lancedb/docs/web/embedding/index.mdx

---
title: "Managing Embeddings"
sidebarTitle: "Managing Embeddings"
description: "Use the embedding API in LanceDB -- registry, functions, schemas, and multi-language SDK support."
icon: "bars"
---

import {
    PyOpenaiEmbeddings,
    PyCreateEmbeddingFunction,
    PyManualQuerySearch,
    PyEmbeddingFunction,
    PyRegisterDevice,
    PyRegisterSecret,
    TsOpenaiEmbeddings,
    TsCreateEmbeddingFunction,
    TsManualQuerySearch,
    TsEmbeddingFunction,
    TsRegisterModelFallback,
    TsRegisterSecret,
    RsOpenaiEmbeddings,
    RsCreateEmbeddingFunction,
    RsManualQuerySearch,
    RsEmbeddingFunction,
} from '/snippets/embedding.mdx';

Modern machine learning models can be trained to convert raw data into embeddings, which are vectors
of floating point numbers. The position of an embedding in vector space captures the semantics of
the data, so vectors that are close to each other are considered similar.

LanceDB provides an embedding function registry in OSS as well as its Enterprise versions
([see below](#embeddings-in-lancedb-enterprise))
that automatically generates vector embeddings during data ingestion. Automatic query-time embedding
generation is available in LanceDB OSS, with SDK-specific query ergonomics. The API abstracts
embedding generation, allowing you to focus on your application logic.

## Embedding Registry {#embedding-registry}

You can get a supported embedding function from the registry, and then use it in your table schema. 
Once configured, the embedding function will automatically generate embeddings when you insert data
into the table. Query-time behavior depends on SDK: Python/TypeScript can query with text directly,
while Rust examples typically compute query embeddings explicitly before vector search.

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

    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
    {TsOpenaiEmbeddings}
    </CodeBlock>

    <CodeBlock filename="Rust" language="rust" icon="rust">
    {RsOpenaiEmbeddings}
    </CodeBlock>
</CodeGroup>

### Using an embedding function {#using-an-embedding-function}

Create an embedding function before you attach it to table or schema metadata. Python and TypeScript fetch
provider implementations from the embedding registry, while Rust constructs the provider embedding function
directly and registers it on the connection before using it in an `EmbeddingDefinition`.

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

    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
    {TsCreateEmbeddingFunction}
    </CodeBlock>

    <CodeBlock filename="Rust" language="rust" icon="rust">
    {RsCreateEmbeddingFunction}
    </CodeBlock>
</CodeGroup>

Provider configuration is SDK-specific, so copy the option names from the provider page for the SDK you use.
For example, the OpenAI model is selected with `name` in Python, `model` in TypeScript, and the model argument
to `OpenAIEmbeddingFunction::new_with_model` in Rust.

| Concept | Python | TypeScript | Rust |
| --- | --- | --- | --- |
| Model | `name="text-embedding-3-small"` | `{ model: "text-embedding-3-small" }` | `new_with_model(api_key, "text-embedding-3-small")` |
| Retry count | `max_retries=7` | Provider/client-specific | Provider/client-specific |
| API key | `api_key="..."`, environment variables, or `$var:` | `apiKey: "..."`, environment variables, or `$var:` | Constructor argument or environment variable |
| Device | Provider-specific, for example `device="cuda"` | Provider-specific | Provider-specific |

When ingesting data with an embedding definition, LanceDB only computes the vector column if that
column is missing from the incoming batch or present but entirely null. If you provide any non-null
values in the vector column, LanceDB treats the column as user-supplied and does not backfill the
remaining rows in that batch.

For reusable runtime configuration, the registry also supports `$var:` placeholders in embedding-function config.
This is useful for provider secrets and environment-specific settings in Python and TypeScript.

- Python uses `registry.set_var(...)`.
- TypeScript uses `registry.setVar(...)`.
- You can provide a fallback with `$var:name:default`.
- Sensitive values such as API keys should be passed through registry variables instead of hardcoding them in config.

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

    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
    {TsRegisterSecret}
    </CodeBlock>
</CodeGroup>

For non-sensitive settings such as inference device selection, you can also use a default fallback:

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

    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
    {TsRegisterModelFallback}
    </CodeBlock>
</CodeGroup>

Find the full list of arguments for each provider in the [integrations](/integrations/embedding) section.

