GPT4ALL สร้าง ChatBot แบบ Local แถมยังทำ RAG (Retrieval-Augmented Generation) ในการคุยกับเอกสารได้

Introduction

In this tutorial, we will guide you through the process of creating a local chatbot using GPT4ALL, incorporating Retrieval-Augmented Generation (RAG) to interact with documents offline. This integration of advanced AI technology enables you to build a chatbot capable of processing and responding to information from various documents without needing an internet connection.

Step 1: Setting Up Your Environment

To start building your local chatbot, you’ll need to configure your development environment.

1. Install Required Software

   • Ensure you have Python installed on your machine. You can download it from the official Python website.
   • Install the necessary libraries. Open your terminal or command prompt and run:

     pip install gpt4all
     pip install langchain
     

2. Download GPT4ALL Model

   • Visit the official GPT4ALL repository to download the model files. Make sure to choose the correct version for your operating system.

3. Set Up Directory Structure

   • Create a dedicated folder for your project. Inside this folder, create subdirectories for:
     • Model files
     • Document storage
     • Code files

Step 2: Implementing the Chatbot Logic

Now that your environment is set up, it’s time to implement the chatbot's core logic.

1. Create the Chatbot Script

   • Create a new Python file (e.g., chatbot.py) in your project folder. Begin by importing the necessary libraries:

     from gpt4all import GPT4All
     from langchain.chains import RetrievalQA
     

2. Initialize the GPT4ALL Model

   • Load the model in your script:

     model = GPT4All('path/to/your/model')
     

3. Set Up Retrieval-Augmented Generation

   • Integrate RAG to allow the chatbot to retrieve information from documents:

     retrieval_qa = RetrievalQA.from_chain_type(
         llm=model,
         chain_type="stuff"
     )
     

Step 3: Adding Document Interaction

To make your chatbot capable of accessing documents, follow these steps:

1. Load Your Documents

   • Prepare your documents in a format the model can process (e.g., PDF, TXT).
   • Write a function in your script to read these documents:

     def load_documents(directory):
         # Logic to read documents from the specified directory
         pass
     

2. Create a Query Function

   • Implement a function to handle user queries and return relevant responses:

     def query_bot(user_input):
         return retrieval_qa.run(user_input)
     

Step 4: Running Your Chatbot

Once everything is set up, it’s time to run your chatbot and interact with it.

1. Start the Chatbot

   • Add the following code to run your chatbot in a loop:

     if __name__ == "__main__":
         load_documents('path/to/documents')
         while True:
             user_input = input("You: ")
             response = query_bot(user_input)
             print("Bot:", response)
     

2. Test Your Chatbot

   • Run your script in the terminal and start chatting with your bot. Test various queries to see how well it interacts with your documents.

Conclusion

You have successfully created a local chatbot using GPT4ALL with Retrieval-Augmented Generation capabilities. This setup allows for efficient offline interaction with documents. As next steps, consider refining your document loading process, enhancing the chatbot's logic for better responses, or exploring additional features like integrating voice commands. Enjoy experimenting with your newly built chatbot!