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Getting Started

Installation

pip install vector-graph-rag

uv add vector-graph-rag

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 the OPENAI_API_KEY environment variable before running.

Configuration

Basic

rag = VectorGraphRAG(
    milvus_uri="./my_data.db",       # local file (Milvus Lite)
    llm_model="gpt-4o",
    embedding_model="text-embedding-3-large",
)

With Remote Milvus

rag = VectorGraphRAG(
    milvus_uri="http://localhost:19530",
    milvus_db="my_database",           # optional: specify database
    collection_prefix="my_project",    # optional: isolate collections
)

Collections will be named my_project_vgrag_entities, my_project_vgrag_relations, my_project_vgrag_passages.

Multiple Knowledge Bases

Use collection_prefix to maintain separate graphs in the same Milvus instance:

# Different documents → different prefixes
legal_rag = VectorGraphRAG(milvus_uri="./data.db", collection_prefix="legal")
finance_rag = VectorGraphRAG(milvus_uri="./data.db", collection_prefix="finance")

With Pre-extracted Triplets

Skip LLM extraction if you already have knowledge graph triplets:

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

Import from URLs and Files

Import web pages, PDFs, and other documents with automatic chunking:

pip install "vector-graph-rag[loaders]"

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)

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

REST API

Run the API server for programmatic access:

uv sync --extra api
uv run uvicorn api.main:app --host 0.0.0.0 --port 8000
Endpoint Method Description
/health GET Health check
/graphs GET List available graphs
/stats GET Get graph statistics
/query POST Query the knowledge graph
/add_documents POST Add documents

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