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DreamDB Python SDK

Python bindings for the DreamDB multimodal versioned data lake — image + audio + text + embeddings + scalar metadata on content-addressed object storage, with vector and metadata filters for ML training pipelines.

Quick start

from pathlib import Path
from tempfile import TemporaryDirectory
import dreamdb as db

with TemporaryDirectory(prefix="dreamdb-quickstart-") as directory:
    backend = Path(directory).as_uri()
    schema = db.Schema().add_scalar_string("label")
    ds = db.Dataset.create("example", schema, backend=backend)
    ds.append_many([{"_anchor": 1, "label": "cat"}])
    reopened = db.Dataset.open("example", backend=backend)
    assert reopened.count() == 1

For trained vector indexes, use the versioned index guide. The main-only feature examples are not the API contract of released Python 0.0.10; they require a source-built SDK.

Build from source

pip install maturin
cd dreamdb-dataset-python
maturin develop --release

This produces the dreamdb package installed into the current virtualenv. Importing it gives the Schema and Dataset classes shown above.

Metadata

Release files for dreamdb 0.0.11

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Table of built distributions (wheels) for dreamdb 0.0.11
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dreamdb-0.0.11-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.8 abi3 Linux glibc 2.17+ x86-64 Details
dreamdb-0.0.11-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.8 abi3 Linux glibc 2.17+ ARM64 Details
dreamdb-0.0.11-cp38-abi3-macosx_11_0_arm64.whl CPython 3.8 abi3 macOS 11.0+ ARM64 Details

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0.0.15

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