Custom AI Assistant (Voice + Chat)

This project aims to build a chatbot & Voice Assistant that responds to the custom data. The Chatbot works with a specific domain related to the IT Industry. The interface is built with React and styled using Tailwind CSS. The backend is powered by Django, and it integrates with Google Generative AI for processing PDF documents to provide responses.

Features

• Handles user input and displays bot responses
• Shows a loading indicator while waiting for a response
• Integrates with a Django backend
• Utilizes Google Generative AI for processing and responding based on PDF document content
• Faiss-cpu as the Vector DB.

Technologies

Frontend

• React
• react-speech-recognition
• Tailwind CSS

Backend

• Django
• Google Generative AI(Gemini)
• PyPDF2
• LangChain
• FAISS
• LangChain-Google-GenAI

Setup

Prerequisites

• Node.js (v14 or later)
• Python (v3.8 or later)
• pip
• Django (v3.2 or later)
• A Google API Key for Google Generative AI

Frontend Setup

1. Clone the repository:

   git clone https://github.com/chandima2000/custom-chatbot-with-langchain-gemini.git
   cd frontend

2. Install the dependencies:

   npm install

3. Run the project

   npm run dev

4. Run the development server:

   Open your browser and go to http://localhost:5173/

Backend Setup

1. Navigate to the backend folder

   cd backend

2. Create a virtual environment: (windows)

   python -m venv venv

3. Activate the virtual environment: (windows)

   venv\Scripts\activate

4. Install the dependencies:

   pip install -r requirements.txt

5. Set up environment variables:

• Create a .env file in the backend directory with the following content:

  GOOGLE_API_KEY=your_google_api_key

6. Start the Django development server:

   python manage.py runserver

Usage

1. Open your browser and go to http://localhost:5173/ for the frontend.
2. Type a message in the chat input and press Send.
3. The chatbot will respond after fetching the answer from the backend.

Backend Implementation

The backend is implemented using Django and integrates Google Generative AI for processing. Here's an overview of the core functionality:

• PDF Processing: The backend reads a PDF document and splits it into chunks.
• Vector Store: It creates a vector store using FAISS to perform similarity searches.
• Google Generative AI: It utilizes Google Generative AI for embeddings and conversational chains.
• LangChain: Simplify the process of building applications that utilize language models
• API Endpoint: The backend exposes an endpoint to handle chat requests and provide responses based on the PDF content.

Contributions

All contributions are welcome. Feel free to open issues or submit pull requests.