Groq API Chat Assistant

The Groq API Chat Assistant is a tool that utilizes the Groq API to create a chat assistant capable of generating responses to user queries or prompts. It leverages large language models (LLMs) to provide informative and contextually relevant answers, making it suitable for a variety of applications such as customer support, information retrieval, and conversational interfaces.

Installation

To run this project, you need to install the necessary dependencies. Run the following command in your Colab notebook:

!pip install -q -U langchain langchain_core langchain_groq gradio

Getting Started:

To use this notebook, you will need to have the following:

• Python 3.6+
• Google Colab account
• Groq API key (you can sign up for a free account at groq.ai)

Usage

1. Initialize the Model:

from google.colab import userdata
groq_api_key = userdata.get('GROQ_API_KEY')

from langchain_groq import ChatGroq

chat = ChatGroq(
    api_key = groq_api_key,
    model_name = "mixtral-8x7b-32768"
)

2. Create a Chain:

from langchain_core.output_parsers import StrOutputParser

chain = prompt | chat | StrOutputParser()

response = chain.invoke({"text":"Why is the sky blue?"})
print(response)

3. Build an App:

import gradio as gr

def fetch_response(user_input):
    chat = ChatGroq(
        api_key = groq_api_key,
        model_name = "mixtral-8x7b-32768"
    )
    system = "You are a helpful assistant."
    human = "{text}"

    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system), ("human", human)
        ]
    )
    chain = prompt | chat | StrOutputParser()
    output = chain.invoke({"text": user_input})
    return output

user_input = "Why is the sky blue?"

fetch_response(user_input)

4. Create an Interface:

iface = gr.Interface(
    fn = fetch_response,
    inputs = "text",
    outputs = "text",
    title = "Groq Chatbot",
    description="Ask a question and get a response."
)

iface.launch()

API Reference

• ChatGroq: This class initializes the Groq API for chat functionality. It requires an API key and a model name as parameters.
• ChatPromptTemplate: This class creates a chat prompt template for interacting with the Groq API.
• StrOutputParser: This class parses the output response from the Groq API into a string format.

Additional Notes

• The code is divided into several sections, including installation instructions, imports, model initialization, and the main function.
• Ensure you have a valid GROQ_API_KEY to access the Groq API.
• The gradio library is used to create an interface for the chatbot.
• The share link provided expires in 72 hours. For permanent hosting and GPU upgrades, consider deploying to Spaces using gradio deploy from Terminal.

Acknowledgements

• Groq API documentation
• LangChain documentation
• Gradio documentation

Authors

• @Shubham Bhatia

Feedback

If you have any feedback, please reach out to me at Shubhambhatia2103@gmail.com