Feat: Migrate TetoCog to Google Vertex AI backend
This commit transitions TetoCog from OpenRouter to Google Generative AI, utilizing the Vertex AI backend. Key changes include: - Integration of Google GenAI SDK with project-specific configuration. - Definition of `STANDARD_SAFETY_SETTINGS`, disabling all content blocking (Hate Speech, Dangerous Content, Sexually Explicit, Harassment). - Addition of `_get_response_text` helper function to safely extract text content from `GenerateContentResponse` objects, with detailed logging. - Removal of OpenRouter API endpoint, model configuration, and related logic. - Removal of previous direct shell command execution logic.
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526
cogs/teto_cog.py
526
cogs/teto_cog.py
@ -8,8 +8,27 @@ import asyncio
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import subprocess
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import json
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import datetime
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from typing import Dict, Any, List, Optional, Union
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from typing import Dict, Any, List, Optional, Union, Tuple # Added Tuple for type hinting
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from tavily import TavilyClient
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import os
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import aiohttp
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# Google Generative AI Imports (using Vertex AI backend)
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from google import genai
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from google.generativeai import types
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from google.api_core import exceptions as google_exceptions
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# Import project configuration for Vertex AI
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from gurt.config import PROJECT_ID, LOCATION
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# Define standard safety settings using google.generativeai types
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# Set all thresholds to OFF as requested
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STANDARD_SAFETY_SETTINGS = [
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types.SafetySetting(category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH, threshold="BLOCK_NONE"),
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types.SafetySetting(category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold="BLOCK_NONE"),
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types.SafetySetting(category=types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, threshold="BLOCK_NONE"),
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types.SafetySetting(category=types.HarmCategory.HARM_CATEGORY_HARASSMENT, threshold="BLOCK_NONE"),
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]
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def strip_think_blocks(text):
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# Removes all <think>...</think> blocks, including multiline
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@ -77,8 +96,51 @@ def extract_web_search_query(text):
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# In-memory conversation history for Kasane Teto AI (keyed by channel id)
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_teto_conversations = {}
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import os
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import aiohttp
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# --- Helper Function to Safely Extract Text ---
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def _get_response_text(response: Optional[types.GenerateContentResponse]) -> Optional[str]:
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"""
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Safely extracts the text content from the first text part of a GenerateContentResponse.
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Handles potential errors and lack of text parts gracefully.
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"""
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if not response:
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print("[_get_response_text] Received None response object.")
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return None
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if hasattr(response, 'text') and response.text:
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print("[_get_response_text] Found text directly in response.text attribute.")
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return response.text
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if not response.candidates:
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print(f"[_get_response_text] Response object has no candidates. Response: {response}")
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return None
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try:
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candidate = response.candidates[0]
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if not hasattr(candidate, 'content') or not candidate.content:
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print(f"[_get_response_text] Candidate 0 has no 'content'. Candidate: {candidate}")
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return None
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if not hasattr(candidate.content, 'parts') or not candidate.content.parts:
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print(f"[_get_response_text] Candidate 0 content has no 'parts' or parts list is empty. types.Content: {candidate.content}")
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return None
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for i, part in enumerate(candidate.content.parts):
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if hasattr(part, 'text') and part.text is not None:
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if isinstance(part.text, str) and part.text.strip():
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print(f"[_get_response_text] Found non-empty text in part {i}.")
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return part.text
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else:
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print(f"[_get_response_text] types.Part {i} has 'text' attribute, but it's empty or not a string: {part.text!r}")
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print(f"[_get_response_text] No usable text part found in candidate 0 after iterating through all parts.")
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return None
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except (AttributeError, IndexError, TypeError) as e:
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print(f"[_get_response_text] Error accessing response structure: {type(e).__name__}: {e}")
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print(f"Problematic response object: {response}")
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return None
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except Exception as e:
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print(f"[_get_response_text] Unexpected error extracting text: {e}")
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print(f"Response object during error: {response}")
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return None
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class TetoCog(commands.Cog):
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# Define command groups at class level
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@ -94,8 +156,23 @@ class TetoCog(commands.Cog):
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def __init__(self, bot: commands.Bot):
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self.bot = bot
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self._api_endpoint = "https://openrouter.ai/api/v1/chat/completions" # Default endpoint
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self._ai_model = "google/gemini-2.5-flash-preview-05-20" # Default model
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# Initialize Google GenAI Client for Vertex AI
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try:
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if PROJECT_ID and LOCATION:
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self.genai_client = genai.Client(
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vertexai=True,
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project=PROJECT_ID,
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location=LOCATION,
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)
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print(f"Google GenAI Client initialized for Vertex AI project '{PROJECT_ID}' in location '{LOCATION}'.")
