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RAG Application with LangChain, Nomic embeddings, Chroma VectorStore and Groq Llama3

This repository contains an example of a Retrieval Augmented Generation (RAG) application built using langchain, Nomic embeddings, and Groq Llama3. The RAG system combines retrieval-based methods with language models to generate coherent and contextually relevant responses based on uploaded PDF

Components

1. LangChain:

   • LangChain provides the core functionality for handling language models, prompts, and text processing.
   • We use the Llama3 LLM (Large Language Model) from llama-index for text generation.

2. Chroma:

   • Chroma is used as the vector store for document embeddings.
   • It organizes and indexes documents based on high-dimensional vectors.

3. Groq Llama3:

   • Groq Llama3 is integrated for querying and retrieving relevant documents.
   • It combines Groq queries with Llama3 embeddings to fetch contextually relevant information from PDF.

Usage

1. Installation:

   • Install the required Python packages using pip install -r requirements.txt.

2. Configuration:

   • Set up your Groq API , NOMIC keys and other necessary credentials.

3. Run the RAG System:

   • Initialize the RAG system with LangChain and Groq Llama3 on Streamlit App
   • Provide your PDF and retrieve contextually relevant information from it with ChatBot.

To run App

streamlit run app_pdf.py