
    hJ                       d Z ddlmZ ddl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       G d de             Z G d deee e f            Z!y)z+Base classes for LLM-powered router chains.    )annotations)AnyOptional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)sinceremovalmessagec                       e Zd ZU dZded<   	  ed      dd       Zedd       Zd fdZ		 d	 	 	 	 	 dd	Z
	 d	 	 	 	 	 dd
Ze	 	 	 	 	 	 	 	 dd       Z x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)modec                d    | j                   j                  }|j                  d}t        |      | 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   msgs      `/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain/chains/router/llm_router.pyvalidate_promptzLLMRouterChain.validate_prompth   s8    &&'  S/!    c                .    | j                   j                  S )zTWill be whatever keys the LLM chain prompt expects.

        :meta private:
        )r   
input_keys)r    s    r"   r&   zLLMRouterChain.input_keysu   s     ~~(((r$   c                V    t         |   |       t        |d   t              st        y )Nnext_inputs)super_validate_outputs
isinstancedictr   )r    outputs	__class__s     r"   r*   z LLMRouterChain._validate_outputs}   s*    !'*'-0$7 8r$   c                "   |xs t        j                         }|j                         } | j                  j                  dd|i|}t        t        t        t        f   | j                  j                  j                  j                  |            S N	callbacks )r	   get_noop_manager	get_childr   predictr   r,   strr   r   r   parse)r    inputsrun_manager_run_managerr1   
predictions         r"   _callzLLMRouterChain._call   sz    
 #S&@&Q&Q&S **,	+T^^++JiJ6J
cNNN!!//55jA
 	
r$   c                   K   |xs t        j                         }|j                         }t        t        t
        t        f    | j                  j                  dd|i| d {         S 7 wr0   )	r	   r3   r4   r   r,   r6   r   r   apredict_and_parse)r    r8   r9   r:   r1   s        r"   _acallzLLMRouterChain._acall   sd     
 #S&@&Q&Q&S **,	cN3$..33RiR6RR
 	
Rs   A!A.#A,
$	A.c                0    t        ||      } | dd|i|S )zConvenience constructor.)llmr   r   r2   r   )clsrA   r   kwargsr   s        r"   from_llmzLLMRouterChain.from_llm   s#     V4	1Y1&11r$   )returnr   )rE   z	list[str])r-   dict[str, Any]rE   None)N)r8   rF   r9   z$Optional[CallbackManagerForChainRun]rE   rF   )r8   rF   r9   z)Optional[AsyncCallbackManagerForChainRun]rE   rF   )rA   r   r   r   rC   r   rE   r   )__name__
__module____qualname____doc____annotations__r   r#   propertyr&   r*   r<   r?   classmethodrD   __classcell__)r.   s   @r"   r   r      s    AF +'"
 #
 ) ) =A

 :
 
	
" BF



 ?

 
	

 22 #2 	2
 
2 2r$   r   c                  D    e Zd ZU dZdZded<   eZded<   dZded<   dd	Z	y
)RouterOutputParserz<Parser for output of router chain in the multi-prompt chain.DEFAULTr6   default_destinationtypenext_inputs_typeinputnext_inputs_inner_keyc                   	 ddg}t        ||      }t        |d   t              sd}t        |      t        |d   | j                        sd| j                   d}t        |      | j
                  |d   i|d<   |d   j                         j                         | j                  j                         k(  rd |d<   |S |d   j                         |d<   	 |S # t        $ r}d| d| }t        |      |d }~ww xY w)Ndestinationr(   z&Expected 'destination' to be a string.zExpected 'next_inputs' to be .zParsing text
z
 raised following error:
)r   r+   r6   	TypeErrorrU   rW   striplowerrS   	Exceptionr
   )r    textexpected_keysparsedr!   es         r"   r7   zRouterOutputParser.parse   s    	4*M:M24GFf]3S9>n$f]3T5J5JK5d6K6K5LANn$%)%?%?AV$WF=!}%++-335++1134 )-}% 	 )/}(=(C(C(E}%   	4"4&(DQCHC',!3	4s   B9C =C 	C8C33C8N)r_   r6   rE   rF   )
rH   rI   rJ   rK   rS   rL   r6   rU   rW   r7   r2   r$   r"   rQ   rQ      s+    F(( d !(3(r$   rQ   N)"rK   
__future__r   typingr   r   r   langchain_core._apir   langchain_core.callbacksr   r	   langchain_core.exceptionsr
   langchain_core.language_modelsr   langchain_core.output_parsersr   langchain_core.promptsr   langchain_core.utils.jsonr   pydanticr   typing_extensionsr   langchain.chainsr   langchain.chains.router.baser   r   r,   r6   rQ   r2   r$   r"   <module>rp      s    1 " & & * < < : 5 C $ " % 4 
	s	D2[ D2D2N)$sCx.9 r$   