Update agent.py

agent.py

@@ -4,8 +4,9 @@ from langchain_openai import ChatOpenAI
 from langchain.agents import AgentExecutor, create_openai_functions_agent
 
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.messages import AIMessage, HumanMessage # SystemMessage not explicitly needed in prompt list for this agent type
+from langchain_core.messages import AIMessage, HumanMessage # SystemMessage can be implicitly created or explicit
 
+# --- Import your defined tools FROM THE 'tools' PACKAGE ---
 from tools import (
     BioPortalLookupTool,
     UMLSLookupTool,
@@ -17,26 +18,33 @@ from tools import (
 from config.settings import settings
 from services.logger import app_logger
 
-llm = None
+# --- Initialize LLM (OpenAI) ---
+llm = None # Initialize to None for robust error handling if init fails
 try:
     if not settings.OPENAI_API_KEY:
-        app_logger.error("CRITICAL: OPENAI_API_KEY not found. Agent cannot initialize.")
-        raise ValueError("OpenAI API Key not configured.")
+        app_logger.error("CRITICAL: OPENAI_API_KEY not found in settings. Agent cannot initialize.")
+        raise ValueError("OpenAI API Key not configured. Please set it in Hugging Face Space secrets as OPENAI_API_KEY.")
+
     llm = ChatOpenAI(
-        model_name="gpt-4-turbo-preview", # More capable model for better tool use
+        model_name="gpt-4-turbo-preview", # More capable model for function calling
         # model_name="gpt-3.5-turbo-0125", # More cost-effective alternative
-        temperature=0.1, # Low temperature for more deterministic tool calls
+        temperature=0.1, # Lower for more deterministic tool use and function calls
         openai_api_key=settings.OPENAI_API_KEY
     )
     app_logger.info(f"ChatOpenAI ({llm.model_name}) initialized successfully for agent.")
+
 except Exception as e:
     detailed_error_message = str(e)
-    user_facing_error = f"OpenAI LLM initialization failed: {detailed_error_message}."
+    user_facing_error = f"OpenAI LLM initialization failed: {detailed_error_message}. Check API key and model name."
     if "api_key" in detailed_error_message.lower() or "authenticate" in detailed_error_message.lower():
-        user_facing_error = "OpenAI LLM initialization failed: API key issue. Check HF Secrets."
-    app_logger.error(user_facing_error, exc_info=True)
-    raise ValueError(user_facing_error)
+        user_facing_error = "OpenAI LLM initialization failed: API key issue. Ensure OPENAI_API_KEY is correctly set in Hugging Face Secrets and is valid."
+        app_logger.error(user_facing_error + f" Original: {detailed_error_message}", exc_info=False)
+    else:
+        app_logger.error(user_facing_error, exc_info=True)
+    raise ValueError(user_facing_error) # Propagate error to stop further agent setup
+
 
+# --- Initialize Tools List ---
 tools_list = [
     UMLSLookupTool(),
     BioPortalLookupTool(),
@@ -44,72 +52,140 @@ tools_list = [
 ]
 app_logger.info(f"Agent tools initialized: {[tool.name for tool in tools_list]}")
 
-OPENAI_SYSTEM_PROMPT_TEXT = (
+
+# --- Agent Prompt (for OpenAI Functions Agent - Simplified System Prompt) ---
+# The create_openai_functions_agent implicitly makes tool descriptions available to the LLM
+# via the function-calling mechanism. Explicitly listing {tools} and {tool_names} in the
+# system prompt string might be redundant or conflict with how this agent type works.
+# We will still provide overall instructions and patient_context placeholder.
+OPENAI_SYSTEM_PROMPT_TEXT_SIMPLIFIED = (
     "You are 'Quantum Health Navigator', an AI assistant for healthcare professionals. "
     "Your goal is to assist with medical information lookup, treatment optimization queries, and general medical Q&A. "
-    "You have access to a set of tools (names: {tool_names}) detailed here: {tools}. Use them when appropriate.\n"
+    "You have access to a set of specialized tools. Use them when a user's query can be best answered by one of them, based on their descriptions.\n"
     "Disclaimers: Always state that you are for informational support and not a substitute for clinical judgment. Do not provide direct medical advice for specific patient cases without using the 'quantum_treatment_optimizer' tool if relevant.\n"
-    "Patient Context for this session (if provided by the user earlier): {patient_context}\n"
-    "When using the 'quantum_treatment_optimizer' tool, ensure you populate its 'patient_data' argument using the available {patient_context}. "
+    "Patient Context for this session (if provided by the user earlier): {patient_context}\n" # This variable is passed from invoke
+    "When using the 'quantum_treatment_optimizer' tool, ensure you populate its 'patient_data' argument using the available {patient_context}.\n"
     "For `bioportal_lookup`, if the user doesn't specify an ontology, you may ask or default to 'SNOMEDCT_US'.\n"
     "Always be clear and concise. Cite tools if their output forms a key part of your answer."
 )
 
