How can I understand the reAct pattern?

Are you looking for an efficient way to understand the reAct pattern? Learn how to quickly create single llm agents using regex, Python and OpenAI.


Custom LLM Agents

Creating single agents from scratch in Python provides numerous benefits...More


James Anthony Phoenix

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Python experience recommended.
1. Scenario
Dushyant Dixit
at Ultra

I'm familiar with language models, however how can I create completely custom tools and agents?

I want to understand what is happening in the reAct (chain of thought) pattern?

It would be great if we have the flexibility to create custom agents from scratch, just in case we need to do something that langchain doesn't let us do easily.

This course is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, data, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

2. Brief

Chain of Thought (CoT) is a novel prompting method designed to encourage large language models (LLMs) to explain their reasoning process by providing exemplars that demonstrate reasoning. This approach often leads to more accurate results in tasks like arithmetic, commonsense, and symbolic reasoning. However, CoT's performance gains are only observed when used with models of around 100 billion parameters, as smaller models tend to produce illogical chains of thought that worsen accuracy.

ReAct, introduced by Yao et al., is a framework that allows LLMs to generate both reasoning traces and task-specific actions in an interleaved manner. By generating reasoning traces, the model can induce, track, and update action plans, handle exceptions, and interact with external sources like knowledge bases or environments. This enables LLMs to retrieve additional information, leading to more reliable and factual responses.

ReAct has proven effective across various tasks such as question answering, fact verification, and interactive decision-making benchmarks, outperforming imitation and reinforcement learning methods while maintaining human interpretability and trustworthiness.

3. Tutorial

β€ŠHey, welcome back. And in this video, we're gonna have a look at how we can potentially use and create custom tools. Uh, using Lang chain, Uh, we're gonna code up a version of react ourselves. So we really understand what Lang chain and a lot of these kinds of auto G B T and baby AGI are doing underneath the hood.

4. Exercises
5. Certificate

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