Building Your First LLM Agent Application

Summary

Building an LLM-Powered API Agent for Task Execution is a comprehensive guide to creating AI agents that can execute tasks by leveraging APIs and external tools. This article explores the concept of LLM agents, their components, and how to build them using NVIDIA’s AI Foundation Models. It provides a step-by-step approach to creating an API agent, including selecting an LLM, defining tools, and integrating planning and execution modules.

Building an LLM-Powered API Agent for Task Execution

Introduction

Large Language Models (LLMs) have revolutionized the way we interact with AI. However, their capabilities can be limited by their lack of access to external tools and data sources. LLM agents address this limitation by providing a framework for AI models to break out of their standard limitations and access external utilities, services, and data sources.

What is an LLM Agent?

An LLM agent is a type of AI agent that uses a large language model to execute tasks by leveraging APIs and external tools. It consists of four components: tools, memory module, planning module, and agent core.

Components of an LLM Agent

Tools

Tools are the individual function calls to the models. They can be API calls, custom functions, or external services. In this example, we will use three models: Mixtral 8x7B Instruct for text generation, Stable Diffusion XL, and Code Llama 34B for code generation.

Memory Module

The memory module stores the history of the chat session and external data sources. It can be a database or a vector store.

Planning Module

The planning module guides the LLM in breaking down complex queries into manageable sub-questions. It uses prompts associated with each tool and a system prompt that dictates the agent’s overall behavior.

Agent Core

The agent core accesses memory and the planning module to direct the query into tool-specific actions. It uses the tools core to interact with external services, perform computations, or execute custom functions.

Building an API Agent

To build an API agent, we need to select an LLM, define tools, and integrate planning and execution modules.

Selecting an LLM

We will use the Mixtral 8x7B LLM available in the NVIDIA NGC catalog. It accelerates various models and makes them available as APIs.

Defining Tools

We will define three tools: text generation, image generation, and code generation. Each tool will have a prompt associated with it, indicating its purpose.

Integrating Planning and Execution Modules

We will use the Plan-and-Execute approach, which fuses the planning module and the agent core. This approach preplans the execution flow, eliminating the need for an iterative planning module.

Key Considerations

When building an API agent, we need to consider scaling the APIs, better planning, and using a retrieval-augmented generation (RAG) system.

Scaling the APIs

We need to build a RAG system to look for the top five most relevant tools, given a user’s question. It’s not possible to continually add all the APIs that can be executed to solve a task.

Better Planning

We can use a better planner, such as ADaPT, to chain different APIs. A better planning algorithm can help tackle more complex cases and failure instances in the plan.

Table: Components of an LLM Agent

Table: Tools Used in the Example

Conclusion

Building an LLM-powered API agent for task execution is a powerful way to leverage AI to execute tasks. By selecting an LLM, defining tools, and integrating planning and execution modules, we can create an API agent that can execute tasks by leveraging APIs and external tools. With careful consideration of scaling the APIs and better planning, we can create a robust and efficient API agent.