a ag@sdZddlmZddlZddlZddlZddlZddlmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZmZmZddlZddlmZddlmZmZmZdd lmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&dd l'm(Z(dd l)m*Z*dd l+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4m5Z5m6Z6ddl7m8Z8ddl9m:Z:ddl;mZ>m?Z?m@Z@ddlAmBZBddlCmDZDddlEmFZFddlGmHZHddlImJZJddlKmLZLddlMmNZNddlOmPZPeQeRZSGddde>ZTGd d!d!e>ZUGd"d#d#e,eeefZVGd$d%d%e,eeeefZWGd&d'd'eTZXGd(d)d)eUZYed*eDd+d,Gd-d.d.eTZZed*eDd+d,Gd/d0d0eTZ[Gd1d2d2e:Z\eeeeefZ]eeXeYfZ^Gd3d4d4eLZ_dS)5zEChain that takes in an input and produces an action and action input.) annotationsN)abstractmethod)Path) Any AsyncIteratorCallableDictIteratorListOptionalSequenceTupleUnioncast) deprecated) AgentAction AgentFinish AgentStep)AsyncCallbackManagerForChainRunAsyncCallbackManagerForToolRunBaseCallbackManagerCallbackManagerForChainRunCallbackManagerForToolRun Callbacks)OutputParserException)BaseLanguageModel) BaseMessage)BaseOutputParser)BasePromptTemplate)FewShotPromptTemplate)PromptTemplate)RunnableRunnableConfig ensure_config) AddableDict)BaseTool)get_color_mapping) BaseModel ConfigDictmodel_validator)Self)AGENT_DEPRECATION_WARNING)AgentExecutorIterator) AgentType) InvalidTool)Chain)LLMChain)asyncio_timeoutcseZdZdZeddddZddddZed-d d d d dddZed.d d d d dddZ eeddddZ dd d ddddZ e d/dddd ddddZ eddd d!Zd d"d#fd$d% Zd&d'd(d)d*Zd"dd+d,ZZS)0BaseSingleActionAgentzBase Single Action Agent class. List[str]returncCsdgSReturn values of the agent.outputselfr9r9d/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/agents/agent.py return_values@sz#BaseSingleActionAgent.return_valuesOptional[List[str]]cCsdSNr9r:r9r9r<get_allowed_toolsEsz'BaseSingleActionAgent.get_allowed_toolsNList[Tuple[AgentAction, str]]rrUnion[AgentAction, AgentFinish]intermediate_steps callbackskwargsr5cKsdS)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. Nr9r;rDrErFr9r9r<planHszBaseSingleActionAgent.plancsdS)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. Nr9rHr9r9r<aplan[szBaseSingleActionAgent.aplancCsdS7Return the input keys. :meta private: Nr9r:r9r9r< input_keysnsz BaseSingleActionAgent.input_keysstrrearly_stopping_methodrDrFr5cKs*|dkrtddidStd|ddS)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. forcer83Agent stopped due to iteration limit or time limit.'Got unsupported early_stopping_method ``Nr ValueErrorr;rQrDrFr9r9r<return_stopped_responsevs z-BaseSingleActionAgent.return_stopped_responserSequence[BaseTool]Optional[BaseCallbackManager])llmtoolscallback_managerrFr5cKstdS)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. NNotImplementedError)clsr^r_r`rFr9r9r<from_llm_and_toolssz(BaseSingleActionAgent.from_llm_and_toolscCstdSz#Return Identifier of an agent type.Nrar:r9r9r< _agent_typesz!BaseSingleActionAgent._agent_typerrFr5c sZt}z |j}Wnty*d}Yn0t|trFt|j|d<n|durV||d<|S)z{Return dictionary representation of agent. Returns: Dict: Dictionary representation of agent. N_type)super model_dumprfrb isinstancer-rOvalue)r;rF_dictrh __class__r9r<dicts     zBaseSingleActionAgent.dictUnion[Path, str]None file_pathr5cCst|trt|}n|}|j}|jddd|}d|vrLtd|d|jdkrt|d }t j ||dd Wd q1s0YnV|j d rt|d }t j ||d d Wd q1s0Ynt |dd S)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_okrhAgent z does not support saving.jsonwindentNz.yamlz.ymlFZdefault_flow_style must be json or yaml)rkrOrparentmkdirrprbsuffixopenjsondumpendswithyamlrY)r;rt save_pathdirectory_path agent_dictfr9r9r<saves    0  0zBaseSingleActionAgent.savecCsiS#Return logging kwargs for tool run.r9r:r9r9r<tool_run_logging_kwargssz-BaseSingleActionAgent.tool_run_logging_kwargs)N)N)N)__name__ __module__ __qualname____doc__propertyr=r@rrIrKrNr[ classmethodrdrfrprr __classcell__r9r9rnr<r2=s,#r2cseZdZdZeddddZddddZed'd d d d dddZed(d d d d dddZ eeddddZ dd d ddddZ eddddZ d ddfdd Z d d!d"d#d$Zddd%d&ZZS))BaseMultiActionAgentzBase Multi Action Agent class.r3r4cCsdgSr6r9r:r9r9r<r=sz"BaseMultiActionAgent.return_valuesr>cCsdS)z]Get allowed tools. Returns: Optional[List[str]]: Allowed tools. Nr9r:r9r9r<r@sz&BaseMultiActionAgent.get_allowed_toolsNrArr%Union[List[AgentAction], AgentFinish]rCcKsdS)a5Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Actions specifying what tool to use. Nr9rHr9r9r<rIszBaseMultiActionAgent.plancsdS)a;Async given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with the observations. callbacks: Callbacks to run. **kwargs: User inputs. Returns: Actions specifying what tool to use. Nr9rHr9r9r<rK szBaseMultiActionAgent.aplancCsdSrLr9r:r9r9r<rNszBaseMultiActionAgent.input_keysrOrrPcKs*|dkrtddidStd|ddS)rRrSr8z$Agent stopped due to max iterations.rUrVrWNrXrZr9r9r<r[%s  z,BaseMultiActionAgent.return_stopped_responsecCstdSrerar:r9r9r<rfAsz BaseMultiActionAgent._agent_typerrgc s4t}zt|j|d<Wnty.Yn0|S)*Return dictionary representation of agent.rh)rirjrOrfrbr;rFrmrnr9r<rpFs   zBaseMultiActionAgent.dictrqrrrscCst|trt|}n|}|}d|vr8td|d|j}|jddd|jdkrt|d }t j ||dd Wd q1s0YnV|j d rt|d }t j ||d d Wd q1s0Ynt |dd S)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") rhrxz does not support saving.Truryrzr{r|Nr~Frr)rkrOrrprbrrrrrrrrrY)r;rtrrrrr9r9r<rOs    0  0zBaseMultiActionAgent.savecCsiSrr9r:r9r9r<rvsz,BaseMultiActionAgent.tool_run_logging_kwargs)N)N)rrrrrr=r@rrIrKrNr[rfrprrrr9r9rnr<rs& 'rc@s$eZdZdZedddddZdS)AgentOutputParserz=Base class for parsing agent output into agent action/finish.rOrBtextr5cCsdS)z$Parse text into agent action/finish.Nr9r;rr9r9r<parseszAgentOutputParser.parseNrrrrrrr9r9r9r<r|src@s$eZdZdZedddddZdS)MultiActionAgentOutputParserzBase class for parsing agent output into agent actions/finish. This is used for agents that can return multiple actions. rOrrcCsdS)zParse text into agent actions/finish. Args: text: Text to parse. Returns: Union[List[AgentAction], AgentFinish]: List of agent actions or agent finish. Nr9rr9r9r<rsz"MultiActionAgentOutputParser.parseNrr9r9r9r<rsrc@seZdZUdZded<gZded<gZded<dZded <edd Z e dd d d Z e dd ddZ ddddddddZ ddddddddZdS) RunnableAgentAgent powered by Runnables.z/Runnable[dict, Union[AgentAction, AgentFinish]]runnabler3input_keys_argreturn_keys_argTboolstream_runnableZarbitrary_types_allowedr4cCs|jSr7rr:r9r9r<r=szRunnableAgent.return_valuescCs|jS)zReturn the input keys.rr:r9r9r<rNszRunnableAgent.input_keysNrArrrBrCcKsdi|d|i}d}|jrL|jj|d|idD]}|dur@|}q.||7}q.n|jj|d|id}|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. rDNrEconfigrrstreamZinvoker;rDrErFinputs final_outputchunkr9r9r<rIs zRunnableAgent.plancsti|d|i}d}|jrV|jj|d|id2z"3dHW}|durH|}q.||7}q.6n|jj|d|idIdH}|SaRAsync 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. rDNrErrrastreamZainvokerr9r9r<rKs zRunnableAgent.aplan)N)Nrrrr__annotations__rrrr(Z model_configrr=rNrIrKr9r9r9r<rs    'rc@seZdZUdZded<gZded<gZded<dZded <edd Z e dd d d Z e dd ddZ ddddddddZ ddddddddZdS)RunnableMultiActionAgentrz5Runnable[dict, Union[List[AgentAction], AgentFinish]]rr3rrTrrrr4cCs|jSrrr:r9r9r<r=sz&RunnableMultiActionAgent.return_valuescCs|jS)QReturn the input keys. Returns: List of input keys. rr:r9r9r<rNsz#RunnableMultiActionAgent.input_keysNrArrrrCcKsdi|d|i}d}|jrL|jj|d|idD]}|dur@|}q.||7}q.n|jj|d|id}|Srrrr9r9r<rI(s zRunnableMultiActionAgent.plancsti|d|i}d}|jrV|jj|d|id2z"3dHW}|durH|}q.||7}q.6n|jj|d|idIdH}|Srrrr9r9r<rKOs zRunnableMultiActionAgent.aplan)N)Nrr9r9r9r<rs     *rz0.1.0z1.0)messageZremovalcseZdZUdZded<ded<ded<eddd d Zd d d fdd Zdddd ddddZdddd ddddZ d dddZ Z S)LLMSingleActionAgentz$Base class for single action agents.r0 llm_chainr output_parserr3stopr4cCstt|jjdhS)rrDlistsetrrNr:r9r9r<rNszLLMSingleActionAgent.input_keysrrrgc st}|d=|Srrrirprrnr9r<rps zLLMSingleActionAgent.dictNrArrBrCcKs(|jjf||j|d|}|j|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. rDrrE)rrunrrrr;rDrErFr8r9r9r<rIszLLMSingleActionAgent.plancs.|jjf||j|d|IdH}|j|S)rJrN)rarunrrrrr9r9r<rKs zLLMSingleActionAgent.aplancCs"dt|jdkrdn|jddS)rrUr llm_prefixobservation_prefix)lenrr:r9r9r<rsz,LLMSingleActionAgent.tool_run_logging_kwargs)N)N) rrrrrrrNrprIrKrrr9r9rnr<r{s  rc seZdZUdZded<ded<dZded<d d d fd d ZddddZeddddZ dddddZ eddddZ dddddZ dKddd d d!d"d#Z dLddd d d!d$d%Zdd d&d'd(d)Zeddd*d+Zed,d-d.dd/d0Zeeddd1d2Zeeddd3d4Zeed5d6d7d8d9Zed5d:d7d;d<Zeed dd d=d>ZedMd?d5d@dAd ddBdCdDZddd dEdFdGdHZd ddIdJZZS)NAgentzAgent 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. r0rrrNr> allowed_toolsrrrgc st}|d=|Srrrrnr9r<rps z Agent.dictr4cCs|jS)zGet allowed tools.)rr:r9r9r<r@szAgent.get_allowed_toolsr3cCsdgSr6r9r:r9r9r<r=szAgent.return_valuesrOrcCs tddS)zsFix the text. Args: text: Text to fix. Returns: str: Fixed text. z(fix_text not implemented for this agent.NrYrr9r9r< _fix_texts zAgent._fix_textcCs d|jd|jgS)N z )rrstripr:r9r9r<_stopsz Agent._stoprAzUnion[str, List[BaseMessage]]rDr5cCs<d}|D].\}}||j7}|d|j|d|j7}q|S)zJConstruct the scratchpad that lets the agent continue its thought process.rUr)logrr)r;rDthoughtsaction observationr9r9r<_construct_scratchpads   zAgent._construct_scratchpadrrBrCcKs4|j|fi|}|jjfd|i|}|j|S)rGrE)get_full_inputsrpredictrr)r;rDrErF full_inputs full_outputr9r9r<rIsz Agent.plancsD|j|fi|}|jjfd|i|IdH}|j|IdH}|S)rJrEN)rrZapredictrZaparse)r;rDrErFrrZ agent_outputr9r9r<rK'sz Agent.aplanDict[str, Any])rDrFr5cKs&||}||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)rr)r;rDrFr new_inputsrr9r9r<r=s   zAgent.get_full_inputscCstt|jjdhS)rMrrr:r9r9r<rNOszAgent.input_keysaftermoder*cCsr|jj}d|jvrntd|jdt|trB|jd7_n,t|t r\|j d7_ nt dt ||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. rzl`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 ) rpromptZinput_variablesloggerwarningappendrkr templaterrrYtype)r;rr9r9r<validate_promptWs    zAgent.validate_promptcCsdS)z&Prefix to append the observation with.Nr9r:r9r9r<rtszAgent.observation_prefixcCsdS)z#Prefix to append the LLM call with.Nr9r:r9r9r<ryszAgent.llm_prefixr\r)r_r5cCsdS)zCreate a prompt for this class. Args: tools: Tools to use. Returns: BasePromptTemplate: Prompt template. Nr9rcr_r9r9r< create_prompt~szAgent.create_promptrrcCsdS)zgValidate that appropriate tools are passed in. Args: tools: Tools to use. Nr9rr9r9r<_validate_toolsszAgent._validate_toolscKsdS)z)Get default output parser for this class.Nr9)rcrFr9r9r<_get_default_output_parsersz Agent._get_default_output_parserrr]zOptional[AgentOutputParser])r^r_r`rrFr5c KsN||t||||d}dd|D}|p6|}|f|||d|S)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. )r^rr`cSsg|] }|jqSr9name.0toolr9r9r< z,Agent.from_llm_and_tools..)rrr)rr0rr) rcr^r_r`rrFrZ tool_namesZ_output_parserr9r9r<rds  zAgent.from_llm_and_toolsrrPc Ks|dkrtddidS|dkrd}|D].\}}||j7}|d|j|d|j7}q&|d7}||jd}i||}|jjfi|} |j| } t | tr| Std| i| Snt d |d S) 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']. rSr8rTrUgeneraterzB I now need to return a final answer based on the previous steps:rzBearly_stopping_method should be one of `force` or `generate`, got N) rrrrrrrrrrkrY) r;rQrDrFrrrrrrZ parsed_outputr9r9r<r[s4      zAgent.return_stopped_responsecCs|j|jdS)rrrr:r9r9r<rszAgent.tool_run_logging_kwargs)N)N)NN)rrrrrrrpr@rr=rrrrIrKrrNr)rrrrrrrrrdr[rrr9r9rnr<rsR      #7rc@sReZdZUdZdZded<dZded<ddddd d d Zddd dd d dZdS) ExceptionToolz!Tool that just returns the query. _ExceptionrOrzException tool descriptionNz#Optional[CallbackManagerForToolRun])query run_managerr5cCs|Sr?r9r;rrr9r9r<_runszExceptionTool._runz(Optional[AsyncCallbackManagerForToolRun]cs|Sr?r9rr9r9r<_arun szExceptionTool._arun)N)N) rrrrrrrrrr9r9r9r<rs    rc@seZdZUdZded<ded<dZded<d Zd ed <d Zd ed<dZded<dZ ded<dZ ded<e d}ddddddddZ e ddddd d!Ze d"de d#dd$d%d&Zed'dd(d)Zd*d+d,d-d.Zd*d+d,d/d0Zd~ddd1ddddd2d3d4d5Zed6dd7d8Zed6dd9d:Zdd;d<d=d>Zd?d@ddAdBdCZddDdEdFdGdHdIdJZddDdEdKdGdHdLdMZdNdOd$dPdQZddRdSdSdTdFdOdUdVdWZddRdSdSdTdFdXdUdYdZZddRdSd[dFd\d]d^d_ZddRdSdSdTdKdOdUd`daZddRdSdSdTdKdbdUdcddZ ddRdSd[dKd\d]dedfZ!ddSdFdGdgdhdiZ"ddSdKdSdgdjdkZ#dldmdndodpZ$dTdTdqdrdsZ%ddtduddvdwdxdyZ&ddtduddzdwd{d|Z'd S) AgentExecutorzAgent that is using tools.z.zAllowed tools (z!) different than provided tools (cSsg|] }|jqSr9rrr9r9r<rur))rr_r@rrY)r;rr_rr9r9r<validate_toolsas  zAgentExecutor.validate_toolsbeforer)valuesr5c Cs|d}|rt|trz |j}Wn(tyJ}zd}WYd}~nd}~00|ttttfk}| dd}|rt ||d|d<nt ||d|d<|S)zConvert runnable to agent if passed in. Args: values: Values to validate. Returns: Dict: Validated values. rFNrT)rr) getrkr!Z OutputType Exceptionrr rrpoprr)rcrr output_type_Z multi_actionrr9r9r<validate_runnable_agentys     z%AgentExecutor.validate_runnable_agentz2Union[BaseSingleActionAgent, BaseMultiActionAgent]cCs"t|jtrtt|jS|jSdS)aCType cast self.agent. If the `agent` attribute is a Runnable, it will be converted one of RunnableAgentType in the validate_runnable_agent root_validator. To support instantiating with a Runnable, here we explicitly cast the type to reflect the changes made in the root_validator. N)rkrr!rRunnableAgentTyper:r9r9r< _action_agents  zAgentExecutor._action_agentrqrrrscCs tddS)zRaise error - saving not supported for Agent Executors. Args: file_path: Path to save to. Raises: ValueError: Saving not supported for agent executors. zpSaving not supported for agent executors. If you are trying to save the agent, please use the `.save_agent(...)`Nrr;rtr9r9r<rs zAgentExecutor.savecCs |j|S)zZSave the underlying agent. Args: file_path: Path to save to. )rrrr9r9r< save_agentszAgentExecutor.save_agent)include_run_infoasync_r,)rrErrr5cCst||||j|dS)agEnables iteration over steps taken to reach final output. Args: inputs: Inputs to the agent. callbacks: Callbacks to run. include_run_info: Whether to include run info. async_: Whether to run async. (Ignored) Returns: AgentExecutorIterator: Agent executor iterator object. )tagsr)r,r)r;rrErrr9r9r<iterszAgentExecutor.iterr3cCs|jjS)rM)rrNr:r9r9r<rNszAgentExecutor.input_keyscCs |jr|jjdgS|jjSdS)z@Return the singular output key. :meta private: rDN)rrr=r:r9r9r< output_keysszAgentExecutor.output_keysr%)rr5cCsdd|jD|S)zLookup tool by name. Args: name: Name of tool. Returns: BaseTool: Tool object. cSsi|] }|j|qSr9rrr9r9r< rz-AgentExecutor.lookup_tool..)r_)r;rr9r9r< lookup_tools zAgentExecutor.lookup_toolintfloat) iterations time_elapsedr5cCs4|jdur||jkrdS|jdur0||jkr0dSdS)NFT)rr)r;r$r%r9r9r<_should_continueszAgentExecutor._should_continuerrz$Optional[CallbackManagerForChainRun]r)r8rDrr5cCs.