RAG PDF Chat — Ask Questions About Any Document

A document question-answering app: upload a PDF, ask questions in plain language, and get answers grounded in the document's actual content instead of the model's guesses.

Large language models don't know what's inside your private documents, and they can make things up. The goal was to give the model the right parts of the document at question time, so its answers stay accurate and relevant.

Ingestion — the PDF is parsed and split into chunks

Embeddings — each chunk is converted into a vector embedding

Vector store — embeddings are indexed in FAISS for fast similarity search

Retrieval — each question is embedded and matched against the most relevant chunks (semantic search)

Generation — the retrieved context is passed to the LLM, which answers based on the document

The complete RAG pipeline with LangChain

Document chunking and embedding generation

FAISS vector store and semantic search

Question-answering flow connecting retrieval to the LLM

Python · LangChain · FAISS · Embeddings · LLM APIs

The same architecture powers customer-support bots over help docs, internal knowledge-base assistants, and contract or policy Q&A — the kind of AI feature I build for clients.