effective_doc_id = doc_ids.pop()
-
-    # Load existing chunks
-    async with acquire_with_retry(pool) as conn:
-        existing_chunks = await chunk_storage.load_existing_chunks(conn, bank_id, effective_doc_id)
-
-    if not existing_chunks:
-        return None
-
-    if any(c.content_hash is None for c in existing_chunks):
-        logger.info(f"Delta retain skipped for {effective_doc_id}: existing chunks lack content_hash (pre-migration)")
-        return None
-
-    # Chunk new content and classify changes
-    step_start = time.time()
-    new_chunks_with_contents = _chunk_contents_for_delta(contents, config)
-    log_buffer.append(
-        f"[delta] Chunked new content: {len(new_chunks_with_contents)} chunks in {time.time() - step_start:.3f}s"
-    )
-
-    existing_by_index = {c.chunk_index: c for c in existing_chunks}
-    new_hashes = {idx: chunk_storage.compute_chunk_hash(text) for idx, text in new_chunks_with_contents.items()}
-
-    unchanged_indices, changed_indices, new_indices, removed_indices = [], [], [], []
-    for idx, new_hash in new_hashes.items():
-        existing = existing_by_index.get(idx)
-        if existing and existing.content_hash == new_hash:
-            unchanged_indices.append(idx)
-        elif existing:
-            changed_indices.append(idx)
-        else:
-            new_indices.append(idx)
-    for idx in existing_by_index:
-        if idx not in new_hashes:
-            removed_indices.append(idx)
-
-    log_buffer.append(
-        f"[delta] Chunk diff: {len(unchanged_indices)} unchanged, "
-        f"{len(changed_indices)} changed, {len(new_indices)} new, "
-        f"{len(removed_indices)} removed"
-    )
-
-    if not unchanged_indices:
-        logger.info(f"Delta retain: no unchanged chunks for {effective_doc_id}, falling back to full retain")
-        return None
-
-    chunks_to_process = changed_indices + new_indices
-
-    if not chunks_to_process and not removed_indices:
-        # Nothing changed — just update document metadata/tags
-        log_buffer.append("[delta] No chunk changes detected — updating document metadata only")
-        return await _delta_metadata_only(
-            pool,
-            bank_id,
-            contents_dicts,
-            contents,
-            effective_doc_id,
-            document_tags,
-            log_buffer,
-            start_time,
-            outbox_callback,
-        )
-
-    # Build content items for only the changed/new chunks
-    delta_contents, delta_chunk_map = _build_delta_contents(contents, new_chunks_with_contents, chunks_to_process)
-
-    if not delta_contents:
-        return await _delta_metadata_only(
-            pool,
-            bank_id,
-            contents_dicts,
-            contents,
-            effective_doc_id,
-            document_tags,
-            log_buffer,
-            start_time,
-            outbox_callback,
-        )
-
-    # Extract facts and generate embeddings (shared pipeline)
-    extracted_facts, processed_facts, new_chunk_metadata, usage = await _extract_and_embed(
-        delta_contents,
-        llm_config,
-        agent_name,
-        config,
-        embeddings_model,
-        format_date_fn,
-        fact_type_override,
-        log_buffer,
-        pool,
-        operation_id,
-        schema,
-    )
-
-    # Database transaction
-    result_unit_ids: list[list[str]] = []
-    log_buffer_pre_db = len(log_buffer)
-
-    async def _run_delta_db_work() -> None:
-        nonlocal result_unit_ids
-        del log_buffer[log_buffer_pre_db:]
-        for pf in processed_facts:
-            pf.document_id = None
-            pf.chunk_id = None
-        entity_resolver.discard_pending_stats()
-
-        async with acquire_with_retry(pool) as conn:
-            async with conn.transaction():
-                # Update document metadata (no delete)
-                step_start = time.time()
-                combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                retain_params, merged_tags = _build_retain_params(contents_dicts, document_tags)
-                await fact_storage.upsert_document_metadata(
-                    conn,
-                    bank_id,
-                    effective_doc_id,
-                    combined_content,
-                    retain_params,
-                    merged_tags,
-                )
-                log_buffer.append(f"  Document metadata update in {time.time() - step_start:.3f}s")
-
-                # Delete changed and removed chunks (cascades to memory_units and links)
-                step_start = time.time()
-                chunks_to_delete = [
-                    existing_by_index[idx].chunk_id
-                    for idx in changed_indices + removed_indices
-                    if idx in existing_by_index
-                ]
-                await chunk_storage.delete_chunks_by_ids(conn, chunks_to_delete)
-                log_buffer.append(
-                    f"  Deleted {len(chunks_to_delete)} chunks "
-                    f"({len(changed_indices)} changed + {len(removed_indices)} removed) "
-                    f"in {time.time() - step_start:.3f}s"
-                )
-
-                # Update tags on unchanged chunks' memory units
-                step_start = time.time()
-                updated_count = await fact_storage.update_memory_units_tags(
-                    conn, bank_id, effective_doc_id, merged_tags
-                )
-                log_buffer.append(
-                    f"  Updated tags on {updated_count} existing memory units in {time.time() - step_start:.3f}s"
-                )
-
-                # Store new/changed chunks
-                step_start = time.time()
-                chunk_id_map_by_doc = {}
-                if new_chunk_metadata:
-                    remapped_chunks = [
-                        ChunkMetadata(
-                            chunk_text=cm.chunk_text,
-                            fact_count=cm.fact_count,
-                            content_index=cm.content_index,
-                            chunk_index=delta_chunk_map.get(cm.chunk_index, cm.chunk_index),
-                        )
-                        for cm in new_chunk_metadata
-                    ]
-                    chunk_id_map = await chunk_storage.store_chunks_batch(
-                        conn, bank_id, effective_doc_id, remapped_chunks
-                    )
-                    for chunk_idx, chunk_id in chunk_id_map.items():
-                        chunk_id_map_by_doc[(effective_doc_id, chunk_idx)] = chunk_id
-                    log_buffer.append(
-                        f"  Stored {len(remapped_chunks)} new/changed chunks in {time.time() - step_start:.3f}s"
-                    )
-
-                # Map chunk_ids and document_ids to processed facts
-                for ef, pf in zip(extracted_facts, processed_facts):
-                    pf.document_id = effective_doc_id
-                    if ef.chunk_index is not None:
-                        original_idx = delta_chunk_map.get(ef.chunk_index, ef.chunk_index)
-                        chunk_id = chunk_id_map_by_doc.get((effective_doc_id, original_idx))
-                        if chunk_id:
-                            pf.chunk_id = chunk_id
-
-                # Insert facts and create all links (shared pipeline)
-                result_unit_ids = await _insert_facts_and_links(
-                    conn,
-                    entity_resolver,
-                    bank_id,
-                    contents,
-                    extracted_facts,
-                    processed_facts,
-                    config,
-                    log_buffer,
-                    outbox_callback,
-                )
-
-            await entity_resolver.flush_pending_stats()
-
-            total_time = time.time() - start_time
-            log_buffer.append(f"{'=' * 60}")
-            log_buffer.append(
-                f"DELTA RETAIN COMPLETE: {len(processed_facts)} new units, "
-                f"{len(unchanged_indices)} chunks unchanged in {total_time:.3f}s"
-            )
-            log_buffer.append(f"Document: {effective_doc_id}")
-            log_buffer.append(f"{'=' * 60}")
-            logger.info("\n" + "\n".join(log_buffer) + "\n")
-
-    await retry_with_backoff(_run_delta_db_work)
-    return result_unit_ids, usage
-
-
-async def _delta_metadata_only(
-    pool,
-    bank_id,
-    contents_dicts,
-    contents,
-    document_id,
-    document_tags,
-    log_buffer,
-    start_time,
-    outbox_callback,
-):
-    """Handle the case where no chunks changed — just update document metadata and tags."""
-    async with acquire_with_retry(pool) as conn:
-        async with conn.transaction():
-            combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-            retain_params, merged_tags = _build_retain_params(contents_dicts, document_tags)
-            await fact_storage.upsert_document_metadata(
-                conn,
-                bank_id,
-                document_id,
-                combined_content,
-                retain_params,
-                merged_tags,
-            )
-            await fact_storage.update_memory_units_tags(conn, bank_id, document_id, merged_tags)
-            if outbox_callback:
-                await outbox_callback(conn)
-
-    total_time = time.time() - start_time
-    log_buffer.append(f"DELTA RETAIN (no changes): metadata updated in {total_time:.3f}s")
-    logger.info("\n" + "\n".join(log_buffer) + "\n")
-    return [[] for _ in contents], TokenUsage()
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-
-def _build_contents(contents_dicts: list[RetainContentDict], document_tags: list[str] | None) -> list[RetainContent]:
-    """Convert content dicts to RetainContent objects."""
-    contents = []
-    for item in contents_dicts:
-        item_tags = item.get("tags", []) or []
-        merged_tags = list(set(item_tags + (document_tags or [])))
-
-        if "event_date" in item and item["event_date"] is None:
-            event_date_value = None
-        elif item.get("event_date"):
-            event_date_value = parse_datetime_flexible(item["event_date"])
-        else:
-            event_date_value = utcnow()
-
-        content = RetainContent(
-            content=item["content"],
-            context=item.get("context", ""),
-            event_date=event_date_value,
-            metadata=item.get("metadata", {}),
-            entities=item.get("entities", []),
-            tags=merged_tags,
-            observation_scopes=item.get("observation_scopes"),
-        )
-        contents.append(content)
-    return contents
-
-
-async def _handle_zero_facts_documents(
-    pool,
-    bank_id,
-    contents_dicts,
-    contents,
-    config,
-    document_id,
-    is_first_batch,
-    document_tags,
-    chunks,
-    log_buffer,
-    start_time,
-):
-    """Handle document tracking when zero facts were extracted."""
-    docs_tracked = 0
-    async with acquire_with_retry(pool) as conn:
-        async with conn.transaction():
-            contents_by_doc = defaultdict(list)
-            for idx, content_dict in enumerate(contents_dicts):
-                doc_id = content_dict.get("document_id")
-                contents_by_doc[doc_id].append((idx, content_dict))
-
-            if document_id:
-                combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                retain_params, merged_tags = _build_retain_params(contents_dicts, document_tags)
-                await fact_storage.handle_document_tracking(
-                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
-                )
-                docs_tracked += 1
-            else:
-                has_any_doc_ids = any(item.get("document_id") for item in contents_dicts)
-                if has_any_doc_ids or chunks:
-                    for original_doc_id, doc_contents in contents_by_doc.items():
-                        should_create_doc = (original_doc_id is not None) or chunks
-                        if not should_create_doc:
-                            continue
-                        actual_doc_id = original_doc_id or str(uuid.uuid4())
-                        combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-                        retain_params, merged_tags = _build_retain_params(
-                            contents_dicts, document_tags, doc_contents=doc_contents
-                        )
-                        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 "
-        f"in {total_time:.3f}s ({doc_status}, no facts)"
-    )
-
-
-def _chunk_contents_for_delta(contents: list[RetainContent], config) -> dict[int, str]:
-    """
-    Chunk contents the same way fact_extraction does, returning a map of
-    global_chunk_index -> chunk_text.
-    """
-    result = {}
-    global_chunk_idx = 0
-    for content in contents:
-        chunk_size = getattr(config, "retain_chunk_size", 120000)
-        chunks = fact_extraction.chunk_text(content.content, chunk_size)
-        for chunk_text in chunks:
-            result[global_chunk_idx] = chunk_text
-            global_chunk_idx += 1
-    return result
-
-
-def _build_delta_contents(
-    original_contents: list[RetainContent],
-    new_chunks_with_contents: dict[int, str],
-    chunks_to_process: list[int],
-) -> tuple[list[RetainContent], dict[int, int]]:
-    """
-    Build RetainContent items containing only the chunks that need processing.
-
-    Returns:
-        - List of RetainContent items (one per chunk to process)
-        - Map of delta_chunk_index -> original_chunk_index
-    """
-    if not chunks_to_process or not original_contents:
-        return [], {}
-
-    template_content = original_contents[0]
-    delta_contents = []
-    delta_chunk_map = {}
-
-    for original_chunk_idx in sorted(chunks_to_process):
-        chunk_text = new_chunks_with_contents.get(original_chunk_idx)
-        if not chunk_text:
-            continue
-        delta_content = RetainContent(
-            content=chunk_text,
-            context=template_content.context,
-            event_date=template_content.event_date,
-            metadata=template_content.metadata,
-            entities=template_content.entities,
-            tags=template_content.tags,
-            observation_scopes=template_content.observation_scopes,
-        )
-        delta_contents.append(delta_content)
-        delta_chunk_map[len(delta_contents) - 1] = original_chunk_idx
-
-    return delta_contents, delta_chunk_map
-
-
-def _map_results_to_contents(
-    contents: list[RetainContent],
-    extracted_facts: list[ExtractedFact],
-    unit_ids: list[str],
-) -> list[list[str]]:
-    """Map created unit IDs back to original content items."""
-    facts_by_content: dict[int, list[int]] = {i: [] for i in range(len(contents))}
-    for i, fact in enumerate(extracted_facts):
-        facts_by_content[fact.content_index].append(i)
-
-    result_unit_ids = []
-    unit_idx = 0
-    for content_index in range(len(contents)):
-        content_unit_ids = []
-        for _ in facts_by_content[content_index]:
-            content_unit_ids.append(unit_ids[unit_idx])
-            unit_idx += 1
-        result_unit_ids.append(content_unit_ids)
-
-    return result_unit_ids

