The Future of RAG is Agentic - Learn this Strategy NOW

Introduction

In this tutorial, we will explore the concept of Agentic Retrieval-Augmented Generation (RAG) and how to implement it effectively. This strategy aims to enhance the performance of AI agents by leveraging external knowledge from curated data. We will walk through the process of building an agentic RAG agent using tools like Pydantic AI and Streamlit, ensuring you can apply these techniques in your projects.

Step 1: Understand Agentic RAG

• Definition: Agentic RAG is a sophisticated approach to enhance the standard RAG technique by incorporating a more powerful methodology for retrieving and generating responses using external knowledge.
• Benefits:
  • Improved context understanding
  • More relevant responses from the AI
  • Higher overall performance of AI agents
• Common Challenges: Many users face issues with the wrong text being returned, and the AI ignoring context. Agentic RAG addresses these issues.

Step 2: Set Up Your Environment

• Install Required Tools:
  • Pydantic AI: A framework to create agents.
  • Streamlit: A tool to build the user interface for your agent.
• Get GPUStack:
  • Sign up for GPUStack, an open-source GPU cluster manager ideal for LLM inference.
  • Understand its capabilities, such as heterogeneous GPU management and distributed inference.

Step 3: Create the Knowledge Base

• Utilize Crawl4AI:
  • Use Crawl4AI to scrape relevant documentation or data for your knowledge base.
  • Store the scraped data in a vector database for easy access.
• Database Setup:
  • Set up Supabase as your database solution to hold the scraped data.
  • Ensure your data is structured correctly for retrieval.

Step 4: Build the Agent

• Create the Basic RAG Agent:
  • Use the scraped data to build a basic RAG agent with Pydantic AI.
  • Ensure the agent can retrieve information accurately from the knowledge base.
• Code Snippet:

  from pydantic import BaseModel
  
  class RAGAgent(BaseModel):
      knowledge_base: str
      
      def get_response(self, query: str):
          # Logic to retrieve relevant information
          pass
  

Step 5: Test Your Basic RAG Agent

• Conduct Tests:
  • Run queries against your agent to ensure it retrieves information correctly.
  • Adjust the parameters or logic if the responses are not satisfactory.

Step 6: Implement Agentic RAG

• Enhance the Agent:
  • Integrate additional features that allow the agent to leverage the context more effectively.
  • Focus on how to optimize the retrieval process for better interaction with the LLM.

Step 7: Build the Frontend Interface

• Using Streamlit:
  • Create a user interface that allows users to interact with the agent seamlessly.
  • Incorporate input fields for queries and display the agent's responses.

Conclusion

You've now learned how to create an agentic RAG agent from scratch, leveraging external knowledge to enhance AI performance. Start by setting up your environment, creating a knowledge base, and building your agent. Testing and refining your setup will help you achieve optimal results. For further exploration, consider diving deeper into advanced features of Pydantic AI or enhancing your UI with Streamlit.