Agentic RAG: Smarter Information Retrieval with Iterative Search
How do AI agents adopt an iterative approach to access information?
1Challenges of RAG Applications
RAG applications can struggle to find relevant information chunks. Similar words may be found, but the right evidence often ranks low or is split across chunks. This leaves the AI model with insufficient context.
2The Idea of Iterative Search
Agentic RAG enables the model to search iteratively. It searches, reads, decides if it has enough evidence, and searches again if needed. This approach can reduce dependency on vector embeddings.
3Case Study: Company Policies
An Agentic RAG agent was built over a company policy collection. Six different policy documents were used to test the model's iterative search capabilities. These documents covered travel, remote work, and approval processes.
4Configuring the Agent
An agent was created using the OpenAI Agents SDK. The agent was equipped with three tools: listing documents, keyword searching, and reading a specific document. These tools enabled the agent to manage the iterative search process.
5How the Agent Works
The agent answered a policy question by first scanning documents, then finding relevant evidence, and finally using supporting documents. This process showed how the agent thinks and makes decisions.
6Example Question and Answer
The agent was tested on a hotel booking question for a conference. It provided the correct answer, identifying that the official conference hotel exceeded the normal cap and explaining the required approval conditions.
7Agent's Tracing Process
The agent's tracing process revealed how it thinks and follows steps. This tracing is an important tool for understanding the agent's iterative search process.
8The Future of Agentic RAG
Agentic RAG can significantly improve information retrieval. The iterative search approach enables AI models to provide more accurate and reliable answers. This technology could find broader applications in the future.
Original article: Towards Data Science — Agentic RAG: İteratif Arama ile Daha Akıllı Bilgi Erişimi