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

@@ -1,493 +0,0 @@
-"""
-Link Expansion graph retrieval.
-
-Expands from semantic/temporal seeds through three parallel, first-class signals
-stored in memory_links:
-
-1. Entity links  — precomputed co-occurrence graph (created at retain time, bounded to
-                   MAX_LINKS_PER_ENTITY per entity). Score = number of distinct shared
-                   entities between the seed set and each candidate.
-2. Semantic links — precomputed kNN graph (each new fact linked to its top-5 most
-                    similar existing facts at insert time, similarity >= 0.7). Checked
-                    in both directions since the graph is not symmetric. Score = weight.
-3. Causal links  — explicit causal chains (causes/caused_by/enables/prevents).
-                   Score = weight + 1.0 (boosted as highest-quality signal).
-
-All three signals are bounded at retain time, so no LATERAL fan-out caps are needed
-at query time. Each expansion is a simple aggregation over a small result set.
-
-For non-observation fact types the three expansions are issued as a single CTE query
-(one roundtrip, one connection) with a `source` discriminator column so the Python
-merge step can apply per-signal score transformations.
-"""
-
-import logging
-import math
-import time
-
-from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
-from .graph_retrieval import GraphRetriever
-from .tags import TagGroup, TagsMatch, filter_results_by_tag_groups, filter_results_by_tags
-from .types import MPFPTimings, RetrievalResult
-
-logger = logging.getLogger(__name__)
-
-
-async def _find_semantic_seeds(
-    conn,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    limit: int = 20,
-    threshold: float = 0.3,
-    tags: list[str] | None = None,
-    tags_match: TagsMatch = "any",
-    tag_groups: list[TagGroup] | None = None,
-) -> list[RetrievalResult]:
-    """Find semantic seeds via embedding search."""
-    from .tags import build_tag_groups_where_clause, build_tags_where_clause_simple
-
-    tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
-    tag_groups_param_start = 6 + (1 if tags else 0)
-    groups_clause, groups_params, _ = build_tag_groups_where_clause(tag_groups, tag_groups_param_start)
-    params = [query_embedding_str, bank_id, fact_type, threshold, limit]
-    if tags:
-        params.append(tags)
-    params.extend(groups_params)
-
-    rows = await conn.fetch(
-        f"""
-        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
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND embedding IS NOT NULL
-          AND fact_type = $3
-          AND (1 - (embedding <=> $1::vector)) >= $4
-          {tags_clause}
-          {groups_clause}
-        ORDER BY embedding <=> $1::vector
-        LIMIT $5
-        """,
-        *params,
-    )
-    return [RetrievalResult.from_db_row(dict(r)) for r in rows]
-
-
-class LinkExpansionRetriever(GraphRetriever):
-    """
-    Graph retrieval via direct link expansion from seeds.
-
-    Runs three expansions through precomputed memory_links: entity co-occurrence,
-    semantic kNN, and causal chains, all bounded at retain time.
-
-    For non-observation fact types the three expansions are issued as a single CTE
-    query (one roundtrip, one connection slot) with a `source` discriminator column.
-    The Python merge step applies per-signal score transformations.
-    """
-
-    def __init__(
-        self,
-        causal_weight_threshold: float = 0.3,
-    ):
-        """
-        Args:
-            causal_weight_threshold: Minimum weight for causal links to follow.
-        """
-        self.causal_weight_threshold = causal_weight_threshold
-
-    @property
-    def name(self) -> str:
-        return "link_expansion"
-
-    async def retrieve(
-        self,
-        pool,
-        query_embedding_str: str,
-        bank_id: str,
-        fact_type: str,
-        budget: int,
-        query_text: str | None = None,
-        semantic_seeds: list[RetrievalResult] | None = None,
-        temporal_seeds: list[RetrievalResult] | None = None,
-        adjacency=None,
-        tags: list[str] | None = None,
-        tags_match: TagsMatch = "any",
-        tag_groups: list[TagGroup] | None = None,
-    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
-        """
-        Retrieve facts by expanding links from seeds.
-
-        Args:
-            pool: Database connection pool
-            query_embedding_str: Query embedding as string
-            bank_id: Memory bank ID
-            fact_type: Fact type to filter
-            budget: Maximum results to return
-            query_text: Original query text (unused)
-            semantic_seeds: Pre-computed semantic entry points
-            temporal_seeds: Pre-computed temporal entry points
-            adjacency: Unused, kept for interface compatibility
-            tags: Optional list of tags for visibility filtering
-
-        Returns:
-            Tuple of (results, timings)
-        """
-        start_time = time.time()
-        timings = MPFPTimings(fact_type=fact_type)
-
-        async with acquire_with_retry(pool) as conn:
-            # Find seeds if not provided
-            if semantic_seeds:
-                all_seeds = list(semantic_seeds)
-            else:
-                seeds_start = time.time()
-                all_seeds = await _find_semantic_seeds(
-                    conn,
-                    query_embedding_str,
-                    bank_id,
-                    fact_type,
-                    limit=20,
-                    threshold=0.3,
-                    tags=tags,
-                    tags_match=tags_match,
-                    tag_groups=tag_groups,
-                )
-                timings.seeds_time = time.time() - seeds_start
-                logger.debug(
-                    f"[LinkExpansion] Found {len(all_seeds)} semantic seeds for fact_type={fact_type} "
-                    f"(tags={tags}, tags_match={tags_match})"
-                )
-
-            if temporal_seeds:
-                all_seeds.extend(temporal_seeds)
-
-            if not all_seeds:
-                return [], timings
-
-            seed_ids = list({s.id for s in all_seeds})
-            timings.pattern_count = len(seed_ids)
-
-            query_start = time.time()
-
-            if fact_type == "observation":
-                entity_rows, semantic_rows, causal_rows = await self._expand_observations(conn, seed_ids, budget)
-            else:
-                entity_rows, semantic_rows, causal_rows = await self._expand_combined(conn, seed_ids, fact_type, budget)
-
-            timings.edge_load_time = time.time() - query_start
-            timings.db_queries = 1
-            timings.edge_count = len(entity_rows) + len(semantic_rows) + len(causal_rows)
-
-        # Merge results with additive intra-score: entity + semantic + causal ∈ [0, 3].
-        #
-        # Entity score: tanh(count × 0.5) maps shared-entity count to [0, 1]:
-        #   1 entity → 0.46,  2 → 0.76,  3 → 0.91,  4 → 0.96  (saturates naturally)
-        # Semantic score: similarity weight, already ∈ [0.7, 1.0].
-        # Causal score:   link weight, already ∈ [0, 1].
-        #
-        # Facts appearing in multiple signals accumulate higher scores, rewarding
-        # convergent evidence. The outer RRF uses rank position from this sorted list.
-        entity_scores: dict[str, float] = {}
-        semantic_scores: dict[str, float] = {}
-        causal_scores: dict[str, float] = {}
-        row_map: dict[str, dict] = {}
-
-        for row in entity_rows:
-            fact_id = str(row["id"])
-            entity_scores[fact_id] = math.tanh(row["score"] * 0.5)
-            row_map[fact_id] = dict(row)
-
-        for row in semantic_rows:
-            fact_id = str(row["id"])
-            semantic_scores[fact_id] = max(semantic_scores.get(fact_id, 0.0), row["score"])
-            row_map.setdefault(fact_id, dict(row))
-
-        for row in causal_rows:
-            fact_id = str(row["id"])
-            causal_scores[fact_id] = max(causal_scores.get(fact_id, 0.0), row["score"])
-            row_map.setdefault(fact_id, dict(row))
-
-        all_ids = set(entity_scores) | set(semantic_scores) | set(causal_scores)
-        score_map = {
-            fid: entity_scores.get(fid, 0.0) + semantic_scores.get(fid, 0.0) + causal_scores.get(fid, 0.0)
-            for fid in all_ids
-        }
-
-        sorted_ids = sorted(score_map.keys(), key=lambda x: score_map[x], reverse=True)[:budget]
-        rows = [row_map[fact_id] for fact_id in sorted_ids]
-
-        results = []
-        for row in rows:
-            result = RetrievalResult.from_db_row(dict(row))
-            result.activation = row["score"]
-            results.append(result)
-
-        if tags:
-            results = filter_results_by_tags(results, tags, match=tags_match)
-
-        if tag_groups:
-            results = filter_results_by_tag_groups(results, tag_groups)
-
-        timings.result_count = len(results)
-        timings.traverse = time.time() - start_time
-
-        logger.debug(
-            f"LinkExpansion: {len(results)} results from {len(seed_ids)} seeds "
-            f"in {timings.traverse * 1000:.1f}ms (query: {timings.edge_load_time * 1000:.1f}ms)"
-        )
-
-        return results, timings
-
-    async def _expand_combined(
-        self,
-        conn,
-        seed_ids: list,
-        fact_type: str,
-        budget: int,
-    ) -> tuple[list, list, list]:
-        """
-        Single-roundtrip CTE query combining entity, semantic, and causal expansions.
-
-        Uses a `source` discriminator column so the caller can apply per-signal
-        score transformations.  The three CTEs share one connection slot — important
-        for asyncpg which does not allow concurrent queries on the same connection.
-
-        Index coverage (requires migration d2e3f4a5b6c7):
-          entity:   idx_memory_links_entity_covering (from_unit_id) INCLUDE (to_unit_id, entity_id)
-                    WHERE link_type = 'entity'  → index-only scan, no heap reads
-          semantic incoming:
-                    idx_memory_links_to_type_weight (to_unit_id, link_type, weight DESC)
-                    → replaces costly BitmapAnd of two separate scans
-        """
-        ml = fq_table("memory_links")
-        mu = fq_table("memory_units")
-        all_rows = await conn.fetch(
-            f"""
-            WITH entity_expanded AS (
-                -- Entity co-occurrence: seeds → their precomputed entity-link neighbors.
-                -- Score = distinct shared entities (bounded at retain time to
-                -- MAX_LINKS_PER_ENTITY=50).  GROUP BY mu.id is sufficient because mu.id
-                -- is the primary key and functionally determines all other mu columns.
-                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,
-                    COUNT(DISTINCT ml.entity_id)::float AS score,
-                    'entity'::text AS source
-                FROM {ml} ml
-                JOIN {mu} mu ON mu.id = ml.to_unit_id
-                WHERE ml.from_unit_id = ANY($1::uuid[])
-                  AND ml.link_type = 'entity'
-                  AND mu.fact_type = $2
-                  AND mu.id != ALL($1::uuid[])
-                GROUP BY mu.id
-                ORDER BY score DESC
-                LIMIT $3
-            ),
-            semantic_expanded AS (
-                -- Semantic kNN: both outgoing (seeds → their kNN at insert time) and
-                -- incoming (facts inserted after seeds that found seeds as kNN).
-                -- Score = max similarity weight across both directions.
-                SELECT
-                    id, text, context, event_date, occurred_start,
-                    occurred_end, mentioned_at,
-                    fact_type, document_id, chunk_id, tags,
-                    MAX(weight) AS score,
-                    'semantic'::text AS source
-                FROM (
-                    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
-                    FROM {ml} ml
-                    JOIN {mu} mu ON mu.id = ml.to_unit_id
-                    WHERE ml.from_unit_id = ANY($1::uuid[])
-                      AND ml.link_type = 'semantic'
-                      AND mu.fact_type = $2
-                      AND mu.id != ALL($1::uuid[])
-                    UNION ALL
-                    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
-                    FROM {ml} ml
-                    JOIN {mu} mu ON mu.id = ml.from_unit_id
-                    WHERE ml.to_unit_id = ANY($1::uuid[])
-                      AND ml.link_type = 'semantic'
-                      AND mu.fact_type = $2
-                      AND mu.id != ALL($1::uuid[])
-                ) sem_raw
-                GROUP BY id, text, context, event_date, occurred_start,
-                         occurred_end, mentioned_at,
-                         fact_type, document_id, chunk_id, tags
-                ORDER BY score DESC
-                LIMIT $3
-            ),
-            causal_expanded AS (
-                -- Causal chains: explicit causes/enables/prevents links from seeds.
-                -- DISTINCT ON handles the case where a seed has multiple causal links
-                -- to the same target; best weight wins.
-                SELECT DISTINCT ON (mu.id)
-                    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 AS score,
-                    'causal'::text AS source
-                FROM {ml} ml
-                JOIN {mu} mu ON ml.to_unit_id = mu.id
-                WHERE ml.from_unit_id = ANY($1::uuid[])
-                  AND ml.link_type IN ('causes', 'caused_by', 'enables', 'prevents')
-                  AND ml.weight >= $4
-                  AND mu.fact_type = $2
-                ORDER BY mu.id, ml.weight DESC
-                LIMIT $3
-            )
-            SELECT * FROM entity_expanded
-            UNION ALL
-            SELECT * FROM semantic_expanded
-            UNION ALL
-            SELECT * FROM causal_expanded
-            """,
-            seed_ids,
-            fact_type,
-            budget,
-            self.causal_weight_threshold,
-        )
-
-        entity_rows = [r for r in all_rows if r["source"] == "entity"]
-        semantic_rows = [r for r in all_rows if r["source"] == "semantic"]
-        causal_rows = [r for r in all_rows if r["source"] == "causal"]
-        return entity_rows, semantic_rows, causal_rows
-
-    async def _expand_observations(
-        self,
-        conn,
-        seed_ids: list,
-        budget: int,
-    ) -> tuple[list, list, list]:
-        """
-        Observation-specific expansion.
-
-        Observations don't have direct entity links in memory_links (they're created
-        by consolidation, not retain).  Instead, traverse source_memory_ids → world
-        facts → entities → other world facts → their observations.
-
-        Semantic and causal expansions run as a second combined CTE query.
-        """
-        source_ids_found: list = []
-        if logger.isEnabledFor(logging.DEBUG):
-            debug_rows = await conn.fetch(
-                f"""
-                SELECT id, source_memory_ids
-                FROM {fq_table("memory_units")}
-                WHERE id = ANY($1::uuid[])
-                """,
-                seed_ids,
-            )
-            for row in debug_rows:
-                if row["source_memory_ids"]:
-                    source_ids_found.extend(row["source_memory_ids"])
-            logger.debug(
-                f"[LinkExpansion] observation graph: {len(seed_ids)} seeds, "
-                f"{len(source_ids_found)} source_memory_ids found"
-            )
-
-        entity_rows = await conn.fetch(
-            f"""
-            WITH seed_sources AS (
-                SELECT DISTINCT unnest(source_memory_ids) AS source_id
-                FROM {fq_table("memory_units")}
-                WHERE id = ANY($1::uuid[])
-                  AND source_memory_ids IS NOT NULL
-            ),
-            connected_sources AS (
-                -- Mirror the non-observation entity expansion: follow pre-bounded entity
-                -- links in memory_links (capped to MAX_LINKS_PER_ENTITY=50 at retain time).
-                -- Score = number of distinct shared entities, same as the non-obs path.
-                SELECT DISTINCT ml.to_unit_id AS source_id
-                FROM seed_sources ss
-                JOIN {fq_table("memory_links")} ml ON ml.from_unit_id = ss.source_id
-                WHERE ml.link_type = 'entity'
-            ),
-            connected_array AS (
-                SELECT array_agg(source_id) AS source_ids FROM connected_sources
-            )
-            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,
-                (SELECT COUNT(DISTINCT s) FROM unnest(mu.source_memory_ids) s WHERE s = ANY(ca.source_ids))::float AS score
-            FROM {fq_table("memory_units")} mu, connected_array ca
-            WHERE mu.fact_type = 'observation'
-              AND mu.id != ALL($1::uuid[])
-              AND ca.source_ids IS NOT NULL
-              AND mu.source_memory_ids && ca.source_ids
-            ORDER BY score DESC
-            LIMIT $2
-            """,
-            seed_ids,
-            budget,
-        )
-        logger.debug(f"[LinkExpansion] observation graph: found {len(entity_rows)} connected observations")
-
-        # Semantic + causal for observations in one query
-        ml = fq_table("memory_links")
-        mu = fq_table("memory_units")
-        sem_causal_rows = await conn.fetch(
-            f"""
-            WITH semantic_expanded AS (
-                SELECT
-                    id, text, context, event_date, occurred_start,
-                    occurred_end, mentioned_at,
-                    fact_type, document_id, chunk_id, tags,
-                    MAX(weight) AS score,
-                    'semantic'::text AS source
-                FROM (
-                    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
-                    FROM {ml} ml JOIN {mu} mu ON mu.id = ml.to_unit_id
-                    WHERE ml.from_unit_id = ANY($1::uuid[])
-                      AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
-                      AND mu.id != ALL($1::uuid[])
-                    UNION ALL
-                    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
-                    FROM {ml} ml JOIN {mu} mu ON mu.id = ml.from_unit_id
-                    WHERE ml.to_unit_id = ANY($1::uuid[])
-                      AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
-                      AND mu.id != ALL($1::uuid[])
-                ) sem_raw
-                GROUP BY id, text, context, event_date, occurred_start, occurred_end,
-                         mentioned_at, fact_type, document_id, chunk_id, tags
-                ORDER BY score DESC LIMIT $2
-            ),
-            causal_expanded AS (
-                SELECT DISTINCT ON (mu.id)
-                    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 AS score, 'causal'::text AS source
-                FROM {ml} ml JOIN {mu} mu ON ml.to_unit_id = mu.id
-                WHERE ml.from_unit_id = ANY($1::uuid[])
-                  AND ml.link_type IN ('causes', 'caused_by', 'enables', 'prevents')
-                  AND ml.weight >= $3 AND mu.fact_type = 'observation'
-                ORDER BY mu.id, ml.weight DESC LIMIT $2
-            )
-            SELECT * FROM semantic_expanded
-            UNION ALL
-            SELECT * FROM causal_expanded
-            """,
-            seed_ids,
-            budget,
-            self.causal_weight_threshold,
-        )
-
-        semantic_rows = [r for r in sem_causal_rows if r["source"] == "semantic"]
-        causal_rows = [r for r in sem_causal_rows if r["source"] == "causal"]
-        return entity_rows, semantic_rows, causal_rows

