mcp-server-rag

# MCP Server RAG RAG (Retrieval-Augmented Generation) server for MCP (Model Control Protocol). This server provides vector-based document retrieval functionality to enhance LLM interactions with contextual information. ## Overview The MCP Server RAG implementation provides a bridge between your LLM applications and document collections, offering: - Vector-based document search using ChromaDB and Sentence Transformers - Multiple specialized collections (liv, ken, ufa, sap commerce) - Strongly typed API responses - Integration with the MCP server infrastructure ## Installation ```bash pip install rag-mcp-server ``` ## Usage After installation, you can run the server with: ```bash mcp-server-rag ``` Or using Python module syntax: ```bash python -m mcp_server_rag ``` ## Features - Vector-based search for contextual document retrieval - Persistent storage of document embeddings via ChromaDB - Multiple collection support for domain-specific searches - Configurable via environment variables - Fully typed response objects with detailed metadata - Seamless integration with the MCP protocol ## Available Collections The server provides access to four specialized collections: - `liv-rag`: LIV document collection (`retrieve_liv_context` tool) - `ken-rag`: Kennametal document collection (`retrieve_ken_context` tool) - `ufa-rag`: UFA document collection (`retrieve_ufa_context` tool) - `sap-comm-rag`: SAP Commerce document collection (`retrieve_sap_comm_context` tool) ## Configuration The following environment variables can be used to configure the server: | Variable | Description | Default | |----------|-------------|--------| | `RAG_PERSIST_DIR` | Directory where ChromaDB will store its data | `~/Documents/chroma_db` | | `RAG_EMBEDDING_MODEL` | Path or name of the Sentence Transformer model | `~/LLM/huggingface/hub/models--sentence-transformers--all-MiniLM-L6-v2/...` | | `RAG_N_RESULTS` | Default number of results to return from searches | `5` | ## Dependenci…