3fi4VdZddlmZddlmZddlmZddlmZm Z ddl m Z m Z ddl mZddlmZmZdd lmZdd lmZmZmZdd lmZdd lmZmZdd lmZddlm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*e+Z,edddGdde&Z-dedeededefdZ.y) zHModule implements an agent that uses OpenAI's APIs function enabled API.)Sequence)Any) deprecated) AgentAction AgentFinish)BaseCallbackManager Callbacks)BaseLanguageModel) BaseMessage SystemMessage)BasePromptTemplate)ChatPromptTemplateHumanMessagePromptTemplateMessagesPlaceholder)BaseMessagePromptTemplate)RunnableRunnablePassthrough)BaseTool)convert_to_openai_function)model_validator)Self)BaseSingleActionAgent"format_to_openai_function_messages) OpenAIFunctionsAgentOutputParserz0.1.0create_openai_functions_agentz1.0) alternativeremovalceZdZUdZeed<eeed<eed<e Z e e ed<de e fdZed defd Zede e fd Zede efd Z dde eee fdedededeezf dZ dde eee fdededeezfdZde de eee fdedefdZee d fde!d zde e"d zde#fdZ$ed d e fdedeede%d zde e"d zde!d zdede&fdZ'y )OpenAIFunctionsAgentaDAn Agent driven by OpenAIs function powered API. Args: llm: This should be an instance of `ChatOpenAI`, specifically a model that supports using `functions`. tools: The tools this agent has access to. prompt: The prompt for this agent, should support agent_scratchpad as one of the variables. For an easy way to construct this prompt, use `OpenAIFunctionsAgent.create_prompt(...)` output_parser: The output parser for this agent. Should be an instance of `OpenAIFunctionsAgentOutputParser`. llmtoolsprompt output_parserreturncT|jDcgc]}|jc}Scc}w)zGet allowed tools.)r"nameselfts r/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_classic/agents/openai_functions_agent/base.pyget_allowed_toolsz&OpenAIFunctionsAgent.get_allowed_tools<s $ +1+++s%after)modecn|j}d|jvrd|j}t||S)zValidate prompt. Args: values: Values to validate. Returns: Validated values. Raises: ValueError: If `agent_scratchpad` is not in the prompt. agent_scratchpadzE`agent_scratchpad` should be one of the variables in the prompt, got )r#input_variables ValueError)r)r#msgs r+validate_promptz$OpenAIFunctionsAgent.validate_prompt@sG&*[[ V%;%; ;--.0 S/ ! cdgS)z0Get input keys. Input refers to user input here.input)r)s r+ input_keyszOpenAIFunctionsAgent.input_keysVs yr5cd|jDcgc]}tt|c}Scc}w)zGet functions.)r"dictrr(s r+ functionszOpenAIFunctionsAgent.functions[s(>BZZH/23HHHs-Nintermediate_steps callbackswith_functionskwargsc t|}|jjDcic]}|dk7s |||}}tdi|d|i}|jjdi|} | j } |r)|j j| |j|} n|j j| |} |jj| Scc}w)aGiven input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to use. with_functions: Whether to use functions. **kwargs: User inputs. Returns: Action specifying what tool to use. If the agent is finished, returns an `AgentFinish`. If the agent is not finished, returns an `AgentAction`. r0r<r>)r>r8) rr#r1r; format_prompt to_messagesr!invoker<r$parse_ai_message) r)r=r>r?r@r0kselected_inputs full_inputsr#messagespredicted_messages r+planzOpenAIFunctionsAgent.plan`s*>>PQ"&++"="= FXAXAvayL  P_P?OP ***9[9%%'  $..#!0!  !%#!0! !!223DEE# s CCcKt|}|jjDcic]}|dk7s |||}}tdi|d|i}|jjdi|}|j } |j j| |j|d{} |jj| Scc}w7$w)aAsync given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to use. **kwargs: User inputs. Returns: Action specifying what tool to use. If the agent is finished, returns an AgentFinish. If the agent is not finished, returns an AgentAction. r0rBNr8) rr#r1r;rCrDr!ainvoker<r$rF) r)r=r>r@r0rGrHrIr#rJrKs r+aplanzOpenAIFunctionsAgent.aplans&>>PQ"&++"="= FXAXAvayL  P_P?OP ***9[9%%'"&(("2"2 nn#3#   !!223DEE  s!$C CCA(C!C"%Cearly_stopping_methodc |dk(rtddidS|dk(r7|j|fddi|}t|tr|Sd|}t|d |}t|) aReturn response when agent has been stopped due to max iterations. Args: early_stopping_method: The early stopping method to use. intermediate_steps: Intermediate steps. **kwargs: User inputs. Returns: AgentFinish. Raises: ValueError: If `early_stopping_method` is not `force` or `generate`. ValueError: If `agent_decision` is not an AgentAction. forceoutputz3Agent stopped due to iteration limit or time limit.generater?Fz,got AgentAction with no functions provided: zBearly_stopping_method should be one of `force` or `generate`, got )rrL isinstancer2)r)rPr=r@agent_decisionr3s r+return_stopped_responsez,OpenAIFunctionsAgent.return_stopped_responses( !G +PQ  !J .