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Chatbot with LangChain, RAG, and Django API

This project implements an AI chatbot leveraging LangChain, Retrieval-Augmented Generation (RAG), and Django to expose the chatbot's functionalities through API endpoints. The chatbot is designed to provide detailed and accurate responses based on structured data stored in a ChromaDB vector database.

Table of Contents

  1. Features
  2. Tech Stack
  3. Installation
  4. Usage
  5. API Documentation
  6. Directory Structure
  7. Contributing
  8. License

Features

  • Conversational AI: Provides intelligent and context-aware responses based on Manu's resume and personality data.
  • RAG-Based Search: Retrieves relevant information using similarity search from vectorized documents stored in ChromaDB.
  • Agent Execution: Uses LangChain's reactive agent architecture for dynamic and task-specific responses.
  • Django API: Exposes chatbot interactions through a RESTful API.

Tech Stack

  • Backend: Python, Django
  • AI Framework: LangChain, LangGraph
  • Vector Database: ChromaDB
  • Embedding Models: OpenAI GPT models
  • Document Processing: LangChain Text Splitter, Docx2txt, PyPDFLoader

Installation

Prerequisites

  1. Python 3.8+
  2. Virtual environment tools like venv or conda
  3. OpenAI API Key (store in a .env file)
  4. Django and required libraries (requirements.txt file provided)

Steps

  1. Clone the repository:

    git clone https://github.com/manugeorge04/PersonaAI.git
cd PersonaAI

  2. Create and activate a virtual environment:

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install dependencies:

    pip install -r requirements.txt

  4. Set up the data directory:

    • Inside the project root directory, create a data folder with the following structure:

      data/
├── Resume_Data/        # Store resume-related .docx files here
└── Personality_Data/   # Store personality-related .docx files here

    • Add .docx files for:

      • Resume Data: Include one or more .docx files with professional experience, projects, and achievements.
      • Personality Data: Include .docx files with personal traits, hobbies, or character-related information.

    • Example:

      data/
├── Resume_Data/
│   ├── resume1.docx
│   ├── resume2.docx
└── Personality_Data/
    ├── personality_traits.docx
    ├── hobbies.docx

    • You can add multiple .docx files to each folder as needed.

  5. Set up the environment variables:

    • Create a .env file in the root directory with the following content: 
      OPENAI_API_KEY=your_openai_api_key

  6. Initialize the vector database and process documents:

    python vectorstore.py

  7. Run the Django development server:

    python manage.py runserver

Usage

Interacting with the API

  1. Start the server (python manage.py runserver).
  2. Use tools like Postman or curl to send a POST request to the /send_message/ endpoint.
    • Endpoint: http://127.0.0.1:8000/send_message/
    • Request Body: 
      {
    "question": "What are Manu's skills?"
}
    • Response: 
      {
    "response": "Manu is skilled in Python, Django, LangChain, and more..."
}

API Documentation

POST /send_message/

  • Description: Handles user queries and returns responses based on structured data.
  • Request Body: 
    {
    "question": "Your question here"
}
  • Response:
    • 200 OK: Returns the chatbot's response.
    • Example: 
      {
    "response": "Manu has experience working with AI, Python, and more..."
}

Directory Structure

chatbot-django-rag/
│
├── llm_utils/
│   ├── chatbot.py        # Main chatbot logic
│   └── vectorstore.py    # Vector database setup
│
├── views.py              # API endpoint logic
│
├── data/
│   ├── Resume_Data/      # Directory for resume documents
│   └── Personality_Data/ # Directory for personality documents
│
├── requirements.txt      # Python dependencies
├── manage.py             # Django management script
├── .env                  # Environment variables
└── README.md             # Project documentation

Contributing

  1. Fork the repository.
  2. Create a new branch (feature/new-feature).
  3. Commit your changes.
  4. Push to the branch.
  5. Create a pull request.

License

This project is licensed under the MIT License.