mirror of
https://github.com/langchain-ai/langgraph-codeact.git
synced 2026-07-14 00:11:48 -04:00
Vadym Barda
116 lines
2.9 KiB
Python
116 lines
2.9 KiB
Python
import builtins
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import contextlib
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import io
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import math
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from typing import Any
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from langchain.chat_models import init_chat_model
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph_codeact import create_codeact
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def eval(code: str, _locals: dict[str, Any]) -> tuple[str, dict[str, Any]]:
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# Store original keys before execution
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original_keys = set(_locals.keys())
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try:
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with contextlib.redirect_stdout(io.StringIO()) as f:
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exec(code, builtins.__dict__, _locals)
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result = f.getvalue()
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if not result:
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result = "<code ran, no output printed to stdout>"
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except Exception as e:
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result = f"Error during execution: {repr(e)}"
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# Determine new variables created during execution
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new_keys = set(_locals.keys()) - original_keys
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new_vars = {key: _locals[key] for key in new_keys}
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return result, new_vars
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def add(a: float, b: float) -> float:
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"""Add two numbers together."""
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return a + b
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def multiply(a: float, b: float) -> float:
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"""Multiply two numbers together."""
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return a * b
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def divide(a: float, b: float) -> float:
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"""Divide two numbers."""
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return a / b
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def subtract(a: float, b: float) -> float:
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"""Subtract two numbers."""
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return a - b
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def sin(a: float) -> float:
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"""Take the sine of a number."""
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return math.sin(a)
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def cos(a: float) -> float:
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"""Take the cosine of a number."""
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return math.cos(a)
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def radians(a: float) -> float:
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"""Convert degrees to radians."""
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return math.radians(a)
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def exponentiation(a: float, b: float) -> float:
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"""Raise one number to the power of another."""
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return a**b
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def sqrt(a: float) -> float:
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"""Take the square root of a number."""
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return math.sqrt(a)
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def ceil(a: float) -> float:
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"""Round a number up to the nearest integer."""
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return math.ceil(a)
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tools = [
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add,
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multiply,
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divide,
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subtract,
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sin,
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cos,
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radians,
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exponentiation,
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sqrt,
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ceil,
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]
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model = init_chat_model("claude-3-7-sonnet-latest", model_provider="anthropic")
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code_act = create_codeact(model, tools, eval)
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agent = code_act.compile(checkpointer=MemorySaver())
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if __name__ == "__main__":
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messages = [
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{
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"role": "user",
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"content": "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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}
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]
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for typ, chunk in agent.stream(
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{"messages": messages},
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stream_mode=["values", "messages"],
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config={"configurable": {"thread_id": 1}},
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):
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if typ == "messages":
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print(chunk[0].content, end="")
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elif typ == "values":
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print("\n\n---answer---\n\n", chunk)
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