feat(python-sdk): add multi-dimensional classification

[doitrous]

Adds Client.classify_dimensions() and the top-level classify_dimensions() convenience function, exposing the POST /v1/classify dimensions API that the MCP classify_dimensions tool already uses.

New types

• DimensionResult — one field result: label, confidence, scores, escalated, unscored, model, ms
• DimensionResponse — tier, model, models_used, usage, and results: List[Dict[str, DimensionResult]]

Usage

from classifier_dev import classify_dimensions

results = classify_dimensions(
    ["Checkout charges me twice"],
    {"team": ["billing", "identity", "platform"],
     "kind": {"labels": ["bug", "request"], "instructions": "bug = broken behavior"}}
)
results[0]["team"].label   # "billing"
results[0]["kind"].label   # "bug"

Tests

Mock server extended with a dimensions handler. Tests cover roundtrip with both label-array and {labels, instructions} shorthand, tier on the wire, batch items, and input validation. All existing tests still pass.

🤖 Generated with Claude Code

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📥 Commits

Reviewing files that changed from the base of the PR and between 5c241af and d2eb309.

📒 Files selected for processing (2)

• sdk/python/classifier_dev/__init__.py
• sdk/python/test_classifier_dev.py

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