mcp-rag-agent
Verified Safeby luisrodriguesphd
Overview
This system provides Retrieval-Augmented Generation (RAG) capabilities, allowing an AI agent to answer questions based on a corpus of internal policy documents using hybrid search with MongoDB Atlas.
Installation
python -m mcp_rag_agent.mcp_server.serverEnvironment Variables
- MONGODB_ATLAS_CLUSTER_URI
- MONGODB_ATLAS_DB_NAME
- OPENAI_API_KEY
- EMBEDDING_MODEL_NAME
- EMBEDDING_DIMENSION
- TEXT_MODEL_NAME
- EVALUATION_MODEL_NAME
- MCP_SERVER_NAME
- MCP_SERVER_HOST
- MCP_SERVER_PORT
- SEMANTIC_WEIGHT
- FEATURE_FLAG_MCPSERVER_ENABLED
- FEATURE_FLAG_WEBSEARCH_ENABLED
- INGESTED_DOC_DIRECTORY
- EVALUATION_DOC_DIRECTORY
- MONGODB_USERS_COLLECTION
- MONGODB_CONVERSATIONS_COLLECTION
- MONGODB_MESSAGES_COLLECTION
- MONGODB_DOCUMENTS_COLLECTION
- MONGODB_VECTOR_COLLECTION
- MONGODB_VECTOR_INDEX_NAME
Security Notes
The project uses standard practices for handling sensitive information by loading API keys and database URIs from environment variables, preventing hardcoded secrets. It relies on external cloud services (OpenAI, MongoDB Atlas), necessitating secure configuration of these services (e.g., IP whitelisting, access controls). No 'eval' or other directly exploitable dangerous functions were found. The general security posture is good for a modern Python application.
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