Learn How to Create Your Own AI Chatbot Agent

Learn How to Create Your Own AI Chatbot Agent

Table of Contents

1. Introduction
2. What are Agents in Large Language Models?
3. Limitations of Chat Interfaces for LLMS
4. Introducing Agents to Overcome Limitations
5. How do Agents Work? 5.1 Running the Model in a Loop 5.2 Promoting Models to Use Outside Tools 5.3 Reducing Hallucination with Chain of Thought
6. Building an Agent from Scratch 6.1 Setting up the Environment 6.2 Installing Required Dependencies 6.3 Setting Environment Variables 6.4 Creating the Custom Agent File
7. Developing Tools for the Agent 7.1 The Search Tool 7.2 The Calculator Tool 7.3 The Save CSV Tool 7.4 Creating Custom Tools
8. Making the Agent More Powerful with GPT-4
9. Conclusion
10. Further Exploration and Adaptation

Introduction

In the world of large language models (LLMs), the concept of agents is gaining popularity among many individuals. While these models offer incredible capabilities, not everyone interacts with them through coding or creating from scratch. In this article, we will explore the idea of agents in LLMS and learn how they can help overcome the limitations of chat interfaces. We will discuss how agents work, provide step-by-step instructions on building an agent from scratch, and explore the potential of incorporating tools into agents. Additionally, we will Delve into the use of GPT-4 to enhance the power of agents and highlight areas for further exploration and adaptation.

What are Agents in Large Language Models?

Agents in large language models (LLMs) are a way to Interact with these models beyond traditional chat interfaces. While chat systems using LLMs have become popular, they have limitations such as hallucination, being trained on limited data, lack of access to private data, and dependence on human intermediaries to work with other tools. Agents attempt to address these limitations by prompting models to call or use outside tools in a specific manner. This allows for a more versatile and comprehensive interaction with the LLM, enhancing its capabilities.

Limitations of Chat Interfaces for LLMS

Chat interfaces with LLMs have proven to be useful but come with inherent limitations. Firstly, these interfaces often hallucinate or generate responses that are not accurate or factual. Secondly, chat systems are typically trained on a fixed dataset, making them limited in terms of information beyond a certain date. Thirdly, chat systems do not have access to private data, which can be necessary for certain use cases. Finally, chat systems struggle to work seamlessly with other tools without human intervention. These limitations Create a need for alternative approaches to interact with LLMs effectively.

Introducing Agents to Overcome Limitations

Agents provide a solution to the limitations of chat interfaces by enabling models to work with external tools and enhance their functionality. By guiding models to utilize outside tools in a specific way, agents offer a more comprehensive and Context-aware interaction. This opens up new possibilities for leveraging the power of LLMs in various domains and use cases. Agents act as intermediaries between the LLM and tools, facilitating their integration and seamless utilization.

How do Agents Work?

Agents work by running the LLM model in a loop and promoting it to use outside tools. The agent receives an initial message and a set of tool descriptions. It then sends this information along with the system message to the LLM model, which generates thoughts as a response. The agent decides on an action Based on these thoughts, either using a tool or interacting with the user. If a tool is used, the agent executes the tool request and feeds the output back into the LLM model. This cycle continues until the agent arrives at a final answer, which it communicates to the user.

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