delete the tests from the react.py and we have react_note in develope…

…r notes

lightrag/lightrag/components/agent/react.py

@@ -306,111 +306,3 @@ def call(
     def _extra_repr(self) -> str:
         s = f"max_steps={self.max_steps}, add_llm_as_fallback={self.add_llm_as_fallback}, "
         return s
-
-
-if __name__ == "__main__":
-    from lightrag.components.model_client import GroqAPIClient
-    from lightrag.core.types import ModelClientType
-    from lightrag.utils import setup_env
-
-    # get_logger()
-
-    setup_env()
-
-    def multiply(a: int, b: int) -> int:
-        """
-        Multiply two numbers.
-        """
-        return a * b
-
-    def add(a: int, b: int) -> int:
-        """
-        Add two numbers.
-        """
-        return a + b
-
-    def divide(a: float, b: float) -> float:
-        """
-        Divide two numbers.
-        """
-        return float(a) / b
-
-    def search(query: str) -> str:
-        """
-        Search the web for the given query.
-        """
-        return "python programming is a great way to learn programming"
-
-    tools = [
-        FunctionTool(fn=multiply),
-        FunctionTool(fn=add),
-        FunctionTool(fn=divide),
-        # FunctionTool.from_defaults(fn=search),
-    ]
-    llm_model_kwargs = {
-        "model": "llama3-70b-8192",  # llama3 is not good with string formatting, llama3 8b is also bad at following instruction, 70b is better but still not as good as gpt-3.5-turbo
-        # mistral also not good: mixtral-8x7b-32768, but with better prompt, it can still work
-        "temperature": 0.0,
-    }
-
-    gpt_3_5_turbo_model_kwargs = {
-        "model": "gpt-3.5-turbo",
-    }
-
-    examples = [
-        # r"""
-        # User: What is 9 - 3?
-        # You: {
-        #     "thought": "I need to subtract 3 from 9, but there is no subtraction tool, so I ask llm_tool to answer the query.",
-        #     "action": "llm_tool('What is 9 - 3?')"
-        # }
-        # """
-    ]
-    # agent = ReActAgent(
-    #     # examples=examples,
-    #     tools=tools,
-    #     max_steps=5,
-    #     model_client=GroqAPIClient,
-    #     model_kwargs=llm_model_kwargs,
-    # )
-    # print(agent)
-    queries = [
-        # "What is 2 times 3?",
-        # "What is 3 plus 4?",
-        "What is the capital of France? and what is 465 times 321 then add 95297 and then divide by 13.2?",
-        # "Li adapted her pet Apple in 2017 when Apple was only 2 months old, now we are at year 2024, how old is Li's pet Apple?",
-        "Give me 5 words rhyming with cool, and make a 4-sentence poem using them",
-    ]
-    """
-    Results: mixtral-8x7b-32768, 0.9s per query
-    llama3-70b-8192, 1.8s per query
-    gpt-3.5-turbo, 2.2s per query
-    """
-    import time
-
-    generator = Generator(
-        model_client=GroqAPIClient(),
-        model_kwargs=llm_model_kwargs,
-    )
-    # for i in range(3):
-    agent = ReActAgent(
-        tools=tools,
-        max_steps=5,
-        model_client=ModelClientType.GROQ(),
-        model_kwargs=llm_model_kwargs,
-    )
-    # agent.llm_planner.print_prompt()
-    # print(agent)
-
-    # vs not using agent
-    # print(agent.tools)
-
-    average_time = 0
-    for query in queries:
-        t0 = time.time()
-        answer = agent(query)
-        average_time += time.time() - t0
-        answer_no_agent = generator(prompt_kwargs={"input_str": query})
-        print(f"Answer with agent: {answer}")
-        print(f"Answer without agent: {answer_no_agent}")
-    print(f"Average time: {average_time / len(queries)}")