Cómo crear un asistente de reuniones GPT en .NET usando agentes GPT

Table of Contents:

1. Introduction
2. Background Information
3. Understanding Large Language Models (LLMs)
4. Creating Autonomous Agents with LLMs
5. The GPT Meeting Agent: A Proof of Concept
6. How the GPT Meeting Agent Works
7. Leveraging Prompt Engineering for Better Results
8. The Role of the Chat GPT Plugin
9. Limitations and Considerations
10. Future Developments and Feedback

Introduction

In today's article, we will explore the concept of creating autonomous agents using large language models (LLMs) like Chat GPT. Specifically, we will Delve into the GPT Meeting Agent, a proof of concept that demonstrates how LLMs can be used to leverage internal APIs and solve a wide range of tasks. We will provide a step-by-step explanation of how the GPT Meeting Agent works and discuss the benefits and limitations of this approach. Additionally, we will explore the concept of prompt engineering and the role of the Chat GPT plugin in this process. So, let's dive in and explore the exciting world of autonomous agents powered by LLMs.

Background Information

Before we delve into the intricacies of the GPT Meeting Agent, it's important to understand some background information about the technologies and techniques involved. In this section, we will provide a high-level overview of LLMs and their capabilities, as well as the concept of creating agents and their decision-making process. We will also introduce the idea of prompt engineering and its significance in building solutions with natural language interfaces.

Understanding Large Language Models (LLMs)

Large language models (LLMs) are advanced AI models that have been trained on massive amounts of data using a framework known as a Transformer. These models have the ability to predict the next token or part of a word in a given text input Based on their training data. This predictive capability forms the basis for creating a wide range of solutions that can interpret and respond to natural language inputs.

One of the most well-known implementations of LLMs is Chat GPT, which is powered by the GPT 3.5 Turbo model developed by OpenAI. This model can take instructions and generate responses based on its training data. By leveraging the predictive capabilities of LLMs, developers can Create agents that can reason and make decisions to solve complex problems.

Creating Autonomous Agents with LLMs

The concept of an autonomous agent comes into play when we construct the prompt text in a specific way that gives the LLM the ability to take actions via commands. These commands are tied to internal APIs and serve as a way to improve decision-making when choosing which APIs to use to solve a specific problem. Prompt engineering techniques, such as chaining commands and using Context learning, allow the agent to break down problems into manageable steps and make informed decisions based on previous actions.

By providing the LLM with additional internal information about your systems, such as the purpose and usage of APIs, it can make more accurate and context-aware decisions. This process, known as prompt engineering, enhances the reasoning ability of LLMs, enabling agents to tackle complex problems and Interact with multiple sources of data via APIs.

The GPT Meeting Agent: A Proof of Concept

In this article, we will focus on the GPT Meeting Agent, which serves as a proof of concept for creating autonomous agents using LLMs. The GPT Meeting Agent showcases a generic approach of exposing internal APIs to LLMs, enabling them to make decisions about which APIs to use to complete a specific task.

The aim of the GPT Meeting Agent is to demonstrate how an LLM, specifically the GPT 3.5 Turbo model, can be used to reason about and book meetings on behalf of a user. The agent dynamically creates and populates forms based on its decisions about which APIs to use. This flexibility allows the agent to solve more complex problems by combining data from various sources via APIs.

In the following sections, we will provide a detailed explanation of how the GPT Meeting Agent works, from the initial prompt to the decision-making process and the resulting actions taken by the agent. We will also highlight the role of the Chat GPT plugin in orchestrating the interaction between the LLM and the server stack.

How the GPT Meeting Agent Works

The GPT Meeting Agent is designed to autonomously book meetings on behalf of a user. To initiate this process, the user provides a prompt, which includes Relevant details such as the person they want to meet, the subject of the meeting, and the preferred date and time. The GPT Meeting Agent then uses this prompt to make decisions about which APIs to utilize.

The prompt is constructed in a way that provides the agent with the necessary context to reach for the right APIs. The agent's behavior is controlled by its configuration, which includes information about the user's role and name. Additionally, the agent's available APIs are defined through matching tag attributes.

