Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
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process.env. You'll be asked to provide them before it can run.DATABASE_URL— "": "postgresql://postgres:postgres@localhost:5432/rag" } } } }EMBEDDINGS_PROVIDEREMBEDDINGS_API_BASE— / EMBEDDINGS_API_KEY / EMBEDDINGS_MODEL, EMBED_DIMEMBEDDINGS_API_KEY— cp .env.example .env # setEMBEDDINGS_MODEL— EMBEDDINGS_API_BASE / EMBEDDINGS_API_KEY / , EMBED_DIMEMBED_DIM— EMBEDDINGS_API_BASE / EMBEDDINGS_API_KEY / EMBEDDINGS_MODEL,CHUNK_SIZE— , CHUNK_OVERLAP, and MCP_TRANSPORT (stdio http).CHUNK_OVERLAP— CHUNK_SIZE, , and MCP_TRANSPORT (stdio http).MCP_TRANSPORT— CHUNK_SIZE, CHUNK_OVERLAP, and (stdio http).MCP_HOSTMCP_PORTMCP_BASE_URLGOOGLE_WEB_CLIENT_IDGOOGLE_WEB_CLIENT_SECRETGOOGLE_ALLOWED_EMAILS[](https://m8ven.ai/mcp/nextlevelmanagementadvisors-rag-mcp-kfniex)