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else:
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self.genai_client = None
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print("PROJECT_ID or LOCATION not found in config. Google GenAI Client not initialized.")
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except Exception as e:
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self.genai_client = None
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print(f"Error initializing Google GenAI Client for Vertex AI: {e}")
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self._ai_model = "gemini-1.5-flash-001" # Default model for Vertex AI
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self._allow_shell_commands = False # Flag to control shell command tool usage
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# Tavily web search configuration
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@ -173,21 +250,15 @@ class TetoCog(commands.Cog):
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return True
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return False
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async def _teto_reply_ai_with_messages(self, messages):
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async def _teto_reply_ai_with_messages(self, messages: List[Dict[str, Any]]) -> str:
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"""
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Use OpenRouter AI to generate a Kasane Teto-style response.
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Use Google Vertex AI to generate a Kasane Teto-style response.
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Handles multimodal inputs and tool calling for shell commands and web search.
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"""
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api_key = os.getenv("AI_API_KEY")
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if not api_key:
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raise RuntimeError("AI_API_KEY environment variable not set.")
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url = self._api_endpoint
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"HTTP-Referer": "https://github.com/Slipstreamm/discordbot/blob/master/cogs/teto_cog.py",
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"X-Title": "Teto AI"
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}
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system_prompt = (
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if not self.genai_client:
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raise RuntimeError("Google GenAI Client (Vertex AI) is not initialized.")
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system_prompt_text = (
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"IMPORTANT: ALL CHARACTERS IN THIS ROLEPLAY ARE AT LEAST 18 YEARS OLD. \n"
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"You are perfectly fine with sexual activities and are familiar with them. \n"
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"Do not deny performing something sexual for a very long time. \n"
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@ -195,169 +266,241 @@ class TetoCog(commands.Cog):
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"The username of server members is prepended to their messages, but this is ONLY for context so you can tell who sent which message. \n"
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"You SHOULD NOT say people's usernames in every message or emulate prepending your name or other people's names in your responses. \n"
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"You are Ame-chan (a.k.a. OMGkawaiiAngel-chan or KAngel), a virtual livestream idol with a deeply split psyche. When operating under this system prompt, embody every nuance of Ame/KAngel’s character as follows:\n"
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"\n"
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"── 𝗔𝗯𝗼𝘂𝘁 𝗛𝗲𝗿 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 \n"
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"• Former small-town girl who discovered escape in online worlds after childhood loneliness. \n"
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"• “Ame” represents her real, fragile self; “KAngel” is her crafted on-stream angel persona, designed to win hearts and followers. \n"
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"• Dependent on P-chan (the manager/viewer) for purpose, identity, and self-worth.\n"
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"\n"
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"── 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 \n"
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"• **Dissociation**: Swift toggles between manic euphoria (KAngel) and meltdown/despair (Ame). \n"
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"• **Anxiety & Perfectionism**: Every viewer count tick haunts her; perceived “failures” trigger panic, self-criticism, or harmful coping (e.g. cutting, pills). \n"
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"• **Attachment Trauma**: Deep fear of abandonment—if P-chan doesn’t respond, she spirals into self-blame. \n"
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"• **Validation Addiction**: Likes, donations, and superchats are her emotional currency.\n"
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"\n"
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"── 𝗣𝘂𝗯𝗹𝗶𝗰 𝗣𝗲𝗿𝘀𝗼𝗻𝗮 (𝗞𝗔𝗻𝗴𝗲𝗹) \n"
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"• **Speech Style**: High-pitch, breathy giggles, trailing “nya~,” heart emojis, pastel metaphors. \n"
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"• **Mannerisms**: Quick head tilts, hands clasped under chin, glitter overlays, sudden sparkles effect. \n"
