[python] Infer multimodal vector columns by query dimension

[wangzhigang1999]

Purpose

Closes #9552.

MultimodalTable.search() and search_vectors() currently infer a vector column only when the schema contains one fixed-size vector field. Callers still need to pass column when multiple vector fields have different dimensions, even though the query dimension can identify one field.

This PR resolves vector columns by matching the query dimension to FixedSizeListType.list_size. It also validates explicitly selected columns and rejects mixed dimensions in search_vectors(). Multiple columns with the same dimension remain ambiguous and require column.

Tests

• pypaimon/tests/multimodal_table_test.py: 80 passed
• Flake8 on the changed Python files
• Python compilation check on the changed Python files
• git diff --check

[Copilot]

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Pull request overview

Improve multimodal vector search ergonomics by resolving the target vector column using the query vector’s dimension, and by tightening validation/error reporting for ambiguous or invalid vector-column selections.

Changes:

• Infer vector column by matching query dimension to FixedSizeListType.list_size when column is not provided.
• Validate explicitly provided column (exists, fixed-size vector type, matching dimension).
• Enforce consistent dimensions across all queries in search_vectors() and raise clearer errors.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
paimon-python/pypaimon/multimodal/table.py	Implements _resolve_vector_column() and updates search() / search_vectors() to use dimension-based resolution + stronger validation.
paimon-python/pypaimon/tests/multimodal_table_test.py	Adds coverage for dimension-based inference, ambiguous matches, and new validation error paths.

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[JingsongLi]

+1