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@ -4682,11 +4682,12 @@ Otherwise, STAY SILENT. Do not respond just to be present or because you *can*.
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async def _get_internal_ai_json_response(
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self,
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prompt_messages: List[Dict[str, Any]],
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json_schema: Dict[str, Any],
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task_description: str,
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model: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 500
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max_tokens: int = 500,
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response_format: Optional[Dict[str, Any]] = None, # Added response_format parameter
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json_schema: Optional[Dict[str, Any]] = None # Keep for backward compatibility or simple cases? Or remove? Let's remove for clarity.
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) -> Optional[Dict[str, Any]]:
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"""
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Makes an AI call expecting a specific JSON response format for internal tasks,
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@ -4694,11 +4695,12 @@ Otherwise, STAY SILENT. Do not respond just to be present or because you *can*.
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Args:
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prompt_messages: The list of messages forming the prompt.
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json_schema: The JSON schema the AI response should conform to.
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task_description: A description of the task for logging/headers.
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model: The specific model to use (defaults to GurtCog's default).
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temperature: The temperature for the AI call.
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max_tokens: The maximum tokens for the response.
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response_format: Optional dictionary defining the structured output format (e.g., for JSON schema).
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max_tokens: The maximum tokens for the response.
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Returns:
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The parsed JSON dictionary if successful, None otherwise.
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@ -4712,25 +4714,35 @@ Otherwise, STAY SILENT. Do not respond just to be present or because you *can*.
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payload = {} # Initialize payload in outer scope for finally block
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try:
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# Add final instruction for JSON format
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json_format_instruction = json.dumps(json_schema, indent=2)
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# Add final instruction for JSON format (remains important for the AI)
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# If response_format is provided and contains a schema, use that for the instruction.
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# Otherwise, fall back to a generic JSON instruction if needed, or omit if not expecting JSON.
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# For this function, we generally expect JSON, so let's keep a generic instruction if no schema provided.
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json_instruction_content = "**CRITICAL: Your response MUST consist *only* of the raw JSON object itself.**"
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if response_format and response_format.get("type") == "json_schema":
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schema_for_prompt = response_format.get("json_schema", {}).get("schema", {})
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if schema_for_prompt:
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json_format_instruction = json.dumps(schema_for_prompt, indent=2)
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json_instruction_content = f"**CRITICAL: Your response MUST consist *only* of the raw JSON object itself, matching this schema:**\n```json\n{json_format_instruction}\n```\n**Ensure nothing precedes or follows the JSON.**"
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prompt_messages.append({
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"role": "user",
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"content": f"**CRITICAL: Your response MUST consist *only* of the raw JSON object itself, matching this schema:**\n```json\n{json_format_instruction}\n```\n**Ensure nothing precedes or follows the JSON.**"
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"content": json_instruction_content
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})
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payload = {
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"model": model or self.default_model,
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"messages": prompt_messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"max_tokens": max_tokens
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# No tools needed for this specific internal call type usually
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# "response_format": { # Potentially use if model supports schema enforcement without tools
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# "type": "json_object", # Or "json_schema" if supported
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# # "json_schema": json_schema # If using json_schema type
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# }
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}
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# Add response_format to payload if provided
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if response_format:
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payload["response_format"] = response_format
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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@ -216,20 +216,68 @@ Current State:
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if current_state.get('avatar_image_data'):
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image_prompt_part = "\n(Current avatar image data is provided below)" # Text hint for the AI
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# Define the expected JSON structure for the AI's response
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# Define the JSON schema for the AI's response content
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response_schema_json = {
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"should_update": "boolean (true if you want to change anything, false otherwise)",
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"updates": {
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"avatar_query": "string | null (Search query for a new avatar, e.g., 'Kasane Teto fanart', or null)",
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"new_bio": "string | null (The new bio text, or null)",
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"role_theme": "string | null (A theme for role selection, e.g., 'cool color roles', 'anime fan roles', or null)",
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"new_activity": {
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"type": "string | null (Activity type: 'playing', 'watching', 'listening', 'competing')",
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"text": "string | null (The activity text)"
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} # Can be null if no activity change
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"type": "object",
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"properties": {
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"should_update": {
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"type": "boolean",
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"description": "True if you want to change anything, false otherwise"
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},
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"reasoning": {
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"type": "string",
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"description": "Your reasoning for the decision and chosen updates (or lack thereof)."
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},
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"updates": {
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"type": "object",
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"properties": {
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"avatar_query": {
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"type": ["string", "null"],
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"description": "Search query for a new avatar, e.g., 'Kasane Teto fanart', or null"
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},
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"new_bio": {
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"type": ["string", "null"],
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"description": "The new bio text, or null"
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},
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"role_theme": {
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"type": ["string", "null"],
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"description": "A theme for role selection, e.g., 'cool color roles', 'anime fan roles', or null"
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},
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"new_activity": {
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"type": ["object", "null"],
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"properties": {
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"type": {
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"type": ["string", "null"],
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"enum": ["playing", "watching", "listening", "competing", None],
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"description": "Activity type: 'playing', 'watching', 'listening', 'competing', or null"
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},
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"text": {
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"type": ["string", "null"],
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"description": "The activity text, or null"
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}
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},
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# If new_activity is not null, both type and text should ideally be present,
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# but allow nulls within the object for flexibility if AI omits one.
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# The handling logic in _update_activity already checks for nulls.
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}
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},
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# No required fields within 'updates' itself, as any part can be null
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}
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},
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"required": ["should_update", "reasoning", "updates"],
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"additionalProperties": False # Enforce strictness at schema level too
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}
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json_format_instruction = json.dumps(response_schema_json, indent=2) # For the prompt
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# Define the payload for the response_format parameter
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response_format_payload = {
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"type": "json_schema",
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"json_schema": {
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"name": "profile_update_decision",
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"strict": True, # Enforce strict adherence to the schema
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"schema": response_schema_json
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}
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}
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json_format_instruction = json.dumps(response_schema_json, indent=2)
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# Construct the full prompt message list for the AI
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prompt_messages = [
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@ -253,20 +301,22 @@ Current State:
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try:
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# Need a way to call GurtCog's core AI logic directly
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# This might require refactoring GurtCog or adding a dedicated method
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# Call the internal AI method from GurtCog
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# Call the internal AI method from GurtCog, specifying the model and structured output format
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result_json = await self.gurt_cog._get_internal_ai_json_response(
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prompt_messages=prompt_messages,
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json_schema=response_schema_json, # Use the schema defined earlier
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model="openai/o4-mini-high", # Use the specified OpenAI model
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response_format=response_format_payload, # Enforce structured output
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task_description="Profile Update Decision",
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temperature=0.5 # Lowered temperature for better instruction following
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temperature=0.5 # Keep temperature for some creativity
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)
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if result_json and isinstance(result_json, dict):
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# Basic validation of the received structure
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if "should_update" in result_json and "updates" in result_json:
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# Basic validation of the received structure (now includes reasoning)
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if "should_update" in result_json and "updates" in result_json and "reasoning" in result_json:
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print(f"ProfileUpdaterTask: AI Reasoning: {result_json.get('reasoning', 'N/A')}") # Log the reasoning
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return result_json
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else:
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print(f"ProfileUpdaterTask: AI response missing required keys. Response: {result_json}")
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print(f"ProfileUpdaterTask: AI response missing required keys (should_update, updates, reasoning). Response: {result_json}")
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return None
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else:
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print(f"ProfileUpdaterTask: AI response was not a dictionary. Response: {result_json}")
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