TLDR/Teaser: Retrieval Augmented Generation (RAG) is the go-to method for feeding external knowledge into Large Language Models (LLMs), but it’s not without its pitfalls. Enter Agentic RAG—a smarter, more dynamic approach that lets your AI agent reason about where and how to find the right information. In this post, we’ll break down what Agentic RAG is, why it’s a game-changer, and how you can implement it to avoid pulling your hair out over failed AI deployments.
Why Agentic RAG Matters
If you’ve ever tried to implement RAG, you know the pain points: irrelevant search results, LLMs ignoring the context you provide, and the general feeling that your AI solution is more of a liability than an asset. These issues aren’t just frustrating—they can derail entire projects, leaving clients skeptical and teams scrambling for fixes.
Agentic RAG solves these problems by giving your AI agent the ability to reason about the data it retrieves. Instead of a one-shot retrieval, your agent can iteratively search, evaluate, and refine its approach to find the most relevant information. This not only improves accuracy but also makes your AI solution more adaptable and reliable—key factors for successful AI adoption.
What Is Agentic RAG?
At its core, RAG works by retrieving relevant documents from a knowledge base, converting them into vectors, and feeding them into an LLM to generate responses. The problem? Traditional RAG is a one-way street: the LLM gets the context, but it can’t decide if it’s enough or if it needs to look elsewhere.
Agentic RAG flips the script. Instead of treating RAG as a static tool, it turns it into a dynamic toolset for the AI agent. The agent can now:
- Search multiple knowledge bases: It’s not limited to a single vector database.
- Reason about the data: It can decide if it needs more context or a different approach.
- Iterate and refine: It can go back and search again if the initial results aren’t sufficient.
This flexibility makes Agentic RAG a powerful upgrade over traditional RAG, especially for complex queries or large knowledge bases.
How to Implement Agentic RAG
Implementing Agentic RAG involves a few key steps:
1. Build Your Knowledge Base
Start by scraping and ingesting your data into a vector database. Tools like Supabase or Quadrant can help you store and manage your knowledge base efficiently. Break your documents into smaller chunks, embed them using a model like OpenAI’s text-embedding-3, and store them with metadata (e.g., source, title, summary) for better filtering.
2. Create the Agent
Using a framework like Pydantic AI, define your agent and its tools. Start with basic RAG functionality, then expand to include tools for listing and fetching specific pages from your knowledge base. This gives your agent the ability to reason about where to find the best information.
3. Test and Iterate
Run your agent through a series of test queries to identify where basic RAG falls short. For example, if your agent struggles to retrieve a full code example from documentation, add tools to let it search specific pages or URLs. This iterative approach ensures your agent can handle real-world use cases.
Real-World Example: Pydantic AI Documentation
Let’s say you’re building an AI agent to help users navigate Pydantic AI’s documentation. With basic RAG, the agent might retrieve a snippet of code but miss the full example. With Agentic RAG, the agent can:
- List all documentation pages.
- Identify the most relevant page for the query.
- Retrieve the full content of that page.
This approach ensures users get complete, accurate answers—not just fragments of information.
Try It Yourself
Ready to implement Agentic RAG in your own projects? Here’s how to get started:
- Scrape and ingest your data: Use tools like Crawl4AI to scrape websites and Supabase to store your knowledge base.
- Define your agent: Use Pydantic AI to create an agent with tools for basic and Agentic RAG.
- Build a UI: Use Streamlit to create a user-friendly interface for interacting with your agent.
By giving your AI agent the ability to reason about its knowledge base, you’ll create a more robust, reliable solution that delivers real value to your clients.
Conclusion
Agentic RAG isn’t just a technical upgrade—it’s a mindset shift. By empowering your AI agent to intelligently explore and leverage its knowledge base, you can overcome the limitations of traditional RAG and deliver solutions that truly meet your clients’ needs. Whether you’re guiding a client through AI adoption or troubleshooting a tricky integration, Agentic RAG is a tool you’ll want in your arsenal.
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