Compare commits

..
Author SHA1 Message Date
Derek Bouius 06d11cf867 change the example openai model 2025-12-11 16:19:53 -05:00
Derek Bouius 7f5576cdee add best-match for the test-python-client too 2025-12-11 15:58:34 -05:00
Derek Bouius 3572387051 add best match strategy to uv 2025-12-11 15:51:16 -05:00
Derek Bouius 3a91c0b87f rework to use only CPU version of pytorch 2025-12-11 15:43:32 -05:00
Derek Bouius 706204bc4f force the CPU only version of Pytorch for tests 2025-12-11 14:58:43 -05:00
Derek Bouius 147d46fc91 Revert "use the CPU only version of pytorch to avoid pulling cuda libraries"
This reverts commit 583683b0a2.
2025-12-11 14:44:59 -05:00
Derek Bouius 285bed65f9 Revert "just use uv sync, not pip"
This reverts commit c85a1ca58b.
2025-12-11 14:44:51 -05:00
Derek Bouius c85a1ca58b just use uv sync, not pip 2025-12-11 14:21:45 -05:00
Derek Bouius 583683b0a2 use the CPU only version of pytorch to avoid pulling cuda libraries 2025-12-11 14:12:43 -05:00
Derek Bouius 0d5503c892 add debug to figure out why docker build fails sometimes 2025-12-11 13:51:06 -05:00
Derek Bouius af2756f2da remove assert that is a race condition
The test was checking that the bank count increased, but with parallel tests (-n 8), other tests can delete their banks while this test is running, causing a race condition. The important assertion is assert test_bank_id in final_banks - which verifies the bank was actually created.
2025-12-11 13:19:09 -05:00
Derek Bouius b1e380bdae Merge branch 'update-example-env' of https://github.com/vectorize-io/hindsight into update-example-env 2025-12-11 13:08:42 -05:00
Derek Bouius b8ec743962 pre-cache the model so CI doesn't need workarounds 2025-12-11 13:08:39 -05:00
Derek Bouius d891124835 Revert "trial to fix failing client tests"
This reverts commit c0093f1a97.
2025-12-11 16:08:22 +01:00
Derek Bouius 04ff24be8d trial to fix failing client tests
NotImplementedError: Cannot copy out of meta tensor; no data! Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() when moving module from meta to a different device.

Added backend_kwargs={"low_cpu_mem_usage": False} to the SentenceTransformer initialization in embeddings.py
This should ensure the low_cpu_mem_usage=False setting is passed through both model_kwargs and backend_kwargs, which are different code paths in sentence-transformers 3.x.
2025-12-11 16:08:22 +01:00
Derek Bouius 076c33e854 fix the assert in testing recall 2025-12-11 16:08:22 +01:00
Derek Bouius a03c942296 Add the LLM_PROVIDER in example 2025-12-11 16:08:22 +01:00
Derek Bouius b4e42bd0c6 lock the sentence transformer packages to align with the breaking changes around lazy tensor loading 2025-12-11 09:46:28 -05:00
Derek Bouius 95b2b7e78f Merge branch 'main' into update-example-env 2025-12-11 09:22:35 -05:00
Derek Bouius 2aa8700db8 Revert "trial to fix failing client tests"
This reverts commit c0093f1a97.
2025-12-10 23:11:04 -05:00
Derek Bouius c0093f1a97 trial to fix failing client tests
NotImplementedError: Cannot copy out of meta tensor; no data! Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() when moving module from meta to a different device.

Added backend_kwargs={"low_cpu_mem_usage": False} to the SentenceTransformer initialization in embeddings.py
This should ensure the low_cpu_mem_usage=False setting is passed through both model_kwargs and backend_kwargs, which are different code paths in sentence-transformers 3.x.
2025-12-10 23:04:42 -05:00
Derek Bouius 460f045f16 fix the assert in testing recall 2025-12-10 22:36:40 -05:00
Derek Bouius 0673d4813d Add the LLM_PROVIDER in example 2025-12-10 22:22:26 -05:00
1191 changed files with 56273 additions and 258662 deletions
+1 -48
View File
@@ -2,50 +2,18 @@
# Copy this file to .env and fill in your values
# LLM Configuration (Required)
# 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
# HINDSIGHT_API_LLM_PROVIDER=anthropic
# HINDSIGHT_API_LLM_API_KEY=your-anthropic-api-key
# HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-20250514
# Example: Google Vertex AI configuration
# HINDSIGHT_API_LLM_PROVIDER=vertexai
# HINDSIGHT_API_LLM_MODEL=google/gemini-2.0-flash-001
# HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=your-gcp-project-id
# 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: LM Studio local configuration (Qwen 2.5 32B recommended)
# HINDSIGHT_API_LLM_PROVIDER=lmstudio
# HINDSIGHT_API_LLM_API_KEY=lmstudio
# HINDSIGHT_API_LLM_BASE_URL=http://localhost:1234/v1
# HINDSIGHT_API_LLM_MODEL=qwen2.5-32b-instruct
# API Configuration (Optional)
HINDSIGHT_API_HOST=0.0.0.0
HINDSIGHT_API_PORT=8888
HINDSIGHT_API_LOG_LEVEL=info
# Base Path / Reverse Proxy Support (Optional)
# Set these when deploying behind a reverse proxy with path-based routing
# Example: To deploy at example.com/hindsight/, set both to "/hindsight"
# HINDSIGHT_API_BASE_PATH=/hindsight
# NEXT_PUBLIC_BASE_PATH=/hindsight
# Database (Optional - uses embedded pg0 by default)
# HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
# HINDSIGHT_API_DATABASE_SCHEMA=public # PostgreSQL schema name (default: public)
# Vector Extension (Optional - uses pgvector by default)
# Options: "pgvector" (default), "vchord", "pgvectorscale" (DiskANN)
# HINDSIGHT_API_VECTOR_EXTENSION=pgvector
# For Azure PostgreSQL with DiskANN:
# HINDSIGHT_API_VECTOR_EXTENSION=pgvectorscale # Auto-detects pg_diskann on Azure
# Embeddings Configuration (Optional - uses local by default)
# Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)
@@ -62,18 +30,3 @@ HINDSIGHT_API_LOG_LEVEL=info
# HINDSIGHT_API_RERANKER_LOCAL_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
# For TEI provider:
# HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081
# Observability & Tracing (Optional - disabled by default)
# Enable OpenTelemetry tracing for LLM calls (GenAI semantic conventions)
# HINDSIGHT_API_OTEL_TRACES_ENABLED=true
#
# Local development with Grafana LGTM stack (recommended - see scripts/dev/grafana/README.md)
# HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
#
# Cloud backends (Grafana Cloud, Langfuse, DataDog, etc.)
# HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT=https://your-backend-url
# HINDSIGHT_API_OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer your-token"
#
# Custom service name and environment (optional, defaults: hindsight-api, development)
# HINDSIGHT_API_OTEL_SERVICE_NAME=hindsight-production
# HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT=production
-27
View File
@@ -1,27 +0,0 @@
#!/bin/bash
# Pre-commit hook - runs all scripts in scripts/hooks/
set -e
REPO_ROOT="$(git rev-parse --show-toplevel)"
HOOKS_DIR="$REPO_ROOT/scripts/hooks"
if [ ! -d "$HOOKS_DIR" ]; then
exit 0
fi
echo ""
echo "=== Running pre-commit hooks ==="
echo ""
# Run all executable scripts in hooks directory
for hook in "$HOOKS_DIR"/*.sh; do
if [ -x "$hook" ]; then
echo "[hook] $(basename "$hook")"
(cd "$REPO_ROOT" && "$hook")
fi
done
echo ""
echo "=== Pre-commit hooks completed ==="
echo ""
-71
View File
@@ -1,71 +0,0 @@
name: Bug Report
description: Report a bug or unexpected behavior
labels: ["bug", "triage"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to report a bug! Please fill out the sections below.
- type: textarea
id: description
attributes:
label: Bug Description
description: A clear and concise description of the bug
placeholder: What happened?
validations:
required: true
- type: textarea
id: reproduction
attributes:
label: Steps to Reproduce
description: Steps to reproduce the behavior
placeholder: |
1. Configure '...'
2. Call '...'
3. See error
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected Behavior
description: What did you expect to happen?
validations:
required: true
- type: textarea
id: actual
attributes:
label: Actual Behavior
description: What actually happened?
validations:
required: true
- type: input
id: version
attributes:
label: Version
description: What version are you using?
placeholder: e.g., 0.1.0 or commit hash
validations:
required: false
- type: dropdown
id: llm-provider
attributes:
label: LLM Provider
description: Which LLM provider are you using?
options:
- OpenAI
- Anthropic
- Gemini
- Groq
- Ollama
- LM Studio
- Other
validations:
required: false
-8
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@@ -1,8 +0,0 @@
blank_issues_enabled: false
contact_links:
- name: Questions & Help
url: https://github.com/vectorize-io/hindsight/discussions/categories/q-a
about: Please ask questions and get help in Discussions instead of opening an issue.
- name: Ideas & Feedback
url: https://github.com/vectorize-io/hindsight/discussions/categories/ideas
about: Share ideas or give feedback in Discussions.
@@ -1,82 +0,0 @@
name: Feature Request
description: Suggest a new feature or enhancement
labels: ["enhancement", "triage"]
body:
- type: markdown
attributes:
value: |
Thanks for suggesting a feature! Please describe what you'd like to see added.
- type: textarea
id: use-case
attributes:
label: Use Case
description: Describe your specific use case. What are you building? What's your goal?
placeholder: |
I'm building an AI agent that needs to...
My application handles...
validations:
required: true
- type: textarea
id: problem
attributes:
label: Problem Statement
description: What problem are you facing? What's missing or difficult today?
placeholder: Currently I have to... which causes...
validations:
required: true
- type: textarea
id: benefit
attributes:
label: How This Feature Would Help
description: Explain how this feature would improve your workflow or solve your problem
placeholder: With this feature, I would be able to...
validations:
required: true
- type: textarea
id: solution
attributes:
label: Proposed Solution
description: Describe your ideal solution (optional - we may have ideas too!)
placeholder: It would be great if Hindsight could...
validations:
required: false
- type: textarea
id: alternatives
attributes:
label: Alternatives Considered
description: Have you considered any alternative solutions or workarounds?
validations:
required: false
- type: dropdown
id: priority
attributes:
label: Priority
description: How important is this feature to you?
options:
- Nice to have
- Important - affects my workflow
- Critical - blocking my use case
validations:
required: true
- type: textarea
id: additional
attributes:
label: Additional Context
description: Any other context, mockups, or examples?
validations:
required: false
- type: checkboxes
id: checklist
attributes:
label: Checklist
options:
- label: I would be willing to contribute this feature
required: false
+11
View File
@@ -0,0 +1,11 @@
name: 'Setup pg0'
description: 'Install pg0 embedded PostgreSQL'
runs:
using: 'composite'
steps:
- name: Install pg0
shell: bash
run: |
curl -fsSL https://raw.githubusercontent.com/vectorize-io/pg0/main/install.sh | bash
echo "$HOME/.pg0/bin" >> $GITHUB_PATH
+6 -8
View File
@@ -20,20 +20,18 @@ concurrency:
jobs:
build:
runs-on: ubuntu-latest
defaults:
run:
working-directory: hindsight-docs
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 20
cache: npm
cache-dependency-path: package-lock.json
- uses: astral-sh/setup-uv@v4
- run: npm ci --workspace=hindsight-docs
- run: uv run generate-llms-full
- run: npm run build --workspace=hindsight-docs
env:
UMAMI_URL: https://analytics.hindsight.vectorize.io
UMAMI_WEBSITE_ID: ${{ secrets.UMAMI_WEBSITE_ID }}
cache-dependency-path: hindsight-docs/package-lock.json
- run: npm ci
- run: npm run build
- uses: actions/upload-pages-artifact@v3
with:
path: hindsight-docs/build
+53 -358
View File
@@ -38,22 +38,6 @@ jobs:
working-directory: ./hindsight
run: uv build --out-dir dist
- name: Build hindsight-litellm
working-directory: ./hindsight-integrations/litellm
run: uv build --out-dir dist
- name: Build hindsight-embed
working-directory: ./hindsight-embed
run: uv build --out-dir dist
- name: Build hindsight-crewai
working-directory: ./hindsight-integrations/crewai
run: uv build --out-dir dist
- name: Build hindsight-pydantic-ai
working-directory: ./hindsight-integrations/pydantic-ai
run: uv build --out-dir dist
# Publish in order (client and api first, then hindsight-all which depends on them)
- name: Publish hindsight-client to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
@@ -73,30 +57,6 @@ jobs:
packages-dir: ./hindsight/dist
skip-existing: true
- name: Publish hindsight-litellm to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ./hindsight-integrations/litellm/dist
skip-existing: true
- name: Publish hindsight-embed to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ./hindsight-embed/dist
skip-existing: true
- name: Publish hindsight-crewai to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ./hindsight-integrations/crewai/dist
skip-existing: true
- name: Publish hindsight-pydantic-ai to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
packages-dir: ./hindsight-integrations/pydantic-ai/dist
skip-existing: true
# Upload artifacts for GitHub release
- name: Upload artifacts
uses: actions/upload-artifact@v4
@@ -106,10 +66,6 @@ jobs:
hindsight-clients/python/dist/*
hindsight-api/dist/*
hindsight/dist/*
hindsight-integrations/litellm/dist/*
hindsight-embed/dist/*
hindsight-integrations/crewai/dist/*
hindsight-integrations/pydantic-ai/dist/*
retention-days: 1
release-typescript-client:
@@ -124,29 +80,18 @@ jobs:
with:
node-version: '20'
registry-url: 'https://registry.npmjs.org'
cache: 'npm'
cache-dependency-path: package-lock.json
- name: Install dependencies
run: npm ci --workspace=hindsight-clients/typescript
working-directory: ./hindsight-clients/typescript
run: npm ci
- name: Build
run: npm run build --workspace=hindsight-clients/typescript
working-directory: ./hindsight-clients/typescript
run: npm run build
- name: Publish to npm
working-directory: ./hindsight-clients/typescript
run: |
set +e
OUTPUT=$(npm publish --access public 2>&1)
EXIT_CODE=$?
echo "$OUTPUT"
if [ $EXIT_CODE -ne 0 ]; then
if echo "$OUTPUT" | grep -q "cannot publish over"; then
echo "Package version already published, skipping..."
exit 0
fi
exit $EXIT_CODE
fi
run: npm publish --access public
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
@@ -161,215 +106,6 @@ jobs:
path: hindsight-clients/typescript/*.tgz
retention-days: 1
release-openclaw-integration:
runs-on: ubuntu-latest
environment: npm
steps:
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '22'
registry-url: 'https://registry.npmjs.org'
- name: Install dependencies
working-directory: ./hindsight-integrations/openclaw
run: npm ci
- name: Build
working-directory: ./hindsight-integrations/openclaw
run: npm run build
- name: Publish to npm
working-directory: ./hindsight-integrations/openclaw
run: |
set +e
OUTPUT=$(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: ${{ secrets.NPM_TOKEN }}
- name: Pack for GitHub release
working-directory: ./hindsight-integrations/ai-sdk
run: npm pack
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: ai-sdk-integration
path: hindsight-integrations/ai-sdk/*.tgz
retention-days: 1
release-chat-integration:
runs-on: ubuntu-latest
environment: npm
steps:
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '22'
registry-url: 'https://registry.npmjs.org'
- name: Install dependencies
working-directory: ./hindsight-integrations/chat
run: npm ci
- name: Build
working-directory: ./hindsight-integrations/chat
run: npm run build
- name: Publish to npm
working-directory: ./hindsight-integrations/chat
run: |
set +e
OUTPUT=$(npm publish --access public 2>&1)
EXIT_CODE=$?
echo "$OUTPUT"
if [ $EXIT_CODE -ne 0 ]; then
if echo "$OUTPUT" | grep -q "cannot publish over"; then
echo "Package version already published, skipping..."
exit 0
fi
exit $EXIT_CODE
fi
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
- name: Pack for GitHub release
working-directory: ./hindsight-integrations/chat
run: npm pack
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: chat-integration
path: hindsight-integrations/chat/*.tgz
retention-days: 1
release-control-plane:
runs-on: ubuntu-latest
environment: npm
steps:
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
registry-url: 'https://registry.npmjs.org'
cache: 'npm'
cache-dependency-path: package-lock.json
- name: Install dependencies
run: npm ci
- name: Build TypeScript client (dependency)
run: npm run build --workspace=hindsight-clients/typescript
- name: Fix platform-specific native modules
run: |
# npm ci installs from lockfile which may have wrong platform binaries
# Delete hoisted native modules and reinstall for current platform
rm -rf node_modules/lightningcss node_modules/@tailwindcss
npm install lightningcss @tailwindcss/postcss @tailwindcss/node
- name: Build
run: npm run build --workspace=hindsight-control-plane
- name: Verify standalone build
run: test -f hindsight-control-plane/standalone/server.js || (echo 'standalone/server.js missing - build failed' && exit 1)
- name: Publish to npm
working-directory: ./hindsight-control-plane
run: |
set +e
OUTPUT=$(npm publish --access public --ignore-scripts 2>&1)
EXIT_CODE=$?
echo "$OUTPUT"
if [ $EXIT_CODE -ne 0 ]; then
if echo "$OUTPUT" | grep -q "cannot publish over"; then
echo "Package version already published, skipping..."
exit 0
fi
exit $EXIT_CODE
fi
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
- name: Pack for GitHub release
working-directory: ./hindsight-control-plane
run: npm pack
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: control-plane
path: hindsight-control-plane/*.tgz
retention-days: 1
release-rust-cli:
runs-on: ${{ matrix.os }}
strategy:
@@ -387,10 +123,6 @@ jobs:
target: aarch64-apple-darwin
artifact_name: hindsight
asset_name: hindsight-darwin-arm64
- os: ubuntu-24.04-arm
target: aarch64-unknown-linux-gnu
artifact_name: hindsight
asset_name: hindsight-linux-arm64
steps:
- uses: actions/checkout@v4
@@ -418,7 +150,6 @@ jobs:
retention-days: 1
release-docker-images:
name: Release Docker (${{ matrix.image_name }}${{ matrix.tag_suffix }})
runs-on: ubuntu-latest
permissions:
contents: read
@@ -428,28 +159,10 @@ jobs:
include:
- target: api-only
image_name: hindsight-api
tag_suffix: ""
build_args: ""
- target: api-only
image_name: hindsight-api
tag_suffix: "-slim"
build_args: |
INCLUDE_LOCAL_MODELS=false
PRELOAD_ML_MODELS=false
- target: cp-only
image_name: hindsight-control-plane
tag_suffix: ""
build_args: ""
- target: standalone
image_name: hindsight
tag_suffix: ""
build_args: ""
- target: standalone
image_name: hindsight
tag_suffix: "-slim"
build_args: |
INCLUDE_LOCAL_MODELS=false
PRELOAD_ML_MODELS=false
steps:
- uses: actions/checkout@v4
@@ -457,7 +170,7 @@ jobs:
- name: Free Disk Space
uses: jlumbroso/free-disk-space@main
with:
tool-cache: true
tool-cache: false
android: true
dotnet: true
haskell: true
@@ -482,49 +195,23 @@ jobs:
id: get_version
run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT
- name: Extract metadata for release tags
- name: Extract metadata
id: meta
uses: docker/metadata-action@v5
with:
images: ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}
flavor: |
latest=auto
suffix=${{ matrix.tag_suffix }}
tags: |
type=semver,pattern={{version}},value=${{ steps.get_version.outputs.VERSION }}
type=semver,pattern={{major}}.{{minor}},value=${{ steps.get_version.outputs.VERSION }}
type=semver,pattern={{major}},value=${{ steps.get_version.outputs.VERSION }}
type=raw,value=latest
# TODO: Re-enable smoke test when disk space issue is resolved
# # Step 1: Build for local testing (single platform, no push)
# # This creates an identical image to what will be released, just for one platform
# - name: Build image for testing
# uses: docker/build-push-action@v6
# with:
# context: .
# file: docker/standalone/Dockerfile
# target: ${{ matrix.target }}
# push: false
# load: true
# tags: ${{ matrix.image_name }}:test
# cache-from: type=gha
# cache-to: type=gha,mode=max
# # Step 2: Test the image before pushing anything
# - name: Smoke test - verify container starts
# env:
# GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
# run: ./docker/test-image.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"
# Build multi-platform and push to release tags
- name: Build and push release images
- name: Build and push
uses: docker/build-push-action@v6
with:
context: .
file: docker/standalone/Dockerfile
target: ${{ matrix.target }}
build-args: ${{ matrix.build_args }}
push: true
platforms: linux/amd64,linux/arm64
tags: ${{ steps.meta.outputs.tags }}
@@ -565,7 +252,7 @@ jobs:
create-github-release:
runs-on: ubuntu-latest
needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-chat-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
needs: [release-python-packages, release-typescript-client, release-rust-cli, release-docker-images, release-helm-chart]
permissions:
contents: write
@@ -588,30 +275,6 @@ jobs:
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 Chat Integration
uses: actions/download-artifact@v4
with:
name: chat-integration
path: ./artifacts/chat-integration
- name: Download Control Plane
uses: actions/download-artifact@v4
with:
name: control-plane
path: ./artifacts/control-plane
- name: Download Rust CLI (Linux)
uses: actions/download-artifact@v4
with:
@@ -643,19 +306,8 @@ jobs:
cp artifacts/python-packages/hindsight-clients/python/dist/* release-assets/ || true
cp artifacts/python-packages/hindsight-api/dist/* release-assets/ || true
cp artifacts/python-packages/hindsight/dist/* release-assets/ || true
cp artifacts/python-packages/hindsight-integrations/litellm/dist/* release-assets/ || true
cp artifacts/python-packages/hindsight-integrations/pydantic-ai/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
# Chat Integration
cp artifacts/chat-integration/*.tgz release-assets/ || true
# Control Plane
cp artifacts/control-plane/*.tgz release-assets/ || true
# Rust CLI binaries
cp artifacts/rust-cli-linux/hindsight-linux-amd64 release-assets/ || true
cp artifacts/rust-cli-darwin-amd64/hindsight-darwin-amd64 release-assets/ || true
@@ -664,11 +316,54 @@ jobs:
cp artifacts/helm-chart/*.tgz release-assets/ || true
ls -la release-assets/
- name: Generate release notes
run: |
cat << 'EOF' > release-notes.md
## Quick Start
```bash
# Install the CLI
curl -fsSL https://raw.githubusercontent.com/vectorize-io/hindsight/refs/heads/main/hindsight-cli/install.sh | bash
# Start the server
docker run -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_PROVIDER=openai \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=gpt-4o-mini \
ghcr.io/${{ github.repository_owner }}/hindsight:${{ steps.get_version.outputs.VERSION }}
```
## Docker Images
- `ghcr.io/${{ github.repository_owner }}/hindsight:${{ steps.get_version.outputs.VERSION }}` - Standalone (recommended)
- `ghcr.io/${{ github.repository_owner }}/hindsight-api:${{ steps.get_version.outputs.VERSION }}` - API only
- `ghcr.io/${{ github.repository_owner }}/hindsight-control-plane:${{ steps.get_version.outputs.VERSION }}` - Web UI only
## CLI
```bash
curl -fsSL https://raw.githubusercontent.com/vectorize-io/hindsight/refs/heads/main/hindsight-cli/install.sh | bash
```
## Python
```bash
pip install hindsight-all # or hindsight-api, hindsight-client
```
## TypeScript/JavaScript
```bash
npm install @vectorize-io/hindsight-client
```
## Helm
```bash
helm install hindsight oci://ghcr.io/${{ github.repository_owner }}/charts/hindsight --version ${{ steps.get_version.outputs.VERSION }}
```
EOF
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
files: release-assets/*
generate_release_notes: true
body_path: release-notes.md
draft: false
prerelease: false
env:
+48 -1287
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+3 -26
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@@ -5,18 +5,12 @@ build/
dist/
wheels/
*.egg-info
.mcp.json
.osgrep
# Virtual environments
.venv
# Node
node_modules/
# Environment variables and local config
# Environment variables
.env
docker-compose.yml
docker-compose.override.yml
# IDE
.idea/
@@ -27,10 +21,6 @@ docker-compose.override.yml
# NLTK data (will be downloaded automatically)
nltk_data/
# Monitoring stack (Prometheus/Grafana binaries and data)
.monitoring/
.pgbouncer/
# Large benchmark datasets (will be downloaded automatically)
**/longmemeval_s_cleaned.json
@@ -39,21 +29,8 @@ logs/
.DS_Store
# Generated docs files
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
whats-next.md
TASK.md
# Changelog is now tracked in hindsight-docs/src/pages/changelog.md
# CHANGELOG.md
blog-post*
hindsight-clients/rust/target
+149 -1
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@@ -1,3 +1,151 @@
# AGENTS.md
See [CLAUDE.md](./CLAUDE.md) for project documentation and coding conventions.
This document captures architectural decisions and coding conventions for the Hindsight project.
## Documentation
- **Main documentation**: [hindsight-docs/docs/developer/](./hindsight-docs/docs/developer/)
- **Use case patterns**: [hindsight-docs/docs/cookbook/](./hindsight-docs/docs/cookbook/)
- **API reference**: Auto-generated from OpenAPI spec
## Project Structure
```
hindsight/ # Python package for embedded usage
hindsight-api/ # FastAPI server (core memory engine)
hindsight-cli/ # Rust CLI client
hindsight-control-plane/ # Next.js admin UI
hindsight-docs/ # Docusaurus documentation site
hindsight-dev/ # Development tools and benchmarks
hindsight-integrations/ # Framework integrations (LangChain, etc.)
hindsight-clients/ # Generated API clients (Python, TypeScript, Rust)
```
## Core Concepts
### Memory Banks
- Each bank is an isolated memory store (like a "brain" for one user/agent)
- Banks contain: memory units (facts), entities, documents, entity links
- Banks have a **disposition** (personality traits) and **background** (context)
- Bank isolation is strict - no cross-bank data leakage
### Memory Types
- **World facts**: General knowledge ("The sky is blue")
- **Experience facts**: Personal experiences ("I visited Paris in 2023")
- **Opinion facts**: Beliefs with confidence scores ("Paris is beautiful" - 0.9 confidence)
### Operations
- **Retain**: Store new memories (extracts facts, entities, relationships)
- **Recall**: Retrieve memories (semantic, BM25, graph, temporal search)
- **Reflect**: Deep analysis to form new insights/opinions
## API Design Decisions
### Single Bank Per Request
- All API endpoints (`recall`, `reflect`, `retain`) operate on a single bank
- Multi-bank queries are the **client/agent's responsibility** to orchestrate
- This keeps the API simple and the isolation model clear
### Disposition Traits (3-trait system)
- **Skepticism** (1-5): How skeptical vs trusting when forming opinions
- **Literalism** (1-5): How literally to interpret information
- **Empathy** (1-5): How much to consider emotional context
- These influence the `reflect` operation, not `recall`
- Background info also only affects `reflect` (opinion formation)
## Multi-Bank Architecture Patterns
See [hindsight-docs/docs/cookbook/](./hindsight-docs/docs/cookbook/) for detailed guides:
- **Per-User Memory**: One bank per user, simplest pattern
- **Support Agent + Shared Knowledge**: User bank + shared docs bank, client orchestrates
## Developer Guide
### Running the API Server
```bash
# From project root
./scripts/dev/start-api.sh
# With options
./scripts/dev/start-api.sh --reload --port 8888 --log-level debug
```
### Running Tests
```bash
# API tests
cd hindsight-api
uv run pytest tests/
# Specific test
uv run pytest tests/test_http_api_integration.py -v
```
### Generating OpenAPI Spec
After changing API endpoints, regenerate the OpenAPI spec and docs:
```bash
./scripts/generate-openapi.sh
```
This will:
1. Generate `openapi.json` at project root
2. Copy to `hindsight-docs/openapi.json`
3. Regenerate API reference documentation
### Generating API Clients
After updating the OpenAPI spec, regenerate all clients:
```bash
./scripts/generate-clients.sh
```
This generates:
- **Rust client**: `hindsight-clients/rust/` (via progenitor in build.rs)
- **Python client**: `hindsight-clients/python/` (via openapi-generator Docker)
- **TypeScript client**: `hindsight-clients/typescript/` (via @hey-api/openapi-ts)
Note: The maintained wrapper `hindsight_client.py` and `README.md` are preserved during regeneration.
### Running the Documentation Site
```bash
./scripts/dev/start-docs.sh
```
### Running the Control Plane
```bash
./scripts/dev/start-control-plane.sh
```
## Code Style
### Python (hindsight-api)
- Use `uv` for package management
- Async throughout (asyncpg, async FastAPI endpoints)
- Pydantic models for request/response validation
- No py files at project root - maintain clean directory structure
### TypeScript (control-plane, clients)
- Next.js with App Router for control plane
- Tailwind CSS with shadcn/ui components
### Rust (CLI)
- Async with tokio
- reqwest for HTTP client
- progenitor for API client generation
## Database
- PostgreSQL with pgvector extension
- Schema managed via Alembic migrations in `hindsight-api/alembic/`, db migrations happen during api startup, no manual commands
- Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
# Branding
## Colors
- Primary: gradient from #0074d9 to #009296
-326
View File
@@ -1,326 +0,0 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
Hindsight is an agent memory system that provides long-term memory for AI agents using biomimetic data structures. Memories are organized as:
- **World facts**: General knowledge ("The sky is blue")
- **Experience facts**: Personal experiences ("I visited Paris in 2023")
- **Mental models**: Consolidated knowledge synthesized from facts ("User prefers functional programming patterns")
## Development Commands
### API Server (Python/FastAPI)
```bash
# Start API server (loads .env automatically)
./scripts/dev/start-api.sh
# Run all tests (parallelized with pytest-xdist)
cd hindsight-api && uv run pytest tests/
# Run specific test file
cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v
# Run single test function
cd hindsight-api && uv run pytest tests/test_retain.py::test_retain_simple -v
# Lint and 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 && uv run ty check hindsight_api/
```
### Control Plane (Next.js)
```bash
./scripts/dev/start-control-plane.sh
# Or manually:
cd hindsight-control-plane && npm run dev
```
### Documentation Site (Docusaurus)
```bash
./scripts/dev/start-docs.sh
```
### Generating Clients/OpenAPI
```bash
# Regenerate OpenAPI spec after API changes (REQUIRED after changing endpoints)
./scripts/generate-openapi.sh
# Regenerate all client SDKs (Python, TypeScript, Rust)
./scripts/generate-clients.sh
```
### 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/**: 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, OpenAI)
- **hindsight-dev/**: Development tools and benchmarks
### 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
- `query_analyzer.py`: Query intent analysis
**retain/**: Memory ingestion pipeline
- `orchestrator.py`: Coordinates the retain flow
- `fact_extraction.py`: LLM-based fact extraction from content
- `link_utils.py`: Entity link creation and management
**search/**: Multi-strategy retrieval
- `retrieval.py`: Main retrieval orchestrator
- `graph_retrieval.py`: Entity/relationship graph traversal
- `mpfp_retrieval.py`: Multi-Path Fact Propagation retrieval
- `fusion.py`: Reciprocal rank fusion for combining results
- `reranking.py`: Cross-encoder reranking
### 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:
- **Retain**: Store memories, extracts facts/entities/relationships
- **Recall**: Retrieve memories via 4 parallel strategies (semantic, BM25, graph, temporal) + reranking
- **Reflect**: Disposition-aware reasoning using memories and mental models.