## Multiple embedding columns {#multiple-embedding-columns}

A single table can include more than one embedding definition when you want to store multiple semantic views
of the same data, or generate embeddings from different source columns. In practice, each embedding definition
maps one source column to one vector column, and the table schema can contain multiple such pairs.

The exact setup differs by SDK, but the underlying pattern is the same: define a distinct source/vector pair
for each embedding function you want applied during ingest.

In TypeScript, automatic query embedding currently uses the first embedding function stored in the
table metadata. If a table has multiple embedding definitions and you need to query a specific vector
column, compute the query embedding explicitly and pass the vector to the search builder.

## Embedding model providers {#embedding-model-providers}

LanceDB supports most popular embedding providers.

### Text embeddings {#text-embeddings}

| Provider | Model ID | Default Model |
|----------|----------|---------------|
| OpenAI | `openai` | `text-embedding-ada-002` |
| Sentence Transformers | `sentence-transformers` | `all-MiniLM-L6-v2` |
| Hugging Face | `huggingface` | `colbert-ir/colbertv2.0` |
| Cohere | `cohere` | `embed-english-v3.0` |
| ... | ... | ... |

### Multimodal embedding {#multimodal-embedding}

| Provider | Model ID | Supported Inputs |
|----------|----------|------------------|
| OpenCLIP | `open-clip` | Text, Images |
| ImageBind | `imagebind` | Text, Images, Audio, Video |
| ... | ... | ... |

You can find all supported embedding models in the [integrations](/integrations/embedding) section.

## Embeddings in LanceDB Enterprise {#embeddings-in-lancedb-enterprise}
<Badge color="red">Enterprise</Badge>

In LanceDB Enterprise, embedding generation during data ingestion is client-side and the resulting vectors are
stored on the remote table.

<Info>
The Enterprise server does not currently generate embeddings from query text on its own. Any automatic
query-time embedding happens on the client side.
</Info>

### How string queries are interpreted {#how-string-queries-are-interpreted}

For the Python remote client, `table.search("hello")` can take two different paths:

- If the selected vector column has embedding metadata
  (i.e., the table schema stores the source-column, vector-column, and
  embedding-function mapping created from fields like `SourceField()` and
  `VectorField()` during table creation), then the embeddings are computed in the Python client process.
  The client uses the same local LanceDB embedding registry used by OSS tables to
  reconstruct the embedding function from schema metadata, compute the query vector in
  the client process, and send that vector to Enterprise for search.
  ```python
  result = table.search("hello").limit(1).to_list()
  # The Python client computes the query embedding locally, then sends a vector search.
  ```
- If the table does not have embedding metadata for that search, `table.search("hello")` in `auto` mode is
  treated as an FTS query instead.
  ```python
  result = table.search("hello").limit(5).to_list()
  # In auto mode this is treated as an FTS query, not a vector query.
  ```

If you want explicit vector or hybrid behavior and the client cannot resolve an embedding function from the
table metadata, generate the query embedding yourself and pass the vector directly.

TypeScript has a similar string-query distinction in `auto` mode: if no embedding providers have been
registered in the process, `search("text")` falls back to FTS. Once an embedding provider has been
imported and registered, the client expects table embedding metadata for automatic vector search and
raises an error if the table has none.

The manual query-embedding flow below works across Enterprise SDKs and is an explicit path you can use when you
want full control over query-time behavior.

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

    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
    {TsManualQuerySearch}
    </CodeBlock>

    <CodeBlock filename="Rust" language="rust" icon="rust">
    {RsManualQuerySearch}
    </CodeBlock>
</CodeGroup>

## Custom Embedding Functions {#custom-embedding-functions}

You can always implement your own embedding function:
- Python/TypeScript: subclass `TextEmbeddingFunction` (text) or `EmbeddingFunction` (multimodal).
- Rust: implement the `EmbeddingFunction` trait.

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

    <CodeBlock filename="TypeScript" language="typescript" icon="square-js">
    {TsEmbeddingFunction}
    </CodeBlock>

    <CodeBlock filename="Rust" language="rust" icon="rust">
    {RsEmbeddingFunction}
    </CodeBlock>
</CodeGroup>