掌握「MemGPT」技巧

掌握「MemGPT」技巧

Table of Contents:

1. Introduction
2. Overview of mgpt code base
3. Running mgpt as a Discord bot
4. Setting up mgpt CLI and basic chat demo
5. Using mgpt to chat with documents
6. Running mgpt with local language models
7. Adding custom functions to mgpt
8. Working with memory in mgpt
9. Tips for using mgpt as a chatbot
10. Conclusion

Introduction

In this MGPT crash course, You will learn from the Creators of MGPT, Vivian and Charles, about five important topics. We will start with a brief overview of the MGPT code base, followed by the easiest way to run MGPT as a Discord bot. Then, we will cover the MGPT CLI setup and basic chat demo, as well as how to use MGPT to chat with documents. Finally, we will explore running MGPT with local language models using different backends. Additionally, we will discuss how to add custom functions to MGPT and how memory works in the MGPT framework. We will provide tips and best practices for using MGPT as a chatbot. By the end of this crash course, you will have a good understanding of how to use MGPT and its various features for your own projects.

Overview of MGPT Code Base

The MGPT code base is organized into several files, with the main file being "agent.py". Within "agent.py", the main logic of the MGPT framework is implemented. The crucial function in the code is the "step" function, which is called every time a user sends a message. The step function appends the user message to the internal message queue and then runs it through either the OpenAI API or a local language model API to get a response. Custom functions can also be added to MGPT by updating the available function bank.

To implement custom functions, you need to touch the code in a few specific places. Firstly, you need to update the available functions bank to include the new function. Then, you need to add the actual call to the custom function within the agent class. For example, you can add a function called "messageChatGPT" that enables MGPT to use chat GPT or GPT-3.5 for answering questions. The implementation involves creating a new function that performs an asynchronous API call and passing the message to be sent to the custom function.

Running MGPT as a Discord Bot

The easiest way to get started with MGPT is by using it as a Discord bot. This approach requires no installation or downloading of code. All you need is a Discord account and an OpenAI API Key. To set up MGPT as a Discord bot, you need to Create a profile and link your OpenAI API key. The profile contains information about the user, which is used to seed the bot's responses. Once the setup is complete, you can start chatting with the MGPT bot in a direct message (DM). The bot will use the information from your profile to provide personalized responses. Customization options, such as selecting default personas or creating your own, are available. You can also toggle the display of function messages and bot's inner thoughts for a cleaner chat experience. The bot's memory can be checked and updated using commands, allowing you to store and recall important information.

Setting up MGPT CLI and Basic Chat Demo

If you prefer to run MGPT from the command line interface (CLI), you can do so by installing the "pymgpt" Package. This allows you to configure and use MGPT as a simple chatbot. You can set the API base variable to specify the location of your web server hosting the MGPT model. The CLI version of MGPT supports various functionalities, such as user profiles, personas, and archival memory. You can also save transcripts of your conversations and load checkpoints to Continue previous chats. This provides flexibility and convenience for managing and persisting conversations with MGPT.

Using MGPT to Chat with Documents

MGPT can also be used to retrieve information from documents through its chatbot interface. By configuring MGPT to preload a folder or file, you can provide a set of reference documents for it to search for answers to specific questions. The archival memory of MGPT can be updated and queried to retrieve Relevant information from the documents. This allows you to utilize MGPT as a powerful document search tool, making it a valuable assistant for tasks requiring information retrieval.

Running MGPT with Local Language Models

In addition to using the OpenAI API, MGPT can be configured to run with local language models hosted on your own server. This approach allows you to use models like Texar and WebUI, leveraging their capabilities for chatbot interactions. You need to download and install the relevant software, such as WebUI, and load the desired local language model. By specifying the backend Type as WebUI and updating the API base variable, you can direct MGPT to use the local language model instead of the OpenAI API. This setup enables you to experiment with smaller models and explore different backends for MGPT.

Adding Custom Functions to MGPT

One of the key features of MGPT is the ability to add custom functions to extend its capabilities. To add a custom function, you need to update the available function bank and modify the agent class to include the function call. The function can perform various tasks, such as integrating with external tools or services. For example, you can add a function that allows MGPT to write to a Word document. By implementing the custom function and updating the persistence manager, you can seamlessly integrate the new functionality into MGPT.

Working with Memory in MGPT

MGPT incorporates memory to store and recall information during conversations. By default, memory is held in Python objects and can be saved to a file using the "save" command. However, you can implement a database-backed memory system for more efficient and persistent memory management. This involves creating new sub-classes for archival or recall memory, depending on your requirements. You can then replace the default memory classes with your custom ones and update the persistence manager accordingly. This allows MGPT to operate with a database for seamless memory management.

Tips for Using MGPT as a Chatbot

Here are some tips to enhance your experience when using MGPT as a chatbot:

1. Experiment with different personas: Customize the personality of your bot by creating or selecting different personas. This allows you to Shape the tone and style of the bot's responses.
2. Store important information in memory: Use the memory feature to store important details from the conversation. This helps the bot remember crucial information and provide more accurate responses.
3. Query Archival Memory: If MGPT is unable to retrieve a desired answer from its initial response, you can instruct it to search the archival memory for more information on a specific topic.
4. Customize function visibility: Toggle the visibility of function messages and inner thoughts to customize the chat view. This allows you to focus on the main conversation without distractions.
5. Use local language models: Explore the option of running MGPT with local language models for faster response times and increased control over the chatbot's behavior.

Conclusion

In this crash course, we covered various aspects of using MGPT for chatbot applications. We started with an overview of the code base and learned how to extend its functionality by adding custom functions. We explored different deployment options, such as running MGPT as a Discord bot or using local language models. We also discussed techniques for working with memory and provided tips for optimizing the chatbot experience. MGPT offers a versatile and customizable platform for building powerful chatbot applications. By leveraging its capabilities, you can create intelligent conversational agents that cater to specific use cases.