📚 Research Assistant RAG

Retrieval-Augmented Generation (RAG) powered document question-answering system built with FastAPI, FAISS, Sentence Transformers, Gemini, and Streamlit.

🔗 Live Demo Frontend: https://research-assistant-rvw6i2kgkdmrdu8mpsnvm8.streamlit.app/

🔗 Backend API https://research-assistant-1-s6ak.onrender.com

📖 API Documentation Swagger Docs: https://research-assistant-1-s6ak.onrender.com/docs

✨ Features

✅ PDF Upload & Processing ✅ Automatic Text Extraction ✅ Smart Text Chunking ✅ Semantic Search with FAISS ✅ Retrieval-Augmented Generation (RAG) ✅ Multi-Document Search ✅ Conversational Memory ✅ Source Attribution ✅ Confidence Scoring ✅ Chat Export ✅ Interactive Streamlit Frontend

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🛠️ Tech Stack

• 🐍 Python
• ⚡ FastAPI
• 🎨 Streamlit
• 🔍 FAISS
• 🤗 Sentence Transformers
• 🤖 Gemini API

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🏗️ Architecture

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📸 Screenshots

🏠 Home Page

📄 Upload PDF

❓ Ask Questions

💬 Chat History

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📂 Project Structure

research-assistant/
│
├── app/
│   ├── services/
│   └── utils/
│
├── ui/
│   └── streamlit_app.py
│
├── uploads/
├── data/
├── vectorstore/
│
├── tests/
├── main.py
├── requirements.txt
└── README.md

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🚀 How It Works

1. Upload one or more PDF documents.
2. Extract text and split it into chunks.
3. Generate embeddings using Sentence Transformers.
4. Store embeddings in a FAISS vector index.
5. Retrieve relevant chunks based on user queries.
6. Use Gemini to generate context-aware answers.
7. Display answers with confidence scores and sources.

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🎯 Key Capabilities

• Semantic document search
• Retrieval-Augmented Generation (RAG)
• Multi-document retrieval
• Conversational memory
• Source inspection
• Confidence scoring
• Chat export functionality

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🔮 Future Improvements

• Hybrid Search (FAISS + BM25)
• Document Deletion & Re-indexing
• User Authentication
• Cloud Deployment
• Advanced Reranking
• Persistent Chat Storage

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