Document Chat in AI & Artificial Intelligence

PDFs were designed for printing, not for reading by machines. They often contain complex layouts, tables, and images that 'break' simple text extractors. To build a robust Document Chat, you must use libraries like pdf-parse or LangChain's PDF loaders. For scanned documents, you may even need to integrate a Vision API to perform OCR. The goal is to extract a clean stream of text that preserves the logical order of headers and paragraphs.

Once you have the text, you can't just send it to the AI. You must break it into Chunks (typically 500-1000 characters). We use a Recursive Character Text Splitter which tries to split at natural boundaries like newlines or periods. We also include a Chunk Overlap (e.g., 100 characters). This ensures that if a sentence about 'Refund Policy' is cut in half, both resulting chunks contain enough context for the Vector DB to identify them correctly during a search.

The biggest problem with AI is trust. When the AI answers a question about a document, the user needs to know *where* the answer came from. In your RAG pipeline, each chunk in the Vector DB should store Metadata (like 'page_number' and 'file_name'). When the AI generates a response, you should display Citations or 'Source Cards' next to the text. This 'Grounding' transforms the AI from a creative writer into a reliable research assistant.