Summary of the Video

The video discusses agentic Retrieval-Augmented Generation (RAG) using an AI reasoning engine and demonstrates how to set up a question-answering agent with IBM’s granite 3.08b instruct model.

Key Points

• Introduction to agentic RAG and its purpose as a reasoning engine.
• Installation of necessary packages and input of API key and project ID.
• Utilization of IBM’s granite 3.08b instruct model, with flexibility to choose any AI model.
• Setting up a prompt template to facilitate multiple question inputs.
• Testing the agent’s responses to basic and complex questions, such as sports played at the U.S. Open.
• Extraction of information through a list of URLs summarizing IBM’s involvement in the 2024 U.S. Open.
• Implementation of an IBM slate model via the Watsonx.ai embedding service.
• Definition of a retriever function for accessing the vector store of embedded documents.
• Creation of a sophisticated multi-question prompt template, including system prompts and memory storage through LangChain.
• Testing the agent’s ability to discern when to call tools for answering user queries.
• Final demonstration of efficient querying where the agent uses its existing knowledge without needing tool calls for certain questions.

Youtube Video: https://www.youtube.com/watch?v=Y1PaM3edYoI
Youtube Channel: IBM Technology
Video Published: 2024-10-21T04:02:00+00:00