a
    ~g<                     @  sB  d Z ddlmZ ddlZddlmZmZmZmZm	Z	m
Z
mZmZ ddlmZ ddlmZmZmZmZmZ ddlmZmZ ddlmZ dd	lmZ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'm(Z(m)Z)m*Z* ddl+m,Z, ddl-m.Z. ddl/m0Z0m1Z1 ddl2m3Z3 eddddG dd de3Z4dddddZ5dS )z2Chain that just formats a prompt and calls an LLM.    )annotationsN)AnyDictListOptionalSequenceTupleUnioncast)
deprecated)AsyncCallbackManagerAsyncCallbackManagerForChainRunCallbackManagerCallbackManagerForChainRun	Callbacks)BaseLanguageModelLanguageModelInput)BaseMessage)BaseLLMOutputParserStrOutputParser)ChatGeneration
Generation	LLMResult)PromptValue)BasePromptTemplatePromptTemplate)RunnableRunnableBindingRunnableBranchRunnableWithFallbacks)DynamicRunnable)get_colored_text)
ConfigDictField)Chainz0.1.17z&RunnableSequence, e.g., `prompt | llm`z1.0)sincealternativeremovalc                   @  s"  e Zd ZU dZeddddZded< ded	< d
Zded< ee	dZ
ded< dZded< eedZded< edddZeddddZeddddZd[ddddd d!Zd\d"dd#d$d%d&Zd]d"d'd#d$d(d)Zd^d"dd*d$d+d,Zd_d"d'd*d$d-d.Zd`d"d/d0d1d2d3Zdad"d/d0d1d4d5Zeddd6d7Zd#d"d8d9d:Zdbdd'ddd;d<Zdcd/d=dd>d?d@Zddd/d=dd>dAdBZded/d=dCd>dDdEZdfd/d=dFd>dGdHZ dgd"d/dId1dJdKZ!d0dIdLdMdNZ"dhd"d/dId1dOdPZ#edddQdRZ$edSdd dTdUdVZ%ddWdXdYdZZ&dS )iLLMChaina^  Chain to run queries against LLMs.

    This class is deprecated. See below for an example implementation using
    LangChain runnables:

        .. code-block:: python

            from langchain_core.output_parsers import StrOutputParser
            from langchain_core.prompts import PromptTemplate
            from langchain_openai import OpenAI

            prompt_template = "Tell me a {adjective} joke"
            prompt = PromptTemplate(
                input_variables=["adjective"], template=prompt_template
            )
            llm = OpenAI()
            chain = prompt | llm | StrOutputParser()

            chain.invoke("your adjective here")

    Example:
        .. code-block:: python

            from langchain.chains import LLMChain
            from langchain_community.llms import OpenAI
            from langchain_core.prompts import PromptTemplate
            prompt_template = "Tell me a {adjective} joke"
            prompt = PromptTemplate(
                input_variables=["adjective"], template=prompt_template
            )
            llm = LLMChain(llm=OpenAI(), prompt=prompt)
    bool)returnc                 C  s   dS )NT selfr+   r+   Y/var/www/html/emsaiapi.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/llm.pyis_lc_serializableM   s    zLLMChain.is_lc_serializabler   promptzSUnion[Runnable[LanguageModelInput, str], Runnable[LanguageModelInput, BaseMessage]]llmtextstr
output_key)default_factoryr   output_parserTreturn_final_onlydict
llm_kwargsforbid)arbitrary_types_allowedextraz	List[str]c                 C  s   | j jS )zJWill be whatever keys the prompt expects.

        :meta private:
        )r0   input_variablesr,   r+   r+   r.   
input_keysf   s    zLLMChain.input_keysc                 C  s   | j r| jgS | jdgS dS )z=Will always return text key.