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

@@ -1,702 +0,0 @@
-"""
-Retrieval module for 4-way parallel search.
-
-Implements:
-1. Semantic retrieval (vector similarity)
-2. BM25 retrieval (keyword/full-text search)
-3. Graph retrieval (via pluggable GraphRetriever interface)
-4. Temporal retrieval (time-aware search with spreading)
-"""
-
-import asyncio
-import logging
-import re
-from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from typing import Optional
-
-from ...config import get_config
-from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
-from .graph_retrieval import BFSGraphRetriever, GraphRetriever
-from .link_expansion_retrieval import LinkExpansionRetriever
-from .mpfp_retrieval import MPFPGraphRetriever
-from .tags import TagGroup, TagsMatch, build_tag_groups_where_clause, build_tags_where_clause_simple
-from .types import MPFPTimings, RetrievalResult
-
-logger = logging.getLogger(__name__)
-
-
-def tokenize_query(query_text: str) -> list[str]:
-    """Normalize query text and split into BM25 tokens.
-
-    Strips punctuation, lowercases, and splits on whitespace.
-    Returns an empty list when the query contains no word characters.
-    """
-    return re.sub(r"[^\w\s]", " ", query_text.lower()).split()
-
-
-@dataclass
-class ParallelRetrievalResult:
-    """Result from parallel retrieval across all methods."""
-
-    semantic: list[RetrievalResult]
-    bm25: list[RetrievalResult]
-    graph: list[RetrievalResult]
-    temporal: list[RetrievalResult] | None
-    timings: dict[str, float] = field(default_factory=dict)
-    temporal_constraint: tuple | None = None  # (start_date, end_date)
-    mpfp_timings: list[MPFPTimings] = field(default_factory=list)  # MPFP sub-step timings per fact type
-    max_conn_wait: float = 0.0  # Maximum connection acquisition wait time across all methods
-
-
-@dataclass
-class MultiFactTypeRetrievalResult:
-    """Result from retrieval across all fact types."""
-
-    # Results per fact type
-    results_by_fact_type: dict[str, ParallelRetrievalResult]
-    # Aggregate timings
-    timings: dict[str, float] = field(default_factory=dict)
-    # Max connection wait across all operations
-    max_conn_wait: float = 0.0
-
-
-# Default graph retriever instance (can be overridden)
-_default_graph_retriever: GraphRetriever | None = None
-
-
-def get_default_graph_retriever() -> GraphRetriever:
-    """Get or create the default graph retriever based on config."""
-    global _default_graph_retriever
-    if _default_graph_retriever is None:
-        config = get_config()
-        retriever_type = config.graph_retriever.lower()
-        if retriever_type == "mpfp":
-            _default_graph_retriever = MPFPGraphRetriever()
-            logger.info(
-                f"Using MPFP graph retriever (top_k_neighbors={_default_graph_retriever.config.top_k_neighbors})"
-            )
-        elif retriever_type == "bfs":
-            _default_graph_retriever = BFSGraphRetriever()
-            logger.info("Using BFS graph retriever")
-        elif retriever_type == "link_expansion":
-            _default_graph_retriever = LinkExpansionRetriever()
-            logger.info("Using LinkExpansion graph retriever")
-        else:
-            logger.warning(f"Unknown graph retriever '{retriever_type}', falling back to link_expansion")
-            _default_graph_retriever = LinkExpansionRetriever()
-    return _default_graph_retriever
-
-
-def set_default_graph_retriever(retriever: GraphRetriever) -> None:
-    """Set the default graph retriever (for configuration/testing)."""
-    global _default_graph_retriever
-    _default_graph_retriever = retriever
-
-
-async def retrieve_semantic_bm25_combined(
-    conn,
-    query_emb_str: str,
-    query_text: str,
-    bank_id: str,
-    fact_types: list[str],
-    limit: int,
-    tags: list[str] | None = None,
-    tags_match: TagsMatch = "any",
-    tag_groups: list[TagGroup] | None = None,
-) -> dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]]:
-    """
-    Combined semantic + BM25 retrieval for multiple fact types in a single query.
-
-    Uses UNION ALL of per-fact_type subqueries so that each arm has its own
-    ORDER BY ... LIMIT, enabling the partial HNSW indexes per fact_type instead
-    of forcing a full sequential scan (which the previous window-function approach
-    caused by using PARTITION BY inside ROW_NUMBER()).
-
-    Requires partial HNSW indexes per fact_type (idx_mu_emb_world,
-    idx_mu_emb_observation, idx_mu_emb_experience), created automatically by
-    Alembic migration a3b4c5d6e7f8_add_partial_hnsw_indexes.py.
-
-    HNSW is approximate — semantic arms over-fetch by 5x (min 100) and trim to
-    limit in Python to compensate.  ef_search=200 is set globally on pool
-    connections at init time (see memory_engine.py) to improve recall on sparse
-    graphs.
-
-    fact_type values are inlined as literals (safe: they come from a controlled
-    internal enum, never from user input).
-
-    Args:
-        conn: Database connection
-        query_emb_str: Query embedding as string
-        query_text: Query text for BM25
-        bank_id: Bank ID
-        fact_types: List of fact types to retrieve
-        limit: Maximum results per method per fact type
-        tags: Optional tags to filter by
-        tags_match: Tag matching mode
-
-    Returns:
-        Dict mapping fact_type -> (semantic_results, bm25_results)
-    """
-    result_dict: dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]] = {ft: ([], []) for ft in fact_types}
-
-    tokens = tokenize_query(query_text)
-
-    # Over-fetch for HNSW approximation; semantic results trimmed to limit in Python.
-    hnsw_fetch = max(limit * 5, 100)
-
-    cols = (
-        "id, text, context, event_date, occurred_start, occurred_end, mentioned_at, "
-        "fact_type, document_id, chunk_id, tags, metadata"
-    )
-    table = fq_table("memory_units")
-
-    # --- Parameter layout ---
-    # $1 = query_emb_str  (semantic arms)
-    # $2 = bank_id
-    # When tokens present:
-    #   $3 = limit          (BM25 LIMIT; semantic uses inlined hnsw_fetch literal)
-    #   $4 = bm25_text
-    #   $5 = tags           (if present)
-    #   $6+ = tag_groups params (one per leaf)
-    # When no tokens ($3 is skipped — not included in params to avoid type inference gap):
-    #   $3 = tags           (if present)
-    #   $4+ = tag_groups params (one per leaf)
-    tags_param_idx = 5 if tokens else 3
-    tags_clause = build_tags_where_clause_simple(tags, tags_param_idx, match=tags_match)
-
-    # tag_groups params start immediately after the tags param slot
-    tag_groups_param_start = tags_param_idx + (1 if tags else 0)
-    groups_clause, groups_params, _ = build_tag_groups_where_clause(tag_groups, tag_groups_param_start)
-
-    # --- Semantic UNION ALL arms (one per fact_type) ---
-    # Each arm has its own ORDER BY embedding <=> $1 LIMIT {hnsw_fetch}, which
-    # lets the planner use the partial HNSW index for that fact_type.
-    sem_arms = []
-    for ft in fact_types:
-        sem_arms.append(
-            f"(SELECT {cols},"
-            f"        1 - (embedding <=> $1::vector) AS similarity,"
-            f"        NULL::float AS bm25_score,"
-            f"        'semantic' AS source"
-            f" FROM {table}"
-            f" WHERE bank_id = $2"
-            f"   AND fact_type = '{ft}'"
-            f"   AND embedding IS NOT NULL"
-            f"   AND (1 - (embedding <=> $1::vector)) >= 0.3"
-            f"   {tags_clause}"
-            f"   {groups_clause}"
-            f" ORDER BY embedding <=> $1::vector"
-            f" LIMIT {hnsw_fetch})"
-        )
-
-    arms = sem_arms
-
-    # --- BM25 UNION ALL arms (one per fact_type, only when tokens present) ---
-    if tokens:
-        config = get_config()
-        if config.text_search_extension == "vchord":
-            bm25_score_expr = (
-                "search_vector <&> to_bm25query('idx_memory_units_text_search', tokenize($4, 'llmlingua2'))"
-            )
-            bm25_order_by = f"{bm25_score_expr} DESC"
-            bm25_where_filter = ""
-            bm25_text_param: str = query_text
-        elif config.text_search_extension == "pg_textsearch":
-            bm25_score_expr = "-(text <@> to_bm25query($4, 'idx_memory_units_text_search'))"
-            bm25_order_by = "text <@> to_bm25query($4, 'idx_memory_units_text_search') ASC"
-            bm25_where_filter = ""
-            bm25_text_param = query_text
-        else:  # native
-            query_tsquery = " | ".join(tokens)
-            bm25_score_expr = "ts_rank_cd(search_vector, to_tsquery('english', $4))"
-            bm25_order_by = f"{bm25_score_expr} DESC"
-            bm25_where_filter = "AND search_vector @@ to_tsquery('english', $4)"
-            bm25_text_param = query_tsquery
-
-        for ft in fact_types:
-            arms.append(
-                f"(SELECT {cols},"
-                f"        NULL::float AS similarity,"
-                f"        {bm25_score_expr} AS bm25_score,"
-                f"        'bm25' AS source"
-                f" FROM {table}"
-                f" WHERE bank_id = $2"
-                f"   AND fact_type = '{ft}'"
-                f"   {bm25_where_filter}"
-                f"   {tags_clause}"
-                f"   {groups_clause}"
-                f" ORDER BY {bm25_order_by}"
-                f" LIMIT $3)"
-            )
-
-    query = "\nUNION ALL\n".join(arms)
-
-    params: list = [query_emb_str, bank_id]
-    if tokens:
-        params.append(limit)  # $3: BM25 LIMIT (only referenced when tokens are present)
-        params.append(bm25_text_param)  # $4
-    if tags:
-        params.append(tags)
-    params.extend(groups_params)
-
-    rows = await conn.fetch(query, *params)
-
-    # Group results; trim semantic to limit (over-fetched for HNSW approximation).
-    sem_counts: dict[str, int] = {ft: 0 for ft in fact_types}
-    for r in rows:
-        row = dict(r)
-        source = row.pop("source")
-        ft = row.get("fact_type")
-        if ft not in result_dict:
-            continue
-        if source == "semantic":
-            if sem_counts[ft] < limit:
-                result_dict[ft][0].append(RetrievalResult.from_db_row(row))
-                sem_counts[ft] += 1
-        else:
-            result_dict[ft][1].append(RetrievalResult.from_db_row(row))
-
-    return result_dict
-
-
-async def retrieve_temporal_combined(
-    conn,
-    query_emb_str: str,
-    bank_id: str,
-    fact_types: list[str],
-    start_date: datetime,
-    end_date: datetime,
-    budget: int,
-    semantic_threshold: float = 0.1,
-    tags: list[str] | None = None,
-    tags_match: TagsMatch = "any",
-    tag_groups: list[TagGroup] | None = None,
-) -> dict[str, list[RetrievalResult]]:
-    """
-    Temporal retrieval for multiple fact types in a single query.
-
-    Batches the entry point query using window functions to get top-N per fact type,
-    then runs spreading for each fact type.
-
-    Args:
-        conn: Database connection
-        query_emb_str: Query embedding as string
-        bank_id: Bank ID
-        fact_types: List of fact types to retrieve
-        start_date: Start of time range
-        end_date: End of time range
-        budget: Node budget for spreading per fact type
-        semantic_threshold: Minimum semantic similarity to include
-
-    Returns:
-        Dict mapping fact_type -> list of RetrievalResult
-    """
-    from ..memory_engine import fq_table
-
-    # Ensure dates are timezone-aware
-    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)
-
-    # Build tags clause
-    # Entry point query: fixed params are $1-$6, tags at $7
-    tags_clause = build_tags_where_clause_simple(tags, 7, match=tags_match)
-    tag_groups_param_start = 7 + (1 if tags else 0)
-    groups_clause, groups_params, _ = build_tag_groups_where_clause(tag_groups, tag_groups_param_start)
-    params: list = [query_emb_str, bank_id, fact_types, start_date, end_date, semantic_threshold]
-    if tags:
-        params.append(tags)
-    params.extend(groups_params)
-
-    # Two-phase entry point query:
-    # Phase 1 (date_ranked): rank by date only — no embedding computation — for all units in
-    #   the temporal window. This lets the planner use date indexes for filtering.
-    # Phase 2 (sim_ranked): join back to memory_units for only the top-50-per-type candidates
-    #   and compute embedding similarity for that small set (≤ 50 × len(fact_types) rows).
-    # This avoids computing embedding distances for potentially thousands of date-range rows.
-    entry_points = await conn.fetch(
-        f"""
-        WITH date_ranked AS MATERIALIZED (
-            SELECT id, fact_type,
-                   ROW_NUMBER() OVER (
-                       PARTITION BY fact_type
-                       ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC NULLS LAST
-                   ) AS rn
-            FROM {fq_table("memory_units")}
-            WHERE bank_id = $2
-              AND fact_type = ANY($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)
-              )
-              {tags_clause}
-              {groups_clause}
-        ),
-        sim_ranked AS (
-            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, mu.metadata,
-                   1 - (mu.embedding <=> $1::vector) AS similarity,
-                   ROW_NUMBER() OVER (PARTITION BY mu.fact_type ORDER BY mu.embedding <=> $1::vector) AS sim_rn
-            FROM date_ranked dr
-            JOIN {fq_table("memory_units")} mu ON mu.id = dr.id
-            WHERE dr.rn <= 50
-              AND (1 - (mu.embedding <=> $1::vector)) >= $6
-        )
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, metadata, similarity
-        FROM sim_ranked
-        WHERE sim_rn <= 10
-        """,
-        *params,
-    )
-
-    if not entry_points:
-        return {ft: [] for ft in fact_types}
-
-    # Group entry points by fact type
-    entries_by_ft: dict[str, list] = {ft: [] for ft in fact_types}
-    for ep in entry_points:
-        ft = ep["fact_type"]
-        if ft in entries_by_ft:
-            entries_by_ft[ft].append(ep)
-
-    # Calculate shared temporal parameters
-    total_days = (end_date - start_date).total_seconds() / 86400
-    mid_date = start_date + (end_date - start_date) / 2
-
-    # Process each fact type (spreading needs to stay per fact type due to link filtering)
-    results_by_ft: dict[str, list[RetrievalResult]] = {}
-
-    for ft in fact_types:
-        ft_entry_points = entries_by_ft.get(ft, [])
-        if not ft_entry_points:
-            results_by_ft[ft] = []
-            continue
-
-        results = []
-        visited = set()
-        node_scores = {}
-
-        # Process entry points
-        for ep in ft_entry_points:
-            unit_id = str(ep["id"])
-            visited.add(unit_id)
-
-            # Calculate temporal proximity
-            best_date = None
-            if ep["occurred_start"] is not None and ep["occurred_end"] is not None:
-                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"]
-
-            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
-
-            ep_result = RetrievalResult.from_db_row(dict(ep))
-            ep_result.temporal_score = temporal_proximity
-            ep_result.temporal_proximity = temporal_proximity
-            results.append(ep_result)
-            node_scores[unit_id] = (ep["similarity"], 1.0)
-
-        # Spreading through temporal links (same as single-fact-type version)
-        frontier = list(node_scores.keys())
-        budget_remaining = budget - len(ft_entry_points)
-        batch_size = 20
-        # Per-source neighbor limit: lets the planner use the composite index
-        # (from_unit_id, link_type, weight DESC) with early termination, avoiding
-        # a full scan of all links from all source nodes before sorting.
-        per_source_limit = 10
-        # Safety cap on BFS iterations to prevent runaway spreading in dense graphs.
-        max_iterations = 5
-        iteration = 0
-
-        # Build tags clause for spreading (use param 7 since 1-6 are used)
-        spreading_tags_clause = build_tags_where_clause_simple(tags, 7, table_alias="mu.", match=tags_match)
-        spreading_groups_param_start = 7 + (1 if tags else 0)
-        spreading_groups_clause, spreading_groups_params, _ = build_tag_groups_where_clause(
-            tag_groups, spreading_groups_param_start, table_alias="mu."
-        )
-
-        while frontier and budget_remaining > 0 and iteration < max_iterations:
-            iteration += 1
-            batch_ids = frontier[:batch_size]
-            frontier = frontier[batch_size:]
-
-            # $1=query_emb, $2=batch_ids, $3=fact_type, $4=threshold, $5=per_source_limit, $6=bank_id, $7=tags, $M+=tag_groups
-            spreading_params = [query_emb_str, batch_ids, ft, semantic_threshold, per_source_limit, bank_id]
-            if tags:
-                spreading_params.append(tags)
-            spreading_params.extend(spreading_groups_params)
-
-            # LATERAL join: for each source node, fetch top-K neighbors by weight using
-            # the existing idx_memory_links_from_type_weight index with early-exit semantics.
-            # This avoids scanning all temporal links from all source nodes before sorting.
-            # bank_id on memory_units lets the planner use idx_memory_units_bank_fact_type.
-            neighbors = await conn.fetch(
-                f"""
-                SELECT src.from_unit_id, 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, mu.metadata,
-                       l.weight, l.link_type,
-                       1 - (mu.embedding <=> $1::vector) AS similarity
-                FROM unnest($2::uuid[]) AS src(from_unit_id)
-                CROSS JOIN LATERAL (
-                    SELECT ml.to_unit_id, ml.weight, ml.link_type
-                    FROM {fq_table("memory_links")} ml
-                    WHERE ml.from_unit_id = src.from_unit_id
-                      AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
-                      AND ml.weight >= 0.1
-                    ORDER BY ml.weight DESC
-                    LIMIT $5
-                ) l
-                JOIN {fq_table("memory_units")} mu ON mu.id = l.to_unit_id
-                WHERE mu.bank_id = $6
-                  AND mu.fact_type = $3
-                  AND mu.embedding IS NOT NULL
-                  AND (1 - (mu.embedding <=> $1::vector)) >= $4
-                  {spreading_tags_clause}
-                  {spreading_groups_clause}
-                """,
-                *spreading_params,
-            )
-
-            for n in neighbors:
-                neighbor_id = str(n["id"])
-                if neighbor_id in visited:
-                    continue
-
-                visited.add(neighbor_id)
-                budget_remaining -= 1
-
-                parent_id = str(n["from_unit_id"])
-                _, parent_temporal_score = node_scores.get(parent_id, (0.5, 0.5))
-
-                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
-
-                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
-
-                propagated_temporal = parent_temporal_score * n["weight"] * causal_boost * 0.7
-                combined_temporal = max(neighbor_temporal_proximity, propagated_temporal)
-
-                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)
-
-                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
-
-        results_by_ft[ft] = results
-
-    return results_by_ft
-
-
-async def retrieve_all_fact_types_parallel(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_types: list[str],
-    thinking_budget: int,
-    question_date: datetime | None = None,
-    query_analyzer: Optional["QueryAnalyzer"] = None,
-    graph_retriever: GraphRetriever | None = None,
-    tags: list[str] | None = None,
-    tags_match: TagsMatch = "any",
-    tag_groups: list[TagGroup] | None = None,
-) -> MultiFactTypeRetrievalResult:
-    """
-    Optimized retrieval for multiple fact types using batched queries.
-
-    This reduces database round-trips by:
-    1. Combining semantic + BM25 into one CTE query for ALL fact types (1 query instead of 2N)
-    2. Running graph retrieval per fact type in parallel (N parallel tasks)
-    3. Running temporal retrieval per fact type in parallel (N parallel tasks)
-
-    Args:
-        pool: Database connection pool
-        query_text: Query text
-        query_embedding_str: Query embedding as string
-        bank_id: Bank ID
-        fact_types: List of fact types to retrieve
-        thinking_budget: Budget for graph traversal and retrieval limits
-        question_date: Optional date when question was asked (for temporal filtering)
-        query_analyzer: Query analyzer to use (defaults to TransformerQueryAnalyzer)
-        graph_retriever: Graph retrieval strategy (defaults to configured retriever)
-
-    Returns:
-        MultiFactTypeRetrievalResult with results organized by fact type
-    """
-    import time
-
-    retriever = graph_retriever or get_default_graph_retriever()
-    start_time = time.time()
-    timings: dict[str, float] = {}
-
-    # Step 1: Extract temporal constraint first (CPU work, no DB)
-    # Do this before DB queries so we know if we need temporal retrieval
-    temporal_extraction_start = time.time()
-    from .temporal_extraction import extract_temporal_constraint
-
-    temporal_constraint = extract_temporal_constraint(query_text, reference_date=question_date, analyzer=query_analyzer)
-    temporal_extraction_time = time.time() - temporal_extraction_start
-    timings["temporal_extraction"] = temporal_extraction_time
-
-    # Step 2: Run semantic + BM25 + temporal combined in ONE connection!
-    # This reduces connection usage from 2 to 1 for these operations
-    semantic_bm25_start = time.time()
-    temporal_results_by_ft: dict[str, list[RetrievalResult]] = {}
-    temporal_time = 0.0
-
-    async with acquire_with_retry(pool) as conn:
-        conn_wait = time.time() - semantic_bm25_start
-
-        # Semantic + BM25 combined
-        semantic_bm25_results = await retrieve_semantic_bm25_combined(
-            conn,
-            query_embedding_str,
-            query_text,
-            bank_id,
-            fact_types,
-            thinking_budget,
-            tags=tags,
-            tags_match=tags_match,
-            tag_groups=tag_groups,
-        )
-        semantic_bm25_time = time.time() - semantic_bm25_start
-
-        # Temporal combined (if constraint detected) - same connection!
-        if temporal_constraint:
-            tc_start, tc_end = temporal_constraint
-            temporal_start = time.time()
-            temporal_results_by_ft = await retrieve_temporal_combined(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_types,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
-                tags=tags,
-                tags_match=tags_match,
-                tag_groups=tag_groups,
-            )
-            temporal_time = time.time() - temporal_start
-
-    timings["semantic_bm25_combined"] = semantic_bm25_time
-    timings["temporal_combined"] = temporal_time
-
-    # Step 3: Run graph retrieval for each fact type in parallel
-    async def run_graph_for_fact_type(ft: str) -> tuple[str, list[RetrievalResult], float, MPFPTimings | None]:
-        graph_start = time.time()
-        results, mpfp_timing = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=ft,
-            budget=thinking_budget,
-            query_text=query_text,
-            semantic_seeds=None,
-            temporal_seeds=None,
-            tags=tags,
-            tags_match=tags_match,
-            tag_groups=tag_groups,
-        )
-        return ft, results, time.time() - graph_start, mpfp_timing
-
-    # Run graph for all fact types in parallel
-    graph_tasks = [run_graph_for_fact_type(ft) for ft in fact_types]
-    graph_results_list = await asyncio.gather(*graph_tasks)
-
-    # Organize results by fact type
-    results_by_fact_type: dict[str, ParallelRetrievalResult] = {}
-    max_conn_wait = conn_wait  # Single connection for semantic+bm25+temporal
-    all_mpfp_timings: list[MPFPTimings] = []
-
-    for ft in fact_types:
-        # Get semantic + bm25 results for this fact type
-        semantic_results, bm25_results = semantic_bm25_results.get(ft, ([], []))
-
-        # Find graph results for this fact type
-        graph_results = []
-        graph_time = 0.0
-        mpfp_timing = None
-        for gr in graph_results_list:
-            if gr[0] == ft:
-                graph_results = gr[1]
-                graph_time = gr[2]
-                mpfp_timing = gr[3]
-                if mpfp_timing:
-                    all_mpfp_timings.append(mpfp_timing)
-                break
-
-        # Get temporal results for this fact type from combined result
-        temporal_results = temporal_results_by_ft.get(ft) if temporal_constraint else None
-        if temporal_results is not None and len(temporal_results) == 0:
-            temporal_results = None
-
-        results_by_fact_type[ft] = ParallelRetrievalResult(
-            semantic=semantic_results,
-            bm25=bm25_results,
-            graph=graph_results,
-            temporal=temporal_results,
-            timings={
-                "semantic": semantic_bm25_time / 2,  # Approximate split
-                "bm25": semantic_bm25_time / 2,
-                "graph": graph_time,
-                "temporal": temporal_time,  # Same for all fact types (single query)
-                "temporal_extraction": temporal_extraction_time,
-            },
-            temporal_constraint=temporal_constraint,
-            mpfp_timings=[mpfp_timing] if mpfp_timing else [],
-            max_conn_wait=max_conn_wait,
-        )
-
-    total_time = time.time() - start_time
-    timings["total"] = total_time
-
-    return MultiFactTypeRetrievalResult(
-        results_by_fact_type=results_by_fact_type,
-        timings=timings,
-        max_conn_wait=max_conn_wait,
-    )

