Create a ChatBot in Python Using Llama2 and LangChain - Ask Questions About Your Own Data

Introduction

In this tutorial, you will learn how to create a ChatBot using the Llama2 language model and LangChain. This ChatBot will be able to read your own data and answer questions based on that information. We will use the 2023 World Chess Championship as our example dataset. By the end of this guide, you’ll have a functional ChatBot and a demo interface created with Gradio.

Step 1: Set Up Your Environment

To get started, you need to set up your development environment. Follow these steps:

1. Install Required Libraries Make sure you have Python installed. You will need the following libraries:

   • Llama2
   • LangChain
   • Gradio

   You can install them using pip:

   pip install llama2 langchain gradio
   

2. Access the Google Colab Notebook Use the provided link to access the Google Colab notebook where you can run the code directly: Google Colab Notebook

Step 2: Prepare Your Dataset

In this step, you will prepare the dataset that your ChatBot will use to answer questions.

1. Collect Data Use a dataset related to the World Chess Championship 2023. You can gather information from articles, reports, or any relevant source.

2. Format Data Ensure your data is in a readable format, such as CSV or JSON. This will make it easy for the ChatBot to process.

3. Load Data into Your Script Use the following code snippet to load your data into the environment:

   import pandas as pd
   
   # Example for loading a CSV file
   data = pd.read_csv('path_to_your_dataset.csv')
   

Step 3: Integrate Llama2 with LangChain

Now, you need to integrate the Llama2 model with LangChain to create the ChatBot functionality.

1. Initialize Llama2 Load the Llama2 model using the LangChain library:

   from langchain import Llama2
   
   model = Llama2()
   

2. Set Up LangChain Create a LangChain instance to process the data:

   from langchain.chains import RetrievalQA
   
   qa_chain = RetrievalQA.from_chain_type(llm=model, chain_type="stuff")
   

3. Add Your Data Feed your dataset into the QA chain:

   qa_chain.add_documents(data)
   

Step 4: Create a Gradio Interface

To make your ChatBot accessible, you will create a Gradio interface.

1. Define Response Function Create a function that will take user queries and return responses from the ChatBot:

   def respond_to_query(query):
       return qa_chain.run(query)
   

2. Set Up Gradio Interface Create a simple Gradio interface for user interaction:

   import gradio as gr
   
   interface = gr.Interface(fn=respond_to_query, inputs="text", outputs="text")
   interface.launch()
   

Conclusion

You have now set up a ChatBot using Llama2 and LangChain, which can answer questions based on your dataset. You also created a user-friendly interface using Gradio.

Next Steps

• Experiment with different datasets to see how the ChatBot's responses vary.
• Explore additional features in LangChain to enhance your ChatBot's capabilities, such as adding more complex query handling or integrating external API data.
• Continue learning about machine learning and natural language processing through more tutorials and practice.