feat(knowledgebase): replace litellm-pgvector connector with memory-retrieval
Per docs/research/langchain-pgvector-vs-litellm-pgvector.md (issue #25): the vendored litellm-pgvector connector (793 lines, Prisma migrations, a fragile git-context build) is replaced by a ~90-line FastAPI service (services/memory-retrieval/) wrapping langchain_postgres.PGVector directly against pgvector-db. Same gateway boundary — it still calls litellm for embeddings, nothing talks to Postgres or the model directly except this service. - New services/memory-retrieval/ (main.py, Dockerfile, requirements.txt): POST /ingest, POST /query, GET /health. - docker-compose.yml: litellm-pgvector service replaced by memory-retrieval; pgvector-db and embedding-server untouched. - litellm-config.yaml: vector_store_registry block removed (no langchain_postgres provider exists to register against; callers query memory-retrieval directly instead of an in-band file_search tool call — that mechanism was never confirmed working per issue #24 anyway). - scripts/ingest-memory.sh rewritten for the new /ingest endpoint (same per-line chunking, no dedup). - .env vars renamed: LITELLM_PGVECTOR_API_KEY/LITELLM_PGVECTOR_EMBEDDING_KEY -> MEMORY_RETRIEVAL_API_KEY/MEMORY_RETRIEVAL_EMBEDDING_KEY. - vendor/litellm-pgvector/ removed entirely. - docs/memory-knowledgebase.md updated for the new setup/query flow. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
@@ -1,4 +0,0 @@
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.env
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__pycache__/*
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venv/*
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venv
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Vendored
-32
@@ -1,32 +0,0 @@
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FROM python:3.11-slim
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# Set environment variables
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ENV PYTHONDONTWRITEBYTECODE=1
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ENV PYTHONUNBUFFERED=1
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ENV PYTHONPATH=/app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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build-essential \
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curl \
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postgresql-client \
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&& rm -rf /var/lib/apt/lists/*
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# Set work directory
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WORKDIR /app
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# Install Python dependencies
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy project
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COPY . .
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# Generate Prisma client
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RUN prisma generate
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# Expose port
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EXPOSE 8000
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# Command to run the application
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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Vendored
-21
@@ -1,21 +0,0 @@
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MIT License
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Copyright (c) 2025 Berri AI
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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Vendored
-388
@@ -1,388 +0,0 @@
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# OpenAI Vector Stores API with PGVector
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A FastAPI application that provides OpenAI-compatible vector store endpoints using PGVector and LiteLLM proxy for embeddings.
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## Features
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- 🔌 OpenAI-compatible API endpoints
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- 🗄️ PGVector for efficient vector storage and similarity search
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- 🎛️ Configurable database field mappings
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- 🔄 LiteLLM proxy integration for any embedding model
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- 🐳 Docker support
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- ⚡ FastAPI with async support
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## API Endpoints
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### 1. Create Vector Store
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```bash
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curl -X POST \
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http://localhost:8000/v1/vector_stores \
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-H "Authorization: Bearer your-api-key" \
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-H "Content-Type: application/json" \
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-d '{
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"name": "Support FAQ"
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}'
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```
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### 2. List Vector Stores
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```bash
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# List all vector stores
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curl -X GET \
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http://localhost:8000/v1/vector_stores \
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-H "Authorization: Bearer your-api-key"
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# List with pagination (limit and after parameters)
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curl -X GET \
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"http://localhost:8000/v1/vector_stores?limit=10&after=vs_abc123" \
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-H "Authorization: Bearer your-api-key"
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```
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### 3. Add Single Embedding to Vector Store
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```bash
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curl -X POST \
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http://localhost:8000/v1/vector_stores/vs_abc123/embeddings \
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-H "Authorization: Bearer your-api-key" \
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-H "Content-Type: application/json" \
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-d '{
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"content": "Our return policy allows returns within 30 days of purchase.",
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"embedding": [0.1, 0.2, 0.3, ...],
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"metadata": {
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"category": "returns",
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"source": "faq",
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"id": "return_policy_1"
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}
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}'
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```
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### 4. Add Multiple Embeddings (Batch)
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```bash
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curl -X POST \
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http://localhost:8000/v1/vector_stores/vs_abc123/embeddings/batch \
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-H "Authorization: Bearer your-api-key" \
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-H "Content-Type: application/json" \
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-d '{
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"embeddings": [
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{
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"content": "Our return policy allows returns within 30 days of purchase.",
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"embedding": [0.1, 0.2, 0.3, ...],
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"metadata": {"category": "returns"}
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},
