langchain.embeddings.dashscope.DashScopeEmbeddings¶
class langchain.embeddings.dashscope.DashScopeEmbeddings[source]¶
Bases: BaseModel, Embeddings
DashScope embedding models.
To use, you should have the dashscope python package installed, and the
environment variable DASHSCOPE_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(dashscope_api_key="my-api-key")
Example
import os
os.environ["DASHSCOPE_API_KEY"] = "your DashScope API KEY"
from langchain.embeddings.dashscope import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(
    model="text-embedding-v1",
)
text = "This is a test query."
query_result = embeddings.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 client: Any = None¶
The DashScope client.
param dashscope_api_key: Optional[str] = None¶
param max_retries: int = 5¶
Maximum number of retries to make when generating.
param model: str = 'text-embedding-v1'¶
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¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.