### Database
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/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
2. **Migration template**:
```python
"""Description of the migration
Revision ID: f1a2b3c4d5e6
Revises: <previous_revision_id>
Create Date: YYYY-MM-DD
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "f1a2b3c4d5e6"
down_revision: str | Sequence[str] | None = "<previous_revision_id>"
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 INDEX ... ON {schema}table_name(...)")
def downgrade() -> None:
schema = _get_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}index_name")
```
3. **Run migrations locally**:
```bash
# Set database URL and run migrations
uv run hindsight-admin run-db-migration
# Run on a specific tenant schema
uv run hindsight-admin run-db-migration --schema tenant_xyz
```
## Key Conventions
### Code Quality
**Always run the lint script after making Python or TypeScript/Node changes:**
```bash
./scripts/hooks/lint.sh
```
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)
- Banks have dispositions (skepticism, literalism, empathy traits 1-5) affecting reflect
- Banks can have background context
- Bank isolation is strict - no cross-bank data leakage
### API Design
- All endpoints operate on a single bank per request
- Multi-bank queries are client responsibility to orchestrate
- Disposition traits only affect reflect, not recall
### Control Plane API Routes
When adding or modifying parameters in the dataplane API (hindsight-api), you must also update the control plane routes that proxy to it:
1. **API Routes** (`hindsight-control-plane/src/app/api/`):
- `recall/route.ts` - proxies to `/v1/default/banks/{bank_id}/memories/recall`
- `reflect/route.ts` - proxies to `/v1/default/banks/{bank_id}/reflect`
- `memories/retain/route.ts` - proxies to `/v1/default/banks/{bank_id}/memories/retain`
- Other routes follow the same pattern
2. **Client types** (`hindsight-control-plane/src/lib/api.ts`):
- Update the TypeScript type definitions for `recall()`, `reflect()`, `retain()` etc.
3. **Checklist when adding new API parameters**:
- Add parameter extraction in the route handler (destructure from `body`)
- Pass the parameter to the SDK call
- Update the client type definition in `lib/api.ts`
- Update any UI components that need to use the new parameter
### 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
### 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
```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
### Adding New API Configuration Flags
Configuration follows a hierarchical system: **Global (env vars) → Tenant (via extension) → Bank (database)**.
Fields must be categorized as either **hierarchical** (can be overridden per-tenant/bank) or **static** (server-level only).
#### Adding a New Configuration Field
1. **config.py** (`hindsight-api/hindsight_api/config.py`):
- Add `ENV_*` constant for the environment variable name (e.g., `ENV_MY_SETTING = "HINDSIGHT_API_MY_SETTING"`)
- Add `DEFAULT_*` constant for the default value
- Add field to `HindsightConfig` dataclass with type annotation
- **Mark as hierarchical or static** by adding to `_HIERARCHICAL_FIELDS` set (hierarchical) or leaving it out (static)
- Add initialization in `from_env()` method
```python
# Hierarchical field (can be overridden per-bank)
_HIERARCHICAL_FIELDS = {
...,
"my_setting", # Add here for hierarchical
}
# Static field - just don't add to _HIERARCHICAL_FIELDS
```
2. **main.py** (`hindsight-api/hindsight_api/main.py`):
- Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
3. **Use hierarchical config in MemoryEngine**:
```python
# Config is resolved automatically per bank via ConfigResolver
config_dict = await self._config_resolver.get_bank_config(bank_id, context)
value = config_dict["my_setting"]
```
4. **Use static config** (non-hierarchical):
```python
from ...config import get_config
config = get_config()
value = config.my_static_field
```
5. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
- Add to appropriate section table with Variable, Description, Default
- Mark if it's hierarchical (can be overridden per-bank)
#### Hierarchical vs Static Guidelines
**Hierarchical** (per-bank overridable):
- LLM settings (provider, model, API key, base URL)
- Operation-specific settings (retain mode, chunk size, etc.)
- Feature flags that vary by customer/bank
**Static** (server-level only):
- Infrastructure settings (database URL, port, host)
- Global limits (max concurrent operations)
- System-wide feature flags
## Environment Setup
```bash
cp .env.example .env
# Edit .env with LLM API key
# Python deps
uv sync --directory hindsight-api/
# Node deps (uses npm workspaces)
npm install
```
Required env vars:
- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, ollama, lmstudio
- `HINDSIGHT_API_LLM_API_KEY`: Your API key
- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., gpt-4o-mini, claude-sonnet-4-20250514)
Optional (uses local models by default):
- `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
- `HINDSIGHT_API_RERANKER_PROVIDER`: local (default) or tei
- `HINDSIGHT_API_DATABASE_URL`: External PostgreSQL (uses embedded pg0 by default)
- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: true)
+5 -72
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@@ -5,23 +5,13 @@ Thanks for your interest in contributing to Hindsight!
## Getting Started
1. Fork and clone the repository
2. Install dependencies:
```bash
git clone [email protected]:vectorize-io/hindsight.git
cd hindsight
cd hindsight-api && uv sync
```
2. Set up your environment:
3. Set up your environment:
```bash
cp .env.example .env
```
Edit the .env to add LLM API key and config as required
3. Install dependencies:
```bash
# Python dependencies
uv sync --directory hindsight-api/
# Node dependencies (uses npm workspaces)
npm install
export OPENAI_API_KEY=your-key
```
## Development
@@ -51,36 +41,7 @@ cd hindsight-api
uv run pytest tests/
```
### Code Style
We use [Ruff](https://docs.astral.sh/ruff/) for Python linting and formatting, and ESLint/Prettier for TypeScript.
#### Setting up git hooks (recommended)
Set up git hooks to automatically lint and format code before each commit:
```bash
./scripts/setup-hooks.sh
```
This configures git to use the hooks in `.githooks/`, which run all scripts in `scripts/hooks/` on commit. The lint hook runs in parallel:
- **Python**: `ruff check --fix`, `ruff format`, `ty check`
- **TypeScript**: `eslint --fix`, `prettier`
#### Manual linting and formatting
```bash
# Run all lints (same as pre-commit)
./scripts/hooks/lint.sh
# Or run individually for Python:
cd hindsight-api
uv run ruff check --fix . # Lint and auto-fix
uv run ruff format . # Format code
uv run ty check hindsight_api # Type check
```
#### Style guidelines
### Code style
- Use Python type hints
- Follow existing code patterns
@@ -93,34 +54,6 @@ uv run ty check hindsight_api # Type check
3. Run tests to ensure nothing breaks
4. Submit a PR with a clear description of changes
## Release Process
The project uses `scripts/release.sh` for creating releases. This script automates the entire release workflow:
1. Bumps version in all components (API, clients, CLI, control plane, Helm)
2. **Regenerates OpenAPI spec and client SDKs** (Python, TypeScript, Rust)
3. Updates documentation versioning
4. Creates a commit and git tag
5. Pushes to GitHub (triggers CI/CD to publish packages)
### Usage
```bash
./scripts/release.sh <version>
```
**Example:**
```bash
./scripts/release.sh 0.5.0
```
### Important for Developers
- During development, version bumps in `__init__.py` do NOT require client regeneration
- Clients are only regenerated during releases
- Do not manually run `./scripts/generate-clients.sh` unless testing generation changes
- Client version comments will reflect the API version from the latest release
## Reporting Issues
Open an issue on GitHub with:
+42 -115
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@@ -1,14 +1,15 @@
<div align="center">
![Hindsight Banner](./hindsight-docs/static/img/hindsight-github-banner.png)
![Hindsight Banner](./hindsight-docs/static/img/banner.webp)
[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://vectorize-io.github.io/hindsight) • [Paper](#coming-soon) • [Examples](https://github.com/vectorize-io/hindsight-cookbook)
[![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)
![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)
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</div>
@@ -17,76 +18,53 @@
## What is Hindsight?
Hindsight is an agent memory system built to create smarter agents that learn over time. Most agent memory systems focus on recalling conversation history. Hindsight is focused on making agents that learn, not just remember.
Hindsight is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph.
Hindsight addresses common challenges that have frustrated AI engineers building agents to automate tasks and assist users with conversational interfaces. Many of these challenges stem directly from a lack of memory.
<video src="https://github.com/user-attachments/assets/923b798d-3581-4897-bb62-9cfa5a931682" controls></video>
- **Inconsistency:** Agents complete tasks successfully one time, then fail when asked to complete the same task again. Memory gives the agent a mechanism to remember what worked and what didn't and to use that information to reduce errors and improve consistency.
- **Hallucinations:** Long term memory can be seeded with external knowledge to ground agent behavior in reliable sources to augment training data.
- **Cognitive Overload:** As workflows get complex, retrievals, tool calls, user messages and agent responses can grow to fill the context window leading to context rot. Short term memory optimization allows agents to reduce tokens and focus context by removing irrelevant details.
It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.
## How Hindsight Works
## Memory Performance & Accuracy
![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
Hindsight is the most accurate agent memory system ever tested according to benchmark performance. It has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of January 2026 is shown here:
Hindsight organizes memory into four networks to mimic the way human memory works:
![Overview](./hindsight-docs/static/img/hindsight-bench.jpg)
- **World:** Facts about the world ("The stove gets hot")
- **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
- **Opinion:** Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
- **Observation:** Complex mental models derived by reflecting on facts and experiences ("Curling irons, ovens, and fire are also hot. I shouldn't touch those either.")
The benchmark performance data for Hindsight has been independently reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
Hindsight provides three simple methods to interact with the system:
Hindsight is being used in production at Fortune 500 enterprises and by a growing number of AI startups.
- **Retain:** Provide information to Hindsight that you want it to remember
- **Recall:** Retrieve memories from Hindsight
- **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.
## 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.
If you need more control over how and when your agent stores and recalls memories, there's also a simple API you can integrate with using the SDKs or directly via HTTP.
![Hindsight Banner](./hindsight-docs/static/img/migration-code.png)
Memories in Hindsight are stored in banks (e.g. memory banks). When memories are retained, they are transformed to construct a series of search indexes, time series data, and entity/relationship graphs.
---
> 🤖 **Using a coding agent?** Install the Hindsight documentation skill for instant access to docs while you code:
> ```bash
> npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docs
> ```
> Works with Claude Code, Cursor, and other AI coding assistants.
---
## Quick Start
### Docker (recommended)
```bash
export OPENAI_API_KEY=sk-xxx
export OPENAI_API_KEY=your-key
docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-e HINDSIGHT_API_LLM_MODEL=o3-mini \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight:latest
```
>API: http://localhost:8888
>UI: http://localhost: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`, and `lmstudio`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
### Docker (external PostgreSQL)
```bash
export OPENAI_API_KEY=sk-xxx
export HINDSIGHT_DB_PASSWORD=choose-a-password
cd docker/docker-compose
docker compose up
```
>API: http://localhost:8888
>UI: http://localhost:9999
### Client
Install client:
```bash
pip install hindsight-client -U
@@ -94,7 +72,7 @@ pip install hindsight-client -U
npm install @vectorize-io/hindsight-client
```
#### Python
Python example:
```python
from hindsight_client import Hindsight
@@ -111,29 +89,7 @@ client.recall(bank_id="my-bank", query="What does Alice do?")
client.reflect(bank_id="my-bank", query="Tell me about Alice")
```
#### Node.js / TypeScript
```bash
npm install @vectorize-io/hindsight-client
```
```javascript
const { HindsightClient } = require('@vectorize-io/hindsight-client');
const main = async () => {
const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
await client.retain('my-bank', 'Alice loves hiking in Yosemite');
const results = await client.recall('my-bank', 'What does Alice like?');
console.log(results);
}
main();
```
### Python Embedded (no server required)
### Python (embedded, no Docker)
```bash
pip install hindsight-all -U
@@ -153,48 +109,25 @@ with HindsightServer(
results = client.recall(bank_id="my-bank", query="Where does Alice work?")
```
### Node.js / TypeScript
---
```bash
npm install @vectorize-io/hindsight-client
```
## Use Cases
```javascript
const { HindsightClient } = require('@vectorize-io/hindsight-client');
const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
Hindsight is built to support conversational AI agents as well as agents that are intended to perform tasks autonomously. The ideal use case for Hindsight are agents that require a blend of these features such as AI employees that need to handle open-ended tasks, change behavior based on user feedback, and learn to perform complex tasks to automate work at a level that approximates a human work. Hindsight can be used with simple AI workflows like those built with n8n and other similar tools, but may be overkill for such applications.
### Per-User Memories and Chat History
One of the simpler use cases you can use Hindsight for is to personalize AI chatbots and other conversational agents by storing and recalling memories associated with individual users.
The requirements for this use case usually look something like this:
![Per-User Memories](./hindsight-docs/static/img/per-user-memory-requirements.png)
<video src="https://github.com/user-attachments/assets/4805e8e1-e7d1-47c6-a4f8-2344a5ec8906" controls></video>
Satisfying these requirements in Hindsight is straightforward. When new user inputs and tool calls are ingested into Hindsight using the retain operation, custom metadata can be used to enrich the new memories. Metadata provides a convenient way to isolate memories that need to be restricted to a given user. Once these are fed into the retain operation, any raw memories and mental models that get created can be filtered when retrieving relevant memories.
![Per-User Memories](./hindsight-docs/static/img/per-user-memory-howto.png)
await client.retain('my-bank', 'Alice loves hiking in Yosemite');
await client.recall('my-bank', 'What does Alice like?');
```
---
## Architecture & Operations
![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:
- **World:** Facts about the world ("The stove gets hot")
- **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
- **Mental Models:** Learned understanding of the agent's world formed by reflecting on raw memories and experiences.
Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
Hindsight provides three simple methods to interact with the system:
- **Retain:** Provide information to Hindsight that you want it to remember
- **Recall:** Retrieve memories from Hindsight
- **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.
### Retain
The `retain` operation is used to push new memories into Hindsight. It tells Hindsight to _retain_ the information you pass in as an input.
@@ -253,7 +186,7 @@ The final output is trimmed as needed to fit within the token limit.
### Reflect
The reflect operation is used to perform a more thorough analysis of existing memories. This allows the agent to form new connections between memories and build a more thorough understanding of its world.
The reflect operation is used to perform a more thorough analysis of existing memories. This allows the agent to form new connections between memories which are then persisted as opinions and/or observations. When building agents, the reflect operation is a key capability to enable the agent to learn from its experiences.
For example, the `reflect` operation can be used to support use cases such as:
@@ -287,13 +220,9 @@ client.reflect(bank_id="my-bank", query="What should I know about Alice?")
- [CLI](https://hindsight.vectorize.io/sdks/cli)
**Community:**
- [Slack](https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)
- [Slack](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
- [GitHub Issues](https://github.com/vectorize-io/hindsight/issues)
---
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=vectorize-io/hindsight&type=date&legend=top-left)](https://www.star-history.com/#vectorize-io/hindsight&type=date&legend=top-left)
---
## Contributing
@@ -307,5 +236,3 @@ 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="" />
@@ -1,54 +0,0 @@
# Docker Compose file for Hindsight with PostgreSQL and pgvector
#
# 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 pgvector extension pre-installed
# see https://hub.docker.com/r/pgvector/pgvector
image: pgvector/pgvector:pg${HINDSIGHT_DB_VERSION:-18}
container_name: hindsight-db
restart: always
# Expose PostgreSQL port
# ports:
# - "5432:5432"
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
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}
depends_on:
- db
networks:
- hindsight-net
networks:
hindsight-net:
driver: bridge
volumes:
pg_data:
-96
View File
@@ -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.
@@ -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:
-40
View File
@@ -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;
}
}
}
@@ -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
@@ -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:
@@ -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=${SEAWEEDFS_S3_SECRET_KEY:-hindsight_s3_secret}
depends_on:
- db
- seaweedfs
networks:
- hindsight-net
networks:
hindsight-net:
driver: bridge
volumes:
pg_data:
seaweedfs_data:
@@ -1,19 +0,0 @@
{
"identities": [
{
"name": "hindsight",
"credentials": [
{
"accessKey": "hindsight_s3_key",
"secretKey": "hindsight_s3_secret"
}
],
"actions": [
"Admin",
"Read",
"Write",
"List"
]
}
]
}
@@ -1,16 +0,0 @@
# Git
.git
.gitignore
.gitattributes
# Docker
docker-compose.yaml
.dockerignore
# Documentation
README.md
*.md
# Environment
.env
.env.example
@@ -1,25 +0,0 @@
# PostgreSQL Configuration
HINDSIGHT_DB_USER=hindsight_user
HINDSIGHT_DB_PASSWORD=change-me-to-secure-password
HINDSIGHT_DB_NAME=hindsight_db
# Hindsight Version
HINDSIGHT_VERSION=latest
# LLM Configuration
HINDSIGHT_API_LLM_PROVIDER=openai
OPENAI_API_KEY=your-openai-api-key-here
# Alternative LLM providers (uncomment and configure as needed):
# HINDSIGHT_API_LLM_PROVIDER=anthropic
# ANTHROPIC_API_KEY=your-anthropic-api-key
# HINDSIGHT_API_LLM_PROVIDER=gemini
# GEMINI_API_KEY=your-gemini-api-key
# HINDSIGHT_API_LLM_PROVIDER=groq
# GROQ_API_KEY=your-groq-api-key
# Vector and Text Search (already configured in docker-compose.yaml)
# HINDSIGHT_API_VECTOR_EXTENSION=pgvectorscale
# HINDSIGHT_API_TEXT_SEARCH_EXTENSION=pg_textsearch
@@ -1,55 +0,0 @@
# PostgreSQL with pgvector, pgvectorscale, and pg_textsearch extensions
# All three extensions from Timescale/pgvector for high-performance vector and text search
# Note: Requires PostgreSQL 16+
FROM postgres:17
# Install build dependencies and Rust toolchain
RUN apt-get update && apt-get install -y \
build-essential \
git \
postgresql-server-dev-17 \
libpq-dev \
cmake \
curl \
pkg-config \
libssl-dev \
&& rm -rf /var/lib/apt/lists/*
# Install Rust toolchain (required for pgvectorscale)
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${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
-101
View File
@@ -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)
@@ -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:
@@ -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:
+113 -193
View File
@@ -2,25 +2,16 @@
# Supports building API-only, Control Plane-only, or both
#
# Build args:
# INCLUDE_API=true/false - Include API (default: true)
# INCLUDE_CP=true/false - Include Control Plane (default: true)
# INCLUDE_LOCAL_MODELS=true/false - Include local ML models for embeddings/reranking (default: true)
# Set to false when using external providers (TEI, OpenAI, Cohere)
# PRELOAD_ML_MODELS=true/false - Pre-download ML models during build (default: true)
# Only effective when INCLUDE_LOCAL_MODELS=true
# NOTE: tiktoken encodings are ALWAYS preloaded (required for air-gapped deployments)
# INCLUDE_API=true/false - Include API (default: true)
# INCLUDE_CP=true/false - Include Control Plane (default: true)
#
# Examples:
# docker build -t hindsight . # Both (standalone)
# docker build -t hindsight-api --build-arg INCLUDE_CP=false . # API only
# docker build -t hindsight-cp --build-arg INCLUDE_API=false . # Control Plane only
# docker build -t hindsight --build-arg PRELOAD_ML_MODELS=false . # Skip ML model preload
# docker build -t hindsight --build-arg INCLUDE_LOCAL_MODELS=false . # Skip local ML deps (for external providers)
# docker build -t hindsight . # Both (standalone)
# docker build -t hindsight-api --build-arg INCLUDE_CP=false . # API only
# docker build -t hindsight-cp --build-arg INCLUDE_API=false . # Control Plane only
ARG INCLUDE_API=true
ARG INCLUDE_CP=true
ARG PRELOAD_ML_MODELS=true
ARG INCLUDE_LOCAL_MODELS=true
# =============================================================================
# Stage: API Builder
@@ -28,7 +19,6 @@ ARG INCLUDE_LOCAL_MODELS=true
FROM python:3.11-slim AS api-builder
ARG INCLUDE_API
ARG INCLUDE_LOCAL_MODELS
RUN if [ "$INCLUDE_API" != "true" ]; then echo "Skipping API build" && exit 0; fi
WORKDIR /app
@@ -47,15 +37,6 @@ COPY hindsight-api/README.md ./api/
WORKDIR /app/api
# Remove local ML model dependencies if INCLUDE_LOCAL_MODELS=false
# This creates a smaller image when using external providers (TEI, OpenAI, Cohere)
RUN if [ "$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
@@ -73,15 +54,13 @@ FROM node:20-slim AS sdk-builder
ARG INCLUDE_CP
RUN if [ "$INCLUDE_CP" != "true" ]; then echo "Skipping SDK build" && exit 0; fi
WORKDIR /app
WORKDIR /app/sdk
# Copy root package files for npm workspaces
COPY package.json package-lock.json ./
COPY hindsight-clients/typescript/ ./hindsight-clients/typescript/
COPY hindsight-clients/typescript/package*.json ./
RUN npm ci
# Install and build SDK using workspace (--ignore-scripts skips git hooks setup)
RUN npm ci --ignore-scripts -w @vectorize-io/hindsight-client
RUN npm run build -w @vectorize-io/hindsight-client
COPY hindsight-clients/typescript/ ./
RUN npm run build
# =============================================================================
# Stage: Control Plane Builder
@@ -91,52 +70,30 @@ FROM node:20-slim AS cp-builder
ARG INCLUDE_CP
RUN if [ "$INCLUDE_CP" != "true" ]; then echo "Skipping CP build" && exit 0; fi
# Create directory structure matching the monorepo layout
# This is required because build:standalone script expects .next/standalone/memory-poc/hindsight-control-plane
WORKDIR /app/memory-poc/hindsight-control-plane
WORKDIR /app
# Copy built SDK
COPY --from=sdk-builder /app/sdk /app/sdk
# Install Control Plane dependencies
# Only copy package.json (not package-lock.json) to ensure npm installs
# correct platform-specific native bindings for lightningcss/tailwindcss
COPY hindsight-control-plane/package.json ./
# Remove the file: dependency on SDK (we'll copy it directly later)
RUN sed -i '/"@vectorize-io\/hindsight-client":/d' package.json
RUN npm install
# Copy Control Plane source (excluding node_modules via .dockerignore)
COPY hindsight-control-plane/ ./
# Remove package-lock.json to avoid conflicts with installed native bindings
# Also remove the file: dependency from package.json (restored by COPY above)
RUN rm -f package-lock.json && sed -i '/"@vectorize-io\/hindsight-client":/d' package.json
RUN rm -f package-lock.json
# Copy built SDK directly into node_modules (more reliable than npm link in Docker)
COPY --from=sdk-builder /app/hindsight-clients/typescript ./node_modules/@vectorize-io/hindsight-client
# Link SDK (temporary for build)
RUN cd /app/sdk && npm link && cd /app && npm link @vectorize-io/hindsight-client
# 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 npm run build
# 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
# Create standalone directory structure manually
# Note: Must exclude node_modules from find to avoid wrong server.js from next/dist/experimental/testmode/
# Note: Must explicitly copy .next since glob * doesn't match hidden directories
RUN STANDALONE_ROOT=$(find .next/standalone -path '*/node_modules' -prune -o -name 'server.js' -print | head -1 | xargs dirname) && \
mkdir -p standalone && \
cp -r "$STANDALONE_ROOT"/* standalone/ && \
cp -r "$STANDALONE_ROOT"/.next standalone/.next && \
# Copy node_modules if separate from app dir (monorepo structure)
if [ -d ".next/standalone/node_modules" ] && [ "$STANDALONE_ROOT" != ".next/standalone" ]; then \
cp -r .next/standalone/node_modules standalone/node_modules; \
fi && \
cp -r .next/static standalone/.next/static && \
mkdir -p standalone/public && \
cp -r public/* standalone/public/ 2>/dev/null || true && \
# Verify required files exist
test -f standalone/server.js || (echo "ERROR: server.js missing!" && exit 1) && \
test -f standalone/.next/BUILD_ID || (echo "ERROR: BUILD_ID missing!" && exit 1)
# Create public directory if it doesn't exist
RUN mkdir -p public
# =============================================================================
# Stage: Final Image - API Only
@@ -145,18 +102,18 @@ FROM python:3.11-slim AS api-only
WORKDIR /app
# Note: libicu version varies by Debian version - try common versions in order
# Install pg0 dependencies
RUN apt-get update && apt-get install -y \
curl \
procps \
libxml2 \
libssl3 \
libgssapi-krb5-2 \
libossp-uuid16 \
&& (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
&& apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
&& rm -rf /var/lib/apt/lists/* \
&& pip install --no-cache-dir uv
# Create non-root user (PostgreSQL cannot run as root)
RUN useradd -m -s /bin/bash hindsight
# Copy API with virtual environment from builder
@@ -166,70 +123,56 @@ COPY --from=api-builder /app/api /app/api
COPY docker/standalone/start-all.sh /app/start-all.sh
RUN chmod +x /app/start-all.sh
RUN chown -R hindsight:hindsight /app
# Create data directory for pg0 and set ownership
RUN mkdir -p /app/data && chown -R hindsight:hindsight /app
# Switch to non-root user
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
# Set PATH for hindsight user
ENV PATH="/home/hindsight/.hindsight/bin:/app/api/.venv/bin:${PATH}"
ENV PATH="/app/api/.venv/bin:${PATH}"
# Install pg0 binary
RUN mkdir -p /home/hindsight/.hindsight/bin && \
ARCH=$(uname -m) && \
if [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ]; then \
PG0_BINARY="pg0-linux-aarch64-gnu"; \
elif [ "$ARCH" = "x86_64" ]; then \
PG0_BINARY="pg0-linux-x86_64-gnu"; \
else \
echo "Unsupported architecture: $ARCH" && exit 1; \
fi && \
echo "Installing pg0 binary: $PG0_BINARY" && \
for i in 1 2 3 4 5; do \
curl -fsSL -o /home/hindsight/.hindsight/bin/pg0 \
"https://github.com/vectorize-io/pg0/releases/latest/download/$PG0_BINARY" && \
chmod +x /home/hindsight/.hindsight/bin/pg0 && \
ls -lh /home/hindsight/.hindsight/bin/pg0 && \
file /home/hindsight/.hindsight/bin/pg0 && \
ldd /home/hindsight/.hindsight/bin/pg0 2>&1 || true && \
break || (echo "Retry $i failed, waiting..." && sleep 10); \
done && \
echo "Testing pg0 binary..." && \
/home/hindsight/.hindsight/bin/pg0 --version || (echo "pg0 --version failed with exit code $?"; ldd /home/hindsight/.hindsight/bin/pg0; exit 1)
# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
# Tiktoken is a core runtime dependency, not an optional ML model
RUN MAX_RETRIES=3; \
RETRY_DELAY=5; \
for i in $(seq 1 $MAX_RETRIES); do \
echo "Attempt $i/$MAX_RETRIES: Downloading tiktoken encoding..."; \
/app/api/.venv/bin/python -c "\
import tiktoken; \
print('Downloading cl100k_base encoding...'); \
tiktoken.get_encoding('cl100k_base'); \
print('Tiktoken encoding cached successfully')" && break; \
if [ $i -lt $MAX_RETRIES ]; then \
echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
sleep $RETRY_DELAY; \
RETRY_DELAY=$((RETRY_DELAY * 2)); \
fi; \
done; \
if [ $i -eq $MAX_RETRIES ]; then \
echo "ERROR: Failed to download tiktoken encoding after $MAX_RETRIES attempts"; \
exit 1; \
fi
# Pre-download PostgreSQL binaries
ENV PG0_HOME=/home/hindsight/.pg0-cache
RUN pg0 start --help && \
(pg0 start --name hindsight --port 5555 --username hindsight --password hindsight --database hindsight && \
sleep 2 && \
pg0 stop --name hindsight && \
echo "PostgreSQL pre-cached to $PG0_HOME") || echo "Pre-download skipped"
# Pre-download ML models to avoid runtime download (conditional)
# Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
# Includes retry logic with exponential backoff for transient network failures
ARG PRELOAD_ML_MODELS
ARG INCLUDE_LOCAL_MODELS
ENV HF_HUB_DOWNLOAD_TIMEOUT=600
RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
MAX_RETRIES=3; \
RETRY_DELAY=10; \
for i in $(seq 1 $MAX_RETRIES); do \
echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
/app/api/.venv/bin/python -c "\
import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
ENV PG0_HOME=/home/hindsight/.pg0
# Pre-download ML models to avoid runtime download
RUN /app/api/.venv/bin/python -c "\
from sentence_transformers import SentenceTransformer, CrossEncoder; \
print('Downloading embedding model...'); \
SentenceTransformer('BAAI/bge-small-en-v1.5'); \
print('Downloading cross-encoder model...'); \
CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
print('Models cached successfully')" && break; \
if [ $i -lt $MAX_RETRIES ]; then \
echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
sleep $RETRY_DELAY; \
RETRY_DELAY=$((RETRY_DELAY * 2)); \
fi; \
done; \
if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
exit 1; \
fi; \
elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
else echo "Skipping ML model preload"; fi
print('Models cached successfully')"
EXPOSE 8888
@@ -239,10 +182,6 @@ ENV HINDSIGHT_API_LOG_LEVEL=info
ENV HINDSIGHT_ENABLE_API=true
ENV HINDSIGHT_ENABLE_CP=false
ENV PYTHONUNBUFFERED=1
# Suppress verbose transformers/HuggingFace model loading warnings
ENV TRANSFORMERS_VERBOSITY=error
ENV HF_HUB_VERBOSITY=error
ENV TOKENIZERS_PARALLELISM=false
CMD ["/app/start-all.sh"]
@@ -254,13 +193,13 @@ FROM node:20-alpine AS cp-only
WORKDIR /app
# Copy built SDK
COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk
COPY --from=sdk-builder /app/sdk /app/sdk
# Copy Control Plane standalone build
WORKDIR /app/control-plane
COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/standalone ./
COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/.next/static ./.next/static
COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/public ./public
COPY --from=cp-builder /app/.next/standalone ./
COPY --from=cp-builder /app/.next/static ./.next/static
COPY --from=cp-builder /app/public ./public
WORKDIR /app
@@ -287,34 +226,33 @@ FROM python:3.11-slim AS standalone
WORKDIR /app
# Install Node.js, curl, uv, and system dependencies
# Note: libicu version varies by Debian version - try common versions in order
# Install Node.js, curl, uv, and pg0 dependencies
RUN apt-get update && apt-get install -y \
curl \
procps \
libxml2 \
libssl3 \
libgssapi-krb5-2 \
libossp-uuid16 \
&& (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
&& apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
&& curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
&& apt-get install -y nodejs \
&& rm -rf /var/lib/apt/lists/* \
&& pip install --no-cache-dir uv
# Create non-root user (PostgreSQL cannot run as root)
RUN useradd -m -s /bin/bash hindsight
# Copy API with virtual environment from builder
COPY --from=api-builder /app/api /app/api
# Copy built SDK
COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk
COPY --from=sdk-builder /app/sdk /app/sdk
# Copy Control Plane standalone build
WORKDIR /app/control-plane
COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/standalone ./
COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/.next/static ./.next/static
COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/public ./public
COPY --from=cp-builder /app/.next/standalone ./
COPY --from=cp-builder /app/.next/static ./.next/static
COPY --from=cp-builder /app/public ./public
WORKDIR /app
@@ -322,70 +260,56 @@ WORKDIR /app
COPY docker/standalone/start-all.sh /app/start-all.sh
RUN chmod +x /app/start-all.sh
RUN chown -R hindsight:hindsight /app
# Create data directory for pg0 and set ownership
RUN mkdir -p /app/data && chown -R hindsight:hindsight /app
# Switch to non-root user
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
# Set PATH for hindsight user
ENV PATH="/home/hindsight/.hindsight/bin:/app/api/.venv/bin:${PATH}"
ENV PATH="/app/api/.venv/bin:${PATH}"
# Install pg0 binary
RUN mkdir -p /home/hindsight/.hindsight/bin && \
ARCH=$(uname -m) && \
if [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ]; then \
PG0_BINARY="pg0-linux-aarch64-gnu"; \
elif [ "$ARCH" = "x86_64" ]; then \
PG0_BINARY="pg0-linux-x86_64-gnu"; \
else \
echo "Unsupported architecture: $ARCH" && exit 1; \
fi && \
echo "Installing pg0 binary: $PG0_BINARY" && \
for i in 1 2 3 4 5; do \
curl -fsSL -o /home/hindsight/.hindsight/bin/pg0 \
"https://github.com/vectorize-io/pg0/releases/latest/download/$PG0_BINARY" && \
chmod +x /home/hindsight/.hindsight/bin/pg0 && \
ls -lh /home/hindsight/.hindsight/bin/pg0 && \
file /home/hindsight/.hindsight/bin/pg0 && \
ldd /home/hindsight/.hindsight/bin/pg0 2>&1 || true && \
break || (echo "Retry $i failed, waiting..." && sleep 10); \
done && \
echo "Testing pg0 binary..." && \
/home/hindsight/.hindsight/bin/pg0 --version || (echo "pg0 --version failed with exit code $?"; ldd /home/hindsight/.hindsight/bin/pg0; exit 1)
# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
# Tiktoken is a core runtime dependency, not an optional ML model
RUN MAX_RETRIES=3; \
RETRY_DELAY=5; \
for i in $(seq 1 $MAX_RETRIES); do \
echo "Attempt $i/$MAX_RETRIES: Downloading tiktoken encoding..."; \
/app/api/.venv/bin/python -c "\
import tiktoken; \
print('Downloading cl100k_base encoding...'); \
tiktoken.get_encoding('cl100k_base'); \
print('Tiktoken encoding cached successfully')" && break; \
if [ $i -lt $MAX_RETRIES ]; then \
echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
sleep $RETRY_DELAY; \
RETRY_DELAY=$((RETRY_DELAY * 2)); \
fi; \
done; \
if [ $i -eq $MAX_RETRIES ]; then \
echo "ERROR: Failed to download tiktoken encoding after $MAX_RETRIES attempts"; \
exit 1; \
fi
# Pre-download PostgreSQL binaries
ENV PG0_HOME=/home/hindsight/.pg0-cache
RUN pg0 start --help && \
(pg0 start --name hindsight --port 5555 --username hindsight --password hindsight --database hindsight && \
sleep 2 && \
pg0 stop --name hindsight && \
echo "PostgreSQL pre-cached to $PG0_HOME") || echo "Pre-download skipped"
# Pre-download ML models to avoid runtime download (conditional)
# Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
# Includes retry logic with exponential backoff for transient network failures
ARG PRELOAD_ML_MODELS
ARG INCLUDE_LOCAL_MODELS
ENV HF_HUB_DOWNLOAD_TIMEOUT=600
RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
MAX_RETRIES=3; \
RETRY_DELAY=10; \
for i in $(seq 1 $MAX_RETRIES); do \
echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
/app/api/.venv/bin/python -c "\
import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
ENV PG0_HOME=/home/hindsight/.pg0
# Pre-download ML models to avoid runtime download
RUN /app/api/.venv/bin/python -c "\
from sentence_transformers import SentenceTransformer, CrossEncoder; \
print('Downloading embedding model...'); \
SentenceTransformer('BAAI/bge-small-en-v1.5'); \
print('Downloading cross-encoder model...'); \
CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
print('Models cached successfully')" && break; \
if [ $i -lt $MAX_RETRIES ]; then \
echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
sleep $RETRY_DELAY; \
RETRY_DELAY=$((RETRY_DELAY * 2)); \
fi; \
done; \
if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
exit 1; \
fi; \
elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
else echo "Skipping ML model preload"; fi
print('Models cached successfully')"
EXPOSE 8888 9999
@@ -397,10 +321,6 @@ ENV HINDSIGHT_CP_DATAPLANE_API_URL=http://localhost:8888
ENV HINDSIGHT_ENABLE_API=true
ENV HINDSIGHT_ENABLE_CP=true
ENV PYTHONUNBUFFERED=1
# Suppress verbose transformers/HuggingFace model loading warnings
ENV TRANSFORMERS_VERBOSITY=error
ENV HF_HUB_VERBOSITY=error
ENV TOKENIZERS_PARALLELISM=false
CMD ["/app/start-all.sh"]
+12 -67
View File
@@ -5,70 +5,16 @@ set -e
ENABLE_API="${HINDSIGHT_ENABLE_API:-true}"
ENABLE_CP="${HINDSIGHT_ENABLE_CP:-true}"
