HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction (2408.04948v1)

Abstract: Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to LLMs even using the current best practices to use Retrieval Augmented Generation (RAG) (referred to as VectorRAG techniques which utilize vector databases for information retrieval) due to challenges such as domain specific terminology and complex formats of the documents. We introduce a novel approach based on a combination, called HybridRAG, of the Knowledge Graphs (KGs) based RAG techniques (called GraphRAG) and VectorRAG techniques to enhance question-answer (Q&A) systems for information extraction from financial documents that is shown to be capable of generating accurate and contextually relevant answers. Using experiments on a set of financial earning call transcripts documents which come in the form of Q&A format, and hence provide a natural set of pairs of ground-truth Q&As, we show that HybridRAG which retrieves context from both vector database and KG outperforms both traditional VectorRAG and GraphRAG individually when evaluated at both the retrieval and generation stages in terms of retrieval accuracy and answer generation. The proposed technique has applications beyond the financial domain

• The paper introduces HybridRAG, a method that integrates knowledge graphs with vector retrieval to improve financial document analysis.
• It employs a dual pipeline with VectorRAG for semantic chunking and GraphRAG for structured entity and relation extraction.
• Evaluation shows HybridRAG outperforms standalone methods with higher faithfulness and answer relevance in financial Q&A.

HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction

The paper "HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction" presents an advanced method for improving information extraction from unstructured financial documents. Financial documents such as earnings call transcripts present unique challenges due to domain-specific terminology, complex data formats, and varied contextual relationships. This paper addresses these complexities by introducing HybridRAG, a methodology that combines the strengths of Knowledge Graphs (KGs) and traditional Vector-based Retrieval Augmented Generation (RAG) techniques to optimize question-answering (Q&A) systems for financial document analysis.

Methodology

The HybridRAG approach integrates two primary retrieval techniques: VectorRAG and GraphRAG. Each of these techniques brings distinct advantages to the information extraction process.

VectorRAG

VectorRAG involves chunking external documents, converting these chunks into vector embeddings using models like OpenAI's text-embedding-ada-002, and storing them in a vector database (e.g., Pinecone). A LLM retrieves relevant chunks based on a given query, integrating these chunks into the generation process to produce contextually relevant and accurate answers.

The implementation utilizes a sophisticated pipeline within the LangChain framework, employing parameterized queries and prompt templates tailored to generate expert-level Q&A responses. The use of vector embeddings facilitates semantic similarity searches, ensuring that the context retrieved for a query is relevant and precise.

Knowledge Graph Construction and GraphRAG

The second component of HybridRAG is the Knowledge Graph (KG) built from financial documents. This involves extracting entities and relationships using advanced prompt engineering and leveraging NLP techniques to distill unstructured text into structured triplets. Entities include financial metrics, corporate events, executive names, and geographical locations among others.

GraphRAG enhances the RAG process by enabling structured retrieval from these KGs. By employing depth-first search strategies and integrating relevant subgraphs into LLM contexts, GraphRAG ensures that generated responses are grounded in explicitly defined relationships, improving the contextual relevance and accuracy.

HybridRAG Synthesis

HybridRAG synthesizes the contexts retrieved from both VectorRAG and GraphRAG, combining them to form a unified input for the LLM. This hybrid approach ensures that responses benefit from the broad retrieval capabilities of VectorRAG and the structured specificity offered by GraphRAG. This combined methodology demonstrates substantial improvements in generating accurate and contextually appropriate answers to complex financial queries.

Evaluation and Results

The efficacy of HybridRAG is evaluated using objective metrics including faithfulness, answer relevance, context precision, and context recall. Faithfulness gauges the extent to which generated answers can be inferred from retrieved contexts, while answer relevance assesses the alignment of generated responses with the original questions. Context precision and recall measure the accuracy and comprehensiveness of the retrieved contextual information, respectively.

Empirical results based on a dataset of financial call transcripts from Nifty 50 companies demonstrate that HybridRAG significantly outperforms both individual VectorRAG and GraphRAG approaches. Faithfulness and answer relevance metrics are notably higher for HybridRAG, reflecting its superior capability in generating coherent and contextually accurate responses. Although context precision is slightly lower due to the combined contexts' trade-off, the overall performance highlights the robust efficiency of the HybridRAG approach.

Implications and Future Directions

The integration of KGs with traditional RAG techniques represents a significant advancement in the field of financial document analysis. HybridRAG not only enhances the accuracy and reliability of extracted information but also provides a framework for developing more sophisticated AI-assisted financial analysis tools. These tools could democratize access to financial insights, enabling a broader range of stakeholders to make informed decisions based on robust and precise data interpretations.

Future research could explore the expansion of HybridRAG to handle multi-modal inputs, integrate real-time financial data streams, and incorporate advanced numerical data analysis capabilities. Additionally, refining evaluation metrics to better capture the nuances of financial language and numerics could further improve the assessment and applicability of such systems.

In conclusion, HybridRAG sets a new benchmark for efficient and accurate information extraction from complex financial documents, facilitating enhanced decision-making in financial contexts and demonstrating considerable potential for cross-domain applications.