Embedding

Create text embeddings with any provider

The embedding and aembedding functions create vector embeddings from text using a unified interface across all providers that support embeddings.

any_llm.embedding()

def embedding(
    model: str,
    inputs: str | list[str],
    *,
    provider: str | LLMProvider | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    client_args: dict[str, Any] | None = None,
    **kwargs: Any,
) -> CreateEmbeddingResponse

any_llm.aembedding()

Async variant with the same parameters.

async def aembedding(
    model: str,
    inputs: str | list[str],
    *,
    provider: str | LLMProvider | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    client_args: dict[str, Any] | None = None,
    **kwargs: Any,
) -> CreateEmbeddingResponse

Parameters

Parameter	Type	Default	Description
model	str	required	Model identifier. Recommended: Use with separate provider parameter (e.g., model='gpt-4', provider='openai'). Alternative: Combined format 'provider:model' (e.g., 'openai:gpt-4'). Legacy format 'provider/model' is also supported but deprecated.
inputs	str | list[str]	required	The input text to embed
provider	str | LLMProvider | None	None	Recommended: Provider name to use for the request (e.g., 'openai', 'mistral'). When provided, the model parameter should contain only the model name.
api_key	str | None	None	API key for the provider
api_base	str | None	None	Base URL for the provider API
client_args	dict[str, Any] | None	None	Additional provider-specific arguments that will be passed to the provider's client instantiation.
**kwargs	Any	required	Additional provider-specific arguments that will be passed to the provider's API call.

Return Value

Returns a CreateEmbeddingResponse containing:

• data -- list of Embedding objects, each with an embedding vector (list[float]) and an index.
• model -- the model used.
• usage -- token usage information with prompt_tokens and total_tokens.

Usage

Single text

from any_llm import embedding

result = embedding(
    model="text-embedding-3-small",
    provider="openai",
    inputs="Hello, world!",
)

vector = result.data[0].embedding
print(f"Dimensions: {len(vector)}")
print(f"Tokens used: {result.usage.total_tokens}")

Batch embedding

result = embedding(
    model="text-embedding-3-small",
    provider="openai",
    inputs=["First sentence", "Second sentence", "Third sentence"],
)

for item in result.data:
    print(f"Index {item.index}: {len(item.embedding)} dimensions")

Async

import asyncio
from any_llm import aembedding

async def main():
    result = await aembedding(
        model="text-embedding-3-small",
        provider="openai",
        inputs="Hello, world!",
    )
    print(f"Dimensions: {len(result.data[0].embedding)}")

asyncio.run(main())

Not all providers support embeddings. Check the providers page for support details, or query ProviderMetadata.embedding programmatically.