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

pip install "vector-graph-rag[hf]"       # HuggingFace transformers
pip install "vector-graph-rag[ollama]"   # Ollama
pip install "vector-graph-rag[jina]"     # Jina AI
pip install "vector-graph-rag[all]"      # all optional providers
# or
uv add "vector-graph-rag[all]"

Skip LLM extraction if you already have knowledge graph triplets:

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

Use upsert_documents_by_source() when a source file, message, or page is created or modified. In Vector Graph RAG, a Document is a passage/chunk; the source object is identified by metadata["source"] or the explicit source argument. The method replaces only that source's chunks and graph references.

from langchain_core.documents import Document

rag.upsert_documents_by_source(
    documents=[
        Document(
            page_content="Einstein developed relativity at Princeton.",
            metadata={
                "source": "sharepoint:file-123",
                "triplets": [
                    ["Einstein", "developed", "relativity"],
                    ["Einstein", "worked at", "Princeton"],
                ],
            },
        ),
    ],
    extract_triplets=False,
)

rag.delete_documents_by_source("sharepoint:file-123")

Migration note: v0.1.5 exposed upsert_documents(document_id=...) and delete_documents(document_id). These names were removed in v0.2.0 because Document means passage/chunk in this project. Use the *_by_source() APIs shown above.

The legacy add_* ingestion helpers rebuild the full knowledge base and are planned for removal in v1.0.0. For explicit full refreshes, use rebuild_texts(), rebuild_documents(), or rebuild_documents_with_triplets().

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.rebuild_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_provider="openai",
    embedding_model="text-embedding-3-large",
    collection_prefix="my_project",     # isolate multiple datasets
)

All settings can also be configured via environment variables with VGRAG_ prefix or a .env file:

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

You can sign up on Zilliz Cloud to get a free cluster and API key.

If you need a dedicated Milvus instance for multi-user or team environments, you can deploy Milvus standalone with Docker Compose. This requires Docker and some infrastructure knowledge. See the official installation guide for detailed steps.

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