&TYY"$N .+6%%@@PQCS/ ! () + or5system_messageextra_prompt_messagesc|xsg}|tur|n td}|r|gng}|jg|tjdt dt |S)aCreate prompt for this agent. Args: system_message: Message to use as the system message that will be the first in the prompt. extra_prompt_messages: Prompt messages that will be placed between the system message and the new human input. Returns: A prompt template to pass into this agent. You are a helpful AI assistant.contentz{input}r0) variable_name)rJ)_NOT_SETr extendr from_templaterr)clsrYrZ_promptssystem_message_rJs r+ create_promptz"OpenAIFunctionsAgent.create_prompts").BX- 'HI  )8O$R  *88C $2DE  "844r5callback_managerc n|tur|n td}|j||}|d||||d|S)aConstruct an agent from an LLM and tools. Args: llm: The LLM to use as the agent. tools: The tools to use. callback_manager: The callback manager to use. extra_prompt_messages: Extra prompt messages to use. system_message: The system message to use. Defaults to a default system message. kwargs: Additional parameters to pass to the agent. r\r])rZrY)r!r#r"rgr8)r`r rf) rcr!r"rgrZrYr@rer#s r+from_llm_and_toolsz'OpenAIFunctionsAgent.from_llm_and_toolssf.X- 'HI  """7*#  -     r5)NT)N)(__name__ __module__ __qualname____doc__r __annotations__rrr rr$typeliststrr,rrr4propertyr9r;r<tuplerr boolrrrLrOrX classmethodr`r rrrfrrrir8r5r+r r &sI   H  (489,49,'"#*DII4:II $# 'F {C'7!89'F'F 'F  'F { " 'FX $F {C'7!89FF F { " FB)")!{C'7!89) )  )V08HL 5%, 5 $$=>E 5  5 5D 8E # &, # #  # # r5r r!r"r#r%c,d|jt|jzvrd|jd}t||j |Dcgc] }t |c}}t jd|z|ztzScc}w)aJ Create an agent that uses OpenAI function calling. Args: llm: LLM to use as the agent. Should work with OpenAI function calling, so either be an OpenAI model that supports that or a wrapper of a different model that adds in equivalent support. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. 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. Raises: ValueError: If `agent_scratchpad` is not in the prompt. Example: Creating an agent with no memory ```python from langchain_openai import ChatOpenAI from langchain_classic.agents import ( AgentExecutor, create_openai_functions_agent, ) from langchain_classic import hub prompt = hub.pull("hwchase17/openai-functions-agent") model = ChatOpenAI() tools = ... agent = create_openai_functions_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 agent prompt must have an `agent_scratchpad` key that is a `MessagesPlaceholder`. Intermediate agent actions and tool output messages will be passed in here. Here's an example: ```python from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder prompt = ChatPromptTemplate.from_messages( [ ("system", "You are a helpful assistant"), MessagesPlaceholder("chat_history", optional=True), ("human", "{input}"), MessagesPlaceholder("agent_scratchpad"), ] ) ``` r0zLPrompt must have input variable `agent_scratchpad`, but wasn't found. Found z instead.)r<ct|dS)Nr=r)xs r+z/create_openai_functions_agent..ws'I&'(r5)r0) r1rppartial_variablesr2bindrrassignr)r!r"r#r3r*llm_with_toolss r+rrsZf&>&>!?? ++,I 7 oXXPU(V1)CA)F(VXWN""       + , - )WsBN)/rmcollections.abcrtypingrlangchain_core._apirlangchain_core.agentsrrlangchain_core.callbacksrr langchain_core.language_modelsr langchain_core.messagesr r langchain_core.promptsr langchain_core.prompts.chatrrrlangchain_core.prompts.messagerlangchain_core.runnablesrrlangchain_core.toolsr%langchain_core.utils.function_callingrpydanticrtyping_extensionsrlangchain_classic.agentsr;langchain_classic.agents.format_scratchpad.openai_functionsr8langchain_classic.agents.output_parsers.openai_functionsrobjectr`r rr8r5r+rsN$*:C<6 EB)L$": 8 G!@%Pu 0u Qu p_ _ H _ _ _r5