Building a Powerful Chatbot with GPT4All and Langchain: A Step-by-Step Tutorial

Ayyaz

@AyyazTech

Introduction: Hello everyone! In this blog post, we will embark on an exciting journey to build a powerful chatbot using GPT4All and Langchain. By following the steps outlined in this tutorial, you'll learn how to integrate GPT4All, an open-source language model, with Langchain to create a chatbot capable of answering questions based on a custom knowledge base. We'll also explore how to enhance the chatbot with embeddings and create a user-friendly interface using Streamlit.

Step 1: Setting Up the Project and Installing Dependencies To get started, let's create a new directory for our project and navigate to it in the terminal:

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Next, create a virtual environment to keep our project's dependencies isolated. You can use the following command:

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Now, let's install the required dependencies using pip:

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We'll be using the Langchain library for integrating with GPT4All, langchain_core and langchain_community for additional functionality, and Streamlit for creating the user interface.

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Step 2: Downloading the GPT4All Model To use GPT4All, we need to download the model file. In this tutorial, we'll be using the "mistral-7b" model. Create a new directory called "models" in your project directory and download the model file using the following command:

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If you prefer, you can also download the model file directly from the GPT4All website.

Step 3: Implementing Simple Integration of GPT4All with Langchain Now, let's create a new Python file called chatbot.py and start implementing the integration of GPT4All with Langchain.

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In this code, we:

1. Import the necessary modules.
2. Create a variable model_path to store the path of the downloaded model file.
3. Create a CallbackManager instance.
4. Create an llm instance using the GPT4All class, passing the model_path, callback_manager, and setting verbose to True.
5. Define a prompt template using a multiline string.
6. Create a prompt variable using the PromptTemplate class, passing the template and input_variables.
7. Create an llm_chain instance using the LLMChain class, passing the prompt and llm.
8. Ask a question by setting the query variable and invoking the llm_chain with the query.
9. Print the result.

Now, run the chatbot.py file in the terminal:

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You should see the model's response printed in the terminal.

Step 4: Enhancing the Chatbot with Embeddings To enable the chatbot to chat with a document, we'll use embeddings and create a vector store. First, create a new directory called "data" in your project and place your text files inside it.

Now, let's modify the chatbot.py file to incorporate embeddings:

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In this updated code, we:

1. Import additional modules for embeddings and document handling.
2. Load the text files from the "data" directory using the DirectoryLoader class.
3. Split the text into chunks using the CharacterTextSplitter class.
4. Create a vector store using the Chroma.from_documents() method, passing the text chunks and embeddings.
5. Create a retriever from the vector store using the as_retriever() method.
6. Load the question-answering chain with the retriever using RetrievalQA.from_chain_type().
7. Ask a question and print the result.

Run the chatbot.py file again to see the chatbot's response based on the provided knowledge base.

Step 5: Creating a User-Friendly Interface with Streamlit To enhance the user experience, let's create a simple user interface using Streamlit. Create a new file called app.py and add the following code:

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In this code, we:

1. Import the necessary modules, including Streamlit.
2. Define a load_model() function to load the GPT4All model.
3. Define a load_vectorstore() function to load the vector store from the "data" directory.
4. Define the main() function, which sets up the Streamlit app.
5. Create a text input for the user to enter their question and a button to trigger the chatbot.
6. When the button is clicked, invoke the question-answering chain with the user's query and display the result.

Run the Streamlit app using the following command:

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This will start the Streamlit server, and you can interact with the chatbot through the web interface.

Conclusion: Congratulations! You have successfully built a powerful chatbot using GPT4All and Langchain. You learned how to integrate GPT4All with Langchain, enhance the chatbot with embeddings, and create a user-friendly interface using Streamlit.

Feel free to experiment with different models, add more documents to your knowledge base, and customize the prompts to suit your needs. If you enjoyed this tutorial and would like to see more videos like this, please let me know in the comments section below. Your feedback and suggestions are highly appreciated!

You can find the GitHub Source code from this url: https://github.com/ayyazzafar/ai_chatbot_powered_by_langchain_gpt4all_streamlit/tree/main

If you prefer to read from Medium then visit this link: https://ayyazzafar.medium.com/building-a-powerful-chatbot-with-gpt4all-and-langchain-a-step-by-step-tutorial-04d28d32fc82

Don't forget to like and share this blog post with others who might find it helpful. If you have any questions or encounter any issues, feel free to ask in the comments, and I'll do my best to assist you.

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Thank you for reading, and happy chatbot building!