|r|j|d|jd|j}|jr*||d<|SNgreen)colorverboserDZon_agent_finishr*r=rr;r8rDrrr9r9r<_returns zAgentExecutor._returnz)Optional[AsyncCallbackManagerForChainRun]cs4|r|j|d|jdIdH|j}|jr0||d<|Sr'r+r,r9r9r<_areturns zAgentExecutor._areturnNextStepOutputz1Union[AgentFinish, List[Tuple[AgentAction, str]]]cCs8t|dtr&t|dksJ|dSdd|DSdS)Nr cSs"g|]}t|tr|j|jfqSr9)rkrrrrar9r9r<r%sz4AgentExecutor._consume_next_step..)rkrr)r;rr9r9r<_consume_next_steps z AgentExecutor._consume_next_stepzDict[str, BaseTool]zDict[str, str]rA)name_to_tool_map color_mappingrrDrr5c Cs"|dd||||||DS)NcSsg|]}|qSr9r9r1r9r9r<r2sz1AgentExecutor._take_next_step..)r3_iter_next_stepr;r4r5rrDrr9r9r<_take_next_step)szAgentExecutor._take_next_stepz4Iterator[Union[AgentFinish, AgentAction, AgentStep]]c csz4||}|jj|fd|r$|ndi|}Wn&tyZ}z t|jtr^|j }nd}|rxtdt |t |} t|jtr|j rt |j } t |j } qd} n2t|jt r|j} nt |jr||} ntdtd| | }|r|j|dd |j} tj|jf|jd|r(|ndd | } t|| d VWYd}~dSd}~00t|trr|VdSt|tr|g} n|} | D] } | Vq| D]} |||| |VqdS) Take a single step in the thought-action-observation loop. Override this to take control of how the agent makes and acts on choices. rENFAn output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Invalid or incomplete response.Got unexpected type of `handle_parsing_errors`rr(r)r*r)rErr)_prepare_intermediate_stepsrrI get_childrrkr rrYrO send_to_llmr llm_outputcallableron_agent_actionrrr tool_inputr*rr_perform_agent_action)r;r4r5rrDrr8e raise_errorrrtool_run_kwargsactions agent_actionr9r9r<r6>sr                zAgentExecutor._iter_next_steprr)r4r5rLrr5c Cs|r|j|dd|j|vrx||j}|j}||j}|j}|rLd|d<|j|jf|j||rh|ndd|} nD|j}t j|jt | df|jd|r|ndd|} t || dS)Nr(r=rUrr>Zrequested_tool_nameZavailable_tool_namesr?) rEr return_directrrrrFr*rAr.rkeysr r;r4r5rLrrrNr)rJrr9r9r<rGs>       z#AgentExecutor._perform_agent_actionc s(|dd||||||2IdHS)Ncsg|z3dHW}|q6Sr?r9r1r9r9r<rsz2AgentExecutor._atake_next_step..)r3_aiter_next_stepr7r9r9r<_atake_next_steps zAgentExecutor._atake_next_stepz9AsyncIterator[Union[AgentFinish, AgentAction, AgentStep]]c sz:|}jj|fdr$ndi|IdH}WntyT}ztjtrdj }nd}|r~tdt |t |} tjtr|j rt |j } t |j } qd} n2tjt rȈj} nt jrވ|} ntdtd| | }j} tj|jfjdrndd| IdH} t|| d VWYd}~dSd}~00t|trl|VdSt|tr|g} n|} | D] } | Vqtjfd d | DIdH}|D] }|VqdS) r9rENFr:r;r<rr>r?csg|]}|qSr9)_aperform_agent_action)rrLr5r4rr;r9r<rsz2AgentExecutor._aiter_next_step..)r@rrKrArrkr rrYrOrBrrCrDrrrrrFr*rrasynciogather)r;r4r5rrDrr8rHrIrrrJrKrLresultrr9rTr<rQst                 zAgentExecutor._aiter_next_stepc s|r|j||jddIdH|j|vr||j}|j}||j}|j}|rVd|d<|j|jf|j||rr|ndd|IdH} nJ|j}t j|jt | df|jd|r|ndd|IdH} t || dS)Nr()r*r)rUrr>rMr?) rEr*rrNrrrrFrAr.rrOrrPr9r9r<rSsB        z$AgentExecutor._aperform_agent_action)rrr5c Csdd|jD}tdd|jDddgd}g}d}d }t}|||r|j|||||d } t| trz|j| ||d S|| t | d kr| d} | | } | d ur|j| ||d S|d 7}t|}q@|j j |j |fi|} |j| ||d S) z(Run text through and get agent response.cSsi|] }|j|qSr9rrr9r9r<r Lrz'AgentExecutor._call..cSsg|] }|jqSr9rrr9r9r<rOrz'AgentExecutor._call..r(redZexcluded_colorsrrr0N)r_r&timer&r8rkrr-extendr_get_tool_returnrr[rQ r;rrr4r5rDr$r% start_timenext_step_outputZnext_step_actionZ tool_returnr8r9r9r<_callEsH     zAgentExecutor._callc sdd|jD}tdd|jDdgd}g}d}d}t}zJt|j4Id H|||r,|j|||||d Id H} t| tr|j | ||d Id HWd Id HWS| | t | d kr| d} | | } | d ur|j | ||d Id HWd Id HWS|d 7}t|}qX|j j|j|fi|} |j | ||d Id HWd Id HWS1Id Hs~0YWnHttjfy|j j|j|fi|} |j | ||d Id HYS0d S) z.Async run text through and get agent response.cSsi|] }|j|qSr9rrr9r9r<r {rz(AgentExecutor._acall..cSsg|] }|jqSr9rrr9r9r<r~rz(AgentExecutor._acall..r(rYrrZNr[r0)r_r&r\r1rr&rRrkrr.r]rr^rr[rQ TimeoutErrorrUr_r9r9r<_acalltsf  "   ">zAgentExecutor._acallzTuple[AgentAction, str]zOptional[AgentFinish])rar5cCs`|\}}dd|jD}d}t|jjdkr8|jjd}|j|vr\||jjr\t||idSdS)z&Check if the tool is a returning tool.cSsi|] }|j|qSr9rrr9r9r<r rz2AgentExecutor._get_tool_return..r8rrUN)r_rrr=rrNr)r;rarLrr4Zreturn_value_keyr9r9r<r^s   zAgentExecutor._get_tool_returnrcCsBt|jtr&|jdkr&||j dSt|jr:||S|SdS)Nr)rkr r"rD)r;rDr9r9r<r@s   z)AgentExecutor._prepare_intermediate_stepszUnion[Dict[str, Any], Any]zOptional[RunnableConfig]zIterator[AddableDict])inputrrFr5cksZt|}t|||df|d|d|d|ddd|}|D] }|VqJdS) aEnables 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. rErmetadatarun_namerun_idTrrfrgrhZ yield_actionsNr#r,rr;rerrFiteratorstepr9r9r<rs   zAgentExecutor.streamzAsyncIterator[AddableDict]cKsdt|}t|||df|d|d|d|ddd|}|2z3dHW}|VqJ6dS) a Async 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. rErrfrgrhTriNrjrkr9r9r<rs   zAgentExecutor.astream)N)N)N)N)N)N)N)N)N)N)N)N)N)N)(rrrrrrrrrQr r rr r)rrrrrrrrNrr!r&r-r.r3r8r6rGrRrQrSrbrdr^r@rrr9r9r9r<rsz          R-Z,2>"r)`r __future__rrUrloggingr\abcrpathlibrtypingrrrrr r r r r rrrZlangchain_core._apirZlangchain_core.agentsrrrZlangchain_core.callbacksrrrrrrZlangchain_core.exceptionsrZlangchain_core.language_modelsrZlangchain_core.messagesrZlangchain_core.output_parsersrZlangchain_core.promptsrZlangchain_core.prompts.few_shotrZlangchain_core.prompts.promptr Zlangchain_core.runnablesr!r"r#Zlangchain_core.runnables.utilsr$Zlangchain_core.toolsr%Zlangchain_core.utils.inputr&Zpydanticr'r(r)Ztyping_extensionsr*Zlangchain._api.deprecationr+Zlangchain.agents.agent_iteratorr,Zlangchain.agents.agent_typesr-Zlangchain.agents.toolsr.Zlangchain.chains.baser/Zlangchain.chains.llmr0Zlangchain.utilities.asyncior1 getLoggerrrr2rrrrrrrrr/rrr9r9r9r<sz   4                     + muS&