Vector Graph RAG

Graph RAG with pure vector search — no graph database needed, single-pass LLM reranking, optimized for knowledge-intensive domains.

Why Vector Graph RAG?

Most Graph RAG systems require a dedicated graph database (Neo4j, etc.) and complex multi-step retrieval with iterative LLM calls. Vector Graph RAG takes a fundamentally different approach:

flowchart LR
    subgraph Traditional["Traditional Graph RAG"]
        direction LR
        A1[Documents] --> B1[Graph DB\nNeo4j]
        B1 --> C1[Cypher\nQueries]
        C1 --> D1[Iterative\nLLM Calls]
        D1 --> E1[Answer]
    end

    subgraph VectorGraphRAG["Vector Graph RAG"]
        direction LR
        A2[Documents] --> B2[Milvus\nVectors]
        B2 --> C2[Vector\nSearch]
        C2 --> D2[Single LLM\nRerank]
        D2 --> E2[Answer]
    end

Key Advantages

• No graph database — The entire knowledge graph lives in Milvus as vectors. No extra infrastructure, no schema management, no graph query language.
• Single-pass reranking — Unlike agentic approaches (IRCoT, multi-step reflection), we call the LLM just once to rerank candidate relations. Simpler, faster, and cheaper.
• Knowledge-intensive friendly — Designed for domains where dense factual knowledge matters: legal documents, financial reports, medical literature, novels, and more.

Features

No Graph Database

Pure vector search with Milvus — no Neo4j, no ArangoDB, no extra infrastructure to deploy or manage.

Single-Pass Reranking

One LLM reranking call, no iterative agent loops. 2 LLM calls total per query vs 5+ for iterative approaches.

Multi-Hop Reasoning

Subgraph expansion discovers connections across documents for complex multi-hop questions.

Zero Configuration

Milvus Lite by default — works as a local file, no server setup needed. Just pip install and go.

State-of-the-Art

87.8% average Recall@5 on standard multi-hop QA benchmarks, competitive with HippoRAG 2.

Visual Explorer

Interactive frontend with step-by-step retrieval visualization showing how the algorithm reasons.

How It Works

flowchart LR
    Q[Question] --> EE["Entity\nExtraction"]
    EE --> VS["Vector Search\n(Milvus)"]
    VS --> SE["Subgraph\nExpansion"]
    SE --> LR["LLM Rerank\n(single pass)"]
    LR --> AG["Answer\nGeneration"]

    style VS fill:#e3f2fd,stroke:#1565c0
    style SE fill:#e3f2fd,stroke:#1565c0
    style LR fill:#fff3e0,stroke:#e65100
    style AG fill:#e8f5e9,stroke:#2e7d32

1 Extract entities from the question 2 Vector search finds similar entities and relations in Milvus 3 Subgraph expansion traverses the graph to discover multi-hop connections 4 LLM reranking selects the most relevant relations (single pass) 5 Generate answer from the selected context

Quick Example

from vector_graph_rag import VectorGraphRAG

rag = VectorGraphRAG()  # reads OPENAI_API_KEY from environment

rag.add_texts([
    "Albert Einstein developed the theory of relativity.",
    "The theory of relativity revolutionized our understanding of space and time.",
])

result = rag.query("What did Einstein develop?")
print(result.answer)

Getting Started

See the Getting Started guide for installation and configuration options.

Performance

Method	MuSiQue	HotpotQA	2WikiMultiHopQA	Average
Naive RAG	55.6%	90.8%	73.7%	73.4%
IRCoT + HippoRAG	57.6%	83.0%	93.9%	78.2%
HippoRAG 2	74.7%	96.3%	90.4%	87.1%
Vector Graph RAG	73.0%	96.3%	94.1%	87.8%

Benchmark Details

Recall@5 on standard multi-hop QA benchmarks. Uses the same pre-extracted triplets as HippoRAG for fair comparison. See Evaluation for full details.