MlflowAIGatewayEmbeddings#

class langchain_community.embeddings.mlflow_gateway.MlflowAIGatewayEmbeddings[source]#

Bases: Embeddings, BaseModel

MLflow AI Gateway embeddings.

To use, you should have the mlflow[gateway] python package installed. For more information, see https://mlflow.org/docs/latest/gateway/index.html.

Example

from langchain_community.embeddings import MlflowAIGatewayEmbeddings

embeddings = MlflowAIGatewayEmbeddings(
    gateway_uri="<your-mlflow-ai-gateway-uri>",
    route="<your-mlflow-ai-gateway-embeddings-route>"
)
param gateway_uri: str | None = None#

The URI for the MLflow AI Gateway API.

param route: str [Required]#

The route to use for the MLflow AI Gateway API.

async aembed_documents(texts: list[str]) → list[list[float]]#

Asynchronous Embed search docs.

Parameters:

texts (list[str]) – List of text to embed.

Returns:

List of embeddings.

Return type:

list[list[float]]

async aembed_query(text: str) → list[float]#

Asynchronous Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

list[float]

embed_documents(texts: List[str]) → List[List[float]][source]#

Embed search docs.

Parameters:

texts (List[str]) – List of text to embed.

Returns:

List of embeddings.

Return type:

List[List[float]]

embed_query(text: str) → List[float][source]#

Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

List[float]

Examples using MlflowAIGatewayEmbeddings