requirements update for llm compat

app.py

@@ -328,59 +328,65 @@ class BasicAgent:
                         }]
                     )
                     
+                    logger.info("Attempting to use primary LLM (Gemini)")
                     response = self.primary_llm.invoke(
                         messages_with_system,
                         tools=[genai_tool]
                     )
+                    logger.info("Successfully used primary LLM")
+                    
                 except Exception as e:
                     error_str = str(e)
-                    if "429" in error_str:
-                        if "GenerateRequestsPerDayPerProjectPerModel-FreeTier" in error_str:
-                            logger.warning("Daily quota limit reached for primary LLM, trying fallback")
-                            if hasattr(self, 'fallback_llm') and self.fallback_llm is not None:
-                                try:
-                                    # For OpenAI, we can use the system message directly
-                                    response = self.fallback_llm.invoke(
-                                        [self.sys_msg] + messages,
-                                        tools=[{
-                                            "type": "function",
-                                            "function": {
-                                                "name": "google_search",
-                                                "description": "Search for information on the web",
-                                                "parameters": {
-                                                    "type": "object",
-                                                    "properties": {
-                                                        "query": {
-                                                            "type": "string",
-                                                            "description": "The search query"
-                                                        }
-                                                    },
-                                                    "required": ["query"]
+                    logger.error(f"Primary LLM error: {error_str}")
+                    
+                    # Check if we should try fallback
+                    if (hasattr(self, 'fallback_llm') and self.fallback_llm is not None and 
+                        ("429" in error_str or "object" in error_str or "string" in error_str)):
+                        try:
+                            logger.info("Attempting to use fallback LLM (OpenAI)")
+                            # For OpenAI, we can use the system message directly
+                            response = self.fallback_llm.invoke(
+                                [self.sys_msg] + messages,
+                                tools=[{
+                                    "type": "function",
+                                    "function": {
+                                        "name": "google_search",
+                                        "description": "Search for information on the web",
+                                        "parameters": {
+                                            "type": "object",
+                                            "properties": {
+                                                "query": {
+                                                    "type": "string",
+                                                    "description": "The search query"
                                                 }
-                                            }
-                                        }]
-                                    )
-                                    logger.info("Successfully used fallback LLM")
-                                except Exception as fallback_error:
-                                    logger.error(f"Fallback LLM also failed: {str(fallback_error)}")
-                                    return {
-                                        "messages": [AIMessage(content="All LLM services are currently unavailable. Please try again later.")], 
-                                        "next": END
+                                            },
+                                            "required": ["query"]
+                                        }
                                     }
+                                }]
+                            )
+                            logger.info("Successfully used fallback LLM")
+                        except Exception as fallback_error:
+                            logger.error(f"Fallback LLM error: {str(fallback_error)}")
+                            if "429" in str(fallback_error):
+                                return {
+                                    "messages": [AIMessage(content="All LLM services are currently rate limited. Please try again later.")], 
+                                    "next": END
+                                }
                             else:
-                                logger.warning("No fallback LLM available")
                                 return {
-                                    "messages": [AIMessage(content="I've reached my daily limit for processing requests. Please try again tomorrow or contact support for assistance.")], 
+                                    "messages": [AIMessage(content="All LLM services are currently unavailable. Please try again later.")], 
                                     "next": END
                                 }
-                        else:
-                            # For other rate limits, wait and retry
+                    else:
+                        # If no fallback available or error not related to rate limits
+                        if "429" in error_str:
                             wait_time = 60 * (retry_count + 1)  # Exponential backoff
                             logger.warning(f"Rate limit hit, waiting {wait_time} seconds before retry...")
                             time.sleep(wait_time)
                             raise  # Re-raise to trigger retry
-                    else:
-                        raise
+                        else:
+                            raise
                 
                 logger.info("\n=== Model Output ===")
                 log_message(response, "  ")