OpenAIAssistantV2Runnable

OpenAIAssistantV2Runnable()

Example using OpenAI tools:

.. code-block:: python

from langchain_classic.agents.openai_assistant import OpenAIAssistantV2Runnable

assistant = OpenAIAssistantV2Runnable.create_assistant( name="math 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 = assistant.invoke({"content": "What's 10 - 4 raised to the 2.7"})

Example using custom tools and AgentExecutor:

.. code-block:: python

from langchain_classic.agents.openai_assistant import OpenAIAssistantV2Runnable from langchain_classic.agents import AgentExecutor from langchain_classic.tools import E2BDataAnalysisTool

tools = [E2BDataAnalysisTool(api_key="...")] agent = OpenAIAssistantV2Runnable.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": "Analyze the data..."})

Example using custom tools and custom execution:

.. code-block:: python

from langchain_classic.agents.openai_assistant import OpenAIAssistantV2Runnable from langchain_classic.agents import AgentExecutor from langchain_core.agents import AgentFinish from langchain_classic.tools import E2BDataAnalysisTool

tools = [E2BDataAnalysisTool(api_key="...")] agent = OpenAIAssistantV2Runnable.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})