hindsight-api-slim/hindsight_api/engine/search/tags.py

@@ -1,390 +0,0 @@
-"""
-Tags filtering utilities for retrieval.
-
-Provides SQL building functions for filtering memories by tags.
-Supports four matching modes via TagsMatch enum:
-- "any": OR matching, includes untagged memories (default, backward compatible)
-- "all": AND matching, includes untagged memories
-- "any_strict": OR matching, excludes untagged memories
-- "all_strict": AND matching, excludes untagged memories
-
-OR matching (any/any_strict): Memory matches if ANY of its tags overlap with request tags
-AND matching (all/all_strict): Memory matches if ALL request tags are present in its tags
-"""
-
-from __future__ import annotations
-
-from typing import Annotated, Literal
-
-from pydantic import BaseModel, ConfigDict, Field
-
-TagsMatch = Literal["any", "all", "any_strict", "all_strict"]
-
-
-def _parse_tags_match(match: TagsMatch) -> tuple[str, bool]:
-    """
-    Parse TagsMatch into operator and include_untagged flag.
-
-    Returns:
-        Tuple of (operator, include_untagged)
-        - operator: "&&" for any/any_strict, "@>" for all/all_strict
-        - include_untagged: True for any/all, False for any_strict/all_strict
-    """
-    if match == "any":
-        return "&&", True
-    elif match == "all":
-        return "@>", True
-    elif match == "any_strict":
-        return "&&", False
-    elif match == "all_strict":
-        return "@>", False
-    else:
-        # Default to "any" behavior
-        return "&&", True
-
-
-def build_tags_where_clause(
-    tags: list[str] | None,
-    param_offset: int = 1,
-    table_alias: str = "",
-    match: TagsMatch = "any",
-) -> tuple[str, list, int]:
-    """
-    Build a SQL WHERE clause for filtering by tags.
-
-    Supports four matching modes:
-    - "any" (default): OR matching, includes untagged memories
-    - "all": AND matching, includes untagged memories
-    - "any_strict": OR matching, excludes untagged memories
-    - "all_strict": AND matching, excludes untagged memories
-
-    Args:
-        tags: List of tags to filter by. If None or empty, returns empty clause (no filtering).
-        param_offset: Starting parameter number for SQL placeholders (default 1).
-        table_alias: Optional table alias prefix (e.g., "mu." for "memory_units mu").
-        match: Matching mode. Defaults to "any".
-
-    Returns:
-        Tuple of (sql_clause, params, next_param_offset):
-        - sql_clause: SQL WHERE clause string
-        - params: List of parameter values to bind
-        - next_param_offset: Next available parameter number
-
-    Example:
-        >>> clause, params, next_offset = build_tags_where_clause(['user_a'], 3, 'mu.', 'any_strict')
-        >>> print(clause)  # "AND mu.tags IS NOT NULL AND mu.tags != '{}' AND mu.tags && $3"
-    """
-    if not tags:
-        return "", [], param_offset
-
-    column = f"{table_alias}tags" if table_alias else "tags"
-    operator, include_untagged = _parse_tags_match(match)
-
-    if include_untagged:
-        # Include untagged memories (NULL or empty array) OR matching tags
-        clause = f"AND ({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_offset})"
-    else:
-        # Strict: only memories with matching tags (exclude NULL and empty)
-        clause = f"AND {column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_offset}"
-
-    return clause, [tags], param_offset + 1
-
-
-def build_tags_where_clause_simple(
-    tags: list[str] | None,
-    param_num: int,
-    table_alias: str = "",
-    match: TagsMatch = "any",
-) -> str:
-    """
-    Build a simple SQL WHERE clause for tags filtering.
-
-    This is a convenience version that returns just the clause string,
-    assuming the caller will add the tags array to their params list.
-
-    Args:
-        tags: List of tags to filter by. If None or empty, returns empty string.
-        param_num: Parameter number to use in the clause.
-        table_alias: Optional table alias prefix.
-        match: Matching mode. Defaults to "any".
-
-    Returns:
-        SQL clause string or empty string.
-    """
-    if not tags:
-        return ""
-
-    column = f"{table_alias}tags" if table_alias else "tags"
-    operator, include_untagged = _parse_tags_match(match)
-
-    if include_untagged:
-        # Include untagged memories (NULL or empty array) OR matching tags
-        return f"AND ({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_num})"
-    else:
-        # Strict: only memories with matching tags (exclude NULL and empty)
-        return f"AND {column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_num}"
-
-
-def filter_results_by_tags(
-    results: list,
-    tags: list[str] | None,
-    match: TagsMatch = "any",
-) -> list:
-    """
-    Filter retrieval results by tags in Python (for post-processing).
-
-    Used when SQL filtering isn't possible (e.g., graph traversal results).
-
-    Args:
-        results: List of RetrievalResult objects with a 'tags' attribute.
-        tags: List of tags to filter by. If None or empty, returns all results.
-        match: Matching mode. Defaults to "any".
-
-    Returns:
-        Filtered list of results.
-    """
-    if not tags:
-        return results
-
-    _, include_untagged = _parse_tags_match(match)
-    is_any_match = match in ("any", "any_strict")
-
-    tags_set = set(tags)
-    filtered = []
-
-    for result in results:
-        result_tags = getattr(result, "tags", None)
-
-        # Check if untagged
-        is_untagged = result_tags is None or len(result_tags) == 0
-
-        if is_untagged:
-            if include_untagged:
-                filtered.append(result)
-            # else: skip untagged
-        else:
-            result_tags_set = set(result_tags)
-            if is_any_match:
-                # Any overlap
-                if result_tags_set & tags_set:
-                    filtered.append(result)
-            else:
-                # All tags must be present
-                if tags_set <= result_tags_set:
-                    filtered.append(result)
-
-    return filtered
-
-
-# =============================================================================
-# Compound tag group models (recursive boolean expressions)
-# =============================================================================
-
-
-class TagGroupLeaf(BaseModel):
-    """A leaf tag filter: matches memories by tag list and match mode."""
-
-    tags: list[str]
-    match: TagsMatch = "any_strict"
-
-
-class TagGroupAnd(BaseModel):
-    """Compound AND group: all child filters must match."""
-
-    model_config = ConfigDict(populate_by_name=True)
-    filters: list[TagGroup] = Field(alias="and")
-
-
-class TagGroupOr(BaseModel):
-    """Compound OR group: at least one child filter must match."""
-
-    model_config = ConfigDict(populate_by_name=True)
-    filters: list[TagGroup] = Field(alias="or")
-
-
-class TagGroupNot(BaseModel):
-    """Compound NOT group: child filter must NOT match."""
-
-    model_config = ConfigDict(populate_by_name=True)
-    filter: TagGroup = Field(alias="not")
-
-
-# TagGroup is a discriminated union; Pydantic will try left-to-right.
-# TagGroupLeaf is identified by the presence of 'tags'.
-# TagGroupAnd / TagGroupOr / TagGroupNot are compound (no 'tags' key).
-TagGroup = Annotated[
-    TagGroupLeaf | TagGroupAnd | TagGroupOr | TagGroupNot,
-    Field(union_mode="left_to_right"),
-]
-
-# Rebuild forward-reference models so recursive TagGroup is resolved.
-TagGroupAnd.model_rebuild()
-TagGroupOr.model_rebuild()
-TagGroupNot.model_rebuild()
-
-
-# =============================================================================
-# SQL builder for compound tag groups
-# =============================================================================
-
-
-def _build_group_clause(
-    group: TagGroup,
-    param_offset: int,
-    table_alias: str,
-) -> tuple[str, list, int]:
-    """
-    Recursively build an inner SQL clause (no leading AND/OR) for a single TagGroup.
-
-    Returns:
-        (inner_clause, params, next_param_offset)
-    """
-    if isinstance(group, TagGroupLeaf):
-        column = f"{table_alias}tags" if table_alias else "tags"
-        operator, include_untagged = _parse_tags_match(group.match)
-        if include_untagged:
-            clause = f"({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_offset})"
-        else:
-            clause = f"({column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_offset})"
-        return clause, [group.tags], param_offset + 1
-
-    elif isinstance(group, TagGroupAnd):
-        parts = []
-        params: list = []
-        offset = param_offset
-        for child in group.filters:
-            child_clause, child_params, offset = _build_group_clause(child, offset, table_alias)
-            parts.append(child_clause)
-            params.extend(child_params)
-        inner = " AND ".join(parts)
-        return f"({inner})", params, offset
-
-    elif isinstance(group, TagGroupOr):
-        parts = []
-        params = []
-        offset = param_offset
-        for child in group.filters:
-            child_clause, child_params, offset = _build_group_clause(child, offset, table_alias)
-            parts.append(child_clause)
-            params.extend(child_params)
-        inner = " OR ".join(parts)
-        return f"({inner})", params, offset
-
-    elif isinstance(group, TagGroupNot):
-        child_clause, child_params, next_offset = _build_group_clause(group.filter, param_offset, table_alias)
-        return f"NOT {child_clause}", child_params, next_offset
-
-    else:
-        # Should never happen with proper Pydantic validation
-        return "", [], param_offset
-
-
-def build_tag_groups_where_clause(
-    tag_groups: list[TagGroup] | None,
-    param_offset: int,
-    table_alias: str = "",
-) -> tuple[str, list, int]:
-    """
-    Build a SQL WHERE clause for compound tag group filtering.
-
-    Top-level groups are AND-ed together. Each group is a recursive boolean
-    expression (leaf, and, or, not).
-
-    Args:
-        tag_groups: List of TagGroup objects. If None or empty, returns empty clause.
-        param_offset: Starting parameter number for SQL placeholders.
-        table_alias: Optional table alias prefix (e.g., "mu." for "memory_units mu").
-
-    Returns:
-        Tuple of (sql_clause, params, next_param_offset):
-        - sql_clause: SQL WHERE clause string starting with "AND" (or empty string)
-        - params: List of parameter values to bind (one per leaf node)
-        - next_param_offset: Next available parameter number
-
-    Example:
-        >>> groups = [TagGroupLeaf(tags=["user:alice"], match="all_strict")]
-        >>> clause, params, next_offset = build_tag_groups_where_clause(groups, 3)
-        >>> print(clause)  # "AND (tags IS NOT NULL AND tags != '{}' AND tags @> $3)"
-    """
-    if not tag_groups:
-        return "", [], param_offset
-
-    all_params: list = []
-    all_clauses: list[str] = []
-    offset = param_offset
-
-    for group in tag_groups:
-        inner_clause, group_params, offset = _build_group_clause(group, offset, table_alias)
-        all_clauses.append(inner_clause)
-        all_params.extend(group_params)
-
-    combined = " AND ".join(all_clauses)
-    return f"AND {combined}", all_params, offset
-
-
-# =============================================================================
-# Python-side filter for compound tag groups (post-retrieval filtering)
-# =============================================================================
-
-
-def _match_group(result: object, group: TagGroup) -> bool:
-    """
-    Recursively evaluate a TagGroup against a retrieval result.
-
-    Args:
-        result: Any object with a 'tags' attribute (list[str] or None).
-        group: The TagGroup to evaluate.
-
-    Returns:
-        True if the result matches the group, False otherwise.
-    """
-    if isinstance(group, TagGroupLeaf):
-        result_tags = getattr(result, "tags", None)
-        is_untagged = result_tags is None or len(result_tags) == 0
-        _, include_untagged = _parse_tags_match(group.match)
-        is_any_match = group.match in ("any", "any_strict")
-        tags_set = set(group.tags)
-
-        if is_untagged:
-            return include_untagged
-        else:
-            result_tags_set = set(result_tags)
-            if is_any_match:
-                return bool(result_tags_set & tags_set)
-            else:
-                return tags_set <= result_tags_set
-
-    elif isinstance(group, TagGroupAnd):
-        return all(_match_group(result, child) for child in group.filters)
-
-    elif isinstance(group, TagGroupOr):
-        return any(_match_group(result, child) for child in group.filters)
-
-    elif isinstance(group, TagGroupNot):
-        return not _match_group(result, group.filter)
-
-    else:
-        return True
-
-
-def filter_results_by_tag_groups(
-    results: list,
-    tag_groups: list[TagGroup] | None,
-) -> list:
-    """
-    Filter retrieval results by compound tag groups in Python (for post-processing).
-
-    Used when SQL filtering isn't possible (e.g., graph traversal results).
-    Top-level groups are AND-ed together.
-
-    Args:
-        results: List of RetrievalResult objects with a 'tags' attribute.
-        tag_groups: List of TagGroup objects. If None or empty, returns all results.
-
-    Returns:
-        Filtered list of results where ALL top-level groups match.
-    """
-    if not tag_groups:
-        return results
-
-    return [r for r in results if all(_match_group(r, group) for group in tag_groups)]

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

@@ -1,79 +0,0 @@
-"""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-slim/hindsight_api/engine/storage/azure.py

@@ -1,62 +0,0 @@
-"""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-slim/hindsight_api/engine/storage/base.py

@@ -1,83 +0,0 @@
-"""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-slim/hindsight_api/engine/storage/gcs.py

@@ -1,105 +0,0 @@
-"""Google Cloud Storage backend using obstore."""
-
-import logging
-import os
-from datetime import datetime, timedelta, timezone
-
-import obstore as obs
-from obstore.store import GCSStore
-
-from .base import FileStorage
-
-logger = logging.getLogger(__name__)
-
-
-def _make_google_auth_credential_provider():
-    """Create a credential provider using google.auth (supports all credential types).
-
-    obstore's built-in credential parsing only supports service_account and
-    authorized_user JSON types.  This provider uses the google-auth library
-    which additionally handles external_account (Workload Identity Federation),
-    impersonated credentials, and metadata-server credentials.
-    """
-    import google.auth
-    import google.auth.transport.requests
-
-    credentials, _ = google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
-    request = google.auth.transport.requests.Request()
-
-    def _provide():
-        credentials.refresh(request)
-        expiry = credentials.expiry
-        if expiry and expiry.tzinfo is None:
-            expiry = expiry.replace(tzinfo=timezone.utc)
-        return {"token": credentials.token, "expires_at": expiry}
-
-    return _provide
-
-
-class GCSFileStorage(FileStorage):
-    """
-    Google Cloud Storage backend.
-
-    Uses obstore (Rust-backed) for high-throughput async access to GCS.
-    Supports Application Default Credentials, service account keys, and explicit credentials.
-    """
-
-    def __init__(
-        self,
-        bucket: str,
-        service_account_key: str | None = None,
-    ):
-        kwargs: dict = {}
-        if service_account_key:
-            kwargs["service_account_key"] = service_account_key
-        else:
-            # Use google.auth credential provider for broad credential type support
-            # (service_account, authorized_user, external_account, metadata server, etc.)
-            try:
-                kwargs["credential_provider"] = _make_google_auth_credential_provider()
-                logger.info("Using google.auth credential provider for GCS")
-            except Exception as e:
-                logger.warning(
-                    f"Failed to create google.auth credential provider, falling back to obstore defaults: {e}"
-                )
-
-        # Workaround for https://github.com/developmentseed/obstore/issues/605
-        # obstore's Rust layer doesn't support external_account credentials (Workload
-        # Identity Federation) and eagerly parses GOOGLE_APPLICATION_CREDENTIALS even
-        # when credential_provider is given. Per the obstore maintainer's guidance,
-        # remove env vars so the Rust code doesn't try to authenticate itself.
-        # google.auth (used by credential_provider above) has already loaded credentials.
-        gac = os.environ.pop("GOOGLE_APPLICATION_CREDENTIALS", None)
-        try:
-            self._store = GCSStore(bucket, **kwargs)
-        finally:
-            if gac is not None:
-                os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = gac
-        logger.info(f"Initialized GCS file storage: bucket={bucket}")
-
-    async def store(self, file_data: bytes, key: str, metadata: dict[str, str] | None = None) -> str:
-        await obs.put_async(self._store, key, file_data)
-        logger.debug(f"Stored file {key} ({len(file_data)} bytes) in GCS")
-        return key
-
-    async def retrieve(self, key: str) -> bytes:
-        try:
-            response = await obs.get_async(self._store, key)
-            return await response.bytes_async()
-        except Exception as e:
-            if "not found" in str(e).lower():
-                raise FileNotFoundError(f"File not found: {key}") from e
-            raise
-
-    async def delete(self, key: str) -> None:
-        await obs.delete_async(self._store, key)
-
-    async def exists(self, key: str) -> bool:
-        try:
-            await obs.head_async(self._store, key)
-            return True
-        except Exception:
-            return False
-
-    async def get_download_url(self, key: str, expires_in: int = 3600) -> str:
-        return await obs.sign_async(self._store, "GET", key, timedelta(seconds=expires_in))

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

@@ -1,153 +0,0 @@
-"""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-slim/hindsight_api/engine/storage/s3.py

@@ -1,71 +0,0 @@
-"""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-slim/hindsight_api/mcp_local.py

@@ -1,40 +0,0 @@
-"""
-Local MCP server entry point for use with Claude Code (HTTP transport).
-
-This is a thin wrapper around the main hindsight-api server that pre-configures
-sensible defaults for local use (embedded PostgreSQL via pg0, warning log level).
-
-The full API runs on localhost:8888. Configure Claude Code's MCP settings:
-    claude mcp add --transport http hindsight http://localhost:8888/mcp/
-
-Or pinned to a specific bank (single-bank mode):
-    claude mcp add --transport http hindsight http://localhost:8888/mcp/default/
-
-Run with:
-    hindsight-local-mcp
-
-Or with uvx:
-    uvx hindsight-api@latest hindsight-local-mcp
-
-Environment variables:
-    HINDSIGHT_API_LLM_API_KEY: Required. API key for LLM provider.
-    HINDSIGHT_API_LLM_PROVIDER: Optional. LLM provider (default: "openai").
-    HINDSIGHT_API_LLM_MODEL: Optional. LLM model (default: "gpt-4o-mini").
-    HINDSIGHT_API_DATABASE_URL: Optional. Override database URL (default: pg0://hindsight-mcp).
-"""
-
-import os
-
-
-def main() -> None:
-    """Start the Hindsight API server with local defaults."""
-    # Set local defaults (only if not already configured by the user)
-    os.environ.setdefault("HINDSIGHT_API_DATABASE_URL", "pg0://hindsight-mcp")
-
-    from hindsight_api.main import main as api_main
-
-    api_main()
-
-
-if __name__ == "__main__":
-    main()

hindsight-api-slim/hindsight_api/webhooks/__init__.py

@@ -1,13 +0,0 @@
-"""Webhook system for Hindsight API event notifications."""
-
-from .manager import WebhookManager
-from .models import ConsolidationEventData, RetainEventData, WebhookConfig, WebhookEvent, WebhookEventType
-
-__all__ = [
-    "WebhookManager",
-    "WebhookConfig",
-    "WebhookEvent",
-    "WebhookEventType",
-    "ConsolidationEventData",
-    "RetainEventData",
-]

