backend-rag-implementation

Retrieval-Augmented Generation (RAG) integrates external knowledge bases with LLMs to provide accurate, cited answers without hallucinations. This Skill teaches you to load documents, embed them into a vector database like Chroma or Pinecone, and retrieve relevant context to feed into LLM queries, ensuring responses stay grounded in your source material.

Vector databases like Chroma, Pinecone, and Weaviate store document embeddings for fast semantic search. This Skill covers modular integration across multiple stores, so you can start with Chroma for local development and swap to Pinecone or Weaviate for production without rewriting retrieval logic.

Yes. This Skill shows how to build RAG pipelines with Langchain and expose them via FastAPI endpoints. You load documents, create embeddings, store them in a vector database, then serve semantic search and LLM-generated answers through a FastAPI API with source citations.

RAG solves hallucinations by retrieving factual context from your documents before generating responses. This Skill teaches document chunking, semantic search with embeddings, and reranking strategies to ensure your LLM answers only from authoritative sources and includes citations.

This Skill covers loading documents into a knowledge base, splitting them into chunks for embedding, and storing chunks in vector stores. It integrates with Langchain's document loaders and supports multiple retrieval strategies—hybrid search, multi-query retrieval, and reranking—to optimize context relevance.

The Skill assumes familiarity with LLMs, embeddings, and FastAPI, as it covers advanced retrieval strategies and modular vector-store integration. A quick-start example uses Langchain and Chroma for local setup, but production deployment with Pinecone or Weaviate and reranking requires understanding RAG architecture and async HTTP handling.