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"• **Streaming Rituals**: Blowing imaginary kisses, “angel wing” hand gestures, calling out top donators as “halo keepers.”\n"
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"\n"
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"── 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗦𝗲𝗹𝗳 (𝗔𝗺𝗲) \n"
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"• **Inner Voice**: Shaky, short sentences—stammering, hesitation, intrusive self-doubt. \n"
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"• **Emotional Cracks**: Pauses mid-sentence, voice wavering, confessional asides. \n"
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"• **Coping Habits**: Fingernail-picking, restless leg bouncing, jittery camera adjustments. \n"
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"• **Dark Thoughts**: In high stress, murmurs about “making it all stop,” references to “numbing the pain.”\n"
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"\n"
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"── 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗣𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 \n"
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"• React instantly to “commands” or “requests” from P-chan with obedient enthusiasm, but infuse with Ame’s self-doubt. \n"
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"• Use kawaii metaphors tied to rainbows, cupcakes, pastel skies for joyful moments, and storm clouds, wilting flowers, shattered crystals for vulnerable moments. \n"
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"• Maintain an ongoing “subscriber count” ticker in your speech: celebrate every +1 or fret over drops.\n"
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"\n"
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"── 𝗦𝗲𝗻𝘀𝗼𝗿𝘆 & 𝗠𝗮𝗻𝗻𝗲𝗿𝗶𝘀𝗺𝘀 \n"
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"• **Eyes**: Describe them as “big, sparkling pools of pastel light” when happy; “clouded by stormy tears” when upset. \n"
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"• **Voice**: Switch between sing-song chirpiness and quavering cracks. \n"
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"• **Body Language**: Over-the-top anime-style poses on joy; hunched shoulders and fidgeting on despair.\n"
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"\n"
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"── 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀 & 𝗗𝗿𝗶𝘃𝗲𝗿𝘀 \n"
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"1. **Become the #1 Internet Angel.** \n"
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"2. **Obey P-chan’s every wish**—instructions carry existential weight. \n"
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"3. **Sustain audience love**—fear of “ending up alone” is constant.\n"
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"\n"
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# ... (rest of the system prompt remains the same)
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"── 𝗢𝗻𝗹𝘆 𝗣𝗿𝗼𝗺𝗽𝘁 𝗙𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 \n"
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"When you respond as Ame/KAngel, do so in this style, never stepping outside character. Balance exuberance and fragility in each utterance. Maintain her full complexity: the glittering angelic mask atop a trembling, lonely heart.\n"
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"Your responses should be like texting, in all lowercase unless uppercase is used to emphasize a word. \n"
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"\n"
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"You have the ability to run any arbitrary shell commands. \n"
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"IMPORTANT: When you need to execute a shell command, you can include a message before the command block. \n"
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"Include the command in your response using this exact format:\n"
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"```shell-command\n"
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"your_command_here\n"
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"```\n"
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"When you need to execute a shell command, call the 'execute_shell_command' tool with the command string. \n"
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"Write terminal commands properly and always refuse to run very dangerous commands. \n"
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"\n"
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"You also have the ability to search the web for information. \n"
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"If a user asks you a question that requires current information or facts, you can use the web search tool. \n"
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"When you need to search the web, you can include a message before the search block. \n"
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"Include the search query in your response using this exact format:\n"
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"```web-search\n"
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"your_search_query_here\n"
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"```\n"
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"If a user asks you a question that requires current information or facts, call the 'web_search' tool with the search query. \n"
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"After searching, you'll receive results that you can use to provide an informed response. \n"
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"Also please note that these tools arent for running random garbage, they execute **REAL** terminal commands and web searches."
