hZdZddlmZddlZddlZddlZddlZddlZddlZddl m Z ddl m Z m Z mZddlmZddlmZmZmZmZmZddlZddlmZdd lmZmZmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&dd l'm(Z(dd l)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4m5Z5m6Z6ddl7m8Z8ddl9m:Z:ddl;mZ>m?Z?m@Z@ddlAmBZBmCZCddlDmEZEddlFmGZGddlHmIZIddlJmKZKddlLmMZMddlNmOZOddlPmQZQejeSZTGdd e>ZUGd!d"e>ZVGd#d$e,eeefZWGd%d&e,eeXeefZYGd'd(eUZZGd)d*eVZ[ed+eEd,-Gd.d/eUZ\ed+eEd,-Gd0d1eUZ]Gd2d3e:Z^eXeeeefZ_eeZe[fZ`Gd4d5eMZay)6zEChain that takes in an input and produces an action and action input.) annotationsN)abstractmethod) AsyncIteratorIteratorSequence)Path)AnyCallableOptionalUnioncast) deprecated) AgentAction AgentFinish AgentStep)AsyncCallbackManagerForChainRunAsyncCallbackManagerForToolRunBaseCallbackManagerCallbackManagerForChainRunCallbackManagerForToolRun Callbacks)OutputParserException)BaseLanguageModel) BaseMessage)BaseOutputParser)BasePromptTemplate)FewShotPromptTemplate)PromptTemplate)RunnableRunnableConfig ensure_config) AddableDict)BaseTool)get_color_mapping) BaseModel ConfigDictmodel_validator)Selfoverride)AGENT_DEPRECATION_WARNING)AgentExecutorIterator) AgentType) InvalidTool)Chain)LLMChain)asyncio_timeoutceZdZdZed dZddZe d ddZe d ddZ eed dZ ddZ e d ddZ edd Zdfd Zdd Zdd ZxZS)BaseSingleActionAgentzBase Single Action Agent class.cdgSReturn values of the agent.outputselfs T/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/langchain/agents/agent.py return_valuesz#BaseSingleActionAgent.return_values= zcyNr7r8s r:get_allowed_toolsz'BaseSingleActionAgent.get_allowed_toolsBsr=c y)0Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Action specifying what tool to use. Nr7r9intermediate_steps callbackskwargss r:planzBaseSingleActionAgent.planEr=c Kyw)6Async given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Action specifying what tool to use. Nr7rCs r:aplanzBaseSingleActionAgent.aplanXcy7Return the input keys. :meta private: Nr7r8s r: input_keysz BaseSingleActionAgent.input_keyskrHr=c J|dk(rtddidSd|d}t|)Return response when agent has been stopped due to max iterations. Args: early_stopping_method: Method to use for early stopping. intermediate_steps: Steps the LLM has taken to date, along with observations. **kwargs: User inputs. Returns: AgentFinish: Agent finish object. Raises: ValueError: If `early_stopping_method` is not supported. forcer63Agent stopped due to iteration limit or time limit.'Got unsupported early_stopping_method ``r ValueErrorr9early_stopping_methodrDrFmsgs r:return_stopped_responsez-BaseSingleActionAgent.return_stopped_responsessA( !G +PQ 88M7NaPor=c t)a.Construct an agent from an LLM and tools. Args: llm: Language model to use. tools: Tools to use. callback_manager: Callback manager to use. kwargs: Additional arguments. Returns: BaseSingleActionAgent: Agent object. NotImplementedError)clsllmtoolscallback_managerrFs r:from_llm_and_toolsz(BaseSingleActionAgent.from_llm_and_toolss &"!r=ctz#Return Identifier of an agent type.r`r8s r: _agent_typez!BaseSingleActionAgent._agent_type "!r=c t|} |j}t |t rt |j|d<|S|||d<|S#t$rd}Y@wxYw)z{Return dictionary representation of agent. Returns: Dict: Dictionary representation of agent. N_type)super model_dumprira isinstancer,strvalue)r9rF_dictrl __class__s r:dictzBaseSingleActionAgent.dictss "$ $$E eY ' -E'N  "E'N # E s A AAc4t|tr t|n|}|j}|j dd|j }d|vrd|d}t ||jdk(r3|jd5}tj||d d d d y |jjd r3|jd5}tj||d d d d y |d}t|#1swYy xYw#1swYy xYw)zSave the agent. Args: file_path: Path to file to save the agent to. 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" """8 "  "  ""(""" "Dr=r2ceZdZdZed dZd dZe d ddZe d ddZ eed dZ ddZ eddZ dfd Z dd Zdd ZxZS)BaseMultiActionAgentzBase Multi Action Agent class.cdgSr4r7r8s r:r;z"BaseMultiActionAgent.return_valuesr<r=cy)z]Get allowed tools. Returns: Optional[List[str]]: Allowed tools. Nr7r8s r:r@z&BaseMultiActionAgent.get_allowed_toolss r=c y)a5Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Actions specifying what tool to use. Nr7rCs r:rGzBaseMultiActionAgent.planrHr=c Kyw)a;Async given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Actions specifying what tool to use. Nr7rCs r:rKzBaseMultiActionAgent.aplanrLrMcyrOr7r8s r:rQzBaseMultiActionAgent.input_keysrHr=c J|dk(rtddidSd|d}t|)rSrTr6z$Agent stopped due to max iterations.rVrWrXrYr[s r:r^z,BaseMultiActionAgent.return_stopped_response s:( !G +*PQSUV V78M7NaPor=ctrhr`r8s r:riz BaseMultiActionAgent._agent_type:rjr=c t|}tjt5t |j |d<ddd|S#1swY|SxYw)*Return dictionary representation of agent.rlN)rmrn contextlibsuppressrarprir9rFrrrss r:rtzBaseMultiActionAgent.dict?sM"$  !4 5 3 !1!12E'N 3  3 s A  Ac4t|tr t|n|}|j}d|vrd|d}t ||j }|j dd|jdk(r3|jd5}tj||d d d d y |jjd r3|jd5}tj||d d d d y |d}t|#1swYy xYw#1swYy xYw)aSave the agent. Args: file_path: Path to file to save the agent to. Raises: NotImplementedError: If agent does not support saving. ValueError: If file_path is not json or yaml. Example: .. code-block:: python # If working with agent executor agent.agent.save(file_path="path/agent.yaml") rlryz does not support saving.Trvrzr{r|r}NrFrr)rorprrtrarrrrrrrrrZ)r9rrrr]rrs r:rzBaseMultiActionAgent.saveFs"(2)S'ADOy YY[ * $4& 9:C%c* *"))TD9   w &$ 3 *a2 3 3    & &'8 9$ C *aEB C CK45CS/ ! 