PDF Q&A Agent (ChatPDF)

In a long contract, technical specification or regulation, the information you need is usually in a single paragraph — the problem is finding it. Classic search matches words, whereas people search for meaning.

This tool works on a RAG (retrieval-augmented generation) approach. An uploaded PDF is first split into meaningful chunks, each chunk is converted into a vector representation and stored in an index. When the user asks a question, the closest chunks are retrieved first, and the language model then produces an answer grounded only in those chunks.

The critical benefit of this design: rather than being pushed into fabrication, the model can say "this information is not in the document" when it finds no match. Because answers are sourced from the document, the user can trace which section they came from.