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

Source builds also expose read-only SqlSession.open(ref_name, backend) with query and explain. See SQL adapters for typed parameters, snapshot semantics and limits. Package release is a separate step.

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 feature examples cover Python 0.0.11, including entity keys, progressive geometry and structured arrays. Older packages do not necessarily expose these methods.

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.

Publisher identity

New Manifest publications record the Python distribution version, shared core build identity and operation in the existing writer tag. Optional dataset.set_application(name, revision) identifies your application on later publications; reopening a Dataset resets that optional pair. This does not label retained payloads or prove execution. See publisher provenance for limits, build configuration and historical/operator behavior.