3 3 C CrciSrr7r8s r:rz,BaseMultiActionAgent.tool_run_logging_kwargsls  r=rrr?rDrrErrFr r%Union[list[AgentAction], AgentFinish]rrrrr)rrrrrr;r@rrGrKrQr^rirtrrrrs@r:rrs(  $ 9    /   $ $ 9    /   $  ":   4""$"Lr=rc"eZdZdZeddZy)AgentOutputParserz=Base class for parsing agent output into agent action/finish.cy)z$Parse text into agent action/finish.Nr7r9texts r:parsezAgentOutputParser.parseurHr=N)rrprrrrrrrrr7r=r:rrrsG33r=rc"eZdZdZeddZy)MultiActionAgentOutputParserzBase class for parsing agent output into agent actions/finish. This is used for agents that can return multiple actions. cy)zParse text into agent actions/finish. Args: text: Text to parse. Returns: Union[List[AgentAction], AgentFinish]: List of agent actions or agent finish. Nr7rs r:rz"MultiActionAgentOutputParser.parserHr=N)rrprrrr7r=r:rrzs     r=rceZdZUdZded< gZded<gZded<dZded < ed Z e dd Z e dd Z d ddZ d ddZy ) RunnableAgentAgent powered by Runnables.z/Runnable[dict, Union[AgentAction, AgentFinish]]runnablerinput_keys_argreturn_keys_argTboolstream_runnablearbitrary_types_allowedc|jSr5rr8s r:r;zRunnableAgent.return_values###r=c|jS)zReturn the input keys.rr8s r:rQzRunnableAgent.input_keyss"""r=Nc i|d|i}d}|jr0|jj|d|iD] }||}||z }|S|jj|d|i}|SaPBased on past history and current inputs, decide what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Action specifying what tool to use. rDNrEconfigrrstreaminvoker9rDrErFinputs final_outputchunks r:rGzRunnableAgent.plans"FFE02DE   --fk9=U-V *'#(L E)L  * ==// Y?W/XLr=cKi|d|i}d}|jr3|jj|d|i23d{}||}||z }|jj|d|id{}|S786|S7 waRAsync based on past history and current inputs, decide what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Action specifying what tool to use. rDNrErrrastreamainvokers r:rKzRunnableAgent.aplans(FFE02DE    $}}44#Y/ 5  * *e '#(L E)L!%!6!6#Y/"7"L *  26A<A7A5A7.A<.A:/A<5A77A<rr?rrrrr__annotations__rrrr& model_configrr;rQrGrKr7r=r:rrs%==/ "NI"!#OY# OT  $L$$## $"9"" " ) "N $*9** *   *r=rceZdZUdZded< gZded<gZded<dZded < ed Z e dd Z e dd Z d ddZ d ddZy )RunnableMultiActionAgentrz5Runnable[dict, Union[list[AgentAction], AgentFinish]]rrrrTrrrc|jSrrr8s r:r;z&RunnableMultiActionAgent.return_valuesrr=c|jS)QReturn the input keys. Returns: List of input keys. rr8s r:rQz#RunnableMultiActionAgent.input_keyss"""r=Nc i|d|i}d}|jr0|jj|d|iD] }||}||z }|S|jj|d|i}|Srrrs r:rGzRunnableMultiActionAgent.plan s(FFE02DE   --fk9=U-V *'#(L E)L  * ==// Y?W/XLr=cKi|d|i}d}|jr3|jj|d|i23d{}||}||z }|jj|d|id{}|S786|S7 wrrrs r:rKzRunnableMultiActionAgent.aplanGs(FFE02DE    $}}44#Y/ 5  * *e '#(L E)L!%!6!6#Y/"7"L  *  rrr?rrr7r=r:rrs%CC0 "NI"!#OY# OT  $L$$## $%9%% %   %T $+9++ +   +r=rz0.1.0z1.0)messageremovalceZdZUdZded< ded< ded< ed dZdfd Z d dd Z d dd Z dd Z xZ S)LLMSingleActionAgentz$Base class for single action agents.r/ llm_chainr output_parserrstopcZtt|jjdhz S)rrDlistsetrrQr8s r:rQzLLMSingleActionAgent.input_keyss'C1126J5KKLLr=c *t|}|d=|Srrrmrtrs r:rtzLLMSingleActionAgent.dict  / " r=c |jjd||j|d|}|jj |S)a4Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Action specifying what tool to use. rDrrEr7)rrunrrrr9rDrErFr6s r:rGzLLMSingleActionAgent.plansO"$## 1    !!''