langchain-rag

To build a LangChain RAG pipeline, load PDFs using document loaders, split text into chunks with RecursiveCharacterTextSplitter, embed them using OpenAI embeddings, index in a vector store like Chroma, and retrieve top matches to ground LLM answers with sources.

Yes, LangChain retrieval-augmented generation supports multiple vector stores including InMemory, FAISS, Chroma, and Pinecone. You can configure these backends to persist your indexed document embeddings and perform similarity searches for relevant context.

You can chunk web pages and filesystem documents using the LangChain RecursiveCharacterTextSplitter. This mechanism splits loaded text into overlapping chunks to produce retrieval-friendly embeddings that maintain contextual continuity for accurate similarity search.

Similarity search retrieves the most semantically relevant documents, while MMR (Maximum Marginal Relevance) retrieves relevant documents while actively reducing redundancy. LangChain RAG pipelines support both retrieval strategies, optionally applying metadata filters for targeted recall.

Yes, LangChain RAG supports agent-style patterns that expose document retrieval as a tool for question answering. This allows iterative workflows where the LLM can autonomously fetch relevant context from indexed vector stores to ground its generated responses.

Using OpenAI embeddings for document question answering requires coordinating loaders, chunking, and indexing steps correctly. Limitations include managing chunk overlap for context continuity, configuring the optimal k value for retrieval, and ensuring metadata filters are properly applied to avoid missing relevant documents.