dhjjee

cogs/profile_updater_cog.py

@@ -309,7 +309,7 @@ Review your current profile state (provided below) and decide if you want to mak
                 prompt_messages=prompt_messages,
                 task_description="Profile Update Decision",
                 response_schema_dict=response_schema_dict, # Pass the schema dict
-                model_name=DEFAULT_MODEL, # Use model from config
+                model_name_override=DEFAULT_MODEL, # Use model from config
                 temperature=0.5, # Keep temperature for some creativity
                 max_tokens=500 # Adjust max tokens if needed
             )
@@ -506,7 +506,7 @@ Review your current profile state (provided below) and decide if you want to mak
                 prompt_messages=role_prompt_messages,
                 task_description=f"Role Selection for Guild {guild.id}",
                 response_schema_dict=role_selection_schema_dict, # Pass the schema dict
-                model_name=DEFAULT_MODEL, # Use model from config
+                model_name_override=DEFAULT_MODEL, # Use model from config
                 temperature=0.5 # More deterministic for role selection
             )
 

gurt/analysis.py

@@ -497,7 +497,7 @@ async def reflect_on_memories(cog: 'GurtCog'):
                     prompt_messages=reflection_prompt,
                     task_description=f"User Fact Reflection ({user_name})",
                     response_schema_dict=synthesis_schema,
-                    model_name=SYNTHESIS_MODEL,
+                    model_name_override=SYNTHESIS_MODEL,
                     temperature=0.4,
                     max_tokens=SYNTHESIS_MAX_TOKENS
                 )
@@ -559,7 +559,7 @@ async def decompose_goal_into_steps(cog: 'GurtCog', goal_description: str) -> Op
             prompt_messages=decomposition_prompt_messages,
             task_description=f"Goal Decomposition ({goal_description[:30]}...)",
             response_schema_dict=GOAL_DECOMPOSITION_SCHEMA['schema'],
-            model_name=cog.fallback_model, # Use fallback model for planning potentially
+            model_name_override=cog.fallback_model, # Use fallback model for planning potentially
             temperature=0.3,
             max_tokens=1000 # Allow more tokens for potentially complex plans
         )

gurt/api.py

@@ -1092,7 +1092,7 @@ async def get_proactive_ai_response(cog: 'GurtCog', message: discord.Message, tr
             prompt_messages=planning_prompt_messages,
             task_description=f"Proactive Planning ({trigger_reason})",
             response_schema_dict=PROACTIVE_PLAN_SCHEMA['schema'],
-            model_name=FALLBACK_MODEL, # Use a potentially faster/cheaper model for planning
+            model_name_override=FALLBACK_MODEL, # Use a potentially faster/cheaper model for planning
             temperature=0.5,
             max_tokens=2000
         )

gurt/background.py

@@ -487,7 +487,7 @@ async def background_processing_task(cog: 'GurtCog'):
                                 prompt_messages=[{"role": "system", "content": follow_up_system_prompt}, {"role": "user", "content": follow_up_user_prompt}],
                                 task_description="Autonomous Follow-up Action Decision",
                                 response_schema_dict=follow_up_schema,
-                                model_name=cog.default_model,
+                                model_name_override=cog.default_model,
                                 temperature=0.5
                             )
 

gurt/tools.py

@@ -292,7 +292,7 @@ async def get_conversation_summary(cog: commands.Cog, channel_id: str = None, me
             prompt_messages=prompt_messages,
             task_description=f"Summarization for channel {target_channel_id}",
             response_schema_dict=SUMMARY_RESPONSE_SCHEMA['schema'], # Pass the schema dict
-            model_name=DEFAULT_MODEL, # Consider a cheaper/faster model if needed
+            model_name_override=DEFAULT_MODEL, # Consider a cheaper/faster model if needed
             temperature=0.3,
             max_tokens=200 # Adjust as needed
         )
@@ -648,7 +648,7 @@ async def _check_command_safety(cog: commands.Cog, command: str) -> Dict[str, An
         prompt_messages=prompt_messages,
         task_description="Command Safety Check",
          response_schema_dict=safety_schema, # Pass the schema dict directly
-         model_name=SAFETY_CHECK_MODEL,
+         model_name_override=SAFETY_CHECK_MODEL,
          temperature=0.1,
          max_tokens=1000 # Increased token limit
      )
@@ -1176,7 +1176,7 @@ async def create_new_tool(cog: commands.Cog, tool_name: str, description: str, p
         prompt_messages=generation_prompt_messages,
         task_description=f"Generate code for new tool '{tool_name}'",
         response_schema_dict=generation_schema,
-        model_name=cog.default_model, # Use default model for generation
+        model_name_override=cog.default_model, # Use default model for generation
         temperature=0.3, # Lower temperature for more predictable code
         max_tokens=5000 # Allow ample space for code generation
     )

wheatley/api.py

@@ -817,7 +817,7 @@ async def get_proactive_ai_response(cog: 'WheatleyCog', message: discord.Message
             prompt_messages=planning_prompt_messages,
             task_description=f"Proactive Planning ({trigger_reason})",
             response_schema_dict=PROACTIVE_PLAN_SCHEMA['schema'],
-            model_name=FALLBACK_MODEL, # Use a potentially faster/cheaper model for planning
+            model_name_override=FALLBACK_MODEL, # Use a potentially faster/cheaper model for planning
             temperature=0.5,
             max_tokens=300
         )