Embedding

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())