~ i1ddlZddlmZmZmZmZddlZddlmZddl m Z ddl m Z m Z ddlmZmZeddd Gd d ee ZdS) N)Dict GeneratorListOptional) deprecated) Embeddings)get_from_dict_or_envpre_init) BaseModel ConfigDictz0.3.16z1.0z)langchain_sambanova.SambaStudioEmbeddings)sinceremovalalternative_importc \eZdZUdZdZeed< dZeed< dZeed< dZ eed< dZ eed< iZ e ed< d Z eed < ed Zed edefdZdefdZdedefdZdeed edefdZ ddeed eedeeefdZdedeefdZdS)SambaStudioEmbeddingsa3SambaNova embedding models. To use, you should have the environment variables ``SAMBASTUDIO_EMBEDDINGS_BASE_URL``, ``SAMBASTUDIO_EMBEDDINGS_BASE_URI`` ``SAMBASTUDIO_EMBEDDINGS_PROJECT_ID``, ``SAMBASTUDIO_EMBEDDINGS_ENDPOINT_ID``, ``SAMBASTUDIO_EMBEDDINGS_API_KEY`` set with your personal sambastudio variable or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.embeddings import SambaStudioEmbeddings embeddings = SambaStudioEmbeddings(sambastudio_embeddings_base_url=base_url, sambastudio_embeddings_base_uri=base_uri, sambastudio_embeddings_project_id=project_id, sambastudio_embeddings_endpoint_id=endpoint_id, sambastudio_embeddings_api_key=api_key, batch_size=32) (or) embeddings = SambaStudioEmbeddings(batch_size=32) (or) # CoE example embeddings = SambaStudioEmbeddings( batch_size=1, model_kwargs={ 'select_expert':'e5-mistral-7b-instruct' } ) sambastudio_embeddings_base_urlsambastudio_embeddings_base_uri!sambastudio_embeddings_project_id"sambastudio_embeddings_endpoint_idsambastudio_embeddings_api_key model_kwargs batch_size)protected_namespacesvaluesreturnct|dd|d<t|ddd|d<t|dd|d<t|d d |d <t|d d |d <|S) z?Validate that api key and python package exists in environment.rSAMBASTUDIO_EMBEDDINGS_BASE_URLrSAMBASTUDIO_EMBEDDINGS_BASE_URIapi/predict/generic)defaultr!SAMBASTUDIO_EMBEDDINGS_PROJECT_IDr"SAMBASTUDIO_EMBEDDINGS_ENDPOINT_IDrSAMBASTUDIO_EMBEDDINGS_API_KEY)r )clsrs C:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\langchain_community/embeddings/sambanova.pyvalidate_environmentz*SambaStudioEmbeddings.validate_environmentKs5I 57X5 5 015I  - -) 5 5 5 01 7K  / /7 7 23 8L  0 08 8 34 4H 46V4 4 /0 cd|jvr|j}n#d|jD}tj|}|S)z Get the tuning parameters to use when calling the model Returns: The tuning parameters as a JSON string. api/v2/predict/genericc^i|]*\}}|t|jt|d+S))typevalue)r.__name__str).0kvs r( z.psE"""AqDGG,s1vv>>"""r*)rritemsjsondumps)selftuning_params_dict tuning_paramss r(_get_tuning_paramsz(SambaStudioEmbeddings._get_tuning_paramsfsd $t'K K K!%!2  ""!.4466"""  #566 r*pathc*|jd|jd|S)z Return the full API URL for a given path. :param str path: the sub-path :returns: the full API URL for the sub-path :rtype: str /)rr)r9r=s r( _get_full_urlz#SambaStudioEmbeddings._get_full_urlws'6ff9]ff`dfffr*textsc#jKtdt||D]}||||zVdS)afGenerator for creating batches in the embed documents method Args: texts (List[str]): list of strings to embed batch_size (int, optional): batch size to be used for the embedding model. Will depend on the RDU endpoint