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HugeGraph-AI

hugegraph-ai provides Python clients for HugeGraph, graph machine learning tools, and LLM tools for knowledge graph construction and GraphRAG applications.

Apache License 2.0 · Ask DeepWiki

Modules

  • hugegraph-llm: knowledge graph construction, GraphRAG, and natural-language graph queries.
  • hugegraph-ml: reads graph data from HugeGraph and runs graph learning models.
  • hugegraph-python-client: a Python SDK for managing schemas and graph data and running Gremlin queries.
  • vermeer-python-client: a Python SDK for the Vermeer graph computing service.

The repository uses a uv workspace whose members are hugegraph-llm and hugegraph-python-client. hugegraph-ml and vermeer-python-client are editable path dependencies rather than workspace members. The current repository version is 1.7.0.

Requirements

  • HugeGraph-LLM: Python 3.10 or 3.11 (>=3.10,<3.12)
  • HugeGraph-ML: Python 3.10 or later
  • HugeGraph Python client and Vermeer Python client: Python 3.9 or later
  • uv 0.7 or later
  • HugeGraph Server 1.3 or later (1.5 or later recommended)

Optional Dependency Groups

The root project declares one extra per module plus a few combined ones:

ExtraInstalls
llmhugegraph-llm
mlhugegraph-ml
python-clienthugegraph-python-client
vermeervermeer-python-client
devpytest, pytest-cov, coverage, pylint, ruff, mypy, ty, pre-commit
nk-llmhugegraph-llm, hugegraph-python-client, and Nuitka for the compiled image
allall four module packages

hugegraph-llm itself declares a vectordb extra that adds pymilvus and qdrant-client.

Deploy with Docker Compose

The repository includes a Compose file that starts both HugeGraph Server and the RAG service:

git clone https://github.com/apache/hugegraph-ai.git
cd hugegraph-ai
cp docker/env.template docker/.env
# Edit docker/.env and set PROJECT_PATH to the absolute path of this repository
touch hugegraph-llm/.env
cd docker
docker compose -f docker-compose-network.yml up -d

Default addresses:

  • HugeGraph Server: http://localhost:8080
  • RAG service and Web UI: http://localhost:8001

Start the RAG Service from Source

git clone https://github.com/apache/hugegraph-ai.git
cd hugegraph-ai
uv sync --extra llm
source .venv/bin/activate
cd hugegraph-llm
python -m hugegraph_llm.demo.rag_demo.app

uv sync creates .venv at the repository root. Do not create a separate environment under hugegraph-llm, because doing so can bypass the dependencies locked by the workspace.

Install ML Dependencies

cd hugegraph-ai
uv sync --extra ml
source .venv/bin/activate
cd hugegraph-ml/src

Example scripts are under hugegraph-ml/src/hugegraph_ml/examples/.

Next Steps