Based on the prompt, the GPT Meeting Agent first searches for the user specified in the prompt using the search users API. It then retrieves the user's schedule using the get user schedule API to analyze their availability. Using a combination of data from these internal services and the APIs it decides to use, the agent is able to construct a meeting request.

The GPT Meeting Agent dynamically creates and populates forms based on the decisions it makes regarding which APIs to use. These forms are built using the auto form component from the service stack view library. This allows the agent to Gather and input the necessary information for each API it utilizes.

The interaction between the browser client, server stack, and agent is orchestrated through the use of the Chat GPT plugin. The browser client sends requests to the plugin, which then communicates with the LLM via the OpenAI API. The plugin injects the necessary service commands and metadata into the prompt, enabling the agent to interact with the server stack via APIs. The responses from the server stack, such as API responses, are injected back into the messages or Prompts sent to the agent in subsequent interactions.

Leveraging Prompt Engineering for Better Results

Prompt engineering plays a crucial role in ensuring that the GPT Meeting Agent performs optimally. By constructing the prompt in a specific way and providing relevant messages and commands, developers can guide the agent's decision-making process and improve its reasoning ability.

The Chat GPT plugin simplifies the process of incorporating AI-driven natural language interfaces into applications by generating service commands based on the metadata of server stack APIs. These commands are injected into the prompt, allowing the agent to interact with the server stack via APIs. This approach provides developers with the flexibility to integrate their own APIs quickly and easily without relying on third-party plugins.

However, it is important to note that the decision-making ability of LLMs is not perfect and can sometimes lead to unexpected or suboptimal choices. The agent's behavior is influenced by the prompt construction and the context it receives, so tweaking the prompts and experimenting with different approaches can help improve performance and accuracy.

In the next section, we will discuss some limitations and considerations when using this approach with LLMs and prompt engineering.

Limitations and Considerations

While the GPT Meeting Agent and similar approaches Show tremendous potential, there are some limitations and considerations that developers should be aware of when working with LLMs and prompt engineering.

Firstly, the decision-making ability of LLMs is not infallible. Depending on the prompt and the context provided, the LLM may make peculiar choices that might not Align with user expectations. This can sometimes lead to frustrating experiences for users.

Another limitation to consider is the complexity of the request DTOs used in APIs. Currently, the plugin only supports flat request DTO structures with primitive types. If your API responses have large nested structures, this can Consume valuable context space and potentially confuse the agent.

To address these limitations, the plugin provides the ability to transform API responses using the register command filter method. This allows developers to modify the API responses and inject only relevant information into the message history, helping the agent focus on critical details.

Despite these limitations, the technology behind the GPT Meeting Agent and similar approaches holds great promise. As the open-source ecosystem of LLMs continues to evolve, specialized models for various domains are expected to emerge. This will enable developers to integrate their own APIs quickly and efficiently, choosing the best model for their specific use case.

Future Developments and Feedback

The GPT Meeting Agent is just the beginning of exploring the possibilities of integrating LLMs and server stack APIs. In the future, we plan to develop a library that simplifies the implementation of similar approaches within the service stack framework. This library will streamline the process and provide developers with more flexibility in solving real-world problems using cutting-edge technologies like Chat GPT.

We Are eager to hear feedback from the service stack developer community regarding use cases, scenarios, and suggestions for improving this approach. Your input will Shape the future developments and help us create a more user-friendly and robust library.

Please share your thoughts and ideas in the comments section below. You can also reach out to us through our Discord Channel, GitHub discussions, or customer forums. We look forward to hearing from You and collaborating to push the boundaries of AI-driven solutions.

Highlights:

• The GPT Meeting Agent showcases the concept of creating autonomous agents using large language models (LLMs).
• LLMs are advanced AI models trained on massive amounts of data using transformers.
• Prompt engineering techniques enhance the reasoning ability of LLMs, allowing them to solve complex problems.
• The GPT Meeting Agent uses internal APIs and dynamically creates forms based on its decisions.
• The Chat GPT plugin simplifies the integration of LLMs with server stack APIs, providing flexibility for developers.
• The limitations of LLMs and prompt engineering must be considered, but the technology holds great promise.
• Future developments include a library to facilitate similar approaches and feedback is welcomed from the developer community.
• The GPT Meeting Agent is an exciting step towards leveraging AI-driven solutions in real-world applications.