o Jjg&m@s|ddlmZddlZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZddlmZmZddlmZddlmZdd lmZmZmZdd lmZdd lm Z dd l!m"Z"m#Z#m$Z$dd l%m&Z&er~ddl'Z'ddl(m)Z)ddl*m+Z+GdddeZ,GdddeZ-d'ddZ.d(ddZ/d)ddZ0d*d!d"Z1ee e-e,e d#e d$fZ2Gd%d&d&ee e2fZ3dS)+) annotationsN)JSONDecodeError)sleep) TYPE_CHECKINGAnyCallableDictListOptionalSequenceTupleTypeUnion) AgentAction AgentFinish)CallbackManager)dumpd)RunnableConfigRunnableSerializable ensure_config)BaseTool)convert_to_openai_tool) BaseModelFieldmodel_validator)Self) ThreadMessage)RequiredActionFunctionToolCallc@s0eZdZUdZded<ded<ed ddZd S) OpenAIAssistantFinishzuAgentFinish with run and thread metadata. Parameters: run_id: Run id. thread_id: Thread id. strrun_id thread_idreturnboolcCdSz]Check if the class is serializable by LangChain. Returns: False Fclsr&r&^/var/www/html/zoom/venv/lib/python3.10/site-packages/langchain/agents/openai_assistant/base.pyis_lc_serializable0z(OpenAIAssistantFinish.is_lc_serializableNr"r#__name__ __module__ __qualname____doc____annotations__ classmethodr*r&r&r&r)r%s rc@s8eZdZUdZded<ded<ded<ed dd Zd S) OpenAIAssistantActionzAgentAction with info needed to submit custom tool output to existing run. Parameters: tool_call_id: Tool call id. run_id: Run id. thread_id: Thread id r tool_call_idr r!r"r#cCr$r%r&r'r&r&r)r*Gr+z(OpenAIAssistantAction.is_lc_serializableNr,r-r&r&r&r)r4:s r4r" openai.OpenAIc CVz ddl}|WSty}ztd|d}~wty*}ztd|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"`.)openaiOpenAI ImportErrorAttributeErrorr9er&r&r)_get_openai_clientQ$ r?openai.AsyncOpenAIc Cr7r8)r9 AsyncOpenAIr;r<r=r&r&r)_get_openai_async_clientar@rCtool:Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]r#cCs"d}t|tod|vo|d|vS)zd?ZdNd@dAZdOdBdCZdPdDdEZdS)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)moder"rcCs,|jdurddl}|jj}|j|d|_|S)Nr)api_key)rRr9rQrYrB)selfr9rYr&r&r)validate_async_clients z-OpenAIAssistantRunnable.validate_async_client)rQname 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&rN.0rDr&r&r) z.r\r]r^r`)rSrQNr&)r?beta assistantscreateid)r(r\r]r^r`rQrb assistantr&r&r)create_assistants  z(OpenAIAssistantRunnable.create_assistantinputrJconfigOptional[RunnableConfig] OutputTypec Cst|}tj|d|d|dd}|jt|||dp#|d}zm|jrC|drC||d}|j j j j j di|}nGd|vr`d |d |d d g|d d}|||}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| |d| d} ~ 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. run_metadata: Metadata to associate with new run. config: Runnable config. Defaults to None. Return: If self.as_agent, will return Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise, will return OpenAI types Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]]. callbackstagsmetadatainheritable_callbacksinheritable_tagsinheritable_metadatarun_namer\intermediate_stepsr!usercontentmessage_metadatarolerrvthread_metadatamessagesrvr rrrvNrvr&)rr configuregeton_chain_startrget_namerV_parse_intermediate_stepsrQrjthreadsrunssubmit_tool_outputs_create_thread_and_runrrl _create_run _wait_for_runrmr! BaseExceptionon_chain_error _get_responserJ on_chain_end) rZrprqcallback_manager run_manager tool_outputsrunthread_r>responser&r&r)invokes`     zOpenAIAssistantRunnable.invoke)rR.riN)rSrRr&)rCrjrkrlrm) r(r\r]r^r`rRrb openai_toolsrnr&r&r)acreate_assistantis  z)OpenAIAssistantRunnable.acreate_assistantc s|pi}tj|d|d|dd}|jt|||dp$|d}z|jrJ|drJ||dIdH}|jj j j j di|IdH}nSd |vrjd |d |d d g|dd}| ||IdH}n3d|vr|jj j jj|d |d d |d dIdH} ||IdH}n|jj j j j di|IdH}||j|jIdH}Wnty} z|| | d} ~ wwz||} Wnty} z |j| |d| d} ~ ww|| | S)aAsync invoke 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 a new message. thread_metadata: Metadata to associate with new thread. Only relevant when a new thread is created. instructions: Additional run instructions. model: Override Assistant model for this run. tools: Override Assistant tools 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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