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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

import dreamdb as vd

# Schema
schema = vd.Schema()
schema.add_image("image", mime="jpeg")
schema.add_embedding("embedding", dim=128)
schema.add_scalar_categorical("label")

# Create against any backend (file://, http(s)://, memory://)
ds = vd.Dataset.create(
    "my-dataset",
    schema,
    backend="file:///tmp/my-ds",
)

# Append a batch of samples (each a dict)
ds.append_many([
    {"image": open("cat.jpg", "rb").read(),
     "embedding": [0.1, 0.2, ...],
     "label": "cat"},
    # ...
])

# Filter::Vector + Filter::Where (And)
batches = ds.iter_vector(
    field="embedding",
    query=[0.1, 0.2, ...],
    top_k=10,
    batch_size=4,
    where_eq={"label": "cat"},
)
for batch in batches:
    images = batch["image"]      # list[bytes]
    embeds = batch["embedding"]  # list[list[float]]
    labels = batch["label"]      # list[str]

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 0.0.10

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Table of built distributions (wheels) for dreamdb 0.0.10
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dreamdb-0.0.10-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.8 abi3 Linux glibc 2.17+ x86-64 Details
dreamdb-0.0.10-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.8 abi3 Linux glibc 2.17+ ARM64 Details
dreamdb-0.0.10-cp38-abi3-macosx_11_0_arm64.whl CPython 3.8 abi3 macOS 11.0+ ARM64 Details

Total release size: 13.7 MB

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