Create a versioned vector space, load asset or frame embeddings, and run scoped search.
Outcome: Create a versioned vector space, load asset or frame embeddings, and run scoped search.
space = client.embedding_spaces.create(
"vision-encoder-2026-07",
dimensions=1024,
model_name="vision-encoder@sha256:...",
)
space.upsert_many(records)
results = space.search(query_vector, limit=50, project_id=project.id)Persist the encoder identity, preprocessing, vector normalization, data source version, dimension, and run ID. Validate that every result resolves to the intended asset or frame. Create a new space for an incompatible model or dimension instead of mixing vector contracts.
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.
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.
Continue with Unitlab: training-data curation workflows