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Brendan Clement
98d13f08c8
Storage configuration from the connection is embedded only, since a deployment configures its own. Training data loading, PyTorch, embedding functions, pandas, Polars and Pydantic work with local and deployment tables, and the Hugging Face Hub integration is embedded only. A local table cannot be pickled for DataLoader workers, so the PyTorch page says to wrap it in a Permutation; a LazyFrame over a whole table, to_pandas in the LangChain example and to_arrow in the DuckDB note are local only; and query text is embedded in the client for both offerings.
97 lines
3.5 KiB
Plaintext
97 lines
3.5 KiB
Plaintext
---
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title: "Pydantic"
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sidebarTitle: "Pydantic"
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availability:
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oss: available
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enterprise: available
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summary: >-
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The SDK converts `LanceModel` schemas to Arrow, and query rows back to
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models, in your process, so they work the same with local and deployment
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tables.
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---
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import {
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PyFrameworksPydanticBaseExample,
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PyFrameworksPydanticBaseModel,
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PyFrameworksPydanticImports,
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PyFrameworksPydanticSetUrl,
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PyFrameworksPydanticTypeConversion,
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PyFrameworksPydanticVectorField,
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} from '/snippets/integrations.mdx';
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[Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python.
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LanceDB integrates with Pydantic for schema inference, data ingestion, and query result casting.
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Using `lancedb.pydantic.LanceModel`, users can seamlessly
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integrate Pydantic with the rest of the LanceDB APIs.
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First, import the necessary LanceDB and Pydantic modules:
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyFrameworksPydanticImports}
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</CodeBlock>
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Next, define your Pydantic model by inheriting from `LanceModel` and specifying your fields including a vector field:
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyFrameworksPydanticBaseModel}
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</CodeBlock>
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Set the database connection URL:
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyFrameworksPydanticSetUrl}
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</CodeBlock>
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Now you can create a table, add data, and perform vector search operations:
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyFrameworksPydanticBaseExample}
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</CodeBlock>
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## Vector Field {#vector-field}
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LanceDB provides a `lancedb.pydantic.Vector` method to define a
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vector Field in a Pydantic Model.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyFrameworksPydanticVectorField}
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</CodeBlock>
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This example demonstrates how LanceDB automatically converts Pydantic field types to their corresponding Apache Arrow data types. The `pydantic_to_schema()` function takes a Pydantic model and generates an Arrow schema where:
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- `int` fields become `pa.int64()` (64-bit integers)
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- `str` fields become `pa.utf8()` (UTF-8 encoded strings)
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- `Vector(768)` becomes `pa.list_(pa.float32(), 768)` (fixed-size list of 768 float32 values)
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- The `False` parameter indicates that the fields are not nullable
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## Type Conversion {#type-conversion}
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LanceDB automatically convert Pydantic fields to
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[Apache Arrow DataType](https://arrow.apache.org/docs/python/generated/pyarrow.DataType.html#pyarrow.DataType).
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Current supported type conversions:
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| Pydantic Field Type | PyArrow Data Type |
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| ------------------- | ----------------- |
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| `int` | `pyarrow.int64` |
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| `float` | `pyarrow.float64` |
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| `bool` | `pyarrow.bool` |
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| `str` | `pyarrow.utf8()` |
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| `list` | `pyarrow.List` |
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| `BaseModel` | `pyarrow.Struct` |
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| `Vector(n)` | `pyarrow.FixedSizeList(float32, n)` |
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LanceDB supports to create Apache Arrow Schema from a
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`pydantic.BaseModel`
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via `lancedb.pydantic.pydantic_to_schema` method.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyFrameworksPydanticTypeConversion}
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</CodeBlock>
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This example shows a more complex Pydantic model with various field types and demonstrates how LanceDB handles:
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- Basic types: `int` and `str` fields
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- Vector fields: `Vector(1536)` creates a fixed-size list of 1536 float32 values
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- List fields: `List[int]` becomes a variable-length list of int64 values
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- Schema generation: The `pydantic_to_schema()` function automatically converts all these types to their Arrow equivalents |