pgvector-azure-openai-mcp-server 1.0.2

A MCP server with RAG capabilities using Azure OpenAI embeddings and pgvector for PostgreSQL.

pgvector Azure OpenAI MCP server

A 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)

# No installation required, run directly with uvx in MCP configuration
# uvx will automatically download and manage packages

Method 2: Install with uv

# 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

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:

wget https://raw.githubusercontent.com/darktohka/pgvector-azure-openai-mcp-server/refs/heads/master/init-db.sql

Then, create the docker-compose.yaml:

services:
  db:
    image: pgvector/pgvector:pg18
    restart: always
    environment:
      POSTGRES_DB: mcp_vectors
      POSTGRES_USER: username
      POSTGRES_PASSWORD: password
    ports:
      - '5432:5432'
    volumes:
      - db_data:/var/lib/postgresql/data
      - ./init-db.sql:/docker-entrypoint-initdb.d/init-db.sql:ro

volumes:
  db_data:

To create the database, finally:

docker compose up -d

By default, the mcp_vectors Postgres database will be used.

If databases need to be set up manually (not necessary when using init-db.sql), use:

# Connect to PostgreSQL and enable the pgvector extension
psql postgres -c "CREATE EXTENSION IF NOT EXISTS vector;"

3. Configure the MCP Client

Add the following configuration to your MCP client configuration file (e.g., VS Code):

Recommended Configuration (using uvx, no pre-installation required):

{
  "servers": {
    "pgvector-azure-openai-mcp-server": {
      "command": "uvx",
      "args": ["pgvector-azure-openai-mcp-server"],
      "env": {
        "DATABASE_URL": "postgresql://username:password@localhost:5432/mcp_vectors",
        "AZURE_OPENAI_API_KEY": "your_azure_openai_api_key_here",
        "AZURE_OPENAI_ENDPOINT": "your_azure_openai_endpoint_here",
        "AZURE_OPENAI_MODEL": "text-embedding-3-small",
        "DEBUG": "false"
      }
    }
  }
}

Alternative Configuration (if already installed):

{
  "servers": {
    "pgvector-azure-openai-mcp-server": {
      "command": "pgvector-azure-openai-mcp-server",
      "env": {
        "DATABASE_URL": "postgresql://username:password@localhost:5432/mcp_vectors",
        "AZURE_OPENAI_API_KEY": "your_azure_openai_api_key_here",
        "AZURE_OPENAI_ENDPOINT": "your_azure_openai_endpoint_here",
        "AZURE_OPENAI_MODEL": "text-embedding-3-small",
        "DEBUG": "false"
      }
    }
  }
}

Traditional Configuration (using Python module):

{
  "servers": {
    "pgvector-azure-openai-mcp-server": {
      "command": "python",
      "args": ["-m", "pgvector_azure_openai_mcp_server"],
      "env": {
        "DATABASE_URL": "postgresql://username:password@localhost:5432/mcp_vectors",
        "AZURE_OPENAI_API_KEY": "your_azure_openai_api_key_here",
        "AZURE_OPENAI_ENDPOINT": "your_azure_openai_endpoint_here",
        "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME": "your_embedding_deployment_name_here",
        "DEBUG": "false"
      }
    }
  }
}

Caution: When using Claude Code, the top-level key is called mcpServers not servers.

4. Verify Installation

Use the MCP client to call the status tool to verify the connection:

{
  "tool": "status",
  "parameters": {}
}

Expected Response:

{
  "success": true,
  "database": {
    "connected": true,
    "pgvector_installed": true
  },
  "embedding_service": {
    "available": true,
    "provider": "Azure OpenAI"
  }
}

MCP Tool Reference

The pgvector MCP Server provides the following 10 tools for MCP clients to call:

1. System Status Check

{
  "tool": "status",
  "parameters": {}
}

2. Collection Management

Create Collection

{
  "tool": "create_collection",
  "parameters": {
    "name": "my_documents",
    "description": "My document collection",
    "dimension": 1536
  }
}

List All Collections

{
  "tool": "list_collections",
  "parameters": {
    "include_documents": true
  }
}

View Collection Details

{
  "tool": "show_collection",
  "parameters": {
    "name": "my_documents",
    "include_stats": true
  }
}

Rename Collection (New Feature)

{
  "tool": "rename_collection",
  "parameters": {
    "old_name": "my_documents",
    "new_name": "document_library"
  }
}

Delete Collection

{
  "tool": "delete_collection",
  "parameters": {
    "name": "my_documents",
    "confirm": true
  }
}

3. Vector Operations

Add Text Vector

{
  "tool": "add_text",
  "parameters": {
    "collection_name": "my_documents",
    "text": "This is a sample document content",
    "metadata": {
      "source": "manual",
      "type": "document",
      "category": "Technical Documentation"
    }
  }
}

Search Similar Content

{
  "tool": "search_collection",
  "parameters": {
    "collection_name": "my_documents",
    "query": "Machine learning related content",
    "limit": 5,
    "search_strategy": "smart",
    "min_similarity": 0.7
  }
}

Add Document File

{
  "tool": "add_document",
  "parameters": {
    "collection_name": "my_documents",
    "file_path": "/path/to/document.pdf",
    "metadata": {
      "category": "manual",
      "language": "en"
    }
  }
}