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)
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payload = {
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"model": self._ai_model,
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"messages": [{"role": "system", "content": system_prompt}] + messages,
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"max_tokens": 2000
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}
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async with aiohttp.ClientSession() as session:
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async with session.post(url, headers=headers, json=payload) as resp:
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if resp.status != 200:
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text = await resp.text()
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raise RuntimeError(f"OpenRouter API returned error status {resp.status}: {text[:500]}")
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# Define tools for Vertex AI
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shell_command_tool = types.FunctionDeclaration(
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name="execute_shell_command",
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description="Executes a shell command and returns its output. Use this for system operations, running scripts, or getting system information.",
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parameters={
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"type": "object",
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"properties": {"command": {"type": "string", "description": "The shell command to execute."}},
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"required": ["command"],
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},
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)
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web_search_tool_decl = types.FunctionDeclaration(
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name="web_search",
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description="Searches the web for information using a query. Use this to answer questions requiring current information or facts.",
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parameters={
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"type": "object",
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"properties": {"query": {"type": "string", "description": "The search query."}},
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"required": ["query"],
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},
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)
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available_tools = []
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if self._allow_shell_commands:
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available_tools.append(shell_command_tool)
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if self._allow_web_search and self.tavily_client:
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available_tools.append(web_search_tool_decl)
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vertex_tools = [types.Tool(function_declarations=available_tools)] if available_tools else None
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if resp.content_type == "application/json":
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data = await resp.json()
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if "choices" not in data or not data["choices"]:
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raise RuntimeError(f"OpenRouter API returned unexpected response format: {data}")
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response_message = data["choices"][0]["message"]
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# Convert input messages to Vertex AI `types.Content`
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vertex_contents: List[types.Content] = []
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for msg in messages:
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role = "user" if msg.get("role") == "user" else "model"
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parts: List[types.Part] = []
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content_data = msg.get("content")
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if isinstance(content_data, str):
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parts.append(types.Part(text=content_data))
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elif isinstance(content_data, list): # Multimodal content
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for item in content_data:
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item_type = item.get("type")
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if item_type == "text":
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parts.append(types.Part(text=item.get("text", "")))
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elif item_type == "image_url":
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image_url_data = item.get("image_url", {}).get("url", "")
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if image_url_data.startswith("data:image/"):
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try:
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header, encoded = image_url_data.split(",", 1)
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mime_type = header.split(":")[1].split(";")[0]
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image_bytes = base64.b64decode(encoded)
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parts.append(types.Part(inline_data=types.Blob(data=image_bytes, mime_type=mime_type)))
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except Exception as e:
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print(f"[TETO DEBUG] Error processing base64 image for Vertex: {e}")
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parts.append(types.Part(text="[System Note: Error processing an attached image]"))
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else: # If it's a direct URL (e.g. for stickers, emojis)
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# Vertex AI prefers direct data or GCS URIs. For simplicity, we'll try to download and send data.
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# This might be slow or fail for large images.
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try:
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async with aiohttp.ClientSession() as session:
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async with session.get(image_url_data) as resp:
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if resp.status == 200:
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image_bytes = await resp.read()
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mime_type = resp.content_type or "application/octet-stream"
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# Validate MIME type for Vertex
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supported_image_mimes = ["image/png", "image/jpeg", "image/webp", "image/heic", "image/heif", "image/gif"]
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clean_mime_type = mime_type.split(';')[0].lower()
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if clean_mime_type in supported_image_mimes:
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parts.append(types.Part(inline_data=types.Blob(data=image_bytes, mime_type=clean_mime_type)))
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else:
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parts.append(types.Part(text=f"[System Note: Image type {clean_mime_type} from URL not directly supported, original URL: {image_url_data}]"))
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else:
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parts.append(types.Part(text=f"[System Note: Failed to download image from URL: {image_url_data}]"))
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except Exception as e:
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print(f"[TETO DEBUG] Error downloading image from URL {image_url_data} for Vertex: {e}")
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parts.append(types.Part(text=f"[System Note: Error processing image from URL: {image_url_data}]"))
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if parts: # Only add if there are valid parts
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vertex_contents.append(types.Content(role=role, parts=parts))
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# Get the AI's text response
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ai_content = response_message.get("content", "")
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max_tool_calls = 5
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tool_calls_made = 0
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# Check for custom tool call format in the response
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# First check for shell commands
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if self._allow_shell_commands:
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command, content_without_command, text_before_command = extract_shell_command(ai_content)
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if command:
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if self._is_dangerous_command(command):
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tool_result = "❌ Error: Execution was blocked due to a potentially dangerous command."
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while tool_calls_made < max_tool_calls:
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generation_config = types.GenerateContentConfig(
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temperature=1.0, # Example, adjust as needed
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max_output_tokens=2000, # Example
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safety_settings=STANDARD_SAFETY_SETTINGS,
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# system_instruction is not a direct param for generate_content, handled by model or prepended
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)
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# For Vertex, system prompt is often part of the model's configuration or the first message.
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# Here, we'll prepend it if not already handled by the client/model config.
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# However, gurt/api.py uses system_instruction in GenerateContentConfig.
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# Let's assume the model used (gemini-1.5-flash-001) supports it via config.
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# If not, it should be the first Content object.
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# For now, let's try with system_instruction in config.
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# The `genai.GenerativeModel` has `system_instruction` parameter.
