You HAVE to Try Agentic RAG with DeepSeek R1 (Insane Results)
Updated: February 25, 2025
Summary
The video introduces the concept of powerful agentic workflows using reasoning language models (LLMs) like R1. It showcases the process of setting up a simple RAG setup with small agents by Hugging Face, focusing on local implementation with R1 distill models. The demonstration includes steps such as importing agents tools, loading reasoning models, setting up the base agent, and creating a knowledge base for efficient information retrieval. The final steps involve optimizing OLLAMA models, adding AMA models, and running the setup to interact with the user interface for conversation generation.
Introduction to Agentic Workflows with LLMs
Introduction to the concept of powerful agentic workflows with LLMs, highlighting the implications and strengths of open-source reasoning LLMs like R1.
Setting Up Agentic RAG Setup
Demonstration of setting up a simple and powerful agentic RAG setup using small agents by Hugging Face, focusing on local implementation with R1 distill models.
Conceptualizing the RAG Implementation
Explanation of the concept behind building a RAG setup using reasoning LLMs like R1 combined with small agents, showcasing the workflow and components involved in the process.
Building an Agentic RAG Tool
Step-by-step guide on building an agentic RAG tool, including importing agents tools, loading reasoning models like R1, setting up the base agent, and utilizing tools such as web search.
Creating Knowledge Base for Agents
Process of creating a knowledge base and preparing LLMS by generating embeddings for text chunks and setting up a vector database for efficient information retrieval.
Finalizing and Running the Agentic RAG Setup
Final steps in setting up the agentic RAG tool, including instructions for optimizing OLLAMA models, adding AMA models, and running the setup to interact with the user interface for conversation generation.
FAQ
Q: What is the concept of agentic workflows with LLMs?
A: Agentic workflows with LLMs involve utilizing reasoning LLMs like R1 in combination with small agents to create powerful setups for various tasks.
Q: Why are open-source reasoning LLMs like R1 highlighted for agentic workflows?
A: Open-source reasoning LLMs like R1 are highlighted for their strengths in open-source reasoning capabilities and their implications in creating powerful agentic setups.
Q: What is a RAG setup in the context of LLMs?
A: A RAG setup involves combining reasoning LLMs like R1 with small agents to facilitate information retrieval and conversation generation.
Q: What are the components involved in building an agentic RAG tool?
A: The components include importing agent tools, loading reasoning models like R1, setting up the base agent, and utilizing tools such as web search for information retrieval.
Q: How is a knowledge base created in the process of setting up an agentic RAG tool?
A: A knowledge base is created by generating embeddings for text chunks and setting up a vector database to efficiently retrieve information during the workflow.
Q: What are the final steps in setting up an agentic RAG tool?
A: The final steps involve optimizing OLLAMA models, adding AMA models, and running the setup to interact with a user interface for conversation generation.
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