OpenAIAssistantRunnable Class — langchain Architecture

Source Code

libs/langchain/langchain_classic/agents/openai_assistant/base.py lines 139–831

class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
    """Run an OpenAI Assistant.

    Example using OpenAI tools:
        ```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:
        ```python
        from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
        from langchain_classic.agents import AgentExecutor
        from langchain_classic.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:
        ```python
        from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
        from langchain_classic.agents import AgentExecutor
        from langchain_core.agents import AgentFinish
        from langchain_classic.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