mirror of
https://github.com/hwchase17/langchain
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9d663f31fa
## Description Updates the `langchain_community.embeddings.fastembed` provider as per the recent updates to [`FastEmbed`](https://github.com/qdrant/fastembed) library.
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
from typing import Any, Dict, List, Literal, Optional
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import numpy as np
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
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class FastEmbedEmbeddings(BaseModel, Embeddings):
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"""Qdrant FastEmbedding models.
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FastEmbed is a lightweight, fast, Python library built for embedding generation.
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See more documentation at:
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* https://github.com/qdrant/fastembed/
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* https://qdrant.github.io/fastembed/
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To use this class, you must install the `fastembed` Python package.
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`pip install fastembed`
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Example:
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from langchain_community.embeddings import FastEmbedEmbeddings
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fastembed = FastEmbedEmbeddings()
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"""
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model_name: str = "BAAI/bge-small-en-v1.5"
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"""Name of the FastEmbedding model to use
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Defaults to "BAAI/bge-small-en-v1.5"
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Find the list of supported models at
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https://qdrant.github.io/fastembed/examples/Supported_Models/
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"""
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max_length: int = 512
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"""The maximum number of tokens. Defaults to 512.
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Unknown behavior for values > 512.
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"""
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cache_dir: Optional[str]
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"""The path to the cache directory.
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Defaults to `local_cache` in the parent directory
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"""
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threads: Optional[int]
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"""The number of threads single onnxruntime session can use.
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Defaults to None
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"""
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doc_embed_type: Literal["default", "passage"] = "default"
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"""Type of embedding to use for documents
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The available options are: "default" and "passage"
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"""
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_model: Any # : :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that FastEmbed has been installed."""
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try:
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from fastembed import TextEmbedding
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model_name = values.get("model_name")
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max_length = values.get("max_length")
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cache_dir = values.get("cache_dir")
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threads = values.get("threads")
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values["_model"] = TextEmbedding(
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model_name=model_name,
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max_length=max_length,
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cache_dir=cache_dir,
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threads=threads,
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)
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except ImportError as ie:
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raise ImportError(
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"'FastEmbedEmbeddings' requires 'fastembed==v0.2.0' or above. "
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"Please install it with `pip install fastembed`."
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) from ie
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Generate embeddings for documents using FastEmbed.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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embeddings: List[np.ndarray]
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if self.doc_embed_type == "passage":
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embeddings = self._model.passage_embed(texts)
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else:
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embeddings = self._model.embed(texts)
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return [e.tolist() for e in embeddings]
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def embed_query(self, text: str) -> List[float]:
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"""Generate query embeddings using FastEmbed.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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query_embeddings: np.ndarray = next(self._model.query_embed(text))
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return query_embeddings.tolist()
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