Source code for langchain_community.embeddings.ernie

import asyncio
import logging
import threading
from functools import partial
from typing import Dict, List, Optional

import requests
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 ErnieEmbeddings(BaseModel, Embeddings): """`Ernie Embeddings V1` embedding models.""" ernie_api_base: Optional[str] = None ernie_client_id: Optional[str] = None ernie_client_secret: Optional[str] = None access_token: Optional[str] = None chunk_size: int = 16 model_name = "ErnieBot-Embedding-V1" _lock = threading.Lock() @root_validator() def validate_environment(cls, values: Dict) -> Dict: values["ernie_api_base"] = get_from_dict_or_env( values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com" ) values["ernie_client_id"] = get_from_dict_or_env( values, "ernie_client_id", "ERNIE_CLIENT_ID", ) values["ernie_client_secret"] = get_from_dict_or_env( values, "ernie_client_secret", "ERNIE_CLIENT_SECRET", ) return values def _embedding(self, json: object) -> dict: base_url = ( f"{self.ernie_api_base}/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings" ) resp = requests.post( f"{base_url}/embedding-v1", headers={ "Content-Type": "application/json", }, params={"access_token": self.access_token}, json=json, ) return resp.json() def _refresh_access_token_with_lock(self) -> None: with self._lock: logger.debug("Refreshing access token") base_url: str = f"{self.ernie_api_base}/oauth/2.0/token" resp = requests.post( base_url, headers={ "Content-Type": "application/json", "Accept": "application/json", }, params={ "grant_type": "client_credentials", "client_id": self.ernie_client_id, "client_secret": self.ernie_client_secret, }, ) self.access_token = str(resp.json().get("access_token"))
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs. Args: texts: The list of texts to embed Returns: List[List[float]]: List of embeddings, one for each text. """ if not self.access_token: self._refresh_access_token_with_lock() 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: resp = self._embedding({"input": [text for text in chunk]}) if resp.get("error_code"): if resp.get("error_code") == 111: self._refresh_access_token_with_lock() resp = self._embedding({"input": [text for text in chunk]}) else: raise ValueError(f"Error from Ernie: {resp}") lst.extend([i["embedding"] for i in resp["data"]]) return lst
[docs] def embed_query(self, text: str) -> List[float]: """Embed query text. Args: text: The text to embed. Returns: List[float]: Embeddings for the text. """ if not self.access_token: self._refresh_access_token_with_lock() resp = self._embedding({"input": [text]}) if resp.get("error_code"): if resp.get("error_code") == 111: self._refresh_access_token_with_lock() resp = self._embedding({"input": [text]}) else: raise ValueError(f"Error from Ernie: {resp}") return resp["data"][0]["embedding"]
[docs] async def aembed_query(self, text: str) -> List[float]: """Asynchronous Embed query text. Args: text: The text to embed. Returns: List[float]: Embeddings for the text. """ return await asyncio.get_running_loop().run_in_executor( None, partial(self.embed_query, text) )
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """Asynchronous Embed search docs. Args: texts: The list of texts to embed Returns: List[List[float]]: List of embeddings, one for each text. """ result = await asyncio.gather(*[self.aembed_query(text) for text in texts]) return list(result)