a bg@sddlmZmZmZmZmZddlZddlmZddl m Z ddl m Z m Z ddlmZddlmZdZd Zd Zd jeed ed ZGddde ZdS))AnyDictListMappingOptionalN)CallbackManagerForLLMRun)LLM)get_from_dict_or_envpre_init) ConfigDict)enforce_stop_tokensz### Instruction:z ### Response:ziBelow is an instruction that describes a task. Write a response that appropriately completes the request.z7{intro} {instruction_key} {instruction} {response_key} z {instruction})ZintroZinstruction_key instructionZ response_keyc@seZdZUdZdZeed<dZeed<dZ e e ed<dZ e ed <dZe eed <ed d Zeeed ddZeeeefdddZeedddZeedddZdee eee eeeedddZdS)MosaicMLa2MosaicML LLM service. To use, you should have the environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.llms import MosaicML endpoint_url = ( "https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict" ) mosaic_llm = MosaicML( endpoint_url=endpoint_url, mosaicml_api_token="my-api-key" ) zDhttps://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict endpoint_urlFinject_instruction_formatN model_kwargsg? retry_sleepmosaicml_api_tokenZforbid)extra)valuesreturncCst|dd}||d<|S)z?Validate that api key and python package exists in environment.rZMOSAICML_API_TOKEN)r )clsrrro/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/llms/mosaicml.pyvalidate_environmentBs zMosaicML.validate_environment)rcCs |jpi}id|jid|iS)zGet the identifying parameters.rr)rr)self _model_kwargsrrr_identifying_paramsKs  zMosaicML._identifying_paramscCsdS)zReturn type of llm.Zmosaicr)rrrr _llm_typeTszMosaicML._llm_type)promptrcCs|jrtj|d}|S)zTransform prompt.)r )rPROMPT_FOR_GENERATION_FORMATformat)rrrrr_transform_promptYs zMosaicML._transform_prompt)rstop run_manageris_retrykwargsrc Ks|jpi}||}d|gi}|||||jdd}ztj|j||d} Wn6tjjy} zt d| WYd} ~ n d} ~ 00z| j dkr|sddl } | |j |j|||d d WSt d | j| } t| trHgd } | D]}|| vr| |}q,qt d d| d| t|trB|d}n|}nt d| ||rr|t|d}Wn@tjjy} z"t d| d| jWYd} ~ n d} ~ 00|durt||}|S)azCall out to a MosaicML LLM inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = mosaic_llm.invoke("Tell me a joke.") inputszapplication/json) Authorizationz Content-Type)headersjsonz$Error raised by inference endpoint: NirT)r%z>Error raised by inference API: rate limit exceeded. Response: )dataoutputoutputszNo valid key (z, z) in response: zUnexpected response type: zError raised by inference API: z . Response: )rr"updaterrequestspostr exceptionsRequestException ValueError status_codetimesleepr_calltextr* isinstancedictjoinlist startswithlenJSONDecodeErrorr )rrr#r$r%r&rpayloadr)responseer5Zparsed_responseZ output_keyskeyZ output_itemr8rrrr7as`     $        zMosaicML._call)NNF)__name__ __module__ __qualname____doc__rstr__annotations__rboolrrr:rfloatrr Z model_configr rrpropertyrrrrr"rrr7rrrrrs8      r)typingrrrrrr/Zlangchain_core.callbacksrZ#langchain_core.language_models.llmsrZlangchain_core.utilsr r Zpydanticr Zlangchain_community.llms.utilsr ZINSTRUCTION_KEYZ RESPONSE_KEYZ INTRO_BLURBr!r rrrrrs"