BookendEmbeddings#
- class langchain_community.embeddings.bookend.BookendEmbeddings[source]#
Bases:
BaseModel
,Embeddings
Bookend AI sentence_transformers embedding models.
Example
from langchain_community.embeddings import BookendEmbeddings bookend = BookendEmbeddings( domain={domain} api_token={api_token} model_id={model_id} ) bookend.embed_documents([ "Please put on these earmuffs because I can't you hear.", "Baby wipes are made of chocolate stardust.", ]) bookend.embed_query( "She only paints with bold colors; she does not like pastels." )
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_token: str [Required]#
Request for an API token at https://bookend.ai/ to use this embeddings module.
- param auth_header: dict [Optional]#
- param domain: str [Required]#
Request for a domain at https://bookend.ai/ to use this embeddings module.
- param model_id: str [Required]#
Embeddings model ID to use.
- 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]
Examples using BookendEmbeddings