o `+ iw@sxddlmZddlZddlZddlmZddlmZddlmZddl m Z m Z m Z m Z mZddlmZmZddlmZdd lmZdd lmZmZmZdd lmZdd lmZdd lmZm Z m!Z!ddl"m#Z#m$Z$e r|ddl%Z%ddl&m'Z'ddl(m)Z)GdddeZ*GdddeZ+d(ddZ,d)ddZ-d*dd Z.d+d"d#Z/ee0e+e*e0d$e0d%fZ1Gd&d'd'ee2e1fZ3dS),) annotationsN)Sequence)JSONDecodeError)sleep) TYPE_CHECKINGAnyCallableOptionalUnion) AgentAction AgentFinish)CallbackManager)dumpd)RunnableConfigRunnableSerializable ensure_config)BaseTool)convert_to_openai_tool) BaseModelFieldmodel_validator)Selfoverride) ThreadMessage)RequiredActionFunctionToolCallc@s0eZdZUdZded<ded<ed ddZd S) OpenAIAssistantFinishzuAgentFinish with run and thread metadata. Parameters: run_id: Run id. thread_id: Thread id. strrun_id thread_idreturnboolcCdSz]Check if the class is serializable by LangChain. Returns: False Fclsr#r#r/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/langchain/agents/openai_assistant/base.pyis_lc_serializable,z(OpenAIAssistantFinish.is_lc_serializableNrr __name__ __module__ __qualname____doc____annotations__ classmethodr'r#r#r#r&r!s rc@s8eZdZUdZded<ded<ded<ed dd Zd S) OpenAIAssistantActionzAgentAction with info needed to submit custom tool output to existing run. Parameters: tool_call_id: Tool call id. run_id: Run id. thread_id: Thread id r tool_call_idrrrr cCr!r"r#r$r#r#r&r'Cr(z(OpenAIAssistantAction.is_lc_serializableNr)r*r#r#r#r&r16s r1r openai.OpenAIc C^z ddl}|WSty}zd}t||d}~wty.}zd}t||d}~wwNrzBUnable to import openai, please install with `pip install openai`.zuPlease make sure you are using a v1.1-compatible version of openai. You can install with `pip install "openai>=1.1"`.)openaiZOpenAI ImportErrorAttributeErrorr6emsgr#r#r&_get_openai_clientM   r<openai.AsyncOpenAIc Cr4r5)r6 AsyncOpenAIr7r8r9r#r#r&_get_openai_async_client]r=r@tool:Union[dict[str, Any], type[BaseModel], Callable, BaseTool]r cCs"d}t|tod|vo|d|vS)zZdMd?d@ZdNdAdBZdOdCdDZdPdEdFZdS)QOpenAIAssistantRunnablea Run an OpenAI Assistant. Example using OpenAI tools: .. code-block:: python from langchain_experimental.openai_assistant import OpenAIAssistantRunnable interpreter_assistant = OpenAIAssistantRunnable.create_assistant( name="langchain assistant", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=[{"type": "code_interpreter"}], model="gpt-4-1106-preview" ) output = interpreter_assistant.invoke({"content": "What's 10 - 4 raised to the 2.7"}) Example using custom tools and AgentExecutor: .. code-block:: python from langchain_experimental.openai_assistant import OpenAIAssistantRunnable from langchain.agents import AgentExecutor from langchain.tools import E2BDataAnalysisTool tools = [E2BDataAnalysisTool(api_key="...")] agent = OpenAIAssistantRunnable.create_assistant( name="langchain assistant e2b tool", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=tools, model="gpt-4-1106-preview", as_agent=True ) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"content": "What's 10 - 4 raised to the 2.7"}) Example using custom tools and custom execution: .. code-block:: python from langchain_experimental.openai_assistant import OpenAIAssistantRunnable from langchain.agents import AgentExecutor from langchain_core.agents import AgentFinish from langchain.tools import E2BDataAnalysisTool tools = [E2BDataAnalysisTool(api_key="...")] agent = OpenAIAssistantRunnable.create_assistant( name="langchain assistant e2b tool", instructions="You are a personal math tutor. Write and run code to answer math questions.", tools=tools, model="gpt-4-1106-preview", as_agent=True ) def execute_agent(agent, tools, input): tool_map = {tool.name: tool for tool in tools} response = agent.invoke(input) while not isinstance(response, AgentFinish): tool_outputs = [] for action in response: tool_output = tool_map[action.tool].invoke(action.tool_input) tool_outputs.append({"output": tool_output, "tool_call_id": action.tool_call_id}) response = agent.invoke( { "tool_outputs": tool_outputs, "run_id": action.run_id, "thread_id": action.thread_id } ) return response response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"}) next_response = execute_agent(agent, tools, {"content": "now add 17.241", "thread_id": response.thread_id}) )default_factoryrclientN async_clientr assistant_idg@@floatcheck_every_msFr as_agentafter)moderrcCs,|jdurddl}|jj}|j|d|_|S)Nr)api_key)rLr6rKrSr?)selfr6rSr#r#r&validate_async_clients z-OpenAIAssistantRunnable.validate_async_client)rKname instructionstoolsSequence[Union[BaseTool, dict]]model2Optional[Union[openai.OpenAI, openai.AzureOpenAI]]kwargscKs@|pt}|jjj||dd|D|d}|d|j|d|S)a*Create an OpenAI Assistant and instantiate the Runnable. Args: name: Assistant name. instructions: Assistant instructions. tools: Assistant tools. Can be passed in OpenAI format or as BaseTools. model: Assistant model to use. client: OpenAI or AzureOpenAI client. Will create a default OpenAI client if not specified. kwargs: Additional arguments. Returns: OpenAIAssistantRunnable configured to run using the created assistant. cSg|]}t|qSr#rH.0rAr#r#r& z.rVrWrXrZ)rMrKNr#)r<beta assistantscreateid)r%rVrWrXrZrKr\ assistantr#r#r&create_assistants  z(OpenAIAssistantRunnable.create_assistantinputrEconfigOptional[RunnableConfig] OutputTypec Kst|}tj|d|d|dd}|jt|||dp#|d}zq|jrC|drC||d}|j j j j j di|}nKd|vrdd |d |d |d d g|dd}|||}n*d|vr|j j j jj|d|d d |d d} ||}n |j j j j j di|}||j|j}Wnty} z|| d} ~ wwz||} Wnty} z |j| |dd} ~ ww|| | S)aInvoke assistant. Args: input: Runnable input dict that can have: content: User message when starting a new run. thread_id: Existing thread to use. run_id: Existing run to use. Should only be supplied when providing the tool output for a required action after an initial invocation. message_metadata: Metadata to associate with new message. thread_metadata: Metadata to associate with new thread. Only relevant when new thread being created. instructions: Additional run instructions. model: Override Assistant model for this run. tools: Override Assistant tools for this run. parallel_tool_calls: Allow Assistant to set parallel_tool_calls for this run. top_p: Override Assistant top_p for this run. temperature: Override Assistant temperature for this run. max_completion_tokens: Allow setting max_completion_tokens for this run. max_prompt_tokens: Allow setting max_prompt_tokens for this run. run_metadata: Metadata to associate with new run. attachments: A list of files attached to the message, and the tools they should be added to. config: Runnable config. Defaults to None. 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Args: input: Runnable input dict that can have: content: User message when starting a new run. thread_id: Existing thread to use. run_id: Existing run to use. Should only be supplied when providing the tool output for a required action after an initial invocation. message_metadata: Metadata to associate with a new message. thread_metadata: Metadata to associate with new thread. Only relevant when a new thread is created. instructions: Overrides the instructions of the assistant. additional_instructions: Appends additional instructions. model: Override Assistant model for this run. tools: Override Assistant tools for this run. parallel_tool_calls: Allow Assistant to set parallel_tool_calls for this run. top_p: Override Assistant top_p for this run. temperature: Override Assistant temperature for this run. max_completion_tokens: Allow setting max_completion_tokens for this run. max_prompt_tokens: Allow setting max_prompt_tokens for this run. run_metadata: Metadata to associate with new run. config: Runnable config. Defaults to None. kwargs: Additional arguments. Return: If self.as_agent, will return Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. 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