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@ -1096,8 +1096,8 @@ async def get_ai_response(cog: 'GurtCog', message: discord.Message, model_name:
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# Start with base config (which now includes system_instruction)
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final_gen_config_dict = base_generation_config_dict.copy()
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final_gen_config_dict.update({
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"response_mime_type": "application/json",
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"response_schema": processed_response_schema,
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# "response_mime_type": "application/json",
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# "response_schema": processed_response_schema,
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# Explicitly exclude tools/tool_config for final JSON generation
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"tools": None,
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"tool_config": None,
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80
print_vertex_schema.py
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80
print_vertex_schema.py
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@ -0,0 +1,80 @@
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import copy
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import json
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from typing import Dict, Any
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# --- Schema Preprocessing Helper ---
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# Copied from discordbot/gurt/api.py
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def _preprocess_schema_for_vertex(schema: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Recursively preprocesses a JSON schema dictionary to replace list types
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(like ["string", "null"]) with the first non-null type, making it
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compatible with Vertex AI's GenerationConfig schema requirements.
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Args:
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schema: The JSON schema dictionary to preprocess.
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Returns:
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A new, preprocessed schema dictionary.
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"""
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if not isinstance(schema, dict):
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return schema # Return non-dict elements as is
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processed_schema = copy.deepcopy(schema) # Work on a copy
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for key, value in processed_schema.items():
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if key == "type" and isinstance(value, list):
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# Find the first non-"null" type in the list
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first_valid_type = next((t for t in value if isinstance(t, str) and t.lower() != "null"), None)
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if first_valid_type:
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processed_schema[key] = first_valid_type
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else:
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# Fallback if only "null" or invalid types are present (shouldn't happen in valid schemas)
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processed_schema[key] = "object" # Or handle as error
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print(f"Warning: Schema preprocessing found list type '{value}' with no valid non-null string type. Falling back to 'object'.")
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elif isinstance(value, dict):
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processed_schema[key] = _preprocess_schema_for_vertex(value) # Recurse for nested objects
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elif isinstance(value, list):
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# Recurse for items within arrays (e.g., in 'properties' of array items)
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processed_schema[key] = [_preprocess_schema_for_vertex(item) if isinstance(item, dict) else item for item in value]
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# Handle 'properties' specifically
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elif key == "properties" and isinstance(value, dict):
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processed_schema[key] = {prop_key: _preprocess_schema_for_vertex(prop_value) for prop_key, prop_value in value.items()}
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# Handle 'items' specifically if it's a schema object
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elif key == "items" and isinstance(value, dict):
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processed_schema[key] = _preprocess_schema_for_vertex(value)
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return processed_schema
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# --- Response Schema ---
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# Copied from discordbot/gurt/config.py
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RESPONSE_SCHEMA = {
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"name": "gurt_response",
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"description": "The structured response from Gurt.",
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"schema": {
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"type": "object",
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"properties": {
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"should_respond": {
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"type": "boolean",
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"description": "Whether the bot should send a text message in response."
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},
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"content": {
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"type": "string",
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"description": "The text content of the bot's response. Can be empty if only reacting."
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},
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"react_with_emoji": {
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"type": ["string", "null"],
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"description": "Optional: A standard Discord emoji to react with, or null/empty if no reaction."
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},
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"reply_to_message_id": {
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"type": ["string", "null"],
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"description": "Optional: The ID of the message this response should reply to. Null or omit for a regular message."
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}
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# Note: tool_requests is handled by Vertex AI's function calling mechanism
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},
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"required": ["should_respond", "content"]
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}
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}
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if __name__ == "__main__":
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processed = _preprocess_schema_for_vertex(RESPONSE_SCHEMA['schema'])
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print(json.dumps(processed, indent=2))
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