Multi-Agent RAG

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AI Summary

Summary: Multi-Agent RAG Discussion and Build

• Introduction:
  • Multi-agent systems are considered “multi-dope,” an advanced level of dopeness.
  • The session aims to technically increase the dopeness of agents using multi-agent frameworks.
  • Dr. Greg and the Whiz discuss multi-agent frameworks and their relevance to building LLM applications.
• Multi-Agent Frameworks:
  • Multi-agent workflows are important for LLM prototyping patterns.
  • The session focuses on understanding and building a multi-agent application.
  • The discussion is rooted in the patterns of geni, prompting, fine-tuning, RAG (retrieval-augmented generation), and agents.
• Key Patterns in LLM Applications:
  • Prompting: Leading to do something, akin to teaching or training.
  • Fine-Tuning: Teaching the LLM to act on task-specific spectrums.
  • Optimization: Enhancing what’s put into the context window using relevant material.
  • RAG: Central to applications creating business value, incorporating context well.
  • Agents: Provide the LLM access to tools, essentially a reasoning action or react pattern.
• Agents as Fancy RAG:
  • Agents can be seen as a form of RAG, augmenting reasoning with retrieved information.
• Multi-Agent Systems:
  • Multi-agents involve using multiple independent agents, each powered by an LLM.
  • They allow for cleaner architecture, separation of prompts, and potentially better results.
  • Multi-agent systems can be complex and are not always necessary for every use case.
• Tools for Multi-Agent Frameworks:
  • Autogen: A conversation framework from Microsoft.
  • Crew AI: A low-code solution for cohesive multi-agent operations.
  • Lang Chain and Lang Graph: For building stateful multiactor applications.
• Building the AIM Editorial AI Maker Space Editorial:
  • A system with a top-level supervisor, a research team using Tavali search, a custom RAG system, and a document team with roles like initial writer, researcher, copy editor, and editor.
  • The system is designed to write a blog on relevant topics, such as extending LLM context windows.
• Implementation:
  • The build involves setting up research and document teams using Lang graph.
  • Each team has a supervisor that routes tasks to the appropriate agents or tools.
  • The meta supervisor at the top directs the overall process, deciding which team to engage for a given task.
• Conclusion:
  • The session demonstrates how multi-agent systems can be built and utilized.
  • The patterns of prompting, fine-tuning, RAG, and agents are integral to the process.
  • Multi-agent systems offer a structured approach to complex LLM applications but can be slow and require careful management of state and communication between agents.