Build Intelligent AI Agents with Autogen Teachable Agents

Table of Contents:

1. Introduction
2. Understanding Teachable Agents
3. Setting up a Simple Teachability Use Case
4. Exploring the Underlying Code
5. Customizing the Teachable Agent
6. Use Cases for Teachable Agents
7. Conclusion

Introduction

In this article, we will explore the concept of teachable agents and how they can be used to create a digital clone of yourself or a business use case. We will start by understanding the basics of teachable agents and their underlying code. Then, we will go through the process of setting up a simple teachability use case using the Autogen framework's official documentation. Next, we will take a closer look at the code and discuss how it works under the hood. After that, we will dive into customizing the teachable agent to create a clone of ourselves or any other business use case. Finally, we will discuss various use cases for teachable agents and conclude with a summary of key takeaways.

Understanding Teachable Agents

Teachable agents are AI agents that can be trained and taught by users to perform specific tasks or solve problems. These agents are capable of learning from conversations and storing information for later retrieval. The concept behind teachable agents is to empower Large Language Models with unlimited memory, allowing them to remember user teachings and provide better assistance based on previously learned information.

Teachable agents utilize a vector database to store useful information from conversations. They make use of a text analyzer agent to analyze conversations and decide which information needs to be stored in the vector database. By analyzing the text, teachable agents can determine if any part of the conversation involves a task or problem to solve. If the text contains advice or information worth remembering, the teachable agent stores it in the vector database for future reference.

Setting up a Simple Teachability Use Case

To set up a simple teachability use case, we will follow the steps outlined in the Autogen framework's official documentation. First, we need to create a Python environment and install the necessary modules, including the Autogen module. Then, we will import the required modules and define our configuration settings.

Next, we will create a teachable agent by initializing the teachability module and adding the teachability capability to an existing conversational agent. We will also create a user proxy agent to represent the user or ourselves in the conversation. Once the agents are set up, we can initiate a chat between the teachable agent and the user proxy agent.

During the chat, the teachable agent will analyze the messages and decide whether to store any information in the vector database. It will consider if the messages involve a task or problem to solve and if there is any useful advice or information that should be remembered. The teachable agent will then save the Relevant information in the vector database for later retrieval.

Exploring the Underlying Code

Let's take a closer look at the underlying code of the teachable agent. The teachable agent is a subclass of the conversible agent and inherits its capabilities. The teachability module adds the teachability capability to any agent that inherits from the conversible agent. This means that we can add teachability to various agents, such as assistant agents or user proxy agents.

The teachability module provides methods for storing information in the vector database and retrieving it based on user queries. The process last message method analyzes the last message in the conversation and decides whether to store the information in the vector database. It also considers any advice or task Mentioned in the message and saves it for future reference.

The consider memo storage and consider memo retrieval functions determine if any relevant information should be retrieved from the vector database and added to the conversation context. This allows the teachable agent to provide accurate and context-based responses to user queries.

Customizing the Teachable Agent

In this section, we will explore how to customize the teachable agent to create a digital clone of ourselves or any other business use case. By modifying the teachability module, we can train the agent to understand specific tasks and provide personalized responses.

To create a digital clone, we need to provide the teachable agent with information about ourselves, such as our personal email address or business details. By training the agent with this information, it can respond to queries as if it were us. This opens up possibilities for automating interactions with customers, clients, or friends when we are unavailable or busy with other tasks.

By customizing the teachability module, we can make the agent more intelligent and capable of handling complex conversations. We can train it to understand and respond to various queries, providing a personalized experience for users.

Use Cases for Teachable Agents

Teachable agents have a wide range of use cases in various industries. Here are a few examples:

1. Customer Support: Teachable agents can be trained to provide customer support and answer frequently asked questions. They can provide Instant responses and handle simple inquiries, freeing up human support agents for more complex issues.

2. Personal Assistance: A teachable agent that mimics your behavior and responses can act as a personal assistant, handling tasks, Scheduling appointments, and providing information on your behalf.

3. Language Learning: Teachable agents can be used to help individuals learn a new language. They can provide language tips, grammar explanations, and interactive practice Sessions.

4. Tutoring and Education: Teachable agents can serve as virtual tutors, providing personalized tutoring sessions, answering questions, and guiding students through their learning journey.

5. Sales and Marketing: Teachable agents can assist with sales and marketing efforts by engaging with potential customers, providing information about products or services, and guiding them towards making a purchase.

These are just a few examples of how teachable agents can be utilized in different industries. The possibilities are endless, and the customization options allow businesses to tailor the agents to their specific needs.

Conclusion

Teachable agents have revolutionized the way AI agents interact with users. By training and teaching these agents, we can enhance their capabilities and create personalized experiences. Through the use of the Autogen framework and the teachability module, we can easily set up and customize teachable agents for various use cases.

In this article, we covered the basics of teachable agents, explored the underlying code, and learned how to create a simple teachability use case. We also discussed the process of customizing the teachable agent and explored different use cases for these agents.

Teachable agents have the potential to transform businesses and automate interactions, providing efficient and personalized experiences for users. With continuous advancements in AI technology, teachable agents will play a significant role in shaping the future of customer support, personal assistance, education, and more.

Highlights

• Teachable agents are AI agents that can be trained and taught by users.
• They utilize a vector database to store useful information from conversations.
• Teachable agents analyze conversations using a text analyzer agent and store relevant information in the vector database.
• The Autogen framework provides a teachability module for adding teachability to conversible agents.
• Teachable agents can be customized to create digital clones or business-specific use cases.
• Use cases for teachable agents include customer support, personal assistance, language learning, tutoring, and sales and marketing.

FAQ

Q: Can teachable agents understand and respond to different languages? A: Yes, teachable agents can be trained to understand and respond to multiple languages by providing language-specific training data.

Q: How accurate are teachable agents in providing responses? A: The accuracy of teachable agents depends on the training data and the customization applied. With sufficient training and fine-tuning, teachable agents can provide highly accurate responses.

Q: Can teachable agents handle complex conversations and tasks? A: Yes, teachable agents can be trained to handle complex conversations and tasks by providing relevant training data and customizing their behavior.

Resources:

• Autogen framework documentation: Link
• Autogen Teachability Module: Link
• Light LM: Link