langchain.embeddings.minimax.MiniMaxEmbeddings¶
class langchain.embeddings.minimax.MiniMaxEmbeddings[source]¶
Bases: BaseModel, Embeddings
MiniMax’s embedding service.
To use, you should have the environment variable MINIMAX_GROUP_ID and
MINIMAX_API_KEY set with your API token, or pass it as a named parameter to
the constructor.
Example
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
document_result = embeddings.embed_documents([document_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 embed_type_db: str = 'db'¶
For embed_documents
param embed_type_query: str = 'query'¶
For embed_query
param endpoint_url: str = 'https://api.minimax.chat/v1/embeddings'¶
Endpoint URL to use.
param minimax_api_key: Optional[str] = None¶
API Key for MiniMax API.
param minimax_group_id: Optional[str] = None¶
Group ID for MiniMax API.
param model: str = 'embo-01'¶
Embeddings model name to use.
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.