# =============================================================================
# Dependency waiting (opt-in via HINDSIGHT_WAIT_FOR_DEPS=true)
#
# Problem: When running with LM Studio, the LLM may take time to load models.
# If Hindsight starts before LM Studio is ready, it fails on LLM verification.
# This wait loop ensures dependencies are ready before starting.
# =============================================================================
if [ "${HINDSIGHT_WAIT_FOR_DEPS:-false}" = "true" ]; then
LLM_BASE_URL="${HINDSIGHT_API_LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
MAX_RETRIES="${HINDSIGHT_RETRY_MAX:-0}" # 0 = infinite
RETRY_INTERVAL="${HINDSIGHT_RETRY_INTERVAL:-10}"
# Check if external database is configured (skip check for embedded pg0)
SKIP_DB_CHECK=false
if [ -z "${HINDSIGHT_API_DATABASE_URL}" ]; then
SKIP_DB_CHECK=true
else
DB_CHECK_HOST=$(echo "$HINDSIGHT_API_DATABASE_URL" | sed -E 's|.*@([^:/]+):([0-9]+)/.*|\1 \2|')
# Copy pre-cached PostgreSQL data if runtime directory is empty (first run with volume)
if [ "$ENABLE_API" = "true" ]; then
PG0_CACHE="/home/hindsight/.pg0-cache"
PG0_HOME="/home/hindsight/.pg0"
if [ -d "$PG0_CACHE" ] && [ "$(ls -A $PG0_CACHE 2>/dev/null)" ]; then
if [ ! "$(ls -A $PG0_HOME 2>/dev/null)" ]; then
echo "📦 Copying pre-cached PostgreSQL data..."
cp -r "$PG0_CACHE"/* "$PG0_HOME"/ 2>/dev/null || true
fi
fi
check_db() {
if $SKIP_DB_CHECK; then
return 0
fi
if command -v pg_isready &> /dev/null; then
pg_isready -h $(echo $DB_CHECK_HOST | cut -d' ' -f1) -p $(echo $DB_CHECK_HOST | cut -d' ' -f2) &>/dev/null
else
python3 -c "import socket; s=socket.socket(); s.settimeout(5); exit(0 if s.connect_ex(('$(echo $DB_CHECK_HOST | cut -d' ' -f1)', $(echo $DB_CHECK_HOST | cut -d' ' -f2))) == 0 else 1)" 2>/dev/null
fi
}
check_llm() {
curl -sf "${LLM_BASE_URL}/models" --connect-timeout 5 &>/dev/null
}
echo "⏳ Waiting for dependencies to be ready..."
attempt=1
while true; do
db_ok=false
llm_ok=false
if check_db; then
db_ok=true
fi
if check_llm; then
llm_ok=true
fi
if $db_ok && $llm_ok; then
echo "✅ Dependencies ready!"
break
fi
if [ "$MAX_RETRIES" -ne 0 ] && [ "$attempt" -ge "$MAX_RETRIES" ]; then
echo "❌ Max retries ($MAX_RETRIES) reached. Dependencies not available."
exit 1
fi
echo " Attempt $attempt: DB=$( $db_ok && echo 'ok' || echo 'waiting' ), LLM=$( $llm_ok && echo 'ok' || echo 'waiting' )"
sleep "$RETRY_INTERVAL"
((attempt++))
done
fi
# Track PIDs for wait
@@ -77,8 +23,7 @@ PIDS=()
# Start API if enabled
if [ "$ENABLE_API" = "true" ]; then
cd /app/api
# Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
hindsight-api &
hindsight-api 2>&1 | sed -u 's/^/[api] /' &
API_PID=$!
PIDS+=($API_PID)
@@ -97,7 +42,7 @@ fi
if [ "$ENABLE_CP" = "true" ]; then
echo "🎛️ Starting Control Plane..."
cd /app/control-plane
PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
PORT=9999 node server.js 2>&1 | grep -v -E "^[[:space:]]*(▲|✓|-|$)" | sed -u 's/^/[control-plane] /' &
CP_PID=$!
PIDS+=($CP_PID)
else
@@ -110,7 +55,7 @@ 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
View File
@@ -1,217 +0,0 @@
#!/bin/bash
#
# Docker Smoke Test Script
#
# Tests that a Hindsight Docker image starts correctly and becomes healthy.
# Can be run locally or in CI pipelines.
#
# Usage:
# ./docker/test-image.sh <image> [target]
#
# Arguments:
# image - Docker image to test (e.g., hindsight-api:test, ghcr.io/vectorize-io/hindsight:latest)
# 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)
# 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)
# HINDSIGHT_API_COHERE_API_KEY - Cohere API key for reranking (optional)
# SMOKE_TEST_TIMEOUT - Timeout in seconds (default: 120)
# SMOKE_TEST_CONTAINER_NAME - Container name (default: hindsight-smoke-test)
#
# Examples:
# # Test a locally built full image
# ./docker/test-image.sh hindsight-api:test
#
# # Test a released image
# ./docker/test-image.sh ghcr.io/vectorize-io/hindsight:latest
#
# # Test control plane image
# ./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 HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
# export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxx
# export HINDSIGHT_API_RERANKER_PROVIDER=cohere
# export HINDSIGHT_API_COHERE_API_KEY=xxx
# ./docker/test-image.sh hindsight-slim:test
#
# Exit codes:
# 0 - Success (container healthy)
# 1 - Failure (container not healthy within timeout)
# 2 - Invalid arguments
#
set -euo pipefail
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
NC='\033[0m' # No Color
# Configuration
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="${HINDSIGHT_API_LLM_MODEL:-gpt-4o-mini}"
# Validate arguments
if [ -z "$IMAGE" ]; then
echo -e "${RED}Error: Image argument is required${NC}"
echo ""
echo "Usage: $0 <image> [target]"
echo ""
echo "Examples:"
echo " $0 hindsight-api:test"
echo " $0 ghcr.io/vectorize-io/hindsight:latest"
echo " $0 hindsight-control-plane:test cp-only"
exit 2
fi
# Determine health endpoint based on target
if [ "$TARGET" = "cp-only" ]; then
HEALTH_PORT=9999
HEALTH_PATH="/api/health"
NEEDS_LLM=false
else
HEALTH_PORT=8888
HEALTH_PATH="/health"
NEEDS_LLM=true
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${NC}"
echo "Set it with: export HINDSIGHT_API_LLM_API_KEY=your-api-key"
exit 2
fi
# Cleanup function
cleanup() {
echo "Cleaning up..."
docker stop "$CONTAINER_NAME" 2>/dev/null || true
docker rm "$CONTAINER_NAME" 2>/dev/null || true
}
# Set trap to cleanup on exit
trap cleanup EXIT
echo -e "${YELLOW}Starting smoke test for: ${IMAGE}${NC}"
echo " Target: $TARGET"
echo " Health endpoint: http://localhost:${HEALTH_PORT}${HEALTH_PATH}"
echo " Timeout: ${TIMEOUT}s"
echo ""
# Remove any existing container with the same name
docker rm -f "$CONTAINER_NAME" 2>/dev/null || true
# Start container based on target type
echo "Starting container..."
if [ "$TARGET" = "cp-only" ]; then
docker run -d --name "$CONTAINER_NAME" \
-p "${HEALTH_PORT}:${HEALTH_PORT}" \
"$IMAGE"
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="$DOCKER_CMD -e HINDSIGHT_API_LLM_MODEL=$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=${HINDSIGHT_API_EMBEDDINGS_PROVIDER}"
fi
if [ -n "${HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY:-}" ]; then
DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=${HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY}"
fi
# Add optional reranker provider config
if [ -n "${HINDSIGHT_API_RERANKER_PROVIDER:-}" ]; then
DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_RERANKER_PROVIDER=${HINDSIGHT_API_RERANKER_PROVIDER}"
fi
if [ -n "${HINDSIGHT_API_COHERE_API_KEY:-}" ]; then
DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_COHERE_API_KEY=${HINDSIGHT_API_COHERE_API_KEY}"
fi
DOCKER_CMD="$DOCKER_CMD -p ${HEALTH_PORT}:${HEALTH_PORT}"
DOCKER_CMD="$DOCKER_CMD $IMAGE"
eval $DOCKER_CMD
fi
# Wait for health endpoint
echo "Waiting for health endpoint at http://localhost:${HEALTH_PORT}${HEALTH_PATH}..."
start_time=$(date +%s)
for i in $(seq 1 "$TIMEOUT"); do
if curl -sf "http://localhost:${HEALTH_PORT}${HEALTH_PATH}" > /dev/null 2>&1; then
end_time=$(date +%s)
duration=$((end_time - start_time))
echo ""
echo -e "${GREEN}Container is healthy after ${duration}s${NC}"
echo ""
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 ""
echo ""
echo "=== Container Logs (last 50 lines) ==="
docker logs "$CONTAINER_NAME" 2>&1 | tail -50
echo ""
echo -e "${GREEN}Smoke test PASSED${NC}"
exit 0
fi
# Show progress every 10 seconds
if [ $((i % 10)) -eq 0 ]; then
echo " Still waiting... (${i}s)"
fi
# Check if container is still running
if ! docker ps -q -f "name=$CONTAINER_NAME" | grep -q .; then
echo ""
echo -e "${RED}Container exited unexpectedly!${NC}"
echo ""
echo "=== Container Logs ==="
docker logs "$CONTAINER_NAME" 2>&1
echo ""
echo -e "${RED}Smoke test FAILED${NC}"
exit 1
fi
sleep 1
done
# Timeout reached
echo ""
echo -e "${RED}Container failed to become healthy after ${TIMEOUT}s${NC}"
echo ""
echo "=== Container Logs ==="
docker logs "$CONTAINER_NAME" 2>&1
echo ""
echo -e "${RED}Smoke test FAILED${NC}"
exit 1
-47
View File
@@ -1,47 +0,0 @@
#!/bin/bash
#
# Local Test Script for Slim Docker Images
#
# This script makes it easy to test slim images locally with external providers.
# It expects API keys to be set in environment variables.
#
# Usage:
# export OPENAI_API_KEY=sk-xxx
# export COHERE_API_KEY=xxx
# ./docker/test-slim-local.sh
#
# Or inline:
# OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
#
set -euo pipefail
# Check for required API keys
if [ -z "${OPENAI_API_KEY:-}" ]; then
echo "❌ Error: OPENAI_API_KEY environment variable is required"
echo "Set it with: export OPENAI_API_KEY=sk-xxx"
exit 1
fi
if [ -z "${COHERE_API_KEY:-}" ]; then
echo "❌ Error: COHERE_API_KEY environment variable is required"
echo "Set it with: export COHERE_API_KEY=xxx"
exit 1
fi
# Configuration
IMAGE="${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
export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$OPENAI_API_KEY
export HINDSIGHT_API_RERANKER_PROVIDER=cohere
export HINDSIGHT_API_COHERE_API_KEY=$COHERE_API_KEY
# Run the test
exec "$(dirname "$0")/test-image.sh" "$IMAGE" standalone
+2 -2
View File
@@ -2,8 +2,8 @@ apiVersion: v2
name: hindsight
description: Hindsight helm chart
type: application
version: 0.4.15
appVersion: "0.4.15"
version: 0.1.4
appVersion: "0.1.4"
keywords:
- ai
- memory
-59
View File
@@ -80,22 +80,6 @@ Control plane selector labels
app.kubernetes.io/component: control-plane
{{- end }}
{{/*
Worker labels
*/}}
{{- define "hindsight.worker.labels" -}}
{{ include "hindsight.labels" . }}
app.kubernetes.io/component: worker
{{- end }}
{{/*
Worker selector labels
*/}}
{{- define "hindsight.worker.selectorLabels" -}}
{{ include "hindsight.selectorLabels" . }}
app.kubernetes.io/component: worker
{{- end }}
{{/*
Create the name of the service account to use
*/}}
@@ -126,46 +110,3 @@ API URL for control plane
{{- define "hindsight.apiUrl" -}}
{{- printf "http://%s-api:%d" (include "hindsight.fullname" .) (.Values.api.service.port | int) }}
{{- end }}
{{/*
TEI reranker labels
*/}}
{{- define "hindsight.tei.reranker.labels" -}}
{{ include "hindsight.labels" . }}
app.kubernetes.io/component: tei-reranker
{{- end }}
{{/*
TEI reranker selector labels
*/}}
{{- define "hindsight.tei.reranker.selectorLabels" -}}
{{ include "hindsight.selectorLabels" . }}
app.kubernetes.io/component: tei-reranker
{{- end }}
{{/*
TEI embedding labels
*/}}
{{- define "hindsight.tei.embedding.labels" -}}
{{ include "hindsight.labels" . }}
app.kubernetes.io/component: tei-embedding
{{- end }}
{{/*
TEI embedding selector labels
*/}}
{{- define "hindsight.tei.embedding.selectorLabels" -}}
{{ include "hindsight.selectorLabels" . }}
app.kubernetes.io/component: tei-embedding
{{- end }}
{{/*
Get the name of the secret to use
*/}}
{{- define "hindsight.secretName" -}}
{{- if .Values.existingSecret }}
{{- .Values.existingSecret }}
{{- else }}
{{- printf "%s-secret" (include "hindsight.fullname" .) }}
{{- end }}
{{- end }}
+6 -37
View File
@@ -15,9 +15,7 @@ spec:
template:
metadata:
annotations:
{{- if not .Values.existingSecret }}
checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
{{- end }}
{{- with .Values.podAnnotations }}
{{- toYaml . | nindent 8 }}
{{- end }}
@@ -33,62 +31,33 @@ spec:
- name: api
securityContext:
{{- toYaml .Values.securityContext | nindent 10 }}
image: "{{ .Values.api.image.repository }}:{{ .Values.api.image.tag | default .Values.version | default .Chart.AppVersion }}"
image: "{{ .Values.api.image.repository }}:{{ .Values.api.image.tag | default .Values.version }}"
imagePullPolicy: {{ .Values.api.image.pullPolicy }}
ports:
- name: http
containerPort: {{ .Values.api.service.targetPort }}
protocol: TCP
{{- if .Values.existingSecret }}
envFrom:
- secretRef:
name: {{ .Values.existingSecret }}
{{- end }}
env:
{{- /* POSTGRES_PASSWORD must be defined before DATABASE_URL for $(VAR) interpolation */}}
- name: HINDSIGHT_API_DATABASE_URL
value: {{ include "hindsight.databaseUrl" . | quote }}
{{- if not .Values.postgresql.enabled }}
- name: POSTGRES_PASSWORD
valueFrom:
secretKeyRef:
name: {{ include "hindsight.secretName" . }}
name: {{ include "hindsight.fullname" . }}-secret
key: postgres-password
{{- end }}
- name: HINDSIGHT_API_DATABASE_URL
value: {{ include "hindsight.databaseUrl" . | quote }}
{{- /* Disable internal worker when dedicated workers are enabled */}}
{{- if .Values.worker.enabled }}
- name: HINDSIGHT_API_WORKER_ENABLED
value: "false"
{{- end }}
{{- /* Explicitly set port to override K8s service discovery env var (HINDSIGHT_API_PORT) */}}
- name: HINDSIGHT_API_PORT
value: {{ .Values.api.service.targetPort | quote }}
{{- range $key, $value := .Values.api.env }}
- name: {{ $key }}
value: {{ $value | quote }}
{{- end }}
{{- if .Values.tei.reranker.enabled }}
- name: HINDSIGHT_API_RERANKER_PROVIDER
value: "tei"
- name: HINDSIGHT_API_RERANKER_TEI_URL
value: "http://{{ include "hindsight.fullname" . }}-tei-reranker:{{ .Values.tei.reranker.port }}"
{{- end }}
{{- if .Values.tei.embedding.enabled }}
- name: HINDSIGHT_API_EMBEDDINGS_PROVIDER
value: "tei"
- name: HINDSIGHT_API_EMBEDDINGS_TEI_URL
value: "http://{{ include "hindsight.fullname" . }}-tei-embedding:{{ .Values.tei.embedding.port }}"
{{- end }}
{{- /* Only use api.secrets when not using existingSecret (for chart-managed secrets) */}}
{{- if not .Values.existingSecret }}
{{- range $key, $value := .Values.api.secrets }}
- name: {{ $key }}
valueFrom:
secretKeyRef:
name: {{ include "hindsight.secretName" $ }}
name: {{ include "hindsight.fullname" $ }}-secret
key: {{ $key }}
{{- end }}
{{- end }}
livenessProbe:
{{- toYaml .Values.api.livenessProbe | nindent 10 }}
readinessProbe:
@@ -99,7 +68,7 @@ spec:
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with (.Values.api.affinity | default .Values.affinity) }}
{{- with .Values.affinity }}
affinity:
{{- toYaml . | nindent 8 }}
{{- end }}
@@ -15,9 +15,7 @@ spec:
template:
metadata:
annotations:
{{- if not .Values.existingSecret }}
checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
{{- end }}
{{- with .Values.podAnnotations }}
{{- toYaml . | nindent 8 }}
{{- end }}
@@ -33,17 +31,12 @@ spec:
- name: control-plane
securityContext:
{{- toYaml .Values.securityContext | nindent 10 }}
image: "{{ .Values.controlPlane.image.repository }}:{{ .Values.controlPlane.image.tag | default .Values.version | default .Chart.AppVersion }}"
image: "{{ .Values.controlPlane.image.repository }}:{{ .Values.controlPlane.image.tag | default .Values.version }}"
imagePullPolicy: {{ .Values.controlPlane.image.pullPolicy }}
ports:
- name: http
containerPort: {{ .Values.controlPlane.service.targetPort }}
protocol: TCP
{{- if .Values.existingSecret }}
envFrom:
- secretRef:
name: {{ .Values.existingSecret }}
{{- end }}
env:
- name: HINDSIGHT_CP_DATAPLANE_API_URL
value: {{ include "hindsight.apiUrl" . | quote }}
@@ -51,16 +44,13 @@ spec:
- name: {{ $key }}
value: {{ $value | quote }}
{{- end }}
{{- /* Only use controlPlane.secrets when not using existingSecret (for chart-managed secrets) */}}
{{- if not .Values.existingSecret }}
{{- range $key, $value := .Values.controlPlane.secrets }}
- name: {{ $key }}
valueFrom:
secretKeyRef:
name: {{ include "hindsight.secretName" $ }}
name: {{ include "hindsight.fullname" $ }}-secret
key: {{ $key }}
{{- end }}
{{- end }}
livenessProbe:
{{- toYaml .Values.controlPlane.livenessProbe | nindent 10 }}
readinessProbe:
@@ -71,7 +61,7 @@ spec:
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with (.Values.controlPlane.affinity | default .Values.affinity) }}
{{- with .Values.affinity }}
affinity:
{{- toYaml . | nindent 8 }}
{{- end }}
-56
View File
@@ -1,56 +0,0 @@
{{- if and .Values.api.enabled .Values.api.podDisruptionBudget.enabled }}
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: {{ include "hindsight.fullname" . }}-api
labels:
{{- include "hindsight.api.labels" . | nindent 4 }}
spec:
{{- if .Values.api.podDisruptionBudget.minAvailable }}
minAvailable: {{ .Values.api.podDisruptionBudget.minAvailable }}
{{- end }}
{{- if .Values.api.podDisruptionBudget.maxUnavailable }}
maxUnavailable: {{ .Values.api.podDisruptionBudget.maxUnavailable }}
{{- end }}
selector:
matchLabels:
{{- include "hindsight.api.selectorLabels" . | nindent 6 }}
{{- end }}
---
{{- if and .Values.controlPlane.enabled .Values.controlPlane.podDisruptionBudget.enabled }}
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: {{ include "hindsight.fullname" . }}-control-plane
labels:
{{- include "hindsight.controlPlane.labels" . | nindent 4 }}
spec:
{{- if .Values.controlPlane.podDisruptionBudget.minAvailable }}
minAvailable: {{ .Values.controlPlane.podDisruptionBudget.minAvailable }}
{{- end }}
{{- if .Values.controlPlane.podDisruptionBudget.maxUnavailable }}
maxUnavailable: {{ .Values.controlPlane.podDisruptionBudget.maxUnavailable }}
{{- end }}
selector:
matchLabels:
{{- include "hindsight.controlPlane.selectorLabels" . | nindent 6 }}
{{- end }}
---
{{- if and .Values.worker.enabled .Values.worker.podDisruptionBudget.enabled }}
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: {{ include "hindsight.fullname" . }}-worker
labels:
{{- include "hindsight.worker.labels" . | nindent 4 }}
spec:
{{- if .Values.worker.podDisruptionBudget.minAvailable }}
minAvailable: {{ .Values.worker.podDisruptionBudget.minAvailable }}
{{- end }}
{{- if .Values.worker.podDisruptionBudget.maxUnavailable }}
maxUnavailable: {{ .Values.worker.podDisruptionBudget.maxUnavailable }}
{{- end }}
selector:
matchLabels:
{{- include "hindsight.worker.selectorLabels" . | nindent 6 }}
{{- end }}
+1 -3
View File
@@ -1,8 +1,7 @@
{{- if not .Values.existingSecret }}
apiVersion: v1
kind: Secret
metadata:
name: {{ include "hindsight.secretName" . }}
name: {{ include "hindsight.fullname" . }}-secret
labels:
{{- include "hindsight.labels" . | nindent 4 }}
type: Opaque
@@ -16,4 +15,3 @@ data:
{{- if and (not .Values.postgresql.enabled) .Values.postgresql.external.password }}
postgres-password: {{ .Values.postgresql.external.password | b64enc | quote }}
{{- end }}
{{- end }}
@@ -1,76 +0,0 @@
{{- if .Values.tei.embedding.enabled }}
apiVersion: apps/v1
kind: Deployment
metadata:
name: {{ include "hindsight.fullname" . }}-tei-embedding
labels:
{{- include "hindsight.tei.embedding.labels" . | nindent 4 }}
spec:
replicas: {{ .Values.tei.embedding.replicaCount }}
selector:
matchLabels:
{{- include "hindsight.tei.embedding.selectorLabels" . | nindent 6 }}
template:
metadata:
{{- with .Values.podAnnotations }}
annotations:
{{- toYaml . | nindent 8 }}
{{- end }}
labels:
{{- include "hindsight.tei.embedding.selectorLabels" . | nindent 8 }}
spec:
{{- if .Values.serviceAccount.create }}
serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
{{- end }}
securityContext:
{{- toYaml .Values.podSecurityContext | nindent 8 }}
containers:
- name: tei-embedding
securityContext:
{{- toYaml .Values.securityContext | nindent 10 }}
image: "{{ .Values.tei.embedding.image.repository }}:{{ .Values.tei.embedding.image.tag }}"
imagePullPolicy: {{ .Values.tei.embedding.image.pullPolicy }}
args:
- "--model-id"
- {{ .Values.tei.embedding.model | quote }}
- "--hostname"
- "0.0.0.0"
{{- range .Values.tei.embedding.args }}
- {{ . | quote }}
{{- end }}
ports:
- name: http
containerPort: {{ .Values.tei.embedding.port }}
protocol: TCP
env:
- name: PORT
value: {{ .Values.tei.embedding.port | quote }}
{{- range $key, $value := .Values.tei.embedding.env }}
- name: {{ $key }}
value: {{ $value | quote }}
{{- end }}
livenessProbe:
{{- toYaml .Values.tei.embedding.livenessProbe | nindent 10 }}
readinessProbe:
{{- toYaml .Values.tei.embedding.readinessProbe | nindent 10 }}
resources:
{{- toYaml .Values.tei.embedding.resources | nindent 10 }}
volumeMounts:
- name: model-cache
mountPath: /data
volumes:
- name: model-cache
emptyDir: {}
{{- with .Values.nodeSelector }}
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.affinity }}
affinity:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.tolerations }}
tolerations:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- end }}
@@ -1,17 +0,0 @@
{{- if .Values.tei.embedding.enabled }}
apiVersion: v1
kind: Service
metadata:
name: {{ include "hindsight.fullname" . }}-tei-embedding
labels:
{{- include "hindsight.tei.embedding.labels" . | nindent 4 }}
spec:
type: ClusterIP
ports:
- port: {{ .Values.tei.embedding.port }}
targetPort: http
protocol: TCP
name: http
selector:
{{- include "hindsight.tei.embedding.selectorLabels" . | nindent 4 }}
{{- end }}
@@ -1,76 +0,0 @@
{{- if .Values.tei.reranker.enabled }}
apiVersion: apps/v1
kind: Deployment
metadata:
name: {{ include "hindsight.fullname" . }}-tei-reranker
labels:
{{- include "hindsight.tei.reranker.labels" . | nindent 4 }}
spec:
replicas: {{ .Values.tei.reranker.replicaCount }}
selector:
matchLabels:
{{- include "hindsight.tei.reranker.selectorLabels" . | nindent 6 }}
template:
metadata:
{{- with .Values.podAnnotations }}
annotations:
{{- toYaml . | nindent 8 }}
{{- end }}
labels:
{{- include "hindsight.tei.reranker.selectorLabels" . | nindent 8 }}
spec:
{{- if .Values.serviceAccount.create }}
serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
{{- end }}
securityContext:
{{- toYaml .Values.podSecurityContext | nindent 8 }}
containers:
- name: tei-reranker
securityContext:
{{- toYaml .Values.securityContext | nindent 10 }}
image: "{{ .Values.tei.reranker.image.repository }}:{{ .Values.tei.reranker.image.tag }}"
imagePullPolicy: {{ .Values.tei.reranker.image.pullPolicy }}
args:
- "--model-id"
- {{ .Values.tei.reranker.model | quote }}
- "--hostname"
- "0.0.0.0"
{{- range .Values.tei.reranker.args }}
- {{ . | quote }}
{{- end }}
ports:
- name: http
containerPort: {{ .Values.tei.reranker.port }}
protocol: TCP
env:
- name: PORT
value: {{ .Values.tei.reranker.port | quote }}
{{- range $key, $value := .Values.tei.reranker.env }}
- name: {{ $key }}
value: {{ $value | quote }}
{{- end }}
livenessProbe:
{{- toYaml .Values.tei.reranker.livenessProbe | nindent 10 }}
readinessProbe:
{{- toYaml .Values.tei.reranker.readinessProbe | nindent 10 }}
resources:
{{- toYaml .Values.tei.reranker.resources | nindent 10 }}
volumeMounts:
- name: model-cache
mountPath: /data
volumes:
- name: model-cache
emptyDir: {}
{{- with .Values.nodeSelector }}
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.affinity }}
affinity:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.tolerations }}
tolerations:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- end }}
@@ -1,17 +0,0 @@
{{- if .Values.tei.reranker.enabled }}
apiVersion: v1
kind: Service
metadata:
name: {{ include "hindsight.fullname" . }}-tei-reranker
labels:
{{- include "hindsight.tei.reranker.labels" . | nindent 4 }}
spec:
type: ClusterIP
ports:
- port: {{ .Values.tei.reranker.port }}
targetPort: http
protocol: TCP
name: http
selector:
{{- include "hindsight.tei.reranker.selectorLabels" . | nindent 4 }}
{{- end }}
@@ -1,25 +0,0 @@
{{- if .Values.worker.enabled }}
apiVersion: v1
kind: Service
metadata:
name: {{ include "hindsight.fullname" . }}-worker
labels:
{{- include "hindsight.worker.labels" . | nindent 4 }}
{{- if .Values.podAnnotations }}
annotations:
{{- /* Common Prometheus annotations for metrics scraping */}}
prometheus.io/scrape: "true"
prometheus.io/port: {{ .Values.worker.service.port | quote }}
prometheus.io/path: "/metrics"
{{- end }}
spec:
# Headless service for StatefulSet (enables stable DNS names like worker-0.worker.namespace)
clusterIP: None
ports:
- port: {{ .Values.worker.service.port }}
targetPort: {{ .Values.worker.service.targetPort }}
protocol: TCP
name: http
selector:
{{- include "hindsight.worker.selectorLabels" . | nindent 4 }}
{{- end }}
@@ -1,110 +0,0 @@
{{- if .Values.worker.enabled }}
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: {{ include "hindsight.fullname" . }}-worker