//r=cK|jjd||j|d|d{}|jj |S7w)rJrNr7)rarunrrrrs r:rKzLLMSingleActionAgent.aplans]"+t~~** 1     !!''//  s.AA Ac^dt|jdk(rddS|jddS)rrVr llm_prefixobservation_prefix)lenrr8s r:rz,LLMSingleActionAgent.tool_run_logging_kwargss8(+DII!(;"  AE1  r=rrr?rr) rrrrrrrQrtrGrKrrrs@r:rrus /$$$) O% MM $0900 0 ) 08 $0900 0 ) 02 r=rceZdZUdZded< ded< dZded< dfd Zdd Zed d Z d!d Z ed d Z d"dZ d# d$dZ d# d$dZ d%dZed dZedd&dZeed'dZeed'dZeed(dZed)dZeed*dZe d+ d,dZ d-dZd.dZxZS)/AgentzAgent that calls the language model and deciding the action. This is driven by a LLMChain. The prompt in the LLMChain MUST include a variable called "agent_scratchpad" where the agent can put its intermediary work. r/rrrNr allowed_toolsc *t|}|d=|Srrrs r:rtz Agent.dictrr=c|jS)zGet allowed tools.)rr8s r:r@zAgent.get_allowed_toolss!!!r=cdgSr4r7r8s r:r;zAgent.return_valuesr<r=cd}t|)zsFix the text. Args: text: Text to fix. Returns: str: Fixed text. z(fix_text not implemented for this agent.rZ)r9rr]s r: _fix_textzAgent._fix_texts9or=cvd|jjd|jjgS)N z )rrstripr8s r:_stopz Agent._stopsB((//12 34**1134 5  r=c~d}|D]5\}}||jz }|d|j|d|jz }7|S)zJConstruct the scratchpad that lets the agent continue its thought process.rVr)logrr )r9rDthoughtsaction observations r:_construct_scratchpadzAgent._construct_scratchpads[ #5 W FK  "H "T445k]"T__DUV VH Wr=c |j|fi|}|jjdd|i|}|jj |S)rBrEr7)get_full_inputsrpredictrrr9rDrErF full_inputs full_outputs r:rGz Agent.plansQ"+d**+=HH ,dnn,,PyPKP !!'' 44r=cK|j|fi|}|jjdd|i|d{}|jj |d{S7'7w)rJrENr7)r$rapredictraparser&s r:rKz Agent.aplan#se"+d**+=HH 3DNN33WiW;WW ''..{;;;X;s!5A#A"A#A!A#!A#c N|j|}||jd}i||S)a1Create the full inputs for the LLMChain from intermediate steps. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. **kwargs: User inputs. Returns: Dict[str, Any]: Full inputs for the LLMChain. agent_scratchpadr)r"r)r9rDrFr new_inputss r:r$zAgent.get_full_inputs8s3--.@A*2DJJG '&'J''r=cZtt|jjdhz S)rPr.rr8s r:rQzAgent.input_keysKs' C1126H5IIJJr=aftermodec||jj}d|jvrtj d|jj dt |tr|xjdz c_|St |tr|xjdz c_ |Sdt|}t||S)aFValidate that prompt matches format. Args: values: Values to validate. Returns: Dict: Validated values. Raises: ValueError: If `agent_scratchpad` is not in prompt.input_variables and prompt is not a FewShotPromptTemplate or a PromptTemplate. r.zl`agent_scratchpad` should be a variable in prompt.input_variables. Did not find it, so adding it at the end.z {agent_scratchpad}zGot unexpected prompt type ) rpromptinput_variablesloggerwarningappendrortemplaterrtyperZ)r9r5r]s r:validate_promptzAgent.validate_promptSs&& V%;%; ; NN=   " " ) )*< =&.1#99  F$9: !77  4DL>B o% r=cy)z&Prefix to append the observation with.Nr7r8s r:rzAgent.observation_prefixqrHr=cy)z#Prefix to append the LLM call with.Nr7r8s r:r zAgent.llm_prefixvrHr=cy)zCreate a prompt for this class. Args: tools: Tools to