Source code for langchain_community.embeddings.volcengine

from __future__ import annotations

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

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.utils import get_from_dict_or_env

logger = logging.getLogger(__name__)

[docs]class VolcanoEmbeddings(BaseModel, Embeddings): """`Volcengine Embeddings` embedding models.""" volcano_ak: Optional[str] = None """volcano access key learn more from:""" volcano_sk: Optional[str] = None """volcano secret key learn more from:""" host: str = "" """host learn more from""" region: str = "cn-beijing" """region learn more from""" model: str = "bge-large-zh" """Model name you could get from for now, we support bge_large_zh """ version: str = "1.0" """ model version """ chunk_size: int = 100 """Chunk size when multiple texts are input""" client: Any """volcano client""" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """ Validate whether volcano_ak and volcano_sk in the environment variables or configuration file are available or not. init volcano embedding client with `ak`, `sk`, `host`, `region` Args: values: a dictionary containing configuration information, must include the fields of volcano_ak and volcano_sk Returns: a dictionary containing configuration information. If volcano_ak and volcano_sk are not provided in the environment variables or configuration file,the original values will be returned; otherwise, values containing volcano_ak and volcano_sk will be returned. Raises: ValueError: volcengine package not found, please install it with `pip install volcengine` """ values["volcano_ak"] = get_from_dict_or_env( values, "volcano_ak", "VOLC_ACCESSKEY", ) values["volcano_sk"] = get_from_dict_or_env( values, "volcano_sk", "VOLC_SECRETKEY", ) try: from volcengine.maas import MaasService client = MaasService(values["host"], values["region"]) client.set_ak(values["volcano_ak"]) client.set_sk(values["volcano_sk"]) values["client"] = client except ImportError: raise ImportError( "volcengine package not found, please install it with " "`pip install volcengine`" ) return values
[docs] def embed_query(self, text: str) -> List[float]: return self.embed_documents([text])[0]
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """ Embeds a list of text documents using the AutoVOT algorithm. Args: texts (List[str]): A list of text documents to embed. Returns: List[List[float]]: A list of embeddings for each document in the input list. Each embedding is represented as a list of float values. """ text_in_chunks = [ texts[i : i + self.chunk_size] for i in range(0, len(texts), self.chunk_size) ] lst = [] for chunk in text_in_chunks: req = { "model": { "name": self.model, "version": self.version, }, "input": chunk, } try: from volcengine.maas import MaasException resp = self.client.embeddings(req) lst.extend([res["embedding"] for res in resp["data"]]) except MaasException as e: raise ValueError(f"embed by volcengine Error: {e}") return lst