Delete Vectors

{
  "tool": "delete_vectors",
  "parameters": {
    "collection_name": "my_documents",
    "file_path": "/path/to/old_document.pdf",
    "confirm": true
  }
}

Usage Examples

Basic Workflow

Call the following tools sequentially via the MCP client:

// 1. Check system status
{
  "tool": "status",
  "parameters": {}
}

// 2. Create document collection
{
  "tool": "create_collection",
  "parameters": {
    "name": "documents",
    "description": "Document knowledge base"
  }
}

// 3. Add document content
{
  "tool": "add_text",
  "parameters": {
    "collection_name": "documents",
    "text": "Machine learning is an important branch of artificial intelligence",
    "metadata": {"type": "knowledge"}
  }
}

// 4. Search for relevant content
{
  "tool": "search_collection",
  "parameters": {
    "collection_name": "documents",
    "query": "Deep learning",
    "limit": 3
  }
}

// 5. View collection statistics
{
  "tool": "show_collection",
  "parameters": {
    "name": "documents",
    "include_stats": true
  }
}

Document Processing Example

// Process PDF document
{
  "tool": "add_document",
  "parameters": {
    "collection_name": "tech_docs",
    "file_path": "/Users/username/documents/manual.pdf",
    "metadata": {
      "category": "technical",
      "language": "en",
      "source": "official_docs"
    }
  }
}

// Search document content
{
  "tool": "search_collection",
  "parameters": {
    "collection_name": "tech_docs",
    "query": "API configuration method",
    "search_strategy": "smart",
    "metadata_filters": {
      "category": "technical"
    }
  }
}

Collection Renaming and Management Example

// Rename collection (new feature)
{
  "tool": "rename_collection",
  "parameters": {
    "old_name": "temp_docs",
    "new_name": "permanent_docs"
  }
}

// List all collections
{
  "tool": "list_collections",
  "parameters": {
    "include_documents": true
  }
}

// Delete unnecessary vectors
{
  "tool": "delete_vectors",
  "parameters": {
    "collection_name": "permanent_docs",
    "file_path": "/old/path/outdated.pdf",
    "confirm": true
  }
}

Feature Description

Windows Encoding Compatibility

• Automatic Encoding Detection: Supports GBK, GB2312, UTF-8, and other encoding formats.
• Encoding Conversion: Automatically converts to UTF-8 for unified processing.

Search Strategies

• smart: Intelligent combination of SQL + semantic search (recommended).
• sql_only: Uses traditional SQL text search only.
• semantic_only: Uses vector similarity search only.

Atomic Operation Guarantee

• Collection Renaming: Database transactions ensure atomicity of operations.
• Batch Insertion: Guarantees consistency during batch processing of vector data.
• Error Recovery: Automatic rollback on operation failure to ensure data integrity.

Performance Optimization

• Vector Indexing: Uses pgvector's ivfflat index to optimize search performance.
• Batch Processing: Supports document chunking and batch vector generation.
• Connection Pool: SQLAlchemy connection pool improves database access efficiency.
• Response Time: Target response time for document processing < 2 seconds.

System Requirements

• Python: 3.11+ (uses modern Python features)
• PostgreSQL: 16+ with pgvector extension enabled
• pgvector: Version 0.8.0+
• MCP Client: AI assistant supporting the MCP protocol (e.g., Claude Desktop)
• Package Management: uv (recommended) or pip
• Azure OpenAI API: Azure OpenAI API key for text embeddings

Development and Deployment

uv Project Development

# Clone the project
git clone https://github.com/darktohka/pgvector-azure-openai-mcp-server
cd pgvector-azure-openai-mcp-server

# Install dependencies with uv
uv sync

# Run tests
uv run pytest

# Build package
uv build

# Publish to PyPI
uv publish

Environment Variable Configuration

# Required configuration
DATABASE_URL=postgresql://username:password@localhost:5432/database
AZURE_OPENAI_API_KEY=your_api_key_here
AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint_here
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=your_embedding_deployment_name_here

# Optional configuration
DEBUG=false  # Debug mode

Installation and Configuration Notes

Advantages of uvx Method

• No Pre-installation Required: uvx automatically downloads and manages packages and their dependencies.
• Isolated Environment: Each tool runs in an independent environment, avoiding dependency conflicts.
• Automatic Updates: Always uses the latest version.
• Simplified Configuration: Configuration files are simpler, no need to specify paths.

Configuration Selection Suggestions

• New Users: Recommended to use uvx configuration, it's the simplest and fastest.
• Developers: Install with uv tool install and then call commands directly.
• Production Environment: Traditional Python module method can be chosen for easier version control.

Troubleshooting

Common Issues:

• Database connection failed: Check DATABASE_URL format and PostgreSQL service status.
• pgvector extension not found: Execute CREATE EXTENSION vector; in PostgreSQL. Make sure init-db.sql is being run.
• API key error: Verify if AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, and AZURE_OPENAI_MODEL are correctly configured.
• Collection renaming failed: Check if the new name already exists.
• uvx network issues: Ensure network connection is normal, uvx needs to download packages from PyPI.