App Link: https://rag-bot-app.streamlit.app/
Intelligent Document Q&A Assistant is an AI-powered chatbot that enables users to ask natural-language questions over their own documents and receive accurate, contextual answers using Retrieval-Augmented Generation (RAG) powered by Google Gemini.
Across industries such as:
- Legal
- Finance
- Healthcare
- Construction
- Research & Consulting
professionals deal with massive volumes of documents including: Contracts, Policy documents, Manuals, SOPs, Technical reports, and more.
Traditional keyword search and static FAQs fail to deliver:
- Context-aware answers
- Cross-document reasoning
- Natural language understanding
As a result:
- Employees spend hours searching PDFs
- Critical insights are missed
- Knowledge remains siloed
There is a strong need for an intelligent document-aware assistant capable of answering questions directly from enterprise knowledge bases.
To build a scalable, enterprise-grade RAG chatbot that enables:
- 📄 Smart ingestion of large PDF and text documents
- 🔍 Semantic retrieval using vector search
- 💬 Natural language Q&A
- 🧠 Context-aware reasoning using LLMs
- ⚡ Instant answers from private knowledge sources
A full-stack Retrieval-Augmented Generation (RAG) system that:
- Accepts document uploads (PDF / text)
- Extracts and chunks text content
- Converts text into vector embeddings
- Stores embeddings in a vector database (FAISS)
- Retrieves relevant chunks based on user queries
- Uses Google Gemini 1.5 Flash to generate precise answers grounded in retrieved context
- Upload multiple PDF or text documents
- Semantic document search using vector embeddings
- Natural language chat interface
- Context-aware answers grounded in documents
- Fast retrieval with FAISS
- Private document-level Q&A (no internet search)
- Simple Streamlit UI
- Cloud-deployed and scalable
| Layer | Technology |
|---|---|
| LLM | Google Gemini 2.5 Flash Lite |
| Framework | LangChain |
| Vector Database | FAISS |
| Embeddings | SentenceTransformers / Gemini-compatible |
| Text Extraction | PyPDF |
| Frontend | Streamlit |
| Backend | Python |
| Deployment | Streamlit Cloud |
-
Legal
- “Which clause discusses penalty on late delivery?”
-
Finance
- “What is the refund timeline for cancelled trips?”
-
Healthcare
- “When should Stage 2 hypertension be escalated?”
-
Research
- “Summarize the methodology used in Section 3.”
-
Operations
- “What is the approval process mentioned in SOP?”
- 90% reduction in document navigation time
- 24×7 AI assistant for internal knowledge access
- Democratized document search for non-technical users
- Faster decision-making and productivity gains
- Secure, private document reasoning