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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.
59 lines
2.2 KiB
Plaintext
59 lines
2.2 KiB
Plaintext
---
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title: "Polars"
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sidebarTitle: "Polars & Arrow"
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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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Polars data goes into `create_table`, and query results come back as Polars
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DataFrames, for local and deployment tables alike; a LazyFrame over a whole
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table is local only.
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---
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import {
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PyPlatformsPolarsCreateTable,
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PyPlatformsPolarsImports,
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PyPlatformsPolarsLazyframe,
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PyPlatformsPolarsPydantic,
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PyPlatformsPolarsVectorSearch,
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} from '/snippets/integrations.mdx';
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LanceDB supports [Polars](https://github.com/pola-rs/polars), a blazingly fast DataFrame library for Python written in Rust. Under the hood, both Lance and Polars speak Arrow, so passing data back and forth stays zero-copy and ergonomic.
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## Create and Query a Table {#create-and-query-a-table}
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Import the required libraries, including the optional Pydantic helpers if you plan to define schemas.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPolarsImports}
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</CodeBlock>
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Build a Polars `DataFrame`, convert it to Arrow, and use it directly when creating a LanceDB table.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPolarsCreateTable}
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</CodeBlock>
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Run vector search and keep the results as a Polars `DataFrame` for further processing or visualization.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPolarsVectorSearch}
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</CodeBlock>
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## Work with LazyFrames {#work-with-lazyframes}
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When you want to operate on the entire table (potentially larger than RAM), convert to a Polars `LazyFrame` so you can chain transformations without loading everything at once. This works for local tables only; against a deployment, `search(...).to_polars()` returns an eager DataFrame of query results.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPolarsLazyframe}
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</CodeBlock>
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## Define Schemas with Pydantic {#define-schemas-with-pydantic}
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You can also describe your table via `LanceModel` and continue ingesting data from Polars. This is useful when multiple teams share a schema or when you want validation.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPolarsPydantic}
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</CodeBlock>
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