mirror of
https://github.com/hwchase17/langchain
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2cf1e73d12
Description: Volcano Ark is an enterprise-grade large-model service platform for developers, providing a full range of functions and services such as model training, inference, evaluation, fine-tuning. You can visit its homepage at https://www.volcengine.com/docs/82379/1099455 for details. This change could help developers use the platform for embedding. Issue: None Dependencies: volcengine Tag maintainer: @baskaryan Twitter handle: @hinnnnnnnnnnnns --------- Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>
129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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from langchain_core.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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class VolcanoEmbeddings(BaseModel, Embeddings):
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"""`Volcengine Embeddings` embedding models."""
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volcano_ak: Optional[str] = None
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"""volcano access key
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learn more from: https://www.volcengine.com/docs/6459/76491#ak-sk"""
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volcano_sk: Optional[str] = None
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"""volcano secret key
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learn more from: https://www.volcengine.com/docs/6459/76491#ak-sk"""
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host: str = "maas-api.ml-platform-cn-beijing.volces.com"
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"""host
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learn more from https://www.volcengine.com/docs/82379/1174746"""
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region: str = "cn-beijing"
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"""region
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learn more from https://www.volcengine.com/docs/82379/1174746"""
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model: str = "bge-large-zh"
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"""Model name
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you could get from https://www.volcengine.com/docs/82379/1174746
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for now, we support bge_large_zh
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"""
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version: str = "1.0"
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""" model version """
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chunk_size: int = 100
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"""Chunk size when multiple texts are input"""
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client: Any
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"""volcano client"""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""
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Validate whether volcano_ak and volcano_sk in the environment variables or
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configuration file are available or not.
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init volcano embedding client with `ak`, `sk`, `host`, `region`
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Args:
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values: a dictionary containing configuration information, must include the
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fields of volcano_ak and volcano_sk
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Returns:
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a dictionary containing configuration information. If volcano_ak and
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volcano_sk are not provided in the environment variables or configuration
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file,the original values will be returned; otherwise, values containing
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volcano_ak and volcano_sk will be returned.
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Raises:
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ValueError: volcengine package not found, please install it with
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`pip install volcengine`
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"""
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values["volcano_ak"] = get_from_dict_or_env(
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values,
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"volcano_ak",
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"VOLC_ACCESSKEY",
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)
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values["volcano_sk"] = get_from_dict_or_env(
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values,
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"volcano_sk",
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"VOLC_SECRETKEY",
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)
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try:
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from volcengine.maas import MaasService
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client = MaasService(values["host"], values["region"])
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client.set_ak(values["volcano_ak"])
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client.set_sk(values["volcano_sk"])
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values["client"] = client
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except ImportError:
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raise ImportError(
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"volcengine package not found, please install it with "
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"`pip install volcengine`"
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)
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return values
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def embed_query(self, text: str) -> List[float]:
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return self.embed_documents([text])[0]
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""
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Embeds a list of text documents using the AutoVOT algorithm.
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Args:
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texts (List[str]): A list of text documents to embed.
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Returns:
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List[List[float]]: A list of embeddings for each document in the input list.
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Each embedding is represented as a list of float values.
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"""
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text_in_chunks = [
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texts[i : i + self.chunk_size]
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for i in range(0, len(texts), self.chunk_size)
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]
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lst = []
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for chunk in text_in_chunks:
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req = {
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"model": {
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"name": self.model,
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"version": self.version,
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},
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"input": chunk,
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}
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try:
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from volcengine.maas import MaasException
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resp = self.client.embeddings(req)
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lst.extend([res["embedding"] for res in resp["data"]])
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except MaasException as e:
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raise ValueError(f"embed by volcengine Error: {e}")
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return lst
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