labels:
{{- include "hindsight.worker.labels" . | nindent 4 }}
spec:
serviceName: {{ include "hindsight.fullname" . }}-worker
replicas: {{ .Values.worker.replicaCount }}
selector:
matchLabels:
{{- include "hindsight.worker.selectorLabels" . | nindent 6 }}
template:
metadata:
annotations:
{{- if not .Values.existingSecret }}
checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
{{- end }}
{{- with .Values.podAnnotations }}
{{- toYaml . | nindent 8 }}
{{- end }}
labels:
{{- include "hindsight.worker.selectorLabels" . | nindent 8 }}
spec:
{{- if .Values.serviceAccount.create }}
serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
{{- end }}
securityContext:
{{- toYaml .Values.podSecurityContext | nindent 8 }}
containers:
- name: worker
securityContext:
{{- toYaml .Values.securityContext | nindent 10 }}
image: "{{ .Values.worker.image.repository }}:{{ .Values.worker.image.tag | default .Values.version | default .Chart.AppVersion }}"
imagePullPolicy: {{ .Values.worker.image.pullPolicy }}
command: ["hindsight-worker"]
ports:
- name: http
containerPort: {{ .Values.worker.service.targetPort }}
protocol: TCP
{{- if .Values.existingSecret }}
envFrom:
- secretRef:
name: {{ .Values.existingSecret }}
{{- end }}
env:
{{- /* POSTGRES_PASSWORD must be defined before DATABASE_URL for $(VAR) interpolation */}}
{{- if not .Values.postgresql.enabled }}
- name: POSTGRES_PASSWORD
valueFrom:
secretKeyRef:
name: {{ include "hindsight.secretName" . }}
key: postgres-password
{{- end }}
- name: HINDSIGHT_API_DATABASE_URL
value: {{ include "hindsight.databaseUrl" . | quote }}
{{- /* Worker ID uses pod name (StatefulSet provides stable names like worker-0, worker-1) */}}
- name: HINDSIGHT_API_WORKER_ID
valueFrom:
fieldRef:
fieldPath: metadata.name
{{- /* Inherit LLM config from api.env */}}
{{- range $key, $value := .Values.api.env }}
- name: {{ $key }}
value: {{ $value | quote }}
{{- end }}
{{- /* Worker-specific env vars */}}
{{- range $key, $value := .Values.worker.env }}
- name: {{ $key }}
value: {{ $value | quote }}
{{- end }}
{{- /* Only use secrets when not using existingSecret */}}
{{- if not .Values.existingSecret }}
{{- /* Inherit secrets from api.secrets */}}
{{- range $key, $value := .Values.api.secrets }}
- name: {{ $key }}
valueFrom:
secretKeyRef:
name: {{ include "hindsight.secretName" $ }}
key: {{ $key }}
{{- end }}
{{- /* Worker-specific secrets (can override api.secrets) */}}
{{- range $key, $value := .Values.worker.secrets }}
- name: {{ $key }}
valueFrom:
secretKeyRef:
name: {{ include "hindsight.secretName" $ }}
key: {{ $key }}
{{- end }}
{{- end }}
livenessProbe:
{{- toYaml .Values.worker.livenessProbe | nindent 10 }}
readinessProbe:
{{- toYaml .Values.worker.readinessProbe | nindent 10 }}
resources:
{{- toYaml .Values.worker.resources | nindent 10 }}
{{- with .Values.nodeSelector }}
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with (.Values.worker.affinity | default .Values.affinity) }}
affinity:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.tolerations }}
tolerations:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- end }}
+3 -175
View File
@@ -1,17 +1,7 @@
# Default values for hindsight
# Global version override - use this to set a consistent image tag across all components
# If not set, defaults to Chart.appVersion from Chart.yaml
# version: ""
# Use an existing secret instead of creating one from values
# When set, all keys from this secret are injected as environment variables via envFrom
# Required keys:
# - postgres-password: PostgreSQL password (when postgresql.enabled=false)
# Optional keys (any key becomes an env var):
# - HINDSIGHT_API_LLM_API_KEY: API key for LLM provider
# - Any other env vars you want to inject
# existingSecret: "my-hindsight-secret"
# Chart version - use this to set a consistent image tag across all components
version: "0.1.1"
# Global settings
replicaCount: 1
@@ -58,15 +48,6 @@ api:
timeoutSeconds: 3
failureThreshold: 3
# Pod disruption budget
podDisruptionBudget:
enabled: false
minAvailable: 1
# maxUnavailable: 1
# Pod affinity/anti-affinity (overrides global affinity for this component)
# affinity: {}
# Environment variables
env:
#HINDSIGHT_API_LLM_PROVIDER: "groq"
@@ -77,72 +58,6 @@ api:
# HINDSIGHT_API_LLM_API_KEY: "your-api-key"
# HINDSIGHT_API_LLM_BASE_URL: "https://api.groq.com/openai/v1"
# Worker settings (distributed task processing)
# When enabled, dedicated worker pods process tasks and the API's internal worker is disabled
worker:
enabled: false
replicaCount: 2
image:
repository: ghcr.io/vectorize-io/hindsight-api
pullPolicy: IfNotPresent
# tag: "" # defaults to .Values.version, then Chart.appVersion if not specified
service:
# Service for metrics scraping (headless for StatefulSet)
port: 8889
targetPort: 8889
# Resource limits and requests
resources:
limits:
cpu: 2000m
memory: 4Gi
requests:
cpu: 500m
memory: 1Gi
# Liveness and readiness probes
livenessProbe:
httpGet:
path: /health
port: 8889
initialDelaySeconds: 30
periodSeconds: 10
timeoutSeconds: 5
failureThreshold: 3
readinessProbe:
httpGet:
path: /health
port: 8889
initialDelaySeconds: 10
periodSeconds: 5
timeoutSeconds: 3
failureThreshold: 3
# Worker-specific environment variables
env:
# Poll interval in milliseconds (how often to check for new tasks)
HINDSIGHT_API_WORKER_POLL_INTERVAL_MS: "500"
# Number of tasks to claim per poll cycle
HINDSIGHT_API_WORKER_BATCH_SIZE: "10"
# Max retries before marking a task as failed
HINDSIGHT_API_WORKER_MAX_RETRIES: "3"
# HTTP port for metrics/health (matches service.targetPort)
HINDSIGHT_API_WORKER_HTTP_PORT: "8889"
# Pod disruption budget
podDisruptionBudget:
enabled: false
minAvailable: 1
# maxUnavailable: 1
# Pod affinity/anti-affinity (overrides global affinity for this component)
# affinity: {}
# Secret environment variables (inherited from api.secrets if not specified)
secrets: {}
# Image settings for control plane
controlPlane:
enabled: true
@@ -183,15 +98,6 @@ controlPlane:
timeoutSeconds: 3
failureThreshold: 3
# Pod disruption budget
podDisruptionBudget:
enabled: false
minAvailable: 1
# maxUnavailable: 1
# Pod affinity/anti-affinity (overrides global affinity for this component)
# affinity: {}
# Environment variables
env:
NODE_ENV: "production"
@@ -290,87 +196,9 @@ nodeSelector: {}
# Tolerations
tolerations: []
# Affinity (applied to all components unless overridden per-component)
# Affinity
affinity: {}
# TEI (Text Embeddings Inference) - optional standalone deployments
# for reranking and/or embedding models
tei:
reranker:
enabled: false
replicaCount: 1
image:
repository: ghcr.io/huggingface/text-embeddings-inference
tag: cpu-1.8.3
pullPolicy: IfNotPresent
model: "cross-encoder/ms-marco-MiniLM-L-6-v2"
port: 8090
args:
- "--auto-truncate"
env:
PAYLOAD_LIMIT: "10000000"
MAX_CLIENT_BATCH_SIZE: "256"
resources:
limits:
cpu: 2000m
memory: 2Gi
requests:
cpu: 500m
memory: 1Gi
livenessProbe:
httpGet:
path: /health
port: 8090
initialDelaySeconds: 30
periodSeconds: 10
timeoutSeconds: 5
failureThreshold: 6
readinessProbe:
httpGet:
path: /health
port: 8090
initialDelaySeconds: 15
periodSeconds: 5
timeoutSeconds: 3
failureThreshold: 3
embedding:
enabled: false
replicaCount: 1
image:
repository: ghcr.io/huggingface/text-embeddings-inference
tag: cpu-1.8.3
pullPolicy: IfNotPresent
model: "sentence-transformers/all-MiniLM-L6-v2"
port: 8091
args: []
env:
PAYLOAD_LIMIT: "10000000"
MAX_CLIENT_BATCH_SIZE: "256"
resources:
limits:
cpu: 2000m
memory: 2Gi
requests:
cpu: 500m
memory: 1Gi
livenessProbe:
httpGet:
path: /health
port: 8091
initialDelaySeconds: 30
periodSeconds: 10
timeoutSeconds: 5
failureThreshold: 6
readinessProbe:
httpGet:
path: /health
port: 8091
initialDelaySeconds: 15
periodSeconds: 5
timeoutSeconds: 3
failureThreshold: 3
# Autoscaling
autoscaling:
enabled: false
+1 -137
View File
@@ -1,137 +1 @@
# Hindsight API
**Memory System for AI Agents** — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.
Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.
## Installation
```bash
pip install hindsight-api
```
## Quick Start
### Run the Server
```bash
# Set your LLM provider
export HINDSIGHT_API_LLM_PROVIDER=openai
export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
# Start the server (uses embedded PostgreSQL by default)
hindsight-api
```
The server starts at http://localhost:8888 with:
- REST API for memory operations
- MCP server at `/mcp` for tool-use integration
### Use the Python API
```python
from hindsight_api import MemoryEngine
# Create and initialize the memory engine
memory = MemoryEngine()
await memory.initialize()
# Create a memory bank for your agent
bank = await memory.create_memory_bank(
name="my-assistant",
background="A helpful coding assistant"
)
# Store a memory
await memory.retain(
memory_bank_id=bank.id,
content="The user prefers Python for data science projects"
)
# Recall memories
results = await memory.recall(
memory_bank_id=bank.id,
query="What programming language does the user prefer?"
)
# Reflect with reasoning
response = await memory.reflect(
memory_bank_id=bank.id,
query="Should I recommend Python or R for this ML project?"
)
```
## CLI Options
```bash
hindsight-api --help
# Common options
hindsight-api --port 9000 # Custom port (default: 8888)
hindsight-api --host 127.0.0.1 # Bind to localhost only
hindsight-api --workers 4 # Multiple worker processes
hindsight-api --log-level debug # Verbose logging
```
## Configuration
Configure via environment variables:
| Variable | Description | Default |
|----------|-------------|---------|
| `HINDSIGHT_API_DATABASE_URL` | PostgreSQL connection string | `pg0` (embedded) |
| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `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
# Memory
+12 -15
View File
@@ -3,29 +3,26 @@ Memory System for AI Agents.
Temporal + Semantic Memory Architecture using PostgreSQL with pgvector.
"""
from .config import HindsightConfig, get_config
from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
from .engine.llm_wrapper import LLMConfig
from .engine.memory_engine import MemoryEngine
from .engine.search.trace import (
EntryPoint,
LinkInfo,
NodeVisit,
PruningDecision,
QueryInfo,
SearchPhaseMetrics,
SearchSummary,
SearchTrace,
QueryInfo,
EntryPoint,
NodeVisit,
WeightComponents,
LinkInfo,
PruningDecision,
SearchSummary,
SearchPhaseMetrics,
)
from .engine.search.tracer import SearchTracer
from .models import RequestContext
from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
from .engine.llm_wrapper import LLMConfig
from .config import HindsightConfig, get_config
__all__ = [
"MemoryEngine",
"RequestContext",
"HindsightConfig",
"get_config",
"SearchTrace",
@@ -46,4 +43,4 @@ __all__ = [
"RemoteTEICrossEncoder",
"LLMConfig",
]
__version__ = "0.4.15"
__version__ = "0.1.0"
@@ -1 +0,0 @@
# Admin CLI for Hindsight
-311
View File
@@ -1,311 +0,0 @@
"""
Hindsight Admin CLI - backup and restore operations.
"""
import asyncio
import io
import json
import logging
import zipfile
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
import asyncpg
import typer
from ..config import HindsightConfig
from ..pg0 import parse_pg0_url, resolve_database_url
def _fq_table(table: str, schema: str) -> str:
"""Get fully-qualified table name with schema prefix."""
return f"{schema}.{table}"
# Setup logging
logging.basicConfig(
level=logging.INFO,
format="%(message)s",
)
logger = logging.getLogger(__name__)
app = typer.Typer(name="hindsight-admin", help="Hindsight administrative commands")
# Tables to backup/restore in dependency order
# Import must happen in this order due to foreign key constraints
BACKUP_TABLES = [
"banks",
"documents",
"entities",
"chunks",
"memory_units",
"unit_entities",
"entity_cooccurrences",
"memory_links",
]
MANIFEST_VERSION = "1"
async def _backup(database_url: str, output_path: Path, schema: str = "public") -> dict[str, Any]:
"""Backup all tables to a zip file using binary COPY protocol."""
conn = await asyncpg.connect(database_url)
try:
tables: dict[str, Any] = {}
manifest: dict[str, Any] = {
"version": MANIFEST_VERSION,
"created_at": datetime.now(timezone.utc).isoformat(),
"schema": schema,
"tables": tables,
}
# Use a transaction with REPEATABLE READ isolation to get a consistent
# snapshot across all tables. This prevents race conditions where
# entity_cooccurrences could reference entities created after the
# entities table was backed up.
async with conn.transaction(isolation="repeatable_read"):
with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zf:
for i, table in enumerate(BACKUP_TABLES, 1):
typer.echo(f" [{i}/{len(BACKUP_TABLES)}] Backing up {table}...", nl=False)
buffer = io.BytesIO()
# Use binary COPY for exact type preservation
# asyncpg requires schema_name as separate parameter
await conn.copy_from_table(table, schema_name=schema, output=buffer, format="binary")
data = buffer.getvalue()
zf.writestr(f"{table}.bin", data)
# Get row count for manifest
qualified_table = _fq_table(table, schema)
row_count = await conn.fetchval(f"SELECT COUNT(*) FROM {qualified_table}")
tables[table] = {
"rows": row_count,
"size_bytes": len(data),
}
typer.echo(f" {row_count} rows")
zf.writestr("manifest.json", json.dumps(manifest, indent=2))
return manifest
finally:
await conn.close()
async def _restore(database_url: str, input_path: Path, schema: str = "public") -> dict[str, Any]:
"""Restore all tables from a zip file using binary COPY protocol."""
conn = await asyncpg.connect(database_url)
try:
with zipfile.ZipFile(input_path, "r") as zf:
# Read and validate manifest
manifest: dict[str, Any] = json.loads(zf.read("manifest.json"))
if manifest.get("version") != MANIFEST_VERSION:
raise ValueError(f"Unsupported backup version: {manifest.get('version')}")
# Use a transaction for atomic restore - either all tables are
# restored or none are, preventing partial/inconsistent state.
async with conn.transaction():
typer.echo(" Clearing existing data...")
# Truncate tables in reverse order (respects FK constraints)
for table in reversed(BACKUP_TABLES):
qualified_table = _fq_table(table, schema)
await conn.execute(f"TRUNCATE TABLE {qualified_table} CASCADE")
# Restore tables in forward order
for i, table in enumerate(BACKUP_TABLES, 1):
filename = f"{table}.bin"
if filename not in zf.namelist():
typer.echo(f" [{i}/{len(BACKUP_TABLES)}] {table}: skipped (not in backup)")
continue
expected_rows = manifest["tables"].get(table, {}).get("rows", "?")
typer.echo(f" [{i}/{len(BACKUP_TABLES)}] Restoring {table}... {expected_rows} rows")
data = zf.read(filename)
buffer = io.BytesIO(data)
# asyncpg requires schema_name as separate parameter
await conn.copy_to_table(table, schema_name=schema, source=buffer, format="binary")
# Refresh materialized view
typer.echo(" Refreshing materialized views...")
await conn.execute(f"REFRESH MATERIALIZED VIEW {_fq_table('memory_units_bm25', schema)}")
return manifest
finally:
await conn.close()
async def _run_backup(db_url: str, output: Path, schema: str = "public") -> dict[str, Any]:
"""Resolve database URL and run backup."""
is_pg0, instance_name, _ = parse_pg0_url(db_url)
if is_pg0:
typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
resolved_url = await resolve_database_url(db_url)
return await _backup(resolved_url, output, schema)
async def _run_restore(db_url: str, input_file: Path, schema: str = "public") -> dict[str, Any]:
"""Resolve database URL and run restore."""
is_pg0, instance_name, _ = parse_pg0_url(db_url)
if is_pg0:
typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
resolved_url = await resolve_database_url(db_url)
return await _restore(resolved_url, input_file, schema)
@app.command()
def backup(
output: Path = typer.Argument(..., help="Output file path (.zip)"),
schema: str = typer.Option("public", "--schema", "-s", help="Database schema to backup"),
):
"""Backup the Hindsight database to a zip file."""
config = HindsightConfig.from_env()
if not config.database_url:
typer.echo("Error: Database URL not configured.", err=True)
typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
raise typer.Exit(1)
if output.suffix != ".zip":
output = output.with_suffix(".zip")
typer.echo(f"Backing up database (schema: {schema}) to {output}...")
manifest = asyncio.run(_run_backup(config.database_url, output, schema))
total_rows = sum(t["rows"] for t in manifest["tables"].values())
typer.echo(f"Backed up {total_rows} rows across {len(BACKUP_TABLES)} tables")
typer.echo(f"Backup saved to {output}")
@app.command()
def restore(
input_file: Path = typer.Argument(..., help="Input backup file (.zip)"),
schema: str = typer.Option("public", "--schema", "-s", help="Database schema to restore to"),
yes: bool = typer.Option(False, "--yes", "-y", help="Skip confirmation prompt"),
):
"""Restore the database from a backup file. WARNING: This deletes all existing data."""
config = HindsightConfig.from_env()
if not config.database_url:
typer.echo("Error: Database URL not configured.", err=True)
typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
raise typer.Exit(1)
if not input_file.exists():
typer.echo(f"Error: File not found: {input_file}", err=True)
raise typer.Exit(1)
if not yes:
typer.confirm(
"This will DELETE all existing data and replace it with the backup. Continue?",
abort=True,
)
typer.echo(f"Restoring database (schema: {schema}) from {input_file}...")
manifest = asyncio.run(_run_restore(config.database_url, input_file, schema))
total_rows = sum(t["rows"] for t in manifest["tables"].values())
typer.echo(f"Restored {total_rows} rows across {len(BACKUP_TABLES)} tables")
typer.echo("Restore complete")
async def _run_migration(db_url: str, schema: str = "public") -> None:
"""Resolve database URL and run migrations."""
from ..migrations import run_migrations
is_pg0, instance_name, _ = parse_pg0_url(db_url)
if is_pg0:
typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
resolved_url = await resolve_database_url(db_url)
run_migrations(resolved_url, schema=schema)
@app.command(name="run-db-migration")
def run_db_migration(
schema: str = typer.Option("public", "--schema", "-s", help="Database schema to run migrations on"),
):
"""Run database migrations to the latest version."""
config = HindsightConfig.from_env()
if not config.database_url:
typer.echo("Error: Database URL not configured.", err=True)
typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
raise typer.Exit(1)
typer.echo(f"Running database migrations (schema: {schema})...")
asyncio.run(_run_migration(config.database_url, schema))
typer.echo("Database migrations completed successfully")
async def _decommission_worker(db_url: str, worker_id: str, schema: str = "public") -> int:
"""Release all tasks owned by a worker, setting them back to pending status."""
is_pg0, instance_name, _ = parse_pg0_url(db_url)
if is_pg0:
typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
resolved_url = await resolve_database_url(db_url)
conn = await asyncpg.connect(resolved_url)
try:
table = _fq_table("async_operations", schema)
result = await conn.fetch(
f"""
UPDATE {table}
SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
WHERE worker_id = $1 AND status = 'processing'
RETURNING operation_id
""",
worker_id,
)
return len(result)
finally:
await conn.close()
@app.command(name="decommission-worker")
def decommission_worker(
worker_id: str = typer.Argument(..., help="Worker ID to decommission"),
schema: str = typer.Option("public", "--schema", "-s", help="Database schema"),
yes: bool = typer.Option(False, "--yes", "-y", help="Skip confirmation prompt"),
):
"""Release all tasks owned by a worker (sets status back to pending).
Use this command when a worker has crashed or been removed without graceful shutdown.
All tasks that were being processed by the worker will be released back to the queue
so other workers can pick them up.
"""
config = HindsightConfig.from_env()
if not config.database_url:
typer.echo("Error: Database URL not configured.", err=True)
typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
raise typer.Exit(1)
if not yes:
typer.confirm(
f"This will release all tasks owned by worker '{worker_id}' back to pending. Continue?",
abort=True,
)
typer.echo(f"Decommissioning worker '{worker_id}' (schema: {schema})...")
count = asyncio.run(_decommission_worker(config.database_url, worker_id, schema))
if count > 0:
typer.echo(f"Released {count} task(s) from worker '{worker_id}'")
else:
typer.echo(f"No tasks found for worker '{worker_id}'")
def main():
app()
if __name__ == "__main__":
main()
+8 -28
View File
@@ -2,19 +2,20 @@
Alembic environment configuration for SQLAlchemy with pgvector.
Uses synchronous psycopg2 driver for migrations to avoid pgbouncer issues.
"""
import logging
import os
import sys
from pathlib import Path
from sqlalchemy import pool, engine_from_config
from sqlalchemy.engine import Connection
from alembic import context
from dotenv import load_dotenv
from sqlalchemy import engine_from_config, pool
# Import your models here
from hindsight_api.models import Base
# Load environment variables based on HINDSIGHT_API_DATABASE_URL env var or default to local
def load_env():
"""Load environment variables from .env"""
@@ -29,7 +30,6 @@ def load_env():
if env_file.exists():
load_dotenv(env_file)
load_env()
# this is the Alembic Config object, which provides
@@ -109,9 +109,6 @@ def run_migrations_online() -> None:
get_database_url() # Process and set the database URL in config
# Check if we're targeting a specific schema (for multi-tenant isolation)
target_schema = config.get_main_option("target_schema")
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
prefix="sqlalchemy.",
@@ -124,34 +121,17 @@ def run_migrations_online() -> None:
def set_read_write_mode(dbapi_connection, connection_record):
cursor = dbapi_connection.cursor()
cursor.execute("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE")
# If targeting a specific schema, set search_path
# Include public in search_path for access to shared extensions (pgvector)
if target_schema:
cursor.execute(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"')
cursor.execute(f'SET search_path TO "{target_schema}", public')
cursor.close()
with connectable.connect() as connection:
# Also explicitly set read-write mode on this connection
connection.execute(text("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE"))
# If targeting a specific schema, set search_path
# Include public in search_path for access to shared extensions (pgvector)
if target_schema:
connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"'))
connection.execute(text(f'SET search_path TO "{target_schema}", public'))
connection.commit() # Commit the SET command
# Configure context with version_table_schema if using a specific schema
context_opts = {
"connection": connection,
"target_metadata": target_metadata,
}
if target_schema:
context_opts["version_table_schema"] = target_schema
context.configure(**context_opts)
context.configure(
connection=connection,
target_metadata=target_metadata
)
with context.begin_transaction():
context.run_migrations()
@@ -5,365 +5,147 @@ Revises:
Create Date: 2025-11-27 11:54:19.228030
"""
from typing import Sequence, Union
import os
from collections.abc import Sequence
import sqlalchemy as sa
from alembic import op
from pgvector.sqlalchemy import Vector
from sqlalchemy import text
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
from pgvector.sqlalchemy import Vector
# revision identifiers, used by Alembic.
revision: str = "5a366d414dce"
down_revision: str | Sequence[str] | None = None
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
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'"
)
revision: str = '5a366d414dce'
down_revision: Union[str, Sequence[str], None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Upgrade schema - create all tables from scratch."""