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{
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"content": "Shipping is free for orders over $50.",
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"embedding": [0.4, 0.5, 0.6, ...],
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"metadata": {"category": "shipping"}
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}
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]
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}'
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```
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### 5. Search Vector Store
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```bash
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curl -X POST \
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http://localhost:8000/v1/vector_stores/vs_abc123/search \
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-H "Authorization: Bearer your-api-key" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "What is the return policy?",
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"limit": 20,
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"filters": {"category": "support"}
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}'
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```
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## Configuration
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### Environment Variables
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Create a `.env` file with the following configuration:
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```bash
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# Database Configuration
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DATABASE_URL="postgresql://username:password@localhost:5432/vectordb?schema=public"
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# API Configuration
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SERVER_API_KEY="your-api-key-here"
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# Server Configuration
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HOST="0.0.0.0"
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PORT=8000
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# LiteLLM Proxy Configuration
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EMBEDDING__MODEL="text-embedding-ada-002"
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EMBEDDING__BASE_URL="http://localhost:4000"
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EMBEDDING__API_KEY="sk-1234"
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EMBEDDING__DIMENSIONS=1536
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# Database Field Configuration (optional)
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DB_FIELDS__ID_FIELD="id"
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DB_FIELDS__CONTENT_FIELD="content"
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DB_FIELDS__METADATA_FIELD="metadata"
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DB_FIELDS__EMBEDDING_FIELD="embedding"
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DB_FIELDS__VECTOR_STORE_ID_FIELD="vector_store_id"
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DB_FIELDS__CREATED_AT_FIELD="created_at"
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```
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### Database Field Mapping
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You can customize the database field names by setting environment variables:
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- `DB_FIELDS__ID_FIELD` - Primary key field (default: "id")
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- `DB_FIELDS__CONTENT_FIELD` - Text content field (default: "content")
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- `DB_FIELDS__METADATA_FIELD` - JSON metadata field (default: "metadata")
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- `DB_FIELDS__EMBEDDING_FIELD` - Vector embedding field (default: "embedding")
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- `DB_FIELDS__VECTOR_STORE_ID_FIELD` - Foreign key field (default: "vector_store_id")
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- `DB_FIELDS__CREATED_AT_FIELD` - Timestamp field (default: "created_at")
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### LiteLLM Proxy Configuration
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The application uses LiteLLM proxy for embeddings. Configure it with:
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- `EMBEDDING__MODEL` - Model name (e.g., "text-embedding-ada-002")
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- `EMBEDDING__BASE_URL` - LiteLLM proxy URL (e.g., "http://localhost:4000")
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- `EMBEDDING__API_KEY` - LiteLLM proxy API key
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- `EMBEDDING__DIMENSIONS` - Embedding dimensions (default: 1536)
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## Setup and Installation
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### 1. Install Dependencies
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```bash
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pip install -r requirements.txt
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```
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### 2. Database Setup
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```bash
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# Generate Prisma client
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prisma generate
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# Run database migrations
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prisma db push
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```
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### 3. Set up LiteLLM Proxy
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Start LiteLLM proxy pointing to your preferred embedding model:
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```bash
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# Example: Start LiteLLM proxy for OpenAI
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litellm --model text-embedding-ada-002 --port 4000
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```
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### 4. Run the Application
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```bash
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python main.py
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```
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Or using uvicorn directly:
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```bash
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uvicorn main:app --host 0.0.0.0 --port 8000 --reload
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```
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## Docker Deployment
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### Build and run with Docker:
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```bash
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# Build the image
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docker build -t vector-store-api .
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# Run the container
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docker run -p 8000:8000 --env-file .env vector-store-api
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```
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## Database Schema
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The application uses two main tables:
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### vector_stores
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- `id` (string, primary key)
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- `name` (string)
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- `file_counts` (json)
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- `status` (string)
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- `usage_bytes` (integer)
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- `created_at` (timestamp)
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- `expires_after` (json, optional)
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- `expires_at` (timestamp, optional)
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- `last_active_at` (timestamp, optional)
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- `metadata` (json, optional)
