> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# embedding_litellm()

> Use LiteLLM to access 100+ embedding providers with a unified interface

Use LiteLLM to generate embeddings from models across multiple providers with a unified interface. LiteLLM supports
OpenAI, Azure, AWS Bedrock, Google Vertex AI, Hugging Face, and 100+ other providers.

You use this function to:

* Define the embedding model to use (from any supported provider)
* Specify the dimensionality of the embeddings
* Configure optional, provider-specific parameters
* Set the name of the environment variable that holds your API key

## Samples

### Hugging Face model

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'code_snippets'::regclass,
    loading => ai.loading_column('code'),
    embedding => ai.embedding_litellm(
        'huggingface/microsoft/codebert-base',
        768,
        api_key_name => 'HUGGINGFACE_API_KEY',
        extra_options => '{"wait_for_model": true}'::jsonb
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### Azure OpenAI

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'documents'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_litellm(
        'azure/my-embedding-deployment',
        1536,
        api_key_name => 'AZURE_API_KEY',
        extra_options => '{
            "api_base": "https://my-resource.openai.azure.com/",
            "api_version": "2023-05-15"
        }'::jsonb
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### AWS Bedrock

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'text_data'::regclass,
    loading => ai.loading_column('text'),
    embedding => ai.embedding_litellm(
        'bedrock/amazon.titan-embed-text-v1',
        1536,
        extra_options => '{
            "aws_region_name": "us-east-1"
        }'::jsonb
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

## Arguments

| Name            | Type    | Default | Required | Description                                                                                                         |
| --------------- | ------- | ------- | -------- | ------------------------------------------------------------------------------------------------------------------- |
| `model`         | `text`  | -       | ✔        | Name of the embedding model with optional provider prefix (e.g., `huggingface/model-name`, `azure/deployment-name`) |
| `dimensions`    | `int`   | -       | ✔        | Number of dimensions for the embedding vectors                                                                      |
| `api_key_name`  | `text`  | -       | ✖        | Name of the environment variable containing the API key                                                             |
| `extra_options` | `jsonb` | -       | ✖        | Provider-specific configuration options (API base URL, region, etc.)                                                |

## Returns

A JSON configuration object for use in [`create_vectorizer()`][create_vectorizer].

## Supported providers

LiteLLM supports 100+ providers including:

* OpenAI and Azure OpenAI
* AWS Bedrock
* Google Vertex AI
* Hugging Face
* Cohere
* Anthropic
* And many more

See the [LiteLLM documentation][litellm-documentation] for the complete list.

## Related functions

* [`embedding_openai()`][embedding_openai]: direct OpenAI integration
* [`embedding_ollama()`][embedding_ollama]: local Ollama models
* [`embedding_voyageai()`][embedding_voyageai]: Voyage AI models

[create_vectorizer]: /api-reference/pgai/vectorizer/create_vectorizer

[embedding_ollama]: /api-reference/pgai/vectorizer/embedding_ollama

[embedding_openai]: /api-reference/pgai/vectorizer/embedding_openai

[embedding_voyageai]: /api-reference/pgai/vectorizer/embedding_voyageai

[litellm-documentation]: https://docs.litellm.ai/docs/embedding/supported_embedding