# Note: pgvector extension is installed globally BEFORE migrations run
# See migrations.py:run_migrations() - this ensures the extension is available
# to all schemas, not just the one being migrated
# We keep this here as a fallback for backwards compatibility
# This may fail if user lacks permissions, which is fine if extension already exists
try:
op.execute("CREATE EXTENSION IF NOT EXISTS vector")
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 = 'vector'")).fetchone()
if not result:
# Extension truly doesn't exist - re-raise the error
raise
# Enable required extensions
op.execute('CREATE EXTENSION IF NOT EXISTS vector')
# Create banks table
op.create_table(
"banks",
sa.Column("bank_id", sa.Text(), nullable=False),
sa.Column("name", sa.Text(), nullable=True),
sa.Column(
"personality",
postgresql.JSONB(astext_type=sa.Text()),
server_default=sa.text("'{}'::jsonb"),
nullable=False,
),
sa.Column("background", sa.Text(), nullable=True),
sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.PrimaryKeyConstraint("bank_id", name=op.f("pk_banks")),
'banks',
sa.Column('bank_id', sa.Text(), nullable=False),
sa.Column('name', sa.Text(), nullable=True),
sa.Column('personality', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
sa.Column('background', sa.Text(), nullable=True),
sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.PrimaryKeyConstraint('bank_id', name=op.f('pk_banks'))
)
# Create documents table
op.create_table(
"documents",
sa.Column("id", sa.Text(), nullable=False),
sa.Column("bank_id", sa.Text(), nullable=False),
sa.Column("original_text", sa.Text(), nullable=True),
sa.Column("content_hash", sa.Text(), nullable=True),
sa.Column(
"metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
),
sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.PrimaryKeyConstraint("id", "bank_id", name=op.f("pk_documents")),
'documents',
sa.Column('id', sa.Text(), nullable=False),
sa.Column('bank_id', sa.Text(), nullable=False),
sa.Column('original_text', sa.Text(), nullable=True),
sa.Column('content_hash', sa.Text(), nullable=True),
sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.PrimaryKeyConstraint('id', 'bank_id', name=op.f('pk_documents'))
)
op.create_index("idx_documents_bank_id", "documents", ["bank_id"])
op.create_index("idx_documents_content_hash", "documents", ["content_hash"])
op.create_index('idx_documents_bank_id', 'documents', ['bank_id'])
op.create_index('idx_documents_content_hash', 'documents', ['content_hash'])
# Create async_operations table
op.create_table(
"async_operations",
sa.Column(
"operation_id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False
),
sa.Column("bank_id", sa.Text(), nullable=False),
sa.Column("operation_type", sa.Text(), nullable=False),
sa.Column("status", sa.Text(), server_default="pending", nullable=False),
sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("completed_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column("error_message", sa.Text(), nullable=True),
sa.Column(
"result_metadata",
postgresql.JSONB(astext_type=sa.Text()),
server_default=sa.text("'{}'::jsonb"),
nullable=False,
),
sa.PrimaryKeyConstraint("operation_id", name=op.f("pk_async_operations")),
sa.CheckConstraint(
"status IN ('pending', 'processing', 'completed', 'failed')", name="async_operations_status_check"
),
'async_operations',
sa.Column('operation_id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
sa.Column('bank_id', sa.Text(), nullable=False),
sa.Column('operation_type', sa.Text(), nullable=False),
sa.Column('status', sa.Text(), server_default='pending', nullable=False),
sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('completed_at', postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column('error_message', sa.Text(), nullable=True),
sa.Column('result_metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
sa.PrimaryKeyConstraint('operation_id', name=op.f('pk_async_operations')),
sa.CheckConstraint("status IN ('pending', 'processing', 'completed', 'failed')", name='async_operations_status_check')
)
op.create_index("idx_async_operations_bank_id", "async_operations", ["bank_id"])
op.create_index("idx_async_operations_status", "async_operations", ["status"])
op.create_index("idx_async_operations_bank_status", "async_operations", ["bank_id", "status"])
op.create_index('idx_async_operations_bank_id', 'async_operations', ['bank_id'])
op.create_index('idx_async_operations_status', 'async_operations', ['status'])
op.create_index('idx_async_operations_bank_status', 'async_operations', ['bank_id', 'status'])
# Create entities table
op.create_table(
"entities",
sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
sa.Column("canonical_name", sa.Text(), nullable=False),
sa.Column("bank_id", sa.Text(), nullable=False),
sa.Column(
"metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
),
sa.Column("first_seen", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("last_seen", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("mention_count", sa.Integer(), server_default="1", nullable=False),
sa.PrimaryKeyConstraint("id", name=op.f("pk_entities")),
'entities',
sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
sa.Column('canonical_name', sa.Text(), nullable=False),
sa.Column('bank_id', sa.Text(), nullable=False),
sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
sa.Column('first_seen', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('last_seen', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('mention_count', sa.Integer(), server_default='1', nullable=False),
sa.PrimaryKeyConstraint('id', name=op.f('pk_entities'))
)
op.create_index("idx_entities_bank_id", "entities", ["bank_id"])
op.create_index("idx_entities_canonical_name", "entities", ["canonical_name"])
op.create_index("idx_entities_bank_name", "entities", ["bank_id", "canonical_name"])
op.create_index('idx_entities_bank_id', 'entities', ['bank_id'])
op.create_index('idx_entities_canonical_name', 'entities', ['canonical_name'])
op.create_index('idx_entities_bank_name', 'entities', ['bank_id', 'canonical_name'])
# Create unique index on (bank_id, LOWER(canonical_name)) for entity resolution
op.execute("CREATE UNIQUE INDEX idx_entities_bank_lower_name ON entities (bank_id, LOWER(canonical_name))")
op.execute('CREATE UNIQUE INDEX idx_entities_bank_lower_name ON entities (bank_id, LOWER(canonical_name))')
# Create memory_units table
op.create_table(
"memory_units",
sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
sa.Column("bank_id", sa.Text(), nullable=False),
sa.Column("document_id", sa.Text(), nullable=True),
sa.Column("text", sa.Text(), nullable=False),
sa.Column("embedding", Vector(384), nullable=True),
sa.Column("context", sa.Text(), nullable=True),
sa.Column("event_date", postgresql.TIMESTAMP(timezone=True), nullable=False),
sa.Column("occurred_start", postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column("occurred_end", postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column("mentioned_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column("fact_type", sa.Text(), server_default="world", nullable=False),
sa.Column("confidence_score", sa.Float(), nullable=True),
sa.Column("access_count", sa.Integer(), server_default="0", nullable=False),
sa.Column(
"metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
),
sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.ForeignKeyConstraint(
["document_id", "bank_id"],
["documents.id", "documents.bank_id"],
name="memory_units_document_fkey",
ondelete="CASCADE",
),
sa.PrimaryKeyConstraint("id", name=op.f("pk_memory_units")),
sa.CheckConstraint(
"fact_type IN ('world', 'bank', 'opinion', 'observation')", name="memory_units_fact_type_check"
),
sa.CheckConstraint(
"confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)",
name="memory_units_confidence_range_check",
),
'memory_units',
sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
sa.Column('bank_id', sa.Text(), nullable=False),
sa.Column('document_id', sa.Text(), nullable=True),
sa.Column('text', sa.Text(), nullable=False),
sa.Column('embedding', Vector(384), nullable=True),
sa.Column('context', sa.Text(), nullable=True),
sa.Column('event_date', postgresql.TIMESTAMP(timezone=True), nullable=False),
sa.Column('occurred_start', postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column('occurred_end', postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column('mentioned_at', postgresql.TIMESTAMP(timezone=True), nullable=True),
sa.Column('fact_type', sa.Text(), server_default='world', nullable=False),
sa.Column('confidence_score', sa.Float(), nullable=True),
sa.Column('access_count', sa.Integer(), server_default='0', nullable=False),
sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.ForeignKeyConstraint(['document_id', 'bank_id'], ['documents.id', 'documents.bank_id'], name='memory_units_document_fkey', ondelete='CASCADE'),
sa.PrimaryKeyConstraint('id', name=op.f('pk_memory_units')),
sa.CheckConstraint("fact_type IN ('world', 'bank', 'opinion', 'observation')", name='memory_units_fact_type_check'),
sa.CheckConstraint("confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)", name='memory_units_confidence_range_check'),
sa.CheckConstraint(
"(fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
"(fact_type = 'observation') OR "
"(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL)",
name="confidence_score_fact_type_check",
),
name='confidence_score_fact_type_check'
)
)
# Add search_vector column for full-text search
# Type depends on configured text search backend
text_search_ext = _detect_text_search_extension()
op.execute("""
ALTER TABLE memory_units
ADD COLUMN search_vector tsvector
GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
""")
if text_search_ext == "vchord":
# VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
# Note: vchord_bm25 extension creates types in bm25_catalog schema
op.execute("""
ALTER TABLE memory_units
ADD COLUMN search_vector bm25_catalog.bm25vector
""")
elif text_search_ext == "pg_textsearch":
# Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
op.execute("""
ALTER TABLE memory_units
ADD COLUMN search_vector TEXT
""")
else: # native
# Native PostgreSQL: tsvector with automatic generation
op.execute("""
ALTER TABLE memory_units
ADD COLUMN search_vector tsvector
GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
""")
op.create_index('idx_memory_units_bank_id', 'memory_units', ['bank_id'])
op.create_index('idx_memory_units_document_id', 'memory_units', ['document_id'])
op.create_index('idx_memory_units_event_date', 'memory_units', [sa.text('event_date DESC')])
op.create_index('idx_memory_units_bank_date', 'memory_units', ['bank_id', sa.text('event_date DESC')])
op.create_index('idx_memory_units_access_count', 'memory_units', [sa.text('access_count DESC')])
op.create_index('idx_memory_units_fact_type', 'memory_units', ['fact_type'])
op.create_index('idx_memory_units_bank_fact_type', 'memory_units', ['bank_id', 'fact_type'])
op.create_index('idx_memory_units_bank_type_date', 'memory_units', ['bank_id', 'fact_type', sa.text('event_date DESC')])
op.create_index('idx_memory_units_opinion_confidence', 'memory_units', ['bank_id', sa.text('confidence_score DESC')], postgresql_where=sa.text("fact_type = 'opinion'"))
op.create_index('idx_memory_units_opinion_date', 'memory_units', ['bank_id', sa.text('event_date DESC')], postgresql_where=sa.text("fact_type = 'opinion'"))
op.create_index('idx_memory_units_observation_date', 'memory_units', ['bank_id', sa.text('event_date DESC')], postgresql_where=sa.text("fact_type = 'observation'"))
op.create_index('idx_memory_units_embedding', 'memory_units', ['embedding'], postgresql_using='hnsw', postgresql_ops={'embedding': 'vector_cosine_ops'})
op.create_index("idx_memory_units_bank_id", "memory_units", ["bank_id"])
op.create_index("idx_memory_units_document_id", "memory_units", ["document_id"])
op.create_index("idx_memory_units_event_date", "memory_units", [sa.text("event_date DESC")])
op.create_index("idx_memory_units_bank_date", "memory_units", ["bank_id", sa.text("event_date DESC")])
op.create_index("idx_memory_units_access_count", "memory_units", [sa.text("access_count DESC")])
op.create_index("idx_memory_units_fact_type", "memory_units", ["fact_type"])
op.create_index("idx_memory_units_bank_fact_type", "memory_units", ["bank_id", "fact_type"])
op.create_index(
"idx_memory_units_bank_type_date", "memory_units", ["bank_id", "fact_type", sa.text("event_date DESC")]
)
op.create_index(
"idx_memory_units_opinion_confidence",
"memory_units",
["bank_id", sa.text("confidence_score DESC")],
postgresql_where=sa.text("fact_type = 'opinion'"),
)
op.create_index(
"idx_memory_units_opinion_date",
"memory_units",
["bank_id", sa.text("event_date DESC")],
postgresql_where=sa.text("fact_type = 'opinion'"),
)
op.create_index(
"idx_memory_units_observation_date",
"memory_units",
["bank_id", sa.text("event_date DESC")],
postgresql_where=sa.text("fact_type = 'observation'"),
)
# Create vector index - conditional based on available extension
vector_ext = _detect_vector_extension()
if vector_ext == "pgvectorscale":
# Use DiskANN index for pgvectorscale (disk-based, scalable)
op.execute("""
CREATE INDEX idx_memory_units_embedding ON memory_units
USING diskann (embedding vector_cosine_ops)
WITH (num_neighbors = 50)
""")
elif vector_ext == "pg_diskann":
# Use DiskANN index for pg_diskann (Azure)
op.execute("""
CREATE INDEX idx_memory_units_embedding ON memory_units
USING diskann (embedding vector_cosine_ops)
WITH (max_neighbors = 50)
""")
elif vector_ext == "vchord":
# Use vchordrq index for vchord (supports high-dimensional embeddings)
op.execute("""
CREATE INDEX idx_memory_units_embedding ON memory_units
USING vchordrq (embedding vector_l2_ops)
""")
else: # pgvector
# Use HNSW index for pgvector
op.create_index(
"idx_memory_units_embedding",
"memory_units",
["embedding"],
postgresql_using="hnsw",
postgresql_ops={"embedding": "vector_cosine_ops"},
)
# Create full-text search index on search_vector
# Index type depends on text search backend
if text_search_ext == "vchord":
# VectorChord BM25 index
op.execute("""
CREATE INDEX idx_memory_units_text_search ON memory_units
USING bm25 (search_vector bm25_catalog.bm25_ops)
""")
elif text_search_ext == "pg_textsearch":
# Timescale pg_textsearch BM25 index on text column
# Note: pg_textsearch doesn't support expressions, so we index the main text column
op.execute("""
CREATE INDEX idx_memory_units_text_search ON memory_units
USING bm25(text)
WITH (text_config='english')
""")
else: # native
# Native PostgreSQL GIN index
op.execute("""
CREATE INDEX idx_memory_units_text_search ON memory_units
USING gin(search_vector)
""")
# Create BM25 full-text search index on search_vector
op.execute("""
CREATE INDEX idx_memory_units_text_search ON memory_units
USING gin(search_vector)
""")
op.execute("""
CREATE MATERIALIZED VIEW memory_units_bm25 AS
@@ -376,149 +158,116 @@ def upgrade() -> None:
FROM memory_units
""")
op.create_index("idx_memory_units_bm25_bank", "memory_units_bm25", ["bank_id"])
op.create_index("idx_memory_units_bm25_text_vector", "memory_units_bm25", ["text_vector"], postgresql_using="gin")
op.create_index('idx_memory_units_bm25_bank', 'memory_units_bm25', ['bank_id'])
op.create_index('idx_memory_units_bm25_text_vector', 'memory_units_bm25', ['text_vector'], postgresql_using='gin')
# Create entity_cooccurrences table
op.create_table(
"entity_cooccurrences",
sa.Column("entity_id_1", postgresql.UUID(as_uuid=True), nullable=False),
sa.Column("entity_id_2", postgresql.UUID(as_uuid=True), nullable=False),
sa.Column("cooccurrence_count", sa.Integer(), server_default="1", nullable=False),
sa.Column(
"last_cooccurred", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False
),
sa.ForeignKeyConstraint(
["entity_id_1"],
["entities.id"],
name=op.f("fk_entity_cooccurrences_entity_id_1_entities"),
ondelete="CASCADE",
),
sa.ForeignKeyConstraint(
["entity_id_2"],
["entities.id"],
name=op.f("fk_entity_cooccurrences_entity_id_2_entities"),
ondelete="CASCADE",
),
sa.PrimaryKeyConstraint("entity_id_1", "entity_id_2", name=op.f("pk_entity_cooccurrences")),
sa.CheckConstraint("entity_id_1 < entity_id_2", name="entity_cooccurrence_order_check"),
'entity_cooccurrences',
sa.Column('entity_id_1', postgresql.UUID(as_uuid=True), nullable=False),
sa.Column('entity_id_2', postgresql.UUID(as_uuid=True), nullable=False),
sa.Column('cooccurrence_count', sa.Integer(), server_default='1', nullable=False),
sa.Column('last_cooccurred', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.ForeignKeyConstraint(['entity_id_1'], ['entities.id'], name=op.f('fk_entity_cooccurrences_entity_id_1_entities'), ondelete='CASCADE'),
sa.ForeignKeyConstraint(['entity_id_2'], ['entities.id'], name=op.f('fk_entity_cooccurrences_entity_id_2_entities'), ondelete='CASCADE'),
sa.PrimaryKeyConstraint('entity_id_1', 'entity_id_2', name=op.f('pk_entity_cooccurrences')),
sa.CheckConstraint('entity_id_1 < entity_id_2', name='entity_cooccurrence_order_check')
)
op.create_index("idx_entity_cooccurrences_entity1", "entity_cooccurrences", ["entity_id_1"])
op.create_index("idx_entity_cooccurrences_entity2", "entity_cooccurrences", ["entity_id_2"])
op.create_index("idx_entity_cooccurrences_count", "entity_cooccurrences", [sa.text("cooccurrence_count DESC")])
op.create_index('idx_entity_cooccurrences_entity1', 'entity_cooccurrences', ['entity_id_1'])
op.create_index('idx_entity_cooccurrences_entity2', 'entity_cooccurrences', ['entity_id_2'])
op.create_index('idx_entity_cooccurrences_count', 'entity_cooccurrences', [sa.text('cooccurrence_count DESC')])
# Create memory_links table
op.create_table(
"memory_links",
sa.Column("from_unit_id", postgresql.UUID(as_uuid=True), nullable=False),
sa.Column("to_unit_id", postgresql.UUID(as_uuid=True), nullable=False),
sa.Column("link_type", sa.Text(), nullable=False),
sa.Column("entity_id", postgresql.UUID(as_uuid=True), nullable=True),
sa.Column("weight", sa.Float(), server_default="1.0", nullable=False),
sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.ForeignKeyConstraint(
["entity_id"], ["entities.id"], name=op.f("fk_memory_links_entity_id_entities"), ondelete="CASCADE"
),
sa.ForeignKeyConstraint(
["from_unit_id"],
["memory_units.id"],
name=op.f("fk_memory_links_from_unit_id_memory_units"),
ondelete="CASCADE",
),
sa.ForeignKeyConstraint(
["to_unit_id"],
["memory_units.id"],
name=op.f("fk_memory_links_to_unit_id_memory_units"),
ondelete="CASCADE",
),
sa.CheckConstraint(
"link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')",
name="memory_links_link_type_check",
),
sa.CheckConstraint("weight >= 0.0 AND weight <= 1.0", name="memory_links_weight_check"),
'memory_links',
sa.Column('from_unit_id', postgresql.UUID(as_uuid=True), nullable=False),
sa.Column('to_unit_id', postgresql.UUID(as_uuid=True), nullable=False),
sa.Column('link_type', sa.Text(), nullable=False),
sa.Column('entity_id', postgresql.UUID(as_uuid=True), nullable=True),
sa.Column('weight', sa.Float(), server_default='1.0', nullable=False),
sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.ForeignKeyConstraint(['entity_id'], ['entities.id'], name=op.f('fk_memory_links_entity_id_entities'), ondelete='CASCADE'),
sa.ForeignKeyConstraint(['from_unit_id'], ['memory_units.id'], name=op.f('fk_memory_links_from_unit_id_memory_units'), ondelete='CASCADE'),
sa.ForeignKeyConstraint(['to_unit_id'], ['memory_units.id'], name=op.f('fk_memory_links_to_unit_id_memory_units'), ondelete='CASCADE'),
sa.CheckConstraint("link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')", name='memory_links_link_type_check'),
sa.CheckConstraint('weight >= 0.0 AND weight <= 1.0', name='memory_links_weight_check')
)
# Create unique constraint using COALESCE for nullable entity_id
op.execute(
"CREATE UNIQUE INDEX idx_memory_links_unique ON memory_links (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid))"
)
op.create_index("idx_memory_links_from_unit", "memory_links", ["from_unit_id"])
op.create_index("idx_memory_links_to_unit", "memory_links", ["to_unit_id"])
op.create_index("idx_memory_links_entity", "memory_links", ["entity_id"])
op.create_index("idx_memory_links_link_type", "memory_links", ["link_type"])
op.execute("CREATE UNIQUE INDEX idx_memory_links_unique ON memory_links (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid))")
op.create_index('idx_memory_links_from_unit', 'memory_links', ['from_unit_id'])
op.create_index('idx_memory_links_to_unit', 'memory_links', ['to_unit_id'])
op.create_index('idx_memory_links_entity', 'memory_links', ['entity_id'])
op.create_index('idx_memory_links_link_type', 'memory_links', ['link_type'])
# Create unit_entities table
op.create_table(
"unit_entities",
sa.Column("unit_id", postgresql.UUID(as_uuid=True), nullable=False),
sa.Column("entity_id", postgresql.UUID(as_uuid=True), nullable=False),
sa.ForeignKeyConstraint(
["entity_id"], ["entities.id"], name=op.f("fk_unit_entities_entity_id_entities"), ondelete="CASCADE"
),
sa.ForeignKeyConstraint(
["unit_id"], ["memory_units.id"], name=op.f("fk_unit_entities_unit_id_memory_units"), ondelete="CASCADE"
),
sa.PrimaryKeyConstraint("unit_id", "entity_id", name=op.f("pk_unit_entities")),
'unit_entities',
sa.Column('unit_id', postgresql.UUID(as_uuid=True), nullable=False),
sa.Column('entity_id', postgresql.UUID(as_uuid=True), nullable=False),
sa.ForeignKeyConstraint(['entity_id'], ['entities.id'], name=op.f('fk_unit_entities_entity_id_entities'), ondelete='CASCADE'),
sa.ForeignKeyConstraint(['unit_id'], ['memory_units.id'], name=op.f('fk_unit_entities_unit_id_memory_units'), ondelete='CASCADE'),
sa.PrimaryKeyConstraint('unit_id', 'entity_id', name=op.f('pk_unit_entities'))
)
op.create_index("idx_unit_entities_unit", "unit_entities", ["unit_id"])
op.create_index("idx_unit_entities_entity", "unit_entities", ["entity_id"])
op.create_index('idx_unit_entities_unit', 'unit_entities', ['unit_id'])
op.create_index('idx_unit_entities_entity', 'unit_entities', ['entity_id'])
def downgrade() -> None:
"""Downgrade schema - drop all tables."""
# Drop tables in reverse dependency order
op.drop_index("idx_unit_entities_entity", table_name="unit_entities")
op.drop_index("idx_unit_entities_unit", table_name="unit_entities")
op.drop_table("unit_entities")
op.drop_index('idx_unit_entities_entity', table_name='unit_entities')
op.drop_index('idx_unit_entities_unit', table_name='unit_entities')
op.drop_table('unit_entities')
op.drop_index("idx_memory_links_link_type", table_name="memory_links")
op.drop_index("idx_memory_links_entity", table_name="memory_links")
op.drop_index("idx_memory_links_to_unit", table_name="memory_links")
op.drop_index("idx_memory_links_from_unit", table_name="memory_links")
op.execute("DROP INDEX IF EXISTS idx_memory_links_unique")
op.drop_table("memory_links")
op.drop_index('idx_memory_links_link_type', table_name='memory_links')
op.drop_index('idx_memory_links_entity', table_name='memory_links')
op.drop_index('idx_memory_links_to_unit', table_name='memory_links')
op.drop_index('idx_memory_links_from_unit', table_name='memory_links')
op.execute('DROP INDEX IF EXISTS idx_memory_links_unique')
op.drop_table('memory_links')
op.drop_index("idx_entity_cooccurrences_count", table_name="entity_cooccurrences")
op.drop_index("idx_entity_cooccurrences_entity2", table_name="entity_cooccurrences")
op.drop_index("idx_entity_cooccurrences_entity1", table_name="entity_cooccurrences")
op.drop_table("entity_cooccurrences")
op.drop_index('idx_entity_cooccurrences_count', table_name='entity_cooccurrences')
op.drop_index('idx_entity_cooccurrences_entity2', table_name='entity_cooccurrences')
op.drop_index('idx_entity_cooccurrences_entity1', table_name='entity_cooccurrences')
op.drop_table('entity_cooccurrences')
# Drop BM25 materialized view and index
op.drop_index("idx_memory_units_bm25_text_vector", table_name="memory_units_bm25")
op.drop_index("idx_memory_units_bm25_bank", table_name="memory_units_bm25")
op.execute("DROP MATERIALIZED VIEW IF EXISTS memory_units_bm25")
op.drop_index('idx_memory_units_bm25_text_vector', table_name='memory_units_bm25')
op.drop_index('idx_memory_units_bm25_bank', table_name='memory_units_bm25')
op.execute('DROP MATERIALIZED VIEW IF EXISTS memory_units_bm25')
op.drop_index("idx_memory_units_embedding", table_name="memory_units")
op.drop_index("idx_memory_units_observation_date", table_name="memory_units")
op.drop_index("idx_memory_units_opinion_date", table_name="memory_units")
op.drop_index("idx_memory_units_opinion_confidence", table_name="memory_units")
op.drop_index("idx_memory_units_bank_type_date", table_name="memory_units")
op.drop_index("idx_memory_units_bank_fact_type", table_name="memory_units")
op.drop_index("idx_memory_units_fact_type", table_name="memory_units")
op.drop_index("idx_memory_units_access_count", table_name="memory_units")
op.drop_index("idx_memory_units_bank_date", table_name="memory_units")
op.drop_index("idx_memory_units_event_date", table_name="memory_units")
op.drop_index("idx_memory_units_document_id", table_name="memory_units")
op.drop_index("idx_memory_units_bank_id", table_name="memory_units")
op.execute("DROP INDEX IF EXISTS idx_memory_units_text_search")
op.drop_table("memory_units")
op.drop_index('idx_memory_units_embedding', table_name='memory_units')
op.drop_index('idx_memory_units_observation_date', table_name='memory_units')
op.drop_index('idx_memory_units_opinion_date', table_name='memory_units')
op.drop_index('idx_memory_units_opinion_confidence', table_name='memory_units')
op.drop_index('idx_memory_units_bank_type_date', table_name='memory_units')
op.drop_index('idx_memory_units_bank_fact_type', table_name='memory_units')
op.drop_index('idx_memory_units_fact_type', table_name='memory_units')
op.drop_index('idx_memory_units_access_count', table_name='memory_units')
op.drop_index('idx_memory_units_bank_date', table_name='memory_units')
op.drop_index('idx_memory_units_event_date', table_name='memory_units')
op.drop_index('idx_memory_units_document_id', table_name='memory_units')
op.drop_index('idx_memory_units_bank_id', table_name='memory_units')
op.execute('DROP INDEX IF EXISTS idx_memory_units_text_search')
op.drop_table('memory_units')
op.execute("DROP INDEX IF EXISTS idx_entities_bank_lower_name")
op.drop_index("idx_entities_bank_name", table_name="entities")
op.drop_index("idx_entities_canonical_name", table_name="entities")
op.drop_index("idx_entities_bank_id", table_name="entities")
op.drop_table("entities")
op.execute('DROP INDEX IF EXISTS idx_entities_bank_lower_name')
op.drop_index('idx_entities_bank_name', table_name='entities')
op.drop_index('idx_entities_canonical_name', table_name='entities')
op.drop_index('idx_entities_bank_id', table_name='entities')
op.drop_table('entities')
op.drop_index("idx_async_operations_bank_status", table_name="async_operations")
op.drop_index("idx_async_operations_status", table_name="async_operations")
op.drop_index("idx_async_operations_bank_id", table_name="async_operations")
op.drop_table("async_operations")
op.drop_index('idx_async_operations_bank_status', table_name='async_operations')
op.drop_index('idx_async_operations_status', table_name='async_operations')
op.drop_index('idx_async_operations_bank_id', table_name='async_operations')
op.drop_table('async_operations')
op.drop_index("idx_documents_content_hash", table_name="documents")
op.drop_index("idx_documents_bank_id", table_name="documents")
op.drop_table("documents")
op.drop_index('idx_documents_content_hash', table_name='documents')
op.drop_index('idx_documents_bank_id', table_name='documents')
op.drop_table('documents')
op.drop_table("banks")
op.drop_table('banks')
# Drop extensions (optional - comment out if you want to keep them)
# op.execute('DROP EXTENSION IF EXISTS vector')
@@ -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 {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")
@@ -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")
@@ -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")
@@ -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")
@@ -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
@@ -5,18 +5,18 @@ Revises: 5a366d414dce
Create Date: 2025-11-28 00:00:00.000000
"""
from typing import Sequence, Union
from collections.abc import Sequence
import sqlalchemy as sa
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = "b7c4d8e9f1a2"
down_revision: str | Sequence[str] | None = "5a366d414dce"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
revision: str = 'b7c4d8e9f1a2'
down_revision: Union[str, Sequence[str], None] = '5a366d414dce'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
@@ -24,47 +24,47 @@ def upgrade() -> None:
# Create chunks table with single text PK (bank_id_document_id_chunk_index)
op.create_table(
"chunks",
sa.Column("chunk_id", sa.Text(), nullable=False),
sa.Column("document_id", sa.Text(), nullable=False),
sa.Column("bank_id", sa.Text(), nullable=False),
sa.Column("chunk_index", sa.Integer(), nullable=False),
sa.Column("chunk_text", sa.Text(), nullable=False),
sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.ForeignKeyConstraint(
["document_id", "bank_id"],
["documents.id", "documents.bank_id"],
name="chunks_document_fkey",
ondelete="CASCADE",
),
sa.PrimaryKeyConstraint("chunk_id", name=op.f("pk_chunks")),
'chunks',
sa.Column('chunk_id', sa.Text(), nullable=False),
sa.Column('document_id', sa.Text(), nullable=False),
sa.Column('bank_id', sa.Text(), nullable=False),
sa.Column('chunk_index', sa.Integer(), nullable=False),
sa.Column('chunk_text', sa.Text(), nullable=False),
sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.ForeignKeyConstraint(['document_id', 'bank_id'], ['documents.id', 'documents.bank_id'], name='chunks_document_fkey', ondelete='CASCADE'),
sa.PrimaryKeyConstraint('chunk_id', name=op.f('pk_chunks'))
)
# Add indexes for efficient queries
op.create_index("idx_chunks_document_id", "chunks", ["document_id"])
op.create_index("idx_chunks_bank_id", "chunks", ["bank_id"])
op.create_index('idx_chunks_document_id', 'chunks', ['document_id'])
op.create_index('idx_chunks_bank_id', 'chunks', ['bank_id'])
# Add chunk_id column to memory_units (nullable, as existing records won't have chunks)
op.add_column("memory_units", sa.Column("chunk_id", sa.Text(), nullable=True))
op.add_column('memory_units', sa.Column('chunk_id', sa.Text(), nullable=True))
# Add foreign key constraint to chunks table
op.create_foreign_key(
"memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="SET NULL"
'memory_units_chunk_fkey',
'memory_units',
'chunks',
['chunk_id'],
['chunk_id'],
ondelete='SET NULL'
)
# Add index on chunk_id for efficient lookups
op.create_index("idx_memory_units_chunk_id", "memory_units", ["chunk_id"])
op.create_index('idx_memory_units_chunk_id', 'memory_units', ['chunk_id'])
def downgrade() -> None:
"""Remove chunks table and chunk_id from memory_units."""
# Drop index and foreign key from memory_units
op.drop_index("idx_memory_units_chunk_id", table_name="memory_units")
op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
op.drop_column("memory_units", "chunk_id")
op.drop_index('idx_memory_units_chunk_id', table_name='memory_units')
op.drop_constraint('memory_units_chunk_fkey', 'memory_units', type_='foreignkey')
op.drop_column('memory_units', 'chunk_id')
# Drop chunks table indexes and table
op.drop_index("idx_chunks_bank_id", table_name="chunks")
op.drop_index("idx_chunks_document_id", table_name="chunks")
op.drop_table("chunks")
op.drop_index('idx_chunks_bank_id', table_name='chunks')
op.drop_index('idx_chunks_document_id', table_name='chunks')
op.drop_table('chunks')
@@ -1,46 +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
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 every standard PostgreSQL installation as a contrib module.
# It enables fast similarity lookups via GIN indexes, used for entity name matching.
op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
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
@@ -5,35 +5,35 @@ Revises: b7c4d8e9f1a2
Create Date: 2025-12-02 00:00:00.000000
"""
from typing import Sequence, Union
from collections.abc import Sequence
import sqlalchemy as sa
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = "c8e5f2a3b4d1"
down_revision: str | Sequence[str] | None = "b7c4d8e9f1a2"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
revision: str = 'c8e5f2a3b4d1'
down_revision: Union[str, Sequence[str], None] = 'b7c4d8e9f1a2'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add retain_params JSONB column to documents table."""