used. Yields: List[str]: list (batch) of strings of size batch size rN)rangelen)r9rAris r(_iterate_over_batchesz+SambaStudioEmbeddings._iterate_over_batchessPq#e**j11 , ,AA N*+ + + + + , ,r*Nc||j}tj}||jd|j}t j|}g}d|j vr| ||D]}||d}| |d|j i|} | j dkrtd| j d | j | d } || #t"$r#t#d | wxYwnd |j vr| ||D]}d t%|D} | |d}| |d|j i|} | j dkrtd| j d | j d| dD} || #t"$r#t#d| wxYwnd|j vr| ||D]}||d}| |d|j i|} | j dkrtd| j d | j |dr| d} n| d} || #t"$r#t#d| wxYwnt)d|j d|S)a<Returns a list of embeddings for the given sentences. Args: texts (`List[str]`): List of texts to encode batch_size (`int`): Batch size for the encoding Returns: `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences Nr?api/predict/nlpinputsparamskeyheadersr71Sambanova /complete call failed with status code . Details: data%'data' not found in endpoint responser,c$g|] \}}d||dS)itemidr/r)r2rErUs r( z9SambaStudioEmbeddings.embed_documents..s7:A!T:!::55r*r6rKcg|] }|d S)r/r)r2rUs r(rXz9SambaStudioEmbeddings.embed_documents..s T T T4g T T Tr*r6&'items' not found in endpoint responser" instancesrK select_expert predictions,'predictions' not found in endpoint responsehandling of endpoint uri:  not implemented)rrequestsSessionr@rrr7loadsr<rrFpostr status_code RuntimeErrortextextendKeyError enumerateget ValueError) r9rAr http_sessionurlrK embeddingsbatchrRresponse embeddingr6s r(embed_documentsz%SambaStudioEmbeddings.embed_documentssW  J'))   5 a a8_ a a  D335566  D D D33E:FF  "'6::',,"D$GH- '3..&N#/NN>FmNN (  7I%%i0000"?  *&)M M M33E:FF  ENuEUEU"'&99',,"D$GH- '3..&N#/NN>FmNN T T8==??7;S T T TI%%i0000"@ % 0#d&J J J33E:FF  %*f==',,"D$GH- '3..&N#/NN>FmNN zz/22C$,MMOOM$B $,MMOOM$B %%i0000"F % 2cT-Qccc s%/D  -D69G>>-H+ AL-L-ricntj}||jd|j}t j|}d|jvr|g|d}| |d|j i|}|j dkrtd|j d|j  |d d }n#t$r#td |wxYwd |jvrd |dg|d}| |d|j i|}|j dkrtd|j d|j  |dd d}n(#t$r#td|wxYwd|jvr|g|d}| |d|j i|}|j dkrtd|j d|j  |dr!|dd }n |dd }nH#t$r#td|wxYwt!d|jd|S)aReturns a list of embeddings for the given sentences. Args: sentences (`List[str]`): List of sentences to encode Returns: `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences r?rHrIrLrMrOrPrQrRrrSr,item0rVrYr6r/r[r"r\r^r_r`rarb)rcrdr@rrr7rer<rrfrrgrhrirkrmrn)r9rirorprKrRrsrts r( embed_queryz!SambaStudioEmbeddings.embed_querys '))   5 a a8_ a a  D335566  D D D#f77D#(( CD)H #s**"J+JJ:B-JJ $MMOOF3A6    ;MMOO  &)M M M%,t<<=PPD#(( CD)H #s**"J+JJ:B-JJ $MMOOG4Q7@    <MMOO  #d&J J J"&6::D#(( CD)H #s**"J+JJ:B-JJ ::o..B (  >q AII (  >q AI   BMMOO cT-Qccc s%7 C-D%&F -F:AI---J)N)r0 __module__ __qualname____doc__rr1__annotations__rrrrrdictrintr model_configr rr)r<r@rrrFrfloatrurxrr*r(rr s !!F,.#S---+-#S----/%s///-.0&000.*,"C,,,L$2J-:2666L $4X4C"g#g#gggg ,49 ,# ,) , , , ,=Abb#Yb,4SMb d5k bbbbHSSU SSSSSSr*r)r7typingrrrrrclangchain_core._api.deprecationrlangchain_core.embeddingsrlangchain_core.utilsr r pydanticr r rrr*r(rs 222222222222666666000000????????********   B tttttIztt  tttr*