hindsight-api-slim/hindsight_api/webhooks/manager.py

@@ -1,242 +0,0 @@
-"""Webhook manager for delivering event notifications."""
-
-import hashlib
-import hmac
-import json
-import logging
-import uuid
-from datetime import datetime, timezone
-from typing import TYPE_CHECKING
-
-import asyncpg
-
-from .models import WebhookConfig, WebhookEvent, WebhookHttpConfig
-
-if TYPE_CHECKING:
-    from hindsight_api.extensions.tenant import TenantExtension
-
-logger = logging.getLogger(__name__)
-
-# Retry delay schedule in seconds: 5 retries after the first attempt.
-# Fast early retries catch transient failures; later retries handle longer outages.
-RETRY_DELAYS = [5, 300, 1800, 7200, 18000]
-MAX_ATTEMPTS = len(RETRY_DELAYS) + 1  # first attempt + len(RETRY_DELAYS) retries
-
-
-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
-
-
-def _parse_http_config(value: str | dict | None) -> WebhookHttpConfig:
-    """Parse http_config column value (JSONB returned as text or dict) into a model."""
-    if value is None:
-        return WebhookHttpConfig()
-    if isinstance(value, str):
-        return WebhookHttpConfig.model_validate_json(value)
-    return WebhookHttpConfig.model_validate(value)
-
-
-class WebhookManager:
-    """
-    Manages webhook registration and event firing.
-
-    Supports both global webhooks (configured via env vars) and per-bank
-    webhooks stored in the database. Deliveries are queued as async_operations
-    tasks (operation_type='webhook_delivery') and picked up by the worker poller.
-    """
-
-    def __init__(
-        self,
-        pool: asyncpg.Pool,
-        global_webhooks: list[WebhookConfig],
-        tenant_extension: "TenantExtension | None" = None,
-    ):
-        self._pool = pool
-        self._global_webhooks = global_webhooks
-        self._tenant_extension = tenant_extension
-
-    def _sign_payload(self, secret: str, payload_bytes: bytes) -> str:
-        """Compute HMAC-SHA256 signature for a payload."""
-        return "sha256=" + hmac.new(secret.encode(), payload_bytes, hashlib.sha256).hexdigest()
-
-    async def fire_event(self, event: WebhookEvent, schema: str | None = None) -> None:
-        """
-        Queue webhook deliveries for an event as async_operations tasks.
-
-        Loads per-bank and global webhooks, inserts pending webhook_delivery tasks for
-        any webhook whose event_types list matches the fired event type. The worker
-        poller picks these up and calls MemoryEngine._handle_webhook_delivery().
-
-        Args:
-            event: The event to deliver.
-            schema: Database schema (for multi-tenant). None = default schema.
-        """
-        webhook_table = _fq_table("webhooks", schema)
-        ops_table = _fq_table("async_operations", schema)
-        now = datetime.now(timezone.utc)
-        payload_str = event.model_dump_json()
-
-        try:
-            # Load per-bank webhooks from DB (bank-specific + global NULL rows)
-            rows = await self._pool.fetch(
-                f"""
-                SELECT id, bank_id, url, secret, event_types, enabled, http_config::text
-                FROM {webhook_table}
-                WHERE (bank_id = $1 OR bank_id IS NULL) AND enabled = true
-                """,
-                event.bank_id,
-            )
-
-            db_webhooks = [
-                WebhookConfig(
-                    id=str(row["id"]),
-                    bank_id=row["bank_id"],
-                    url=row["url"],
-                    secret=row["secret"],
-                    event_types=list(row["event_types"]) if row["event_types"] else [],
-                    enabled=row["enabled"],
-                    http_config=_parse_http_config(row["http_config"]),
-                )
-                for row in rows
-            ]
-
-            # Merge with global webhooks from env config
-            all_webhooks = self._global_webhooks + db_webhooks
-            matched = 0
-
-            for webhook in all_webhooks:
-                if not webhook.enabled:
-                    continue
-                if event.event.value not in webhook.event_types:
-                    continue
-
-                operation_id = uuid.uuid4()
-                webhook_id = webhook.id if webhook.id else None
-
-                task_payload = json.dumps(
-                    {
-                        "type": "webhook_delivery",
-                        "operation_id": str(operation_id),
-                        "bank_id": event.bank_id,
-                        "url": webhook.url,
-                        "secret": webhook.secret,
-                        "event_type": event.event.value,
-                        "payload": payload_str,
-                        "webhook_id": webhook_id,
-                        "http_config": webhook.http_config.model_dump(),
-                    }
-                )
-
-                await self._pool.execute(
-                    f"""
-                    INSERT INTO {ops_table}
-                      (operation_id, bank_id, operation_type, status, task_payload, result_metadata, created_at, updated_at)
-                    VALUES ($1, $2, 'webhook_delivery', 'pending', $3::jsonb, '{{}}'::jsonb, $4, $4)
-                    """,
-                    operation_id,
-                    event.bank_id,
-                    task_payload,
-                    now,
-                )
-                matched += 1
-
-            logger.debug(f"Fired webhook event {event.event} for bank {event.bank_id}: {matched} delivery(ies) queued")
-
-        except Exception as e:
-            logger.error(f"Failed to queue webhook deliveries for event {event.event}: {e}")
-
-    async def fire_event_with_conn(
-        self, event: WebhookEvent, conn: asyncpg.Connection, schema: str | None = None
-    ) -> None:
-        """
-        Queue webhook deliveries within an existing database connection/transaction.
-
-        Identical to fire_event() but uses the provided connection instead of acquiring
-        one from the pool. Use this to atomically insert delivery tasks in the same
-        transaction as the primary operation (transactional outbox pattern).
-
-        Args:
-            event: The event to deliver.
-            conn: Existing asyncpg connection (may be inside an active transaction).
-            schema: Database schema (for multi-tenant). None = default schema.
-        """
-        webhook_table = _fq_table("webhooks", schema)
-        ops_table = _fq_table("async_operations", schema)
-        now = datetime.now(timezone.utc)
-        payload_str = event.model_dump_json()
-
-        try:
-            rows = await conn.fetch(
-                f"""
-                SELECT id, bank_id, url, secret, event_types, enabled, http_config::text
-                FROM {webhook_table}
-                WHERE (bank_id = $1 OR bank_id IS NULL) AND enabled = true
-                """,
-                event.bank_id,
-            )
-
-            db_webhooks = [
-                WebhookConfig(
-                    id=str(row["id"]),
-                    bank_id=row["bank_id"],
-                    url=row["url"],
-                    secret=row["secret"],
-                    event_types=list(row["event_types"]) if row["event_types"] else [],
-                    enabled=row["enabled"],
-                    http_config=_parse_http_config(row["http_config"]),
-                )
-                for row in rows
-            ]
-
-            all_webhooks = self._global_webhooks + db_webhooks
-            matched = 0
-
-            for webhook in all_webhooks:
-                if not webhook.enabled:
-                    continue
-                if event.event.value not in webhook.event_types:
-                    continue
-
-                operation_id = uuid.uuid4()
-                webhook_id = webhook.id if webhook.id else None
-
-                task_payload = json.dumps(
-                    {
-                        "type": "webhook_delivery",
-                        "operation_id": str(operation_id),
-                        "bank_id": event.bank_id,
-                        "url": webhook.url,
-                        "secret": webhook.secret,
-                        "event_type": event.event.value,
-                        "payload": payload_str,
-                        "webhook_id": webhook_id,
-                        "http_config": webhook.http_config.model_dump(),
-                    }
-                )
-
-                await conn.execute(
-                    f"""
-                    INSERT INTO {ops_table}
-                      (operation_id, bank_id, operation_type, status, task_payload, result_metadata, created_at, updated_at)
-                    VALUES ($1, $2, 'webhook_delivery', 'pending', $3::jsonb, '{{}}'::jsonb, $4, $4)
-                    """,
-                    operation_id,
-                    event.bank_id,
-                    task_payload,
-                    now,
-                )
-                matched += 1
-
-            logger.debug(
-                f"Fired webhook event {event.event} for bank {event.bank_id}: {matched} delivery(ies) queued (in-transaction)"
-            )
-
-        except Exception as e:
-            logger.error(
-                f"Failed to queue webhook deliveries (in-transaction) for event {event.event}: {e}. "
-                "CRITICAL: The enclosing database transaction is now aborted and will roll back all changes."
-            )
-            raise

hindsight-api-slim/hindsight_api/webhooks/models.py

@@ -1,51 +0,0 @@
-"""Pydantic models for the webhook system."""
-
-from datetime import datetime
-from enum import StrEnum
-
-from pydantic import BaseModel, Field
-
-
-class WebhookEventType(StrEnum):
-    CONSOLIDATION_COMPLETED = "consolidation.completed"
-    RETAIN_COMPLETED = "retain.completed"
-
-
-class ConsolidationEventData(BaseModel):
-    observations_created: int | None = None
-    observations_updated: int | None = None
-    observations_deleted: int | None = None
-    error_message: str | None = None
-
-
-class RetainEventData(BaseModel):
-    document_id: str | None = None
-    tags: list[str] | None = None
-
-
-class WebhookEvent(BaseModel):
-    event: WebhookEventType
-    bank_id: str
-    operation_id: str
-    status: str  # "completed" or "failed"
-    timestamp: datetime
-    data: ConsolidationEventData | RetainEventData
-
-
-class WebhookHttpConfig(BaseModel):
-    """HTTP delivery configuration for a webhook."""
-
-    method: str = Field(default="POST", description="HTTP method: GET or POST")
-    timeout_seconds: int = Field(default=30, description="HTTP request timeout in seconds")
-    headers: dict[str, str] = Field(default_factory=dict, description="Custom HTTP headers")
-    params: dict[str, str] = Field(default_factory=dict, description="Custom HTTP query parameters")
-
-
-class WebhookConfig(BaseModel):
-    id: str
-    bank_id: str | None
-    url: str
-    secret: str | None
-    event_types: list[str]
-    enabled: bool
-    http_config: WebhookHttpConfig = Field(default_factory=WebhookHttpConfig)

hindsight-api-slim/hindsight_api/worker/exceptions.py

@@ -1,9 +0,0 @@
-from datetime import datetime
-
-
-class RetryTaskAt(Exception):
-    """Raise from a task handler to schedule a retry at a specific time."""
-
-    def __init__(self, retry_at: datetime, message: str = ""):
-        self.retry_at = retry_at
-        super().__init__(message)

hindsight-api-slim/pyproject.toml

@@ -1,211 +0,0 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[project]
-name = "hindsight-api-slim"
-version = "0.4.22"
-description = "Hindsight: Agent Memory That Works Like Human Memory"
-readme = "README.md"
-requires-python = ">=3.11"
-dependencies = [
-    "asyncpg>=0.29.0",
-    "python-dotenv>=1.0.0",
-    "openai>=1.0.0",
-    "pydantic>=2.0.0",
-    "rich>=13.0.0",
-    "langchain-text-splitters>=0.3.0",
-    "fastapi[standard]>=0.120.3",
-    "uvicorn>=0.38.0",
-    "wsproto>=1.0.0",
-    "sqlalchemy>=2.0.44",
-    "alembic>=1.17.1",
-    "pgvector>=0.4.1",
-    "greenlet>=3.2.4",
-    "psycopg2-binary>=2.9.11",
-    "tiktoken>=0.12.0",
-    "httpx>=0.27.0",
-    "PyJWT[crypto]>=2.8.0",
-    "fastmcp>=2.14.0", # CVE-2025-66416
-    "python-dateutil>=2.8.0",
-    "opentelemetry-api>=1.20.0",
-    "opentelemetry-sdk>=1.20.0",
-    "opentelemetry-instrumentation-fastapi>=0.41b0",
-    "opentelemetry-exporter-prometheus>=0.41b0",
-    "opentelemetry-exporter-otlp-proto-http>=1.20.0",
-    "opentelemetry-semantic-conventions>=0.41b0",
-    "dateparser>=1.2.2",
-    "google-genai>=1.0.0",
-    "google-auth>=2.0.0",
-    "anthropic>=0.40.0",
-    "typer>=0.9.0",
-    "cohere>=5.0.0",
-    "litellm>=1.0.0,<=1.82.6", # 1.82.7+ contains a supply chain attack (malicious .pth credential stealer)
-    "markitdown[pdf,docx,pptx,xlsx,xls]>=0.1.4", # File to markdown conversion
-    "obstore>=0.4.0", # S3/GCS/Azure object storage client (Rust-backed)
-    "winloop>=0.1.0; sys_platform == 'win32'",
-    "uvloop>=0.22.1; sys_platform != 'win32'",
-    # Transitive dependency security fixes
-    "pyasn1>=0.6.3", # DoS vulnerability fix
-    "urllib3>=2.6.3", # Decompression-bomb safeguards bypass fix
-    "langchain-core>=1.2.11", # Serialization injection + SSRF vulnerability fix
-    "langsmith>=0.6.3", # SSRF via tracing header injection fix
-    "protobuf>=6.33.5", # JSON recursion depth bypass fix
-    "pillow>=12.1.1", # Out-of-bounds write in PSD image loading fix
-    "cryptography>=46.0.5", # Subgroup attack vulnerability fix
-    "filelock>=3.20.1", # TOCTOU race condition fix
-    "authlib>=1.6.9", # Account takeover/JWS header injection vulnerability fix
-    "pyjwt>=2.12.0", # Accepts unknown crit header extensions fix
-    "orjson>=3.11.6", # Unbounded recursion DoS fix
-    "python-multipart>=0.0.22", # Arbitrary file write via non-default configuration fix
-    "tornado>=6.5.5", # DoS multipart/incomplete cookie validation fix
-    "aiohttp>=3.13.3", # Multiple DoS vulnerabilities
-    "claude-agent-sdk>=0.1.27",
-    "boto3>=1.42.74",
-]
-
-[project.optional-dependencies]
-local-ml = [
-    # Local ML models for embeddings/reranking
-    "sentence-transformers>=3.3.0",
-    "transformers>=4.53.0", # Security fixes for ReDoS vulnerabilities
-    "torch>=2.6.0", # CVE fix for remote code execution
-    "einops>=0.8.2",
-    "flashrank>=0.2.0",
-    # Apple Silicon local inference
-    "mlx>=0.31.0",
-    "mlx-lm>=0.31.1",
-    "safetensors>=0.6.2",
-]
-embedded-db = [
-    "pg0-embedded>=0.11.0",
-]
-all = [
-    "hindsight-api-slim[local-ml,embedded-db]",
-]
-test = [
-    "pytest>=7.0.0",
-    "pytest-asyncio>=0.21.0",
-    "pytest-timeout>=2.4.0",
-    "pytest-xdist>=3.0.0",
-    "filelock>=3.20.1",  # TOCTOU race condition fix
-    "testcontainers>=4.0.0",
-]
-
-[project.scripts]
-hindsight-api = "hindsight_api.main:main"
-hindsight-worker = "hindsight_api.worker.main:main"
-hindsight-local-mcp = "hindsight_api.mcp_local:main"
-hindsight-admin = "hindsight_api.admin.cli:main"
-
-[tool.hatch.build.targets.wheel]
-packages = ["hindsight_api"]
-
-[tool.hatch.build.targets.wheel.sources]
-"hindsight_api" = "hindsight_api"
-
-[tool.hatch.build.targets.sdist]
-include = [
-    "hindsight_api/**/*",
-]
-
-[tool.hatch.build]
-include = [
-    "hindsight_api/**/*.py",
-    "hindsight_api/alembic/**/*",
-]
-
-[tool.pytest.ini_options]
-log_cli = true
-log_cli_level = "INFO"
-log_cli_format = "%(asctime)s - %(levelname)s - %(name)s - %(message)s"
-log_cli_date_format = "%Y-%m-%d %H:%M:%S"
-addopts = "--timeout 300 -n 8 --dist loadgroup --durations=10 -v"
-asyncio_mode = "auto"
-asyncio_default_fixture_loop_scope = "function"
-log_auto_indent = true
-filterwarnings = [
-    "ignore:The @wait_container_is_ready decorator is deprecated:DeprecationWarning",
-    "ignore::RuntimeWarning:asyncio",
-]
-
-[dependency-groups]
-dev = [
-    "pytest>=9.0.0",
-    "pytest-asyncio>=1.3.0",
-    "pytest-timeout>=2.4.0",
-    "pytest-xdist>=3.8.0",
-    "python-dotenv>=1.2.1",
-    "filelock>=3.20.1",  # TOCTOU race condition fix
-    "ruff>=0.8.0",
-    "ty>=0.0.1",
-    "testcontainers>=4.0.0",
-]
-
-[tool.ruff]
-line-length = 120
-target-version = "py311"
-exclude = [
-    "tests/",
-    "**/tests/",
-]
-
-[tool.ruff.lint]
-select = [
-    "E",      # pycodestyle errors
-    "W",      # pycodestyle warnings
-    "F",      # Pyflakes
-    "I",      # isort
-]
-ignore = [
-    "E501",   # line too long (handled by formatter)
-    "E402",   # module import not at top of file
-    "F401",   # unused import (too noisy during development)
-    "F841",   # unused variable (too noisy during development)
-    "F811",   # redefined while unused
-    "F821",   # undefined name (forward references in type hints)
-]
-
-[tool.ruff.lint.isort]
-known-third-party = ["alembic"]
-
-[tool.ruff.format]
-quote-style = "double"
-indent-style = "space"
-
-[tool.uv]
-# Use explicit index for PyTorch to prevent the pytorch index from serving
-# non-pytorch packages (e.g. markupsafe) with incompatible wheels
-[[tool.uv.index]]
-name = "pytorch-cpu"
-url = "https://download.pytorch.org/whl/cpu"
-explicit = true
-
-[tool.uv.sources]
-# Route torch to the CPU-only PyTorch index; everything else uses PyPI
-torch = { index = "pytorch-cpu" }
-
-[tool.ty]
-# Type checking configuration
-# ty is an extremely fast Python type checker from Astral (same team as ruff/uv)
-
-[tool.ty.environment]
-python-version = "3.11"
-
-[tool.ty.src]
-exclude = [
-    "tests/",
-    "hindsight_api/alembic/",
-]
-
-[tool.ty.rules]
-# Disable noisy rules while keeping important ones
-invalid-argument-type = "ignore"    # False positives with **kwargs patterns
-invalid-return-type = "ignore"      # Often intentional in async code
-invalid-parameter-default = "ignore" # Optional params with None default
-possibly-missing-attribute = "ignore" # Common with Optional types
-invalid-raise = "ignore"            # False positives with exception tracking
-call-non-callable = "ignore"        # False positives with Optional types
-invalid-key = "ignore"              # Pydantic ConfigDict not understood
-invalid-method-override = "ignore"  # Intentional signature differences
-unresolved-reference = "ignore"     # Forward references not always resolved