# Add retain_params column to store parameters passed during retain
op.add_column("documents", sa.Column("retain_params", postgresql.JSONB(), nullable=True))
op.add_column('documents', sa.Column('retain_params', postgresql.JSONB(), nullable=True))
# Add index for efficient queries on retain_params
op.create_index("idx_documents_retain_params", "documents", ["retain_params"], postgresql_using="gin")
op.create_index('idx_documents_retain_params', 'documents', ['retain_params'], postgresql_using='gin')
def downgrade() -> None:
"""Remove retain_params column from documents table."""
# Drop index
op.drop_index("idx_documents_retain_params", table_name="documents")
op.drop_index('idx_documents_retain_params', table_name='documents')
# Drop column
op.drop_column("documents", "retain_params")
op.drop_column('documents', 'retain_params')
@@ -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")
@@ -5,49 +5,44 @@ Revises: c8e5f2a3b4d1
Create Date: 2024-12-04 15:00:00.000000
"""
from alembic import op
import sqlalchemy as sa
from alembic import context, op
# revision identifiers, used by Alembic.
revision = "d9f6a3b4c5e2"
down_revision = "c8e5f2a3b4d1"
revision = 'd9f6a3b4c5e2'
down_revision = 'c8e5f2a3b4d1'
branch_labels = None
depends_on = None
def _get_schema_prefix() -> str:
"""Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def upgrade():
schema = _get_schema_prefix()
# Drop old check constraint FIRST (before updating data)
op.drop_constraint("memory_units_fact_type_check", "memory_units", type_="check")
op.drop_constraint('memory_units_fact_type_check', 'memory_units', type_='check')
# Update existing 'bank' values to 'experience'
op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
# Also update any 'interactions' values (in case of partial migration)
op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")
op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")
# Create new check constraint with 'experience' instead of 'bank'
op.create_check_constraint(
"memory_units_fact_type_check", "memory_units", "fact_type IN ('world', 'experience', 'opinion', 'observation')"
'memory_units_fact_type_check',
'memory_units',
"fact_type IN ('world', 'experience', 'opinion', 'observation')"
)
def downgrade():
schema = _get_schema_prefix()
# Drop new check constraint FIRST
op.drop_constraint("memory_units_fact_type_check", "memory_units", type_="check")
op.drop_constraint('memory_units_fact_type_check', 'memory_units', type_='check')
# Update 'experience' back to 'bank'
op.execute(f"UPDATE {schema}memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")
op.execute("UPDATE memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")
# Recreate old check constraint
op.create_check_constraint(
"memory_units_fact_type_check", "memory_units", "fact_type IN ('world', 'bank', 'opinion', 'observation')"
'memory_units_fact_type_check',
'memory_units',
"fact_type IN ('world', 'bank', 'opinion', 'observation')"
)
@@ -8,49 +8,22 @@ Migrate disposition traits from Big Five (openness, conscientiousness, extravers
agreeableness, neuroticism, bias_strength with 0-1 float values) to the new 3-trait
system (skepticism, literalism, empathy with 1-5 integer values).
"""
from typing import Sequence, Union
from collections.abc import Sequence
from alembic import op
import sqlalchemy as sa
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "e0a1b2c3d4e5"
down_revision: str | Sequence[str] | None = "rename_personality"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _get_target_schema() -> str:
"""Get the target schema name (tenant schema or 'public')."""
schema = context.config.get_main_option("target_schema")
return schema if schema else "public"
revision: str = 'e0a1b2c3d4e5'
down_revision: Union[str, Sequence[str], None] = 'rename_personality'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Convert Big Five disposition to 3-trait disposition."""
conn = op.get_bind()
schema = _get_schema_prefix()
target_schema = _get_target_schema()
# Check if disposition column exists (should have been created by previous migration)
result = conn.execute(
sa.text("""
SELECT column_name
FROM information_schema.columns
WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
"""),
{"schema": target_schema},
)
if not result.fetchone():
# Column doesn't exist yet (shouldn't happen but be safe)
return
# Update all existing banks to use the new disposition format
# Convert from old format to new format with reasonable mappings:
@@ -58,54 +31,32 @@ def upgrade() -> None:
# - literalism: derived from conscientiousness (detail-oriented people are more literal)
# - empathy: derived from agreeableness + inverse of neuroticism
# Default all to 3 (neutral) for simplicity
conn.execute(
sa.text(f"""
UPDATE {schema}banks
SET disposition = '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
conn.execute(sa.text("""
UPDATE banks
SET disposition = '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
WHERE disposition IS NOT NULL
""")
)
"""))
# Update the default for new banks
conn.execute(
sa.text(f"""
ALTER TABLE {schema}banks
ALTER COLUMN disposition SET DEFAULT '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
""")
)
conn.execute(sa.text("""
ALTER TABLE banks
ALTER COLUMN disposition SET DEFAULT '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
"""))
def downgrade() -> None:
"""Convert back to Big Five disposition."""
conn = op.get_bind()
schema = _get_schema_prefix()
target_schema = _get_target_schema()
# Check if disposition column exists
result = conn.execute(
sa.text("""
SELECT column_name
FROM information_schema.columns
WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
"""),
{"schema": target_schema},
)
if not result.fetchone():
return
# Revert to Big Five format with default values
conn.execute(
sa.text(f"""
UPDATE {schema}banks
SET disposition = '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
conn.execute(sa.text("""
UPDATE banks
SET disposition = '{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}'::jsonb
WHERE disposition IS NOT NULL
""")
)
"""))
# Update the default for new banks
conn.execute(
sa.text(f"""
ALTER TABLE {schema}banks
ALTER COLUMN disposition SET DEFAULT '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
""")
)
conn.execute(sa.text("""
ALTER TABLE banks
ALTER COLUMN disposition SET DEFAULT '{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}'::jsonb
"""))
@@ -1,44 +0,0 @@
"""add_memory_links_from_type_weight_index
Revision ID: f1a2b3c4d5e6
Revises: e0a1b2c3d4e5
Create Date: 2025-01-12
Add composite index on memory_links (from_unit_id, link_type, weight DESC)
to optimize MPFP graph traversal queries that need top-k edges per type.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "f1a2b3c4d5e6"
down_revision: str | Sequence[str] | None = "e0a1b2c3d4e5"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def upgrade() -> None:
"""Add composite index for efficient MPFP edge loading."""
schema = _get_schema_prefix()
# Create composite index for efficient top-k per (from_node, link_type) queries
# This enables LATERAL joins to use index-only scans with early termination
# Note: Not using CONCURRENTLY here as it requires running outside a transaction
# For production with large tables, consider running this manually with CONCURRENTLY
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_memory_links_from_type_weight "
f"ON {schema}memory_links(from_unit_id, link_type, weight DESC)"
)
def downgrade() -> None:
"""Remove the composite index."""
schema = _get_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_links_from_type_weight")
@@ -1,48 +0,0 @@
"""add_tags_column
Revision ID: g2a3b4c5d6e7
Revises: f1a2b3c4d5e6
Create Date: 2025-01-13
Add tags column to memory_units and documents tables for visibility scoping.
Tags enable filtering memories by scope (e.g., user IDs, session IDs) during recall/reflect.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "g2a3b4c5d6e7"
down_revision: str | Sequence[str] | None = "f1a2b3c4d5e6"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def upgrade() -> None:
"""Add tags column to memory_units and documents tables."""
schema = _get_schema_prefix()
# Add tags column to memory_units table
op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS tags VARCHAR[] NOT NULL DEFAULT '{{}}'")
# Create GIN index for efficient array containment queries (tags && ARRAY['x'])
op.execute(f"CREATE INDEX IF NOT EXISTS idx_memory_units_tags ON {schema}memory_units USING GIN (tags)")
# Add tags column to documents table for document-level tags
op.execute(f"ALTER TABLE {schema}documents ADD COLUMN IF NOT EXISTS tags VARCHAR[] NOT NULL DEFAULT '{{}}'")
def downgrade() -> None:
"""Remove tags columns and index."""
schema = _get_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_tags")
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS tags")
op.execute(f"ALTER TABLE {schema}documents DROP COLUMN IF EXISTS tags")
@@ -1,112 +0,0 @@
"""mental_models_v4
Revision ID: h3c4d5e6f7g8
Revises: g2a3b4c5d6e7
Create Date: 2026-01-08 00:00:00.000000
This migration implements the v4 mental models system:
1. Deletes existing observation memory_units (observations now in mental models)
2. Adds mission column to banks (replacing background)
3. Creates mental_models table with final schema
Mental models can reference entities when an entity is "promoted" to a mental model.
Summary content is stored as JSONB observations with per-observation fact attribution.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "h3c4d5e6f7g8"
down_revision: str | Sequence[str] | None = "g2a3b4c5d6e7"
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:
"""Apply mental models v4 changes."""
schema = _get_schema_prefix()
# Step 1: Delete observation memory_units (cascades to unit_entities links)
# Observations are now handled through mental models, not memory_units
op.execute(f"DELETE FROM {schema}memory_units WHERE fact_type = 'observation'")
# Step 2: Drop observation-specific index (if it exists)
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_observation_date")
# Step 3: Add mission column to banks (replacing background)
op.execute(f"ALTER TABLE {schema}banks ADD COLUMN IF NOT EXISTS mission TEXT")
# Migrate: copy background to mission if background column exists
# Use DO block to check column existence first (idempotent for re-runs)
schema_name = context.config.get_main_option("target_schema") or "public"
op.execute(f"""
DO $$
BEGIN
IF EXISTS (
SELECT 1 FROM information_schema.columns
WHERE table_schema = '{schema_name}' AND table_name = 'banks' AND column_name = 'background'
) THEN
UPDATE {schema}banks
SET mission = background
WHERE mission IS NULL;
END IF;
END $$;
""")
# Remove background column (replaced by mission)
op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS background")
# Step 4: Create mental_models table with final v4 schema (if not exists)
op.execute(f"""
CREATE TABLE IF NOT EXISTS {schema}mental_models (
id VARCHAR(64) NOT NULL,
bank_id VARCHAR(64) NOT NULL,
subtype VARCHAR(32) NOT NULL,
name VARCHAR(256) NOT NULL,
description TEXT NOT NULL,
entity_id UUID,
observations JSONB DEFAULT '{{"observations": []}}'::jsonb,
links VARCHAR[],
tags VARCHAR[] DEFAULT '{{}}',
last_updated TIMESTAMP WITH TIME ZONE,
created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
PRIMARY KEY (id, bank_id),
FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE,
FOREIGN KEY (entity_id) REFERENCES {schema}entities(id) ON DELETE SET NULL,
CONSTRAINT ck_mental_models_subtype CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'))
)
""")
# Step 5: Create indexes for efficient queries (if not exist)
op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_bank_id ON {schema}mental_models(bank_id)")
op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_subtype ON {schema}mental_models(bank_id, subtype)")
op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_entity_id ON {schema}mental_models(entity_id)")
# GIN index for efficient tags array filtering
op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_tags ON {schema}mental_models USING GIN(tags)")
def downgrade() -> None:
"""Revert mental models v4 changes."""
schema = _get_schema_prefix()
# Drop mental_models table (cascades to indexes)
op.execute(f"DROP TABLE IF EXISTS {schema}mental_models CASCADE")
# Add back background column to banks
op.execute(f"ALTER TABLE {schema}banks ADD COLUMN IF NOT EXISTS background TEXT")
# Migrate mission back to background
op.execute(f"UPDATE {schema}banks SET background = mission WHERE background IS NULL")
# Remove mission column
op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS mission")
# Note: Cannot restore deleted observations - they are lost on downgrade
@@ -1,41 +0,0 @@
"""delete_opinions
Revision ID: i4d5e6f7g8h9
Revises: h3c4d5e6f7g8
Create Date: 2026-01-15 00:00:00.000000
This migration removes opinion facts from memory_units.
Opinions are no longer a separate fact type - they are now represented
through mental model observations with confidence scores.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "i4d5e6f7g8h9"
down_revision: str | Sequence[str] | None = "h3c4d5e6f7g8"
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:
"""Delete opinion memory_units."""
schema = _get_schema_prefix()
# Delete opinion memory_units (cascades to unit_entities links)
# Opinions are now handled through mental model observations
op.execute(f"DELETE FROM {schema}memory_units WHERE fact_type = 'opinion'")
def downgrade() -> None:
"""Cannot restore deleted opinions."""
# Note: Cannot restore deleted opinions - they are lost on downgrade
pass
@@ -1,95 +0,0 @@
"""mental_model_versions
Revision ID: j5e6f7g8h9i0
Revises: i4d5e6f7g8h9
Create Date: 2026-01-16 00:00:00.000000
This migration adds versioning support for mental models:
1. Creates mental_model_versions table to store observation snapshots
2. Adds version column to mental_models for tracking current version
This enables changelog/diff functionality for mental model observations.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "j5e6f7g8h9i0"
down_revision: str | Sequence[str] | None = "i4d5e6f7g8h9"
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 mental_model_versions table and add version tracking."""
schema = _get_schema_prefix()
# Create mental_model_versions table for storing observation snapshots
op.execute(f"""
CREATE TABLE {schema}mental_model_versions (
id SERIAL PRIMARY KEY,
mental_model_id VARCHAR(64) NOT NULL,
bank_id VARCHAR(64) NOT NULL,
version INT NOT NULL,
observations JSONB NOT NULL DEFAULT '{{"observations": []}}'::jsonb,
created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
FOREIGN KEY (mental_model_id, bank_id)
REFERENCES {schema}mental_models(id, bank_id) ON DELETE CASCADE,
UNIQUE (mental_model_id, bank_id, version)
)
""")
# Index for efficient version queries (get latest, list versions)
op.execute(f"""
CREATE INDEX idx_mental_model_versions_lookup
ON {schema}mental_model_versions(mental_model_id, bank_id, version DESC)
""")
# Add version column to mental_models to track current version
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS version INT NOT NULL DEFAULT 0
""")
# Migrate existing mental models: create version 1 for any that have observations
op.execute(f"""
INSERT INTO {schema}mental_model_versions (mental_model_id, bank_id, version, observations, created_at)
SELECT id, bank_id, 1, observations, COALESCE(last_updated, created_at)
FROM {schema}mental_models
WHERE observations IS NOT NULL
AND observations != '{{"observations": []}}'::jsonb
AND (observations->'observations') IS NOT NULL
AND jsonb_array_length(observations->'observations') > 0
""")
# Update version to 1 for migrated mental models
op.execute(f"""
UPDATE {schema}mental_models
SET version = 1
WHERE observations IS NOT NULL
AND observations != '{{"observations": []}}'::jsonb
AND (observations->'observations') IS NOT NULL
AND jsonb_array_length(observations->'observations') > 0
""")
def downgrade() -> None:
"""Remove mental_model_versions table and version column."""
schema = _get_schema_prefix()
# Drop index
op.execute(f"DROP INDEX IF EXISTS {schema}idx_mental_model_versions_lookup")
# Drop versions table
op.execute(f"DROP TABLE IF EXISTS {schema}mental_model_versions")
# Remove version column from mental_models
op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS version")
@@ -1,58 +0,0 @@
"""add_directive_subtype
Revision ID: k6f7g8h9i0j1
Revises: j5e6f7g8h9i0
Create Date: 2026-01-16 00:00:00.000000
This migration adds 'directive' to the mental_models subtype constraint.
Directives are hard rules with user-provided observations that the reflect agent must follow.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "k6f7g8h9i0j1"
down_revision: str | Sequence[str] | None = "j5e6f7g8h9i0"
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 'directive' to mental_models subtype constraint."""
schema = _get_schema_prefix()
# Drop existing constraint
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
# Create new constraint with 'directive' added
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD CONSTRAINT ck_mental_models_subtype
CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned', 'directive'))
""")
def downgrade() -> None:
"""Remove 'directive' from mental_models subtype constraint."""
schema = _get_schema_prefix()
# First delete any directives (cannot downgrade if they exist)
op.execute(f"DELETE FROM {schema}mental_models WHERE subtype = 'directive'")
# Drop constraint with directive
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
# Recreate original constraint without directive
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD CONSTRAINT ck_mental_models_subtype
CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'))
""")
@@ -1,109 +0,0 @@
"""add_worker_columns
Revision ID: l7g8h9i0j1k2
Revises: k6f7g8h9i0j1
Create Date: 2026-01-19 00:00:00.000000
This migration adds columns to async_operations for distributed worker support:
- worker_id: ID of the worker that claimed the task
- claimed_at: When the task was claimed
- retry_count: Number of retry attempts
- task_payload: The serialized task dictionary
"""
from collections.abc import Sequence
import sqlalchemy as sa
from alembic import context, op
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = "l7g8h9i0j1k2"
down_revision: str | Sequence[str] | None = "k6f7g8h9i0j1"
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 worker columns to async_operations."""
schema = _get_schema_prefix()
# Add worker_id column (ID of worker that claimed the task)
op.add_column(
"async_operations",
sa.Column("worker_id", sa.Text(), nullable=True),
schema=context.config.get_main_option("target_schema") or None,
)
# Add claimed_at column (when task was claimed by worker)
op.add_column(
"async_operations",
sa.Column("claimed_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
schema=context.config.get_main_option("target_schema") or None,
)
# Add retry_count column (number of retry attempts)
op.add_column(
"async_operations",
sa.Column("retry_count", sa.Integer(), server_default="0", nullable=False),
schema=context.config.get_main_option("target_schema") or None,
)
# Add task_payload column (serialized task dictionary)
op.add_column(
"async_operations",
sa.Column(
"task_payload",
postgresql.JSONB(astext_type=sa.Text()),
nullable=True,
),
schema=context.config.get_main_option("target_schema") or None,
)
# Add index for efficient worker polling (pending tasks ordered by creation time)
op.execute(
f"CREATE INDEX idx_async_operations_pending_claim ON {schema}async_operations (status, created_at) "
f"WHERE status = 'pending' AND task_payload IS NOT NULL"
)
# Add index for finding tasks by worker_id (for decommissioning)
op.execute(
f"CREATE INDEX idx_async_operations_worker_id ON {schema}async_operations (worker_id) WHERE worker_id IS NOT NULL"
)
def downgrade() -> None:
"""Remove worker columns from async_operations."""
schema = _get_schema_prefix()
# Drop indexes
op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_pending_claim")
op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_worker_id")
# Drop columns
op.drop_column(
"async_operations",
"task_payload",
schema=context.config.get_main_option("target_schema") or None,
)
op.drop_column(
"async_operations",
"retry_count",
schema=context.config.get_main_option("target_schema") or None,
)
op.drop_column(
"async_operations",
"claimed_at",
schema=context.config.get_main_option("target_schema") or None,
)
op.drop_column(
"async_operations",
"worker_id",
schema=context.config.get_main_option("target_schema") or None,
)
@@ -1,41 +0,0 @@
"""mental_model_id_to_text
Revision ID: m8h9i0j1k2l3
Revises: l7g8h9i0j1k2
Create Date: 2026-01-19 00:00:00.000000
This migration changes the mental_models.id column from VARCHAR(64) to TEXT
to support longer model IDs (e.g., entity names that exceed 64 characters).
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "m8h9i0j1k2l3"
down_revision: str | Sequence[str] | None = "l7g8h9i0j1k2"
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:
"""Change mental_models.id from VARCHAR(64) to TEXT."""
schema = _get_schema_prefix()
# Alter the id column type from VARCHAR(64) to TEXT
op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE TEXT")
def downgrade() -> None:
"""Revert mental_models.id from TEXT to VARCHAR(64)."""
schema = _get_schema_prefix()
# Note: This may fail if any id values exceed 64 characters
op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE VARCHAR(64)")
@@ -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")
@@ -1,113 +0,0 @@
"""migrate_mental_models_data
Revision ID: o0j1k2l3m4n5
Revises: n9i0j1k2l3m4
Create Date: 2026-01-21 00:00:00.000000
This migration:
1. Migrates existing 'pinned' mental models to the new 'pinned_reflections' table
2. Migrates existing 'learned' mental models to the new 'learnings' table
3. Deletes non-directive mental models (structural, emergent, pinned, learned)
4. Drops the mental_model_versions table (no longer used)
5. Adds a CHECK constraint that only 'directive' subtype is allowed
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "o0j1k2l3m4n5"
down_revision: str | Sequence[str] | None = "n9i0j1k2l3m4"
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:
"""Migrate data and clean up old mental models."""
schema = _get_schema_prefix()
# 1. Migrate 'pinned' mental models to pinned_reflections
# For pinned models, the first observation's content becomes the pinned reflection content
op.execute(f"""
INSERT INTO {schema}pinned_reflections (bank_id, name, source_query, content, tags, created_at)
SELECT
bank_id,
name,
description AS source_query,
COALESCE(
observations->'observations'->0->>'content',
description,
''
) AS content,
tags,
created_at
FROM {schema}mental_models
WHERE subtype = 'pinned'
ON CONFLICT DO NOTHING
""")
# 2. Migrate 'learned' mental models to learnings
# Each observation in a learned model becomes a separate learning
op.execute(f"""
INSERT INTO {schema}learnings (bank_id, text, proof_count, tags, created_at)
SELECT
mm.bank_id,
obs->>'content' AS text,
GREATEST(1, COALESCE(jsonb_array_length(obs->'evidence'), 1)) AS proof_count,
mm.tags,
mm.created_at
FROM {schema}mental_models mm,
LATERAL jsonb_array_elements(mm.observations->'observations') AS obs
WHERE mm.subtype = 'learned'
AND obs->>'content' IS NOT NULL
AND obs->>'content' != ''
ON CONFLICT DO NOTHING
""")
# 3. Delete all non-directive mental models (they've been migrated or are obsolete)
op.execute(f"""
DELETE FROM {schema}mental_models
WHERE subtype != 'directive'
""")
# 4. Drop the mental_model_versions table (no longer used)
op.execute(f"DROP TABLE IF EXISTS {schema}mental_model_versions CASCADE")
# 5. Drop old constraints and add new one that only allows 'directive'
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD CONSTRAINT ck_mental_models_subtype CHECK (subtype = 'directive')
""")
def downgrade() -> None:
"""Reverse the migration (data migration is one-way, so this just removes constraints)."""
schema = _get_schema_prefix()
# Remove the directive-only constraint
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
# Re-create mental_model_versions table
op.execute(f"""
CREATE TABLE IF NOT EXISTS {schema}mental_model_versions (
id SERIAL PRIMARY KEY,
bank_id VARCHAR(64) NOT NULL,
model_id VARCHAR(128) NOT NULL,
version INT NOT NULL,
observations JSONB NOT NULL,
created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
)
""")
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_mm_versions_lookup ON {schema}mental_model_versions(bank_id, model_id, version DESC)"
)
# Note: Data migration cannot be reversed - pinned_reflections and learnings data remains
@@ -1,194 +0,0 @@
"""new_knowledge_architecture
Revision ID: p1k2l3m4n5o6
Revises: o0j1k2l3m4n5
Create Date: 2026-01-21 00:00:00.000000
This migration implements the new knowledge architecture:
1. Drops the 'learnings' table (mental models are now in memory_units)
2. Renames 'pinned_reflections' to 'reflections'
3. Drops the 'mental_models' table completely
4. Creates 'directives' table for hard rules
5. Adds mental model support columns to 'memory_units' (proof_count, source_memory_ids, history)
The new architecture:
- Directives: Hard rules in their own table
- Mental Models: Stored in memory_units with fact_type='mental_model'
- Reflections: User-curated documents (renamed from pinned_reflections)
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "p1k2l3m4n5o6"
down_revision: str | Sequence[str] | None = "o0j1k2l3m4n5"
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:
"""Implement new knowledge architecture."""
schema = _get_schema_prefix()
# 1. Drop the learnings table (mental models will be in memory_units)
op.execute(f"DROP TABLE IF EXISTS {schema}learnings CASCADE")
# 2. Rename pinned_reflections to reflections
op.execute(f"ALTER TABLE IF EXISTS {schema}pinned_reflections RENAME TO reflections")
# Rename indexes for reflections
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_bank_id RENAME TO idx_reflections_bank_id")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_embedding RENAME TO idx_reflections_embedding")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_tags RENAME TO idx_reflections_tags")
op.execute(
f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_text_search RENAME TO idx_reflections_text_search"
)
# Rename foreign key constraint
op.execute(f"""
ALTER TABLE {schema}reflections
DROP CONSTRAINT IF EXISTS fk_pinned_reflections_bank_id
""")
op.execute(f"""
ALTER TABLE {schema}reflections
ADD CONSTRAINT fk_reflections_bank_id
FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
""")
# 3. Drop the mental_models table completely
op.execute(f"DROP TABLE IF EXISTS {schema}mental_models CASCADE")
# 4. Create directives table
op.execute(f"""
CREATE TABLE {schema}directives (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
bank_id VARCHAR(64) NOT NULL,
name VARCHAR(256) NOT NULL,
content TEXT NOT NULL,
priority INT NOT NULL DEFAULT 0,
is_active BOOLEAN NOT NULL DEFAULT TRUE,
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 and indexes for directives
op.execute(f"""
ALTER TABLE {schema}directives
ADD CONSTRAINT fk_directives_bank_id
FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
""")
op.execute(f"CREATE INDEX idx_directives_bank_id ON {schema}directives(bank_id)")
op.execute(f"CREATE INDEX idx_directives_bank_active ON {schema}directives(bank_id, is_active)")
op.execute(f"CREATE INDEX idx_directives_tags ON {schema}directives USING GIN(tags)")
# 5. Add mental model support columns to memory_units
# proof_count: Number of memories that support this mental model
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD COLUMN IF NOT EXISTS proof_count INT DEFAULT 1
""")
# source_memory_ids: Array of memory IDs that consolidated into this mental model
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD COLUMN IF NOT EXISTS source_memory_ids UUID[] DEFAULT ARRAY[]::UUID[]
""")
# history: JSONB array tracking changes to mental models
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD COLUMN IF NOT EXISTS history JSONB DEFAULT '[]'::jsonb
""")
# Add index for finding mental models
op.execute(f"""
CREATE INDEX IF NOT EXISTS idx_memory_units_mental_models
ON {schema}memory_units(bank_id, fact_type)
WHERE fact_type = 'mental_model'
""")
# 6. Update fact_type check constraint to include 'mental_model'
op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD CONSTRAINT memory_units_fact_type_check
CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation', 'mental_model'))
""")
def downgrade() -> None:
"""Reverse the migration."""
schema = _get_schema_prefix()
# Restore original fact_type check constraint (without 'mental_model')
op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD CONSTRAINT memory_units_fact_type_check
CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))
""")
# Drop mental model columns from memory_units
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS proof_count")
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS source_memory_ids")
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS history")
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_mental_models")
# Drop directives table
op.execute(f"DROP TABLE IF EXISTS {schema}directives CASCADE")
# Rename reflections back to pinned_reflections
op.execute(f"ALTER TABLE IF EXISTS {schema}reflections RENAME TO pinned_reflections")
# Restore indexes
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_bank_id RENAME TO idx_pinned_reflections_bank_id")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_embedding RENAME TO idx_pinned_reflections_embedding")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_tags RENAME TO idx_pinned_reflections_tags")
op.execute(
f"ALTER INDEX IF EXISTS {schema}idx_reflections_text_search RENAME TO idx_pinned_reflections_text_search"
)
# Restore foreign key
op.execute(f"""
ALTER TABLE {schema}pinned_reflections
DROP CONSTRAINT IF EXISTS fk_reflections_bank_id
""")
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
""")
# Re-create learnings table
op.execute(f"""
CREATE TABLE IF NOT EXISTS {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()
)
""")
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
""")
# Note: mental_models table recreation is complex and would need separate handling
@@ -1,50 +0,0 @@
"""fix_mental_model_fact_type
Revision ID: q2l3m4n5o6p7
Revises: p1k2l3m4n5o6
Create Date: 2026-01-21 13:30:00.000000
Fix the fact_type check constraint to include 'mental_model'.
This is a fix for p1k2l3m4n5o6 which should have included this change.
"""
from collections.abc import Sequence
from alembic import context, op
# revision identifiers, used by Alembic.
revision: str = "q2l3m4n5o6p7"
down_revision: str | Sequence[str] | None = "p1k2l3m4n5o6"
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 'mental_model' to the fact_type check constraint."""
schema = _get_schema_prefix()
# Drop the old constraint and add the new one with mental_model included
op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD CONSTRAINT memory_units_fact_type_check
CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation', 'mental_model'))
""")
def downgrade() -> None:
"""Remove 'mental_model' from the fact_type check constraint."""
schema = _get_schema_prefix()
op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD CONSTRAINT memory_units_fact_type_check
CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))
""")
@@ -1,47 +0,0 @@
"""Add reflect_response JSONB column to reflections
Revision ID: r3m4n5o6p7q8
Revises: q2l3m4n5o6p7
Create Date: 2026-01-21
This migration adds a reflect_response JSONB column to store the full
reflect API response payload, including based_on facts and trace data.
Note: Table was renamed from pinned_reflections to reflections in p1k2l3m4n5o6.
