mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
daa9ccae52
- **Description:** add deprecated warning for ErnieBotChat and ErnieEmbeddings. - These two classes **lack maintenance** and do not use the sdk provided by qianfan, which means hard to implement some key feature like streaming. - The alternative `langchain_community.chat_models.QianfanChatEndpoint` and `langchain_community.embeddings.QianfanEmbeddingsEndpoint` can completely replace these two classes, only need to change configuration items. - **Issue:** None, - **Dependencies:** None, - **Twitter handle:** None --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
157 lines
4.9 KiB
Python
157 lines
4.9 KiB
Python
import asyncio
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import logging
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import threading
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from typing import Dict, List, Optional
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import requests
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from langchain_core._api.deprecation import deprecated
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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.runnables.config import run_in_executor
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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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@deprecated(
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since="0.0.13",
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alternative="langchain_community.embeddings.QianfanEmbeddingsEndpoint",
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)
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class ErnieEmbeddings(BaseModel, Embeddings):
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"""`Ernie Embeddings V1` embedding models."""
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ernie_api_base: Optional[str] = None
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ernie_client_id: Optional[str] = None
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ernie_client_secret: Optional[str] = None
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access_token: Optional[str] = None
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chunk_size: int = 16
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model_name = "ErnieBot-Embedding-V1"
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_lock = threading.Lock()
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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values["ernie_api_base"] = get_from_dict_or_env(
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values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com"
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)
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values["ernie_client_id"] = get_from_dict_or_env(
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values,
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"ernie_client_id",
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"ERNIE_CLIENT_ID",
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)
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values["ernie_client_secret"] = get_from_dict_or_env(
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values,
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"ernie_client_secret",
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"ERNIE_CLIENT_SECRET",
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)
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return values
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def _embedding(self, json: object) -> dict:
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base_url = (
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f"{self.ernie_api_base}/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings"
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)
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resp = requests.post(
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f"{base_url}/embedding-v1",
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headers={
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"Content-Type": "application/json",
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},
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params={"access_token": self.access_token},
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json=json,
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)
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return resp.json()
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def _refresh_access_token_with_lock(self) -> None:
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with self._lock:
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logger.debug("Refreshing access token")
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base_url: str = f"{self.ernie_api_base}/oauth/2.0/token"
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resp = requests.post(
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base_url,
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headers={
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"Content-Type": "application/json",
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"Accept": "application/json",
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},
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params={
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"grant_type": "client_credentials",
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"client_id": self.ernie_client_id,
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"client_secret": self.ernie_client_secret,
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},
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)
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self.access_token = str(resp.json().get("access_token"))
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed search docs.
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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[List[float]]: List of embeddings, one for each text.
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"""
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if not self.access_token:
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self._refresh_access_token_with_lock()
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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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resp = self._embedding({"input": [text for text in chunk]})
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if resp.get("error_code"):
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if resp.get("error_code") == 111:
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self._refresh_access_token_with_lock()
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resp = self._embedding({"input": [text for text in chunk]})
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else:
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raise ValueError(f"Error from Ernie: {resp}")
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lst.extend([i["embedding"] for i in resp["data"]])
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return lst
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def embed_query(self, text: str) -> List[float]:
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"""Embed query text.
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Args:
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text: The text to embed.
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Returns:
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List[float]: Embeddings for the text.
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"""
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if not self.access_token:
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self._refresh_access_token_with_lock()
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resp = self._embedding({"input": [text]})
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if resp.get("error_code"):
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if resp.get("error_code") == 111:
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self._refresh_access_token_with_lock()
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resp = self._embedding({"input": [text]})
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else:
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raise ValueError(f"Error from Ernie: {resp}")
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return resp["data"][0]["embedding"]
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async def aembed_query(self, text: str) -> List[float]:
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"""Asynchronous Embed query text.
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Args:
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text: The text to embed.
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Returns:
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List[float]: Embeddings for the text.
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"""
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return await run_in_executor(None, self.embed_query, text)
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async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Asynchronous Embed search docs.
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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[List[float]]: List of embeddings, one for each text.
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"""
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result = await asyncio.gather(*[self.aembed_query(text) for text in texts])
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return list(result)
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