hindsight-api-slim/tests/test_async_batch_retain.py

@@ -1,435 +0,0 @@
-"""Test async batch retain with smart batching and parent-child operations."""
-
-import asyncio
-import json
-import uuid
-
-import pytest
-
-from hindsight_api.extensions import RequestContext
-
-
-async def _ensure_bank(pool, bank_id: str) -> None:
-    """Upsert a minimal bank row so FK on async_operations passes."""
-    await pool.execute(
-        "INSERT INTO banks (bank_id, name) VALUES ($1, $2) ON CONFLICT DO NOTHING",
-        bank_id,
-        bank_id,
-    )
-
-
-@pytest.mark.asyncio
-async def test_duplicate_document_ids_rejected_async(memory, request_context):
-    """Test that async retain rejects batches with duplicate document_ids."""
-    bank_id = "test_duplicate_async"
-    contents = [
-        {"content": "First item", "document_id": "doc1"},
-        {"content": "Second item", "document_id": "doc2"},
-        {"content": "Third item", "document_id": "doc1"},  # Duplicate!
-    ]
-
-    # Should raise ValueError due to duplicate document_ids
-    with pytest.raises(ValueError, match="duplicate document_ids.*doc1"):
-        await memory.submit_async_retain(
-            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
-        )
-
-
-@pytest.mark.asyncio
-async def test_duplicate_document_ids_rejected_sync(memory, request_context):
-    """Test that sync retain also rejects batches with duplicate document_ids."""
-    bank_id = "test_duplicate_sync"
-    contents = [
-        {"content": "First item", "document_id": "doc1"},
-        {"content": "Second item", "document_id": "doc1"},  # Duplicate!
-    ]
-
-    # Should raise ValueError due to duplicate document_ids
-    with pytest.raises(ValueError, match="duplicate document_ids.*doc1"):
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
-        )
-
-
-@pytest.mark.asyncio
-async def test_small_async_batch_no_splitting(memory, request_context):
-    """Test that small async batches create parent with single child (simplified code path)."""
-    bank_id = "test_small_async"
-    contents = [{"content": "Alice works at Google", "document_id": f"doc{i}"} for i in range(5)]
-
-    # Calculate total chars (should be well under threshold)
-    total_chars = sum(len(item["content"]) for item in contents)
-    assert total_chars < 10_000, "Test batch should be small"
-
-    # Submit async retain
-    result = await memory.submit_async_retain(
-        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
-    )
-
-    # Verify we got an operation_id back
-    assert "operation_id" in result
-    assert "items_count" in result
-    assert result["items_count"] == 5
-
-    operation_id = result["operation_id"]
-
-    # Wait for task to complete (SyncTaskBackend executes immediately)
-    await asyncio.sleep(0.1)
-
-    # Check operation status
-    status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=operation_id,
-        request_context=request_context,
-    )
-
-    # Should be a parent operation with single child (simplified code path)
-    assert status["status"] == "completed"
-    assert status["operation_type"] == "batch_retain"
-    assert "child_operations" in status
-    assert status["result_metadata"]["num_sub_batches"] == 1  # Single sub-batch
-    assert len(status["child_operations"]) == 1
-    assert status["child_operations"][0]["status"] == "completed"
-
-
-@pytest.mark.asyncio
-async def test_large_async_batch_auto_splits(memory, request_context):
-    """Test that large async batches automatically split into sub-batches with parent operation."""
-    from hindsight_api.engine.memory_engine import count_tokens
-
-    bank_id = "test_large_async"
-
-    # Create a large batch that exceeds the threshold (10k tokens default)
-    # Repeating "A"s gets heavily compressed by tokenizer, use varied content
-    # Use ~22k chars per item = ~5.5k tokens per item, 2 items = ~11k tokens total (exceeds 10k)
-    large_content = "The quick brown fox jumps over the lazy dog. " * 500  # ~22k chars = ~5.5k tokens
-    contents = [{"content": large_content + f" item {i}", "document_id": f"doc{i}"} for i in range(2)]
-
-    # Calculate total tokens (should exceed threshold)
-    total_tokens = sum(count_tokens(item["content"]) for item in contents)
-    assert total_tokens > 10_000, "Test batch should exceed threshold"
-
-    # Submit async retain
-    result = await memory.submit_async_retain(
-        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
-    )
-
-    # Verify we got an operation_id back
-    assert "operation_id" in result
-    assert "items_count" in result
-    assert result["items_count"] == 2
-
-    parent_operation_id = result["operation_id"]
-
-    # Wait for tasks to complete
-    await asyncio.sleep(0.5)
-
-    # Check parent operation status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=parent_operation_id,
-        request_context=request_context,
-    )
-
-    # Should be a parent operation with children
-    assert parent_status["operation_type"] == "batch_retain"
-    assert "child_operations" in parent_status
-    assert "num_sub_batches" in parent_status["result_metadata"]
-    assert parent_status["result_metadata"]["num_sub_batches"] >= 2  # Should split into at least 2 batches
-    assert parent_status["result_metadata"]["items_count"] == 2
-
-    # Verify child operations
-    child_ops = parent_status["child_operations"]
-    assert len(child_ops) >= 2, "Should have at least 2 child operations"
-
-    # All children should be completed (SyncTaskBackend executes immediately)
-    for child in child_ops:
-        assert child["status"] == "completed"
-        assert child["sub_batch_index"] is not None
-        assert child["items_count"] > 0
-
-    # Parent status should be aggregated as "completed"
-    assert parent_status["status"] == "completed"
-
-
-@pytest.mark.asyncio
-async def test_parent_operation_status_aggregation_pending(memory, request_context):
-    """Test that parent operation shows 'pending' when children are pending."""
-    bank_id = "test_parent_pending"
-    pool = await memory._get_pool()
-    await _ensure_bank(pool, bank_id)
-
-    # Manually create a parent operation
-    parent_id = uuid.uuid4()
-    async with pool.acquire() as conn:
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            parent_id,
-            bank_id,
-            "batch_retain",
-            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
-            "pending",
-        )
-
-        # Create 2 child operations - one completed, one pending
-        child1_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child1_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 1,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-        child2_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child2_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 2,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "pending",
-        )
-
-    # Check parent status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=str(parent_id),
-        request_context=request_context,
-    )
-
-    # Parent should aggregate as "pending" since one child is still pending
-    assert parent_status["status"] == "pending"
-    assert len(parent_status["child_operations"]) == 2
-
-
-@pytest.mark.asyncio
-async def test_parent_operation_status_aggregation_failed(memory, request_context):
-    """Test that parent operation shows 'failed' when any child fails."""
-    bank_id = "test_parent_failed"
-    pool = await memory._get_pool()
-    await _ensure_bank(pool, bank_id)
-
-    # Manually create a parent operation
-    parent_id = uuid.uuid4()
-    async with pool.acquire() as conn:
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            parent_id,
-            bank_id,
-            "batch_retain",
-            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
-            "pending",
-        )
-
-        # Create 2 child operations - one completed, one failed
-        child1_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child1_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 1,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-        child2_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status, error_message)
-            VALUES ($1, $2, $3, $4, $5, $6)
-            """,
-            child2_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 2,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "failed",
-            "Test error",
-        )
-
-    # Check parent status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=str(parent_id),
-        request_context=request_context,
-    )
-
-    # Parent should aggregate as "failed" since one child failed
-    assert parent_status["status"] == "failed"
-    assert len(parent_status["child_operations"]) == 2
-
-    # Verify child with error is included
-    failed_child = [c for c in parent_status["child_operations"] if c["status"] == "failed"][0]
-    assert failed_child["error_message"] == "Test error"
-
-
-@pytest.mark.asyncio
-async def test_parent_operation_status_aggregation_completed(memory, request_context):
-    """Test that parent operation shows 'completed' when all children are completed."""
-    bank_id = "test_parent_completed"
-    pool = await memory._get_pool()
-    await _ensure_bank(pool, bank_id)
-
-    # Manually create a parent operation
-    parent_id = uuid.uuid4()
-    async with pool.acquire() as conn:
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            parent_id,
-            bank_id,
-            "batch_retain",
-            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
-            "pending",
-        )
-
-        # Create 2 child operations - both completed
-        child1_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child1_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 1,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-        child2_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child2_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 2,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-    # Check parent status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=str(parent_id),
-        request_context=request_context,
-    )
-
-    # Parent should aggregate as "completed" since all children are completed
-    assert parent_status["status"] == "completed"
-    assert len(parent_status["child_operations"]) == 2
-    assert all(c["status"] == "completed" for c in parent_status["child_operations"])
-
-
-@pytest.mark.asyncio
-async def test_config_retain_batch_tokens_respected(memory, request_context):
-    """Test that the retain_batch_tokens config setting is respected."""
-    from hindsight_api.config import get_config
-    from hindsight_api.engine.memory_engine import count_tokens
-
-    bank_id = "test_config_batch_tokens"
-    config = get_config()
-
-    # Check that config has the retain_batch_tokens setting
-    assert hasattr(config, "retain_batch_tokens")
-    assert config.retain_batch_tokens > 0
-
-    # Create a batch that's just under the threshold
-    # Use content that produces roughly half the token limit per item
-    content_size = config.retain_batch_tokens * 2  # chars (rough estimate: 1 token ~= 4 chars)
-    contents = [{"content": "A" * content_size, "document_id": f"doc{i}"} for i in range(2)]
-
-    total_tokens = sum(count_tokens(item["content"]) for item in contents)
-    # Should be equal to threshold (boundary case, no splitting since we use > not >=)
-    assert total_tokens <= config.retain_batch_tokens
-
-    # Submit - should NOT split
-    result = await memory.submit_async_retain(
-        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
-    )
-
-    # Wait for completion
-    await asyncio.sleep(0.1)
-
-    # Check status - should be a parent with single child (even for small batches)
-    status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=result["operation_id"],
-        request_context=request_context,
-    )
-
-    # Even small batches use parent-child pattern now (simpler code path)
-    assert "child_operations" in status
-    assert status["result_metadata"]["num_sub_batches"] == 1

hindsight-api-slim/tests/test_async_retain_tags.py

@@ -1,95 +0,0 @@
-"""Unit tests for async retain tag propagation."""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from hindsight_api.engine.memory_engine import MemoryEngine
-from hindsight_api.models import RequestContext
-
-
-@pytest.mark.asyncio
-async def test_submit_async_retain_includes_document_tags_in_task_payload():
-    """submit_async_retain should include document_tags in queued task payload."""
-    engine = MemoryEngine.__new__(MemoryEngine)
-    engine._initialized = True
-    engine._authenticate_tenant = AsyncMock()
-    engine._operation_validator = None
-    engine._submit_async_operation = AsyncMock(return_value={"operation_id": "op-1"})
-
-    # Mock the pool and connection for parent operation creation
-    mock_conn = AsyncMock()
-    mock_conn.execute = AsyncMock()
-    mock_conn.transaction = MagicMock()
-    mock_conn.transaction.return_value.__aenter__ = AsyncMock()
-    mock_conn.transaction.return_value.__aexit__ = AsyncMock()
-
-    mock_pool = AsyncMock()
-    mock_pool.acquire = AsyncMock(return_value=mock_conn)
-    mock_pool.release = AsyncMock()
-
-    engine._get_pool = AsyncMock(return_value=mock_pool)
-
-    request_context = RequestContext(tenant_id="tenant-a", api_key_id="key-a")
-    contents = [{"content": "Async retain payload test."}]
-    document_tags = ["scope:tools", "user:alice"]
-
-    with patch("hindsight_api.engine.memory_engine.bank_utils.get_bank_profile", new_callable=AsyncMock):
-        result = await MemoryEngine.submit_async_retain(
-            engine,
-            bank_id="bank-1",
-            contents=contents,
-            document_tags=document_tags,
-            request_context=request_context,
-        )
-
-    # Check result structure
-    assert "operation_id" in result
-    assert "items_count" in result
-    assert result["items_count"] == 1
-
-    # Verify authentication was called
-    engine._authenticate_tenant.assert_awaited_once_with(request_context)
-
-    # Verify child operation was submitted
-    engine._submit_async_operation.assert_awaited_once()
-
-    # Verify child operation payload contains document_tags
-    kwargs = engine._submit_async_operation.await_args.kwargs
-    assert kwargs["bank_id"] == "bank-1"
-    assert kwargs["operation_type"] == "retain"
-    assert kwargs["task_type"] == "batch_retain"
-    assert kwargs["task_payload"]["contents"] == contents
-    assert kwargs["task_payload"]["document_tags"] == document_tags
-    assert kwargs["task_payload"]["_tenant_id"] == "tenant-a"
-    assert kwargs["task_payload"]["_api_key_id"] == "key-a"
-
-
-@pytest.mark.asyncio
-async def test_handle_batch_retain_forwards_document_tags_to_retain_batch_async():
-    """Worker handler should forward document_tags from task payload."""
-    engine = MemoryEngine.__new__(MemoryEngine)
-    engine._initialized = True
-    engine.retain_batch_async = AsyncMock(return_value={"items_count": 1})
-
-    task_dict = {
-        "bank_id": "bank-1",
-        "contents": [{"content": "Forward tags test."}],
-        "document_tags": ["scope:client"],
-        "_tenant_id": "tenant-a",
-        "_api_key_id": "key-a",
-    }
-
-    await MemoryEngine._handle_batch_retain(engine, task_dict)
-
-    engine.retain_batch_async.assert_awaited_once()
-    kwargs = engine.retain_batch_async.await_args.kwargs
-    assert kwargs["bank_id"] == "bank-1"
-    assert kwargs["contents"] == task_dict["contents"]
-    assert kwargs["document_tags"] == ["scope:client"]
-
-    request_context = kwargs["request_context"]
-    assert request_context.internal is True
-    assert request_context.user_initiated is True
-    assert request_context.tenant_id == "tenant-a"
-    assert request_context.api_key_id == "key-a"

hindsight-api-slim/tests/test_audit_log.py

@@ -1,449 +0,0 @@
-"""
-Tests for the audit log feature.
-
-Tests the audit log list, stats, filtering, and pagination endpoints.
-Verifies that audit entries are created for operations when audit logging is enabled.
-"""
-
-import asyncio
-
-import httpx
-import pytest
-import pytest_asyncio
-
-from hindsight_api.api import create_app
-from hindsight_api.config import get_config
-
-
-@pytest_asyncio.fixture
-async def audit_api_client(memory):
-    """Create a test client with audit logging enabled."""
-    # Enable audit logging on the memory engine's audit logger
-    memory._audit_logger._enabled = True
-    memory._audit_logger._allowed_actions = None  # All actions
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        yield client
-
-
-@pytest.fixture
-def bank_id():
-    """Provide a unique bank ID for audit tests."""
-    from datetime import datetime
-
-    return f"audit_test_{datetime.now().timestamp()}"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_list_empty(audit_api_client, bank_id):
-    """Test listing audit logs for a bank with no entries returns empty."""
-    # Create the bank first
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    # Small delay for fire-and-forget audit writes
-    await asyncio.sleep(0.5)
-
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["bank_id"] == bank_id
-    assert "total" in data
-    assert "items" in data
-    assert "limit" in data
-    assert "offset" in data
-    assert isinstance(data["items"], list)
-
-
-@pytest.mark.asyncio
-async def test_audit_log_created_for_retain(audit_api_client, bank_id):
-    """Test that a retain operation creates an audit log entry."""
-    # Create bank
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    # Perform a retain
-    response = await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories",
-        json={
-            "items": [{"content": "Alice likes cats", "context": "preferences"}],
-        },
-    )
-    assert response.status_code == 200
-
-    # Wait for fire-and-forget audit writes
-    await asyncio.sleep(1.0)
-
-    # List audit logs - should have entries for create_bank and retain
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] >= 1
-
-    actions = [item["action"] for item in data["items"]]
-    assert "retain" in actions, f"Expected 'retain' in audit actions, got: {actions}"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_entry_fields(audit_api_client, bank_id):
-    """Test that audit log entries have all expected fields."""
-    # Create bank + recall to generate entries
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "test query"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] >= 1
-
-    # Check the recall entry has all fields
-    recall_entries = [item for item in data["items"] if item["action"] == "recall"]
-    assert len(recall_entries) >= 1, f"Expected recall entry, got actions: {[i['action'] for i in data['items']]}"
-
-    entry = recall_entries[0]
-    assert entry["id"] is not None
-    assert entry["action"] == "recall"
-    assert entry["transport"] == "http"
-    assert entry["bank_id"] == bank_id
-    assert entry["started_at"] is not None
-    assert entry["ended_at"] is not None
-    # Request should contain the recall parameters
-    assert entry["request"] is not None
-    assert "query" in entry["request"]
-    # Response should contain the recall results
-    assert entry["response"] is not None
-
-
-@pytest.mark.asyncio
-async def test_audit_log_filter_by_action(audit_api_client, bank_id):
-    """Test filtering audit logs by action type."""
-    # Create bank and do retain + recall
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories",
-        json={"items": [{"content": "test content", "context": "test"}]},
-    )
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "test"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    # Filter by retain only
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"action": "retain"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for item in data["items"]:
-        assert item["action"] == "retain"
-
-    # Filter by recall only
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"action": "recall"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for item in data["items"]:
-        assert item["action"] == "recall"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_filter_by_transport(audit_api_client, bank_id):
-    """Test filtering audit logs by transport type."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await asyncio.sleep(0.5)
-
-    # Filter by http transport
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"transport": "http"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for item in data["items"]:
-        assert item["transport"] == "http"
-
-    # Filter by mcp transport - should be empty (no MCP calls in this test)
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"transport": "mcp"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] == 0
-
-
-@pytest.mark.asyncio
-async def test_audit_log_filter_by_date_range(audit_api_client, bank_id):
-    """Test filtering audit logs by date range."""
-    from datetime import datetime, timedelta, timezone
-
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await asyncio.sleep(0.5)
-
-    now = datetime.now(timezone.utc)
-
-    # Filter with start_date in the past - should include entries
-    past = (now - timedelta(hours=1)).isoformat()
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"start_date": past},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] >= 1
-
-    # Filter with start_date in the future - should be empty
-    future = (now + timedelta(hours=1)).isoformat()
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"start_date": future},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] == 0
-
-
-@pytest.mark.asyncio
-async def test_audit_log_pagination(audit_api_client, bank_id):
-    """Test audit log pagination with limit and offset."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    # Generate multiple audit entries
-    for i in range(5):
-        await audit_api_client.post(
-            f"/v1/default/banks/{bank_id}/memories/recall",
-            json={"query": f"test query {i}"},
-        )
-
-    await asyncio.sleep(1.5)
-
-    # Get first page
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"limit": 2, "offset": 0},
-    )
-    assert response.status_code == 200
-    page1 = response.json()
-    assert len(page1["items"]) == 2
-    assert page1["limit"] == 2
-    assert page1["offset"] == 0
-    assert page1["total"] >= 5  # At least 5 recall + 1 create_bank
-
-    # Get second page
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"limit": 2, "offset": 2},
-    )
-    assert response.status_code == 200
-    page2 = response.json()
-    assert len(page2["items"]) == 2
-    assert page2["offset"] == 2
-
-    # Entries should be different between pages
-    page1_ids = {item["id"] for item in page1["items"]}
-    page2_ids = {item["id"] for item in page2["items"]}
-    assert page1_ids.isdisjoint(page2_ids), "Pages should not overlap"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_stats(audit_api_client, bank_id):
-    """Test the audit log stats endpoint returns correct structure."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "stats test"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    # Get stats for last 24h
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs/stats",
-        params={"period": "1d"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["bank_id"] == bank_id
-    assert data["period"] == "1d"
-    assert data["trunc"] == "day"
-    assert "buckets" in data
-    assert isinstance(data["buckets"], list)
-
-    # Should have at least one bucket with our operations
-    assert len(data["buckets"]) >= 1
-    bucket = data["buckets"][0]
-    assert "time" in bucket
-    assert "actions" in bucket
-    assert "total" in bucket
-    assert bucket["total"] >= 1
-
-
-@pytest.mark.asyncio
-async def test_audit_log_stats_filter_by_action(audit_api_client, bank_id):
-    """Test stats endpoint filters by action."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "test"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    # Stats filtered by recall
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs/stats",
-        params={"period": "1d", "action": "recall"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for bucket in data["buckets"]:
-        # All actions in buckets should be "recall" only
-        for action_name in bucket["actions"]:
-            assert action_name == "recall"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_stats_periods(audit_api_client, bank_id):
-    """Test stats endpoint supports different periods."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await asyncio.sleep(0.5)
-
-    for period, expected_trunc in [("1d", "day"), ("7d", "day"), ("30d", "day")]:
-        response = await audit_api_client.get(
-            f"/v1/default/banks/{bank_id}/audit-logs/stats",
-            params={"period": period},
-        )
-        assert response.status_code == 200
-        data = response.json()
-        assert data["period"] == period
-        assert data["trunc"] == expected_trunc
-
-
-@pytest.mark.asyncio
-async def test_audit_log_disabled(memory):
-    """Test that no audit logs are created when audit logging is disabled."""
-    # Ensure audit logging is disabled
-    memory._audit_logger._enabled = False
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        from datetime import datetime
-
-        bid = f"audit_disabled_test_{datetime.now().timestamp()}"
-
-        await client.put(f"/v1/default/banks/{bid}", json={"name": "No Audit"})
-        await client.post(
-            f"/v1/default/banks/{bid}/memories/recall",
-            json={"query": "test"},
-        )
-
-        await asyncio.sleep(0.5)
-
-        response = await client.get(f"/v1/default/banks/{bid}/audit-logs")
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total"] == 0, "No audit entries should exist when audit logging is disabled"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_action_allowlist(memory):
-    """Test that only allowed actions are audited when allowlist is set."""
-    memory._audit_logger._enabled = True
-    memory._audit_logger._allowed_actions = frozenset({"recall"})  # Only audit recall
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        from datetime import datetime
-
-        bid = f"audit_allowlist_test_{datetime.now().timestamp()}"
-
-        # create_bank should NOT be audited
-        await client.put(f"/v1/default/banks/{bid}", json={"name": "Allowlist Test"})
-        # recall should be audited
-        await client.post(
-            f"/v1/default/banks/{bid}/memories/recall",
-            json={"query": "allowlist test"},
-        )
-
-        await asyncio.sleep(1.0)
-
-        response = await client.get(f"/v1/default/banks/{bid}/audit-logs")
-        assert response.status_code == 200
-        data = response.json()
-        actions = [item["action"] for item in data["items"]]
-        assert "recall" in actions, "recall should be audited"
-        assert "create_bank" not in actions, "create_bank should NOT be audited (not in allowlist)"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_ordered_by_most_recent(audit_api_client, bank_id):
-    """Test that audit logs are returned ordered by most recent first."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Order Test Bank"},
-    )
-
-    for i in range(3):
-        await audit_api_client.post(
-            f"/v1/default/banks/{bank_id}/memories/recall",
-            json={"query": f"order test {i}"},
-        )
-        await asyncio.sleep(0.2)  # Small gap between requests
-
-    await asyncio.sleep(1.0)
-
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-
-    # Check descending order by started_at
-    timestamps = [item["started_at"] for item in data["items"] if item["started_at"]]
-    assert timestamps == sorted(timestamps, reverse=True), "Audit logs should be ordered most recent first"

