Embedding spaces

Create a fixed-dimension embedding space and inspect readiness before loading vectors.

Outcome: Create a fixed-dimension embedding space and inspect readiness before loading vectors.

space = client.embedding_spaces.create(
    "clip-vit-l14",
    dimensions=768,
    model_name="ViT-L/14",
)

space = client.embedding_spaces.get(space.id)
print(space.id, space.dimensions, space.status)

Dimensions are part of the vector contract. Create a new space when the embedding model or dimension changes materially. list(), get(id), and delete() manage the space lifecycle. Delete only after confirming that curation and search workflows no longer depend on the index.

Operating contract

Concern	Required behavior
Execution surface	Pinned unitlab==3.0.0 application environment on Python 3.10+.
Identity	Least-privilege API key supplied through approved configuration.
Target resolution	Stable resource IDs and an explicitly bounded target set.
Success evidence	Typed return fields, server-side state, and downstream acceptance of the result.

Failure and recovery boundary

Condition	Response
Authentication or authorization fails	Stop, correct the service identity or access model, rotate exposed credentials, and rerun a read-only check.
Validation or entitlement rejects the operation	Correct the input or entitlement; do not retry an unchanged request.
A request times out	Inspect remote state before repeating a mutation because the server may have accepted it.
Asynchronous processing exceeds its deadline	Preserve the Batch Queue or release ID, continue bounded monitoring, and inspect item-level failures.
Only part of a batch succeeds	Keep successful identifiers, isolate failed rows, and retry only the corrected subset.

Pin and test the production SDK version. Use stable IDs, keep secrets out of logs and command history, and record the correlation ID, target IDs, counts, final state, and redacted error details for material state changes.

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Related Unitlab capability guides: Unitlab’s data curation platform