fix: model fields set and model config access

[joein]

due to incorrect access to model_fields_set the following upsert was failing in pydantic v1

from qdrant_client import QdrantClient, models

if __name__ == '__main__':
    client = QdrantClient(":memory:")
    collection_name = "test"

    # Create collection with sparse vector support
    client.create_collection(
        collection_name=collection_name,
        sparse_vectors_config={
            "text": models.SparseVectorParams(),
        },
    )

    client.upsert(
        collection_name=collection_name,
        points=[
            models.PointStruct(
                id=1,
                vector={
                    "text": models.SparseVector(
                        indices=[1, 3, 5, 6],
                        values=[0.1, 0.2, 0.3, 0.4],
                    )
                },
            )
        ],
    )

[coderabbitai]

📝 Walkthrough

Walkthrough

The pull request introduces a new function, model_config, in the compatibility module to uniformly retrieve a Pydantic model's configuration. The function checks the PYDANTIC_V2 flag and either returns model.model_config or constructs a dictionary from model.__config__. Additionally, the PR updates the type inspector to use a function call to model_fields_set—imported from the compatibility module—instead of directly accessing the field set attribute. Similarly, the logic in the inspection cache population tool now accesses model configurations by calling model_config(model) rather than directly reading an attribute. These changes standardize the retrieval of model configuration and field details across different parts of the codebase while accounting for variations in Pydantic versions.

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[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (1)

qdrant_client/_pydantic_compat.py (1)

56-57: Consistent spacing

The added blank lines maintain the module's consistent style of separating function definitions, which is good for readability.

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 64311d3 and a2ed898.

📒 Files selected for processing (3)

• qdrant_client/_pydantic_compat.py (1 hunks)
• qdrant_client/embed/type_inspector.py (2 hunks)
• tools/populate_inspection_cache.py (2 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (5)

• GitHub Check: Python 3.13.x on ubuntu-latest test
• GitHub Check: Python 3.12.x on ubuntu-latest test
• GitHub Check: Python 3.11.x on ubuntu-latest test
• GitHub Check: Python 3.10.x on ubuntu-latest test
• GitHub Check: Python 3.9.x on ubuntu-latest test

🔇 Additional comments (5)

qdrant_client/embed/type_inspector.py (2)

5-5: Good addition of import from compatibility module

This import allows the code to use the model_fields_set function from the compatibility layer, which handles differences between Pydantic v1 and v2.

---

66-66: Proper implementation of Pydantic version compatibility

Replacing direct access to member.model_fields_set with model_fields_set(member) ensures compatibility with both Pydantic v1 and v2. This is essential since in v1, field sets are accessed via __fields_set__ while in v2 they're accessed via model_fields_set.

tools/populate_inspection_cache.py (2)

9-9: Good addition of import from compatibility module

Adding the import for model_config enables the code to use the compatibility function for accessing model configurations consistently across Pydantic versions.

---

44-45: Correct implementation of Pydantic version compatibility

Replacing direct access to model.model_config with model_config(model) ensures the code works with both Pydantic v1 (which uses __config__) and v2 (which uses model_config). The subsequent check for the "extra" configuration property is maintained correctly.

qdrant_client/_pydantic_compat.py (1)

58-62: Well-implemented compatibility function for model config access

The new model_config function follows the established pattern in this compatibility module. It correctly handles the difference between Pydantic v1 (where configuration is accessed via __config__) and v2 (where it's accessed via model_config).

The implementation gracefully returns a dictionary representation regardless of the Pydantic version, ensuring consistent behavior throughout the codebase.

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