RAG Chatbot

Inspiration

We wanted to solve the problem of searching and understanding large document collections—whether technical docs, research papers, or business files—by combining the power of retrieval and generative AI. Our goal was to make document Q&A as easy and transparent as chatting with an expert.

What it does

• Lets you upload PDF, DOCX, and TXT files
• Processes and chunks documents for semantic search
• Finds the most relevant content for any question
• Generates context-aware answers with source citations
• Provides an interactive Streamlit chat interface
• Allows exporting chat history for review or sharing

How we built it

• Streamlit for the chat UI and user experience
• LangChain for robust text chunking and processing
• Sentence Transformers for fast, high-quality embeddings
• FAISS for scalable vector similarity search
• Hugging Face Transformers for answer generation
• Modular Python architecture for easy extension and testing
• Docker and GitHub Actions for deployment and CI/CD

Challenges we ran into

• Handling noisy or fragmented text from PDFs
• Ensuring fast embedding and retrieval for large files
• Making the UI responsive during long processing steps
• Integrating multiple AI libraries and frameworks smoothly
• Providing clear, readable answers with accurate source attribution

Accomplishments that we're proud of

• Built a complete RAG pipeline from scratch
• Achieved instant, accurate answers with transparent citations
• Created a beautiful, user-friendly chat interface
• Enabled support for multiple document formats
• Automated deployment with Docker and CI/CD

What we learned

• The importance of robust text cleaning and chunking
• How to combine retrieval and generation for better answers
• Best practices for scalable, production-ready AI systems
• The value of transparent source attribution in AI applications
• How to optimize user experience for real-world document Q&A

What's next for RAG Chatbot

• Integrate OpenAI GPT for even smarter responses
• Add support for images, tables, and multi-modal documents
• Enable real-time collaboration and annotation
• Expand to enterprise authentication and permissions
• Provide advanced analytics and insights on document collections

Built With

• docker
• faiss
• github
• hugging
• langchain
• python
• sentence
• streamlit
• transformers