Crafting an AI-Powered Chatbot for Document Q&A using RAG, Langchain, and Streamlit
Leverage Langchain, OpenAI’s GPT, and Meta’s FAISS for Contextual Document Q&A and Rapid Insight Retrieval
A few years ago, I took part in a consulting case competition analyzing punitive damages for a mobile phone manufacturer, stemming from a competitor’s pricing and anti-competitive practices. A crucial detail that boosted our claim was tucked in a 60-page report’s… footnotes. If we hadn’t found that tiny, obscure clause, our case would have left money on the table and well, our team wouldn’t have won (I lie — we came in second but that’s hardly the point).
Fact is, these types of situations happen all the time — people are busy and skim through documents, risks can be glossed over and opportunities are left on the table. Whether it’s a lease document with hidden caveats or a dense report with elusive investment data, not many people have the time or bandwidth to capture every detail in lengthy reports.
Oftentimes, the challenge lies in swiftly sieving through heaps of documents to extract the nuggets of insight that drive informed decisions. For time-crunched professionals, each minute saved from scrolling through reports can result in meaningful improvements in efficiency and building analysis.
