DataHub Python Builds

These prebuilt wheel files can be used to install our Python packages as of a specific commit.

Build context

Built at 2026-04-26T20:52:34.388525+00:00.

{
  "timestamp": "2026-04-26T20:52:34.388525+00:00",
  "branch": "worktree-feat+local-embedding-provider",
  "commit": {
    "hash": "1afc93fe230fadc3888a0d3f33a4a11663e1e7ed",
    "message": "fix(search): address code review feedback on local embedding provider\n\n- Split LocalEmbeddingProvider timeout into CONNECT_TIMEOUT=10s and\n  REQUEST_TIMEOUT=120s; cold GGUF model loading can take 60s+ so a longer\n  read timeout is needed while the connect timeout stays short\n- Fix ConnectException detection: HttpClient sometimes wraps it inside\n  IOException; add getCause() check so the helpful \"ollama serve\" hint\n  fires in both cases; extract to newConnectError() helper\n- Add testWrappedConnectException and testIoExceptionRetryExhausted tests\n  (15 total, was 12)\n- Fix _validate_provider_config: add 'local' branch so test_connection\n  correctly reports capability instead of always returning False\n- Fix SemanticContent model_key: use model_embedding_key from server config\n  when available (authoritative), fall back to derivation only when not set\n- Extract _LOCAL_EMBEDDING_DEFAULT_ENDPOINT constant in chunking_config.py\n  to keep Java and Python defaults in sync\n- Ollama-model-init: add warmup embedding request after model pull so GGUF\n  is loaded into memory before the container exits; add restart: \"no\"\n- application.yaml: make nomic_embed_text vectorDimension configurable via\n  LOCAL_EMBEDDING_VECTOR_DIMENSION env var\n- datahub_dev.py: add --no-ai flag to clear AI env vars; add\n  --embeddings-endpoint (BYO server, skips Ollama container) and\n  --embeddings-model flags; add _wait_for_ollama_model_ready() probe so\n  'start --ai' blocks until model is loaded and the first search query\n  is warm; future AI capabilities (chat etc.) can add --chat-endpoint\n\nCo-Authored-By: Claude Sonnet 4.6 "
  },
  "base": {
    "hash": "34c878a4484f895ba65e87a35ff4c4760252f6f2",
    "message": "ci(security): Trivy/Grype scan workflow, Linear sync, and registry profiles (#17159)"
  },
  "pr": {
    "number": 17201,
    "title": "feat(search): add local embedding provider for on-premise semantic search (Ollama)",
    "url": "https://github.com/datahub-project/datahub/pull/17201"
  }
}

Usage

Current base URL: unknown

Package	Size	Install command
acryl-datahub	3.590 MB	uv pip install 'acryl-datahub @ <base-url>/artifacts/wheels/acryl_datahub-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-actions	0.105 MB	uv pip install 'acryl-datahub-actions @ <base-url>/artifacts/wheels/acryl_datahub_actions-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-airflow-plugin	0.108 MB	uv pip install 'acryl-datahub-airflow-plugin @ <base-url>/artifacts/wheels/acryl_datahub_airflow_plugin-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-dagster-plugin	0.020 MB	uv pip install 'acryl-datahub-dagster-plugin @ <base-url>/artifacts/wheels/acryl_datahub_dagster_plugin-0.0.0.dev1-py3-none-any.whl'
acryl-datahub-gx-plugin	0.011 MB	uv pip install 'acryl-datahub-gx-plugin @ <base-url>/artifacts/wheels/acryl_datahub_gx_plugin-0.0.0.dev1-py3-none-any.whl'
prefect-datahub	0.011 MB	uv pip install 'prefect-datahub @ <base-url>/artifacts/wheels/prefect_datahub-0.0.0.dev1-py3-none-any.whl'