Simple Gemini RAG Chatbot Framework

Just add your gemini api key in .env file, URLS to scrape, and modify System_Prompt in /configs/config.py. That's it, you're good to deploy a RAG-enabled-chatbot with a reasoning Gemini Model(if a reasoning model is used).

Setup

1. Create virtual enviornment for python and source it.

   python3 -m venv .venv

• For linux/macOS:

  source .venv/bin/activate

• For Windows:

   .venv\Scripts\Activate.ps1

2. Install required packages from requirements.txt

   pip install -r requirements.txt

3. Run Application

   python app.py

Done, your GEMINI Powered RAG-CHATBOT is READY!

Developers:

1. To switch to a different LLM, modify llm_setup/llm_setup.py.
2. This framework follows a layered architecture:
   • Service Layer: Contains all business logic.
   • Store Layer: Handles adding/retrieving embeddings from Chroma DB (basic functionality).