hindsight-api-slim/tests/test_base_path.py

@@ -1,189 +0,0 @@
-"""
-Integration test for API base path support.
-
-Tests that the API works correctly when deployed with a base path (e.g., /hindsight)
-for reverse proxy deployments.
-"""
-import os
-import pytest
-import pytest_asyncio
-import httpx
-from hindsight_api.api import create_app
-from hindsight_api.config import clear_config_cache
-
-
-@pytest_asyncio.fixture
-async def api_client_with_base_path(memory):
-    """Create an async test client for the FastAPI app with a base path."""
-    # Set base path in environment
-    base_path = "/hindsight"
-    os.environ["HINDSIGHT_API_BASE_PATH"] = base_path
-
-    # Clear config cache to force reload with new base_path
-    clear_config_cache()
-
-    # Memory is already initialized by the conftest fixture (with migrations)
-    app = create_app(memory, initialize_memory=False)
-
-    # Use base_url with base path
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(
-        transport=transport,
-        base_url=f"http://test{base_path}"
-    ) as client:
-        yield client
-
-    # Cleanup: unset base path
-    os.environ.pop("HINDSIGHT_API_BASE_PATH", None)
-    clear_config_cache()
-
-
-@pytest_asyncio.fixture
-async def api_client_without_base_path(memory):
-    """Create an async test client for the FastAPI app without a base path (root)."""
-    # Ensure no base path is set
-    os.environ.pop("HINDSIGHT_API_BASE_PATH", None)
-    clear_config_cache()
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        yield client
-
-
-@pytest.mark.asyncio
-async def test_base_path_health_endpoint(api_client_with_base_path):
-    """Test that health endpoint works with base path."""
-    # With base path set to /hindsight, health should be at /hindsight/health
-    # But since our client base_url is already http://test/hindsight, we request /health
-    response = await api_client_with_base_path.get("/health")
-    assert response.status_code == 200
-    data = response.json()
-    assert "status" in data
-    assert data["status"] in ["ok", "healthy"]  # Accept both formats
-
-
-@pytest.mark.asyncio
-async def test_base_path_banks_endpoint(api_client_with_base_path):
-    """Test that banks endpoint works with base path."""
-    response = await api_client_with_base_path.get("/v1/default/banks")
-    assert response.status_code == 200
-    data = response.json()
-    assert "banks" in data
-
-
-@pytest.mark.asyncio
-async def test_base_path_openapi_schema(api_client_with_base_path):
-    """Test that OpenAPI schema includes correct base path in servers."""
-    response = await api_client_with_base_path.get("/openapi.json")
-    assert response.status_code == 200
-    openapi_schema = response.json()
-
-    # Check that servers array includes base path
-    assert "servers" in openapi_schema
-    servers = openapi_schema["servers"]
-    assert len(servers) > 0
-    # FastAPI should set server URL to the root_path
-    assert servers[0]["url"] == "/hindsight"
-
-
-@pytest.mark.asyncio
-async def test_base_path_docs_redirect(api_client_with_base_path):
-    """Test that /docs redirects correctly with base path."""
-    # FastAPI docs endpoint should work
-    response = await api_client_with_base_path.get("/docs", follow_redirects=False)
-    # Should either return 200 (direct) or 307 (redirect to trailing slash)
-    assert response.status_code in [200, 307]
-
-
-@pytest.mark.asyncio
-async def test_base_path_metrics(api_client_with_base_path):
-    """Test that metrics endpoint works with base path."""
-    response = await api_client_with_base_path.get("/metrics")
-    assert response.status_code == 200
-    # Metrics should be in Prometheus format
-    assert "# HELP" in response.text or "# TYPE" in response.text
-
-
-@pytest.mark.asyncio
-async def test_base_path_full_workflow(api_client_with_base_path):
-    """
-    Test a full retain/recall workflow with base path.
-
-    This ensures that all memory operations work correctly when the API
-    is deployed with a base path.
-    """
-    bank_id = "test_base_path_bank"
-
-    # 1. Create/get bank
-    response = await api_client_with_base_path.get(f"/v1/default/banks/{bank_id}/profile")
-    assert response.status_code == 200
-
-    # 2. Store a memory
-    response = await api_client_with_base_path.post(
-        f"/v1/default/banks/{bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "The API supports base path deployment for reverse proxy use cases.",
-                    "context": "testing base path feature"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-    assert result["success"] is True
-
-    # 3. Recall the memory
-    response = await api_client_with_base_path.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={
-            "query": "base path support"
-        }
-    )
-    assert response.status_code == 200
-    recall_result = response.json()
-    # API returns "results" not "memories"
-    assert "results" in recall_result
-    assert len(recall_result["results"]) > 0
-
-
-@pytest.mark.asyncio
-async def test_without_base_path_still_works(api_client_without_base_path):
-    """
-    Regression test: ensure default behavior (no base path) still works.
-
-    This test verifies that when HINDSIGHT_API_BASE_PATH is not set,
-    the API works at the root path as before.
-    """
-    # Health check at root
-    response = await api_client_without_base_path.get("/health")
-    assert response.status_code == 200
-
-    # Banks endpoint at root
-    response = await api_client_without_base_path.get("/v1/default/banks")
-    assert response.status_code == 200
-
-    # OpenAPI schema should have empty or "/" server path
-    response = await api_client_without_base_path.get("/openapi.json")
-    assert response.status_code == 200
-    openapi_schema = response.json()
-    servers = openapi_schema.get("servers", [])
-    if servers:
-        # Server URL should be empty string (root) or "/"
-        assert servers[0]["url"] in ["", "/"]
-
-
-@pytest.mark.skip(reason="MCP endpoint routing with base path needs investigation")
-@pytest.mark.asyncio
-async def test_base_path_mcp_endpoint(api_client_with_base_path):
-    """Test that MCP endpoint is accessible with base path."""
-    bank_id = "test_mcp_bank"
-
-    # MCP endpoint should be mounted at /mcp/{bank_id}/
-    # The MCP server uses a different protocol, so just check the root exists
-    response = await api_client_with_base_path.get(f"/mcp/{bank_id}/")
-    # MCP may return various status codes, but should not be 404 (not found)
-    # Accept 405 (method not allowed), 400 (bad request), etc.
-    assert response.status_code != 404, "MCP endpoint should exist"

hindsight-api-slim/tests/test_batch_api.py

@@ -1,507 +0,0 @@
-"""
-Test OpenAI Batch API integration for retain fact extraction.
-
-Tests cover:
-- Normal batch API flow (submit, poll, complete)
-- Crash recovery (resume from existing batch_id)
-- Provider fallback (when batch API not supported)
-- Worker recovery on restart
-"""
-import pytest
-import asyncio
-import logging
-import json
-import uuid
-from datetime import datetime, timezone
-from unittest.mock import AsyncMock, MagicMock, patch
-from hindsight_api import RequestContext
-from hindsight_api.engine.retain.fact_extraction import (
-    extract_facts_from_contents_batch_api,
-    extract_facts_from_contents,
-    RetainContent,
-)
-from hindsight_api.config import HindsightConfig
-from hindsight_api.engine.llm_wrapper import create_llm_provider
-from hindsight_api.worker.poller import WorkerPoller
-
-logger = logging.getLogger(__name__)
-
-
-@pytest.fixture
-def mock_llm_config():
-    """Create a mock LLM config with batch API support."""
-    mock = MagicMock()
-    mock.provider = "openai"
-    mock.model = "gpt-4o-mini"
-    mock._provider_impl = AsyncMock()
-    return mock
-
-
-@pytest.fixture
-def test_contents():
-    """Create test content for fact extraction."""
-    return [
-        RetainContent(
-            content="Alice is a senior software engineer at TechCorp. She specializes in distributed systems.",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            context="team overview",
-        ),
-        RetainContent(
-            content="Bob joined the team last month as a junior developer. He is learning React.",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            context="team overview",
-        ),
-    ]
-
-
-@pytest.fixture
-def hindsight_config():
-    """Create test config with batch API enabled."""
-    config = HindsightConfig.from_env()
-    config.retain_batch_enabled = True
-    config.retain_batch_poll_interval_seconds = 1  # Fast polling for tests
-    config.retain_chunk_size = 4000
-    config.retain_extraction_mode = "concise"
-    config.retain_extract_causal_links = False
-    return config
-
-
-@pytest.mark.asyncio
-async def test_batch_api_normal_flow(mock_llm_config, test_contents, hindsight_config, memory, request_context):
-    """Test normal batch API flow: submit, poll, complete."""
-    bank_id = f"test_batch_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Mock batch API responses
-        batch_id = "batch_test123"
-
-        # Mock supports_batch_api
-        mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=True)
-
-        # Mock submit_batch - returns batch metadata
-        mock_llm_config._provider_impl.submit_batch = AsyncMock(
-            return_value={
-                "batch_id": batch_id,
-                "status": "validating",
-                "request_counts": {"total": 2, "completed": 0, "failed": 0},
-            }
-        )
-
-        # Mock get_batch_status - simulate polling sequence
-        status_sequence = [
-            {"status": "in_progress", "request_counts": {"total": 2, "completed": 1, "failed": 0}},
-            {"status": "completed", "request_counts": {"total": 2, "completed": 2, "failed": 0}},
-        ]
-        mock_llm_config._provider_impl.get_batch_status = AsyncMock(side_effect=status_sequence)
-
-        # Mock retrieve_batch_results - returns fact extraction results
-        mock_results = [
-            {
-                "custom_id": "chunk_0",
-                "response": {
-                    "body": {
-                        "choices": [
-                            {
-                                "message": {
-                                    "content": json.dumps({
-                                        "facts": [
-                                            {
-                                                "what": "Alice is a senior software engineer at TechCorp",
-                                                "when": "present",
-                                                "where": "TechCorp",
-                                                "who": "Alice",
-                                                "why": "Professional background information",
-                                                "fact_type": "world",
-                                                "fact_kind": "conversation",
-                                            }
-                                        ]
-                                    })
-                                }
-                            }
-                        ],
-                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
-                    }
-                },
-            },
-            {
-                "custom_id": "chunk_1",
-                "response": {
-                    "body": {
-                        "choices": [
-                            {
-                                "message": {
-                                    "content": json.dumps({
-                                        "facts": [
-                                            {
-                                                "what": "Bob joined the team last month as a junior developer",
-                                                "when": "last month",
-                                                "where": "team",
-                                                "who": "Bob",
-                                                "why": "New team member information",
-                                                "fact_type": "world",
-                                                "fact_kind": "conversation",
-                                            }
-                                        ]
-                                    })
-                                }
-                            }
-                        ],
-                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
-                    }
-                },
-            },
-        ]
-        mock_llm_config._provider_impl.retrieve_batch_results = AsyncMock(return_value=mock_results)
-
-        # Call batch API extraction
-        facts, chunks, usage = await extract_facts_from_contents_batch_api(
-            contents=test_contents,
-            llm_config=mock_llm_config,
-            agent_name="test_agent",
-            config=hindsight_config,
-            pool=None,  # No DB pool for this test
-            operation_id=None,
-            schema=None,
-        )
-
-        # Verify results
-        assert len(facts) == 2, "Should extract 2 facts (one per chunk)"
-        # Facts are ExtractedFact objects with .fact_text field
-        assert "Alice" in facts[0].fact_text and "senior software engineer" in facts[0].fact_text
-        assert "Bob" in facts[1].fact_text and "junior developer" in facts[1].fact_text
-
-        # Verify chunks metadata
-        assert len(chunks) == 2, "Should have 2 chunks metadata"
-        assert chunks[0].fact_count == 1
-        assert chunks[1].fact_count == 1
-
-        # Verify token usage
-        assert usage.input_tokens == 200  # 100 per chunk
-        assert usage.output_tokens == 100  # 50 per chunk
-        assert usage.total_tokens == 300
-
-        # Verify API calls
-        mock_llm_config._provider_impl.submit_batch.assert_called_once()
-        assert mock_llm_config._provider_impl.get_batch_status.call_count == 2
-        mock_llm_config._provider_impl.retrieve_batch_results.assert_called_once_with(batch_id)
-
-        logger.info("✅ Normal batch API flow test passed")
-
-    finally:
-        # Cleanup
-        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
-        except Exception:
-            pass
-
-
-@pytest.mark.asyncio
-async def test_batch_api_crash_recovery(mock_llm_config, test_contents, hindsight_config, memory, request_context):
-    """Test crash recovery: resume polling from existing batch_id."""
-    bank_id = f"test_crash_{datetime.now(timezone.utc).timestamp()}"
-    operation_id = str(uuid.uuid4())  # Must be UUID for async_operations table
-
-    try:
-        # Ensure bank exists
-        await memory.get_bank_profile(bank_id, request_context=request_context)
-
-        # Setup: Store batch_id in async_operations table (simulates partial execution)
-        batch_id = "batch_recovered_456"
-        pool = memory._pool
-        schema = request_context.tenant_id
-
-        from hindsight_api.engine.task_backend import fq_table
-        table = fq_table("async_operations", schema)
-
-        # Create operation with batch_id already stored
-        await pool.execute(
-            f"""
-            INSERT INTO {table} (operation_id, operation_type, bank_id, status, result_metadata)
-            VALUES ($1, 'retain', $2, 'processing', $3::jsonb)
-            """,
-            operation_id,
-            bank_id,
-            json.dumps({
-                "batch_id": batch_id,
-                "batch_provider": "openai",
-                "chunk_count": 2,
-            }),
-        )
-
-        # Mock batch API responses for resume scenario
-        mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=True)
-
-        # Mock get_batch_status - batch already in progress
-        mock_llm_config._provider_impl.get_batch_status = AsyncMock(
-            return_value={
-                "status": "completed",
-                "request_counts": {"total": 2, "completed": 2, "failed": 0},
-            }
-        )
-
-        # Mock retrieve_batch_results
-        mock_results = [
-            {
-                "custom_id": "chunk_0",
-                "response": {
-                    "body": {
-                        "choices": [
-                            {
-                                "message": {
-                                    "content": json.dumps({
-                                        "facts": [
-                                            {
-                                                "what": "Alice is a senior software engineer",
-                                                "when": "present",
-                                                "where": "TechCorp",
-                                                "who": "Alice",
-                                                "why": "Background",
-                                                "fact_type": "world",
-                                                "fact_kind": "conversation",
-                                            }
-                                        ]
-                                    })
-                                }
-                            }
-                        ],
-                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
-                    }
-                },
-            },
-            {
-                "custom_id": "chunk_1",
-                "response": {
-                    "body": {
-                        "choices": [
-                            {
-                                "message": {
-                                    "content": json.dumps({
-                                        "facts": [
-                                            {
-                                                "what": "Bob is a junior developer",
-                                                "when": "last month",
-                                                "where": "team",
-                                                "who": "Bob",
-                                                "why": "New member",
-                                                "fact_type": "world",
-                                                "fact_kind": "conversation",
-                                            }
-                                        ]
-                                    })
-                                }
-                            }
-                        ],
-                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
-                    }
-                },
-            },
-        ]
-        mock_llm_config._provider_impl.retrieve_batch_results = AsyncMock(return_value=mock_results)
-
-        # Call batch API extraction with operation_id (crash recovery scenario)
-        facts, chunks, usage = await extract_facts_from_contents_batch_api(
-            contents=test_contents,
-            llm_config=mock_llm_config,
-            agent_name="test_agent",
-            config=hindsight_config,
-            pool=pool,
-            operation_id=operation_id,  # Provides crash recovery context
-            schema=schema,
-        )
-
-        # Verify results
-        assert len(facts) == 2, "Should extract 2 facts after recovery"
-
-        # CRITICAL: Verify submit_batch was NOT called (because batch_id already exists)
-        mock_llm_config._provider_impl.submit_batch.assert_not_called()
-
-        # Verify get_batch_status WAS called (polling resumed)
-        mock_llm_config._provider_impl.get_batch_status.assert_called()
-
-        # Verify retrieve_batch_results was called with the recovered batch_id
-        mock_llm_config._provider_impl.retrieve_batch_results.assert_called_once_with(batch_id)
-
-        logger.info("✅ Crash recovery test passed - resumed polling without re-submission")
-
-    finally:
-        # Cleanup
-        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
-        except Exception:
-            pass
-
-
-@pytest.mark.asyncio
-async def test_batch_api_fallback_unsupported_provider(mock_llm_config, test_contents, hindsight_config):
-    """Test fallback to sync mode when provider doesn't support batch API."""
-
-    # Mock provider that doesn't support batch API
-    mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=False)
-    mock_llm_config.provider = "groq"  # Example of provider
-
-    # Patch the sync mode function to verify it's called
-    with patch(
-        "hindsight_api.engine.retain.fact_extraction.extract_facts_from_contents"
-    ) as mock_sync_extract:
-        mock_sync_extract.return_value = ([], [], MagicMock())
-
-        # Call batch API extraction (should fallback to sync)
-        await extract_facts_from_contents_batch_api(
-            contents=test_contents,
-            llm_config=mock_llm_config,
-            agent_name="test_agent",
-            config=hindsight_config,
-            pool=None,
-            operation_id=None,
-            schema=None,
-        )
-
-        # Verify fallback occurred
-        mock_sync_extract.assert_called_once()
-
-        # Verify batch API methods were NOT called
-        mock_llm_config._provider_impl.submit_batch.assert_not_called()
-
-        logger.info("✅ Fallback to sync mode test passed")
-
-
-@pytest.mark.asyncio
-async def test_worker_batch_recovery(memory, request_context):
-    """Test that WorkerPoller._recover_batch_operations finds and resets orphaned batches."""
-    bank_id = f"test_worker_recovery_{datetime.now(timezone.utc).timestamp()}"
-    operation_id = str(uuid.uuid4())  # Must be UUID for async_operations table
-
-    try:
-        # Ensure bank exists
-        await memory.get_bank_profile(bank_id, request_context=request_context)
-
-        pool = memory._pool
-        schema = request_context.tenant_id
-
-        from hindsight_api.engine.task_backend import fq_table
-        table = fq_table("async_operations", schema)
-
-        # Create orphaned batch operation (simulates worker crash during polling)
-        batch_id = "batch_orphaned_999"
-        task_payload = {
-            "operation_type": "retain",
-            "bank_id": bank_id,
-            "contents": [{"content": "test", "event_date": "2024-01-15T00:00:00Z"}],
-        }
-
-        await pool.execute(
-            f"""
-            INSERT INTO {table} (operation_id, operation_type, bank_id, status, worker_id, result_metadata, task_payload)
-            VALUES ($1, 'retain', $2, 'processing', 'worker_crashed', $3::jsonb, $4::jsonb)
-            """,
-            operation_id,
-            bank_id,
-            json.dumps({
-                "batch_id": batch_id,
-                "batch_provider": "openai",
-                "chunk_count": 1,
-            }),
-            json.dumps(task_payload),
-        )
-
-        # Create WorkerPoller
-        from hindsight_api.extensions.builtin.tenant import DefaultTenantExtension
-        tenant_extension = DefaultTenantExtension(config={"schema": schema} if schema else {})
-
-        poller = WorkerPoller(
-            pool=pool,
-            worker_id="test_worker_recovery",
-            executor=memory,
-            poll_interval_ms=100,
-            schema=schema,
-            tenant_extension=tenant_extension,
-            max_slots=5,
-            consolidation_max_slots=2,
-        )
-
-        # Run recovery
-        recovered_count = await poller._recover_batch_operations(schema)
-
-        # Verify recovery
-        assert recovered_count == 1, "Should recover 1 batch operation"
-
-        # Verify operation was reset to pending
-        row = await pool.fetchrow(
-            f"SELECT status, worker_id FROM {table} WHERE operation_id = $1",
-            operation_id,
-        )
-
-        assert row["status"] == "pending", "Operation should be reset to pending"
-        assert row["worker_id"] is None, "Worker ID should be cleared"
-
-        logger.info("✅ Worker batch recovery test passed")
-
-    finally:
-        # Cleanup
-        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
-        except Exception:
-            pass
-
-
-@pytest.mark.asyncio
-async def test_batch_api_via_extract_facts_from_contents(
-    mock_llm_config, test_contents, hindsight_config, memory, request_context
-):
-    """Test that extract_facts_from_contents routes to batch API when enabled."""
-    bank_id = f"test_routing_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Enable batch API in config
-        hindsight_config.retain_batch_enabled = True
-
-        # Mock batch API support
-        mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=True)
-        mock_llm_config._provider_impl.submit_batch = AsyncMock(
-            return_value={"batch_id": "batch_123", "status": "validating", "request_counts": {}}
-        )
-        mock_llm_config._provider_impl.get_batch_status = AsyncMock(
-            return_value={"status": "completed", "request_counts": {"total": 1, "completed": 1, "failed": 0}}
-        )
-        mock_llm_config._provider_impl.retrieve_batch_results = AsyncMock(
-            return_value=[
-                {
-                    "custom_id": "chunk_0",
-                    "response": {
-                        "body": {
-                            "choices": [
-                                {
-                                    "message": {
-                                        "content": json.dumps({"facts": []})
-                                    }
-                                }
-                            ],
-                            "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
-                        }
-                    },
-                }
-            ]
-        )
-
-        # Call main extract_facts_from_contents (should route to batch API)
-        facts, chunks, usage = await extract_facts_from_contents(
-            contents=test_contents,
-            llm_config=mock_llm_config,
-            agent_name="test_agent",
-            config=hindsight_config,
-            pool=None,
-            operation_id=None,
-            schema=None,
-        )
-
-        # Verify batch API was called
-        mock_llm_config._provider_impl.submit_batch.assert_called_once()
-
-        logger.info("✅ Routing to batch API test passed")
-
-    finally:
-        # Cleanup
-        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
-        except Exception:
-            pass