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### embeddings
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- `id` (string, primary key)
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- `vector_store_id` (string, foreign key)
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- `content` (string)
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- `embedding` (vector(1536))
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- `metadata` (json, optional)
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- `created_at` (timestamp)
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## Supported Models
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Any embedding model supported by LiteLLM proxy can be used. Examples:
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- OpenAI: `text-embedding-ada-002`, `text-embedding-3-small`, `text-embedding-3-large`
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- Cohere: `embed-english-v3.0`, `embed-multilingual-v3.0`
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- Voyage: `voyage-2`, `voyage-large-2`
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- And many more...
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## API Response Format
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### Vector Store Response
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```json
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{
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"id": "vs_abc123",
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"object": "vector_store",
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"created_at": 1699024800,
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"name": "Support FAQ",
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"usage_bytes": 0,
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"file_counts": {
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"in_progress": 0,
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"completed": 0,
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"failed": 0,
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"cancelled": 0,
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"total": 0
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},
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"status": "completed",
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"metadata": {}
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}
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```
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### Vector Store List Response
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```json
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{
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"object": "list",
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"data": [
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{
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"id": "vs_abc123",
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"object": "vector_store",
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"created_at": 1699024800,
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"name": "Support FAQ",
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"usage_bytes": 1024,
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"file_counts": {"completed": 5, "total": 5},
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"status": "completed",
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"metadata": {}
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}
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],
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"first_id": "vs_abc123",
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"last_id": "vs_def456",
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"has_more": false
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}
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```
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|
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### Search Response
|
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```json
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{
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"object": "vector_store.search",
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"data": [
|
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{
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"id": "emb_123",
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"content": "Return policy text...",
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"score": 0.95,
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"metadata": {"category": "support"}
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}
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],
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"usage": {
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"total_tokens": 1
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}
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}
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```
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|
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## Example Search Request
|
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|
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```bash
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curl -X POST \
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http://localhost:8000/v1/vector_stores/vs_support_faq/search \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "How do I return an item?",
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"limit": 5,
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"return_metadata": true
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}'
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```
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|
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## Health Check
|
||||
|
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```bash
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curl http://localhost:8000/health
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```
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|
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## Migrating Existing Data
|
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|
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If you have an existing database with embeddings and content, you can easily migrate using the embedding APIs:
|
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|
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### 1. Create Vector Store
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First, create a vector store for your data:
|
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|
||||
```bash
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curl -X POST \
|
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http://localhost:8000/v1/vector_stores \
|
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-H "Authorization: Bearer your-api-key" \
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-H "Content-Type: application/json" \
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-d '{
|
||||
"name": "Migrated Data",
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"metadata": {"source": "legacy_system"}
|
||||
}'
|
||||
```
|
||||
|
||||
### 2. Batch Insert Embeddings
|
||||
Use the batch endpoint to efficiently insert multiple embeddings:
|
||||
|
||||
```bash
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curl -X POST \
|
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http://localhost:8000/v1/vector_stores/vs_your_id/embeddings/batch \
|
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-H "Authorization: Bearer your-api-key" \
|
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-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"embeddings": [
|
||||
{
|
||||
"content": "Your text content here",
|
||||
"embedding": [0.1, 0.2, 0.3, ...1536 dimensions...],
|
||||
"metadata": {"source_id": "doc_123", "category": "support"}
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
### 3. Migration Script Example
|
||||
|
||||
Here's a Python script example for migrating from an existing database:
|
||||
|
||||
```python
|
||||
import psycopg2
|
||||
import requests
|
||||
import json
|
||||
|
||||
# Connect to your existing database
|
||||
