a
    agR                     @  s   d Z ddlmZ ddlmZmZmZmZmZm	Z	 ddl
mZ ddlmZmZ ddlmZ ddlmZ ddlmZ dd	lmZ dd
lmZ ddlmZ ddlmZ ddlmZ ddlm Z  eddddG dd de Z!G dd deee"e"f  Z#dS )z+Base classes for LLM-powered router chains.    )annotations)AnyDictListOptionalTypecast)
deprecated)AsyncCallbackManagerForChainRunCallbackManagerForChainRun)OutputParserException)BaseLanguageModel)BaseOutputParser)BasePromptTemplate)parse_and_check_json_markdown)model_validator)SelfLLMChain)RouterChainz0.2.12z1.0zUse RunnableLambda to select from multiple prompt templates. See example in API reference: https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.LLMRouterChain.html)ZsinceZremovalmessagec                      s   e Zd ZU dZded< edddddd	Zed
dddZddd fddZ	d ddddddZ
d!ddddddZedddd dddZ  ZS )"LLMRouterChaina
	  A router chain that uses an LLM chain to perform routing.

    This class is deprecated. See below for a replacement, which offers several
    benefits, including streaming and batch support.

    Below is an example implementation:

        .. code-block:: python

            from operator import itemgetter
            from typing import Literal
            from typing_extensions import TypedDict

            from langchain_core.output_parsers import StrOutputParser
            from langchain_core.prompts import ChatPromptTemplate
            from langchain_core.runnables import RunnableLambda, RunnablePassthrough
            from langchain_openai import ChatOpenAI

            llm = ChatOpenAI(model="gpt-4o-mini")

            prompt_1 = ChatPromptTemplate.from_messages(
                [
                    ("system", "You are an expert on animals."),
                    ("human", "{query}"),
                ]
            )
            prompt_2 = ChatPromptTemplate.from_messages(
                [
                    ("system", "You are an expert on vegetables."),
                    ("human", "{query}"),
                ]
            )

            chain_1 = prompt_1 | llm | StrOutputParser()
            chain_2 = prompt_2 | llm | StrOutputParser()

            route_system = "Route the user's query to either the animal or vegetable expert."
            route_prompt = ChatPromptTemplate.from_messages(
                [
                    ("system", route_system),
                    ("human", "{query}"),
                ]
            )


            class RouteQuery(TypedDict):
                """Route query to destination."""
                destination: Literal["animal", "vegetable"]


            route_chain = (
                route_prompt
                | llm.with_structured_output(RouteQuery)
                | itemgetter("destination")
            )

            chain = {
                "destination": route_chain,  # "animal" or "vegetable"
                "query": lambda x: x["query"],  # pass through input query
            } | RunnableLambda(
                # if animal, chain_1. otherwise, chain_2.
                lambda x: chain_1 if x["destination"] == "animal" else chain_2,
            )

            chain.invoke({"query": "what color are carrots"})
    r   	llm_chainafter)moder   )returnc                 C  s   | j j}|jd u rtd| S )NzLLMRouterChain requires base llm_chain prompt to have an output parser that converts LLM text output to a dictionary with keys 'destination' and 'next_inputs'. Received a prompt with no output parser.)r   promptoutput_parser
ValueError)selfr    r    p/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/router/llm_router.pyvalidate_prompth   s    
zLLMRouterChain.validate_promptz	List[str]c                 C  s   | j jS )zTWill be whatever keys the LLM chain prompt expects.

        :meta private:
        )r   
input_keys)r   r    r    r!   r#   t   s    zLLMRouterChain.input_keysDict[str, Any]None)outputsr   c                   s"   t  | t|d tstd S )Nnext_inputs)super_validate_outputs
isinstancedictr   )r   r&   	__class__r    r!   r)   |   s    z LLMRouterChain._validate_outputsNz$Optional[CallbackManagerForChainRun])inputsrun_managerr   c                 C  sL   |p
t  }| }| jjf d|i|}ttttf | jj	j
|}|S N	callbacks)r   get_noop_manager	get_childr   Zpredictr   r   strr   r   r   parse)r   r.   r/   _run_managerr1   Z
predictionoutputr    r    r!   _call   s    
zLLMRouterChain._callz)Optional[AsyncCallbackManagerForChainRun]c                   sB   |p
t  }| }ttttf | jjf d|i|I d H }|S r0   )	r   r2   r3   r   r   r4   r   r   Zapredict_and_parse)r   r.   r/   r6   r1   r7   r    r    r!   _acall   s    
zLLMRouterChain._acallr   r   r   )llmr   kwargsr   c                 K  s   t ||d}| f d|i|S )zConvenience constructor.)r:   r   r   r   )clsr:   r   r;   r   r    r    r!   from_llm   s    zLLMRouterChain.from_llm)N)N)__name__
__module____qualname____doc____annotations__r   r"   propertyr#   r)   r8   r9   classmethodr=   __classcell__r    r    r,   r!   r      s   

C  r   c                   @  sF   e Zd ZU dZdZded< eZded< dZded< dd	d
ddZ	dS )RouterOutputParserz<Parser for output of router chain in the multi-prompt chain.DEFAULTr4   default_destinationr   next_inputs_typeinputnext_inputs_inner_keyr$   )textr   c              
   C  s   zddg}t ||}t|d ts*tdt|d | jsLtd| j d| j|d i|d< |d   | j krd |d< n|d  |d< |W S  t	y } z t
d| d| W Y d }~n
d }~0 0 d S )Ndestinationr'   z&Expected 'destination' to be a string.zExpected 'next_inputs' to be .zParsing text
z
 raised following error:
)r   r*   r4   r   rI   rK   striplowerrH   	Exceptionr   )r   rL   Zexpected_keysparseder    r    r!   r5      s*    

zRouterOutputParser.parseN)
r>   r?   r@   rA   rH   rB   r4   rI   rK   r5   r    r    r    r!   rF      s
   
rF   N)$rA   
__future__r   typingr   r   r   r   r   r   Zlangchain_core._apir	   Zlangchain_core.callbacksr
   r   Zlangchain_core.exceptionsr   Zlangchain_core.language_modelsr   Zlangchain_core.output_parsersr   Zlangchain_core.promptsr   Zlangchain_core.utils.jsonr   Zpydanticr   Ztyping_extensionsr   Zlangchain.chainsr   Zlangchain.chains.router.baser   r   r4   rF   r    r    r    r!   <module>   s*    	 