a ag4@s2dZddlmZmZmZmZmZmZmZddl m Z ddl m Z m Z ddlmZmZddlmZddlmZmZddlmZdd lmZmZmZmZdd lmZmZdd l m!Z!dd l"m#Z#dd l$m%Z%ddl&m'Z'ddl(m)Z)ddl*m+Z+ddl,m-Z-e ddddGddde)Z.eee!eedddZ/dS)zHModule implements an agent that uses OpenAI's APIs function enabled API.)AnyListOptionalSequenceTupleTypeUnion) deprecated) AgentAction AgentFinish)BaseCallbackManager Callbacks)BaseLanguageModel) BaseMessage SystemMessage)BasePromptTemplate)BaseMessagePromptTemplateChatPromptTemplateHumanMessagePromptTemplateMessagesPlaceholder)RunnableRunnablePassthrough)BaseToolconvert_to_openai_function)model_validator)Self)BaseSingleActionAgent"format_to_openai_function_messages) OpenAIFunctionsAgentOutputParserz0.1.0create_openai_functions_agentz1.0) alternativeZremovalc @sxeZdZUdZeed<eeed<eed<e Z e e ed<e e dddZed d edd d Zee e dd dZee edddZd$e eee feeeeeefdddZd%e eee feeeeefdddZe e eee feedddZe e!dddfe"e!e"e e#e$ddd Z%e dde!ddfeeee"e&e"e e#e"e!ee'd!d"d#Z(dS)&OpenAIFunctionsAgentazAn Agent driven by OpenAIs function powered API. Args: llm: This should be an instance of ChatOpenAI, specifically a model that supports using `functions`. tools: The tools this agent has access to. prompt: The prompt for this agent, should support agent_scratchpad as one of the variables. For an easy way to construct this prompt, use `OpenAIFunctionsAgent.create_prompt(...)` output_parser: The output parser for this agent. Should be an instance of OpenAIFunctionsAgentOutputParser. Defaults to OpenAIFunctionsAgentOutputParser. llmtoolsprompt output_parser)returncCsdd|jDS)zGet allowed tools.cSsg|] }|jqS)name.0tr)r)z/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/agents/openai_functions_agent/base.py <z:OpenAIFunctionsAgent.get_allowed_tools..r%selfr)r)r.get_allowed_tools:sz&OpenAIFunctionsAgent.get_allowed_toolsafter)modecCs$|j}d|jvr td|j|S)zValidate prompt. Args: values: Values to validate. Returns: Validated values. 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If the agent is not finished, returns an AgentAction. csi|]}|dkr||qSr7r)r,krBr)r. tsz-OpenAIFunctionsAgent.plan..r7r>r@)r@) rr&r8r= format_prompt to_messagesr$Zpredict_messagesr>r'_parse_ai_message) r3r?r@rArBr7selected_inputs full_inputsr&messagespredicted_messageagent_decisionr)rFr.plan^s&  zOpenAIFunctionsAgent.plan)r?r@rBr(c sxt|}fdd|jjD}tfi|d|i}|jjfi|}|}|jj||j|dIdH} |j | } | S)aAsync given input, decided what to do. Args: intermediate_steps: Steps the LLM has taken to date, along with observations. callbacks: Callbacks to use. Defaults to None. **kwargs: User inputs. Returns: Action specifying what tool to use. If the agent is finished, returns an AgentFinish. If the agent is not finished, returns an AgentAction. csi|]}|dkr||qSrCr)rDrFr)r.rGsz.OpenAIFunctionsAgent.aplan..r7rHN) rr&r8r=rIrJr$Zapredict_messagesr>r'rK) r3r?r@rBr7rLrMr&rNrOrPr)rFr.aplans   zOpenAIFunctionsAgent.aplan)early_stopping_methodr?rBr(cKsd|dkrtddidS|dkrR|j|fddi|}t|trB|Std|ntd |d S) 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.generaterAFz,got AgentAction with no functions provided: zBearly_stopping_method should be one of `force` or `generate`, got N)r rQ isinstancer9)r3rSr?rBrPr)r)r.return_stopped_responses, z,OpenAIFunctionsAgent.return_stopped_responsezYou are a helpful AI assistant.)content)system_messageextra_prompt_messagesr(cCsD|pg}|r|g}ng}|g|tdtddt|dS)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}r7)Z variable_name)rN)extendrZ from_templaterr)clsr[r\Z_promptsrNr)r)r. create_promptsz"OpenAIFunctionsAgent.create_prompt)r$r%callback_managerr\r[rBr(cKs&|j||d}|f||||d|S)aConstruct an agent from an LLM and tools. Args: llm: The LLM to use as the agent. tools: The tools to use. callback_manager: The callback manager to use. Defaults to None. extra_prompt_messages: Extra prompt messages to use. Defaults to None. system_message: The system message to use. Defaults to a default system message. kwargs: Additional parameters to pass to the agent. )r\r[)r$r&r%r`)r_)r^r$r%r`r\r[rBr&r)r)r.from_llm_and_toolssz'OpenAIFunctionsAgent.from_llm_and_tools)NT)N))__name__ __module__ __qualname____doc__r__annotations__rrrr r'rrstrr4rrr:propertyr<r=r>rr r boolrrr rQrRrY classmethodrrrrr_r rrar)r)r)r.r##sv   - " * " r#)r$r%r&r(cCs^d|jt|jvr&td|jd|jdd|Dd}tjddd |B|BtB}|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"), ] ) r7zLPrompt must have input variable `agent_scratchpad`, but wasn't found. 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