AI-powered document analysis and question answering.

Inspiration

People often spend a lot of time reading lengthy documents to find specific information. I wanted to build an AI tool that makes documents easier to understand, search, and interact with.

What it does

Document Intelligence allows users to upload documents, ask questions in natural language, and receive relevant AI-generated answers. It uses RAG to retrieve useful information from the documents before generating responses.

How we built it

The project was built using Python and Flask, with document processing, text chunking, embeddings, vector search, and an LLM API. The retrieved document content is provided to the LLM to generate context-aware answers.

Challenges we ran into

The main challenges were extracting useful information from different documents, creating effective chunks, retrieving relevant content, and improving the accuracy of generated answers while keeping the application responsive.

Accomplishments that we're proud of

We built a working end-to-end AI application that can process documents and answer user questions using their content. The project demonstrates a practical use of RAG for document analysis.

What we learned

We learned about document processing, embeddings, vector search, RAG pipelines, prompt design, LLM integration, and building an end-to-end AI application.

What's next for AI-powered document analysis and question answering

We plan to add support for more document formats, source citations, better answer verification, conversation history, and improved retrieval to make the system more accurate and useful.