RAG-Powered Chatbot with Chroma and Groq

RAG-Powered Chatbot with Embeddings is a project designed to build an intelligent chatbot that interacts with users by understanding and retrieving information from a large set of documents. The system utilizes advanced techniques to enhance the chatbot's responses based on the document content it has access to.

Main Objectives

• Compare Text Embeddings: Use the Groq API to measure the similarity between different pieces of text.
• Manage a Document Database: Create and maintain a document database using Chroma, with text embedded by HuggingFace models.
• Interactive Chatbot: Allow users to ask questions, retrieve relevant information from the database, and get responses generated through Groq.

Key Functionalities

• Text Similarity: Compare and analyze the similarity between different texts.
• Database Creation: Set up and manage a database for storing and retrieving documents.
• Contextual Responses: Query the database and provide answers based on the content of the documents.

Overview

The project consists of three main scripts:

1. compare_embedding.py: Compares embeddings for text and measures similarity using the Groq API.
2. create_database.py: Creates and manages a document database with Chroma, using HuggingFace embeddings.
3. query_system.py: Queries the database and interacts with the Groq API to answer questions based on document context.

Features

• Compare embeddings for text and measure similarity.
• Create and manage a document database with Chroma.
• Query the database and interact with the Groq API.
• Use HuggingFace embeddings for text representation.

Technologies Used

• Embeddings: HuggingFace Transformers
• Database: Chroma
• API: Groq
• Environment Variables: dotenv

Installation

Follow these steps to set up the project on your local machine.

Prerequisites

• Python version: Ensure you have Python 3.8 or higher installed.

Setup

1. Clone the repository:

   git clone https://github.com/anmol52490/RAG.git
   cd your-repository

2. Install Python dependencies: Create a requirements.txt file with the following content:

   dotenv
   langchain
   transformers
   langchain-chroma
   langchain-huggingface
   groq
   

   Then, install the dependencies using pip:

   pip install -r requirements.txt

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

   GROQ_API_KEY=your_groq_api_key
   CACHE_DIR=./default_cache_path

4. Run the scripts:

   • To compare embeddings:

     python compare_embedding.py

   • To create and manage the document database:

     python create_database.py

   • To query the database and interact with the Groq API:

     python query_system.py

Contributors

• Anmol Bhusal

Acknowledgements

Thanks to HuggingFace for providing powerful models and to Groq for their API. Special thanks to LangChain for their extensive libraries and tools.