conn = psycopg2.connect("your_existing_db_url")
|
||||
cur = conn.cursor()
|
||||
|
||||
# Fetch existing data
|
||||
cur.execute("SELECT content, embedding, metadata FROM your_table")
|
||||
rows = cur.fetchall()
|
||||
|
||||
# Prepare batch data
|
||||
embeddings = []
|
||||
for content, embedding, metadata in rows:
|
||||
embeddings.append({
|
||||
"content": content,
|
||||
"embedding": embedding.tolist(), # Convert numpy array to list
|
||||
"metadata": metadata or {}
|
||||
})
|
||||
|
||||
# Send batch to API
|
||||
response = requests.post(
|
||||
"http://localhost:8000/v1/vector_stores/your_vector_store_id/embeddings/batch",
|
||||
headers={
|
||||
"Authorization": "Bearer your-api-key",
|
||||
"Content-Type": "application/json"
|
||||
},
|
||||
json={"embeddings": embeddings}
|
||||
)
|
||||
|
||||
print(f"Migrated {len(embeddings)} embeddings")
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
MIT License
|
||||
Vendored
-8
@@ -1,8 +0,0 @@
|
||||
Vendored from https://github.com/BerriAI/litellm-pgvector at commit
|
||||
`b553f84a32f580b4303297df5567f25912b59d93` (main, 2026-09-02) — no changes
|
||||
made to the source. See `docker-compose.yml`'s `litellm-pgvector` service
|
||||
comment for why this is vendored instead of built from a remote git context.
|
||||
|
||||
To update: `git clone https://github.com/BerriAI/litellm-pgvector.git`
|
||||
somewhere, copy everything except `.git/` over this directory, update the
|
||||
commit hash above, and run `./scripts/update.sh`.
|
||||
Vendored
-60
@@ -1,60 +0,0 @@
|
||||
from typing import Dict, Optional
|
||||
from pydantic import BaseModel
|
||||
from pydantic_settings import BaseSettings
|
||||
|
||||
|
||||
class DatabaseFieldConfig(BaseModel):
|
||||
"""Configuration for database field mappings"""
|
||||
id_field: str = "id"
|
||||
content_field: str = "content"
|
||||
metadata_field: str = "metadata"
|
||||
embedding_field: str = "embedding"
|
||||
vector_store_id_field: str = "vector_store_id"
|
||||
created_at_field: str = "created_at"
|
||||
|
||||
|
||||
class EmbeddingConfig(BaseModel):
|
||||
"""Configuration for embedding generation via LiteLLM proxy"""
|
||||
model: str = "text-embedding-ada-002"
|
||||
base_url: str = "http://localhost:4000" # LiteLLM proxy URL
|
||||
api_key: str = "sk-1234" # LiteLLM proxy API key
|
||||
dimensions: int = 1536
|
||||
|
||||
|
||||
class Settings(BaseSettings):
|
||||
"""Application settings"""
|
||||
# Database configuration
|
||||
database_url: str = "postgresql://username:password@localhost:5432/vectordb?schema=public"
|
||||
|
||||
# API configuration
|
||||
server_api_key: str = "your-api-key-here"
|
||||
port: int = 8000
|
||||
host: str = "0.0.0.0"
|
||||
|
||||
# Database field mappings
|
||||
db_fields: DatabaseFieldConfig = DatabaseFieldConfig()
|
||||
|
||||
# Embedding configuration
|
||||
embedding: EmbeddingConfig = EmbeddingConfig()
|
||||
|
||||
class Config:
|
||||
env_file = ".env"
|
||||
env_nested_delimiter = "__"
|
||||
case_sensitive = False
|
||||
|
||||
# Allow environment variables like:
|
||||
# DB_FIELDS__ID_FIELD=custom_id
|
||||
# EMBEDDING__MODEL=text-embedding-3-small
|
||||
# EMBEDDING__API_BASE=https://api.openai.com/v1
|
||||
|
||||
@property
|
||||
def table_names(self) -> Dict[str, str]:
|
||||
"""Get table names"""
|
||||
return {
|
||||
"vector_stores": "vector_stores",
|
||||
"embeddings": "embeddings"
|
||||
}
|
||||
|
||||
|
||||
# Global settings instance
|
||||
settings = Settings()
|
||||
-90
@@ -1,90 +0,0 @@
|
||||
from typing import List, Optional
|
||||
from config import settings, EmbeddingConfig
|
||||
from litellm.types.utils import EmbeddingResponse
|
||||
import litellm
|
||||
import logging
|
||||
|
||||
class EmbeddingService:
|
||||
"""Service for generating embeddings using OpenAI SDK pointed at LiteLLM proxy"""
|
||||
|
||||
def __init__(self, config: Optional[EmbeddingConfig] = None):
|
||||
self.config = config or settings.embedding
|
||||
|
||||
|
||||
|
||||
async def generate_embedding(self, text: str) -> List[float]:
|
||||
"""
|
||||
Generate embedding for a single text using LiteLLM proxy
|
||||
|
||||
Args:
|
||||
text: Text to embed
|
||||
|
||||
Returns:
|
||||
List of floats representing the embedding vector
|
||||
"""
|
||||
try:
|
||||
response: EmbeddingResponse = await litellm.aembedding(
|
||||
model=self.config.model,
|
||||
input=[text],
|
||||
api_base=self.config.base_url,
|
||||
api_key=self.config.api_key
|
||||
)
|
||||
logging.debug(f"Embedding response: {response}")
|
||||
|
||||
# Extract embedding from response
|
||||
embedding = response.data[0]["embedding"]
|
||||
|
||||
# Validate embedding dimensions
|
||||
if len(embedding) != self.config.dimensions:
|
||||
raise ValueError(
|
||||
f"Expected embedding dimension {self.config.dimensions}, "
|
||||
f"got {len(embedding)}"
|
||||
)
|
||||
|
||||
return embedding
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to generate embedding: {str(e)}")
|
||||
|
||||
async def generate_embeddings(self, texts: List[str]) -> List[List[float]]:
|
||||
"""
|
||||
Generate embeddings for multiple texts
|
||||
|
||||
Args:
|
||||
texts: List of texts to embed
|
||||
|
||||
Returns:
|
||||
List of embedding vectors
|
||||
"""
|
||||
try:
|
||||
# Generate embeddings using LiteLLM
|
||||
response = await litellm.aembedding(
|
||||
model=self.config.model,
|
||||
input=texts,
|
||||
api_base=self.config.base_url,
|
||||
api_key=self.config.api_key
|
||||
)
|
||||
|
||||
# Extract embeddings from response
|
||||
embeddings = [item.embedding for item in response.data]
|
||||
|
||||
# Validate embedding dimensions
|
||||
for i, embedding in enumerate(embeddings):
|
||||
if len(embedding) != self.config.dimensions:
|
||||
raise ValueError(
|
||||
f"Expected embedding dimension {self.config.dimensions} for text {i}, "
|
||||
f"got {len(embedding)}"
|
||||
)
|
||||
|
||||
return embeddings
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to generate embeddings: {str(e)}")
|
||||
|
||||
def update_config(self, new_config: EmbeddingConfig):
|
||||
"""Update the embedding configuration"""
|
||||
self.config = new_config
|
||||
|
||||
|
||||
# Global embedding service instance
|
||||
embedding_service = EmbeddingService()
|
||||
Vendored
-522
@@ -1,522 +0,0 @@
|
||||
import os
|
||||
import asyncio
|
||||
import time
|
||||
from typing import List, Optional
|
||||
from fastapi import FastAPI, HTTPException, Depends, Header
|
||||
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from prisma import Prisma
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from models import (
|
||||
VectorStoreCreateRequest,
|
||||
VectorStoreResponse,
|
||||
VectorStoreSearchRequest,
|
||||
VectorStoreSearchResponse,
|
||||
SearchResult,
|
||||
EmbeddingCreateRequest,
|
||||
EmbeddingResponse,
|
||||
EmbeddingBatchCreateRequest,
|
||||
EmbeddingBatchCreateResponse,
|
||||
VectorStoreListResponse,
|
||||
ContentChunk
|
||||
)
|
||||
from config import settings
|
||||
from embedding_service import embedding_service
|
||||
|
||||
load_dotenv()
|
||||
|
||||
app = FastAPI(
|
||||
title="OpenAI Vector Stores API",
|
||||
description="OpenAI-compatible Vector Stores API using PGVector",
|
||||
version="1.0.0"
|
||||
)
|
||||
|
||||
# CORS middleware
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
# Global Prisma client
|
||||
db = Prisma()
|
||||
|
||||
security = HTTPBearer()
|
||||
|
||||
|
||||
async def get_api_key(credentials: HTTPAuthorizationCredentials = Depends(security)):
|
||||
"""Validate API key from Authorization header"""
|
||||
expected_key = settings.server_api_key
|
||||
if credentials.credentials != expected_key:
|
||||
raise HTTPException(status_code=401, detail="Invalid API key")
|
||||
return credentials.credentials
|
||||
|
||||
|
||||
@app.on_event("startup")
|
||||
async def startup():
|
||||
"""Connect to database on startup"""
|
||||
await db.connect()
|
||||
|
||||
|
||||
@app.on_event("shutdown")
|
||||
async def shutdown():
|
||||
"""Disconnect from database on shutdown"""
|
||||
await db.disconnect()
|
||||
|
||||
|
||||
async def generate_query_embedding(query: str) -> List[float]:
|
||||
"""
|
||||
Generate an embedding for the query using LiteLLM
|
||||
"""
|
||||
return await embedding_service.generate_embedding(query)
|
||||
|
||||
|
||||
@app.post("/v1/vector_stores", response_model=VectorStoreResponse)
|
||||
async def create_vector_store(
|
||||
request: VectorStoreCreateRequest,
|
||||
api_key: str = Depends(get_api_key)
|
||||
):
|
||||
"""
|
||||
Create a new vector store.
