rag-retrieval-mcp 0.2.0

A generic RAG MCP server with pluggable embedding providers and vector stores

MCP Server for RAG Retrieval

A generic Retrieval-Augmented Generation (RAG) Model Context Protocol (MCP) server with pluggable embedding providers and vector stores.

Why this server?

Vendor MCP servers usually only support their (own) integrated embedding models. If your index uses external embeddings (e.g., OpenAI), those servers can't query it. This server fills that gap — it embeds your query with the provider of your choice, then searches any supported vector store.

Currently Supports

Embedding Providers:

• OpenAI (text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002, etc.)

Vector Stores:

• Pinecone
• pgvector (PostgreSQL)

Tools

retrieve

Search a knowledge base and return relevant content.

Parameters:

• query (string, required) — The search query to find relevant content.

Returns a JSON array of results, each with text, score, and metadata fields.

Install & Run

Run directly with uvx (no install needed):

uvx rag-retrieval-mcp[all]

Or install with pip:

pip install rag-retrieval-mcp[all]
rag-retrieval-mcp

MCP client configuration

{
  "mcpServers": {
    "rag-retrieval": {
      "command": "uvx",
      "args": ["rag-retrieval-mcp[all]"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key",
        "PINECONE_API_KEY": "your-pinecone-api-key",
        "PINECONE_HOST": "your-pinecone-index-host-url"
      }
    }
  }
}

Environment Variables

Variable	Required	Default	Description
RAG_EMBEDDING_PROVIDER	No	openai	Embedding provider to use
RAG_VECTOR_STORE	No	pinecone	Vector store to use
RAG_TOP_K	No	5	Number of results to return
OPENAI_API_KEY	Yes (if using OpenAI)		OpenAI API key
OPENAI_EMBEDDING_MODEL	No	text-embedding-3-small	OpenAI embedding model
PINECONE_API_KEY	Yes (if using Pinecone)		Pinecone API key
PINECONE_HOST	Yes (if using Pinecone)		Pinecone index host URL
PINECONE_TEXT_FIELD	No	text	Metadata field containing text
PGVECTOR_CONNECTION_STRING	Yes (if using pgvector)		PostgreSQL connection string
PGVECTOR_TABLE	No	embeddings	Table name containing vectors
PGVECTOR_TEXT_COLUMN	No	text	Column containing text content
PGVECTOR_EMBEDDING_COLUMN	No	embedding	Column containing embedding vectors
PGVECTOR_METADATA_COLUMN	No		jsonb column containing metadata (if any)

Adding New Providers

Implement the EmbeddingProvider or VectorStore abstract base class and register it in server.py's factory function. See src/rag_retrieval_mcp/embedding_providers/base.py and src/rag_retrieval_mcp/vector_stores/base.py for the interfaces.

License

Apache License 2.0