3fi*ddlZddlmZddlmZddlmZddlmZddl m Z ddl m Z ddl mZdd lmZmZmZdd lmZmZdd lmZdd lmZdd lmZddlmZddlmZm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(m)Z)m*Z*ddl+m,Z,ddl-m.Z.dZ/edddGddeZ0e.fddde d eed!ed"ed#e1e2e3zd$ef d%Z4y)&N)Sequence)Any) deprecated) AgentAction)BaseCallbackManager)BaseLanguageModel)BasePromptTemplate)ChatPromptTemplateHumanMessagePromptTemplateSystemMessagePromptTemplate)RunnableRunnablePassthrough)BaseTool) ToolsRenderer)Field)override)AgentAgentOutputParserformat_log_to_str)JSONAgentOutputParser)%StructuredChatOutputParserWithRetries)FORMAT_INSTRUCTIONSPREFIXSUFFIX)LLMChain) render_text_description_and_argsz{input} {agent_scratchpad}z0.1.0create_structured_chat_agentz1.0) alternativeremovalceZdZUdZeeZeed< e de fdZ e de fdZ de eee fde ffd Zed eedd fd Zee dd ed zd edefdZe ede e fdZeeeeeed d fd eede de de de de e d zde ed zdefdZed d eeeed d fd ed eede d zded zde de de de de e d zde ed zd ede!fdZ"e de fdZ#xZ$S)StructuredChatAgentzStructured Chat Agent.)default_factory output_parserreturncy)z&Prefix to append the observation with.z Observation: selfs k/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_classic/agents/structured_chat/base.pyobservation_prefixz&StructuredChatAgent.observation_prefix/scy)z#Prefix to append the llm call with.zThought:r'r(s r* llm_prefixzStructuredChatAgent.llm_prefix4sr,intermediate_stepscnt||}t|ts d}t ||rd|S|S)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)r/agent_scratchpadmsg __class__s r*r2z)StructuredChatAgent._construct_scratchpad9sQ!789KL*C0>CS/ ! 11A0BD   r,toolsNcyNr')clsr9s r*_validate_toolsz#StructuredChatAgent._validate_toolsIs r,llmkwargsc .tj|S)Nr>)rfrom_llm)r<r>r?s r*_get_default_output_parserz.StructuredChatAgent._get_default_output_parserMs5==#FFr,cdgS)Nz Observation:r'r(s r*_stopzStructuredChatAgent._stopVs r,prefixsuffixhuman_message_templateformat_instructionsinput_variablesmemory_promptsc 4g}|D]n} tjddtjddt| j} |j | j d| j d| pdj|} dj|D cgc]} | j c} } |j| }|d | d |d |} |d d g}|xsg}tj| g|tj|}t|| Scc} w)N}z}}{z{{z: z, args:  , ) tool_namesz inputr6)rJmessages) resubr4argsappendname descriptionjoinformatr from_templater r )r<r9rFrGrHrIrJrK tool_stringstool args_schemaformatted_toolsrQtemplate_memory_promptsrSs r* create_promptz!StructuredChatAgent.create_prompt[s.  YD&&dBFF3c$))n,MNK   499+R0@0@/A+ W X Y))L1YYepromptrdrA) llm_chain allowed_toolsr$r')r=rcrrXrC)r<r>r9rdr$rFrGrHrIrJrKr?rfrgr^rQ_output_parsers r*from_llm_and_toolsz&StructuredChatAgent.from_llm_and_toolsys E""" #9 3+)# - -22Ddii2 2&Q#*H*HS*H*Q $(    3sA3ctr;)r5r(s r* _agent_typezStructuredChatAgent._agent_typesr,r;)%__name__ __module__ __qualname____doc__rrr$r__annotations__propertyr4r+r.listtuplerr2 classmethodrrr=rrrrCrErrHUMAN_MESSAGE_TEMPLATErr rcrrrjrl __classcell__)r8s@r*r"r"&s ',=(M$' CC  {C'7!89    HX$6 4   )-G  %GG  GG  tCy   &<#6,0:>V!VV V !$ V ! VcT)V/047V VV8 8<26&<#6,0:>% % !% .4 % )4/ %  % % !$% !% cT)% /047% %  % % NSr,r"T) stop_sequencer>r9rftools_rendererrxr%c hdj|jt|jz}|rd|}t ||j |t|dj |Dcgc]}|jc}}|r|durdgn|}|j|} n|} tjd |z| ztzScc}w) a Create 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. 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. 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: ```python from langchain_classic import hub from langchain_openai import ChatOpenAI from langchain_classic.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: ```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: ```txt {{ "action": $TOOL_NAME, "action_input": $INPUT }} ``` Follow this format: Question: input question to answer Thought: consider previous and subsequent steps Action: ``` $JSON_BLOB ``` Observation: action result ... (repeat Thought/Action/Observation N times) Thought: I know what to respond Action: ```txt {{ "action": "Final Answer", "action_input": "Final response to human" }} Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$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), ] ) ``` >r9rQr6z#Prompt missing required variables: rP)r9rQTz Observation)stopct|dS)Nr/r)xs r*z.create_structured_chat_agent..8s'8;O9P'Qr,)r6) differencerJrspartial_variablesr5partialrZrXbindrassignr) r>r9rfryrx missing_varsr7tr{ llm_with_stops r*rrs~?IIf&>&>!??L3L>Bo ^^T%[)99e4aff45F$1T$9 }d+   ""Q       !  "5s0C)5rTcollections.abcrtypingrlangchain_core._apirlangchain_core.agentsrlangchain_core.callbacksrlangchain_core.language_modelsrlangchain_core.promptsr langchain_core.prompts.chatr r r langchain_core.runnablesr rlangchain_core.toolsrlangchain_core.tools.renderrpydanticrtyping_extensionsrlangchain_classic.agents.agentrr*langchain_classic.agents.format_scratchpadr'langchain_classic.agents.output_parsersr6langchain_classic.agents.structured_chat.output_parserr/langchain_classic.agents.structured_chat.promptrrrlangchain_classic.chains.llmrlangchain_classic.tools.renderrrvr"boolrsr4rr'r,r*rs $*-8<5 C)5&CHI 2K8 G!?O|%|P|F%E W '+ W W H W W" W $s)# WWr,