蘺hnddlZddlmZmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZmZmZddlmZdd lmZmZdd lmZmZdd lmZdd lmZdd lm Z ddl!m"Z"m#Z#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,dZ-edddGddeZ.de dee*dedefdZ/dS)N)AnyListOptionalSequenceTuple) deprecated) AgentAction)BaseLanguageModel)BasePromptTemplate)ChatPromptTemplateHumanMessagePromptTemplateSystemMessagePromptTemplate)Field)RunnableRunnablePassthrough)AgentAgentOutputParserformat_log_to_str)JSONAgentOutputParser)%StructuredChatOutputParserWithRetries)FORMAT_INSTRUCTIONSPREFIXSUFFIX)BaseCallbackManager)LLMChain)BaseTool) render_text_description_and_argsz{input} {agent_scratchpad}z0.1.0create_structured_chat_agentz0.2.0) alternativeremovalczeZdZUdZeeZeed< e de fdZ e de fdZ de eee fde ffd Zed eedd fd Ze dd eed edefdZe de e fdZeeeeed d fd eede de de de dee e dee edefdZed d eeeed d fd ed eedee deede de de de dee e dee ed ede!fdZ"e de fdZ#xZ$S)StructuredChatAgentzStructured Chat Agent.)default_factory output_parserreturncdS)z&Prefix to append the observation with.z Observation: selfs m/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/langchain/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_prefix-s zr-intermediate_stepsct|}t|tst d|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*r0agent_scratchpad __class__s r+r3z)StructuredChatAgent._construct_scratchpad2sk!77889KLL*C00 KIJJ J  $D1ADD  $ #r-toolsNcdSNr()clsr9s r+_validate_toolsz#StructuredChatAgent._validate_toolsAs r-llmkwargsc ,tj|S)Nr>)rfrom_llm)r<r>r?s r+_get_default_output_parserz.StructuredChatAgent._get_default_output_parserEs5=#FFFFr-cdgS)Nz Observation:r(r)s r+_stopzStructuredChatAgent._stopKs r-prefixsuffixhuman_message_templateformat_instructionsinput_variablesmemory_promptsc (g}|D]e} tjddtjddt| j} || jd| jd| fd|} dd |D} || }d || ||g} |d d g}|pg}tj | g|tj |}t||S)N}z}}{z{{z: z, args:  , cg|] }|j Sr(name.0tools r+ z5StructuredChatAgent.create_prompt.._s<<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_toolsls E""""" #9 3+)#  -   32E222 &Q#*H*HS*H*Q*Qs $(      r-ctr;)r6r)s r+ _agent_typezStructuredChatAgent._agent_typesr-r;)%__name__ __module__ __qualname____doc__rrr%r__annotations__propertyr5r,r/rrr r3 classmethodrrr=rr rrCrErrHUMAN_MESSAGE_TEMPLATErr rhrrrprr __classcell__)r8s@r+r#r#s;  ',u=(((M$' CXCX $"&u[#-='>"? $  $ $ $ $ $ $ HX$6 4   [ 04GG,-G@CG GGG[G  tCy   X &<#6/3=AVV!VV V !$ V ! V"$s),V!&8!9:V VVV[V8 ;?59&<#6/3=A% % % !% ##67 %  12 %  % % !$% !% "$s),% !&8!9:% %  % % % [% NSXr-r#r>r9rkr&chd|j}|rtd||t t |dd|D}|dg}tj d |z|ztz}|S) aCreate an agent aimed at supporting tools with multiple inputs. 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?"), ], } ) Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use, must have input keys of `tools`, `tool_names`, and `agent_scratchpad`. 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. >r9rXr7z#Prompt missing required variables: rPcg|] }|j Sr(rR)rUts r+rWz0create_structured_chat_agent..s444af444r-)r9rX Observation)stopc,t|dS)Nr0r)xs r+z.create_structured_chat_agent..s'8;O9P'Q'Qr-)r7) differencerJr6partialrlistr`bindrassignr)r>r9rk missing_vars llm_with_stopagents r+rrs^?>>IILOM|MMNNN ^^.tE{{;;9944e44455FHH=/H22M "QQ         ! !  "  Lr-)0r[typingrrrrrlangchain_core._apirlangchain_core.agentsr langchain_core.language_modelsr langchain_core.promptsr langchain_core.prompts.chatr r rlangchain_core.pydantic_v1rlangchain_core.runnablesrrlangchain.agents.agentrr"langchain.agents.format_scratchpadrlangchain.agents.output_parsersr.langchain.agents.structured_chat.output_parserr'langchain.agents.structured_chat.promptrrrlangchain.callbacks.baserlangchain.chains.llmrlangchain.toolsrlangchain.tools.renderrrzr#rr(r-r+rsG 77777777777777******------<<<<<<555555 -,,,,,BBBBBBBB;;;;;;;;@@@@@@AAAAAAXWWWWWWWWW888888))))))$$$$$$CCCCCC8 G!?QQQvvvvv%vvRQvrC C#+H#5C?QC CCCCCCr-