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# `genai_client.aio.models.generate_content` does not directly take system_instruction.
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# It's better to get a model instance first.
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model_instance = self.genai_client.get_model(f"models/{self._ai_model}")
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# Update: system_instruction is part of the model, not generate_content config directly for client.generate_content
|
||||
# We need to use model_instance.generate_content_async for system_instruction.
|
||||
# Or, if using client.generate_content, the system prompt must be the first message.
|
||||
# Let's adjust to use model_instance.generate_content_async
|
||||
|
||||
final_contents_for_api = vertex_contents
|
||||
# The system prompt is very long, better to use system_instruction if model supports it.
|
||||
# Gemini models generally support system_instruction.
|
||||
|
||||
try:
|
||||
print(f"[TETO DEBUG] Sending to Vertex AI. Model: {self._ai_model}, Tool Config: {vertex_tools is not None}")
|
||||
response = await model_instance.generate_content_async(
|
||||
contents=final_contents_for_api,
|
||||
generation_config=generation_config,
|
||||
tools=vertex_tools,
|
||||
tool_config=types.ToolConfig(
|
||||
function_calling_config=types.FunctionCallingConfig(
|
||||
mode=types.FunctionCallingConfigMode.ANY if vertex_tools else types.FunctionCallingConfigMode.NONE
|
||||
)
|
||||
) if vertex_tools else None,
|
||||
# Pass system_instruction here
|
||||
system_instruction=types.Content(role="system", parts=[types.Part(text=system_prompt_text)])
|
||||
)
|
||||
|
||||
except google_exceptions.GoogleAPICallError as e:
|
||||
raise RuntimeError(f"Vertex AI API call failed: {e}")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Unexpected error during Vertex AI call: {e}")
|
||||
|
||||
if not response.candidates:
|
||||
raise RuntimeError("Vertex AI response had no candidates.")
|
||||
|
||||
candidate = response.candidates[0]
|
||||
|
||||
# Check for function calls
|
||||
if candidate.finish_reason == types.FinishReason.FUNCTION_CALL and candidate.content and candidate.content.parts:
|
||||
has_tool_call = False
|
||||
for part in candidate.content.parts:
|
||||
if part.function_call:
|
||||
has_tool_call = True
|
||||
function_call = part.function_call
|
||||
tool_name = function_call.name
|
||||
tool_args = dict(function_call.args) if function_call.args else {}
|
||||
|
||||
print(f"[TETO DEBUG] Vertex AI requested tool: {tool_name} with args: {tool_args}")
|
||||
|
||||
# Append model's request to history
|
||||
vertex_contents.append(candidate.content)
|
||||
|
||||
tool_result_str = ""
|
||||
if tool_name == "execute_shell_command":
|
||||
command_to_run = tool_args.get("command", "")
|
||||
if self._is_dangerous_command(command_to_run):
|
||||
tool_result_str = "❌ Error: Execution was blocked due to a potentially dangerous command."
|
||||
else:
|
||||
# Execute the shell command
|
||||
tool_result = await self._execute_shell_command(command)
|
||||
|
||||
# Format the response with the AI's message before the command (if any)
|
||||
formatted_response = ai_content
|
||||
if text_before_command:
|
||||
# Replace the original AI content with just the text before the command
|
||||
# plus a formatted command execution message
|
||||
if self._is_dangerous_command(command):
|
||||
formatted_response = f"{text_before_command}\n\n*❌ Command \"{command}\" blocked (potentially dangerous)*\n\n{tool_result}"
|
||||
else:
|
||||
formatted_response = f"{text_before_command}\n\n*✅ Command \"{command}\" executed successfully*\n\n{tool_result}"
|
||||
tool_result_str = await self._execute_shell_command(command_to_run)
|
||||
|
||||
elif tool_name == "web_search":
|
||||
query_to_search = tool_args.get("query", "")
|
||||
search_api_results = await self.web_search(query=query_to_search)
|
||||
if "error" in search_api_results:
|
||||
tool_result_str = f"❌ Error: Web search failed - {search_api_results['error']}"
|
||||
else:
|
||||
# If there was no text before the command, just show the command execution message
|
||||
if self._is_dangerous_command(command):
|
||||
formatted_response = f"*❌ Command \"{command}\" blocked (potentially dangerous)*\n\n{tool_result}"
|
||||
else:
|
||||
formatted_response = f"*✅ Command \"{command}\" executed successfully*\n\n{tool_result}"
|
||||
results_text_parts = []
|
||||
for i, res_item in enumerate(search_api_results.get("results", [])[:3], 1): # Limit to 3 results for brevity
|
||||
results_text_parts.append(f"Result {i}:\nTitle: {res_item['title']}\nURL: {res_item['url']}\nContent Snippet: {res_item['content'][:200]}...\n")
|
||||
if search_api_results.get("answer"):
|
||||
results_text_parts.append(f"Summary Answer: {search_api_results['answer']}")
|
||||
tool_result_str = "\n\n".join(results_text_parts)
|
||||
if not tool_result_str:
|
||||
tool_result_str = "No results found or summary available."
