dhSSKJr SSKrSSKJrJrJrJr SSKJ r SSK J r J r J r SSKJrJrJrJr \R&"\5r"SS\\ 5rg) ) annotationsN)AnyDictListOptional) Embeddings)convert_to_secret_strget_from_dict_or_envpre_init) BaseModel ConfigDictField SecretStrc\rSrSr%Sr\"SSS9rS\S'\"SSS9rS\S 'S r S \S '\"SS 9r S\S'Sr S\S'Sr S\S'\"\ S9rS\S'\"\ S9rS\S'\"SS9r\S Sj5rS!SjrS"SjrS!SjrS"SjrSrg)#QianfanEmbeddingsEndpoint aBaidu Qianfan Embeddings embedding models. Setup: To use, you should have the ``qianfan`` python package installed, and set environment variables ``QIANFAN_AK``, ``QIANFAN_SK``. .. code-block:: bash pip install qianfan export QIANFAN_AK="your-api-key" export QIANFAN_SK="your-secret_key" Instantiate: .. code-block:: python from langchain_community.embeddings import QianfanEmbeddingsEndpoint embeddings = QianfanEmbeddingsEndpoint() Embed: .. code-block:: python # embed the documents vectors = embeddings.embed_documents([text1, text2, ...]) # embed the query vectors = embeddings.embed_query(text) # embed the documents with async vectors = await embeddings.aembed_documents([text1, text2, ...]) # embed the query with async vectors = await embeddings.aembed_query(text) Napi_key)defaultaliaszOptional[SecretStr] qianfan_ak secret_key qianfan_skint chunk_sizerz Optional[str]modelstrendpointrclient)default_factoryzDict[str, Any] init_kwargs model_kwargs)protected_namespacesc [[USSSS95US'[[USSSS95US'SSKn0URS 05ES US 0EnUSR 5S:waUSR 5US 'USR 5S:waUSR 5US 'US bUS S:waUS US 'UR "S0UD6US'U$![ a [ S5ef=f)a Validate whether qianfan_ak and qianfan_sk in the environment variables or configuration file are available or not. init qianfan embedding client with `ak`, `sk`, `model`, `endpoint` Args: values: a dictionary containing configuration information, must include the fields of qianfan_ak and qianfan_sk Returns: a dictionary containing configuration information. If qianfan_ak and qianfan_sk are not provided in the environment variables or configuration file,the original values will be returned; otherwise, values containing qianfan_ak and qianfan_sk will be returned. Raises: ValueError: qianfan package not found, please install it with `pip install qianfan` r QIANFAN_AKrrr QIANFAN_SKrNr#rakskr r!zGqianfan package not found, please install it with `pip install qianfan`r%)r r qianfangetget_secret_value Embedding ImportError)clsvaluesr,paramss m/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/embeddings/baidu_qianfan_endpoint.pyvalidate_environment.QianfanEmbeddingsEndpoint.validate_environmentVs@. 5    | 5    |  **]B/Fl#446"<%l3DDFt l#446"<%l3DDFt j!-&2D2J%+J%7z"&00:6:F8    (  s B%CC/c0URU/5nUS$Nr)embed_documents)selftextresps r4 embed_query%QianfanEmbeddingsEndpoint.embed_querys##TF+AwcF[S[U5UR5Vs/sHnXX R-PM nn/nUHQnURR"SSU0UR D6nUR USVs/sHowSPM sn5 MS U$s snfs snf)a' Embeds a list of text documents using the AutoVOT algorithm. Args: texts (List[str]): A list of text documents to embed. Returns: List[List[float]]: A list of embeddings for each document in the input list. Each embedding is represented as a list of float values. rtextsdata embeddingr%)rangelenrr!dor$extendr:rAitext_in_chunkslstchunkr<ress r4r9)QianfanEmbeddingsEndpoint.embed_documentss1c%j$//: : a//) *:  #E;;>>CC1B1BCD JJDLALSK(LA B$  Bs B? B cL# URU/5IShvN nUS$N 7fr8)aembed_documents)r:r; embeddingss r4 aembed_query&QianfanEmbeddingsEndpoint.aembed_querys)00$88 !}9s $" $cN# [S[U5UR5Vs/sHnXX R-PM nn/nUHTnURR"SSU0UR D6IShvN nUSHnUR US/5 M MV U$s snfN07f)NrrArBrCr%)rDrErr!ador$rGrHs r4rP*QianfanEmbeddingsEndpoint.aembed_documentss1c%j$//: : a//) *:  #EJuJ8I8IJJDF| C ,-.$$  Ks#B%B4B%2B#31B%)r2rreturnr)r;rrWz List[float])rAz List[str]rWzList[List[float]])__name__ __module__ __qualname____firstlineno____doc__rr__annotations__rrrr r!dictr#r$r model_configr r5r=r9rRrP__static_attributes__r%r?r4rr s!F',D &JJ#J$&+D &MJ#M'J2 .E=. HcKFC"'"=K=@$)#>L.>826L ::x* r?r) __future__rloggingtypingrrrrlangchain_core.embeddingsrlangchain_core.utilsr r r pydanticr r rr getLoggerrXloggerrr%r?r4ris@",,0VV<<   8 $m :mr?