gh|dZddlmZmZmZddlmZmZmZm Z ddl m Z m Z ddl mZddlmZddlmZddlmZmZdd lmZdd lmZmZmZmZdd lmZeGd d eZdS)0Structured prompt template for a language model.)IteratorMappingSequence)AnyCallableOptionalUnion) BaseModelField)override)beta)BaseLanguageModel)ChatPromptTemplateMessageLikeRepresentation)PromptTemplateFormat)OtherRunnableRunnableSequenceRunnableSerializable)get_pydantic_field_namesceZdZUdZeeefed< eeZ ee e fed< ddddde e deeeeefdeee e fd ed e d df fd Zed ee fd Zede e deeefd e d efdZedeee efee gefeee geefee eee efee gefe fffd eeeffdZdddeee efee gefeee geefee eee efee gefe fffdee d eeeffdZxZ S)StructuredPromptrschema_)default_factorystructured_output_kwargsNzf-string)rtemplate_formatmessagesrkwargsreturnc |p|d}|pi}t|t|jD]}||||<t jd||||d|dS)a+Create a structured prompt template. Args: messages: sequence of messages. schema_: schema for the structured prompt. structured_output_kwargs: additional kwargs for structured output. template_format: template format for the prompt. schema)rrrrN)popset differencer __class__super__init__)selfrrrrrkr's ^/var/www/FlaskApp/flask-venv/lib/python3.11/site-packages/langchain_core/prompts/structured.pyr)zStructuredPrompt.__init__&s"1VZZ11#;#Ar V''(@(P(PQQ 8 8A*0**Q-- $Q ' ' %=+         c6|jdS)zGet the namespace of the langchain object. For example, if the class is `langchain.llms.openai.OpenAI`, then the namespace is ["langchain", "llms", "openai"] .) __module__split)clss r,get_lc_namespacez!StructuredPrompt.get_lc_namespaceCs~##C(((r-r"c |||fi|S)aCreate a chat prompt template from a variety of message formats. Examples: Instantiation from a list of message templates: .. code-block:: python from langchain_core.prompts import StructuredPrompt class OutputSchema(BaseModel): name: str value: int template = StructuredPrompt( [ ("human", "Hello, how are you?"), ("ai", "I'm doing well, thanks!"), ("human", "That's good to hear."), ], OutputSchema, ) Args: messages: sequence of message representations. A message can be represented using the following formats: (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of (message type, template); e.g., ("human", "{user_input}"), (4) 2-tuple of (message class, template), (5) a string which is shorthand for ("human", template); e.g., "{user_input}" schema: a dictionary representation of function call, or a Pydantic model. kwargs: Any additional kwargs to pass through to ``ChatModel.with_structured_output(schema, **kwargs)``. Returns: a structured prompt template r#)r2rr"rs r,from_messages_and_schemaz)StructuredPrompt.from_messages_and_schemaLsVs8V..v...r-otherc,||SN)pipe)r*r6s r,__or__zStructuredPrompt.__or__ysyyr-)nameothersr;c|rt|dtst|ddr5t||dj|jfi|jg|ddRd|iSd}t|)aPipe the structured prompt to a language model. Args: others: The language model to pipe the structured prompt to. name: The name of the pipeline. Defaults to None. Returns: A RunnableSequence object. Raises: NotImplementedError: If the first element of `others` is not a language model. rwith_structured_outputNr;z8Structured prompts need to be piped to a language model.) isinstancerhasattrrr>rrNotImplementedError)r*r;r<msgs r,r9zStructuredPrompt.pipes0  6!9&788 vay":;;  $0q 0L$($A     I!#&&&r-r8)!__name__r0 __qualname____doc__r dicttype__annotations__r rstrrrrr r rr) classmethodlistr3rr5r rrrrrrr:r9 __classcell__)r's@r,rrs:: 4: +/4uT/J/J/Jd38nJJJ ;? >B0:    45 %d9o 567 #+4S>":  .          :)c)))[)*/45*/dDj!*/ */  */*/*/[*/X   S%Z cUE\ " hsm_huo5 6 CxU 3XseUl5KSPQQ R T    dEk *    X  &#%'%'%' S%Z cUE\ " hsm_huo5 6 CxU 3XseUl5KSPQQ R T %'sm%' dEk *%'%'%'%'%'%'%'%'r-rN) rFcollections.abcrrrtypingrrr r pydanticr r typing_extensionsr "langchain_core._api.beta_decoratorr#langchain_core.language_models.baserlangchain_core.prompts.chatrrlangchain_core.prompts.stringrlangchain_core.runnables.baserrrrlangchain_core.utilsrrr#r-r,rXs667777777777&%%%%%%%&&&&&&333333AAAAAA?>>>>> :99999K'K'K'K'K')K'K'K'K'K'r-