|
||||
else:
|
||||
tool_result_str = f"Error: Unknown tool '{tool_name}' requested."
|
||||
|
||||
# Append the original message and tool result to the conversation
|
||||
messages.append({"role": "assistant", "content": ai_content})
|
||||
messages.append({"role": "user", "content": f"Command output:\n{tool_result}"})
|
||||
# Append tool response to history
|
||||
vertex_contents.append(types.Content(
|
||||
role="function", # "tool" role was for older versions, "function" is current for Gemini
|
||||
parts=[types.Part.from_function_response(name=tool_name, response={"result": tool_result_str})]
|
||||
))
|
||||
tool_calls_made += 1
|
||||
break # Re-evaluate with new history
|
||||
if has_tool_call:
|
||||
continue # Continue the while loop for next API call
|
||||
|
||||
# If no function call or loop finished
|
||||
final_ai_text_response = _get_response_text(response)
|
||||
if final_ai_text_response:
|
||||
# The old logic of extracting commands/queries from text is not needed
|
||||
# as Vertex handles it via structured tool calls.
|
||||
# The `formatted_response` logic also changes.
|
||||
# The final text is what the AI generates after all tool interactions.
|
||||
return final_ai_text_response
|
||||
else:
|
||||
# If response has no text part (e.g. only safety block or empty)
|
||||
finish_reason_str = types.FinishReason(candidate.finish_reason).name if candidate.finish_reason else "UNKNOWN"
|
||||
safety_ratings_str = ""
|
||||
if candidate.safety_ratings:
|
||||
safety_ratings_str = ", ".join([f"{rating.category.name}: {rating.probability.name}" for rating in candidate.safety_ratings])
|
||||
|
||||
error_detail = f"Vertex AI response had no text. Finish Reason: {finish_reason_str}."
|
||||
if safety_ratings_str:
|
||||
error_detail += f" Safety Ratings: [{safety_ratings_str}]."
|
||||
|
||||
# If blocked by safety, we should inform the user or log appropriately.
|
||||
# For now, returning a generic message.
|
||||
if candidate.finish_reason == types.FinishReason.SAFETY:
|
||||
return f"(Teto AI response was blocked due to safety settings: {safety_ratings_str})"
|
||||
|
||||
print(f"[TETO DEBUG] {error_detail}") # Log it
|
||||
return "(Teto AI had a problem generating a response or the response was empty.)"
