Cognitive RAG-Chain of Thought Approach in Retrieval Augmented Generation on Graph Data

Introduction

In recent years, Retrieval-Augmented Generation (RAG) has transformed how large language models (LLMs) interact with structured and unstructured data. One of the most promising advancements in this field is integrating Chain-of-Thought (CoT) reasoning with RAG, especially when working with graph-based knowledge representations. This approach enhances reasoning, improves retrieval efficiency, and generates more accurate responses. In this blog, we explore how CoT-enhanced RAG can improve performance when dealing with graph data.

Understanding RAG and Graph Data

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an AI paradigm that enhances the capabilities of generative models by incorporating an external retrieval mechanism. Instead of relying solely on parametric memory, RAG fetches relevant information from external knowledge sources. This improves factual accuracy, reduces hallucinations, and provides contextually grounded responses.

Why Use Graph Data?

Graphs are widely used to represent relationships between entities in various domains, such as knowledge graphs, social networks, and biomedical databases. Unlike textual data, graphs encode rich relational structures, making them highly expressive. However, they also pose challenges for traditional retrieval models, which often struggle to leverage these interconnected relationships effectively.

Motivation for Graph-Augmented LLMs

Large Language Models (LLMs) sometimes generate content that appears plausible but lacks a factual foundation, a problem known as hallucination. Existing RAG models mitigate this issue by incorporating external text corpora as knowledge sources, treating each document as an independent knowledge unit.

However, real-world information is rarely isolated; instead, it is interconnected, forming a graph of relationships. Knowledge graphs store information not just in textual form but also through structured connections between entities. These graphs often contain different types of edges, representing various relationships such as "is a part of," "is similar to," "was influenced by," and "has property." This diversity in relationships allows for richer retrieval and reasoning processes.

Example of Knowledge Graphs:

Traditional RAG with Graph Databases vs. Graph-CoT

Traditional RAG with Graph Databases (e.g., Neo4j)

• Uses predefined query languages (like Cypher) to fetch relevant nodes and edges.
• Relies on direct lookup of structured graph data rather than reasoning iteratively.
• Typically retrieves subgraphs and provides them as static context to LLMs.
• Struggles with complex multi-hop reasoning beyond predefined paths.
• Does not dynamically adjust retrieval strategies based on the query.

Graph-CoT (Our Proposed System)

• Iteratively reasons through the graph, identifying the necessary steps dynamically.
• Uses an LLM-driven reasoning mechanism to decide on retrieval steps rather than predefined queries.
• Expands queries dynamically based on previous retrievals, allowing multi-hop and inductive reasoning.
• Provides structured evidence aggregation, leading to more coherent and contextually aware responses.
• Adapts retrieval dynamically, selecting only the most relevant information instead of entire subgraphs.

By enabling step-wise reasoning, Graph-CoT allows LLMs to retrieve and process information more effectively than traditional RAG approaches that rely solely on direct lookups in graph databases.

Proposed Method: Graph Chain-of-Thought (Graph-CoT)

We introduce Graph Chain-of-Thought (Graph-CoT), a step-by-step framework that enables LLMs to traverse knowledge graphs iteratively, identifying key information rather than processing entire subgraphs at once.

Graph-CoT Framework

Each iteration of Graph-CoT consists of three key sub-steps:

1. Reasoning: The LLM analyzes the current information and determines what additional data is needed from the graph.
2. Interaction: The LLM formulates actions to retrieve relevant nodes, check connections, or refine the search.
3. Execution: The system executes the requested retrieval, fetching the necessary data from the graph.

This iterative process continues until the LLM gathers sufficient information to generate an answer.

Implementation

We implemented Graph-CoT using the Claude Sonnet API for the LLM component. The retrieval and reasoning pipeline follows these steps:

1. Initial Question Processing: The LLM first determines the subject category of the query.
2. Self-Reasoning: If the LLM can confidently answer using its internal knowledge, it does so. Otherwise, it proceeds to retrieval.
3. Graph Retrieval with Embeddings: Using Sentence Transformer embeddings, the system identifies the most relevant node.
4. Information Sufficiency Check: The LLM evaluates whether the retrieved node contains enough information. If not, it reasons again and explores further connections in the knowledge graph.
5. Graph Traversal: The process repeats, leveraging named edges and multi-hop connections until the LLM determines that it has gathered sufficient context.
6. Final Answer Generation: Once enough relevant data is retrieved, the LLM synthesizes an accurate and coherent response.

Comparison with Hop-Based Retrieval

We also compared Graph-CoT with hop-based retrieval strategies:

• Hop-0: Retrieves only the most relevant node found using sentence transformer embeddings.
• Hop-1: Retrieves the most relevant node along with its directly connected neighbors.
• Hop-2: Retrieves the most relevant node, its neighbors, and their respective neighbors, expanding the context further.

While increasing the hop level provides more contextual information, our proposed Graph-CoT system consistently outperforms all hop-based approaches by dynamically reasoning and retrieving only the most relevant information at each step.

Conclusion

Integrating Chain-of-Thought reasoning with Retrieval-Augmented Generation offers a powerful approach to handling graph-based knowledge representations. By enabling step-wise reasoning, contextual query expansion, and structured retrieval, Graph-CoT significantly improves the efficiency and accuracy of knowledge graph augmentation for LLMs. Future research will focus on optimizing retrieval efficiency, exploring more advanced graph traversal strategies, and evaluating performance across diverse real-world applications.

By structuring the thought process and leveraging graph-based retrieval, Graph-CoT paves the way for more reliable and interpretable AI systems capable of handling complex, interconnected knowledge sources.

GitHub Repository: Cognitive-RAG