
    m
i
                         d dl 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  e j        e          Zd
Z ej        e          Z G d de          ZdS )    N)#AsyncCallbackManagerForRetrieverRunCallbackManagerForRetrieverRun)Document)BaseLLMStrOutputParser)BasePromptTemplate)PromptTemplate)BaseRetriever)RunnablezYou are an assistant tasked with taking a natural language query from a user and converting it into a query for a vectorstore. In this process, you strip out information that is not relevant for the retrieval task. Here is the user query: {question}c            
           e Zd ZU dZeed<   eed<   eefdede	de
dd fd            Zded	edee         fd
Zded	edee         fdZdS )RePhraseQueryRetrieverzbGiven a query, use an LLM to re-phrase it.

    Then, retrieve docs for the re-phrased query.
    	retriever	llm_chainllmpromptreturnc                 D    ||z  t                      z  } | ||          S )ap  Initialize from llm using default template.

        The prompt used here expects a single input: `question`

        Args:
            retriever: retriever to query documents from
            llm: llm for query generation using DEFAULT_QUERY_PROMPT
            prompt: prompt template for query generation

        Returns:
            RePhraseQueryRetriever
        )r   r   r   )clsr   r   r   r   s        C:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\langchain_classic/retrievers/re_phraser.pyfrom_llmzRePhraseQueryRetriever.from_llm$   s7    & SL?#4#44	s
 
 
 	
    queryrun_managerc                    | j                             |d|                                i          }t                              d|           | j                            |d|                                i          S )zGet relevant documents given a user question.

        Args:
            query: user question
            run_manager: callback handler to use

        Returns:
            Relevant documents for re-phrased question
        	callbackszRe-phrased question: %s)config)r   invoke	get_childloggerinfor   )selfr   r   re_phrased_questions       r   _get_relevant_documentsz.RePhraseQueryRetriever._get_relevant_documents=   s     #n33+//112
 
 	-/BCCC~$$!6!6!8!89 % 
 
 	
r   c                   K   t           )N)NotImplementedError)r"   r   r   s      r   _aget_relevant_documentsz/RePhraseQueryRetriever._aget_relevant_documentsV   s       "!r   N)__name__
__module____qualname____doc__r   __annotations__r   classmethodDEFAULT_QUERY_PROMPTr   r	   r   strr   listr   r$   r   r'    r   r   r   r      s          
 
 &:	
 
 
 
 #	

 
"
 
 
 [
0

 4	

 
h
 
 
 
2"" 9	"
 
h" " " " " "r   r   )logginglangchain_core.callbacksr   r   langchain_core.documentsr   langchain_core.language_modelsr   langchain_core.output_parsersr   langchain_core.promptsr	   langchain_core.prompts.promptr
   langchain_core.retrieversr   langchain_core.runnablesr   	getLoggerr(   r    DEFAULT_TEMPLATEfrom_templater.   r   r1   r   r   <module>r>      s$           . - - - - - 2 2 2 2 2 2 9 9 9 9 9 9 5 5 5 5 5 5 8 8 8 8 8 8 3 3 3 3 3 3 - - - - - -		8	$	$:  4~34DEE A" A" A" A" A"] A" A" A" A" A"r   