a agu@sddlmZddlZddlZddlmZddlmZddlmZm 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&erddl'Z'ddl(m)Z)ddl*m+Z+GdddeZ,GdddeZ-ddddZ.ddddZ/dddddZ0dd dd!d"Z1ee e-e,e d#e d$fZ2Gd%d&d&ee e2fZ3dS)') annotationsN)JSONDecodeError)sleep) TYPE_CHECKINGAnyCallableDictListOptionalSequenceTupleTypeUnion) AgentAction AgentFinish)CallbackManager)dumpd)RunnableConfigRunnableSerializable ensure_config)BaseTool)convert_to_openai_tool) BaseModelFieldmodel_validator)Self) ThreadMessage)RequiredActionFunctionToolCallc@s4eZdZUdZded<ded<eddddZd S) OpenAIAssistantFinishzuAgentFinish with run and thread metadata. Parameters: run_id: Run id. thread_id: Thread id. strrun_id thread_idboolreturncCsdSz]Check if the class is serializable by LangChain. Returns: False Fclsr&r&t/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/agents/openai_assistant/base.pyis_lc_serializable0sz(OpenAIAssistantFinish.is_lc_serializableN__name__ __module__ __qualname____doc____annotations__ classmethodr*r&r&r&r)r%s rc@s<eZdZUdZded<ded<ded<edddd Zd S) OpenAIAssistantActionzAgentAction with info needed to submit custom tool output to existing run. Parameters: tool_call_id: Tool call id. run_id: Run id. thread_id: Thread id r tool_call_idr r!r"r#cCsdSr%r&r'r&r&r)r*Gsz(OpenAIAssistantAction.is_lc_serializableNr+r&r&r&r)r2:s r2z openai.OpenAIr#c Csrzddl}|WSty@}ztd|WYd}~n6d}~0tyl}ztd|WYd}~n d}~00dSNrzBUnable to import openai, please install with `pip install openai`.zuPlease make sure you are using a v1.1-compatible version of openai. You can install with `pip install "openai>=1.1"`.)openaiZOpenAI ImportErrorAttributeErrorr5er&r&r)_get_openai_clientQs r:zopenai.AsyncOpenAIc Csrzddl}|WSty@}ztd|WYd}~n6d}~0tyl}ztd|WYd}~n d}~00dSr4)r5 AsyncOpenAIr6r7r8r&r&r)_get_openai_async_clientas r<z:Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]r")toolr$cCs"d}t|to d|vo |d|vS)zd?Zdddd5d@dAZdS)DOpenAIAssistantRunnablea 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)moderr#cCs,|jdur(ddl}|jj}|j|d|_|S)Nr)api_key)rFr5rErMr;)selfr5rMr&r&r)validate_async_clients  z-OpenAIAssistantRunnable.validate_async_client)rEzSequence[Union[BaseTool, dict]]z2Optional[Union[openai.OpenAI, openai.AzureOpenAI]])name instructionstoolsmodelrEkwargsr$cKs@|pt}|jjj||dd|D|d}|f|j|d|S)a*Create an OpenAI Assistant and instantiate the Runnable. Args: name: Assistant name. instructions: Assistant instructions. tools: Assistant tools. Can be passed in OpenAI format or as BaseTools. model: Assistant model to use. client: OpenAI or AzureOpenAI client. Will create a default OpenAI client if not specified. kwargs: Additional arguments. Returns: OpenAIAssistantRunnable configured to run using the created assistant. cSsg|] }t|qSr&rB.0r=r&r&r) z.rPrQrRrS)rGrE)r:beta assistantscreateid)r(rPrQrRrSrErT assistantr&r&r)create_assistants  z(OpenAIAssistantRunnable.create_assistantr@zOptional[RunnableConfig] OutputType)inputconfigrTr$c Kst|}tj|d|d|dd}|jt|||dpF|d}z|jr|dr||d}|j j j j j fi|}nd|vrd |d |d d g|d d}|||}nTd|vr|j j j jj|d|d d |d d} ||}n|j j j j j fi|}||j|j}Wn4tyZ} z|| | WYd} ~ n d} ~ 00z||} Wn<ty} z"|j| |d| WYd} ~ nd} ~ 00|| | SdS)a/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 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. config: Runnable config. Defaults to None. Return: If self.as_agent, will return Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. 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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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