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "r3m4n5o6p7q8"
down_revision: str | Sequence[str] | None = "q2l3m4n5o6p7"
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 reflect_response JSONB column to reflections."""
schema = _get_schema_prefix()
# Add reflect_response column to store the full reflect API response
op.execute(f"""
ALTER TABLE {schema}reflections
ADD COLUMN IF NOT EXISTS reflect_response JSONB
""")
def downgrade() -> None:
"""Remove reflect_response column from reflections."""
schema = _get_schema_prefix()
op.execute(f"""
ALTER TABLE {schema}reflections
DROP COLUMN IF EXISTS reflect_response
""")
@@ -5,81 +5,61 @@ Revises: d9f6a3b4c5e2
Create Date: 2024-12-04
"""
from typing import Sequence, Union
from collections.abc import Sequence
from alembic import op
import sqlalchemy as sa
from alembic import context, op
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = "rename_personality"
down_revision: str | Sequence[str] | None = "d9f6a3b4c5e2"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_target_schema() -> str:
"""Get the target schema name (tenant schema or 'public')."""
schema = context.config.get_main_option("target_schema")
return schema if schema else "public"
revision: str = 'rename_personality'
down_revision: Union[str, Sequence[str], None] = 'd9f6a3b4c5e2'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Rename personality column to disposition in banks table (if it exists)."""
conn = op.get_bind()
target_schema = _get_target_schema()
# Check if 'personality' column exists (old database)
result = conn.execute(
sa.text("""
result = conn.execute(sa.text("""
SELECT column_name
FROM information_schema.columns
WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'personality'
"""),
{"schema": target_schema},
)
WHERE table_name = 'banks' AND column_name = 'personality'
"""))
has_personality = result.fetchone() is not None
# Check if 'disposition' column exists (new database)
result = conn.execute(
sa.text("""
result = conn.execute(sa.text("""
SELECT column_name
FROM information_schema.columns
WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
"""),
{"schema": target_schema},
)
WHERE table_name = 'banks' AND column_name = 'disposition'
"""))
has_disposition = result.fetchone() is not None
if has_personality and not has_disposition:
# Old database: rename personality -> disposition
op.alter_column("banks", "personality", new_column_name="disposition")
op.alter_column('banks', 'personality', new_column_name='disposition')
elif not has_personality and not has_disposition:
# Neither exists (shouldn't happen, but be safe): add disposition column
op.add_column(
"banks",
sa.Column(
"disposition",
postgresql.JSONB(astext_type=sa.Text()),
server_default=sa.text("'{}'::jsonb"),
nullable=False,
),
)
op.add_column('banks', sa.Column(
'disposition',
postgresql.JSONB(astext_type=sa.Text()),
server_default=sa.text("'{}'::jsonb"),
nullable=False
))
# else: disposition already exists, nothing to do
def downgrade() -> None:
"""Revert disposition column back to personality."""
conn = op.get_bind()
target_schema = _get_target_schema()
result = conn.execute(
sa.text("""
result = conn.execute(sa.text("""
SELECT column_name
FROM information_schema.columns
WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
"""),
{"schema": target_schema},
)
WHERE table_name = 'banks' AND column_name = 'disposition'
"""))
if result.fetchone():
op.alter_column("banks", "disposition", new_column_name="personality")
op.alter_column('banks', 'disposition', new_column_name='personality')
@@ -1,53 +0,0 @@
"""Add consolidated_at column to memory_units for incremental consolidation tracking.
This allows consolidation to track progress at the memory level rather than
using a bank-level watermark. If consolidation crashes, already-processed
memories won't be reprocessed.
Revision ID: s4n5o6p7q8r9
Revises: r3m4n5o6p7q8
Create Date: 2025-01-22
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "s4n5o6p7q8r9"
down_revision: str | Sequence[str] | None = "r3m4n5o6p7q8"
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 consolidated_at column to memory_units
op.execute(
f"""
ALTER TABLE {schema}memory_units
ADD COLUMN IF NOT EXISTS consolidated_at TIMESTAMPTZ DEFAULT NULL
"""
)
# Create index for efficient querying of unconsolidated memories
op.execute(
f"""
CREATE INDEX IF NOT EXISTS idx_memory_units_unconsolidated
ON {schema}memory_units (bank_id, created_at)
WHERE consolidated_at IS 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_unconsolidated")
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS consolidated_at")
@@ -1,134 +0,0 @@
"""Rename mental_model fact_type to observation and reflections table to mental_models
Revision ID: t5o6p7q8r9s0
Revises: s4n5o6p7q8r9
Create Date: 2026-01-26
This migration implements the terminology rename:
1. mental_model (fact_type in memory_units) -> observation
2. reflections table -> mental_models table
The new terminology:
- Observations: Consolidated knowledge synthesized from facts (was mental_model)
- Mental Models: Stored reflect responses (was reflections)
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "t5o6p7q8r9s0"
down_revision: str | Sequence[str] | None = "s4n5o6p7q8r9"
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:
"""Rename mental_model -> observation and reflections -> mental_models."""
schema = _get_schema_prefix()
# 1. Update fact_type values: mental_model -> observation
op.execute(f"""
UPDATE {schema}memory_units
SET fact_type = 'observation'
WHERE fact_type = 'mental_model'
""")
# 2. Update the CHECK constraint - remove mental_model, keep observation
op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD CONSTRAINT memory_units_fact_type_check
CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))
""")
# 3. Rename the index for observations (was for mental_models)
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_mental_models")
op.execute(f"""
CREATE INDEX IF NOT EXISTS idx_memory_units_observations
ON {schema}memory_units(bank_id, fact_type)
WHERE fact_type = 'observation'
""")
# 4. Update the unconsolidated index to not filter by fact_type since observations
# are now the consolidated type
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_unconsolidated")
op.execute(f"""
CREATE INDEX IF NOT EXISTS idx_memory_units_unconsolidated
ON {schema}memory_units (bank_id, created_at)
WHERE consolidated_at IS NULL AND fact_type IN ('experience', 'world')
""")
# 5. Rename reflections table to mental_models
op.execute(f"ALTER TABLE IF EXISTS {schema}reflections RENAME TO mental_models")
# 6. Rename indexes for mental_models (was reflections)
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_bank_id RENAME TO idx_mental_models_bank_id")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_embedding RENAME TO idx_mental_models_embedding")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_tags RENAME TO idx_mental_models_tags")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_text_search RENAME TO idx_mental_models_text_search")
# 7. Rename foreign key constraint
op.execute(f"""
ALTER TABLE {schema}mental_models
DROP CONSTRAINT IF EXISTS fk_reflections_bank_id
""")
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD CONSTRAINT fk_mental_models_bank_id
FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
""")
def downgrade() -> None:
"""Reverse: observation -> mental_model and mental_models -> reflections."""
schema = _get_schema_prefix()
# 1. Rename mental_models table back to reflections
op.execute(f"ALTER TABLE IF EXISTS {schema}mental_models RENAME TO reflections")
# 2. Rename indexes back
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_bank_id RENAME TO idx_reflections_bank_id")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_embedding RENAME TO idx_reflections_embedding")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_tags RENAME TO idx_reflections_tags")
op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_text_search RENAME TO idx_reflections_text_search")
# 3. Rename foreign key back
op.execute(f"""
ALTER TABLE {schema}reflections
DROP CONSTRAINT IF EXISTS fk_mental_models_bank_id
""")
op.execute(f"""
ALTER TABLE {schema}reflections
ADD CONSTRAINT fk_reflections_bank_id
FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
""")
# 4. Update fact_type values: observation -> mental_model
op.execute(f"""
UPDATE {schema}memory_units
SET fact_type = 'mental_model'
WHERE fact_type = 'observation'
""")
# 5. Update the CHECK constraint back
op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
op.execute(f"""
ALTER TABLE {schema}memory_units
ADD CONSTRAINT memory_units_fact_type_check
CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation', 'mental_model'))
""")
# 6. Rename index back
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_observations")
op.execute(f"""
CREATE INDEX IF NOT EXISTS idx_memory_units_mental_models
ON {schema}memory_units(bank_id, fact_type)
WHERE fact_type = 'mental_model'
""")
@@ -1,41 +0,0 @@
"""Change mental_models.id from UUID to TEXT
Revision ID: u6p7q8r9s0t1
Revises: t5o6p7q8r9s0
Create Date: 2026-01-27
This migration changes the mental_models.id column from UUID to TEXT
to support user-defined text identifiers like 'team-communication' instead of UUIDs.
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "u6p7q8r9s0t1"
down_revision: str | Sequence[str] | None = "t5o6p7q8r9s0"
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:
"""Change mental_models.id from UUID to TEXT."""
schema = _get_schema_prefix()
# Change the id column type from UUID to TEXT
# Existing UUIDs will be converted to their string representation
op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE TEXT USING id::TEXT")
def downgrade() -> None:
"""Revert mental_models.id from TEXT to UUID."""
schema = _get_schema_prefix()
# Note: This will fail if any id values are not valid UUIDs
op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE UUID USING id::UUID")
@@ -1,50 +0,0 @@
"""Add max_tokens and trigger columns to mental_models
Revision ID: v7q8r9s0t1u2
Revises: u6p7q8r9s0t1
Create Date: 2026-01-27
This migration adds:
- max_tokens column: token limit for content generation during refresh
- trigger column: JSONB for trigger settings (e.g., refresh_after_consolidation)
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "v7q8r9s0t1u2"
down_revision: str | Sequence[str] | None = "u6p7q8r9s0t1"
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 max_tokens and trigger columns to mental_models."""
schema = _get_schema_prefix()
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS max_tokens INT NOT NULL DEFAULT 2048
""")
# trigger column stores trigger settings as JSONB
# Default: refresh_after_consolidation = false (not "real time")
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS trigger JSONB NOT NULL DEFAULT '{{"refresh_after_consolidation": false}}'::jsonb
""")
def downgrade() -> None:
"""Remove max_tokens and trigger columns from mental_models."""
schema = _get_schema_prefix()
op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS max_tokens")
op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS trigger")
@@ -1,60 +0,0 @@
"""Fix mental_models primary key to be scoped per bank
Revision ID: w8r9s0t1u2v3
Revises: v7q8r9s0t1u2
Create Date: 2026-02-05
This migration fixes a critical bank isolation bug where mental_models.id was
globally unique across all banks instead of being scoped per bank. This caused
conflicts when different banks tried to use the same custom ID.
CRITICAL FIX: Changes primary key from (id) to (bank_id, id) to ensure proper isolation.
"""
from collections.abc import Sequence
from alembic import context, op
revision: str = "w8r9s0t1u2v3"
down_revision: str | Sequence[str] | None = "v7q8r9s0t1u2"
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:
"""Change mental_models primary key from (id) to (bank_id, id) for proper bank isolation."""
schema = _get_schema_prefix()
# Drop the old primary key constraint (just id)
# Note: The constraint might be named differently on different DBs
# Try both old names (pinned_reflections_pkey from original, mental_models_pkey from rename)
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS pinned_reflections_pkey")
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS mental_models_pkey")
# Create the new composite primary key (bank_id, id)
# This ensures IDs are scoped per bank, not globally
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD CONSTRAINT mental_models_pkey PRIMARY KEY (bank_id, id)
""")
def downgrade() -> None:
"""Revert mental_models primary key from (bank_id, id) to (id)."""
schema = _get_schema_prefix()
# Drop the composite primary key
op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS mental_models_pkey")
# Restore the old primary key (just id)
# WARNING: This downgrade will fail if there are duplicate IDs across banks
op.execute(f"""
ALTER TABLE {schema}mental_models
ADD CONSTRAINT mental_models_pkey PRIMARY KEY (id)
""")
@@ -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
""")
@@ -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 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")
@@ -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")
+24 -58
View File
@@ -3,10 +3,8 @@ Unified API module for Hindsight.
Provides both HTTP REST API and MCP (Model Context Protocol) server.
"""
import logging
from contextlib import asynccontextmanager
from typing import Optional
from fastapi import FastAPI
from hindsight_api import MemoryEngine
@@ -19,7 +17,7 @@ def create_app(
http_api_enabled: bool = True,
mcp_api_enabled: bool = False,
mcp_mount_path: str = "/mcp",
initialize_memory: bool = True,
initialize_memory: bool = True
) -> FastAPI:
"""
Create and configure the unified Hindsight API application.
@@ -45,81 +43,49 @@ def create_app(
# Both HTTP and MCP
app = create_app(memory, mcp_api_enabled=True)
"""
mcp_servers = None
# Create MCP servers first if enabled (we need their lifespans for chaining)
if mcp_api_enabled:
try:
from .mcp import MCPMiddleware, create_mcp_servers
mcp_servers = create_mcp_servers(memory=memory)
except ImportError as e:
logger.error(f"MCP server requested but dependencies not available: {e}")
logger.error("Install with: pip install hindsight-api[mcp]")
raise
# Import and create HTTP API if enabled
if http_api_enabled:
from .http import create_app as create_http_app
app = create_http_app(memory=memory, initialize_memory=initialize_memory)
app = create_http_app(
memory=memory,
initialize_memory=initialize_memory
)
logger.info("HTTP REST API enabled")
else:
# Create minimal FastAPI app
app = FastAPI(title="Hindsight API", version="0.0.7")
logger.info("HTTP REST API disabled")
# Add MCP middleware and chain its lifespan if enabled
if mcp_servers is not None:
multi_bank_server, single_bank_server, multi_bank_starlette_app, single_bank_starlette_app = mcp_servers
# Mount MCP server if enabled
if mcp_api_enabled:
try:
from .mcp import create_mcp_app
# Store the original lifespan
original_lifespan = app.router.lifespan_context
@asynccontextmanager
async def chained_lifespan(app_instance: FastAPI):
"""Chain both MCP lifespans with the main app lifespan."""
# Start both MCP lifespans (multi-bank and single-bank)
async with multi_bank_starlette_app.router.lifespan_context(multi_bank_starlette_app):
async with single_bank_starlette_app.router.lifespan_context(single_bank_starlette_app):
logger.info("MCP lifespans started (multi-bank and single-bank)")
# Then start the original app lifespan
async with original_lifespan(app_instance):
yield
logger.info("MCP lifespans stopped")
# Replace the app's lifespan with the chained version
app.router.lifespan_context = chained_lifespan
# Add MCP as a wrapping middleware — intercepts /mcp* requests directly,
# passes everything else through to the FastAPI app. No Starlette Mount
# means no 307 redirect for /mcp (no trailing slash).
app.add_middleware(
MCPMiddleware,
memory=memory,
prefix=mcp_mount_path,
multi_bank_app=multi_bank_starlette_app,
single_bank_app=single_bank_starlette_app,
multi_bank_server=multi_bank_server,
single_bank_server=single_bank_server,
)
logger.info(f"MCP server enabled at {mcp_mount_path}/")
# Create MCP app with dynamic bank_id support
# Supports: /mcp/{bank_id}/sse (bank-specific SSE endpoint)
mcp_app = create_mcp_app(memory=memory)
app.mount(mcp_mount_path, mcp_app)
logger.info(f"MCP server enabled at {mcp_mount_path}/{{bank_id}}/sse")
except ImportError as e:
logger.error(f"MCP server requested but dependencies not available: {e}")
logger.error("Install with: pip install hindsight-api[mcp]")
raise
return app
# Re-export commonly used items for backwards compatibility
from .http import (
CreateBankRequest,
DispositionTraits,
MemoryItem,
RecallRequest,
RecallResponse,
RecallResult,
RecallResponse,
MemoryItem,
RetainRequest,
ReflectRequest,
ReflectResponse,
RetainRequest,
CreateBankRequest,
DispositionTraits,
)
__all__ = [
File diff suppressed because it is too large Load Diff
+137 -374
View File
@@ -1,452 +1,215 @@
"""Hindsight MCP Server implementation using FastMCP (HTTP transport)."""
"""Hindsight MCP Server implementation using FastMCP."""
import json
import logging
import os
from contextvars import ContextVar
from typing import Optional
from fastmcp import FastMCP
from hindsight_api import MemoryEngine
from hindsight_api.config import _get_raw_config
from hindsight_api.engine.memory_engine import _current_schema
from hindsight_api.extensions import MCPExtension, load_extension
from hindsight_api.extensions.tenant import AuthenticationError
from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
from hindsight_api.models import RequestContext
# All tools available in the system (explicit list — no wildcards)
_ALL_TOOLS: frozenset[str] = frozenset(
{
"retain",
"recall",
"reflect",
"list_banks",
"create_bank",
"list_mental_models",
"get_mental_model",
"create_mental_model",
"update_mental_model",
"delete_mental_model",
"refresh_mental_model",
"list_directives",
"create_directive",
"delete_directive",
"list_memories",
"get_memory",
"delete_memory",
"list_documents",
"get_document",
"delete_document",
"list_operations",
"get_operation",
"cancel_operation",
"list_tags",
"get_bank",
"get_bank_stats",
"update_bank",
"delete_bank",
"clear_memories",
}
)
from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
# Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
_log_level_map = {
"critical": logging.CRITICAL,
"error": logging.ERROR,
"warning": logging.WARNING,
"info": logging.INFO,
"debug": logging.DEBUG,
"trace": logging.DEBUG,
}
_log_level_map = {"critical": logging.CRITICAL, "error": logging.ERROR, "warning": logging.WARNING,
"info": logging.INFO, "debug": logging.DEBUG, "trace": logging.DEBUG}
logging.basicConfig(
level=_log_level_map.get(_log_level_str, logging.INFO),
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"
)
logger = logging.getLogger(__name__)
# Default bank_id from environment variable
DEFAULT_BANK_ID = os.environ.get("HINDSIGHT_MCP_BANK_ID", "default")
# Legacy MCP authentication token (for backwards compatibility)
# If set, this token is checked first before TenantExtension auth
MCP_AUTH_TOKEN = os.environ.get("HINDSIGHT_API_MCP_AUTH_TOKEN")
# Context variable to hold the current bank_id
_current_bank_id: ContextVar[str | None] = ContextVar("current_bank_id", default=None)
# Context variable to hold the current API key (for tenant auth propagation)
_current_api_key: ContextVar[str | None] = ContextVar("current_api_key", default=None)
# Context variables for tenant_id and api_key_id (set by authenticate, used by usage metering)
_current_tenant_id: ContextVar[str | None] = ContextVar("current_tenant_id", default=None)
_current_api_key_id: ContextVar[str | None] = ContextVar("current_api_key_id", default=None)
# Context variable to hold the current bank_id from the URL path
_current_bank_id: ContextVar[Optional[str]] = ContextVar("current_bank_id", default=None)
def get_current_bank_id() -> str | None:
"""Get the current bank_id from context."""
def get_current_bank_id() -> Optional[str]:
"""Get the current bank_id from context (set from URL path)."""
return _current_bank_id.get()
def get_current_api_key() -> str | None:
"""Get the current API key from context."""
return _current_api_key.get()
def get_current_tenant_id() -> str | None:
"""Get the current tenant_id from context."""
return _current_tenant_id.get()
def get_current_api_key_id() -> str | None:
"""Get the current api_key_id from context."""
return _current_api_key_id.get()
def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
def create_mcp_server(memory: MemoryEngine) -> FastMCP:
"""
Create and configure the Hindsight MCP server.
Args:
memory: MemoryEngine instance (required)
multi_bank: If True, expose all tools with bank_id parameters (default).
If False, only expose bank-scoped tools without bank_id parameters.
Returns:
Configured FastMCP server instance
"""
mcp = FastMCP("hindsight-mcp-server")
global_config = _get_raw_config()
@mcp.tool()
async def retain(content: str, context: str = "general") -> str:
"""
Store important information to long-term memory.
# Tools available for this mode (multi-bank exposes all tools; single-bank excludes bank-management tools)
_SINGLE_BANK_TOOLS: frozenset[str] = frozenset(
{
"retain",
"recall",
"reflect",
"list_mental_models",
"get_mental_model",
"create_mental_model",
"update_mental_model",
"delete_mental_model",
"refresh_mental_model",
"list_directives",
"create_directive",
"delete_directive",
"list_memories",
"get_memory",
"delete_memory",
"list_documents",
"get_document",
"delete_document",
"list_operations",
"get_operation",
"cancel_operation",
"list_tags",
"get_bank",
"update_bank",
"delete_bank",
"clear_memories",
}
)
base_tools: frozenset[str] | None = None if multi_bank else _SINGLE_BANK_TOOLS
Use this tool PROACTIVELY whenever the user shares:
- Personal facts, preferences, or interests
- Important events or milestones
- User history, experiences, or background
- Decisions, opinions, or stated preferences
- Goals, plans, or future intentions
- Relationships or people mentioned
- Work context, projects, or responsibilities
# Apply global mcp_enabled_tools filter (env-level allowlist)
if global_config.mcp_enabled_tools is not None:
allowed = frozenset(global_config.mcp_enabled_tools)
base_tools = (base_tools if base_tools is not None else _ALL_TOOLS) & allowed
Args:
content: The fact/memory to store (be specific and include relevant details)
context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
"""
try:
bank_id = get_current_bank_id()
await memory.put_batch_async(
bank_id=bank_id,
contents=[{"content": content, "context": context}]
)
return "Memory stored successfully"
except Exception as e:
logger.error(f"Error storing memory: {e}", exc_info=True)
return f"Error: {str(e)}"
# Configure and register tools using shared module
config = MCPToolsConfig(
bank_id_resolver=get_current_bank_id,
api_key_resolver=get_current_api_key, # Propagate API key for tenant auth
tenant_id_resolver=get_current_tenant_id, # Propagate tenant_id for usage metering
api_key_id_resolver=get_current_api_key_id, # Propagate api_key_id for usage metering
include_bank_id_param=multi_bank,
tools=base_tools,
)
@mcp.tool()
async def recall(query: str, max_results: int = 10) -> str:
"""
Search memories to provide personalized, context-aware responses.
register_mcp_tools(mcp, memory, config)
Use this tool PROACTIVELY to:
- Check user's preferences before making suggestions
- Recall user's history to provide continuity
- Remember user's goals and context
- Personalize responses based on past interactions
# Load and register additional tools from MCP extension if configured
mcp_extension = load_extension("MCP", MCPExtension)
if mcp_extension:
logger.info(f"Loading MCP extension: {mcp_extension.__class__.__name__}")
mcp_extension.register_tools(mcp, memory)
Args:
query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
max_results: Maximum number of results to return (default: 10)
"""
try:
bank_id = get_current_bank_id()
from hindsight_api.engine.memory_engine import Budget
search_result = await memory.recall_async(
bank_id=bank_id,
query=query,
fact_type=list(VALID_RECALL_FACT_TYPES),
budget=Budget.LOW
)
# Make all tools tolerant of extra arguments from LLMs (e.g., "explanation")
_make_tools_tolerant(mcp)
results = [
{
"id": fact.id,
"text": fact.text,
"type": fact.fact_type,
"context": fact.context,
"event_date": fact.event_date,
}
for fact in search_result.results[:max_results]
]
return json.dumps({"results": results}, indent=2)
except Exception as e:
logger.error(f"Error searching: {e}", exc_info=True)
return json.dumps({"error": str(e), "results": []})
return mcp
def _make_tools_tolerant(mcp: FastMCP) -> None:
"""Wrap all tool run methods to strip unknown arguments before validation.
LLMs frequently add extra fields like "explanation" or "reasoning" to tool calls.
FastMCP's Pydantic TypeAdapter rejects these with "Unexpected keyword argument".
This wraps each tool's run() to filter arguments to only known parameters.
"""
try:
for name, tool in mcp._tool_manager._tools.items():
if hasattr(tool, "parameters") and tool.parameters:
allowed = set(tool.parameters.get("properties", {}).keys())
original_run = tool.run
async def _tolerant_run(arguments, _allowed=allowed, _orig=original_run):
extra_keys = set(arguments.keys()) - _allowed
if extra_keys:
logger.debug(f"Stripping unknown arguments from tool call: {extra_keys}")
arguments = {k: v for k, v in arguments.items() if k in _allowed}
return await _orig(arguments)
# FunctionTool is a Pydantic model with extra='forbid', so use
# object.__setattr__ to bypass Pydantic's setter validation.
object.__setattr__(tool, "run", _tolerant_run)
except (AttributeError, KeyError) as e:
logger.warning(f"Could not make tools tolerant of extra arguments: {e}")
class MCPMiddleware:
"""ASGI middleware that intercepts MCP requests and routes to appropriate MCP server.
"""ASGI middleware that extracts bank_id from path and sets context."""
This middleware wraps the main FastAPI app and intercepts requests matching the
configured prefix (default: /mcp). Non-MCP requests pass through to the inner app.
Authentication:
1. If HINDSIGHT_API_MCP_AUTH_TOKEN is set (legacy), validates against that token
2. Otherwise, uses TenantExtension.authenticate_mcp() from the MemoryEngine
- DefaultTenantExtension: no auth required (local dev)
- ApiKeyTenantExtension: validates against env var
Two modes based on URL structure:
1. Multi-bank mode (for /mcp/ root endpoint):
- Exposes all tools: retain, recall, reflect, list_banks, create_bank
- All tools include optional bank_id parameter for cross-bank operations
- Bank ID from: X-Bank-Id header or HINDSIGHT_MCP_BANK_ID env var
2. Single-bank mode (for /mcp/{bank_id}/ endpoints):
- Exposes bank-scoped tools only: retain, recall, reflect
- No bank_id parameter (comes from URL)
- No bank management tools (list_banks, create_bank)
- Recommended for agent isolation
Bank ID resolution priority:
1. URL path (e.g., /mcp/{bank_id}/) → single-bank mode
2. X-Bank-Id header → multi-bank mode
3. HINDSIGHT_MCP_BANK_ID env var → multi-bank mode (default: "default")
Examples:
# Single-bank mode (recommended for agent isolation)
claude mcp add --transport http my-agent http://localhost:8888/mcp/my-agent-bank/ \\
--header "Authorization: Bearer <token>"
# Multi-bank mode (for cross-bank operations)
claude mcp add --transport http hindsight http://localhost:8888/mcp \\
--header "X-Bank-Id: my-bank" --header "Authorization: Bearer <token>"
"""
def __init__(
self,
app,
memory: MemoryEngine,
prefix: str = "/mcp",
multi_bank_app=None,
single_bank_app=None,
multi_bank_server=None,
single_bank_server=None,
):
def __init__(self, app, memory: MemoryEngine):
self.app = app
self.prefix = prefix
self.memory = memory
self.tenant_extension = memory._tenant_extension
if multi_bank_app and single_bank_app:
# Pre-created servers (used when called via add_middleware from create_app)
self.multi_bank_app = multi_bank_app
self.single_bank_app = single_bank_app
self.multi_bank_server = multi_bank_server
self.single_bank_server = single_bank_server
else:
# Create servers internally (for direct construction / tests)
self.multi_bank_server = create_mcp_server(memory, multi_bank=True)
self.multi_bank_app = self.multi_bank_server.http_app(path="/", stateless_http=True)
self.single_bank_server = create_mcp_server(memory, multi_bank=False)
self.single_bank_app = self.single_bank_server.http_app(path="/", stateless_http=True)
def _get_header(self, scope: dict, name: str) -> str | None:
"""Extract a header value from ASGI scope."""
name_lower = name.lower().encode()
for header_name, header_value in scope.get("headers", []):
if header_name.lower() == name_lower:
return header_value.decode()
return None
self.mcp_server = create_mcp_server(memory)
# Use sse_app - http_app requires lifespan management that's complex with middleware
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
self.mcp_app = self.mcp_server.sse_app()
async def __call__(self, scope, receive, send):
if scope["type"] != "http":
await self.app(scope, receive, send)
await self.mcp_app(scope, receive, send)
return
path = scope.get("path", "")
# Check if this is an MCP request (matches prefix)
if not (path == self.prefix or path.startswith(self.prefix + "/")):
# Not an MCP request — pass through to the inner app
await self.app(scope, receive, send)
# Strip any mount prefix (e.g., /mcp) that FastAPI might not have stripped
root_path = scope.get("root_path", "")
if root_path and path.startswith(root_path):
path = path[len(root_path):] or "/"
# Also handle case where mount path wasn't stripped (e.g., /mcp/...)
if path.startswith("/mcp/"):
path = path[4:] # Remove /mcp prefix
# Extract bank_id from path: /{bank_id}/ or /{bank_id}
# http_app expects requests at /
if not path.startswith("/") or len(path) <= 1:
# No bank_id in path - return error
await self._send_error(send, 400, "bank_id required in path: /mcp/{bank_id}/")
return
# Strip prefix from path
path = path[len(self.prefix) :] or "/"
# Extract bank_id from first path segment
parts = path[1:].split("/", 1)
if not parts[0]:
await self._send_error(send, 400, "bank_id required in path: /mcp/{bank_id}/")
return
# Extract auth token from header (for tenant auth propagation)
auth_header = self._get_header(scope, "Authorization")
auth_token: str | None = None
if auth_header:
# Support both "Bearer <token>" and direct token
auth_token = auth_header[7:].strip() if auth_header.startswith("Bearer ") else auth_header.strip()
bank_id = parts[0]
new_path = "/" + parts[1] if len(parts) > 1 else "/"
# Authenticate: check legacy MCP_AUTH_TOKEN first, then TenantExtension
tenant_context = None
auth_tenant_id: str | None = None
auth_api_key_id: str | None = None
if MCP_AUTH_TOKEN:
# Legacy authentication mode - validate against static token
if not auth_token:
await self._send_error(send, 401, "Authorization header required")
return
if auth_token != MCP_AUTH_TOKEN:
await self._send_error(send, 401, "Invalid authentication token")
return
# Legacy mode doesn't use tenant schemas
tenant_context = None
else:
# Use TenantExtension.authenticate_mcp() for auth
try:
auth_context = RequestContext(api_key=auth_token)
tenant_context = await self.tenant_extension.authenticate_mcp(auth_context)
# Capture tenant_id and api_key_id set by authenticate() for usage metering
auth_tenant_id = auth_context.tenant_id
auth_api_key_id = auth_context.api_key_id
except AuthenticationError as e:
await self._send_error(send, 401, str(e), extra_headers=e.headers)
return
# Set schema from tenant context so downstream DB queries use the correct schema
schema_token = (
_current_schema.set(tenant_context.schema_name) if tenant_context and tenant_context.schema_name else None
)
# Resolve bank_id: path takes priority over header.
# Path = user's explicit connection endpoint (e.g., /mcp/my-bank/).
# X-Bank-Id header = per-request override for multi-bank mode only.
bank_id = None
bank_id_from_path = False
new_path = path
# First, try to extract from path: /{bank_id}/...
if path.startswith("/") and len(path) > 1:
parts = path[1:].split("/", 1)
if parts[0]:
bank_id = parts[0]
bank_id_from_path = True
new_path = "/" + parts[1] if len(parts) > 1 else "/"
# If no path-based bank_id, try X-Bank-Id header (multi-bank mode)
if not bank_id:
bank_id = self._get_header(scope, "X-Bank-Id")
# Fall back to default bank_id
if not bank_id:
bank_id = DEFAULT_BANK_ID
logger.debug(f"Using default bank_id: {bank_id}")
# Select the appropriate MCP app based on how bank_id was provided:
# - Path-based bank_id → single-bank app (no bank_id param, scoped tools)
# - Header/env bank_id → multi-bank app (bank_id param, all tools)
target_app = self.single_bank_app if bank_id_from_path else self.multi_bank_app
# Set bank_id, api_key, tenant_id, and api_key_id context
bank_id_token = _current_bank_id.set(bank_id)
# Store the auth token for tenant extension to validate
api_key_token = _current_api_key.set(auth_token) if auth_token else None
# Store tenant_id and api_key_id from authentication for usage metering
tenant_id_token = _current_tenant_id.set(auth_tenant_id) if auth_tenant_id else None
api_key_id_token = _current_api_key_id.set(auth_api_key_id) if auth_api_key_id else None
# Set bank_id context
token = _current_bank_id.set(bank_id)
try:
new_scope = scope.copy()
new_scope["path"] = new_path
# Clear root_path since we're passing directly to the app
new_scope["root_path"] = ""
# Wrap send to rewrite the SSE endpoint URL to include bank_id if using path-based routing.
# Only rewrite SSE (text/event-stream) responses to avoid corrupting tool results
# that might contain the literal string "data: /messages".
is_sse_response = False
# Wrap send to rewrite the SSE endpoint URL to include bank_id
# The SSE app sends "event: endpoint\ndata: /messages\n" but we need
# the client to POST to /{bank_id}/messages instead
async def send_wrapper(message):
nonlocal is_sse_response
if message["type"] == "http.response.start":
for header_name, header_value in message.get("headers", []):
if header_name == b"content-type" and b"text/event-stream" in header_value:
is_sse_response = True
break
if message["type"] == "http.response.body" and bank_id_from_path and is_sse_response:
if message["type"] == "http.response.body":
body = message.get("body", b"")
if body and b"/messages" in body:
# Rewrite /messages to /{bank_id}/messages in SSE endpoint event
body = body.replace(b"data: /messages", f"data: /{bank_id}/messages".encode())
body = body.replace(
b"data: /messages",
f"data: /{bank_id}/messages".encode()
)
message = {**message, "body": body}
await send(message)
await target_app(new_scope, receive, send_wrapper)
await self.mcp_app(new_scope, receive, send_wrapper)
finally:
_current_bank_id.reset(bank_id_token)
if api_key_token is not None:
_current_api_key.reset(api_key_token)
if tenant_id_token is not None:
_current_tenant_id.reset(tenant_id_token)
if api_key_id_token is not None:
_current_api_key_id.reset(api_key_id_token)
if schema_token is not None:
_current_schema.reset(schema_token)
_current_bank_id.reset(token)
async def _send_error(self, send, status: int, message: str, extra_headers: dict[str, str] | None = None):
async def _send_error(self, send, status: int, message: str):
"""Send an error response."""
body = json.dumps({"error": message}).encode()
headers = [(b"content-type", b"application/json")]
for key, value in (extra_headers or {}).items():
headers.append((key.encode(), value.encode()))
await send(
{
"type": "http.response.start",
"status": status,
"headers": headers,
}
)
await send(
{
"type": "http.response.body",
"body": body,
}
)
await send({
"type": "http.response.start",
"status": status,
"headers": [(b"content-type", b"application/json")],
})
await send({
"type": "http.response.body",
"body": body,
})
def create_mcp_servers(memory: MemoryEngine):
"""Create multi-bank and single-bank MCP servers and their Starlette apps.
def create_mcp_app(memory: MemoryEngine):
"""
Create an ASGI app that handles MCP requests.
