---
title: "Pydantic"
sidebarTitle: "Pydantic"
availability:
  oss: available
  enterprise: available
  summary: >-
    The SDK converts `LanceModel` schemas to Arrow, and query rows back to
    models, in your process, so they work the same with local and deployment
    tables.

---

import {
  PyFrameworksPydanticBaseExample,
  PyFrameworksPydanticBaseModel,
  PyFrameworksPydanticImports,
  PyFrameworksPydanticSetUrl,
  PyFrameworksPydanticTypeConversion,
  PyFrameworksPydanticVectorField,
} from '/snippets/integrations.mdx';

[Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python.
LanceDB integrates with Pydantic for schema inference, data ingestion, and query result casting.
Using `lancedb.pydantic.LanceModel`, users can seamlessly
integrate Pydantic with the rest of the LanceDB APIs.

First, import the necessary LanceDB and Pydantic modules:

<CodeBlock filename="Python" language="Python" icon="python">
  {PyFrameworksPydanticImports}
</CodeBlock>

Next, define your Pydantic model by inheriting from `LanceModel` and specifying your fields including a vector field:

<CodeBlock filename="Python" language="Python" icon="python">
  {PyFrameworksPydanticBaseModel}
</CodeBlock>

Set the database connection URL:

<CodeBlock filename="Python" language="Python" icon="python">
  {PyFrameworksPydanticSetUrl}
</CodeBlock>

Now you can create a table, add data, and perform vector search operations:

<CodeBlock filename="Python" language="Python" icon="python">
  {PyFrameworksPydanticBaseExample}
</CodeBlock>


## Vector Field {#vector-field}

LanceDB provides a `lancedb.pydantic.Vector` method to define a
vector Field in a Pydantic Model.

<CodeBlock filename="Python" language="Python" icon="python">
  {PyFrameworksPydanticVectorField}
</CodeBlock>

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:
- `int` fields become `pa.int64()` (64-bit integers)
- `str` fields become `pa.utf8()` (UTF-8 encoded strings)  
- `Vector(768)` becomes `pa.list_(pa.float32(), 768)` (fixed-size list of 768 float32 values)
- The `False` parameter indicates that the fields are not nullable

## Type Conversion {#type-conversion}

LanceDB automatically convert Pydantic fields to
[Apache Arrow DataType](https://arrow.apache.org/docs/python/generated/pyarrow.DataType.html#pyarrow.DataType).

Current supported type conversions:

| Pydantic Field Type | PyArrow Data Type |
| ------------------- | ----------------- |
| `int`               | `pyarrow.int64`   |
| `float`              | `pyarrow.float64`  |
| `bool`              | `pyarrow.bool`    |
| `str`               | `pyarrow.utf8()`    |
| `list`              | `pyarrow.List`    |
| `BaseModel`         | `pyarrow.Struct`    |
| `Vector(n)`         | `pyarrow.FixedSizeList(float32, n)` |

LanceDB supports to create Apache Arrow Schema from a
`pydantic.BaseModel`
via `lancedb.pydantic.pydantic_to_schema` method.

<CodeBlock filename="Python" language="Python" icon="python">
  {PyFrameworksPydanticTypeConversion}
</CodeBlock>

This example shows a more complex Pydantic model with various field types and demonstrates how LanceDB handles:
- Basic types: `int` and `str` fields
- Vector fields: `Vector(1536)` creates a fixed-size list of 1536 float32 values
- List fields: `List[int]` becomes a variable-length list of int64 values
- Schema generation: The `pydantic_to_schema()` function automatically converts all these types to their Arrow equivalents