cleaned up all react agent code and finished first draft of react agent

developer_notes/react_note.py

@@ -0,0 +1,74 @@
+from lightrag.components.agent import ReActAgent
+from lightrag.core import Generator, ModelClientType, ModelClient
+from lightrag.utils import setup_env
+
+setup_env()
+
+
+# Define tools
+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
+
+
+llama3_model_kwargs = {
+    "model": "llama3-70b-8192",  # llama3 70b works better than 8b here.
+    "temperature": 0.0,
+}
+gpt_model_kwargs = {
+    "model": "gpt-3.5-turbo",
+    "temperature": 0.0,
+}
+
+
+def test_react_agent(model_client: ModelClient, model_kwargs: dict):
+    tools = [multiply, add, divide]
+    queries = [
+        "What is the capital of France? and what is 465 times 321 then add 95297 and then divide by 13.2?",
+        "Give me 5 words rhyming with cool, and make a 4-sentence poem using them",
+    ]
+    # define a generator without tools for comparison
+
+    generator = Generator(
+        model_client=model_client,
+        model_kwargs=model_kwargs,
+    )
+
+    react = ReActAgent(
+        max_steps=6,
+        add_llm_as_fallback=True,
+        tools=tools,
+        model_client=model_client,
+        model_kwargs=model_kwargs,
+    )
+    # print(react)
+
+    for query in queries:
+        print(f"Query: {query}")
+        agent_response = react.call(query)
+        llm_response = generator.call(prompt_kwargs={"input_str": query})
+        print(f"Agent response: {agent_response}")
+        print(f"LLM response: {llm_response}")
+        print("")
+
+
+if __name__ == "__main__":
+    test_react_agent(ModelClientType.GROQ(), llama3_model_kwargs)
+    # test_react_agent(ModelClientType.OPENAI(), gpt_model_kwargs)
+    print("Done")