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An Introduction to LLM Agents | From OpenAI Function Calling to LangChain Agents
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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:
Support the Channel!
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:
Support the Channel!
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