Vector Graph RAG

Graph RAG with pure vector search — no graph database needed.

💡 Encode entities and relations as vectors in Milvus, replace iterative LLM agents with a single reranking pass — achieve state-of-the-art multi-hop retrieval at a fraction of the operational and computational cost.

✨ Features

• No Graph Database Required — Pure vector search with Milvus, no Neo4j or other graph databases needed
• Single-Pass LLM Reranking — One LLM call to rerank, no iterative agent loops (unlike IRCoT or multi-step reflection)
• Knowledge-Intensive Friendly — Optimized for domains with dense factual content: legal, finance, medical, literature, etc.
• Zero Configuration — Uses Milvus Lite by default, works out of the box with a single file
• Multi-hop Reasoning — Subgraph expansion enables complex multi-hop question answering
• State-of-the-Art Performance — 87.8% avg Recall@5 on multi-hop QA benchmarks, outperforming HippoRAG

📦 Installation

pip install vector-graph-rag
# or
uv add vector-graph-rag

pip install "vector-graph-rag[loaders]"
# or
uv add "vector-graph-rag[loaders]"

pip install "vector-graph-rag[hf]"
# or
uv add "vector-graph-rag[hf]"

🚀 Quick Start

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)

Note: Set OPENAI_API_KEY environment variable before running.

rag.add_documents_with_triplets([
    {
        "passage": "Einstein developed relativity at Princeton.",
        "triplets": [
            ["Einstein", "developed", "relativity"],
            ["Einstein", "worked at", "Princeton"],
        ],
    },
])

from vector_graph_rag import VectorGraphRAG
from vector_graph_rag.loaders import DocumentImporter

# Import from URLs, PDFs, DOCX, etc. (with automatic chunking)
importer = DocumentImporter(chunk_size=1000, chunk_overlap=200)
result = importer.import_sources([
    "https://en.wikipedia.org/wiki/Albert_Einstein",
    "/path/to/document.pdf",
    "/path/to/report.docx",
])

rag = VectorGraphRAG(milvus_uri="./my_graph.db")
rag.add_documents(result.documents, extract_triplets=True)

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

rag = VectorGraphRAG(
    milvus_uri="./my_data.db",          # or remote Milvus / Zilliz Cloud
    llm_model="gpt-4o",
    embedding_model="text-embedding-3-large",
    collection_prefix="my_project",     # isolate multiple datasets
)

VGRAG_LLM_MODEL=gpt-4o
VGRAG_EMBEDDING_MODEL=text-embedding-3-large
VGRAG_MILVUS_URI=http://localhost:19530

📖 Full Python API reference → Python API docs

🔬 How It Works

Indexing:

Documents → Triplet Extraction (LLM) → Entities + Relations → Embedding → Milvus

Query:

Question → Entity Extraction → Vector Search → Subgraph Expansion → LLM Reranking → Answer

Example: "What did Einstein develop?"

1. Extract entity: Einstein
2. Vector search finds similar entities and relations in Milvus
3. Subgraph expansion collects neighboring relations
4. Single-pass LLM reranking selects the most relevant passages
5. Generate answer from selected passages

📖 Detailed pipeline walkthrough with diagrams → How It Works · Design Philosophy

📊 Evaluation Results

Evaluated on three multi-hop QA benchmarks (Recall@5):

¹ HippoRAG (NeurIPS 2024) ² HippoRAG 2 (2025)

📖 Detailed analysis and reproduction steps → Evaluation

🗄️ Milvus Backend

Just change milvus_uri to switch between deployment modes:

Milvus Lite (default) — zero config, single-process, data stored in a local file. Great for prototyping and small datasets:

rag = VectorGraphRAG(milvus_uri="./my_graph.db")  # just works

⭐ Zilliz Cloud — fully managed, free tier available — sign up 👇:

rag = VectorGraphRAG(
    milvus_uri="https://in03-xxx.api.gcp-us-west1.zillizcloud.com",
    milvus_token="your-api-key",
)

rag = VectorGraphRAG(milvus_uri="http://localhost:19530")

🖥️ Frontend & REST API

Vector Graph RAG includes a React-based frontend for interactive graph visualization and a FastAPI backend.

# Backend
uv sync --extra api
uv run uvicorn vector_graph_rag.api.app:app --host 0.0.0.0 --port 8000

# Frontend
cd frontend && npm install && npm run dev

See API docs at http://localhost:8000/docs after starting the server.

📖 Full endpoint reference → REST API docs · Frontend guide

📚 Links

• Documentation — full guides, API reference, and architecture details
• How It Works — pipeline walkthrough with diagrams
• Design Philosophy — why pure vector search, no graph DB
• Milvus — the vector database powering Vector Graph RAG
• FAQ — common questions and troubleshooting

Contributing

Bug reports, feature requests, and pull requests are welcome! For questions and discussions, join us on Discord.

📄 License

MIT