langchain_community.embeddings.infinity.InfinityEmbeddings

class langchain_community.embeddings.infinity.InfinityEmbeddings

Bases: BaseModel, Embeddings

Embedding models for self-hosted https://github.com/michaelfeil/infinity This should also work for text-embeddings-inference and other self-hosted openai-compatible servers.

Infinity is a class to interact with Embedding Models on https://github.com/michaelfeil/infinity

Example

from langchain_community.embeddings import InfinityEmbeddings
InfinityEmbeddings(
    model="BAAI/bge-small",
    infinity_api_url="http://localhost:7797/v1",
)

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

Infinity client.

param infinity_api_url: str = 'http://localhost:7797/v1'

Endpoint URL to use.

param model: str [Required]

Underlying Infinity model id.

async aembed_documents(texts: List[str]) → List[List[float]]

Async call out to Infinity’s embedding endpoint.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

async aembed_query(text: str) → List[float]

Async call out to Infinity’s embedding endpoint.

Parameters

text – The text to embed.

Returns

Embeddings for the 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_documents(texts: List[str]) → List[List[float]]

Call out to Infinity’s embedding endpoint.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float]

Call out to Infinity’s 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