🤖 ChatBot with Conversation Memory : Streamlit App, LangChain, StreamlitChatMessageHistory, Groq API to : llama3, Mixtral, Gemma

This repository contains an example of a Memero Conversational ChatBot (RAG) application built using LangChain and Groq Llama3.

🛠️ Components

1. LangChain:

   • llama-index 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 key and other necessary credentials.

3. Run the RAG System:

   • Initialize the app with Groq Api on Streamlit App

To run App

streamlit run main.py