AI-Powered Portfolio Chatbot 🚀

📌 Overview

This project is an interactive chatbot integrated into my portfolio website, designed to provide dynamic responses about my work, projects, and experiences. It leverages Retrieval-Augmented Generation (RAG) with FAISS for efficient data retrieval and the Gemini-2.0-Flash model for response generation.

Instead of manually navigating through my portfolio, visitors can ask questions and get precise, AI-driven answers.

🔗 Try the AI-Powered Chatbot

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🚀 Features

• ✅ Uses FAISS for fast and scalable similarity search.
• ✅ Converts textual data into embeddings using all-MiniLM-L6-v2.
• ✅ Employs Gemini-2.0-Flash for intelligent response generation.
• ✅ Built with FastAPI and deployed on Render for seamless API access.
• ✅ Frontend implemented in HTML, CSS, and JavaScript for an interactive experience.

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📁 Project Structure

• app.py - The main script running on Render. Handles query processing, FAISS retrieval, LLM interaction, and final response generation.
• portfolioContent.docx - Contains structured textual data about my portfolio, including projects, experiences, and publications.
• requirements.txt - Lists all dependencies required for the project.
• vectorDatabaseInit.py - Generates the FAISS vector database by embedding the textual data.
• vector_database.index - The prebuilt FAISS database containing indexed vector embeddings for fast retrieval.

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🛠️ Technologies Used

• Python for backend logic and processing
• FastAPI for building the API
• FAISS for efficient similarity search
• SentenceTransformer (all-MiniLM-L6-v2) for text embeddings
• Gemini-2.0-Flash for AI-generated responses
• Render for API deployment
• HTML, CSS, JavaScript for frontend

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⚡ Setup & Usage

There are two ways you can run this file.

1. Using docker
2. Cloning the repository

Tip

Create a .env file with your api key to access google's gemini models. Look at .env.example for reference

🐳 Run with Docker

You can run the entire backend API using Docker for an easy and consistent setup. Follow the steps below:

1️⃣ Build the Docker Image

From the root directory of the project, run:

docker build -t chatbot .

2️⃣ Run the Docker Container

docker run -d -p 8000:8000 chatbot

This will start the FastAPI server inside the Docker container and expose it at http://localhost:8000

You can access the interactive API at:

http://localhost:8000/docs

It comes with a swagger UI capable of sending and receiving prompts and messages.

📦 Notes

Make sure Docker is installed and running on your system.

The Dockerfile takes care of installing all dependencies and running vectorDatabaseInit.py to generate the FAISS index before starting the API server.

Now you’re all set to use the chatbot locally via Docker! 🚀

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📥 Cloning the repository

1️⃣ Install Dependencies

pip install -r requirements.txt

2️⃣ Initialize the FAISS Vector Database

To prepare the database, run:

python vectorDatabaseInit.py

This will generate the FAISS index file (vector_database.index) containing the embeddings of the portfolio content.

3️⃣ Run the API Locally

Start the FastAPI server using:

uvicorn app:app --host 0.0.0.0 --port 8000

Once the server is running, you can access the API documentation and test endpoints by visiting:

http://127.0.0.1:8000/docs

4️⃣ Interact with the Chatbot

Use the frontend interface to input queries. The frontend sends the query to the API, which retrieves relevant information using FAISS. The Gemini-2.0-Flash model generates a response based on the retrieved data. The chatbot displays the AI-generated response dynamically.

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📢 Live Demo

Check out the live chatbot here: 🔗 Try it Now

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📜 License

This project is licensed under the MIT License – feel free to use and modify it!