|
||||
"""
|
||||
try:
|
||||
# Use raw SQL to insert the vector store with configurable table/field names
|
||||
vector_store_table = settings.table_names["vector_stores"]
|
||||
|
||||
result = await db.query_raw(
|
||||
f"""
|
||||
INSERT INTO {vector_store_table} (id, name, file_counts, status, usage_bytes, expires_after, metadata, created_at)
|
||||
VALUES (gen_random_uuid(), $1, $2, $3, $4, $5, $6, NOW())
|
||||
RETURNING id, name, file_counts, status, usage_bytes, expires_after, expires_at, last_active_at, metadata,
|
||||
EXTRACT(EPOCH FROM created_at)::bigint as created_at_timestamp
|
||||
""",
|
||||
request.name,
|
||||
{"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0},
|
||||
"completed",
|
||||
0,
|
||||
request.expires_after,
|
||||
request.metadata or {}
|
||||
)
|
||||
|
||||
if not result:
|
||||
raise HTTPException(status_code=500, detail="Failed to create vector store")
|
||||
|
||||
vector_store = result[0]
|
||||
|
||||
# Convert to response format
|
||||
created_at = int(vector_store["created_at_timestamp"])
|
||||
expires_at = int(vector_store["expires_at"].timestamp()) if vector_store.get("expires_at") else None
|
||||
last_active_at = int(vector_store["last_active_at"].timestamp()) if vector_store.get("last_active_at") else None
|
||||
|
||||
return VectorStoreResponse(
|
||||
id=vector_store["id"],
|
||||
created_at=created_at,
|
||||
name=vector_store["name"],
|
||||
usage_bytes=vector_store["usage_bytes"] or 0,
|
||||
file_counts=vector_store["file_counts"] or {"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0},
|
||||
status=vector_store["status"],
|
||||
expires_after=vector_store["expires_after"],
|
||||
expires_at=expires_at,
|
||||
last_active_at=last_active_at,
|
||||
metadata=vector_store["metadata"]
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=f"Failed to create vector store: {str(e)}")
|
||||
|
||||
|
||||
@app.get("/v1/vector_stores", response_model=VectorStoreListResponse)
|
||||
async def list_vector_stores(
|
||||
limit: Optional[int] = 20,
|
||||
after: Optional[str] = None,
|
||||
before: Optional[str] = None,
|
||||
api_key: str = Depends(get_api_key)
|
||||
):
|
||||
"""
|
||||
List vector stores with optional pagination.
|
||||
"""
|
||||
try:
|
||||
limit = min(limit or 20, 100) # Cap at 100 results
|
||||
|
||||
vector_store_table = settings.table_names["vector_stores"]
|
||||
|
||||
# Build base query
|
||||
base_query = f"""
|
||||
SELECT id, name, file_counts, status, usage_bytes, expires_after, expires_at, last_active_at, metadata,
|
||||
EXTRACT(EPOCH FROM created_at)::bigint as created_at_timestamp
|
||||
FROM {vector_store_table}
|
||||
"""
|
||||
|
||||
# Add pagination conditions
|
||||
conditions = []
|
||||
params = []
|
||||
param_count = 1
|
||||
|
||||
if after:
|
||||
conditions.append(f"id > ${param_count}")
|
||||
params.append(after)
|
||||
param_count += 1
|
||||
|
||||
if before:
|
||||
conditions.append(f"id < ${param_count}")
|
||||
params.append(before)
|
||||
param_count += 1
|
||||
|
||||
if conditions:
|
||||
base_query += " WHERE " + " AND ".join(conditions)
|
||||
|
||||
# Add ordering and limit
|
||||
final_query = base_query + f" ORDER BY created_at DESC LIMIT {limit + 1}"
|
||||
|
||||
# Execute query
|
||||
results = await db.query_raw(final_query, *params)
|
||||
|
||||
# Check if there are more results
|
||||
has_more = len(results) > limit
|
||||
if has_more:
|
||||
results = results[:limit] # Remove extra result
|
||||
|
||||
# Convert to response format
|
||||
vector_stores = []
|
||||
for row in results:
|
||||
created_at = int(row["created_at_timestamp"])
|
||||
expires_at = int(row["expires_at"].timestamp()) if row.get("expires_at") else None
|
||||
last_active_at = int(row["last_active_at"].timestamp()) if row.get("last_active_at") else None
|
||||
|
||||
vector_store = VectorStoreResponse(
|
||||
id=row["id"],
|
||||
created_at=created_at,
|
||||
name=row["name"],
|
||||
usage_bytes=row["usage_bytes"] or 0,
|
||||
file_counts=row["file_counts"] or {"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0},
|
||||
status=row["status"],
|
||||
expires_after=row["expires_after"],
|
||||
expires_at=expires_at,
|
||||
last_active_at=last_active_at,
|
||||
metadata=row["metadata"]
|
||||
)
|
||||
vector_stores.append(vector_store)
|
||||
|
||||
# Determine first_id and last_id
|
||||
first_id = vector_stores[0].id if vector_stores else None
|
||||
last_id = vector_stores[-1].id if vector_stores else None
|
||||
|
||||
return VectorStoreListResponse(
|
||||
data=vector_stores,
|
||||
first_id=first_id,
|
||||
last_id=last_id,
|
||||
has_more=has_more
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
raise HTTPException(status_code=500, detail=f"Failed to list vector stores: {str(e)}")
|
||||
|
||||
|
||||
@app.post("/v1/vector_stores/{vector_store_id}/search", response_model=VectorStoreSearchResponse)
|
||||
@app.post("/vector_stores/{vector_store_id}/search", response_model=VectorStoreSearchResponse)
|
||||
async def search_vector_store(
|
||||
vector_store_id: str,
|
||||
request: VectorStoreSearchRequest,
|
||||
api_key: str = Depends(get_api_key)
|
||||
):
|
||||
"""
|
||||
Search a vector store for similar content.
