A retrieval-augmented generation (RAG) pipeline for question answering over a custom document corpus.

It combines Google’s FLAN-T5 model with a Facebook AI Similarity Search (FAISS) vector database, orchestrated in LangChain — documents are embedded and indexed, the most relevant chunks are retrieved for a query, and the model generates an answer grounded in that retrieved context.