tj!*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$ef d%Z4dS)&N)Sequence)Any) deprecated) AgentAction)BaseCallbackManager)BaseLanguageModel)BasePromptTemplate)ChatPromptTemplateHumanMessagePromptTemplateSystemMessagePromptTemplate)RunnableRunnablePassthrough)BaseTool) ToolsRenderer)Field)override)AgentAgentOutputParserformat_log_to_str)JSONAgentOutputParser)%StructuredChatOutputParserWithRetries)FORMAT_INSTRUCTIONSPREFIXSUFFIX)LLMChain) render_text_description_and_argsz{input} {agent_scratchpad}z0.1.0create_structured_chat_agentz2.0.0) alternativeremovalceZdZUdZeeZeed< e de fdZ e de fdZ de eee fde ffd Zed eedd fd Zee dd ed zd edefdZe ede e fdZeeeeeed d fd eede de de de de e d zde ed zdefdZed d eeeed d fd ed eede d zded zde de de de de e d zde ed zd ede!fdZ"e de fdZ#xZ$S)StructuredChatAgentzStructured Chat Agent.)default_factory output_parserreturncdS)z&Prefix to append the observation with.z Observation: selfs k/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_classic/agents/structured_chat/base.pyobservation_prefixz&StructuredChatAgent.observation_prefix/s cdS)z#Prefix to append the llm call with.zThought:r'r(s r* llm_prefixzStructuredChatAgent.llm_prefix4s zr,intermediate_stepsct|}t|tsd}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_scratchpad9sl!77889KLL*C00 ">CS// !  D1ADD   r,toolsNcdSNr')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=#FFFFr,cdgS)Nz Observation:r'r(s r*_stopzStructuredChatAgent._stopVs r,prefixsuffixhuman_message_templateformat_instructionsinput_variablesmemory_promptsc g}|D]e} tjddtjddt| j} || jd| jd| fd|} dd |D} || }|d | d |d |} |d d g}|pg}tj | g|tj |}t||S)N}z}}{z{{z: z, args:  , cg|] }|j Sr'name.0tools r* z5StructuredChatAgent.create_prompt..ls<<promptricg|] }|j Sr'rRrTs r*rWz:StructuredChatAgent.from_llm_and_tools..s222Ddi222r,rA) llm_chain allowed_toolsr$r')r=rhrrC)r<r>r9rir$rFrGrHrIrJrKr?rkrmrX_output_parsers r*from_llm_and_toolsz&StructuredChatAgent.from_llm_and_toolsys E""""" #9 3+)#  -   32E222 &Q#*H*HS*H*Q*Qs $(      r,ctr;)r5r(s r* _agent_typezStructuredChatAgent._agent_typesr,r;)%__name__ __module__ __qualname____doc__rrr$r__annotations__propertyr4r+r.listtuplerr2 classmethodrrr=rrrrCrErrHUMAN_MESSAGE_TEMPLATErr rhrrrprr __classcell__)r8s@r*r"r"&s^  ',u=(((M$' CXCX  {C'7!89         HX$6 4   [  )-GG  %GG  GGGX[G  tCy   XX  &<#6,0:>VV!VV V !$ V ! VcT)V/047V VVVX[V8 8<26&<#6,0:>% % % !% .4 % )4/ %  % % !$% !% cT)% /047% %  % % % [% NSXr,r"T) stop_sequencer>r9rktools_rendererr~r%chd|jt|jz}|rd|}t |||t|dd|D}|r |durdgn|}||}n|}tj d |z|ztzS) 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), ] ) ``` >r9rXr6z#Prompt missing required variables: rPcg|] }|j Sr'rR)rUts r*rWz0create_structured_chat_agent...s444af444r,)r9rXTz Observation)stopc,t|dS)Nr/r)xs r*z.create_structured_chat_agent..8s'8;O9P'Q'Qr,)r6) differencerJrypartial_variablesr5partialr`bindrassignr) r>r9rkrr~ missing_varsr7r llm_with_stops r*rrs#~?>>IIf&>!?!??LBLBBoo ^^nT%[[))9944e44455F$1T$9$9  }d++   "QQ         ! !  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