A PDF RAG Pipeline That Doesn’t Break Legal Docs
At DocuChat, we built a RAG pipeline for PDF files: document → chunks → embeddings → vector DB → context for the LLM. A user uploads a PDF and chats with it without having to manually search for the right clause.
At DocuChat, we built a RAG pipeline for PDF files: document → chunks → embeddings → vector DB → context for the LLM. A user just uploads a PDF and chats with it — no need to go on a quest to find clause 7.4 on page 113.
The main pain point was files with 100+ pages: if you feed everything to the LLM at once, it quickly loses the thread. So we implemented chunking + retrieval so the model can pull relevant pieces without losing the document’s overall logic.
Another nice part is editing through chat. The model does not rewrite the PDF however it wants; instead, it shows diff suggestions and preserves the layout, tables, and numbering. For legal review, this is a must-have: you need changes to be precise, not in the style of “oops, the entire formatting shifted.”
Against the backdrop of the hype around AI assistants for documents, this is no longer a nice-to-have, but a practical team tool.