docs: Improve README.md with technical details and integrations#743
docs: Improve README.md with technical details and integrations#743luisremis wants to merge 4 commits into
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Pull request overview
This PR refreshes README.md to present the project as the “ApertureDB Python SDK”, adding badges and expanding documentation around installation, integrations, development setup, and test execution.
Changes:
- Added PyPI/Python/License/CI badges and restructured the intro content.
- Documented integrations/capabilities (ML frameworks, embeddings, Dask, cloud storage, Croissant, SPARQL) and reorganized documentation links.
- Reworked development + testing instructions and runtime configuration environment variable table.
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This README refresh looks great! The addition of badges, clear integration outlines, and a clean environment variable table really improves the onboarding experience. Approving.
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Addressed the review comments by Copilot: fixed the CI badge workflow link, corrected the integrations text regarding dependencies, quoted the pip install command, and updated the dbinfo instructions to use integers and ephemeral ports.
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Addressed the remaining PR review comments: updated the integrations section to mention explicit dependency requirements for |
| ## 🚀 Integrations & Capabilities | ||
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| The SDK is designed for modern ML workflows and offers seamless integrations with (some require additional dependencies): | ||
| * **Deep Learning Frameworks:** Seamless conversion of ApertureDB queries into `PyTorch` (`PyTorchDataset`) and `TensorFlow` (`TensorFlowDataset`) data loaders for immediate model training. |
| * **Vector Search & Embeddings:** First-class support for storing and retrieving high-dimensional descriptors, including native embedding extraction utilizing `CLIP` (requires `openai-clip`) and `Facenet`. | ||
| * **Distributed Data Processing:** Integration with `Dask` to handle parallelized data loading and large-scale query execution. | ||
| * **Cloud Storage Integrations:** Easy handling of assets stored remotely using `Boto3` (AWS S3) and Google Cloud Storage. | ||
| * **ML Croissant:** Native parsing and handling of datasets aligned with the ML Croissant metadata format. |
| ``` | ||
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| or an installation with only the core part of the SDK | ||
| To install just the lightweight core client without the heavy ML dependencies: |
| ```bash | ||
| git clone https://github.com/aperture-data/aperturedb-python.git | ||
| cd aperturedb-python | ||
| pip install -e .[dev] |
| DB_TCP_PORT = 55551 # Replace with the mapped host port for lenz | ||
| DB_REST_PORT = 8443 # Replace with the mapped host port for nginx 443 |
| DB_REST_HOST = 'localhost' | ||
| DB_TCP_PORT = 55551 # Replace with the mapped host port for lenz | ||
| DB_REST_PORT = 8443 # Replace with the mapped host port for nginx 443 | ||
| VERIFY_HOSTNAME = False # Required when connecting to localhost to skip cert hostname validation |
| # Reporting bugs | ||
| Any error in the functionality / documentation / tests maybe reported by creating a | ||
| [github issue](https://github.com/aperture-data/aperturedb-python/issues). | ||
| | `ADB_DEBUGGABLE` | `boolean` | Allows the application to register a fault handler that dumps a trace when `SIGUSR1` is sent to the process. | *Not set* | |
This PR updates the
README.mdby adding: