@hNSrSSKJr SSKrSSKrSSKrSSKrSSKrSSKrSSK J r SSK J r J r Jr SSKJr SSKJrJrJrJrJr SSKrSSKJr SS KJrJrJr SS KJrJ r J!r!J"r"J#r#J$r$ SS K%J&r& SS K'J(r( SS K)J*r* SSK+J,r, SSK-J.r. SSK/J0r0 SSK1J2r2 SSK3J4r4J5r5J6r6 SSK7J8r8 SSK9J:r: SSK;Jr>J?r?J@r@ SSKAJBrBJCrC SSKDJErE SSKFJGrG SSKHJIrI SSKJJKrK SSKLJMrM SSKNJOrO SSKPJQrQ \R"\S5rT"SS \>5rU"S!S"\>5rV"S#S$\,\\\45rW"S%S&\,\\X\\45rY"S'S(\U5rZ"S)S*\V5r[\"S+\ES,S-9"S.S/\U55r\\"S+\ES,S-9"S0S1\U55r]"S2S3\:5r^\X\\\\4r_\\Z\[4r`"S4S5\M5rag)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_timeoutc*^\rSrSrSr\SSj5rSSjr\SSSjj5r \SSSjj5r \\SSj55r SSjr \ SSS jj5r\SS j5rSU4S jjrSS jrSS jrSrU=r$)BaseSingleActionAgent:zBase Single Action Agent class.cS/$Return values of the agent.outputselfs N/var/www/html/shao/venv/lib/python3.13/site-packages/langchain/agents/agent.py return_values#BaseSingleActionAgent.return_values= zcgNr8r9s r;get_allowed_tools'BaseSingleActionAgent.get_allowed_toolsBsr?c g)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. Nr8r:intermediate_steps callbackskwargss r;planBaseSingleActionAgent.planEr?c # g7f)Async 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. Nr8rFs r;aplanBaseSingleActionAgent.aplanXcg'Return the input keys. :meta private: Nr8r9s r; input_keys BaseSingleActionAgent.input_keyskrLr?c LUS:Xa[SS0S5$SUS3n[U5e)rReturn response when agent has been stopped due to max iterations. Args: early_stopping_method: Method to use for early stopping. intermediate_steps: Steps the LLM has taken to date, along with observations. **kwargs: User inputs. Returns: AgentFinish: Agent finish object. Raises: ValueError: If `early_stopping_method` is not supported. forcer73Agent stopped due to iteration limit or time limit.'Got unsupported early_stopping_method ``r ValueErrorr:early_stopping_methodrGrImsgs r;return_stopped_response-BaseSingleActionAgent.return_stopped_responsessA( !G +PQ 88M7NaPor?c [e)zConstruct an agent from an LLM and tools. Args: llm: Language model to use. tools: Tools to use. callback_manager: Callback manager to use. kwargs: Additional arguments. Returns: BaseSingleActionAgent: Agent object. NotImplementedError)clsllmtoolscallback_managerrIs r;from_llm_and_tools(BaseSingleActionAgent.from_llm_and_toolss &"!r?c[ez#Return Identifier of an agent type.rgr9s r; _agent_type!BaseSingleActionAgent._agent_type "!r?c >[TU]5nURn[ U[ 5(a[ UR5US'U$UbX2S'U$![a SnNFf=f)zcReturn dictionary representation of agent. Returns: Dict: Dictionary representation of agent. N_type)super model_dumprqrh isinstancer,strvalue)r:rI_dictru __class__s r;dictBaseSingleActionAgent.dictst "$ $$E eY ' ' -E'N  "'N # E s A A$#A$cP[U[5(a [U5OUnURnUR SSS9 UR 5nSU;aSUS3n[ U5eURS:Xa0URS5n[R"XFSS 9 S S S 5 g URRS 5(a0URS5n[R"XFS S 9 S S S 5 g US3n[U5e!,(df  g =f!,(df  g =f)zSave the agent. Args: file_path: Path to file to save the agent to. Example: .. code-block:: python # If working with agent executor agent.agent.save(file_path="path/agent.yaml") Tparentsexist_okruAgent z does not support saving.jsonwindentNz.yamlz.ymlFdefault_flow_style must be json or yaml)rxryrparentmkdirr}rhsuffixopenjsondumpendswithyamlr`)r: file_path save_pathdirectory_path agent_dictrcfs r;saveBaseSingleActionAgent.saves(2)S'A'ADOy "))TD9YY[ * $4& 89C%c* *   w &$ *2%$    & &'8 9 9$ *EB%$K45CS/ !%$%$DD D D%c0$#Return logging kwargs for tool run.r8r9s r;tool_run_logging_kwargs-BaseSingleActionAgent.tool_run_logging_kwargss r?r8return list[str]rOptional[list[str]]rArGlist[tuple[AgentAction, str]]rHrrIr rUnion[AgentAction, AgentFinish]rbryrGrrIr rr) rjrrkSequence[BaseTool]rlOptional[BaseCallbackManager]rIr rr2rryrIr r builtins.dictrzUnion[Path, str]rNonerr)__name__ __module__ __qualname____firstlineno____doc__propertyr<rBrrJrOrVrd classmethodrmrqr}rr__static_attributes__ __classcell__r|s@r;r2r2:sT)  $ 9    )   $ $ 9    )   $  ":   : ;? " """8 "  "  ""(""" "Dr?r2c^\rSrSrSr\SSj5rSSjr\SSSjj5r \SSSjj5r \\SSj55r SSjr \SS j5r SU4S jjrSS jrSS jrS rU=r$)BaseMultiActionAgentzBase Multi Action Agent class.cS/$r5r8r9s r;r<"BaseMultiActionAgent.return_valuesr>r?cg)zEGet allowed tools. Returns: Optional[List[str]]: Allowed tools. Nr8r9s r;rB&BaseMultiActionAgent.get_allowed_toolss r?c g)zGiven 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. Nr8rFs r;rJBaseMultiActionAgent.planrLr?c # g7f)zAsync 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. Nr8rFs r;rOBaseMultiActionAgent.aplanrQrRcgrTr8r9s r;rVBaseMultiActionAgent.input_keysrLr?c LUS:Xa[SS0S5$SUS3n[U5e)rYrZr7z$Agent stopped due to max iterations.r\r]r^r_ras r;rd,BaseMultiActionAgent.return_stopped_response s:( !G +*PQSUV V78M7NaPor?c[erprgr9s r;rq BaseMultiActionAgent._agent_type:rsr?c >[TU]5n[R"[5 [ UR 5US'SSS5 U$!,(df  U$=f)*Return dictionary representation of agent.ruN)rvrw contextlibsuppressrhryrqr:rIr{r|s r;r}BaseMultiActionAgent.dict?sK"$  !4 5 !1!12E'N6 6 5 s A  AcP[U[5(a [U5OUnUR5nSU;aSUS3n[ U5eUR nUR SSS9 URS:Xa0URS5n[R"X6SS 9 S S S 5 g URRS 5(a0URS5n[R"X6S S 9 S S S 5 g US3n[U5e!,(df  g =f!,(df  g =f)a8Save 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") rurz does not support saving.TrrrrrNrFrr)rxryrr}rhrrrrrrrrr`)r:rrrrcrrs r;rBaseMultiActionAgent.saveFs"(2)S'A'ADOy YY[ * $4& 9:C%c* *"))TD9   w &$ *2%$    & &'8 9 9$ *EB%$K45CS/ !%$%$rc0$rr8r9s r;r,BaseMultiActionAgent.tool_run_logging_kwargsls  r?r8rrrArGrrHrrIr r%Union[list[AgentAction], AgentFinish]rrrrr)rrrrrrr<rBrrJrOrVrdrqr}rrrrrs@r;rrs (  $ 9    /   $ $ 9    /   $  ":   4""$"Lr?rc,\rSrSrSr\SSj5rSrg)AgentOutputParserirz=Base class for parsing agent output into agent action/finish.cg)z$Parse text into agent action/finish.Nr8r:texts r;parseAgentOutputParser.parseurLr?r8N)rryrrrrrrrrrrr8r?r;rrrsG33r?rc,\rSrSrSr\SSj5rSrg)MultiActionAgentOutputParserizzzBase class for parsing agent output into agent actions/finish. This is used for agents that can return multiple actions. cg)zParse text into agent actions/finish. Args: text: Text to parse. Returns: Union[List[AgentAction], AgentFinish]: List of agent actions or agent finish. Nr8rs r;r"MultiActionAgentOutputParser.parserLr?r8N)rryrrrr8r?r;rrzs     r?rc\rSrSr%SrS\S'/rS\S'/rS\S'SrS \S '\ "SS 9r \ SS j5r \ SS j5r SSSjjrSSSjjrSrg) RunnableAgentiAgent powered by Runnables.z/Runnable[dict, Union[AgentAction, AgentFinish]]runnablerinput_keys_argreturn_keys_argTboolstream_runnablearbitrary_types_allowedcUR$r6rr9s r;r<RunnableAgent.return_values###r?cUR$)zReturn the input keys.rr9s r;rVRunnableAgent.input_keyss"""r?Nc 0UESU0EnSnUR(a0URRUSU0S9HnUcUnM XV- nM U$URRUSU0S9nU$aBased 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. rGNrHconfigrrstreaminvoker:rGrHrIinputs final_outputchunks r;rJRunnableAgent.plans"FFE02DE   --fk9=U-V'#(L )L W ==// Y?W/XLr?c# 0UESU0EnSnUR(a2URRUSU0S9ShvN nUcUnMXV- nMURRUSU0S9IShvN nU$N7 U$N 7faAsync 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. rGNrHrrrastreamainvokers r;rORunnableAgent.aplans(FFE02DE    $}}44#Y/ 5  *e '#(L )L!%!6!6#Y/"7"L *  28A=A8A6A8-A=/A;0A=6A88A=r8rrArrrrrr__annotations__rrrr& model_configrr<rVrJrOrr8r?r;rrs%==/ "NI"!#OY# OT  $L$$## $"9"" " ) "N $*9** *   **r?rc\rSrSr%SrS\S'/rS\S'/rS\S'SrS \S '\ "SS 9r \ SS j5r \ SS j5r SSSjjrSSSjjrSrg)RunnableMultiActionAgentirz5Runnable[dict, Union[list[AgentAction], AgentFinish]]rrrrTrrrcUR$rrr9s r;r<&RunnableMultiActionAgent.return_valuesrr?cUR$)9Return the input keys. Returns: List of input keys. rr9s r;rV#RunnableMultiActionAgent.input_keyss"""r?Nc 0UESU0EnSnUR(a0URRUSU0S9HnUcUnM XV- nM U$URRUSU0S9nU$rrrs r;rJRunnableMultiActionAgent.plan s(FFE02DE   --fk9=U-V'#(L )L W ==// Y?W/XLr?c# 0UESU0EnSnUR(a2URRUSU0S9ShvN nUcUnMXV- nMURRUSU0S9IShvN nU$N7 U$N 7frrrs r;rORunnableMultiActionAgent.aplanGs(FFE02DE    $}}44#Y/ 5  *e '#(L )L!%!6!6#Y/"7"L  *  r r8rrArr r8r?r;rrs%CC0 "NI"!#OY# OT  $L$$## $%9%% %   %T $+9++ +   ++r?rz0.1.0z1.0)messageremovalc^\rSrSr%SrS\S'S\S'S\S'\SS j5rSU4S jjrSSS jjr SSS jjr SS jr Sr U=r $)LLMSingleActionAgentiuz$Base class for single action agents.r/ llm_chainr output_parserrstopcZ[[URR5S1- 5$)rrGlistsetrrVr9s r;rVLLMSingleActionAgent.input_keyss'C1126J5KKLLr?c *>[TU]5nUS U$rrrvr}rs r;r}LLMSingleActionAgent.dict  / " r?c URR"SUURUS.UD6nURR U5$)zGiven input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Action specifying what tool to use. rGrrHr8)rrunrrrr:rGrHrIr7s r;rJLLMSingleActionAgent.plansM"## 1    !!''//r?c# URR"SUURUS.UD6IShvN nURR U5$N7f)rNr+Nr8)rarunrrrr-s r;rOLLMSingleActionAgent.aplansZ"~~** 1     !!''//  s.AA Ac`S[UR5S:XaSS.$URSS.$)rr\r llm_prefixobservation_prefix)lenrr9s r;r,LLMSingleActionAgent.tool_run_logging_kwargss8(+DII!(;"  AE1  r?r8rrrArr)rrrrrr rrVr}rJrOrrrrs@r;rrus /$$$) O% MM $0900 0 ) 08 $0900 0 ) 02  r?rc ^\rSrSr%SrS\S'S\S'SrS\S 'S U4S jjrS!S jr\ S"S j5r S#S jr \ S"Sj5r S$Sjr S%S&SjjrS%S&SjjrS'Sjr\ S"Sj5r\"SS9S(Sj5r\ \S)Sj55r\ \S)Sj55r\\S*Sj55r\S+Sj5r\\S,Sj55r\S-S.Sjj5rS/SjrS0SjrSrU=r$)1AgentizAgent that calls the language model and deciding the action. This is driven by a LLMChain. The prompt in the LLMChain MUST include a variable called "agent_scratchpad" where the agent can put its intermediary work. r/rrrNr allowed_toolsc *>[TU]5nUS U$r&r'rs r;r} Agent.dictr)r?cUR$)zGet allowed tools.)r:r9s r;rBAgent.get_allowed_toolss!!!r?cS/$r5r8r9s r;r<Agent.return_valuesr>r?cSn[U5e)zKFix the text. Args: text: Text to fix. Returns: str: Fixed text. z(fix_text not implemented for this agent.r`)r:rrcs r; _fix_textAgent._fix_texts9or?cvSURR53SURR53/$)N z )r5rstripr9s r;_stop Agent._stopsB((//12 34**1134 5  r?c~SnUH4up4X#R- nUSURUSUR3- nM6 U$)zJConstruct the scratchpad that lets the agent continue its thought process.r\rF)logr5r4)r:rGthoughtsaction observations r;_construct_scratchpadAgent._construct_scratchpadsP #5 F  "H "T445k]"T__DUV VH$6r?c UR"U40UD6nURR"SSU0UD6nURR U5$)rErHr8)get_full_inputsrpredictrrr:rGrHrI full_inputs full_outputs r;rJ Agent.plansM"**+=HH nn,,PyPKP !!'' 44r?c# UR"U40UD6nURR"SSU0UD6IShvN nURR U5IShvN $N'N7f)rNrHNr8)rRrapredictraparserTs r;rO Agent.aplan#s_"**+=HH  NN33WiW;WW ''..{;;;X;s!5A#A"A#A!A#!A#c LURU5nX0RS.n0UEUE$)zCreate 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)rOrH)r:rGrIrL new_inputss r;rRAgent.get_full_inputs8s1--.@A*2JJG '&'J''r?cZ[[URR5S1- 5$)rUr^r!r9s r;rVAgent.input_keysKs' C1126H5IIJJr?aftermodecURRnSUR;a[R S5 URR S5 [ U[5(aU=RS- slU$[ U[5(aU=RS- sl U$S[U53n[U5eU$)aValidate 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_variablesloggerwarningappendrxrtemplaterrtyper`)r:rgrcs r;validate_promptAgent.validate_promptSs&& V%;%; ; NN=   " " ) )*< =&.11#99  F$9:: !77  4DL>B o% r?cg)z&Prefix to append the observation with.Nr8r9s r;r5Agent.observation_prefixqrLr?cg)z#Prefix to append the LLM call with.Nr8r9s r;r4Agent.llm_prefixvrLr?cg)zsCreate a prompt for this class. Args: tools: Tools to use. Returns: BasePromptTemplate: Prompt template. Nr8rirks r; create_promptAgent.create_prompt{rLr?cg)zOValidate that appropriate tools are passed in. Args: tools: Tools to use. Nr8rus r;_validate_toolsAgent._validate_toolsrLr?c g)z)Get default output parser for this class.Nr8)rirIs r;_get_default_output_parser Agent._get_default_output_parserrLr?c URU5 [UURU5US9nUVs/sHowRPM nnU=(d UR 5n U"SUUU S.UD6$s snf)aConstruct 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. )rjrgrl)rr:rr8)ryr/rvnamer|) rirjrkrlrrIrtool tool_names_output_parsers r;rmAgent.from_llm_and_toolss* E"$$U+- -22EDiiE 2&J#*H*H*J $(    3sA+c US:Xa[SS0S5$US:XaSnUH4upVXER- nUSURUSUR3- nM6 US- nX@RS.n0UEUEnUR R "S 0UD6n URRU 5n [U [5(aU $[SU 0U 5$S U3n [U 5e) aReturn response when agent has been stopped due to max iterations. Args: early_stopping_method: Method to use for early stopping. intermediate_steps: Steps the LLM has taken to date, along with observations. **kwargs: User inputs. Returns: AgentFinish: Agent finish object. Raises: ValueError: If `early_stopping_method` is not in ['force', 'generate']. rZr7r[r\generaterFzB I now need to return a final answer based on the previous steps:r]zBearly_stopping_method should be one of `force` or `generate`, got r8) rrKr5r4rHrrSrrrxr`) r:rbrGrIrLrMrNr_rUrV parsed_outputrcs r;rdAgent.return_stopped_responses( !G +PQ  !J .H'9#JJ&001+b@QR(: V H/7 KJ2V2z2K..00?;?K ..44[AM-55$$+6 D D () + or?c4URURS.$)rr3r3r9s r;rAgent.tool_run_logging_kwargss//"&"9"9  r?r8rrr)rryrry)rGrrzUnion[str, list[BaseMessage]]rAr)rGrrIr rzbuiltins.dict[str, Any]rr(r)rkrrr)rkrrr)rIr rr)NN) rjrrkrrlrrzOptional[AgentOutputParser]rIr rr9rr) rrrrrr r:r}rBrr<rCrHrOrJrOrRrVr'rnrr5r4rrvryr|rmrdrrrrs@r;r9r9s9 $$$))-M&-F "   9  '  $5955 5 ) 50 $<9<< < ) <*(9(( ! (&KK'"#:5522    88 ;?59 ! ! "! 8 ! 3 !  !  ! ! F5"5:5 5  5n  r?r9cn\rSrSr%SrSrS\S'SrS\S'S S S jjrS SS jjr S r g) ExceptionTooliz!Tool that just returns the query. _ExceptionryrzException tool descriptionNcU$rAr8r:query run_managers r;_runExceptionTool._runs  r?c# U$7frAr8rs r;_arunExceptionTool._aruns  sr8rA)rryrz#Optional[CallbackManagerForToolRun]rry)rryrz(Optional[AsyncCallbackManagerForToolRun]rry) rrrrrrr rrrrr8r?r;rrss+D#'K'" <@9 AE> r?rcb\rSrSr%SrS\S'S\S'SrS\S 'S rS \S 'S rS\S'Sr S\S'Sr S\S'Sr S\S'\ S7S8Sjj5r \"SS9S9Sj5r\"SS9\ S:Sj55r\S;Sj5rSS#j5r\S>S$j5rS?S%jrS@S&jrS7SAS'jjrS7SBS(jjrSCS)jrS7SDS*jjrS7SES+jjrS7SFS,jjrS7SGS-jjr S7SHS.jjr!S7SIS/jjr"S7SJS0jjr#S7SKS1jjr$SLS2jr%SMS3jr&\'S7SNS4jj5r(\'S7SOS5jj5r)S6r*g )P AgentExecutorizAgent that is using tools.Expected a single AgentFinish output, but got multiple values.)rxrr6r`rrMrN)r:rrcas r;_consume_next_step AgentExecutor._consume_next_stepsk fRj+ . .6{aV o%": 39V6aZ9=U)1==)6VVVs A9A9c \UR[URUUUUU555$rA)rr"_iter_next_step)r:name_to_tool_map color_mappingrrGrs r;_take_next_stepAgentExecutor._take_next_step$s<&& $$$!&    r?c## URU5nURR"U4SU(aUR5OS0UD6n[ U[,5(aUv g[ U[5(aU/OUn U HnUv M U HnUR/UUUU5v M g![Gan[ UR [5(aUR (+nOSnU(aSU<3n [U 5Ue[U5n [ UR [5(a?UR(a+[UR5n [UR5n OiSn Of[ UR [5(a UR n O:[UR 5(aUR U5n OSn [U 5Ue[SX5nU(aURUSS 9 URR!5n [#5R$"UR&4UR(SU(aUR5OSS .U D6n [+XkS 9v SnAgSnAff=f7f) Take a single step in the thought-action-observation loop. Override this to take control of how the agent makes and acts on choices. rHNFAn 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`rrrrrrHrMrN)_prepare_intermediate_stepsrrJ get_childrrxrrr`ry send_to_llmrN llm_outputcallableron_agent_actionrrr, tool_inputrrr_perform_agent_action)r:rrrrGrr7e raise_errorrcrrNtool_run_kwargsactions agent_actions r;rAgentExecutor._iter_next_step8s<0 !%!A!ABT!U '',,"5@+//1dF^ fk * *L )==6(6#L $#L,,   $e%' $44d;;"&"<"<< # *+,0 !o1,q6D$44d;;=="%amm"4Kq||,D"BKD66<<"88 $4455"88; F o1, {AF++F'+B"00HHJO'/--!! 5@+//1d  " K6C C O' s1I1AB, A!I1, I.7F-I)$I1)I..I1cU(aURUSS9 URU;aXRnURnX#RnURR 5nU(aSUS'UR "UR 4URUU(aUR5OSS.UD6n O~URR 5n[5R "UR[UR55S.4URSU(aUR5OSS.UD6n [X9S9$)Nrrr\r4rrequested_tool_nameavailable_tool_namesr) rr return_directrrr,rrrr-r"keysr r:rrrrrrrrrNs r;r#AgentExecutor._perform_agent_actions1   ' ' G ' D    0 0#$5$56D ..M!"3"34E"00HHJO02 -(('' 5@+//1d  " K#00HHJO%-+++7+<+<,01A1F1F1H,I   5@+//1d " K FFr?c # URURUUUUU5Vs/sShvN nUPM N sn5$s snf7frA)r_aiter_next_step)r:rrrrGrrs r;_atake_next_stepAgentExecutor._atake_next_stepsV&& $44$!&        s&#A>6 46 >6 > Ac L# URU5nURR"U4SU(aUR5OS0UD6IShvN n[ U[*5(aU7v g[ U[5(aU/OUn U HnU7v M [,R."U Vs/sHnUR1UUUU5PM sn6IShvN nUHnU7v M gN![Gan[ UR [5(aUR (+nOSnU(aSU<3n [U 5Ue[U5n [ UR [5(a?UR(a+[UR5n [UR5n OiSn Of[ UR [5(a UR n O:[UR 5(aUR U5n OSn [U 5Ue[SX5nURR5n [!5R""UR$4UR&SU(aUR5OSS.U D6IShvN n [)XkS 97v SnAgSnAff=fs snfGN7f) rrHNFrrrrrr)rrrOrrrxrrr`ryrrNrrrrrr0rrrrasynciogather_aperform_agent_action)r:rrrrGrr7rrrcrrNrrrresultrs r;r AgentExecutor._aiter_next_stepsz. !%!A!ABT!U  --33"5@+//1dFZ fk * *L )==6(6#L $~~%,%,L ++$!   %,   EKI %% $44d;;"&"<"<< # *+,0 !o1,q6D$44d;;=="%amm"4Kq||,D"BKD66<<"88 $4455"88; F o1, {AF"00HHJO - 4 4!!! 5@+//1d ! " !K6C C K% f  sgJ$A C$C"C$AJ$*JJ$J!J$"C$$ J/FJ=J>JJ$J J$c# U(a#URUURSS9IShvN URU;aXRnURnX#RnURR 5nU(aSUS'UR "UR4URUU(aUR5OSS.UD6IShvN n OURR 5n[5R "UR[UR55S.4URSU(aUR5OSS.UD6IShvN n [X9S9$GN=NN7f)Nr)rrr\r4rrr) rrrrrrr0rrr-r"rrrs r;r$AgentExecutor._aperform_agent_actionsg -- .      0 0#$5$56D ..M!"3"34E"00HHJO02 - $ ''! 5@+//1d ! " !K#00HHJO + 2 2+7+<+<,01A1F1F1H,I !  5@+//1d !" ! K FFC  s5&E,E%B'E,E(BE,E*E,(E,*E,cURVs0sHo3RU_M nn[URVs/sHo3RPM snSS/S9n/nSnSn[R"5n UR Xx5(aUR UUUUUS9n [ U [5(aURU UUS9$URU 5 [U 5S:Xa*U Sn URU 5n U bURU UUS9$US- n[R"5U - nUR Xx5(aMURR"URU40UD6n URXUS9$s snfs snf)z(Run text through and get agent response.rredexcluded_colorsrrr)rkrr$timerrrxrrextendr6_get_tool_returnrrdrbr:rrrrrrGrr start_timenext_step_outputnext_step_action tool_returnr7s r;_callAgentExecutor._callEs9= C IItO C)#':: .:4YY: .$e, =?  