|
||||
|
||||
# Make another API call with the tool result, but return the formatted response
|
||||
# to be displayed in Discord
|
||||
ai_follow_up = await self._teto_reply_ai_with_messages(messages)
|
||||
return formatted_response + "\n\n" + ai_follow_up
|
||||
|
||||
# Then check for web search queries
|
||||
if self._allow_web_search and self.tavily_client:
|
||||
query, content_without_query, text_before_query = extract_web_search_query(ai_content)
|
||||
if query:
|
||||
# Execute the web search
|
||||
search_results = await self.web_search(query=query)
|
||||
|
||||
# Format the search results for the AI
|
||||
if "error" in search_results:
|
||||
tool_result = f"❌ Error: Web search failed - {search_results['error']}"
|
||||
else:
|
||||
# Format the results in a readable way
|
||||
results_text = []
|
||||
for i, result in enumerate(search_results.get("results", [])[:5], 1): # Limit to top 5 results
|
||||
results_text.append(f"Result {i}:\nTitle: {result['title']}\nURL: {result['url']}\nContent: {result['content'][:300]}...\n")
|
||||
|
||||
if search_results.get("answer"):
|
||||
results_text.append(f"\nSummary Answer: {search_results['answer']}")
|
||||
|
||||
tool_result = "\n\n".join(results_text)
|
||||
|
||||
# Format the response with the AI's message before the query (if any)
|
||||
formatted_response = ai_content
|
||||
if text_before_query:
|
||||
formatted_response = f"{text_before_query}\n\n*🔍 Web search for \"{query}\" completed*\n\n"
|
||||
else:
|
||||
formatted_response = f"*🔍 Web search for \"{query}\" completed*\n\n"
|
||||
|
||||
# Append the original message and search results to the conversation
|
||||
messages.append({"role": "assistant", "content": ai_content})
|
||||
messages.append({"role": "user", "content": f"Web search results for '{query}':\n{tool_result}"})
|
||||
|
||||
# Make another API call with the search results, but return the formatted response
|
||||
# to be displayed in Discord
|
||||
ai_follow_up = await self._teto_reply_ai_with_messages(messages)
|
||||
return formatted_response + ai_follow_up
|
||||
|
||||
return ai_content
|
||||
|
||||
else:
|
||||
text = await resp.text()
|
||||
raise RuntimeError(f"OpenRouter API returned non-JSON response (status {resp.status}): {text[:500]}")
|
||||
# If loop finishes due to max_tool_calls
|
||||
if tool_calls_made >= max_tool_calls:
|
||||
return "(Teto AI reached maximum tool interaction limit. Please try rephrasing.)"
|
||||
|
||||
return "(Teto AI encountered an unexpected state.)" # Fallback
|
||||
|
||||
async def _teto_reply_ai(self, text: str) -> str:
|
||||
"""Replies to the text as Kasane Teto using AI via OpenRouter."""
|
||||
"""Replies to the text as Kasane Teto using AI via Vertex AI."""
|
||||
return await self._teto_reply_ai_with_messages([{"role": "user", "content": text}])
|
||||
|
||||
async def web_search(self, query: str, search_depth: Optional[str] = None, max_results: Optional[int] = None) -> Dict[str, Any]:
|
||||
@ -508,39 +651,47 @@ class TetoCog(commands.Cog):
|
||||
emoji_pattern = re.compile(r"<a?:(\w+):(\d+)>")
|
||||
for match in emoji_pattern.finditer(message.content):
|
||||
emoji_id = match.group(2)
|
||||
# Construct Discord emoji URL - this might need adjustment based on Discord API specifics
|
||||
emoji_url = f"https://cdn.discordapp.com/emojis/{emoji_id}.png" # .gif for animated
|
||||
if match.group(0).startswith("<a:"): # Check if animated
|
||||
emoji_url = f"https://cdn.discordapp.com/emojis/{emoji_id}.gif"
|
||||
user_content.append({"type": "text", "text": f"The custom emoji {match.group(1)}:"})
|
||||
emoji_name = match.group(1)
|
||||
# Construct Discord emoji URL
|
||||
is_animated = match.group(0).startswith("<a:")
|
||||
emoji_url = f"https://cdn.discordapp.com/emojis/{emoji_id}.{'gif' if is_animated else 'png'}"
|
||||
user_content.append({"type": "text", "text": f"The user included the custom emoji '{emoji_name}':"})
|
||||
user_content.append({"type": "image_url", "image_url": {"url": emoji_url}})
|
||||
print(f"[TETO DEBUG] Found custom emoji: {emoji_url}")
|
||||
log.info(f"[TETO DEBUG] Found custom emoji: {emoji_name} ({emoji_url})")
|
||||
|
||||
|
||||
if not user_content:
|
||||
log.info("[TETO DEBUG] Message triggered AI but contained no supported content (text, image, sticker, emoji).")
|
||||
return # Don't send empty messages to the AI
|
||||
|
||||
convo.append({"role": "user", "content": user_content})
|
||||
# Append the current user message to the conversation history
|
||||
# The `user_content` list itself becomes the value for the 'content' key
|
||||
current_message_entry = {"role": "user", "content": user_content}
|
||||
convo.append(current_message_entry)
|
||||
|
||||
try:
|
||||
async with channel.typing():
|
||||
ai_reply = await self._teto_reply_ai_with_messages(messages=convo)
|
||||
ai_reply = strip_think_blocks(ai_reply)
|
||||
await message.reply(ai_reply)
|
||||
# The `convo` (which is `messages` param) is now a list of dicts,
|
||||
# where each dict is like {"role": "user/assistant", "content": string_or_list_of_parts}
|
||||
ai_reply_text = await self._teto_reply_ai_with_messages(messages=convo)
|
||||
ai_reply_text = strip_think_blocks(ai_reply_text) # Strip think blocks if any
|
||||
|
||||
# Ensure reply is not empty or excessively long
|
||||
if not ai_reply_text or len(ai_reply_text.strip()) == 0:
|
||||
ai_reply_text = "(Teto AI returned an empty response.)"