use. Returns: BasePromptTemplate: Prompt template. Nr7rbrds r: create_promptzAgent.create_prompt{rHr=cy)zgValidate that appropriate tools are passed in. Args: tools: Tools to use. Nr7r@s r:_validate_toolszAgent._validate_toolsrHr=c y)z)Get default output parser for this class.Nr7)rbrFs r:_get_default_output_parserz Agent._get_default_output_parserrHr=c |j|t||j||}|Dcgc]}|j}}|xs|j } |d||| d|Scc}w)aOConstruct an agent from an LLM and tools. Args: llm: Language model to use. tools: Tools to use. callback_manager: Callback manager to use. output_parser: Output parser to use. kwargs: Additional arguments. Returns: Agent: Agent object. )rcr5re)rrrr7)rCr/rAnamerE) rbrcrdrerrFrtool tool_names_output_parsers r:rfzAgent.from_llm_and_toolss* E"$$U+- -22Ddii2 2&J#*H*H*J $(    3sA(c |dk(rtddidS|dk(rd}|D]5\}}||jz }|d|j|d|jz }7|dz }||jd}i||}|j j d i|} |jj| } t| tr| Std| i| Sd |} t| ) aReturn response when agent has been stopped due to max iterations. Args: early_stopping_method: Method to use for early stopping. intermediate_steps: Steps the LLM has taken to date, along with observations. **kwargs: User inputs. Returns: AgentFinish: Agent finish object. Raises: ValueError: If `early_stopping_method` is not in ['force', 'generate']. rTr6rUrVgeneraterzB I now need to return a final answer based on the previous steps:r-zBearly_stopping_method should be one of `force` or `generate`, got r7) rrrr rrr%rrrorZ) r9r\rDrFrr r!r/r'r( parsed_outputr]s r:r^zAgent.return_stopped_responses"( !G +PQ  !J .H'9 # FJJ&001+b@QR  V H/7 KJ2V2z2K0$..00?;?K ..44[AM-5$$+6 D D () + or=c4|j|jdS)rr r r8s r:rzAgent.tool_run_logging_kwargss//"&"9"9  r=rrr)rrprrp)rDrrzUnion[str, list[BaseMessage]]r?r)rDrrFr rzbuiltins.dict[str, Any]rr(r)rdrrr)rdrrr)rFr rr)NN) rcrrdrrerrzOptional[AgentOutputParser]rFr rrrr)rrrrrrrtr@rr;rrr"rGrKr$rQr'r<rrr rrArCrErfr^rrrs@r:rrs4 $$$))-M&-F "   9  '  $5955 5 ) 50 $<9<< < ) <*(9(( ! (&KK'"#:5522    88 ;?59 ! ! "! 8 ! 3 !  !  ! ! F5"5:5 5  5n r=rc^eZdZUdZdZded< dZded< d d dZ d d d Zy) ExceptionToolz!Tool that just returns the query. _ExceptionrprGzException tool descriptionNc|Sr?r7r9query run_managers r:_runzExceptionTool._runs  r=cK|Swr?r7rUs r:_arunzExceptionTool._aruns  sr?)rVrprWz#Optional[CallbackManagerForToolRun]rrp)rVrprWz(Optional[AsyncCallbackManagerForToolRun]rrp) rrrrrGrrSrXrZr7r=r:rQrQsn+D#'K'" <@9 AE> r=rQceZdZUdZded< ded< dZded< d Zd ed < d Zd ed< dZded< dZ ded< dZ ded< e d5 d6dZ e dd7dZe de d8dZed9dZd:dZd:dZ d5ddd  d;d!Zedd%Z d5 d?d&Z d5 d@d'Z dAd(Z d5 dBd)Z d5 dCd*Z d5 dDd+Z d5 dEd,Z d5 dFd-Z d5 dGd.Z! d5 dHd/Z" d5 dId0Z# dJd1Z$ dKd2Z%e& d5 dLd3Z'e& d5 dMd4Z(y )N AgentExecutorzAgent that is using tools.Expected a single AgentFinish output, but got multiple values.)rorrrZrr r!)r9rqr]as r:_consume_next_stepz AgentExecutor._consume_next_stepsa fRj+ .6{aV o%": 39VaZ9=U1==)VVVs A+A+c \|jt|j|||||Sr?)rr_iter_next_step)r9name_to_tool_map color_mappingrrDrWs r:_take_next_stepzAgentExecutor._take_next_step$s<&& $$$!