Source code for langchain_community.embeddings.baidu_qianfan_endpoint

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, Field, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

logger = logging.getLogger(__name__)

[docs]class QianfanEmbeddingsEndpoint(BaseModel, Embeddings): """`Baidu Qianfan Embeddings` embedding models.""" qianfan_ak: Optional[str] = None """Qianfan application apikey""" qianfan_sk: Optional[str] = None """Qianfan application secretkey""" chunk_size: int = 16 """Chunk size when multiple texts are input""" model: str = "Embedding-V1" """Model name you could get from for now, we support Embedding-V1 and - Embedding-V1 (默认模型) - bge-large-en - bge-large-zh preset models are mapping to an endpoint. `model` will be ignored if `endpoint` is set """ endpoint: str = "" """Endpoint of the Qianfan Embedding, required if custom model used.""" client: Any """Qianfan client""" init_kwargs: Dict[str, Any] = Field(default_factory=dict) """init kwargs for qianfan client init, such as `query_per_second` which is associated with qianfan resource object to limit QPS""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """extra params for model invoke using with `do`.""" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """ Validate whether qianfan_ak and qianfan_sk in the environment variables or configuration file are available or not. init qianfan embedding client with `ak`, `sk`, `model`, `endpoint` Args: values: a dictionary containing configuration information, must include the fields of qianfan_ak and qianfan_sk Returns: a dictionary containing configuration information. If qianfan_ak and qianfan_sk are not provided in the environment variables or configuration file,the original values will be returned; otherwise, values containing qianfan_ak and qianfan_sk will be returned. Raises: ValueError: qianfan package not found, please install it with `pip install qianfan` """ values["qianfan_ak"] = convert_to_secret_str( get_from_dict_or_env( values, "qianfan_ak", "QIANFAN_AK", default="", ) ) values["qianfan_sk"] = convert_to_secret_str( get_from_dict_or_env( values, "qianfan_sk", "QIANFAN_SK", default="", ) ) try: import qianfan params = { **values.get("init_kwargs", {}), "model": values["model"], } if values["qianfan_ak"].get_secret_value() != "": params["ak"] = values["qianfan_ak"].get_secret_value() if values["qianfan_sk"].get_secret_value() != "": params["sk"] = values["qianfan_sk"].get_secret_value() if values["endpoint"] is not None and values["endpoint"] != "": params["endpoint"] = values["endpoint"] values["client"] = qianfan.Embedding(**params) except ImportError: raise ImportError( "qianfan package not found, please install it with " "`pip install qianfan`" ) return values
[docs] def embed_query(self, text: str) -> List[float]: resp = self.embed_documents([text]) return resp[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: resp =, **self.model_kwargs) lst.extend([res["embedding"] for res in resp["data"]]) return lst
[docs] async def aembed_query(self, text: str) -> List[float]: embeddings = await self.aembed_documents([text]) return embeddings[0]
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: 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 = await self.client.ado(texts=chunk, **self.model_kwargs) for res in resp["data"]: lst.extend([res["embedding"]]) return lst