|
||||
"""
|
||||
try:
|
||||
# Check if vector store exists
|
||||
vector_store_table = settings.table_names["vector_stores"]
|
||||
vector_store_result = await db.query_raw(
|
||||
f"SELECT id FROM {vector_store_table} WHERE id = $1",
|
||||
vector_store_id
|
||||
)
|
||||
if not vector_store_result:
|
||||
raise HTTPException(status_code=404, detail="Vector store not found")
|
||||
|
||||
# Generate embedding for query
|
||||
query_embedding = await generate_query_embedding(request.query)
|
||||
query_vector_str = "[" + ",".join(map(str, query_embedding)) + "]"
|
||||
|
||||
# Build the raw SQL query for vector similarity search
|
||||
limit = min(request.limit or 20, 100) # Cap at 100 results
|
||||
|
||||
# Base query with vector similarity using cosine distance
|
||||
# Use configurable field names
|
||||
fields = settings.db_fields
|
||||
table_name = settings.table_names["embeddings"]
|
||||
|
||||
# Build query with proper parameter placeholders for Prisma
|
||||
param_count = 1
|
||||
query_params = [query_vector_str, vector_store_id]
|
||||
|
||||
base_query = f"""
|
||||
SELECT
|
||||
{fields.id_field},
|
||||
{fields.content_field},
|
||||
{fields.metadata_field},
|
||||
({fields.embedding_field} <=> ${param_count}::vector) as distance
|
||||
FROM {table_name}
|
||||
WHERE {fields.vector_store_id_field} = ${param_count + 1}
|
||||
"""
|
||||
param_count += 2
|
||||
|
||||
# Add metadata filters if provided
|
||||
filter_conditions = []
|
||||
|
||||
if request.filters:
|
||||
for key, value in request.filters.items():
|
||||
filter_conditions.append(f"{fields.metadata_field}->>${param_count} = ${param_count + 1}")
|
||||
query_params.extend([key, str(value)])
|
||||
param_count += 2
|
||||
|
||||
if filter_conditions:
|
||||
base_query += " AND " + " AND ".join(filter_conditions)
|
||||
|
||||
# Add ordering and limit
|
||||
final_query = base_query + f" ORDER BY distance ASC LIMIT {limit}"
|
||||
|
||||
# Execute the query
|
||||
results = await db.query_raw(final_query, *query_params)
|
||||
|
||||
# Convert results to SearchResult objects
|
||||
search_results = []
|
||||
for row in results:
|
||||
# Convert distance to similarity score (1 - normalized_distance)
|
||||
# Cosine distance ranges from 0 (identical) to 2 (opposite)
|
||||
similarity_score = max(0, 1 - (row['distance'] / 2))
|
||||
|
||||
# Extract filename from metadata or use a default
|
||||
metadata = row[fields.metadata_field] or {}
|
||||
filename = metadata.get('filename', 'document.txt')
|
||||
|
||||
content_chunks = [ContentChunk(type="text", text=row[fields.content_field])]
|
||||
|
||||
result = SearchResult(
|
||||
file_id=row[fields.id_field],
|
||||
filename=filename,
|
||||
score=similarity_score,
|
||||
attributes=metadata if request.return_metadata else None,
|
||||
content=content_chunks
|
||||
)
|
||||
search_results.append(result)
|
||||
|
||||
return VectorStoreSearchResponse(
|
||||
search_query=request.query,
|
||||
data=search_results,
|
||||
has_more=False, # TODO: Implement pagination
|
||||
next_page=None
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
raise HTTPException(status_code=500, detail=f"Search failed: {str(e)}")
|
||||
|
||||
|
||||
@app.post("/v1/vector_stores/{vector_store_id}/embeddings", response_model=EmbeddingResponse)
|
||||
async def create_embedding(
|
||||
vector_store_id: str,
|
||||
request: EmbeddingCreateRequest,
|
||||
api_key: str = Depends(get_api_key)
|
||||
):
|
||||
"""
|
||||
Add a single embedding to a vector store.
