💬 AI Chatbot

An AI-powered personalized chatbot built using RAG + Gemini API, integrated with FastAPI backend, ChromaDB for vector storage, and a responsive JS frontend. Deployed seamlessly on Render.

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🚀 Features

• 🧠 Retrieval-Augmented Generation (RAG) for contextual answers from personal/project data
• ⚡ FastAPI Backend for handling chatbot requests
• 📂 ChromaDB as vector database for efficient embeddings & retrieval
• 🤖 Gemini API integration for LLM responses
• 🌐 Frontend with HTML/CSS/JS for chat interface
• ☁️ Deployed on Render with environment-based API key management

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🛠 Tech Stack

• Backend: FastAPI, LangChain
• Vector DB: ChromaDB
• LLM API: Gemini API
• Frontend: HTML, CSS, JavaScript
• Deployment: Render

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⚙️ Setup & Installation

1. Clone the repo

   git clone https://github.com/anikchand461/AkBot.git
   cd ai-chatbot

2. Create virtual environment & install dependencies

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Set up environment variables
   Create a .env file in the project root:

   GEMINI_API_KEY=your_api_key_here

4. Run the backend

   uvicorn main:app --reload

5. Open the frontend
   Open index.html in your browser, or serve via any static hosting.

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📦 Deployment on Render

1. Push your repo to GitHub.
2. Create a Render Web Service → select FastAPI backend.
3. Add GEMINI_API_KEY under Environment Variables in Render Dashboard.
4. Deploy 🚀

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