langchain_community.embeddings.voyageai.VoyageEmbeddings¶
class langchain_community.embeddings.voyageai.VoyageEmbeddings[source]¶
Bases: BaseModel, Embeddings
Voyage embedding models.
To use, you should have the environment variable VOYAGE_API_KEY set with
your API key or pass it as a named parameter to the constructor.
Example
from langchain_community.embeddings import VoyageEmbeddings
voyage = VoyageEmbeddings(voyage_api_key="your-api-key")
text = "This is a test query."
query_result = voyage.embed_query(text)
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 batch_size: int = 8¶
Maximum number of texts to embed in each API request.
param max_retries: int = 6¶
Maximum number of retries to make when generating.
param model: str = 'voyage-01'¶
param request_timeout: Optional[Union[float, Tuple[float, float]]] = None¶
Timeout in seconds for the API request.
param show_progress_bar: bool = False¶
Whether to show a progress bar when embedding. Must have tqdm installed if set
to True.
param voyage_api_base: str = 'https://api.voyageai.com/v1/embeddings'¶
param voyage_api_key: Optional[pydantic.types.SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