ChatGroq RAG

This repository demonstrates how to create a webpage retrieval system using model named mixtral-8x7b-32768 on Groq and LangChain with Streamlit for interactive querying. The app uses Ollama Embeddings to handle document vectorization and retrieval.

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Installation Instructions

Follow the steps below to set up your environment on your local machine:

1. Clone the Repository

First, clone the repository to your local machine:

git clone <repository_url>
cd <repository_name>

2. Install Required Libraries

Install the required dependencies using pip:

The necessary packages include:

• Streamlit: for the web interface.
• LangChain: for webpage loading, splitting, and querying.
• FAISS: for vector-based document retrieval.
• Ollama: for embeddings.

To manually install some of the key dependencies:

pip install streamlit langchain faiss-cpu ollama python-dotenv

3. Install and Set Up Ollama

Install Ollama to pull Llama-2 (run cmd/powershell as admin in Windows):

choco install ollama #Windows

Then, pull the Llama-2 model using Ollama:

ollama pull llama2

5. Obtain API Keys

• Groq API Key: You will need a Groq API key.

  os.environ["GROQ_API_KEY"] = "your_groq_api_key_here"

6. Provide the website link

 st.session_state.loader=WebBaseLoader("https://docs.smith.langchain.com/")

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Running the Application

Once you have the environment set up and the necessary dependencies installed, run the app using Streamlit:

streamlit run app.py

After starting the app, you can input your query in the text box and it will retrieve and answer based on the provided webpage.

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Screenshot

Here’s an example of the app's interface:

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How it Works

1. Webpage Loading: The app uses the WebBaseLoader from LangChain to load webpage from a URL.
2. Text Splitting: It splits the loaded webpages into chunks using RecursiveCharacterTextSplitter for efficient querying.
3. Embeddings: The webpage embeddings are generated using Ollama Embeddings, which are stored in FAISS for fast retrieval.
4. Retrieval Chain: The system builds a retrieval chain using ChatGroq and LangChain’s document chain functions.
5. Prompting: You can ask questions about the webpage, and the system will return relevant answers based on context from the webpage.