langchain_community.embeddings.fastembed.FastEmbedEmbeddings

class langchain_community.embeddings.fastembed.FastEmbedEmbeddings[source]

Bases: BaseModel, Embeddings

Qdrant FastEmbedding models. FastEmbed is a lightweight, fast, Python library built for embedding generation. See more documentation at: * https://github.com/qdrant/fastembed/ * https://qdrant.github.io/fastembed/

To use this class, you must install the fastembed Python package.

pip install fastembed .. rubric:: Example

from langchain_community.embeddings import FastEmbedEmbeddings fastembed = FastEmbedEmbeddings()

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 cache_dir: Optional[str] = None

The path to the cache directory. Defaults to local_cache in the parent directory

param doc_embed_type: Literal['default', 'passage'] = 'default'

Type of embedding to use for documents “default”: Uses FastEmbed’s default embedding method “passage”: Prefixes the text with “passage” before embedding.

param max_length: int = 512

The maximum number of tokens. Defaults to 512. Unknown behavior for values > 512.

param model_name: str = 'BAAI/bge-small-en-v1.5'

Name of the FastEmbedding model to use Defaults to “BAAI/bge-small-en-v1.5” Find the list of supported models at https://qdrant.github.io/fastembed/examples/Supported_Models/

param threads: Optional[int] = None

The number of threads single onnxruntime session can use. Defaults to None

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_documents(texts: List[str]) → List[List[float]][source]

Generate embeddings for documents using FastEmbed.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float][source]

Generate query embeddings using FastEmbed.

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