o JjgO*@snddlZddlmZmZmZmZmZmZddlm Z ddl m Z ddl m Z ddlmZddlmZddlmZmZmZdd lmZmZdd lmZdd lmZdd lmZdd lm Z m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)m*Z*m+Z+ddl,m-Z-ddl.m/Z/dZ0e ddddGddde Z1e/fdddedeeded ed!ee2ee3fd"ef d#dZ4dS)$N)AnyListOptionalSequenceTupleUnion) 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) alternativeremovalcseZdZUdZeedZeed< e de fddZ e de fddZ d e eee fde ffd d Zed eedd fddZe d#deededefddZe de e fddZeeeeed d fd eede de de de dee e dee edefddZed d eeeed d fded eedee deede de de de dee e dee edede!fdd Z"e de fd!d"Z#Z$S)$StructuredChatAgentzStructured Chat Agent.)default_factory output_parserreturncCdS)z&Prefix to append the observation with.z Observation: selfr(r(]/var/www/html/zoom/venv/lib/python3.10/site-packages/langchain/agents/structured_chat/base.pyobservation_prefix)z&StructuredChatAgent.observation_prefixcCr')z#Prefix to append the llm call with.zThought:r(r)r(r(r+ llm_prefix.r-zStructuredChatAgent.llm_prefixintermediate_stepscs0t|}t|tstd|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_scratchpad __class__r(r+r13s  z)StructuredChatAgent._construct_scratchpadtoolsNcCsdSNr()clsr8r(r(r+_validate_toolsBz#StructuredChatAgent._validate_toolsllmkwargscKs tj|dS)Nr=)rfrom_llm)r:r=r>r(r(r+_get_default_output_parserFs z.StructuredChatAgent._get_default_output_parsercCsdgS)Nz Observation:r(r)r(r(r+_stopLszStructuredChatAgent._stopprefixsuffixhuman_message_templateformat_instructionsinput_variablesmemory_promptsc Csg}|D] } tddtddt| j} || jd| jd| qd|} dd d |D} |j| d }d || ||g} |durKd dg}|pNg}t | g|t |}t ||dS)N}z}}{z{{z: z, args:  , cSg|]}|jqSr(name.0toolr(r(r+ `z5StructuredChatAgent.create_prompt..) tool_namesz inputr5)rGmessages) resubr3argsappendrO descriptionjoinformatr from_templaterr )r:r8rCrDrErFrGrH tool_stringsrR args_schemaformatted_toolsrUtemplate_memory_promptsrWr(r(r+ create_promptPs$     z!StructuredChatAgent.create_promptcallback_managerc  Ksd|||j|||||| | d} t|| |d} dd|D}|p&|j|d}|d| ||d| S) z)Construct an agent from an LLM and tools.)rCrDrErFrGrH)r=promptrfcSrMr(rNrPr(r(r+rSrTz:StructuredChatAgent.from_llm_and_tools..r?) llm_chain allowed_toolsr%Nr()r;rerrA)r:r=r8rfr%rCrDrErFrGrHr>rgrhrU_output_parserr(r(r+from_llm_and_toolsms0  z&StructuredChatAgent.from_llm_and_toolscCstr9)r4r)r(r(r+ _agent_typer<zStructuredChatAgent._agent_typer9)%__name__ __module__ __qualname____doc__rrr%r__annotations__propertyr3r,r.rrr r1 classmethodrrr;rr rrArBrrHUMAN_MESSAGE_TEMPLATErr rer rrkrl __classcell__r(r(r6r+r# s          'r#T) stop_sequencer=r8rgtools_rendererrvr&c Cshd|jt|j}|rtd||j|t|ddd|Dd}|r;|dur2dgn|}|j|d }n|}tj d d d |B|Bt B}|S) 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), ] ) >r8rUr5z#Prompt missing required variables: rLcSrMr(rN)rQtr(r(r+rSrTz0create_structured_chat_agent..)r8rUTz Observation)stopcSs t|dS)Nr/r)xr(r(r+&s z.create_structured_chat_agent..)r5) differencerGlistpartial_variablesr4partialr]bindrassignr) r=r8rgrwrv missing_varsry llm_with_stopagentr(r(r+r s0z )5rXtypingrrrrrrlangchain_core._apirlangchain_core.agentsr langchain_core.callbacksr langchain_core.language_modelsr langchain_core.promptsr langchain_core.prompts.chatr rrlangchain_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.renderrrtr#boolr3r r(r(r(r+sL               }