|
||||
"""
|
||||
try:
|
||||
# Check if vector store exists
|
||||
vector_store_table = settings.table_names["vector_stores"]
|
||||
vector_store_result = await db.query_raw(
|
||||
f"SELECT id FROM {vector_store_table} WHERE id = $1",
|
||||
vector_store_id
|
||||
)
|
||||
if not vector_store_result:
|
||||
raise HTTPException(status_code=404, detail="Vector store not found")
|
||||
|
||||
# Convert embedding to vector string format
|
||||
embedding_vector_str = "[" + ",".join(map(str, request.embedding)) + "]"
|
||||
|
||||
# Insert embedding using configurable field names
|
||||
fields = settings.db_fields
|
||||
table_name = settings.table_names["embeddings"]
|
||||
|
||||
result = await db.query_raw(
|
||||
f"""
|
||||
INSERT INTO {table_name} ({fields.id_field}, {fields.vector_store_id_field}, {fields.content_field},
|
||||
{fields.embedding_field}, {fields.metadata_field}, {fields.created_at_field})
|
||||
VALUES (gen_random_uuid(), $1, $2, $3::vector, $4, NOW())
|
||||
RETURNING {fields.id_field}, {fields.vector_store_id_field}, {fields.content_field},
|
||||
{fields.metadata_field}, EXTRACT(EPOCH FROM {fields.created_at_field})::bigint as created_at_timestamp
|
||||
""",
|
||||
vector_store_id,
|
||||
request.content,
|
||||
embedding_vector_str,
|
||||
request.metadata or {}
|
||||
)
|
||||
|
||||
if not result:
|
||||
raise HTTPException(status_code=500, detail="Failed to create embedding")
|
||||
|
||||
embedding = result[0]
|
||||
|
||||
# Update vector store statistics
|
||||
await db.query_raw(
|
||||
f"""
|
||||
UPDATE {vector_store_table}
|
||||
SET
|
||||
file_counts = jsonb_set(
|
||||
jsonb_set(
|
||||
COALESCE(file_counts, '{{"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0}}'::jsonb),
|
||||
'{{completed}}',
|
||||
(COALESCE(file_counts->>'completed', '0')::int + 1)::text::jsonb
|
||||
),
|
||||
'{{total}}',
|
||||
(COALESCE(file_counts->>'total', '0')::int + 1)::text::jsonb
|
||||
),
|
||||
usage_bytes = COALESCE(usage_bytes, 0) + LENGTH($2),
|
||||
last_active_at = NOW()
|
||||
WHERE id = $1
|
||||
""",
|
||||
vector_store_id,
|
||||
request.content
|
||||
)
|
||||
|
||||
return EmbeddingResponse(
|
||||
id=embedding[fields.id_field],
|
||||
vector_store_id=embedding[fields.vector_store_id_field],
|
||||
content=embedding[fields.content_field],
|
||||
metadata=embedding[fields.metadata_field],
|
||||
created_at=int(embedding["created_at_timestamp"])
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
raise HTTPException(status_code=500, detail=f"Failed to create embedding: {str(e)}")
|
||||
|
||||
|
||||
@app.post("/v1/vector_stores/{vector_store_id}/embeddings/batch", response_model=EmbeddingBatchCreateResponse)
|
||||
async def create_embeddings_batch(
|
||||
vector_store_id: str,
|
||||
request: EmbeddingBatchCreateRequest,
|
||||
api_key: str = Depends(get_api_key)
|
||||
):
|
||||
"""
|
||||
Add multiple embeddings to a vector store in batch.
|
||||
"""
|
||||
try:
|
||||
# Check if vector store exists
|
||||
vector_store_table = settings.table_names["vector_stores"]
|
||||
vector_store_result = await db.query_raw(
|
||||
f"SELECT id FROM {vector_store_table} WHERE id = $1",
|
||||
vector_store_id
|
||||
)
|
||||
if not vector_store_result:
|
||||
raise HTTPException(status_code=404, detail="Vector store not found")
|
||||
|
||||
if not request.embeddings:
|
||||
raise HTTPException(status_code=400, detail="No embeddings provided")
|
||||
|
||||
# Prepare batch insert
|
||||
fields = settings.db_fields
|
||||
table_name = settings.table_names["embeddings"]
|
||||
|
||||
# Build VALUES clause for batch insert
|
||||
values_clauses = []
|
||||
params = []
|
||||
param_count = 1
|
||||
|
||||
for embedding_req in request.embeddings:
|
||||
embedding_vector_str = "[" + ",".join(map(str, embedding_req.embedding)) + "]"
|
||||
values_clauses.append(f"(gen_random_uuid(), ${param_count}, ${param_count + 1}, ${param_count + 2}::vector, ${param_count + 3}, NOW())")
|
||||
params.extend([
|
||||
vector_store_id,
|
||||
embedding_req.content,
|
||||
embedding_vector_str,
|
||||
embedding_req.metadata or {}
|
||||
])
|
||||
param_count += 4
|
||||
|
||||
values_clause = ", ".join(values_clauses)
|
||||
|
||||
# Execute batch insert
|
||||
result = await db.query_raw(
|
||||
f"""
|
||||
INSERT INTO {table_name} ({fields.id_field}, {fields.vector_store_id_field}, {fields.content_field},
|
||||
{fields.embedding_field}, {fields.metadata_field}, {fields.created_at_field})
|
||||
VALUES {values_clause}
|
||||
RETURNING {fields.id_field}, {fields.vector_store_id_field}, {fields.content_field},
|
||||
{fields.metadata_field}, EXTRACT(EPOCH FROM {fields.created_at_field})::bigint as created_at_timestamp
|
||||
""",
|
||||
*params
|
||||
)
|
||||
|
||||
if not result:
|
||||
raise HTTPException(status_code=500, detail="Failed to create embeddings")
|
||||
|
||||
# Calculate total content length for usage bytes update
|
||||
total_content_length = sum(len(emb.content) for emb in request.embeddings)
|
||||
|
||||
# Update vector store statistics
|
||||
await db.query_raw(
|
||||
f"""
|
||||
UPDATE {vector_store_table}
|
||||
SET
|
||||
file_counts = jsonb_set(
|
||||
jsonb_set(
|
||||
COALESCE(file_counts, '{{"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0}}'::jsonb),
|
