An Introduction to LLM Agents | From OpenAI Function Calling to LangChain Agents

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AI Summary

Video Summary: Introduction to Agents

• Introduction
  • The presenter is an instructor at R media, specializing in live trainings on Lang chain, agents, Doge language models, and prompt engineering.
  • The video aims to introduce agents and demonstrate their capabilities using Lang chain.
• Concept of Agents
  • Agents combine thought and action.
  • They are defined as a combination of Large Language Models (LLMs) and tools.
  • Agents think, plan, and use tools like internet browsing to perform actions.
• Key Papers and Concepts
  • Two papers, “ToolFormer” and “React,” are fundamental to understanding current agent applications.
  • ”ToolFormer” shows LLMs learning to call external tools.
  • ”React” demonstrates LLMs interleaving thoughts, actions, and observations to solve problems.
• Surge in Agent Popularity
  • There has been a significant increase in papers on LLM-based agents in recent years.
  • Agents are becoming popular due to their ability to perform actions beyond text output.
• Popular Agent Implementations
  • Babi, Autog GPT, GPT Researcher, and OpenGPT are some notable implementations.
  • Each has unique features like task prioritization, execution, and planning.
• Understanding Agents in Three Complexity Levels
  • Level 1: Python functions within prompts.
  • Level 2: OpenAI’s function calling API.
  • Level 3: Lang chain framework for building agents.
• Lang Chain Framework
  • Lang chain offers components, chains, and a language expression language (LCL) for building agents.
  • Core elements include the model, prompt templator, and output parser.
  • The agent loop involves user input, model processing, tool actions, observations, and output.
• Building Agents with Lang Chain
  • Lang chain provides structured ways to interact with components using schemas.
  • Tools in Lang chain are functions that agents can invoke.
  • Lang chain focuses on practical applications and provides toolkits for specific objectives.
• Conclusion
  • The presenter will share demos in future videos and encourages viewers to explore building agents with Lang chain.
  • References for the presentation are provided.

References

• The presenter lists several references for the presentation, including papers and Lang chain documentation.