&    r=c#tK |j|}|jj|fd|r|jndi|}t |t,r|yt |tr|gn|} | D]}|| D]}|j/||||y#t$r}t |j tr|j }nd}|rd|} t| |t|} t |j tr:|jr+t|j} t|j} n_d} n\t |j tr |j } n5t|j r|j |} nd} t| |td| | }|r|j|d |jj!} t#j$|j&f|j(d|r|jndd | } t+|| Yd}~yd}~wwxYww) Take a single step in the thought-action-observation loop. Override this to take control of how the agent makes and acts on choices. rENFAn output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Invalid or incomplete response.Got unexpected type of `handle_parsing_errors`rRrrrrrEr r!)_prepare_intermediate_stepsrxrG get_childrrorcrrZrp send_to_llmr! llm_outputcallableron_agent_actionrrQr tool_inputrrr_perform_agent_action)r9rrrrDrWr6e raise_errorr]rr!tool_run_kwargsactions agent_actions r:rzAgentExecutor._iter_next_step8s=0 !%!A!ABT!U -T'',,"5@+//1dF^ fk *L )=6(6# L  # L,,    e%' $44d;"&"<"<< # *+,0 !o1,q6D$44d;=="%amm"4Kq||,D"BKD66<"88 $445"88; F o1, {DAF++F'+B"00HHJO--/--!! 5@+//1d  " K6{C C O' s1H8ABAH8 H5#FH0+H80H55H8cf|r|j|d|j|vr||j}|j}||j}|jj }|rd|d<|j |j f|j||r|jndd|} ny|jj }tj |jt|jdf|jd|r|jndd|} t|| S)NrrrVr rrequested_tool_nameavailable_tool_namesr) rrH return_directrxrrrrrr-rkeysr r9rrrrWrHrrrr!s r:rz#AgentExecutor._perform_agent_actions;   ' ' G ' D    0 0#L$5$56D ..M!,"3"34E"00HHJO02 -"$(('' 5@+//1d  " K#00HHJO++-+++7+<+<,01A1F1F1H,I   5@+//1d " K +FFr=c K|j|j|||||2cgc3d{}| 76c}Scc}wwr?)r_aiter_next_step)r9rrrrDrWrs r:_atake_next_stepzAgentExecutor._atake_next_steps]&& $44$!&         s&#A=5 35 =5 = Ac K |j|}|jj|fd|r|jndi|d{}t |t*r|yt |tr|gn|} | D]}| t-j.| Dcgc]}|j1||||c}d{}|D]}| y7#t$r}t |j tr|j }nd}|rd|} t| |t|} t |j tr:|jr+t|j} t|j} n_d} n\t |j tr |j } n5t|j r|j |} nd} t| |td| | }|jj} t!j"|j$f|j&d|r|jndd| d{7} t)|| Yd}~yd}~wwxYwcc}w7w) rrENFrrrrRrr)rrxrKrrrorcrrZrprr!rrrrrQr rrrrasynciogather_aperform_agent_action)r9rrrrDrWr6rrr]rr!rrrresultrs r:rzAgentExecutor._aiter_next_stepst. !%!A!ABT!U 44--33"5@+//1dFZ fk *L )=6(6# L  ~~%,! ++$!      EK I %% $44d;"&"<"<< # *+,0 !o1,q6D$44d;=="%amm"4Kq||,D"BKD66<"88 $445"88; F o1, {DAF"00HHJO 4  4 4!!! 5@+//1d ! " !K6{C C K% f  sgI*AC C  CA I*I"3I*:I';I* C IE'I?III*I I*cK|r&|j||jdd{|j|vr||j}|j}||j}|jj }|rd|d<|j |jf|j||r|jndd|d{} n|jj }tj |jt|jdf|jd|r|jndd|d{} t|| S7277w)Nr)rrrVr rrr) rrrHrrxrr rrr-rrrrs r:rz$AgentExecutor._aperform_agent_actionst -- .      0 0#L$5$56D ..M!,"3"34E"00HHJO02 - ) ''! 5@+//1d ! " !K#00HHJO 2 2 2+7+<+<,01A1F1F1H,I !  