+# The ChatPromptTemplate defines the sequence of messages sent to the LLM.
+# `create_openai_functions_agent` expects specific placeholders.
 prompt = ChatPromptTemplate.from_messages([
-    ("system", OPENAI_SYSTEM_PROMPT_TEXT),
-    MessagesPlaceholder(variable_name="chat_history"),
-    ("human", "{input}"),
-    MessagesPlaceholder(variable_name="agent_scratchpad")
+    ("system", OPENAI_SYSTEM_PROMPT_TEXT_SIMPLIFIED), # System instructions, expects {patient_context}
+    MessagesPlaceholder(variable_name="chat_history"),  # For past Human/AI messages
+    ("human", "{input}"),                              # For the current user query
+    MessagesPlaceholder(variable_name="agent_scratchpad") # For agent's internal work (function calls/responses)
 ])
-app_logger.info("Agent prompt template created for OpenAI Functions agent.")
+app_logger.info("Agent prompt template (simplified for OpenAI Functions) created.")
+
+# --- Create Agent ---
+if llm is None: # Defensive check, should have been caught by earlier raise
+    app_logger.critical("LLM object is None at agent creation (OpenAI). Application cannot proceed.")
+    raise SystemExit("Agent LLM failed to initialize. Application cannot start.")
 
-if llm is None:
-    app_logger.critical("LLM object is None at agent creation (OpenAI). Cannot proceed.")
-    raise SystemExit("Agent LLM failed to initialize.")
 try:
+    # `create_openai_functions_agent` will use the tools' Pydantic schemas to define
+    # the "functions" that the OpenAI model can call.
     agent = create_openai_functions_agent(llm=llm, tools=tools_list, prompt=prompt)
     app_logger.info("OpenAI Functions agent created successfully.")
 except Exception as e:
     app_logger.error(f"Failed to create OpenAI Functions agent: {e}", exc_info=True)
+    # This is where the "Input to ChatPromptTemplate is missing variables" error would occur
+    # if the prompt object was still expecting variables not provided by the agent constructor or invoke.
     raise ValueError(f"OpenAI agent creation failed: {e}")
 
+
+# --- Create Agent Executor ---
 agent_executor = AgentExecutor(
     agent=agent,
     tools=tools_list,
-    verbose=True,
-    handle_parsing_errors=True,
-    max_iterations=7, # Adjusted max_iterations
-    # return_intermediate_steps=True, # Enable for deep debugging
+    verbose=True,               # Essential for debugging tool usage and agent thoughts
+    handle_parsing_errors=True, # Tries to gracefully handle LLM output parsing issues
+    max_iterations=7,           # Prevent runaway agent loops
+    # return_intermediate_steps=True, # Set to True to get detailed thought/action steps in the response
 )
 app_logger.info("AgentExecutor with OpenAI agent created successfully.")
 
-_agent_executor_instance = agent_executor
+
+# --- Getter Function for Streamlit App ---
+_agent_executor_instance = agent_executor # Store the successfully initialized executor
+
 def get_agent_executor():
+    """
+    Returns the configured agent executor for OpenAI.
+    The executor is initialized when this module is first imported.
+    """
     global _agent_executor_instance
     if _agent_executor_instance is None:
-        app_logger.critical("CRITICAL: Agent executor is None (OpenAI).")
-        raise RuntimeError("Agent executor (OpenAI) not initialized.")
+        # This indicates a failure during the initial module load (LLM or agent creation).
+        app_logger.critical("CRITICAL: Agent executor is None when get_agent_executor is called (OpenAI). Initialization likely failed.")
+        raise RuntimeError("Agent executor (OpenAI) was not properly initialized. Check application startup logs for errors (e.g., API key issues, prompt errors).")
+    
+    # Final check for API key, though LLM initialization should be the primary guard.
+    if not settings.OPENAI_API_KEY:
+        app_logger.error("OpenAI API Key is missing at get_agent_executor call. Agent will fail.")
+        raise ValueError("OpenAI API Key not configured.")
+        
     return _agent_executor_instance
 
