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][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- param api_key: SecretStr [Optional]#
API Key for authentication
- param api_url: str [Optional]#
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]
Examples using TextEmbedEmbeddings