RAG Chatbot with Conversational Memory

A Retrieval-Augmented Generation (RAG) chatbot built with LangChain that supports conversational memory for follow-up questions. Features FastAPI backend and Streamlit frontend with document upload capabilities.

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🚀 Features

• Document Upload: Supports PDF, DOCX, HTML files (200MB limit per file)
• Conversational Memory: Maintains context for follow-up questions
• Multiple Models: Choose between GPT-4o-mini and other model options
• Vector Storage: Efficient document retrieval with ChromaDB
• Interactive API: FastAPI backend with Swagger documentation
• LangSmith Integration: Built-in tracing and monitoring

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⚙️ Configuration

Create a .env file in the root with the following variables:

OPENAI_API_KEY=your_openai_api_key
LANGSMITH_API_KEY=your_langsmith_api_key

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📦 Project Structure

RAG-CHATBOT/
├── api/                         # FastAPI backend server
│   ├── __pycache__/             # Python bytecode cache
│   ├── chroma_db/               # ChromaDB vector storage
│   ├── app.log                  # Logging file
│   ├── chroma_utils.py          # ChromaDB utilities
│   ├── db_utils.py              # Chat history & metadata DB logic
│   ├── langchain_utils.py       # LangChain RAG pipeline
│   ├── main.py                  # FastAPI entry point
│   ├── pydantic_models.py       # Request/response validation
│   └── rag_app.db               # SQLite DB
├── app/                         # Streamlit frontend
│   ├── __pycache__/             # Python bytecode cache
│   ├── api_utils.py             # FastAPI client utils
│   ├── chat_interface.py        # Chat UI
│   ├── sidebar.py               # File upload & model switch
│   └── streamlit_app.py         # Streamlit app
├── docs/                        # Sample documents
├── documentation/               # Guides & screenshots
│   ├── screenshots/             # UI screenshots
│   ├── api_reference.md         # API docs
│   └── user_guide.md            # Manual
├── .env                         # Env variables
├── .gitignore                   # Git ignore rules
├── LICENSE                      # MIT License
├── notes.txt                    # Dev notes
├── README.md                    # This file
└── requirements.txt             # Dependencies

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⚡ Quick Start

🔧 Prerequisites

• Python 3.9+
• OpenAI API Key
• LangSmith API Key (optional)

🛠 Installation

1. Clone the repository

   git clone https://github.com/YOUR-USERNAME/rag-chatbot.git
   cd rag-chatbot

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment

   Create a .env file using the sample and add your keys:

   cp .env.example .env

4. Run the FastAPI backend

   cd api
   uvicorn main:app --reload --port 8000

5. Run the Streamlit frontend

   In a new terminal:

   cd app
   streamlit run streamlit_app.py --server.port 8500

6. Access the application

   • Streamlit UI: http://localhost:8500
   • Swagger Docs: http://localhost:8000/docs

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📚 Usage Guide

📤 Upload Documents

1. Open the Streamlit UI
2. Use the sidebar to upload PDF, DOCX, or HTML files
3. Uploaded docs are indexed into ChromaDB

💬 Chat with Documents

1. Ask a question related to the uploaded content
2. Ask follow-up questions — context is remembered
3. Switch models via the sidebar dropdown

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🧪 API Usage Example (Python)

import requests

# Upload a document
files = {'file': open('document.pdf', 'rb')}
upload_res = requests.post('http://localhost:8000/upload-doc', files=files)

# Chat with the document
chat_payload = {
    "message": "What is this document about?",
    "session_id": "user123"
}
chat_res = requests.post('http://localhost:8000/chat', json=chat_payload)
print(chat_res.json())

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🔌 API Endpoints

Endpoint	Method	Description
/chat	POST	Chat with uploaded documents
/upload-doc	POST	Upload and index documents
/list-docs	GET	List all uploaded documents
/delete-doc	POST	Delete a specific document

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🧠 Architecture Overview

1. Document Ingestion: Files are split into text chunks
2. Embedding: Text is embedded using OpenAI embeddings
3. Storage: Embeddings are stored in ChromaDB
4. Retrieval: Relevant chunks are fetched for user queries
5. Generation: LangChain passes context and query to LLM
6. Memory: Session IDs preserve conversation history

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🛠 Environment Setup

1. Copy template and configure

   cp .env.example .env

2. Edit .env and add keys

   • Get your OpenAI API key from: https://platform.openai.com/account/api-keys
   • (Optional) Get LangSmith API key from: https://smith.langchain.com/

3. Keep .env private

   .env is included in .gitignore to avoid committing secrets.

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🔑 Required API Keys

• OPENAI_API_KEY: Required for embeddings and completions
• LANGSMITH_API_KEY: For request tracing and logging

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🖼 Screenshots

Screenshots are located in:

documentation/screenshots/

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🤝 Contributing

We welcome contributions!

1. Fork the repository
2. Create a feature branch
3. Make your changes and commit
4. Open a pull request

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📜 License

This project is licensed under the MIT License.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:
...

See the full LICENSE file for more details.

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🙏 Acknowledgments

• Built with LangChain
• Vector storage by ChromaDB
• UI by Streamlit
• Backend by FastAPI
• Observability via LangSmith

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📬 Contact

Have feedback, issues, or ideas?

• Open an issue on GitHub
• Submit a pull request
• Contact the maintainer (aryanmahawar205@gmail.com)