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])
field embed_type_db: str = 'db'#
For embed_documents
field embed_type_query: str = 'query'#
For embed_query
field endpoint_url: str = 'https://api.minimax.chat/v1/embeddings'#
Endpoint URL to use.
field minimax_api_key: Optional[str] = None#
API Key for MiniMax API.
field minimax_group_id: Optional[str] = None#
Group ID for MiniMax API.
field model: str = 'embo-01'#
Embeddings model name to use.
embed_documents(texts: List[str]) → List[List[float]][source]#
Embed documents using a MiniMax embedding endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Embed a query using a MiniMax embedding endpoint.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.ModelScopeEmbeddings[source]#
Wrapper around modelscope_hub embedding models.
To use, you should have the modelscope python package installed.
Example
from langchain.embeddings import ModelScopeEmbeddings
model_id = "damo/nlp_corom_sentence-embedding_english-base"
embed = ModelScopeEmbeddings(model_id=model_id)
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html