||||
'{{completed}}',
|
||||
(COALESCE(file_counts->>'completed', '0')::int + $2)::text::jsonb
|
||||
),
|
||||
'{{total}}',
|
||||
(COALESCE(file_counts->>'total', '0')::int + $2)::text::jsonb
|
||||
),
|
||||
usage_bytes = COALESCE(usage_bytes, 0) + $3,
|
||||
last_active_at = NOW()
|
||||
WHERE id = $1
|
||||
""",
|
||||
vector_store_id,
|
||||
len(request.embeddings),
|
||||
total_content_length
|
||||
)
|
||||
|
||||
# Convert results to response format
|
||||
embeddings = []
|
||||
for row in result:
|
||||
embeddings.append(EmbeddingResponse(
|
||||
id=row[fields.id_field],
|
||||
vector_store_id=row[fields.vector_store_id_field],
|
||||
content=row[fields.content_field],
|
||||
metadata=row[fields.metadata_field],
|
||||
created_at=int(row["created_at_timestamp"])
|
||||
))
|
||||
|
||||
return EmbeddingBatchCreateResponse(
|
||||
data=embeddings,
|
||||
created=int(time.time())
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
raise HTTPException(status_code=500, detail=f"Failed to create embeddings batch: {str(e)}")
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint"""
|
||||
return {"status": "healthy", "timestamp": int(time.time())}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run("main:app", host=settings.host, port=settings.port, reload=True)
|
||||
Vendored
-86
@@ -1,86 +0,0 @@
|
||||
from typing import Optional, Dict, Any, List
|
||||
from pydantic import BaseModel
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class VectorStoreCreateRequest(BaseModel):
|
||||
name: str
|
||||
file_ids: Optional[List[str]] = None
|
||||
expires_after: Optional[Dict[str, Any]] = None
|
||||
chunking_strategy: Optional[Dict[str, Any]] = None
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
|
||||
|
||||
class VectorStoreResponse(BaseModel):
|
||||
id: str
|
||||
object: str = "vector_store"
|
||||
created_at: int
|
||||
name: str
|
||||
usage_bytes: int
|
||||
file_counts: Dict[str, int]
|
||||
status: str
|
||||
expires_after: Optional[Dict[str, Any]] = None
|
||||
expires_at: Optional[int] = None
|
||||
last_active_at: Optional[int] = None
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
|
||||
|
||||
class VectorStoreSearchRequest(BaseModel):
|
||||
query: str
|
||||
limit: Optional[int] = 20
|
||||
filters: Optional[Dict[str, Any]] = None
|
||||
return_metadata: Optional[bool] = True
|
||||
|
||||
|
||||
class ContentChunk(BaseModel):
|
||||
type: str = "text"
|
||||
text: str
|
||||
|
||||
|
||||
class SearchResult(BaseModel):
|
||||
file_id: str
|
||||
filename: str
|
||||
score: float
|
||||
attributes: Optional[Dict[str, Any]] = None
|
||||
content: List[ContentChunk]
|
||||
|
||||
|
||||
class VectorStoreSearchResponse(BaseModel):
|
||||
object: str = "vector_store.search_results.page"
|
||||
search_query: str
|
||||
data: List[SearchResult]
|
||||
has_more: bool = False
|
||||
next_page: Optional[str] = None
|
||||
|
||||
|
||||
class EmbeddingCreateRequest(BaseModel):
|
||||
content: str
|
||||
embedding: List[float]
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
|
||||
|
||||
class EmbeddingResponse(BaseModel):
|
||||
id: str
|
||||
object: str = "embedding"
|
||||
vector_store_id: str
|
||||
content: str
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
created_at: int
|
||||
|
||||
|
||||
class EmbeddingBatchCreateRequest(BaseModel):
|
||||
embeddings: List[EmbeddingCreateRequest]
|
||||
|
||||
|
||||
class EmbeddingBatchCreateResponse(BaseModel):
|
||||
object: str = "embedding.batch"
|
||||
data: List[EmbeddingResponse]
|
||||
created: int
|
||||
|
||||
|
||||
class VectorStoreListResponse(BaseModel):
|
||||
object: str = "list"
|
||||
data: List[VectorStoreResponse]
|
||||
first_id: Optional[str] = None
|
||||
last_id: Optional[str] = None
|
||||
has_more: bool = False
|
||||
-40
@@ -1,40 +0,0 @@
|
||||
// This is your Prisma schema file,
|
||||
// learn more about it in the docs: https://pris.ly/d/prisma-schema
|
||||
|
||||
generator client {
|
||||
provider = "prisma-client-py"
|
||||
}
|
||||
|
||||
datasource db {
|
||||
provider = "postgresql"
|
||||
url = env("DATABASE_URL")
|
||||
}
|
||||
|
||||
model VectorStore {
|
||||
id String @id @default(cuid())
|
||||
name String
|
||||
file_counts Json?
|
||||
status String @default("completed")
|
||||
usage_bytes Int? @default(0)
|
||||
created_at DateTime @default(now())
|
||||
expires_after Json?
|
||||
expires_at DateTime?
|
||||
last_active_at DateTime?
|
||||
metadata Json?
|
||||
embeddings Embedding[]
|
||||
|
||||
@@map("vector_stores")
|
||||
}
|
||||
|
||||
model Embedding {
|
||||
id String @id @default(cuid())
|
||||
vector_store_id String
|
||||
content String
|
||||
embedding Unsupported("vector(1536)")
|
||||
metadata Json?
|
||||
created_at DateTime @default(now())
|
||||
|
||||
vector_store VectorStore @relation(fields: [vector_store_id], references: [id], onDelete: Cascade)
|
||||
|
||||
@@map("embeddings")
|
||||
}
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
fastapi==0.104.1
|
||||
uvicorn[standard]==0.24.0
|
||||
prisma==0.11.0
|
||||
python-dotenv==1.0.0
|
||||
pydantic>=2.5.0
|
||||
psycopg2-binary==2.9.7
|
||||
pgvector==0.2.4
|
||||
python-multipart==0.0.6
|
||||
litellm==1.74.3
|
||||
pydantic-settings==2.1.0
|
||||
Reference in New Issue
Block a user