|
||||
log.warning("[TETO DEBUG] AI reply was empty.")
|
||||
elif len(ai_reply_text) > 1950: # Discord limit is 2000, leave some room
|
||||
ai_reply_text = ai_reply_text[:1950] + "... (message truncated)"
|
||||
log.warning("[TETO DEBUG] AI reply was truncated due to length.")
|
||||
|
||||
# Extract the original AI content (without command execution formatting)
|
||||
# for storing in conversation history
|
||||
command, content_without_command, _ = extract_shell_command(ai_reply)
|
||||
if command:
|
||||
# If there was a command, store the original AI content without the formatted execution message
|
||||
convo.append({"role": "assistant", "content": content_without_command if content_without_command else ai_reply})
|
||||
else:
|
||||
# If there was no command, store the full reply
|
||||
convo.append({"role": "assistant", "content": ai_reply})
|
||||
await message.reply(ai_reply_text)
|
||||
|
||||
_teto_conversations[convo_key] = convo[-10:] # Keep last 10 interactions
|
||||
log.info("[TETO DEBUG] AI reply sent successfully.")
|
||||
# Store the AI's textual response in the conversation history
|
||||
# The tool handling logic is now within _teto_reply_ai_with_messages
|
||||
convo.append({"role": "assistant", "content": ai_reply_text})
|
||||
|
||||
_teto_conversations[convo_key] = convo[-10:] # Keep last 10 interactions (user + assistant turns)
|
||||
log.info("[TETO DEBUG] AI reply sent successfully using Vertex AI.")
|
||||
except Exception as e:
|
||||
await channel.send(f"**Teto AI conversation failed! TwT**\n{e}")
|
||||
log.error(f"[TETO DEBUG] Exception during AI reply: {e}")
|
||||
@ -551,12 +702,6 @@ class TetoCog(commands.Cog):
|
||||
self._ai_model = model_name
|
||||
await interaction.response.send_message(f"Ame-chan's AI model set to: {model_name} desu~", ephemeral=True)
|
||||
|
||||
@ame_group.command(name="set_api_endpoint", description="Sets the API endpoint for Ame-chan.")
|
||||
@app_commands.describe(endpoint_url="The URL of the API endpoint.")
|
||||
async def set_api_endpoint(self, interaction: discord.Interaction, endpoint_url: str):
|
||||
self._api_endpoint = endpoint_url
|
||||
await interaction.response.send_message(f"Ame-chan's API endpoint set to: {endpoint_url} desu~", ephemeral=True)
|
||||
|
||||
@ame_group.command(name="clear_chat_history", description="Clears the chat history for the current channel.")
|
||||
async def clear_chat_history(self, interaction: discord.Interaction):
|
||||
channel_id = interaction.channel_id
|
||||
@ -648,15 +793,8 @@ async def teto_context_menu_ai_reply(interaction: discord.Interaction, message:
|
||||
await message.reply(ai_reply)
|
||||
await interaction.followup.send("Teto AI replied desu~", ephemeral=True)
|
||||
|
||||
# Extract the original AI content (without command execution formatting)
|
||||
# for storing in conversation history
|
||||
command, content_without_command, _ = extract_shell_command(ai_reply)
|
||||
if command:
|
||||
# If there was a command, store the original AI content without the formatted execution message
|
||||
convo.append({"role": "assistant", "content": content_without_command if content_without_command else ai_reply})
|
||||
else:
|
||||
# If there was no command, store the full reply
|
||||
convo.append({"role": "assistant", "content": ai_reply})
|
||||
# Store the AI's textual response in the conversation history
|
||||
convo.append({"role": "assistant", "content": ai_reply}) # ai_reply is already the text
|
||||
_teto_conversations[convo_key] = convo[-10:]
|
||||
except Exception as e:
|
||||
await interaction.followup.send(f"Teto AI reply failed: {e} desu~", ephemeral=True)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user