DreamDB

Python SDK

Version scope: Python 0.0.12 / JavaScript 0.5.4. Entity keys, progressive geometry, structured ragged/CSR and additional index capabilities are included in these releases. See feature examples and limits for the supported boundaries; older packages may not expose these methods.

dreamdb is where data gets in. It creates datasets, trains spatial indexes, ingests video, and feeds PyTorch — the parts of DreamDB the browser SDK deliberately doesn't carry.

bash
pip install dreamdb numpy

It is a compiled extension around the same Rust core as the JavaScript SDK, so the two write identical bytes. numpy is optional but every example uses it.

Two classes

Schema

Declare media, embeddings, fixed-shape numeric arrays, and six scalar types. Chainable.

Dataset

Everything else — create, append, query, snapshot, branch, merge, compact, delete.

Module-level functions cover the jobs that happen outside a dataset's lifetime: training indexes and compressors, garbage collection, and comparing refs.

What only Python can do

PythonJavaScript
Create a datasetAnywhereNode only
Train a spatial indexYesNo
dreamdb.ivf-cosine / imi-cosine fieldsYesCannot be created
Build a text (BM25) indexYesNo
Video ingest with fragmentingYes, via ffmpegNo
Typed numeric array fields and constantsYesYes, from 0.5.2
Read VideoItems by stable key or timeYesYes, from 0.5.2
PyTorch / Arrow batchesYesNo

If a pipeline writes data or builds an index, it wants this SDK. If an application reads and searches, either will do.

A local directory is a real backend

Nothing needs to be running to use DreamDB from Python:

python
ds = db.Dataset.create("photos", schema, backend="file:///tmp/photos")

memory:// exists for tests, and http(s):// for any S3-compatible endpoint. See Storage backends.

Where to go next