TextEmbedEmbeddings#

class langchain_community.embeddings.textembed.TextEmbedEmbeddings[source]#

Bases: BaseModel, Embeddings

A class to handle embedding requests to the TextEmbed API.

model#: The TextEmbed model ID to use for embeddings.

api_url#: The base URL for the TextEmbed API.

api_key#: The API key for authenticating with the TextEmbed API.

client#: The TextEmbed client instance.

Example

from langchain_community.embeddings import TextEmbedEmbeddings

embeddings = TextEmbedEmbeddings(
    model="sentence-transformers/clip-ViT-B-32",
    api_url="http://localhost:8000/v1",
    api_key="<API_KEY>"
)

For more information: kevaldekivadiya2415/textembed

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param api_key: str = 'None'#: API Key for authentication

param api_url: str = 'http://localhost:8000/v1'#: Endpoint URL to use.

param client: Any = None#: TextEmbed client.

param model: str [Required]#: Underlying TextEmbed model id.

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

Async call out to TextEmbed’s embedding endpoint.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

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

Async call out to TextEmbed’s embedding endpoint for a single query.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

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

Call out to TextEmbed’s embedding endpoint.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

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

Call out to TextEmbed’s embedding endpoint for a single query.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using TextEmbedEmbeddings

TextEmbed - Embedding Inference Server