hindsight-api-slim/tests/test_batch_api_integration.py

@@ -1,263 +0,0 @@
-"""
-Real integration test for OpenAI Batch API.
-
-This test makes REAL API calls to OpenAI and measures actual timing.
-It will be slow (minutes to hours) depending on OpenAI's queue.
-
-To run:
-    pytest tests/test_batch_api_integration.py -v -s
-
-To skip in CI:
-    Add @pytest.mark.skip at the test level
-"""
-import pytest
-import os
-import asyncio
-import logging
-import time
-from datetime import datetime, timezone
-from dotenv import load_dotenv
-from hindsight_api import RequestContext
-from hindsight_api.engine.retain.fact_extraction import (
-    extract_facts_from_contents_batch_api,
-    RetainContent,
-)
-from hindsight_api.config import HindsightConfig
-from hindsight_api.engine.llm_wrapper import LLMProvider
-
-logger = logging.getLogger(__name__)
-
-# Load .env file for API keys
-load_dotenv()
-
-
-@pytest.fixture
-def openai_api_key():
-    """Get OpenAI API key from environment."""
-    # Try both current and commented keys from .env
-    api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
-
-    # Check if it's an OpenAI key (starts with sk-proj- or sk-)
-    if not api_key or not api_key.startswith("sk-"):
-        # Try the OpenAI-specific env var (if set separately)
-        api_key = os.getenv("OPENAI_API_KEY")
-
-    if not api_key or not api_key.startswith("sk-"):
-        pytest.skip("OpenAI API key not found in environment. Set OPENAI_API_KEY or uncomment OpenAI config in .env")
-
-    return api_key
-
-
-@pytest.fixture
-def real_llm_config(openai_api_key):
-    """Create real LLM config for OpenAI."""
-    # Create config with OpenAI settings
-    config = HindsightConfig.from_env()
-
-    # Use LLMProvider wrapper (which creates _provider_impl internally)
-    llm_config = LLMProvider(
-        provider="openai",
-        api_key=openai_api_key,
-        base_url="https://api.openai.com/v1",
-        model="gpt-4o-mini",  # Fast, cheap model for testing
-        reasoning_effort="medium",  # Required parameter
-    )
-
-    return llm_config
-
-
-@pytest.fixture
-def test_contents_real():
-    """Create realistic test content for fact extraction."""
-    return [
-        RetainContent(
-            content="""
-            Alice is a senior software engineer at TechCorp, where she has been working for 5 years.
-            She specializes in distributed systems and microservices architecture. Alice graduated
-            from MIT with a degree in Computer Science in 2015. She is known for writing clean,
-            well-documented code and mentoring junior developers.
-            """,
-            event_date=datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc),
-            context="team member profile",
-        ),
-        RetainContent(
-            content="""
-            Bob joined TechCorp last month as a junior developer. He is learning React and Node.js
-            and recently completed his first feature, which was a user authentication flow. Bob
-            graduated from Berkeley with a degree in Computer Science in 2023. He is enthusiastic
-            and asks great questions during code reviews.
-            """,
-            event_date=datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc),
-            context="team member profile",
-        ),
-        RetainContent(
-            content="""
-            The team uses Kubernetes for container orchestration and deploys to AWS. They follow
-            agile methodologies with two-week sprints. Code reviews are mandatory before merging
-            any pull request. The team meets every morning for a 15-minute standup to discuss
-            progress and blockers.
-            """,
-            event_date=datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc),
-            context="team processes",
-        ),
-    ]
-
-
-@pytest.fixture
-def integration_config():
-    """Create config for integration test."""
-    config = HindsightConfig.from_env()
-    config.retain_batch_enabled = True
-    config.retain_batch_poll_interval_seconds = 30  # Poll every 30 seconds (reasonable for real API)
-    config.retain_chunk_size = 4000
-    config.retain_extraction_mode = "concise"
-    config.retain_extract_causal_links = False
-    return config
-
-
-@pytest.mark.skip(reason="Real API test - takes minutes and costs money. Run manually with: pytest tests/test_batch_api_integration.py::test_real_openai_batch_api -v -s")
-@pytest.mark.integration  # Mark as integration test
-@pytest.mark.slow  # Mark as slow test
-@pytest.mark.asyncio
-async def test_real_openai_batch_api(real_llm_config, test_contents_real, integration_config, memory, request_context):
-    """
-    REAL integration test: Submit actual batch to OpenAI and measure timing.
-
-    WARNING: This test:
-    - Makes real API calls to OpenAI
-    - Will take minutes to hours to complete
-    - Costs money (though very little with gpt-4o-mini)
-    - Requires valid OpenAI API key
-
-    To skip this test:
-        pytest tests/test_batch_api_integration.py --skip-integration
-    """
-    bank_id = f"test_real_batch_{datetime.now(timezone.utc).timestamp()}"
-
-    logger.info("=" * 80)
-    logger.info("STARTING REAL OPENAI BATCH API INTEGRATION TEST")
-    logger.info("=" * 80)
-    logger.info(f"Test contents: {len(test_contents_real)} items")
-    logger.info(f"Poll interval: {integration_config.retain_batch_poll_interval_seconds}s")
-    logger.info(f"Model: {real_llm_config.model}")
-    logger.info("This may take several minutes to hours depending on OpenAI's queue...")
-    logger.info("=" * 80)
-
-    try:
-        # Ensure bank exists
-        await memory.get_bank_profile(bank_id, request_context=request_context)
-
-        # Get database pool and schema for crash recovery testing
-        pool = memory._pool
-        schema = request_context.tenant_id
-
-        # Track overall timing
-        test_start_time = time.time()
-
-        # Call REAL batch API extraction
-        logger.info("\n📤 Submitting batch to OpenAI...")
-
-        facts, chunks, usage = await extract_facts_from_contents_batch_api(
-            contents=test_contents_real,
-            llm_config=real_llm_config,
-            agent_name="test_agent",
-            config=integration_config,
-            pool=pool,
-            operation_id=None,  # No crash recovery for this test
-            schema=schema,
-        )
-
-        test_end_time = time.time()
-        total_duration = test_end_time - test_start_time
-
-        # Log results
-        logger.info("\n" + "=" * 80)
-        logger.info("✅ BATCH COMPLETED SUCCESSFULLY")
-        logger.info("=" * 80)
-        logger.info(f"Total duration: {total_duration:.1f} seconds ({total_duration/60:.1f} minutes)")
-        logger.info(f"Facts extracted: {len(facts)}")
-        logger.info(f"Chunks processed: {len(chunks)}")
-        logger.info(f"Token usage: {usage.input_tokens} input + {usage.output_tokens} output = {usage.total_tokens} total")
-        logger.info(f"Estimated cost: ${(usage.input_tokens * 0.00015 / 1000 + usage.output_tokens * 0.0006 / 1000):.4f}")
-        logger.info("=" * 80)
-
-        # Log sample facts
-        logger.info("\n📋 Sample extracted facts:")
-        for i, fact in enumerate(facts[:5]):  # Show first 5 facts
-            logger.info(f"\nFact {i+1}:")
-            logger.info(f"  Type: {fact.fact_type}")
-            logger.info(f"  Text: {fact.fact_text[:100]}...")
-            logger.info(f"  Entities: {fact.entities}")
-
-        # Verify results
-        assert len(facts) > 0, "Should extract at least some facts"
-        assert len(chunks) == len(test_contents_real), f"Should have {len(test_contents_real)} chunks"
-        assert usage.total_tokens > 0, "Should have token usage"
-
-        # Verify fact structure
-        for fact in facts:
-            assert hasattr(fact, "fact_text"), "Fact should have fact_text"
-            assert hasattr(fact, "fact_type"), "Fact should have fact_type"
-            assert fact.fact_type in ["world", "experience", "opinion"], f"Invalid fact_type: {fact.fact_type}"
-
-        logger.info("\n✅ All assertions passed!")
-
-        # Write timing report to file for later analysis
-        report_path = "/tmp/openai_batch_api_timing_report.txt"
-        with open(report_path, "w") as f:
-            f.write(f"OpenAI Batch API Integration Test Report\n")
-            f.write(f"={'=' * 60}\n\n")
-            f.write(f"Test Date: {datetime.now(timezone.utc).isoformat()}\n")
-            f.write(f"Model: {real_llm_config.model}\n")
-            f.write(f"Contents: {len(test_contents_real)} items\n")
-            f.write(f"Poll Interval: {integration_config.retain_batch_poll_interval_seconds}s\n\n")
-            f.write(f"Results:\n")
-            f.write(f"  Total Duration: {total_duration:.1f}s ({total_duration/60:.1f} min)\n")
-            f.write(f"  Facts Extracted: {len(facts)}\n")
-            f.write(f"  Chunks Processed: {len(chunks)}\n")
-            f.write(f"  Token Usage: {usage.total_tokens} ({usage.input_tokens} in + {usage.output_tokens} out)\n")
-            f.write(f"  Estimated Cost: ${(usage.input_tokens * 0.00015 / 1000 + usage.output_tokens * 0.0006 / 1000):.4f}\n")
-
-        logger.info(f"\n📄 Timing report written to: {report_path}")
-
-    finally:
-        # Cleanup
-        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
-            logger.info(f"\n🧹 Cleaned up test bank: {bank_id}")
-        except Exception as e:
-            logger.error(f"Failed to cleanup bank: {e}")
-
-
-@pytest.mark.skip(reason="Real API test - requires Groq API key. Run manually if needed.")
-@pytest.mark.integration
-@pytest.mark.slow
-@pytest.mark.asyncio
-async def test_real_batch_supports_groq(integration_config):
-    """
-    Test that Groq also supports batch API (if configured).
-
-    Groq has the same batch API interface as OpenAI.
-    """
-    groq_api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
-
-    if not groq_api_key or not groq_api_key.startswith("gsk_"):
-        pytest.skip("Groq API key not found in environment")
-
-    llm_config = LLMProvider(
-        provider="groq",
-        api_key=groq_api_key,
-        base_url="https://api.groq.com/openai/v1",
-        model="llama-3.1-8b-instant",
-        reasoning_effort="medium",
-    )
-
-    # Check if Groq supports batch API
-    supports_batch = await llm_config._provider_impl.supports_batch_api()
-
-    logger.info(f"Groq batch API support: {supports_batch}")
-
-    # Groq should support batch API (same interface as OpenAI)
-    assert supports_batch, "Groq should support batch API"
-
-    logger.info("✅ Groq batch API support confirmed")

hindsight-api-slim/tests/test_batch_api_validation.py

@@ -1,38 +0,0 @@
-"""
-Test validation for batch API + synchronous retain.
-
-When HINDSIGHT_API_RETAIN_BATCH_ENABLED=true, synchronous retain operations
-should be rejected with a 400 error since they will timeout.
-"""
-
-import os
-import pytest
-from hindsight_api.engine.memory_engine import MemoryEngine
-from hindsight_api.config import HindsightConfig
-from hindsight_api import RequestContext
-
-
-@pytest.mark.asyncio
-async def test_batch_api_validation(memory, request_context):
-    """
-    Test that attempting synchronous retain with batch API enabled
-    raises an error at the HTTP layer.
-
-    This test verifies the validation logic exists - actual HTTP testing
-    would require full FastAPI app setup.
-    """
-    # Create config with batch API enabled
-    config = HindsightConfig.from_env()
-    config.retain_batch_enabled = True
-    config.retain_batch_poll_interval_seconds = 1
-
-    # Verify the validation exists in memory engine
-    # The actual HTTP validation happens in http.py api_retain()
-    # This test documents the expected behavior
-
-    assert config.retain_batch_enabled is True
-    assert config.retain_batch_poll_interval_seconds == 1
-
-    # When batch API is enabled and async=false, the HTTP endpoint
-    # should return 400 with message:
-    # "Batch API is enabled (HINDSIGHT_API_RETAIN_BATCH_ENABLED=true) but async=false"

hindsight-api-slim/tests/test_codex_tool_choice.py

@@ -1,88 +0,0 @@
-"""
-Regression tests for Codex provider tool_choice normalization.
-
-The reflect agent forces tool selection via named tool_choice dicts on early iterations:
-  {"type": "function", "function": {"name": "recall"}}
-
-The Codex Responses API expects the function name at the top level instead:
-  {"type": "function", "name": "recall"}
-
-Without normalization, Codex rejects the request with:
-  400 Unknown parameter: 'tool_choice.function'
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from hindsight_api.engine.providers.codex_llm import CodexLLM
-
-TOOLS = [
-    {
-        "type": "function",
-        "function": {
-            "name": "recall",
-            "description": "Recall semantic memories",
-            "parameters": {
-                "type": "object",
-                "properties": {"query": {"type": "string"}},
-                "required": ["query"],
-            },
-        },
-    }
-]
-
-
-def build_llm() -> CodexLLM:
-    with patch.object(CodexLLM, "_load_codex_auth", return_value=("token", "account")):
-        return CodexLLM(
-            provider="openai-codex",
-            api_key="ignored",
-            base_url="https://chatgpt.com/backend-api",
-            model="gpt-5.4-mini",
-        )
-
-
-@pytest.mark.asyncio
-async def test_codex_normalizes_legacy_named_tool_choice_shape():
-    llm = build_llm()
-    response = MagicMock()
-    response.status_code = 200
-    response.raise_for_status.return_value = None
-    with patch.object(llm._client, "post", new_callable=AsyncMock) as mock_post:
-        mock_post.return_value = response
-        with patch.object(llm, "_parse_sse_tool_stream", new_callable=AsyncMock) as mock_parse:
-            mock_parse.return_value = (None, [])
-            await llm.call_with_tools(
-                messages=[{"role": "user", "content": "recall the memory"}],
-                tools=TOOLS,
-                tool_choice={"type": "function", "function": {"name": "recall"}},
-                max_retries=0,
-            )
-        sent_payload = mock_post.call_args.kwargs["json"]
-
-    assert sent_payload["tool_choice"] == {"type": "function", "name": "recall"}
-
-
-@pytest.mark.asyncio
-async def test_codex_forced_tool_choice_still_yields_tool_calls():
-    llm = build_llm()
-    response = MagicMock()
-    response.status_code = 200
-    response.raise_for_status.return_value = None
-    tool_call = {"id": "call-1", "name": "recall", "arguments": {"query": "memory"}}
-    with patch.object(llm._client, "post", new_callable=AsyncMock) as mock_post:
-        mock_post.return_value = response
-        with patch.object(llm, "_parse_sse_tool_stream", new_callable=AsyncMock) as mock_parse:
-            mock_parse.return_value = (None, [tool_call])
-            result = await llm.call_with_tools(
-                messages=[{"role": "user", "content": "recall the memory"}],
-                tools=TOOLS,
-                tool_choice={"type": "function", "function": {"name": "recall"}},
-                max_retries=0,
-            )
-        sent_payload = mock_post.call_args.kwargs["json"]
-
-    assert len(result.tool_calls) == 1
-    assert result.tool_calls[0].name == "recall"
-    assert sent_payload["tool_choice"] == {"type": "function", "name": "recall"}