Build Multi-Agent RAG Applications From Scratch!

The video discusses the creation of multi-agent applications using a framework called Autogen, which facilitates multi-agent conversations and task automation. Key points include:

• Multi-Agent RAG System: The setup involves a user query that is complex and requires subdivision into tasks. An orchestrator agent assigns these tasks to various specialized agents, such as a retriever, researcher, and writer. A vector database (Vector DB) stores the necessary information.
• Autogen Framework: Autogen is an open-source programming framework designed for building multi-agent AI applications. It allows for the configuration of different agents to work collaboratively.
• Building the Application:
  • Installation: The process starts with installing necessary libraries including Autogen, Langchain, OpenAI, and Unstructured.
  • Data Preparation: Documents are loaded and processed, which includes splitting and creating embeddings.
  • Vector Database Setup: Single Store is utilized as the vector database to store embeddings.
  • Agent Configuration: Multiple agents are configured, such as a boss agent, senior Python engineer, product manager, and code reviewer, each with specific roles.
• Tutorial and Practical Demonstration: The speaker provides a step-by-step guide on how to build the application, including creating a workspace and database in Single Store, setting up the notebook environment, and coding the interactions among agents.
• RAG Setup Comparison: The video demonstrates the difference in responses from the agents with and without the RAG Setup, showing that RAG provides more accurate and contextually relevant answers.
• Resources: The speaker mentions that a link to the notebook containing the complete code will be shared for viewers to follow along and try out the setup themselves.

Main Speakers/Sources:

• The speaker of the video, who provides the tutorial and insights on the multi-agent application development using Autogen and Single Store.