 Название: What Are AI Agents? When and How to Use LLM Agents
Автор: Benjamin Labaschin
Издательство: O’Reilly Media, Inc.
Год: 2024
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
AI agents represent the latest milestone in humanity's computational toolbox. Powered by large language models (LLMs) and the data they were trained on, AI agents are tools that let you interact with specialized LLMs to achieve more productive or creative workflows with less technical hassle.
The last century in the history of computing has had many notable milestones: the invention of the computer; the development of the personal computer as we know it; the internet; the smart phone; machine learning; and cloud computing. Every few decades it seems societies are thrust forward, riding the wave of some new computational innovation. If you are reading this, then congratulations (or I’m sorry?), you are living during another one of these milestones in computational advancement.
Artificial Intelligence (AI) agents, as powered by large language models (LLMs) and the user data they are provided, have emerged in recent years as powerful new tools in humanity’s computational repertoire. But what are AI agents? And how can we be so certain AI agents will be so impactful? Readers of this report will not only learn the answers to these questions but will also be exposed to such insights as when to use AI agents and, critically, how to get started!
What Are AI Agents? So, before we get ahead of ourselves, what exactly are AI agents, and why should you want to learn about them in the first place? AI agents are tools designed to allow users to interact with LLMs to achieve a more productive or creative workflow as seamlessly as possible. Before AI agents, users would be forced to build their own statistical language models—a time-consuming, technical, and expensive endeavor! Now, with AI agents, users who want to interact with AI simply get to log in to an interface and conduct business ranging from asking questions of their documents to getting help with their homework.
At a more granular level, you might think of AI agents as UI “wrappers” around the models that power them. That is to say, AI agents are often user-friendly “frontends” that make using the models that fuel them easier, often by focusing and limiting just how users interact with the model. Take ChatGPT, for instance. The models fueling ChatGPT (GPT-3.5 Turbo or GPT-4) are massively complex, powerful, and difficult to use and operate on their own. As an AI agent, ChatGPT abstracts away these models’ technical features and allows users to interact with them simply via text.
Code assistant agents are AI agents that are fueled by models specifically designed to help users like you write code more productively and efficiently. Popular code assistant agents include GitHub Copilot, Amazon CodeWhisperer, and Hugging Face’s StarCoder. Code assistant agents are tools designed to edit error-ridden code, autocomplete simple functions to common coding problems, or design templates for more difficult coding problems. Whereas in the very recent past, software engineers and developers might resort to search engines, chatrooms, or colleagues to solve their problems, code assistant agents reduce the need for such costly context switching, allowing for large improvements in productivity.
Imagine your boss at Very Fake Company, Inc., asks you to translate legacy code from Python into Java. The problem is, while you know Python, you’re not that familiar with Java. Luckily, as you log in to Very Fake Company, Inc.’s code editor, you see that it comes equipped with a code assistant agent. As you log in to the repository that contains the legacy Python code you’ve been instructed to change, you see a Python function that converts marketing copy from lowercase, to all uppercase.
Contents:
What Are AI Agents?
What Are AI Agents?
How Do AI Agents Relate to LLMs?
When to Use AI Agents
Document Question and Answer Agents
Chatbot Agents
Code Assistant Agents
AI Agent Tools and How to Use Them
Document Q&A Agents with Chainlit and PandasAI
Dynamic Chatbot Agents with HuggingFace Chat, Llama 2, and Streamlit
Coding Assistant Agents with OpenAI ChatGPT
Moving Forward with AI Agents
Augmented Search
Product Integration and Isolation
Infrastructure
Security, Legislation, and Ethics
Conclusion
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