Agentic RAG: Enhancing LLMs with Intelligent Knowledge Retrieval

Agentic RAG: Enhancing LLMs with Intelligent Knowledge Retrieval

Retrieval Augmented Generation (RAG) has become a standard method for integrating external knowledge into Large Language Models (LLMs). It enables LLMs to become experts in specific domains. However, basic RAG implementations often suffer from pitfalls, such as retrieving irrelevant text or the LLM ignoring the provided context. Agentic RAG addresses these issues by empowering LLMs to actively reason about and explore knowledge sources.

The Problem with Basic RAG

In basic RAG, a knowledge base is divided into chunks, converted into vector representations using an embedding model, and stored in a vector database. When a user query arrives, it's also converted into a vector and matched against the stored vectors to retrieve relevant documents. These documents are then added to the prompt, providing the LLM with context to answer the query.

This approach, often referred to as "one-shot retrieval," has limitations. The LLM cannot reason about the retrieved context, determine if it's sufficient, or refine the search. It lacks the ability to improve upon the initial context.

Agentic RAG: A More Intelligent Approach

Agentic RAG transforms the RAG process by treating it as a set of tools for the agent to interact with. This unlocks new possibilities, such as searching through different vector databases or employing various knowledge retrieval methods. The agent can reason about the user's question and intelligently select the appropriate knowledge source.

Instead of passively receiving context, the agent actively explores the data, enabling it to overcome the limitations of basic RAG.

Building an Agentic RAG Solution

Building an agentic RAG solution involves several steps:

1. Crawling and Ingestion: Web crawlers extract content from websites and put it into a database, creating a RAG knowledge base.
2. Database Setup: A database (e.g., Supabase) is configured to store the knowledge base.
3. Agent Creation: Use a framework like Pydantic AI to create the foundation of an AI agent. Start with basic RAG and then extend it to implement agentic RAG.
4. UI Integration: A user interface (e.g. Streamlit) is created to interact with the agent.

Chunking Text

To optimize knowledge retrieval, documents are split into chunks. This process should respect the document structure, such as code blocks and paragraphs, to ensure that each chunk contains coherent information.

Processing Chunks

Each chunk is processed to extract relevant information, including a title, summary, and embedding. The title and summary provide context for the agent to reason about when to use a specific piece of knowledge. Metadata, such as the source of the information, can also be added to enable filtering and organization.

Storing Chunks

Finally, the processed chunks are stored in a database, such as Supabase. This involves defining a schema that captures all the relevant information, including the URL, chunk number, title, summary, content, metadata, and embedding.

Tools for Agentic RAG

Agentic RAG requires specialized tools that allow the agent to interact with the knowledge base in a more intelligent way. Some examples include:

• List Documentation Pages: A tool to retrieve a list of all available URLs in the knowledge base.
• Get Page Content: A tool to retrieve the content of a specific page, given its URL.

These tools enable the agent to reason about which pages to visit and extract the necessary information to answer the user's question.

Supabase vs. Quadrant

When building an RAG solution, a key decision is choosing the right database. Supabase and Quadrant are two popular options, each with its own strengths and weaknesses.

• Supabase: A comprehensive platform that provides both vector storage and structured data storage. It simplifies development by allowing to store both embeddings and metadata in a single place.
• Quadrant: A dedicated vector database optimized for speed and efficiency. It's a good choice if performance is critical, but requires a separate database for structured data.

The Power of Intelligent Knowledge Leverage

Agentic RAG empowers LLMs to intelligently leverage knowledge bases. By giving the LLM the ability to reason about where and how to get the right information, the results are more consistent and accurate. It enhances the ability of LLMs to solve complex and open-ended question, which is the key for building future AI system.

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