📄 DocChat – RAG-based Document Chat Application

DocChat is an end-to-end Retrieval-Augmented Generation (RAG) application that allows users to upload documents and ask questions about their content. The system retrieves the most relevant document chunks using vector similarity search and generates accurate, context-aware answers using a Large Language Model.

This project demonstrates practical use of LLMs, vector databases, and backend system design in a real-world application.

🔮 Features

• Source citations in responses
• Multi-document comparison
• Conversation memory
• Document Categorization

🧠 RAG Workflow

User Query
   ↓
Query Embedding
   ↓
Vector Similarity Search (pgvector)
   ↓
Top-K Relevant Document Chunks
   ↓
Prompt Construction (Query + Context)
   ↓
LLM Generates Final Answer

🛠 Tech Stack & Libraries

Backend & Database

• Python
• Peewee ORM – database modeling and queries
• PostgreSQL (Supabase) – structured data and embeddings storage
• pgvector – vector similarity search
• psycopg2-binary – PostgreSQL driver

AI / ML

• Embedding Model – Hugging Face
• Large Language Model (LLM) – Groq/OpenAI

Frontend

• Streamlit – interactive web interface

⚙️ Setup & Installation

1️⃣ Clone the repository

git clone https://github.com/your-username/docchat.git
cd docchat

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Configure environment variables

4️⃣ Run the application

streamlit run app.py

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