Initial llm fn calling experiment

experiment/llm_test.py

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+import os
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, SystemMessage
+
+# Load environment variables
+load_dotenv()
+
+def check_weather(location: str) -> str:
+    return f"The weather in {location} is sunny!"
+
+def ask_human(query: str) -> str:
+    print()
+    print(f"Assistant: {query}")
+    user_input = input("\nYou: ").strip()
+    return user_input
+
+def output_message(message: str, prompt_user_input: bool = False) -> str:
+    print()
+    print(f"Assistant: {message.strip()}")
+    if prompt_user_input:
+        user_input = input("\nYou: ").strip()
+        return user_input
+    return ""
+
+def evaluate_response(code: str) -> any:
+    """
+    Evaluates a single function call and returns its result
+    """
+    globals_dict = {
+        'check_weather': check_weather,
+        'ask_human': ask_human,
+        'output_message': output_message
+    }
+    
+    try:
+        # Using eval() instead of exec() since we're evaluating a single expression
+        result = eval(code, globals_dict)
+        return result
+    except Exception as e:
+        return f"Error executing code: {str(e)}"
+
+def create_chat_interface():
+    # Initialize the chat model
+    chat = ChatOpenAI(
+        api_key=os.getenv("OPENROUTER_API_KEY"),
+        temperature=0.3,
+        base_url="https://openrouter.ai/api/v1",
+        # model="deepseek/deepseek-chat"
+        model="qwen/qwen-2.5-coder-32b-instruct"
+    )
+    
+    # Chat loop
+    print("Welcome to the Chat Interface! (Type 'quit' to exit)")
+    
+    chat_history = []
+    last_result = None
+    first_iteration = True
+    
+    while True:
+        base_prompt = ""
+        
+        # Add the last result to the prompt if it's not the first iteration
+        if not first_iteration and last_result is not None:
+            base_prompt += f"\n<last result>{last_result}</last result>"
+            
+        # Construct the tool documentation and context
+        base_prompt += """
+        <available functions>
+        # Get the weather at a location:
+        check_weather(location: str) -> str
+        
+        # Output a message and optionally get their response:
+        output_message(message: str, prompt_user_input: bool = False) -> str
+        </available functions>
+        """
+        
+        base_prompt += """
+        <agent instructions>
+            You are a ReAct agent. You run in a loop and use ONE of the available functions per iteration.
+            If the current query does not require a function call, just use output_message to say what you would normally say.
+            The result of that function call will be given to you in the next message.
+            Call one function at a time. Function arguments can be complex objects, long strings, etc. if needed.
+            The user cannot see the results of function calls, so you have to explicitly call output_message if you want them to see something.
+            You must always respond with a single line of python that calls one of the available tools.
+            Start by asking the user what they want.
+        </agent instructions>
+        
+        <example response>
+            check_weather("London")
+        </example response>
+            
+        <example response>
+            output_message(\"\"\"
+            How can I help you today?
+            \"\"\", True)
+        </example response>
+        """
+        
+        base_prompt += "\nOutput **ONLY THE CODE** and **NO MARKDOWN BACKTICKS**"
+            
+        # Add user message to history
+        # Remove the previous messages if they exist
+        # if len(chat_history) > 1:
+        #     chat_history.pop()  # Remove the last assistant message
+        #     chat_history.pop()  # Remove the last human message
+            
+        chat_history.append(HumanMessage(content=base_prompt))
+        
+        try:
+            # Get response from model
+            response = chat.invoke(chat_history)
+            
+            # # Print the code response
+            # print("\nAssistant generated code:")
+            # print(response.content)
+            
+            # Evaluate the code
+            # print("\nExecuting code:")
+            last_result = evaluate_response(response.content.strip())
+            # if last_result is not None:
+                # print(f"Result: {last_result}")
+            
+            # Add assistant response to history
+            chat_history.append(response)
+            
+            # Set first_iteration to False after the first loop
+            first_iteration = False
+            
+        except Exception as e:
+            print(f"\nError: {str(e)}")
+
+if __name__ == "__main__":
+    create_chat_interface()