5@+//1d !" ! K +FFC  s5$EEBEEBEEEEEc|jDcic]}|j|}}t|jDcgc]}|jc}ddg}g}d}d}tj} |j ||r|j |||||} t | tr|j| ||S|j| t| dk(r,| d} |j| } | |j| ||S|dz }tj| z }|j ||r|jj|j|fi|} |j| ||Scc}wcc}w)z(Run text through and get agent response.rredexcluded_colorsrrWr)rdrGr$timerrrorrextendr_get_tool_returnrxr^r\r9rrWrHrrrDrr start_timenext_step_outputnext_step_action tool_returnr6s r:_callzAgentExecutor._callEs9= CDIItOCC)#':: .4TYY .$e, =?  YY[ ##J =#33 "' 4  *K8||$& +$  % %&6 7#$)#3A#6 "334DE *<<#*$/( !OJ99;3L7##J =8<##;;  & &    ||F$6K|PP[D /s E2E7cK|jDcic]}|j|}}t|jDcgc]}|jc}dg}g}d}d}tj} t |j 4d{|j ||r|j|||||d{} t| tr-|j| ||d{cdddd{S|j| t| dk(rE| d} |j| } | -|j| ||d{cdddd{S|dz }tj| z }|j ||r|jj|j |fi|} |j| ||d{cdddd{Scc}wcc}w7d7777777/7!#1d{7swYyxYw#t"t$j"f$rH|jj|j |fi|} |j| ||d{7cYSwxYww)z.Async run text through and get agent response.rrrrNrr)rdrGr$rr0rbrrrorrrrrrxr^r\ TimeoutErrorrrs r:_acallzAgentExecutor._acall{s9= CDIItOCC)#':: .4TYY .$I =?  YY[ 4 &t'>'>?' ' ++J E-1-B-B(%*$/ .C.($""2K@%)]],.(3&3& ' ' '  '--.>?+,1+;A+>(&*&;&;4B7B>BBr?) r^r]rdrrErrFr rr\rO)rqrtrr )rz2Union[BaseSingleActionAgent, BaseMultiActionAgent]r) rr rErr|rr}rrr+r)rGrprr#)rrrfloatrr)r6rrDrrW$Optional[CallbackManagerForChainRun]rdict[str, Any])r6rrDrrW)Optional[AsyncCallbackManagerForChainRun]rr)rqNextStepOutputr1Union[AgentFinish, list[tuple[AgentAction, str]]]) rdict[str, BaseTool]rdict[str, str]rrrDrrWrrr) rrrrrrrDrrWrrz4Iterator[Union[AgentFinish, AgentAction, AgentStep]]) rrrrrrrWrrr) rrrrrrrDrrWrrr) rrrrrrrDrrWrrz9AsyncIterator[Union[AgentFinish, AgentAction, AgentStep]]) rrrrrrrWrrr)rrrWrrr)rrrWrrr)rztuple[AgentAction, str]rzOptional[AgentFinish])rDrrr)rUnion[dict[str, Any], Any]rOptional[RunnableConfig]rFr rzIterator[AddableDict])rrrrrFr rzAsyncIterator[AddableDict]))rrrrrr_rarbr\rcrerrgr'rjrurrxrr{rrQrrrrrrrrrrrrrrrrr)rrr7r=r:r\r\s$ GG3 -&+t+3$&NM&9+/.")3( S   $  K "        2'"#2(#$>   2 $ "'          6--00 >Y=A  ! :   "BF !?   " W W ; W"=A  - &   :  :   ; 4=A M-M&M M : M : M >Mh=A %G-%G&%G" %G : %G  %GZBF  - &   :  ?   ; 6BF U-U&U U : U ? U CUxBF )G-)G&)G" )G ? )G  )G\=A4Q4Q:4Q  4QrBFGG?G  GR1 * "9 " ' ",0))   <,0))  $ r=r\)br __future__rrbuiltinsrrloggingrabcrcollections.abcrrrpathlibrtypingr r r r r rlangchain_core._apirlangchain_core.agentsrrrlangchain_core.callbacksrrrrrrlangchain_core.exceptionsrlangchain_core.language_modelsrlangchain_core.messagesrlangchain_core.output_parsersrlangchain_core.promptsrlangchain_core.prompts.few_shotrlangchain_core.prompts.promptrlangchain_core.runnablesrr r!langchain_core.runnables.utilsr"langchain_core.toolsr#langchain_core.utils.inputr$pydanticr%r&r'typing_extensionsr(r)langchain._api.deprecationr*langchain.agents.agent_iteratorr+langchain.agents.agent_typesr,langchain.agents.toolsr-langchain.chains.baser.langchain.chains.llmr/langchain.utilities.asyncior0 getLoggerrr7r2rrrrrrrrrQrrwr\r7r=r:r sK"  == *EE<</:5A8LL6)8;;,@A2.')7   8 $eIePM9M`3({K/G)HI3 U4 ,k9:; *l)l^t3tn  %  P 0P  P f  %  b !b  b J H.eKi?@A-)AABR ER r=