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. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

embed(texts: List[str], embed_type: str) List[List[float]][source]
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.

classmethod from_orm(obj: Any) Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model

Examples using MiniMaxEmbeddings