dh1SSKrSSKJrJrJrJr SSKrSSKJr SSK J r SSK J r J r SSKJrJr \"SSS S 9"S S \\ 55rg) N)Dict GeneratorListOptional) deprecated) Embeddings)get_from_dict_or_envpre_init) BaseModel ConfigDictz0.3.16z1.0z)langchain_sambanova.SambaStudioEmbeddings)sinceremovalalternative_importc :\rSrSr%SrSr\\S'Sr\\S'Sr \\S'Sr \\S'Sr \\S'0r \ \S 'S r\\S '\"S S 9r\S\S\4Sj5rS\4SjrS\S\4SjrS\\S \S\4SjrSS\\S \\S\\\4SjjrS\S\\4SjrS rg)SambaStudioEmbeddings aSambaNova 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_namespacesvaluesreturnc[USS5US'[USSSS9US'[USS5US'[US S 5US '[US S 5US 'U$) z?Validate that api key and python package exists in environment.rSAMBASTUDIO_EMBEDDINGS_BASE_URLrSAMBASTUDIO_EMBEDDINGS_BASE_URIapi/predict/generic)defaultr!SAMBASTUDIO_EMBEDDINGS_PROJECT_IDr"SAMBASTUDIO_EMBEDDINGS_ENDPOINT_IDrSAMBASTUDIO_EMBEDDINGS_API_KEY)r )clsrs `/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/embeddings/sambanova.pyvalidate_environment*SambaStudioEmbeddings.validate_environmentKs5I 57X5 015I  - -) 5 01 7K  / /7 23 8L  0 08 34 4H 46V4 /0 c SUR;a URnOMURR5VVs0sH'up#U[U5R[ U5S._M) nnn[ R"U5nU$s snnf)zo Get the tuning parameters to use when calling the model Returns: The tuning parameters as a JSON string. api/v2/predict/generic)typevalue)rritemsr/__name__strjsondumps)selftuning_params_dictkv tuning_paramss r)_get_tuning_params(SambaStudioEmbeddings._get_tuning_paramsfs $t'K'K K!%!2!2 "..446"6DADG,,s1v>>6 " #56  "s-Bpathc>URSURSU3$)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)r6r=s r) _get_full_url#SambaStudioEmbeddings._get_full_urlws,667q9]9]8^^_`d_effr,textsc#V# [S[U5U5H nXX2-v M g7f)a.Generator 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)r6rBris r)_iterate_over_batches+SambaStudioEmbeddings._iterate_over_batchess+q#e*j1AAN+ +2s')NcUc URn[R"5nURURSUR 35n[ R"UR55n/nSUR;aURX5H~nXuS.nURUSUR0US9n U RS:wa%[SU RSU R35eU R 5S n UR!U 5 M U$S UR;aURX5Hn[%U5V V s/sH upS U 3U S .PM n n n XS.nURUSUR0US9n U RS:wa%[SU RSU R35eU R 5SV s/sHoSPM n n UR!U 5 M U$SUR;aURX5HnXuS.nURUSUR0US9n U RS:wa%[SU RSU R35eUR'S5(aU R 5Sn OU R 5Sn UR!U 5 M U$[)SURS35e!["a [#S U R 55ef=fs sn n fs sn f!["a [#SU R 55ef=f!["a [#SU R 55ef=f)aReturns a list of embeddings for the given sentences. Args: texts (`List[str]`): List of texts to encode batch_size (`int`): Batch size for the encoding Returns: `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences r?api/predict/nlpinputsparamskeyheadersr41Sambanova /complete call failed with status code . Details: data%'data' not found in endpoint responser.itemidr0r1rMr1r0&'items' not found in endpoint responser# instancesrM select_expert predictions,'predictions' not found in endpoint responsehandling of endpoint uri:  not implemented)rrequestsSessionr@rrr4loadsr;rrGpostr status_code RuntimeErrortextextendKeyError enumerateget ValueError)r6rBr http_sessionurlrM embeddingsbatchrTresponse embeddingrFrVr1s r)embed_documents%SambaStudioEmbeddings.embed_documentss  J'')   556a8_8_7` a D3356  D D D33EF"':',,"D$G$GH- ''3.&K#//0 hmm_N (  7I%%i0GXo&)M)M M33EFENuEUEU'!T!:5EU"'9',,"D$G$GH- ''3.&K#//0 hmm_N;C==?7;S T;S4g;SI T%%i0#Gl=#d&J&J J33EF%*=',,"D$G$GH- ''3.&K#//0 hmm_N zz/22$,MMOM$B $,MMOM$B %%i0#G: ,T-Q-Q,RRbc s "? !U"@ 2 "F s=0$K L9L L L/AL7%L L%L47%MrhcZ[R"5nURURSUR35n[ R "UR55nSUR;aqU/US.nURUSUR0US9nURS:wa%[SURSUR35eUR 5S S nU$S UR;awS US./US.nURUSUR0US9nURS:wa%[SURSUR35eUR 5SS SnU$SUR;aU/US.nURUSUR0US9nURS:wa%[SURSUR35eURS5(aUR 5SS nU$UR 5SS nU$[!SURS35e![a [S UR 55ef=f![a [SUR 55ef=f![a [SUR 55ef=f)zReturns 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?rJrKrNrOrQrRrSrTrrUr.item0rWrYr1r0rZr#r[r]r^r_r`ra)rbrcr@rrr4rdr;rrerrfrgrhrjrlrm)r6rhrnrorMrTrrrss r) embed_query!SambaStudioEmbeddings.embed_querys '')   556a8_8_7` a D3356  D D D#f7D#(( C CD)H ##s*"G++,M(--J $MMOF3A6 na&)M)M M%,t<=PD#(( C CD)H ##s*"G++,M(--J $MMOG4Q7@ F9#d&J&J J"&6:D#(( C CD)H ##s*"G++,M(--J ::o.. (  >q AI!)  >q AI ,T-Q-Q,RRbc e ;MMO ( <MMO . BMMO s* H5I,JJ5%I%J%J*)N)r2 __module__ __qualname____firstlineno____doc__rr3__annotations__rrrrrdictrintr model_configr rr*r;r@rrrGrfloatrtrx__static_attributes__rr,r)rr s' !F,.#S-+-#S--/%s/-.0&0.*,"C,L$2J-26L $44C"g#g#g ,49 ,# ,) ,=Ab#Yb,4SMb d5k bHSSU Sr,r)r4typingrrrrrblangchain_core._api.deprecationrlangchain_core.embeddingsrlangchain_core.utilsr r pydanticr r rrr,r)rsK 2260?*  B tIzt  tr,