Issue #82 - Introduction to Agentic RAG

💊 Pill of the week

Agentic RAG represents a significant advancement in AI, moving beyond the limitations of traditional RAG systems by introducing autonomous agents capable of dynamic task execution. This evolution enables AI to address more complex, real-world problems with increased precision and adaptability.

What is (traditional) RAG?

Retrieval-Augmented Generation (RAG) is a technique for building LLM-powered applications that goes beyond the limitations of relying solely on an LLM's internal knowledge. RAG enhances LLMs by connecting them to external knowledge sources like vector databases, APIs, and even real-time data feeds. This allows the LLM to access and process the most relevant and up-to-date information when responding to a user query.

Instead of relying solely on pre-trained knowledge, RAG systems can access specific and current information, making their responses more accurate and relevant. For instance, a RAG system can pull real-time weather data or access a company's internal knowledge base when answering a question. This integration of external data makes RAG a powerful tool for building more dynamic and informed LLM-based applications.

More info here:

Issue #56 - Retrieval-Augmented Generation

Looking for an implementation of RAG? Here you have it:

DIY #11 - Build RAG System with LangChain

What is Agentic RAG?

Agentic RAG builds upon traditional RAG by introducing agents, which are essentially autonomous decision-making entities capable of dynamic task execution. Unlike static retrieval processes in traditional RAG, Agentic RAG utilises agents to adapt workflows based on context. These agents integrate tools such as APIs, databases, and external functions to enhance their capabilities.

Traditional RAG follows a linear, predictable path:

• Receive a query

• Retrieve relevant documents

• Generate a response

In contrast, agents in Agentic RAG systems possess more freedom to act and can take multiple non-linear steps to achieve a specific goal.

Agentic RAG systems allow the agent to utilise external knowledge sources to make decisions, primarily determining when external knowledge is required. This breaks the linear flow of a standard RAG system and empowers the agent to take multiple steps to reach its goal.

Let’s explain this in more detail:

1. User Query: The process begins with a user posing a question or request. This query initiates the Agentic RAG workflow.

2. Agent Decision: The agent, acting as the core decision-maker, analyses the user query and determines the appropriate course of action. This decision-making capability is a key feature of Agentic RAG, distinguishing it from the linear flow of traditional RAG.

3. Retrieve Documents: