Boost RAG Accuracy with GraphRAG: Combining Knowledge Graphs and Vectors on PolarDB

Knowledge Graph Overview

Knowledge Graph (KG) is a structured representation of entities and their relationships, offering advantages such as structured information, semantic understanding, knowledge association, reasoning support, and visualisation, and is widely used in finance, knowledge management, and social network analysis.

Retrieval‑Augmented Generation (RAG)

RAG enhances large language models (LLMs) by retrieving relevant data from various sources, using keyword matching or semantic similarity (vector embeddings) to provide more accurate, context‑aware responses.

GraphRAG Advantages

Improved information retrieval : understands entity relationships for more precise results.

Enhanced context understanding : knowledge graphs supply richer context for query interpretation.

Reduced hallucinations : answers are grounded in factual graph data.

Implementation Example

Using an open‑source stock knowledge graph, we construct a GraphRAG system with PolarDB, Tongyi Qianwen, and LangChain.

PolarDB

PolarDB PostgreSQL is a cloud‑native relational database fully compatible with PostgreSQL and Oracle syntax. It supports the Apache AGE graph engine and the pgvector extension, enabling unified storage and retrieval of graph and vector data.

Tongyi Qianwen

Tongyi Qianwen is Alibaba Cloud’s large language model used for generating embeddings and answering queries, providing personalized responses in RAG scenarios.

LangChain

LangChain is an open‑source framework that connects LLMs with external data sources. It includes support for the AGE graph plugin and pgvector, allowing combined graph‑and‑vector searches.

Query Workflow

User poses a question.

The RAG system retrieves relevant graph nodes.

Graph results are fed to a vector retriever.

Combined results are passed to the LLM.

The LLM generates the final answer.

Code snippets illustrate installing required Python packages, configuring PolarDB, creating the AGE and vector extensions, loading data, and defining custom retrievers and prompt templates.

Result

Graph‑plus‑vector search returns detailed stock information for the query “李士祎关联的股票信息?” whereas vector‑only or graph‑only searches return incomplete or inaccurate answers.

Conclusion

Combining knowledge graphs with vector embeddings in PolarDB provides higher‑quality RAG answers, demonstrating an effective approach to leveraging private data in AI applications.