An MCP server with RAG capabilities using Azure OpenAI embeddings and pgvector for PostgreSQL.
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# pgvector Azure OpenAI MCP server An MCP server with RAG capabilities using Azure OpenAI embeddings and pgvector for PostgreSQL. Focuses on Azure OpenAI embeddings. ## Features - **MCP Compatible**: Fully based on the Model Context Protocol, seamlessly integrating with AI assistants. - **Collection Management**: Create, list, rename, and delete vector collections. - **Vector Operations**: Add vectors, search for similar content, and process documents in batches. - **Embedding Service**: Integrates Azure OpenAI embeddings. - **Modern Package Management**: Based on the uv package manager for fast installation and dependency management. - **Atomic Operations**: Database transactions ensure atomicity for operations like collection renaming. - **Cross-Platform Support**: Compatible with Windows, macOS, and Linux. ## Quick Start ### 1. Installation Methods **Method 1: Run directly with uvx (Most Recommended)** ```bash # No installation required, run directly with uvx in MCP configuration # uvx will automatically download and manage packages ``` **Method 2: Install with uv** ```bash # Global tool installation uv tool install pgvector-azure-openai-mcp-server # Or project dependency installation uv add pgvector-azure-openai-mcp-server ``` **Method 3: Install with pip** ```bash pip install pgvector-azure-openai-mcp-server ``` ### 2. Set up the Database using Docker Use `docker-compose.yaml` to set up a database with `pgvector` support. To install `pgvector` into the database, an `init-db.sql` script must be ran when the PostgreSQL container is first ran. Download this `init-db.sql` script first: ```bash wget https://raw.githubusercontent.com/darktohka/pgvector-azure-openai-mcp-server/refs/heads/master/init-db.sql ``` Then, create the `docker-compose.yaml`: ```yaml services: db: image: pgvector/pgvector:pg18 restart: always environment: POSTGRES_DB: mcp_vectors POSTGRES_USER: username POSTGRES_PASSWORD: password ports: …
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