Groq Chat App

A chatbot application built using Streamlit, LangChain, and Groq for conversational AI. This app enables users to interact with a conversational agent powered by advanced language models, providing engaging and intelligent responses. Features

Interactive chatbot interface.
Supports multiple language models (e.g., mixtral-8x7b-32768, llama2-70b-4096).
Adjustable conversational memory length for context-aware responses.
Tracks and displays chat history during a session.

Technologies Used

Streamlit: For building the web-based user interface.
LangChain: For managing conversational flows and memory.
Groq: For leveraging AI models in conversations.
Python Libraries:
    os
    dotenv
    langchain
    langchain_groq
    streamlit

Installation Step 1: Clone the Repository

git clone repo_URL cd groq-chat-app

Step 2: Set Up Environment

Create a .env file in the root directory:

touch .env

Add your Groq API Key to the .env file:

GROQ_API_KEY=your_groq_api_key_here

Step 3: Install Dependencies

Make sure Python 3.9+ is installed. Then, run:

pip install -r requirements.txt

Usage Run the Application

Start the Streamlit app by running:

streamlit run app.py

Open the App

After running the command, you will see a URL (e.g., http://localhost:8501). Open this in your browser. App Workflow

Select the language model from the sidebar.
Adjust the conversational memory length using the slider.
Enter your question in the text area and hit Enter.
The chatbot will respond, and your conversation history will be displayed.

Directory Structure

groq-chat-app/ │ ├── app.py # Main application file ├── requirements.txt # Python dependencies ├── .env # Environment file for secrets ├── README.md # Documentation

Requirements

Python 3.9+
Groq API key
Supported AI models from Groq

Customizations

Add or modify available models in the sidebar under:

model = st.sidebar.selectbox( 'Choose a model', ['mixtral-8x7b-32768', 'llama2-70b-4096'] )

Adjust the default conversational memory length in:

conversational_memory_length = st.sidebar.slider('Conversational memory length:', 1, 10, value=5)

Contributing

Feel free to fork this repository, make updates, and submit pull requests. Contributions are welcome!