Reasoning & Recursive Retrieval With Deepseek-r1, Tavily, and LangGraph

system combining DeepSeek-r1's reasoning with Tavily search for recursive RAG workflows. Built on LangGraph, it processes complex nested queries through intelligent information retrieval and reasoning.

Introduction

This article explores how to incorporate a reasoning model with an agentic loop to enhance the performance of Retrieval-Augmented Generation (RAG) systems.

Key Concepts

• Deepseek-r1: A powerful reasoning model used for information retrieval.
• Tavily: An information-retrieval technology used for web search.
• LangGraph: A framework for building agentic workflows.
• Recursive Retrieval: The agent repeatedly queries Tavily for more information until it can answer the question.

Workflow

1. User submits a query.
2. Deepseek-r1 evaluates whether the retrieved information is sufficient to answer the question.
3. If sufficient, it generates the answer.
4. If not, it uses Tavily to retrieve more information.
5. Steps 2-4 are repeated until a complete answer is obtained.

Advantages

• Improved RAG Performance: Reasoning models can significantly improve the performance of RAG systems.
• Simplified RAG Techniques: Reduces the need for “hacky” RAG techniques.
• Handles Complex Queries: Able to answer complex and nested queries.

Summary

Incorporating a reasoning model with an agentic loop is a promising future architecture for RAG systems, significantly improving performance.