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Sr/\"SSSS9"SS\55r0\.4SS.S\S\\S \S!\S"\\1\2\34S#\4 S$jjjr4g)%N)Sequence)AnyOptionalUnion) deprecated) AgentAction)BaseCallbackManager)BaseLanguageModel)BasePromptTemplate)ChatPromptTemplateHumanMessagePromptTemplateSystemMessagePromptTemplate)RunnableRunnablePassthrough)BaseTool) ToolsRenderer)Field)AgentAgentOutputParserformat_log_to_str)JSONAgentOutputParser)%StructuredChatOutputParserWithRetries)FORMAT_INSTRUCTIONSPREFIXSUFFIX)LLMChain) render_text_description_and_argsz{input} {agent_scratchpad}z0.1.0create_structured_chat_agentz1.0) alternativeremovalc^\rSrSr%Sr\"\S9r\\ S'\ S\ 4Sj5r \ S\ 4Sj5r S\\\\ 4S\ 4U4S jjr\S \\SS 4S j5r\SS \\S\S\4Sjj5r\ S\\ 4Sj5r\\\\\S S 4S \\S\ S\ S\ S\ S\\\ S\\\S\4Sjj5r \S S \\\\S S 4S \S \\S\\!S\\S\ S\ S\ S\ S\\\ S\\\S\S\"4Sjj5r#\ S\ 4Sj5r$Sr%U=r&$)StructuredChatAgent!zStructured Chat Agent.)default_factory output_parserreturncg)z&Prefix to append the observation with.z Observation: selfs ]/var/www/html/shao/venv/lib/python3.13/site-packages/langchain/agents/structured_chat/base.pyobservation_prefix&StructuredChatAgent.observation_prefix*scg)z#Prefix to append the llm call with.zThought:r)r*s r, llm_prefixStructuredChatAgent.llm_prefix/sr/intermediate_stepsc>[TU]U5n[U[5(d Sn[ U5eU(aSU3$U$)Nz*agent_scratchpad should be of type string.zhThis was your previous work (but I haven't seen any of it! I only see what you return as final answer): )super_construct_scratchpad isinstancestr ValueError)r+r3agent_scratchpadmsg __class__s r,r6)StructuredChatAgent._construct_scratchpad4sT!789KL*C00>CS/ ! 11A0BD   r/toolsNcgNr))clsr>s r,_validate_tools#StructuredChatAgent._validate_toolsDs r/llmkwargsc *[R"US9$)NrD)rfrom_llm)rArDrEs r,_get_default_output_parser.StructuredChatAgent._get_default_output_parserHs 5==#FFr/cS/$)Nz Observation:r)r*s r,_stopStructuredChatAgent._stopPs r/prefixsuffixhuman_message_templateformat_instructionsinput_variablesmemory_promptsc @/nUHqn [R"SS[R"SS[U R555n UR U R SU R SU 35 Ms SRU5n SRUV s/sHoR PM sn 5n URU S 9nUS U S US U3n UcS S /nU=(d /n[R"U 5/UQ[R"U5Pn[XoS 9$s sn f)N}z}}{z{{z: z, args:  , ) tool_namesz inputr:)rRmessages) resubr8argsappendname descriptionjoinformatr from_templater r )rAr>rNrOrPrQrRrS tool_stringstool args_schemaformatted_toolsrYtemplate_memory_promptsr[s r, create_prompt!StructuredChatAgent.create_promptTs' D&&dBFF3c$))n,MNK   499+R0@0@/A+ W X))L1YYermr&rNrOrPrQrRrSrErorprfrY_output_parsers r,from_llm_and_tools&StructuredChatAgent.from_llm_and_toolsqs E""" #9 3+)# - -22EDiiE 2&Q#*H*HS*H*Q $(    3sA0c[er@)r9r*s r, _agent_typeStructuredChatAgent._agent_typesr/r)r@)'__name__ __module__ __qualname____firstlineno____doc__rrr&r__annotations__propertyr8r-r1listtuplerr6 classmethodrrrBrr rrIrLrrHUMAN_MESSAGE_TEMPLATErr rkr rrsrv__static_attributes__ __classcell__)r<s@r,r#r#!s~ ',=(M$' CC  {C'7!89    HX$6 4  ,0G ' (GG  GG tCy  &<#6/3=AV!VV V !$ V ! V"$s),V!&8!9:V VV8 ;?59&<#6/3=A% % !% ##67 %  12 %  % % !$% !% "$s),% !&8!9:% %  % % NSr/r#T) stop_sequencerDr>rotools_rendererrr'c 1SkRUR[UR5-5nU(aSU3n[ U5eUR U"[U55SR UVs/sHowRPM sn5S9nU(aUSLaS/OUnURUS9n OUn [R"SS 9U-U -[5-$s snf) aCreate an agent aimed at supporting tools with multiple inputs. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. stop_sequence: bool or list of str. If True, adds a stop token of "Observation:" to avoid hallucinates. If False, does not add a stop token. If a list of str, uses the provided list as the stop tokens. Default is True. You may to set this to False if the LLM you are using does not support stop sequences. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. Default is `render_text_description`. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an AgentAction or AgentFinish. Examples: .. code-block:: python from langchain import hub from langchain_community.chat_models import ChatOpenAI from langchain.agents import AgentExecutor, create_structured_chat_agent prompt = hub.pull("hwchase17/structured-chat-agent") model = ChatOpenAI() tools = ... agent = create_structured_chat_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Using with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", "chat_history": [ HumanMessage(content="hi! my name is bob"), AIMessage(content="Hello Bob! How can I assist you today?"), ], } ) Prompt: The prompt must have input keys: * `tools`: contains descriptions and arguments for each tool. * `tool_names`: contains all tool names. * `agent_scratchpad`: contains previous agent actions and tool outputs as a string. Here's an example: .. code-block:: python from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Respond to the human as helpfully and accurately as possible. You have access to the following tools: {tools} Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). Valid "action" values: "Final Answer" or {tool_names} Provide only ONE action per $JSON_BLOB, as shown: ``` {{ "action": $TOOL_NAME, "action_input": $INPUT }} ``` Follow this format: Question: input question to answer Thought: consider previous and subsequent steps Action: ``` $JSON_BLOB ``` Observation: action result ... (repeat Thought/Action/Observation N times) Thought: I know what to respond Action: ``` {{ "action": "Final Answer", "action_input": "Final response to human" }} Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation''' human = '''{input} {agent_scratchpad} (reminder to respond in a JSON blob no matter what)''' prompt = ChatPromptTemplate.from_messages( [ ("system", system), MessagesPlaceholder("chat_history", optional=True), ("human", human), ] ) >r>rYr:z#Prompt missing required variables: rX)r>rYTz Observation)stopc[US5$)Nr3r)xs r,.create_structured_chat_agent..+s'8;O9P'Qr/)r:) differencerRrpartial_variablesr9partialrbr`bindrassignr) rDr>rorr missing_varsr;tr llm_with_stops r,rrst?IIf&>&>!??L3L>Bo ^^T%[)99e4effe45F$1T$9 }d+   ""Q       !  "5s5C)5r\collections.abcrtypingrrrlangchain_core._apirlangchain_core.agentsrlangchain_core.callbacksr langchain_core.language_modelsr langchain_core.promptsr langchain_core.prompts.chatr r rlangchain_core.runnablesrrlangchain_core.toolsrlangchain_core.tools.renderrpydanticrlangchain.agents.agentrr"langchain.agents.format_scratchpadrlangchain.agents.output_parsersr.langchain.agents.structured_chat.output_parserr'langchain.agents.structured_chat.promptrrrlangchain.chains.llmrlangchain.tools.renderrrr#boolrr8rr)r/r,rs $''*-8<5 C)5;@AXW)C8 G!?Oy%yPy@%E R -1 R R H R R" R tCy) RRr/