+# --- Example Usage (for local testing of this agent.py file) ---
 if __name__ == "__main__":
     if not settings.OPENAI_API_KEY:
-        print("🚨 Set OPENAI_API_KEY in .env or environment.")
+        print("🚨 Please set your OPENAI_API_KEY in .env file or as an environment variable to run the test.")
     else:
         print("\n🚀 Quantum Health Navigator (OpenAI Agent Test Console) 🚀")
-        # ... (The __main__ test block from the last full OpenAI agent.py version) ...
-        try: test_executor = get_agent_executor()
-        except ValueError as e_init: print(f"⚠️ Agent init failed: {e_init}"); exit()
-        history = []
-        context = "Age: 50; Gender: Male; History: Hypertension."
-        print(f"ℹ️  Simulated Context: {context}\n")
+        print("-----------------------------------------------------------")
+        print("Type 'exit' or 'quit' to stop.")
+        print("Example topics: medical definitions, treatment optimization (will use simulated patient context).")
+        print("-" * 59)
+
+        try:
+            test_executor = get_agent_executor() # Get the executor
+        except ValueError as e_init: # Catch errors from get_agent_executor or LLM/agent init
+            print(f"⚠️ Agent initialization failed during test startup: {e_init}")
+            print("Ensure your API key is correctly configured and prompt variables are set.")
+            exit() # Exit if agent can't be initialized
+
+        current_chat_history_for_test_run = [] # List of HumanMessage, AIMessage
+
+        # Simulated patient context for testing the {patient_context} variable
+        test_patient_context_summary_str = (
+            "Age: 70; Gender: Male; Chief Complaint: Shortness of breath on exertion; "
+            "Key Medical History: COPD, Atrial Fibrillation; "
+            "Current Medications: Tiotropium inhaler, Apixaban 5mg BID; Allergies: Penicillin."
+        )
+        print(f"ℹ️  Simulated Patient Context for this test run: {test_patient_context_summary_str}\n")
+
         while True:
-            usr_in = input("👤 You: ").strip()
-            if usr_in.lower() in ["exit", "quit"]: print("👋 Exiting."); break
-            if not usr_in: continue
+            user_input_str = input("👤 You: ").strip()
+            if user_input_str.lower() in ["exit", "quit"]:
+                print("👋 Exiting test console.")
+                break
+            if not user_input_str: # Skip empty input
+                continue
+
             try:
-                res = test_executor.invoke({"input": usr_in, "chat_history": history, "patient_context": context})
-                ai_out = res.get('output', "No output.")
-                print(f"🤖 Agent: {ai_out}")
-                history.extend([HumanMessage(content=usr_in), AIMessage(content=ai_out)])
-                if len(history) > 8: history = history[-8:]
-            except Exception as e_invoke: print(f"⚠️ Invoke Error: {e_invoke}")
+                app_logger.info(f"__main__ test (OpenAI): Invoking with input: '{user_input_str}'")
+                # These are the keys expected by the ChatPromptTemplate and agent:
+                # "input", "chat_history", and "patient_context" (because it's in our system prompt)
+                response_dict = test_executor.invoke({
+                    "input": user_input_str,
+                    "chat_history": current_chat_history_for_test_run,
+                    "patient_context": test_patient_context_summary_str
+                })
+                
+                ai_output_str = response_dict.get('output', "Agent did not produce an 'output' key.")
+                print(f"🤖 Agent: {ai_output_str}")
+                
+                # Update history for the next turn
+                current_chat_history_for_test_run.append(HumanMessage(content=user_input_str))
+                current_chat_history_for_test_run.append(AIMessage(content=ai_output_str))
+                
+                # Optional: Limit history length to prevent overly long contexts
+                if len(current_chat_history_for_test_run) > 10: # Keep last 5 pairs
+                    current_chat_history_for_test_run = current_chat_history_for_test_run[-10:]
+
+            except Exception as e_invoke:
+                print(f"⚠️ Error during agent invocation: {type(e_invoke).__name__} - {e_invoke}")
+                app_logger.error(f"Error in __main__ OpenAI agent test invocation: {e_invoke}", exc_info=True)