        :meta private:
        full_generationN)r7   r4   r,   r+   r+   r.   output_keysn   s    zLLMChain.output_keysNzDict[str, Any]z$Optional[CallbackManagerForChainRun]zDict[str, str])inputsrun_managerr*   c                 C  s   | j |g|d}| |d S NrB   r   )generatecreate_outputsr-   rA   rB   responser+   r+   r.   _cally   s    zLLMChain._callzList[Dict[str, Any]]r   )
input_listrB   r*   c           	      C  s   | j ||d\}}|r| nd}t| jtrJ| jj||fd|i| jS | jjf d|i| jt	t
|d|i}g }|D ]4}t|tr|t|dg q||t|dg q|t|dS dS z Generate LLM result from inputs.rD   N	callbacksstop)message)r2   )generations)prep_prompts	get_child
isinstancer1   r   generate_promptr9   bindbatchr
   r   r   appendr   r   r   	r-   rJ   rB   promptsrM   rL   resultsrO   resr+   r+   r.   rE      s(    
zLLMChain.generatez)Optional[AsyncCallbackManagerForChainRun]c           	        s   | j ||dI dH \}}|r$| nd}t| jtrV| jj||fd|i| jI dH S | jjf d|i| jt	t
|d|iI dH }g }|D ]4}t|tr|t|dg q|t|dg qt|dS dS rK   )aprep_promptsrQ   rR   r1   r   agenerate_promptr9   rT   abatchr
   r   r   rV   r   r   r   rW   r+   r+   r.   	agenerate   s(    

zLLMChain.ageneratez-Tuple[List[PromptValue], Optional[List[str]]]c           	        s   d}t |dkrg |fS d|d v r0|d d }g }|D ]~  fdd| jjD }| jjf i |}t| d}d| }|r|j|d| jd	 d v r d |krtd
|	| q8||fS )Prepare prompts from inputs.Nr   rM   c                   s   i | ]}| | qS r+   r+   .0krA   r+   r.   
<dictcomp>       z)LLMChain.prep_prompts.<locals>.<dictcomp>greenPrompt after formatting:

endverbose=If `stop` is present in any inputs, should be present in all.
lenr0   r=   format_promptr!   	to_stringon_textrk   
ValueErrorrV   	r-   rJ   rB   rM   rX   Zselected_inputsr0   Z_colored_text_textr+   rc   r.   rP      s&    zLLMChain.prep_promptsc           	        s   d}t |dkrg |fS d|d v r0|d d }g }|D ]  fdd| jjD }| jjf i |}t| d}d| }|r|j|d| jd	I dH  d v r d |krtd
|	| q8||fS )r_   Nr   rM   c                   s   i | ]}| | qS r+   r+   r`   rc   r+   r.   rd      re   z*LLMChain.aprep_prompts.<locals>.<dictcomp>rf   rg   rh   ri   rl   rm   rs   r+   rc   r.   r[      s&    zLLMChain.aprep_promptsr   zList[Dict[str, str]])rJ   rL   r*   c              
   C  s   t || j| j}|jdd|i|  d}z| j||d}W n2 tyn } z|| |W Y d}~n
d}~0 0 | 	|}|
d|i |S z0Utilize the LLM generate method for speed gains.NrJ   )namerD   outputs)r   	configurerL   rk   on_chain_startget_namerE   BaseExceptionon_chain_errorrF   on_chain_endr-   rJ   rL   callback_managerrB   rH   erw   r+   r+   r.   apply   s     


zLLMChain.applyc              
     s   t || j| j}|jdd|i|  dI dH }z| j||dI dH }W n8 ty } z ||I dH  |W Y d}~n
d}~0 0 | 	|}|
d|iI dH  |S ru   )r   rx   rL   rk   ry   rz   r^   r{   r|   rF   r}   r~   r+   r+   r.   aapply   s     

zLLMChain.aapplyc                 C  s   | j S Nr4   r,   r+   r+   r.   _run_output_key  s    zLLMChain._run_output_key)
llm_resultr*   c                   s0    fdd|j D } jr, fdd|D }|S )zCreate outputs from response.c                   s"   g | ]} j  j|d |iqS )r?   )r4   r6   parse_result)ra   
generationr,   r+   r.   
<listcomp>  s   z+LLMChain.create_outputs.<locals>.<listcomp>c                   s   g | ]} j | j  iqS r+   r   )ra   rr,   r+   r.   r   $  re   )rO   r7   )r-   r   resultr+   r,   r.   rF     s    
zLLMChain.create_outputsc                   s$   | j |g|dI d H }| |d S rC   )r^   rF   rG   r+   r+   r.   _acall'  s    zLLMChain._acallr   )rL   kwargsr*   c                 K  s   | ||d| j  S )S  Format prompt with kwargs and pass to LLM.

