mirror of
https://github.com/langchain-ai/langchain-sandbox.git
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vbarda
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
# pip install langgraph "langchain[anthropic]"
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import asyncio
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import uuid
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from langchain_sandbox import PyodideSandbox, PyodideSandboxTool
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from langgraph.prebuilt import create_react_agent
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from langgraph.checkpoint.memory import InMemorySaver
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# Create a sandbox instance
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sandbox = PyodideSandbox(
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"./sessions", # Directory to store session files
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# Allow Pyodide to install python packages that
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# might be required.
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allow_net=True,
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)
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# Define the sandbox tool
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sandbox_tool = PyodideSandboxTool(sandbox=sandbox)
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checkpointer = InMemorySaver()
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# Create an agent with the sandbox tool
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agent = create_react_agent(
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"anthropic:claude-3-7-sonnet-latest", [sandbox_tool], checkpointer=checkpointer
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)
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query = """A batter hits a baseball at 45.847 m/s at an angle of 23.474° above the horizontal. The outfielder, who starts facing the batter, picks up the baseball as it lands, then throws it back towards the batter at 24.12 m/s at an angle of 39.12 degrees. How far is the baseball from where the batter originally hit it? Assume zero air resistance."""
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async def run_agent(query: str, thread_id: str):
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config = {"configurable": {"thread_id": thread_id}}
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# Stream agent outputs
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async for chunk in agent.astream({"messages": query}, config):
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print(chunk)
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print("\n")
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if __name__ == "__main__":
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# Run the agent
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asyncio.run(run_agent(query, str(uuid.uuid4())))
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