HybridRAG

HybridRAG is a novel retrieval-augmented generation (RAG) approach that combines VectorRAG (retrieving text chunks using vector embeddings) with GraphRAG (retrieving structured knowledge from knowledge graphs). This hybrid strategy improves information extraction and generation accuracy, particularly for complex domains like financial analysis.

Why HybridRAG?

Financial documents, like earnings call transcripts, annual reports, and news articles, contain domain-specific language, structured financial data, and complex contextual relationships. Traditional VectorRAG struggles with:

• Ambiguous terminology that lacks direct matches in embeddings.
• Hierarchical financial data that requires relational reasoning.
• Inconsistent retrieval accuracy, leading to hallucinations in generated answers.

On the other hand, GraphRAG improves information retrieval by leveraging structured relationships but struggles with abstractive tasks or when queries lack explicit entity mentions.

HybridRAG integrates the best of both worlds, using:

• VectorRAG for semantic similarity-based retrieval.
• GraphRAG for structured reasoning over knowledge graphs.
• A unified generation model that combines both retrieval outputs.

How HybridRAG Differs from VectorRAG and GraphRAG

Feature	VectorRAG (Text Search)	GraphRAG (Structured Search)	HybridRAG (Combined Approach)
Data type	Unstructured text chunks	Structured knowledge graphs	Combines text and graphs
Retrieval	Semantic similarity search	Graph traversal, entity linking	Hybrid search strategy
Context handling	Independent text chunks	Interconnected entities & relations	Merges structured & unstructured context
Reasoning	Lexical similarity-based	Multi-hop relational reasoning	Combines both for better accuracy
Best for	Open-domain QA, summaries	Structured financial QA, reasoning	Complex queries in finance, healthcare, etc.

How HybridRAG Works

HybridRAG enhances financial information extraction by combining two retrieval techniques before generating an answer.

1. Query Processing

• VectorRAG query: Converts text queries into vector embeddings using OpenAI's text-embedding-ada-002.
• GraphRAG query: Extracts entities and relationships using knowledge graph traversal.
• Hybrid query integration: Merges results from both methods for a comprehensive context.

2. Retrieval

• Vector-based retrieval: Searches a vector database (e.g., Pinecone) for similar text chunks.
• Graph-based retrieval: Extracts related entities and relationships from a Knowledge Graph (KG).
• Hybrid retrieval: Combines vector search and graph traversal results.

3. Context Organization

• Merges retrieved data from both approaches.
• Re-ranks information based on relevance and relationship structure.
• Optimizes knowledge formatting for Large Language Models (LLMs).

4. Generation

• LLM-based Generation: Uses GPT-3.5-turbo to generate responses.
• Graph-Aware Generation: Incorporates structured knowledge from GraphRAG.
• Hybrid Output Synthesis: Generates a contextually accurate, structured response.

Results: HybridRAG Performance

• HybridRAG balances both extractive (GraphRAG) and abstractive (VectorRAG) tasks.

Why HybridRAG Works Better?

• For extractive questions (e.g., direct facts from earnings reports), GraphRAG is superior.
• For abstractive questions (e.g., summarization, market analysis), VectorRAG is better.
• HybridRAG dynamically switches between both for optimal results.

Conclusion

HybridRAG bridges the gap between structured knowledge retrieval (GraphRAG) and semantic text search (VectorRAG), improving the accuracy and relevance of generated responses.

Footnotes

1. Han, H., Wang, Y., Shomer, H., Guo, K., Ding, J., Lei, Y., Halappanavar, M., Rossi, R. A., Mukherjee, S., Tang, X., He, Q., Hua, Z., Long, B., Zhao, T., Shah, N., Javari, A., Xia, Y., & Tang, J. (2025). Retrieval-Augmented Generation with Graphs (GraphRAG). https://arxiv.org/abs/2501.00309 ↩