Builds a production-ready RAG pipeline leveraging FastMCP, PostgreSQL, and pgvector for efficient data ingestion and semantic querying.
Sponsored
The RAG Template offers a comprehensive, production-ready framework for developing Retrieval-Augmented Generation (RAG) pipelines. It integrates a FastMCP server for seamless LLM client interaction, utilizing PostgreSQL with pgvector for robust vector similarity search. This template streamlines the entire RAG workflow, from ingesting CSV data, cleaning, chunking, and embedding with local models like `all-MiniLM-L6-v2`, to storing it efficiently. It's designed to accelerate the development of applications requiring powerful semantic search and contextualized information retrieval.
Key Features
01FastMCP Server for LLM client integration
02PostgreSQL + pgvector for vector similarity search
03Automated RAG pipeline: CSV ingestion to storage
04Local embedding generation using sentence-transformers
05Configurable text chunking with overlap
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Use Cases
01Quickly setting up new RAG projects
02Ingesting and processing custom document data
03Developing applications requiring semantic search and contextual queries