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

arXiv: https://arxiv.org/abs/2408.04948 · HTML: https://mlsanity.com/paper/2408.04948/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Placed in

- Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [FinanceBench: A New Benchmark for Financial Question Answering](https://mlsanity.com/paper/2311.11944/) (2023)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [FinTextQA: A Dataset for Long-form Financial Question Answering](https://mlsanity.com/paper/2405.09980/) (2024)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [Towards reducing hallucination in extracting information from financial reports using Large Language Models](https://mlsanity.com/paper/2310.10760/) (2023)

## Built on by (most cited first, 30 of 93)

- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG](https://mlsanity.com/paper/2502.09304/) (2025)
- [Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey](https://mlsanity.com/paper/2504.10499/) (2025)
- [FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.15800/) (2025)
- [Graph RAG-Tool Fusion](https://mlsanity.com/paper/2502.07223/) (2025)
- [Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems](https://mlsanity.com/paper/2511.01854/) (2025)
- [Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching](https://mlsanity.com/paper/2501.08686/) (2025)
- [AssertionForge: Enhancing Formal Verification Assertion Generation with Structured Representation of Specifications and RTL](https://mlsanity.com/paper/2503.19174/) (2025)
- [BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering](https://mlsanity.com/paper/2507.04127/) (2025)
- [Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval](https://mlsanity.com/paper/2509.21710/) (2025)
- [Cog-RAG: Cognitive-Inspired Dual-Hypergraph with Theme Alignment Retrieval-Augmented Generation](https://mlsanity.com/paper/2511.13201/) (2025)
- [Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study](https://mlsanity.com/paper/2409.13694/) (2024)
- [FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs](https://mlsanity.com/paper/2501.09957/) (2025)
- [Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance](https://mlsanity.com/paper/2503.05185/) (2025)
- [KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing](https://mlsanity.com/paper/2505.20245/) (2025)
- [DRAE: Dynamic Retrieval-Augmented Expert Networks for Lifelong Learning and Task Adaptation in Robotics](https://mlsanity.com/paper/2507.04661/) (2025)
- [DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning](https://mlsanity.com/paper/2507.13396/) (2025)
- [RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval](https://mlsanity.com/paper/2505.18541/) (2025)
- [TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework](https://mlsanity.com/paper/2511.05385/) (2025)
- [MEG: Medical Knowledge-Augmented Large Language Models for Question Answering](https://mlsanity.com/paper/2411.03883/) (2024)
- [Multi-Document Financial Question Answering using LLMs](https://mlsanity.com/paper/2411.07264/) (2024)
- [OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices](https://mlsanity.com/paper/2510.24145/) (2025)
- [RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing](https://mlsanity.com/paper/2602.00296/) (2026)
- [Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments](https://mlsanity.com/paper/2503.07670/) (2025)
- [Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents](https://mlsanity.com/paper/2505.20368/) (2025)
- [SymRAG: Efficient Neuro-Symbolic Retrieval Through Adaptive Query Routing](https://mlsanity.com/paper/2506.12981/) (2025)
- [TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking](https://mlsanity.com/paper/2505.07891/) (2025)
- [FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph](https://mlsanity.com/paper/2508.00961/) (2025)

## Abstract (arXiv)

Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to large language models (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