YY[ ##J==#33 "' 4  *K88||$& +$  % %&6 7#$)#3A#6 "334DE *<<#*$/( !OJ99;3L7##J==8##;;  & &    ||FK|PP[D /s E6E;c  # URVs0sHo3RU_M nn[URVs/sHo3RPM snS/S9n/nSnSn[R"5n [ UR 5IShvN UR Xx5(aURUUUUUS9IShvN n [U [5(a*URU UUS9IShvN sSSS5IShvN $URU 5 [U 5S:XaCU Sn URU 5n U b*URU UUS9IShvN sSSS5IShvN $US- n[R"5U - nUR Xx5(aMURR"UR U40UD6n URU UUS9IShvN sSSS5IShvN $s snfs snfGNiGN;GNGNNNN/N!!,IShvN (df  g=f!["[$R"4aE URR"UR U40UD6n URU UUS9IShvN s$f=f7f)z.Async run text through and get agent response.rrrrNrr)rkrr$rr0rrr rxrrrr6rrrdrb TimeoutErrorr rs r;_acallAgentExecutor._acall{s9= C IItO C)#':: .:4YY: .$I =?  YY[ 4 &t'>'>??++JEE-1-B-B(%*$/ .C.($""2K@@%)]],.(3&3& @?? '--.>?+,1+;A+>(&*&;&;JJJcPUup#URVs0sHoDRU_M nnSn[URR5S:aURRSnUR U;a*XRR R (a [Xc0S5$gs snf)z&Check if the tool is a returning tool.r7rr\N)rkrr6rr<rrr)r:rrrNrrreturn_value_keys r;rAgentExecutor._get_tool_returns %5! 8< C IItO C# t!!// 01 4#11??B    !1 1 !2!23AA!/ DsB#c[UR[5(aURS:aXR*S$[UR5(aURU5$U$)Nr)rxrintr)r:rGs r;r)AgentExecutor._prepare_intermediate_stepssc t33S 9 9,,q0%'C'C&C&EF F D00 1 1//0BC C!!r?c +# [U5n[UUURS54URS5URS5URS5URS5SS.UD6nUShvN gN7f) zEnables streaming over steps taken to reach final output. Args: input: Input to the agent. config: Config to use. kwargs: Additional arguments. Yields: AddableDict: Addable dictionary. rHrmetadatarun_namerun_idTrr/r0r1 yield_actionsNr!r+r)r:inputrrIiterators r;rAgentExecutor.streams|"v&(   JJ{ #  F#ZZ +ZZ +::h'    sA0A:2A83A:c  # [U5n[UUURS54URS5URS5URS5URS5SS.UD6nUShvN nU7v MN g7f) zAsync enables streaming over steps taken to reach final output. Args: input: Input to the agent. config: Config to use. kwargs: Additional arguments. Yields: AddableDict: Addable dictionary. rHrr/r0r1Tr2Nr4)r:r5rrIr6steps r;rAgentExecutor.astreams$v&(   JJ{ #  F#ZZ +ZZ +::h'    # $J (s*A-B/B3A?4B7B?BBr8rA) rrrkrrHrrIr rrr)rr}rr )rz2Union[BaseSingleActionAgent, BaseMultiActionAgent]r) rr rHrrrrrrr+r)rryrr#)rr,rfloatrr)r7rrGr"r$Optional[CallbackManagerForChainRun]rdict[str, Any])r7rrGr"r)Optional[AsyncCallbackManagerForChainRun]rr=)rNextStepOutputr1Union[AgentFinish, list[tuple[AgentAction, str]]]) rdict[str, BaseTool]rdict[str, str]rrBrGrrr<rr@) rrArrBrrBrGrrr<rz4Iterator[Union[AgentFinish, AgentAction, AgentStep]]) rrArrBrrrr<rr) rrArrBrrBrGrrr>rr@) rrArrBrrBrGrrr>rz9AsyncIterator[Union[AgentFinish, AgentAction, AgentStep]]) rrArrBrrrr>rr)rrBrr<rr=)rrBrr>rrB)rztuple[AgentAction, str]rzOptional[AgentFinish])rGrrr)r5Union[dict[str, Any], Any]rOptional[RunnableConfig]rIr rzIterator[AddableDict])r5rCrrDrIr rzAsyncIterator[AddableDict])+rrrrrr rrrrbrrrrr'rrrrrrrrVrrrrrrrrrr r rr"r&rrr)rrrr8r?r;rrs$ 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__rr builtinsrrloggingrabcrcollections.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 getLoggerrrir2rrr"rrrrr9rr?rrr8r?r;rdsK"  == *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?