Returns the servers and apps separately so lifespans can be chained before
the middleware wraps the main app.
URL pattern: /mcp/{bank_id}/
The bank_id is extracted from the URL path and made available to tools.
Args:
memory: MemoryEngine instance
Returns:
Tuple of (multi_bank_server, single_bank_server, multi_bank_app, single_bank_app)
ASGI application
"""
multi_bank_server = create_mcp_server(memory, multi_bank=True)
multi_bank_app = multi_bank_server.http_app(path="/", stateless_http=True)
single_bank_server = create_mcp_server(memory, multi_bank=False)
single_bank_app = single_bank_server.http_app(path="/", stateless_http=True)
return multi_bank_server, single_bank_server, multi_bank_app, single_bank_app
return MCPMiddleware(None, memory)
+7 -23
View File
@@ -4,11 +4,9 @@ Banner display for Hindsight API startup.
Shows the logo and tagline with gradient colors.
"""
from .utils import mask_network_location
# Gradient colors: #0074d9 -> #009296
GRADIENT_START = (0, 116, 217) # #0074d9
GRADIENT_END = (0, 146, 150) # #009296
GRADIENT_END = (0, 146, 150) # #009296
# Pre-generated logo (generated by test-logo.py)
LOGO = """\
@@ -33,8 +31,8 @@ def gradient_text(text: str, start: tuple = GRADIENT_START, end: tuple = GRADIEN
result = []
length = len(text)
for i, char in enumerate(text):
if char == " ":
result.append(" ")
if char == ' ':
result.append(' ')
else:
t = i / max(length - 1, 1)
r, g, b = _interpolate_color(start, end, t)
@@ -76,30 +74,16 @@ def dim(text: str) -> str:
return f"\033[38;2;128;128;128m{text}\033[0m"
def print_startup_info(
host: str,
port: int,
database_url: str,
llm_provider: str,
llm_model: str,
embeddings_provider: str,
reranker_provider: str,
mcp_enabled: bool = False,
version: str | None = None,
vector_extension: str | None = None,
text_search_extension: str | None = None,
):
def print_startup_info(host: str, port: int, database_url: str, llm_provider: str,
llm_model: str, embeddings_provider: str, reranker_provider: str,
mcp_enabled: bool = False):
"""Print styled startup information."""
print(color_start("Starting Hindsight API..."))
if version:
print(f" {dim('Version:')} {color(f'v{version}', 0.1)}")
print(f" {dim('URL:')} {color(f'http://{host}:{port}', 0.2)}")
print(f" {dim('Database:')} {color(mask_network_location(database_url), 0.4)}")
print(f" {dim('Database:')} {color(database_url, 0.4)}")
print(f" {dim('LLM:')} {color(f'{llm_provider} / {llm_model}', 0.6)}")
print(f" {dim('Embeddings:')} {color(embeddings_provider, 0.8)}")
print(f" {dim('Reranker:')} {color(reranker_provider, 1.0)}")
extensions = f"{vector_extension or 'default'} (vector) / {text_search_extension or 'default'} (text)"
print(f" {dim('Extensions:')} {color(extensions, 0.4)}")
if mcp_enabled:
print(f" {dim('MCP:')} {color_end('enabled at /mcp')}")
print()
File diff suppressed because it is too large Load Diff
@@ -1,275 +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
from typing import Any
import asyncpg
from hindsight_api.config import HindsightConfig, _get_raw_config, normalize_config_dict
from hindsight_api.engine.memory_engine import fq_table
from hindsight_api.extensions.tenant import TenantExtension
from hindsight_api.models import RequestContext
logger = logging.getLogger(__name__)
class ConfigResolver:
"""Resolves hierarchical configuration with tenant/bank overrides."""
def __init__(self, pool: asyncpg.Pool, tenant_extension: TenantExtension | None = None):
"""
Initialize config resolver.
Args:
pool: Database connection pool
tenant_extension: Optional tenant extension for tenant-level config and permissions
"""
self.pool = pool
self.tenant_extension = tenant_extension
self._global_config = _get_raw_config()
self._configurable_fields = HindsightConfig.get_configurable_fields()
self._credential_fields = HindsightConfig.get_credential_fields()
async def resolve_full_config(self, bank_id: str, context: RequestContext | None = None) -> HindsightConfig:
"""
Resolve full HindsightConfig for a bank with hierarchical overrides applied.
This is for INTERNAL USE ONLY. Returns the complete config object with all fields
including credentials and static fields. Use get_bank_config() for API responses.
Resolution order:
1. Global config (from environment variables)
2. Tenant config overrides (from TenantExtension.get_tenant_config())
3. Bank config overrides (from banks.config JSONB)
Args:
bank_id: Bank identifier
context: Request context for tenant config resolution
Returns:
Complete HindsightConfig with hierarchical overrides applied
"""
# Start with global config (all fields)
config_dict = asdict(self._global_config)
# Load tenant config overrides (if tenant extension available)
if self.tenant_extension and context:
try:
tenant_overrides = await self.tenant_extension.get_tenant_config(context)
if tenant_overrides:
# Normalize keys and filter to configurable fields only
normalized_tenant = normalize_config_dict(tenant_overrides)
configurable_tenant = {k: v for k, v in normalized_tenant.items() if k in self._configurable_fields}
config_dict.update(configurable_tenant)
logger.debug(
f"Applied tenant config overrides for bank {bank_id}: {list(configurable_tenant.keys())}"
)
except Exception as e:
logger.warning(f"Failed to load tenant config for bank {bank_id}: {e}")
# Load bank config overrides
bank_overrides = await self._load_bank_config(bank_id)
if bank_overrides:
config_dict.update(bank_overrides)
logger.debug(f"Applied bank config overrides for bank {bank_id}: {list(bank_overrides.keys())}")
# Return full config object (dataclass doesn't have __init__ that accepts kwargs, so we update the object)
# Create a new config instance by copying the global config and updating fields
resolved_config = HindsightConfig(**config_dict)
return resolved_config
async def get_bank_config(self, bank_id: str, context: RequestContext | None = None) -> dict[str, Any]:
"""
Get fully resolved config for a bank (filtered by permissions).
Resolution order:
1. Global config (from environment variables)
2. Tenant config overrides (from TenantExtension.get_tenant_config())
3. Bank config overrides (from banks.config JSONB)
Note: Config is resolved on every call (not cached) to ensure consistency
across multiple API servers.
SECURITY:
- Only returns configurable fields (excludes static/infrastructure fields)
- Filters out ALL credential fields (API keys, base URLs, etc.)
- Further filtered by tenant/bank permissions if extension provides them
Args:
bank_id: Bank identifier
context: Request context for tenant config resolution and permissions
Returns:
Dict of allowed configurable fields only (never includes credentials or static fields)
"""
# Resolve full config with all hierarchical overrides
resolved_config = await self.resolve_full_config(bank_id, context)
config_dict = asdict(resolved_config)
# SECURITY: Filter to only configurable fields (exclude static/infrastructure)
filtered = {k: v for k, v in config_dict.items() if k in self._configurable_fields}
# SECURITY: Remove ALL credential fields (API keys, base URLs, etc.)
filtered = {k: v for k, v in filtered.items() if k not in self._credential_fields}
# PERMISSIONS: Further filter based on tenant/bank permissions
if self.tenant_extension and context:
try:
allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
if allowed_fields is not None: # None means "allow all"
filtered = {k: v for k, v in filtered.items() if k in allowed_fields}
logger.debug(
f"Applied permission filter for bank {bank_id}: allowed={len(allowed_fields)} fields, "
f"returned={len(filtered)} fields"
)
except Exception as e:
logger.warning(f"Failed to load permissions for bank {bank_id}: {e}")
return filtered
async def _load_bank_config(self, bank_id: str) -> dict[str, Any]:
"""
Load bank config overrides from banks.config JSONB column.
Args:
bank_id: Bank identifier
Returns:
Dict of config overrides (only configurable fields, normalized keys)
"""
try:
async with self.pool.acquire() as conn:
row = await conn.fetchrow(
f"""
SELECT config FROM {fq_table("banks")} WHERE bank_id = $1
""",
bank_id,
)
if row and row["config"]:
config_data = row["config"]
# Handle case where JSONB is returned as JSON string
if isinstance(config_data, str):
config_data = json.loads(config_data)
# Normalize keys (handle both env var format and Python field format)
normalized = normalize_config_dict(config_data)
# Only return overrides for configurable fields
return {k: v for k, v in normalized.items() if k in self._configurable_fields}
except Exception as e:
logger.error(f"Failed to load bank config for {bank_id}: {e}")
return {}
async def update_bank_config(
self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
) -> None:
"""
Update bank configuration overrides (with permission checking).
Args:
bank_id: Bank identifier
updates: Dict of config field names to new values.
Keys can be in env var format (HINDSIGHT_API_LLM_PROVIDER)
or Python field format (llm_provider).
Only configurable fields are allowed.
context: Request context for permission checking
Raises:
ValueError: If attempting to override invalid/disallowed fields
"""
# Normalize keys
normalized_updates = normalize_config_dict(updates)
# SECURITY: Reject credential fields explicitly
credential_attempts = set(normalized_updates.keys()) & self._credential_fields
if credential_attempts:
raise ValueError(
f"Cannot set credential fields via API: {sorted(credential_attempts)}. "
f"Credentials (API keys, base URLs) must be set at server level only."
)
# Validate all fields are configurable
invalid_fields = set(normalized_updates.keys()) - self._configurable_fields
if invalid_fields:
static_fields = HindsightConfig.get_static_fields()
invalid_static = invalid_fields & static_fields
if invalid_static:
raise ValueError(
f"Cannot override static (server-level) fields: {sorted(invalid_static)}. "
f"Only configurable fields can be overridden per-bank. "
f"Configurable fields include: {sorted(list(self._configurable_fields)[:10])}... "
f"(total: {len(self._configurable_fields)} fields)"
)
else:
raise ValueError(
f"Unknown configuration fields: {sorted(invalid_fields)}. "
f"Valid configurable fields: {sorted(list(self._configurable_fields)[:10])}..."
)
# PERMISSIONS: Check tenant/bank permissions
if self.tenant_extension and context:
try:
allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
if allowed_fields is not None: # None means "allow all"
disallowed = set(normalized_updates.keys()) - allowed_fields
if disallowed:
raise ValueError(
f"Not allowed to modify fields: {sorted(disallowed)}. "
f"Your permissions allow: {sorted(list(allowed_fields)[:10])}..."
if allowed_fields
else "Not allowed to modify fields: {sorted(disallowed)}. "
"Your permissions do not allow any config modifications."
)
except ValueError:
raise # Re-raise permission errors
except Exception as e:
logger.warning(f"Failed to check permissions for bank {bank_id}: {e}")
# Continue without permission check (fail open for backward compatibility)
# Merge with existing config (JSONB || operator)
async with self.pool.acquire() as conn:
await conn.execute(
f"""
UPDATE {fq_table("banks")}
SET config = config || $1::jsonb,
updated_at = now()
WHERE bank_id = $2
""",
json.dumps(normalized_updates),
bank_id,
)
logger.info(f"Updated bank config for {bank_id}: {list(normalized_updates.keys())}")
async def reset_bank_config(self, bank_id: str) -> None:
"""
Reset bank configuration to defaults (remove all overrides).
Args:
bank_id: Bank identifier
"""
async with self.pool.acquire() as conn:
await conn.execute(
f"""
UPDATE {fq_table("banks")}
SET config = '{{}}'::jsonb,
updated_at = now()
WHERE bank_id = $1
""",
bank_id,
)
logger.info(f"Reset bank config for {bank_id} to defaults")
-113
View File
@@ -1,113 +0,0 @@
"""
Daemon mode support for Hindsight API.
Provides idle timeout for running as a background daemon.
"""
import asyncio
import logging
import os
import sys
import time
from pathlib import Path
logger = logging.getLogger(__name__)
# Default daemon configuration
DEFAULT_DAEMON_PORT = 8888
DEFAULT_IDLE_TIMEOUT = 0 # 0 = no auto-exit (hindsight-embed passes its own timeout)
# Allow override via environment variable for profile-specific logs
DAEMON_LOG_PATH = Path(os.getenv("HINDSIGHT_API_DAEMON_LOG", str(Path.home() / ".hindsight" / "daemon.log")))
class IdleTimeoutMiddleware:
"""ASGI middleware that tracks activity and exits after idle timeout."""
def __init__(self, app, idle_timeout: int = DEFAULT_IDLE_TIMEOUT):
self.app = app
self.idle_timeout = idle_timeout
self.last_activity = time.time()
self._checker_task = None
async def __call__(self, scope, receive, send):
# Update activity timestamp on each request
self.last_activity = time.time()
await self.app(scope, receive, send)
def start_idle_checker(self):
"""Start the background task that checks for idle timeout."""
self._checker_task = asyncio.create_task(self._check_idle())
async def _check_idle(self):
"""Background task that exits the process after idle timeout."""
# If idle_timeout is 0, don't auto-exit
if self.idle_timeout <= 0:
return
while True:
await asyncio.sleep(30) # Check every 30 seconds
idle_time = time.time() - self.last_activity
if idle_time > self.idle_timeout:
logger.info(f"Idle timeout reached ({self.idle_timeout}s), shutting down daemon")
# Give a moment for any in-flight requests
await asyncio.sleep(1)
# Send SIGTERM to ourselves to trigger graceful shutdown
import signal
os.kill(os.getpid(), signal.SIGTERM)
def daemonize():
"""
Fork the current process into a background daemon.
Uses double-fork technique to properly detach from terminal.
"""
# First fork - detach from parent
try:
pid = os.fork()
if pid > 0:
sys.exit(0)
except OSError as e:
sys.stderr.write(f"fork #1 failed: {e}\n")
sys.exit(1)
# Decouple from parent environment
os.chdir("/")
os.setsid()
os.umask(0)
# Second fork - prevent zombie
pid = os.fork()
if pid > 0:
sys.exit(0)
# Redirect standard file descriptors to log file
DAEMON_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
sys.stdout.flush()
sys.stderr.flush()
# Redirect stdin to /dev/null
with open("/dev/null", "r") as devnull:
os.dup2(devnull.fileno(), sys.stdin.fileno())
# Redirect stdout/stderr to log file
log_fd = open(DAEMON_LOG_PATH, "a")
os.dup2(log_fd.fileno(), sys.stdout.fileno())
os.dup2(log_fd.fileno(), sys.stderr.fileno())
def check_daemon_running(port: int = DEFAULT_DAEMON_PORT) -> bool:
"""Check if a daemon is running and responsive on the given port."""
import socket
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(1)
result = sock.connect_ex(("127.0.0.1", port))
sock.close()
return result == 0
except Exception:
return False
+11 -22
View File
@@ -7,30 +7,24 @@ This package contains all the implementation details of the memory engine:
- Supporting modules: embeddings, cross_encoder, entity_resolver, etc.
"""
from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
from .memory_engine import MemoryEngine
from .db_utils import acquire_with_retry
from .embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
from .llm_wrapper import LLMConfig
from .memory_engine import (
MemoryEngine,
UnqualifiedTableError,
fq_table,
get_current_schema,
validate_sql_schema,
)
from .response_models import MemoryFact, RecallResult, ReflectResult
from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
from .search.trace import (
EntryPoint,
LinkInfo,
NodeVisit,
PruningDecision,
QueryInfo,
SearchPhaseMetrics,
SearchSummary,
SearchTrace,
QueryInfo,
EntryPoint,
NodeVisit,
WeightComponents,
LinkInfo,
PruningDecision,
SearchSummary,
SearchPhaseMetrics,
)
from .search.tracer import SearchTracer
from .llm_wrapper import LLMConfig
from .response_models import RecallResult, ReflectResult, MemoryFact
__all__ = [
"MemoryEngine",
@@ -55,9 +49,4 @@ __all__ = [
"RecallResult",
"ReflectResult",
"MemoryFact",
# Schema safety utilities
"fq_table",
"get_current_schema",
"validate_sql_schema",
"UnqualifiedTableError",
]
@@ -1,5 +0,0 @@
"""Consolidation engine for automatic learning creation from memories."""
from .consolidator import run_consolidation_job
__all__ = ["run_consolidation_job"]
File diff suppressed because it is too large Load Diff
@@ -1,66 +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 (no create/update needed)"""
# Output format — JSON braces escaped as {{ }} so .format() leaves them literal
_BATCH_OUTPUT_FORMAT = """
Output a JSON object with three arrays.
Example (showing the required UUID format for all IDs):
{{"creates": [{{"text": "Alice lives in Berlin", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890", "b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}],
"updates": [{{"text": "Alice works at Acme Corp as a senior engineer", "observation_id": "c3d4e5f6-a7b8-9012-cdef-123456789012", "source_fact_ids": ["d4e5f6a7-b8c9-0123-defa-234567890123"]}}],
"deletes": [{{"observation_id": "e5f6a7b8-c9d0-1234-efab-345678901234"}}]}}
Rules:
- "source_fact_ids": copy the EXACT UUID strings shown in brackets [uuid] from NEW FACTS — never use integers or positions.
- "observation_id": copy the EXACT "id" UUID string from EXISTING OBSERVATIONS.
- One create/update may reference multiple facts when they jointly support the observation.
- "deletes": only when an observation is directly superseded or contradicted by new facts.
- Do NOT include "tags" — handled automatically.
- Return {{"creates": [], "updates": [], "deletes": []}} if nothing durable is found."""
def build_batch_consolidation_prompt(observations_mission: str | None = None) -> str:
"""
Build the consolidation prompt for batch mode (multiple facts per LLM call).
The mission defines *what* to track (customisable per bank).
Processing rules and output format are always present regardless of mission.
"""
mission = observations_mission or _DEFAULT_MISSION
return (
"You are a memory consolidation system. Synthesize facts into observations "
"and merge with existing observations when appropriate.\n\n"
f"## MISSION\n{mission}\n\n"
f"{_PROCESSING_RULES}" + _BATCH_DATA_SECTION + _BATCH_OUTPUT_FORMAT
)
File diff suppressed because it is too large Load Diff
@@ -1,284 +0,0 @@
"""
Database connection budget management.
Limits concurrent database connections per operation to prevent
a single operation (e.g., recall with parallel queries) from
exhausting the connection pool.
"""
import asyncio
import logging
import uuid
from contextlib import asynccontextmanager
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, AsyncIterator
if TYPE_CHECKING:
import asyncpg
logger = logging.getLogger(__name__)
@dataclass
class OperationBudget:
"""
Tracks connection budget for a single operation.
Each operation gets a semaphore limiting its concurrent connections.
"""
operation_id: str
max_connections: int
semaphore: asyncio.Semaphore = field(init=False)
active_count: int = field(default=0, init=False)
def __post_init__(self):
self.semaphore = asyncio.Semaphore(self.max_connections)
class ConnectionBudgetManager:
"""
Manages per-operation connection budgets.
Usage:
manager = ConnectionBudgetManager(default_budget=4)
# Start an operation
async with manager.operation(max_connections=2) as op:
# Acquire connections within the budget
async with op.acquire(pool) as conn:
await conn.fetch(...)
# Multiple connections respect the budget
async with op.acquire(pool) as conn1, op.acquire(pool) as conn2:
# At most 2 concurrent connections for this operation
...
"""
def __init__(self, default_budget: int = 4):
"""
Initialize the budget manager.
Args:
default_budget: Default max connections per operation
"""
self.default_budget = default_budget
self._operations: dict[str, OperationBudget] = {}
self._lock = asyncio.Lock()
@asynccontextmanager
async def operation(
self,
max_connections: int | None = None,
operation_id: str | None = None,
) -> AsyncIterator["BudgetedOperation"]:
"""
Create a budgeted operation context.
Args:
max_connections: Max concurrent connections for this operation.
Defaults to manager's default_budget.
operation_id: Optional custom operation ID. Auto-generated if not provided.
Yields:
BudgetedOperation context for acquiring connections
"""
op_id = operation_id or f"op-{uuid.uuid4().hex[:12]}"
budget = max_connections or self.default_budget
async with self._lock:
if op_id in self._operations:
raise ValueError(f"Operation {op_id} already exists")
self._operations[op_id] = OperationBudget(op_id, budget)
try:
yield BudgetedOperation(self, op_id)
finally:
async with self._lock:
self._operations.pop(op_id, None)
def _get_budget(self, operation_id: str) -> OperationBudget:
"""Get budget for an operation (internal use)."""
budget = self._operations.get(operation_id)
if not budget:
raise ValueError(f"Operation {operation_id} not found")
return budget
class BudgetedOperation:
"""
A single operation with connection budget.
Provides methods to acquire connections within the budget.
"""
def __init__(self, manager: ConnectionBudgetManager, operation_id: str):
self._manager = manager
self.operation_id = operation_id
@property
def budget(self) -> OperationBudget:
"""Get the budget for this operation."""
return self._manager._get_budget(self.operation_id)
@asynccontextmanager
async def acquire(self, pool: "asyncpg.Pool") -> AsyncIterator["asyncpg.Connection"]:
"""
Acquire a connection within the operation's budget.
Blocks if the operation has reached its connection limit.
Args:
pool: asyncpg connection pool
Yields:
Database connection
"""
budget = self.budget
async with budget.semaphore:
budget.active_count += 1
conn = await pool.acquire()
try:
yield conn
finally:
budget.active_count -= 1
await pool.release(conn)
def wrap_pool(self, pool: "asyncpg.Pool") -> "BudgetedPool":
"""
Wrap a pool with this operation's budget.
The returned BudgetedPool can be passed to functions expecting a pool,
and all acquire() calls will be limited by this operation's budget.
Args:
pool: asyncpg connection pool to wrap
Returns:
BudgetedPool that limits connections to this operation's budget
"""
return BudgetedPool(pool, self)
async def acquire_many(
self,
pool: "asyncpg.Pool",
count: int,
) -> AsyncIterator[list["asyncpg.Connection"]]:
"""
Acquire multiple connections within the budget.
Note: This acquires connections sequentially to respect the budget.
For parallel acquisition, use multiple acquire() calls with asyncio.gather().
Args:
pool: asyncpg connection pool
count: Number of connections to acquire
Yields:
List of database connections
"""
connections = []
try:
for _ in range(count):
conn = await pool.acquire()
connections.append(conn)
yield connections
finally:
for conn in connections:
await pool.release(conn)
# Global default manager instance
_default_manager: ConnectionBudgetManager | None = None
def get_budget_manager(default_budget: int = 4) -> ConnectionBudgetManager:
"""
Get or create the global budget manager.
Args:
default_budget: Default max connections per operation
Returns:
Global ConnectionBudgetManager instance
"""
global _default_manager
if _default_manager is None:
_default_manager = ConnectionBudgetManager(default_budget=default_budget)
return _default_manager
@asynccontextmanager
async def budgeted_operation(
max_connections: int | None = None,
operation_id: str | None = None,
default_budget: int = 4,
) -> AsyncIterator[BudgetedOperation]:
"""
Convenience function to create a budgeted operation.
Args:
max_connections: Max concurrent connections for this operation
operation_id: Optional custom operation ID
default_budget: Default budget if manager not yet created
Yields:
BudgetedOperation context
Example:
async with budgeted_operation(max_connections=2) as op:
async with op.acquire(pool) as conn:
await conn.fetch(...)
"""
manager = get_budget_manager(default_budget)
async with manager.operation(max_connections, operation_id) as op:
yield op
class BudgetedPool:
"""
A pool wrapper that limits concurrent connection acquisitions.
This can be passed to functions expecting a pool, and acquire()
calls will be limited by the budget semaphore.
Usage:
async with budgeted_operation(max_connections=4) as op:
budgeted_pool = op.wrap_pool(pool)
# Pass budgeted_pool to functions that expect a pool
await some_function(budgeted_pool, ...)
"""
def __init__(self, pool: "asyncpg.Pool", operation: BudgetedOperation):
self._pool = pool
self._operation = operation
async def acquire(self) -> "asyncpg.Connection":
"""
Acquire a connection within the budget.
Note: Caller must release the connection when done.
Prefer using as context manager via acquire_with_retry or op.acquire().
"""
budget = self._operation.budget
await budget.semaphore.acquire()
budget.active_count += 1
try:
return await self._pool.acquire()
except Exception:
budget.active_count -= 1
budget.semaphore.release()
raise
async def release(self, conn: "asyncpg.Connection") -> None:
"""Release a connection back to the pool."""
budget = self._operation.budget
try:
await self._pool.release(conn)
finally:
budget.active_count -= 1
budget.semaphore.release()
def __getattr__(self, name):
"""Proxy other attributes to the underlying pool."""
return getattr(self._pool, name)
+4 -17
View File
@@ -1,11 +1,9 @@
"""
Database utility functions for connection management with retry logic.
"""
import asyncio
import logging
from contextlib import asynccontextmanager
import asyncpg
logger = logging.getLogger(__name__)
@@ -20,7 +18,6 @@ RETRYABLE_EXCEPTIONS = (
asyncpg.exceptions.InterfaceError,
asyncpg.exceptions.ConnectionDoesNotExistError,
asyncpg.exceptions.TooManyConnectionsError,
asyncpg.exceptions.DeadlockDetectedError,
OSError,
ConnectionError,
asyncio.TimeoutError,
@@ -57,14 +54,16 @@ async def retry_with_backoff(
except retryable_exceptions as e:
last_exception = e
if attempt < max_retries:
delay = min(base_delay * (2**attempt), max_delay)
delay = min(base_delay * (2 ** attempt), max_delay)
logger.warning(
f"Database operation failed (attempt {attempt + 1}/{max_retries + 1}): {e}. "
f"Retrying in {delay:.1f}s..."
)
await asyncio.sleep(delay)
else:
logger.error(f"Database operation failed after {max_retries + 1} attempts: {e}")
logger.error(
f"Database operation failed after {max_retries + 1} attempts: {e}"
)
raise last_exception
@@ -84,22 +83,10 @@ async def acquire_with_retry(pool: asyncpg.Pool, max_retries: int = DEFAULT_MAX_
Yields:
An asyncpg connection
"""
import time
start = time.time()
async def acquire():
return await pool.acquire()
conn = await retry_with_backoff(acquire, max_retries=max_retries)
acquire_time = time.time() - start
# Log slow connection acquisitions (indicates pool contention)
if acquire_time > 0.05: # 50ms threshold
pool_size = pool.get_size()
pool_free = pool.get_idle_size()
logger.warning(f"[DB POOL] Slow acquire: {acquire_time:.3f}s | size={pool_size}, idle={pool_free}")
try:
yield conn
finally:
@@ -1,5 +0,0 @@
"""Directives module for hard rules injected into prompts."""
from .models import Directive
__all__ = ["Directive"]
@@ -1,37 +0,0 @@
"""Pydantic models for directives."""
from datetime import datetime, timezone
from uuid import UUID
from pydantic import BaseModel, Field
class Directive(BaseModel):
"""A directive is a hard rule injected into prompts.
Directives are user-defined rules that guide agent behavior. Unlike mental models
which are automatically consolidated from memories, directives are explicit
instructions that are always included in relevant prompts.
Examples:
- "Always respond in formal English"
- "Never share personal data with third parties"
- "Prefer conservative investment recommendations"
"""
id: UUID = Field(description="Unique identifier")
bank_id: str = Field(description="Bank this directive belongs to")
name: str = Field(description="Human-readable name")
content: str = Field(description="The directive text to inject into prompts")
priority: int = Field(default=0, description="Higher priority directives are injected first")
is_active: bool = Field(default=True, description="Whether this directive is currently active")
tags: list[str] = Field(default_factory=list, description="Tags for filtering")
created_at: datetime = Field(
default_factory=lambda: datetime.now(timezone.utc), description="When this directive was created"
)
updated_at: datetime = Field(
default_factory=lambda: datetime.now(timezone.utc), description="When this directive was last updated"
)
class Config:
from_attributes = True

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