gh4bdZddlmZddlmZmZmZddlmZddl m Z m Z ddl m Z mZddlmZddlmZmZdd lmZdd lmZmZmZmZdd lmZmZdd lmZdd l m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)ddl*m+Z+edddGdde'Z,dedeededefdZ-dS)zHModule implements an agent that uses OpenAI's APIs function enabled API.)Sequence)AnyOptionalUnion) deprecated) AgentAction AgentFinish)BaseCallbackManager Callbacks)BaseLanguageModel) BaseMessage SystemMessage)BasePromptTemplate)BaseMessagePromptTemplateChatPromptTemplateHumanMessagePromptTemplateMessagesPlaceholder)RunnableRunnablePassthrough)BaseToolconvert_to_openai_function)model_validator)Self)BaseSingleActionAgent"format_to_openai_function_messages) OpenAIFunctionsAgentOutputParserz0.1.0create_openai_functions_agentz1.0) alternativeremovalceZdZUdZeed<eeed<eed<e Z e e ed<de e fdZed defd Zede e fd Zede efd Z dde eee fdedededeeeff dZ dde eee fdededeeeffdZde de eee fdedefdZe e!dd fde"e!de"e e#de$fdZ%e d d e!dfdedeede"e&de"e e#de"e!dede'fdZ(d S) OpenAIFunctionsAgentazAn 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. Defaults to OpenAIFunctionsAgentOutputParser. llmtoolsprompt output_parserreturnc$d|jDS)zGet allowed tools.cg|] }|j S)name.0ts i/var/www/FlaskApp/flask-venv/lib/python3.11/site-packages/langchain/agents/openai_functions_agent/base.py z:OpenAIFunctionsAgent.get_allowed_tools..=s+++1+++r%selfs r0get_allowed_toolsz&OpenAIFunctionsAgent.get_allowed_tools;s++ ++++r2after)modecT|j}d|jvrtd|j|S)zValidate prompt. Args: values: Values to validate. Returns: Validated values. Raises: ValueError: If `agent_scratchpad` is not in the prompt. agent_scratchpadzE`agent_scratchpad` should be one of the variables in the prompt, got )r&input_variables ValueError)r5r&s r0validate_promptz$OpenAIFunctionsAgent.validate_prompt?sG&*[ V%; ; ;0-00  r2cdgS)z0Get input keys. Input refers to user input here.inputr+r4s r0 input_keyszOpenAIFunctionsAgent.input_keysTs yr2c$d|jDS)zGet functions.cFg|]}tt|Sr+)dictrr-s r0r1z2OpenAIFunctionsAgent.functions..]s)HHH/2233HHHr2r3r4s r0 functionszOpenAIFunctionsAgent.functionsYsIHTZHHHHr2NTintermediate_steps callbackswith_functionskwargsc xt|}fd|jjD}tdi|d|i}|jjdi|}|} |r#|j| |j|} n|j| |} |j | } | S)a Given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to use. Defaults to None. with_functions: Whether to use functions. Defaults to True. **kwargs: User inputs. Returns: Action specifying what tool to use. If the agent is finished, returns an AgentFinish. If the agent is not finished, returns an AgentAction. c.i|]}|dk||Sr:r+r.krHs r0 z-OpenAIFunctionsAgent.plan..u1   FXAXAXAvayAXAXAXr2r:rDrF)rFr+) rr&r;rC format_prompt to_messagesr$predict_messagesrDr'_parse_ai_message) r5rErFrGrHr:selected_inputs full_inputsr&messagespredicted_messageagent_decisions ` r0planzOpenAIFunctionsAgent.plan_s*>>PQQ    "&+"=   PP_PP?OPPP **99[99%%''   $ 9 9.#!:!!   !% 9 9#!:!! +==>OPPr2cJKt|}fd|jjD}tdi|d|i}|jjdi|}|}|j||j|d{V} |j | } | S)aAsync given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to use. Defaults to None. **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. c.i|]}|dk||SrKr+rLs r0rNz.OpenAIFunctionsAgent.aplan..rOr2r:rPNr+) rr&r;rCrQrRr$apredict_messagesrDr'rT) r5rErFrHr:rUrVr&rWrXrYs ` r0aplanzOpenAIFunctionsAgent.aplans&>>PQQ    "&+"=   PP_PP?OPPP **99[99%%''"&("<"< )#=# #       +==>OPPr2early_stopping_methodc |dkrtddidS|dkr9|j|fddi|}t|tr|Std|td |) 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.generaterGFz,got AgentAction with no functions provided: zBearly_stopping_method should be one of `force` or `generate`, got )r rZ isinstancer<)r5r_rErHrYs r0return_stopped_responsez,OpenAIFunctionsAgent.return_stopped_responses( !G + +PQSU #j 0 0&TY"38SS/,// r2zYou are a helpful AI assistant.)contentsystem_messageextra_prompt_messagesc|pg}|r|g}ng}|g|tjdtdt |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. z{input}r:) variable_name)rW)extendr from_templaterr)clsrhri_promptsrWs r0 create_promptz"OpenAIFunctionsAgent.create_prompts&).B  &'HHH  *8CC $2DEEE    "84444r2callback_managerc J|||}|d||||d|S)aConstruct an agent from an LLM and tools. Args: llm: The LLM to use as the agent. tools: The tools to use. callback_manager: The callback manager to use. Defaults to None. extra_prompt_messages: Extra prompt messages to use. Defaults to None. system_message: The system message to use. Defaults to a default system message. kwargs: Additional parameters to pass to the agent. )rirh)r$r&r%rqr+)rp)rnr$r%rqrirhrHr&s r0from_llm_and_toolsz'OpenAIFunctionsAgent.from_llm_and_toolssZ."""7)#  s -       r2)NT)N))__name__ __module__ __qualname____doc__r __annotations__rrrrr'typeliststrr6rrr=propertyr@rCrDtuplerr boolrrr rZr^rf classmethodrrrrrpr rrsr+r2r0r#r#$sB    H  (489,49,,,,_'"""#"(DIXI4:IIIXI $# (( {C'7!89(( (  ( {K' ( ((((Z $ {C'7!89  {K' ( @("(!{C'7!89( (  ((((T3@-53 3 3 LP 5 5 / 5 (-F(GH 5  5 5 5[ 5D ;?KO2?-53 3 3     !  ##67  (-F(GH  !/         [    r2r#r$r%r&r(cd|jt|jzvrtd|jd|d|D}t jd|z|ztz}|S)a 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 .. code-block:: python from langchain_community.chat_models import ChatOpenAI from langchain.agents import AgentExecutor, create_openai_functions_agent from langchain 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: .. code-block:: 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"), ] ) r:zLPrompt must have input variable `agent_scratchpad`, but wasn't found. 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