Master PDF Chat with LangChain - Your essential guide to queries on documents

Building a “chat with your PDF” system hinges on one practical fix: plain prompting can’t reliably handle long books because the context window is finite. The solution is retrieval—turning the PDF into a searchable semantic index so questions pull in only the most relevant passages before the language model tries to answer.

The workflow starts by loading a PDF (Reid Hoffman’s book on GPT-4 and AI, available as a free PDF) and converting it into text. Because a model can’t ingest hundreds of thousands of characters at once, the text is split into overlapping chunks. In the example, the chunk size is set to 1,000 characters with a 200-character overlap, producing 448 chunk-sized “mini documents.” The overlap matters: key information often straddles boundaries, so sliding windows reduce the chance that a question’s answer gets cut in half.

Next comes embeddings and a vector store. Each chunk is embedded into a high-dimensional vector using OpenAI embeddings (specifically the model text embedding ADA 0 0 2). Those vectors are stored in a FAISS in-memory index, which functions like a database for semantic search. When a user asks a question—such as “How does GPT-4 change social media?”—the question itself is embedded and compared against the chunk vectors using similarity matching. The vector store returns the closest chunks (by default, four), and those retrieved passages are then fed into a language-model chain along with the user’s question.

The chain setup demonstrates how answers depend on retrieval quality and how many chunks are included. A basic “stuff” approach stuffs the retrieved context into a single prompt, which works as long as the combined text stays within the model’s context limit. When the system retrieves too little, answers can miss details. For instance, asking “Who are the authors of the book?” returns Reid Hoffman correctly but incorrectly omits GPT-4 as a co-author—an error attributed to the model’s interpretation of “authors” and the retrieved context. A separate test with an unrelated query (“Has it rained this week?”) yields “not specified,” illustrating the intended behavior when relevant passages aren’t found.

To manage context limits, the transcript contrasts chain types. Increasing K (the number of retrieved documents) can trigger context-length errors (the example cites a maximum context length around 4,097 tokens). Alternatives include “map-reduce”-style chains such as map-rerank, which run the language model on multiple retrieved chunks and then merge results using scores. Another option is retrieval QA, which wraps the retriever and question-answering logic together and can return both the final answer and the source documents.

Finally, the system is stress-tested with questions that should be answerable from the book’s table-of-contents chapters (e.g., what GPT-4 means for creativity) and with a nonsense query (“Beagle Bard”). The nonsense question is used to show that when the term isn’t present in retrieved context, the system should not confidently invent an answer. The takeaway is a production-minded checklist: chunking strategy, embedding model, vector store retrieval settings (like top-k), and chain type all determine whether the language model gets the right evidence to respond accurately.