U h@sddlmZmZmZmZmZddlZddlmZddl m Z ddl m Z m Z ddlmZdZdZd Zd jeed ed ZGd dde ZdS))AnyDictListMappingOptionalN)CallbackManagerForLLMRun)LLM)get_from_dict_or_envpre_init)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 <Gd d 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_tokenc@seZdZdZdS)zMosaicML.ConfigZforbidN)__name__ __module__ __qualname__extrarrE/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/llms/mosaicml.pyConfig=sr)valuesreturncCst|dd}||d<|S)z?Validate that api key and python package exists in environment.rZMOSAICML_API_TOKEN)r )clsrrrrrvalidate_environment@szMosaicML.validate_environment)rcCs|jpi}d|jid|iS)zGet the identifying parameters.rr)rr)self _model_kwargsrrr_identifying_paramsIs zMosaicML._identifying_paramscCsdS)zReturn type of llm.Zmosaicr)rrrr _llm_typeRszMosaicML._llm_type)promptrcCs|jrtj|d}|S)zTransform prompt.)r )rPROMPT_FOR_GENERATION_FORMATformat)rr"rrr_transform_promptWs zMosaicML._transform_prompt)r"stop run_manageris_retrykwargsrc Ks|jpi}||}d|gi}|||||jdd}ztj|j||d} Wn4tjjk r} zt d| W5d} ~ XYnXz| j dkr|sddl } | |j |j|||d d WSt d | j| } t| trHd d dg} | D]}|| kr| |}q,qt dd| d| t|trB|d}n|}nt d| ||rr|t|d}Wn>tjjk r} zt d| d| jW5d} ~ XYnX|dk rt||}|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$Error raised by inference endpoint: NirT)r(z>Error raised by inference API: rate limit exceeded. Response: dataoutputoutputszNo valid key (z, z) in response: zUnexpected response type: zError raised by inference API: z . Response: )rr%updaterrequestspostr exceptionsRequestException ValueError status_codetimesleepr_calltextr- isinstancedictjoinlist startswithlenJSONDecodeErrorr )rr"r&r'r(r)rpayloadr,responseer8Zparsed_responseZ output_keyskeyZ output_itemr;rrrr:_sX                zMosaicML._call)NNF)rrr__doc__rstr__annotations__rboolrrr=rfloatrrr rrpropertyrrr r!r%rrr:rrrrr s4      r )typingrrrrrr2Zlangchain_core.callbacksrZ#langchain_core.language_models.llmsrZlangchain_core.utilsr r Zlangchain_community.llms.utilsr ZINSTRUCTION_KEYZ RESPONSE_KEYZ INTRO_BLURBr$r#r rrrrs