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#automation #python #LLM #langchain #chatgpt In this video, we’ll go through an introduction on LLM-based agents. We'll start with discussing some intuitions about agents in general and their implementation, covering from Python + API implementations, to OpenAI function calling and LangChain agentic implementations. Then we dive into LangChain as a framework to build interesting agents exploring some core features. 📚 Chapters: 00:00: Introduction to the video and the topic of agents. 00:36: Personal introduction and definition of agents as a combination of thought and action. 00:51: Tools and three complexity levels of agents. 01:03: Discussion on the OpenAI's Function API. 01:38: Defining an agent in simple terms and the decision-making process. 02:05: Example of the decision-making process applied to attending a live training. 02:58: Simplistic definition of an agent in the context of LLMs. 03:20: Introduction to LLMs and their basic function. 03:51: Example of LLM output and introduction to tools for real-world actions. 04:19: Discussion on seminal papers on combining LLMs with tools. 05:02: Python functions as tools for LLMs and system setup. 05:36: Introduction to the paper "React" and its contributions to agents. 06:41: Recap of fundamental papers on agents and LLMs. 07:00: Surge in popularity of LLM-based agents and applications. 08:04: Popular agent implementations and their features. 09:14: Discussion on GPT-based agents and their functionalities. 10:01: Complexity levels in building agents and setting up task executions. 11:02: Level one of agent implementation using Python functions. 12:27: Execution of Python functions and the limitations of this approach. 13:37: Introduction to OpenAI's Function API and its usage. 14:43: Detailed explanation of setting up and using OpenAI's Function API. 17:12: Introduction to LangChain as a framework for agents. 17:52: Cognitive architecture and its relevance to agents. 18:53: The routing process in agent implementation. 19:19: LangChain's framework features and core elements. 20:07: Use of LangChain for common tasks and integrations. 21:21: Prompt templating and dynamic prompts in LangChain. 22:25: Output parsing with LangChain and integration with Pantic. 23:09: LangChain expression language for building application chains. 24:21: The agent loop and its key components in LangChain. 25:31: Schema and structured interactions in LangChain. 26:28: Inputs to the agent and the loop structure. 27:19: Discussion on the agent loop code and runtime. 28:15: Tools in LangChain as functions for agents to invoke. 29:37: LangChain's focus on action and real-world applications. 30:38: The future of LangChain and its ease of use. 31:18: LangChain toolkits and integrations for LLMs. 31:31: References for the presentation and closing remarks. 🔗 Links: - Subscribe!: https://www.youtube.com/channel/UCu8WF59Scx9f3H1N_FgZUwQ - Join Medium: https://lucas-soares.medium.com/membership - Tiktok: https://www.tiktok.com/@enkrateialucca?lang=en - Twitter: https://twitter.com/LucasEnkrateia - LinkedIn: https://www.linkedin.com/in/lucas-soares-969044167/ Support the Channel! - Buy me a cup of coffee: https://tr.ee/7tYsD-tUu2 - Paypal: https://paypal.me/lucasenkrateia?country.x=PT&locale.x=pt_PT

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