JavelinAIGatewayEmbeddings#

class langchain_community.embeddings.javelin_ai_gateway.JavelinAIGatewayEmbeddings[source]#

Bases: Embeddings, BaseModel

Javelin AI Gateway embeddings.

To use, you should have the javelin_sdk python package installed. For more information, see https://docs.getjavelin.io

Example

from langchain_community.embeddings import JavelinAIGatewayEmbeddings

embeddings = JavelinAIGatewayEmbeddings(
    gateway_uri="<javelin-ai-gateway-uri>",
    route="<your-javelin-gateway-embeddings-route>"
)

param client: Any [Required]#: javelin client.

param gateway_uri: str | None = None#: The URI for the Javelin AI Gateway API.

param javelin_api_key: str | None = None#: The API key for the Javelin AI Gateway API.

param route: str [Required]#: The route to use for the Javelin AI Gateway API.

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

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][source]#

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 JavelinAIGatewayEmbeddings