        Args:
            callbacks: Callbacks to pass to LLMChain
            **kwargs: Keys to pass to prompt template.

        Returns:
            Completion from LLM.

        Example:
            .. code-block:: python

                completion = llm.predict(adjective="funny")
        rL   r   r-   rL   r   r+   r+   r.   predict/  s    zLLMChain.predictc                   s   | j ||dI dH | j S )r   r   N)acallr4   r   r+   r+   r.   apredict@  s    zLLMChain.apredictz%Union[str, List[str], Dict[str, Any]]c                 K  s@   t d | jf d|i|}| jjdur8| jj|S |S dS )z(Call predict and then parse the results.z_The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.rL   N)warningswarnr   r0   r6   parser-   rL   r   r   r+   r+   r.   predict_and_parseQ  s    zLLMChain.predict_and_parsez%Union[str, List[str], Dict[str, str]]c                   sF   t d | jf d|i|I dH }| jjdur>| jj|S |S dS )z)Call apredict and then parse the results.z`The apredict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.rL   N)r   r   r   r0   r6   r   r   r+   r+   r.   apredict_and_parse_  s    zLLMChain.apredict_and_parsez/Sequence[Union[str, List[str], Dict[str, str]]]c                 C  s"   t d | j||d}| |S )&Call apply and then parse the results.z]The apply_and_parse method is deprecated, instead pass an output parser directly to LLMChain.r   )r   r   r   _parse_generationr-   rJ   rL   r   r+   r+   r.   apply_and_parsem  s
    zLLMChain.apply_and_parse)r   r*   c                   s&    j jd ur fdd|D S |S d S )Nc                   s    g | ]} j j| j qS r+   )r0   r6   r   r4   )ra   rZ   r,   r+   r.   r   |  s   z.LLMChain._parse_generation.<locals>.<listcomp>)r0   r6   )r-   r   r+   r,   r.   r   x  s
    
zLLMChain._parse_generationc                   s(   t d | j||dI dH }| |S )r   z^The aapply_and_parse method is deprecated, instead pass an output parser directly to LLMChain.r   N)r   r   r   r   r   r+   r+   r.   aapply_and_parse  s
    zLLMChain.aapply_and_parsec                 C  s   dS )N	llm_chainr+   r,   r+   r+   r.   _chain_type  s    zLLMChain._chain_typer   )r1   templater*   c                 C  s   t |}| ||dS )z&Create LLMChain from LLM and template.)r1   r0   )r   from_template)clsr1   r   prompt_templater+   r+   r.   from_string  s    
zLLMChain.from_stringint)r2   r*   c                 C  s   t | j|S r   )_get_language_modelr1   get_num_tokens)r-   r2   r+   r+   r.   _get_num_tokens  s    zLLMChain._get_num_tokens)N)N)N)N)N)N)N)N)N)N)N)N)N)N)'__name__
__module____qualname____doc__classmethodr/   __annotations__r4   r#   r   r6   r7   r8   r9   r"   model_configpropertyr>   r@   rI   rE   r^   rP   r[   r   r   r   rF   r   r   r   r   r   r   r   r   r   r   r   r+   r+   r+   r.   r(   &   sh   
!            r(   r   r   )llm_liker*   c                 C  sd   t | tr| S t | tr"t| jS t | tr6t| jS t | ttfrNt| j	S t
dt|  d S )NzAUnable to extract BaseLanguageModel from llm_like object of type )rR   r   r   r   boundr   runnabler   r    defaultrr   type)r   r+   r+   r.   r     s    





r   )6r   
__future__r   r   typingr   r   r   r   r   r   r	   r
   langchain_core._apir   langchain_core.callbacksr   r   r   r   r   langchain_core.language_modelsr   r   langchain_core.messagesr   langchain_core.output_parsersr   r   langchain_core.outputsr   r   r   langchain_core.prompt_valuesr   langchain_core.promptsr   r   langchain_core.runnablesr   r   r   r   %langchain_core.runnables.configurabler    langchain_core.utils.inputr!   pydanticr"   r#   langchain.chains.baser$   r(   r   r+   r+   r+   r.   <module>   s2   (  s