Source code for langchain_community.embeddings.fastembed

from typing import Any, Dict, List, Literal, Optional

import numpy as np
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator

[docs]class FastEmbedEmbeddings(BaseModel, Embeddings): """Qdrant FastEmbedding models. FastEmbed is a lightweight, fast, Python library built for embedding generation. See more documentation at: * * To use this class, you must install the `fastembed` Python package. `pip install fastembed` Example: from langchain_community.embeddings import FastEmbedEmbeddings fastembed = FastEmbedEmbeddings() """ 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 """ max_length: int = 512 """The maximum number of tokens. Defaults to 512. Unknown behavior for values > 512. """ cache_dir: Optional[str] """The path to the cache directory. Defaults to `local_cache` in the parent directory """ threads: Optional[int] """The number of threads single onnxruntime session can use. Defaults to None """ doc_embed_type: Literal["default", "passage"] = "default" """Type of embedding to use for documents The available options are: "default" and "passage" """ _model: Any # : :meta private: class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that FastEmbed has been installed.""" model_name = values.get("model_name") max_length = values.get("max_length") cache_dir = values.get("cache_dir") threads = values.get("threads") try: # >= v0.2.0 from fastembed import TextEmbedding values["_model"] = TextEmbedding( model_name=model_name, max_length=max_length, cache_dir=cache_dir, threads=threads, ) except ImportError as ie: try: # < v0.2.0 from fastembed.embedding import FlagEmbedding values["_model"] = FlagEmbedding( model_name=model_name, max_length=max_length, cache_dir=cache_dir, threads=threads, ) except ImportError: raise ImportError( "Could not import 'fastembed' Python package. " "Please install it with `pip install fastembed`." ) from ie return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Generate embeddings for documents using FastEmbed. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ embeddings: List[np.ndarray] if self.doc_embed_type == "passage": embeddings = self._model.passage_embed(texts) else: embeddings = self._model.embed(texts) return [e.tolist() for e in embeddings]
[docs] def embed_query(self, text: str) -> List[float]: """Generate query embeddings using FastEmbed. Args: text: The text to embed. Returns: Embeddings for the text. """ query_embeddings: np.ndarray = next(self._model.query_embed(text)) return query_embeddings.tolist()