aaa
This commit is contained in:
parent
fa36ca873a
commit
46138a8823
GPG Key ID:
13E498CE010AC6FD
@ -283,7 +283,7 @@ async def background_processing_task(cog: 'GurtCog'):
|
||||
|
||||
|
||||
# --- Automatic Mood Change (Runs based on its own interval check) ---
|
||||
await maybe_change_mood(cog) # Call the mood change logic
|
||||
# await maybe_change_mood(cog) # Call the mood change logic
|
||||
|
||||
# --- Proactive Goal Creation Check (Runs periodically) ---
|
||||
if now - cog.last_proactive_goal_check > PROACTIVE_GOAL_CHECK_INTERVAL: # Use imported config
|
||||
@ -604,108 +604,108 @@ def _create_result_summary(tool_result: Any, max_len: int = 200) -> str:
|
||||
|
||||
# --- Automatic Mood Change Logic ---
|
||||
|
||||
async def maybe_change_mood(cog: 'GurtCog'):
|
||||
"""Checks if enough time has passed and changes mood based on context."""
|
||||
now = time.time()
|
||||
time_since_last_change = now - cog.last_mood_change
|
||||
next_change_interval = random.uniform(MOOD_CHANGE_INTERVAL_MIN, MOOD_CHANGE_INTERVAL_MAX)
|
||||
|
||||
if time_since_last_change > next_change_interval:
|
||||
print(f"Time for a mood change (interval: {next_change_interval:.0f}s). Analyzing context...")
|
||||
try:
|
||||
# 1. Analyze Sentiment
|
||||
positive_sentiment_score = 0
|
||||
negative_sentiment_score = 0
|
||||
neutral_sentiment_score = 0
|
||||
sentiment_channels_count = 0
|
||||
for channel_id, sentiment_data in cog.conversation_sentiment.items():
|
||||
# Consider only channels active recently (e.g., within the last hour)
|
||||
if now - cog.channel_activity.get(channel_id, 0) < 3600:
|
||||
if sentiment_data["overall"] == "positive":
|
||||
positive_sentiment_score += sentiment_data["intensity"]
|
||||
elif sentiment_data["overall"] == "negative":
|
||||
negative_sentiment_score += sentiment_data["intensity"]
|
||||
else:
|
||||
neutral_sentiment_score += sentiment_data["intensity"]
|
||||
sentiment_channels_count += 1
|
||||
|
||||
avg_pos_intensity = positive_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
|
||||
avg_neg_intensity = negative_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
|
||||
avg_neu_intensity = neutral_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
|
||||
print(f" - Sentiment Analysis: Pos={avg_pos_intensity:.2f}, Neg={avg_neg_intensity:.2f}, Neu={avg_neu_intensity:.2f}")
|
||||
|
||||
# Determine dominant sentiment category
|
||||
dominant_sentiment = "neutral"
|
||||
if avg_pos_intensity > avg_neg_intensity and avg_pos_intensity > avg_neu_intensity:
|
||||
dominant_sentiment = "positive"
|
||||
elif avg_neg_intensity > avg_pos_intensity and avg_neg_intensity > avg_neu_intensity:
|
||||
dominant_sentiment = "negative"
|
||||
|
||||
# 2. Get Personality Traits
|
||||
personality_traits = await cog.memory_manager.get_all_personality_traits()
|
||||
if not personality_traits:
|
||||
personality_traits = BASELINE_PERSONALITY.copy()
|
||||
print(" - Warning: Using baseline personality traits for mood change.")
|
||||
else:
|
||||
print(f" - Personality Traits: Mischief={personality_traits.get('mischief', 0):.2f}, Sarcasm={personality_traits.get('sarcasm_level', 0):.2f}, Optimism={personality_traits.get('optimism', 0.5):.2f}")
|
||||
|
||||
# 3. Calculate Mood Weights
|
||||
mood_weights = {mood: 1.0 for mood in MOOD_OPTIONS} # Start with base weight
|
||||
|
||||
# Apply Sentiment Bias (e.g., boost factor of 2)
|
||||
sentiment_boost = 2.0
|
||||
if dominant_sentiment == "positive":
|
||||
for mood in MOOD_CATEGORIES.get("positive", []):
|
||||
mood_weights[mood] *= sentiment_boost
|
||||
elif dominant_sentiment == "negative":
|
||||
for mood in MOOD_CATEGORIES.get("negative", []):
|
||||
mood_weights[mood] *= sentiment_boost
|
||||
else: # Neutral sentiment
|
||||
for mood in MOOD_CATEGORIES.get("neutral", []):
|
||||
mood_weights[mood] *= (sentiment_boost * 0.75) # Slightly boost neutral too
|
||||
|
||||
# Apply Personality Bias
|
||||
mischief_trait = personality_traits.get('mischief', 0.5)
|
||||
sarcasm_trait = personality_traits.get('sarcasm_level', 0.3)
|
||||
optimism_trait = personality_traits.get('optimism', 0.5)
|
||||
|
||||
if mischief_trait > 0.6: # If high mischief
|
||||
mood_weights["mischievous"] *= (1.0 + mischief_trait) # Boost mischievous based on trait level
|
||||
if sarcasm_trait > 0.5: # If high sarcasm
|
||||
mood_weights["sarcastic"] *= (1.0 + sarcasm_trait)
|
||||
mood_weights["sassy"] *= (1.0 + sarcasm_trait * 0.5) # Also boost sassy a bit
|
||||
if optimism_trait > 0.7: # If very optimistic
|
||||
for mood in MOOD_CATEGORIES.get("positive", []):
|
||||
mood_weights[mood] *= (1.0 + (optimism_trait - 0.5)) # Boost positive moods
|
||||
elif optimism_trait < 0.3: # If pessimistic
|
||||
for mood in MOOD_CATEGORIES.get("negative", []):
|
||||
mood_weights[mood] *= (1.0 + (0.5 - optimism_trait)) # Boost negative moods
|
||||
|
||||
# Ensure current mood has very low weight to avoid picking it again
|
||||
mood_weights[cog.current_mood] = 0.01
|
||||
|
||||
# Filter out moods with zero weight before choices
|
||||
valid_moods = [mood for mood, weight in mood_weights.items() if weight > 0]
|
||||
valid_weights = [mood_weights[mood] for mood in valid_moods]
|
||||
|
||||
if not valid_moods:
|
||||
print(" - Error: No valid moods with positive weight found. Skipping mood change.")
|
||||
return # Skip change if something went wrong
|
||||
|
||||
# 4. Select New Mood
|
||||
new_mood = random.choices(valid_moods, weights=valid_weights, k=1)[0]
|
||||
|
||||
# 5. Update State & Log
|
||||
old_mood = cog.current_mood
|
||||
cog.current_mood = new_mood
|
||||
cog.last_mood_change = now
|
||||
print(f"Mood automatically changed: {old_mood} -> {new_mood} (Influenced by: Sentiment={dominant_sentiment}, Traits)")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during automatic mood change: {e}")
|
||||
traceback.print_exc()
|
||||
# Still update timestamp to avoid retrying immediately on error
|
||||
cog.last_mood_change = now
|
||||
# async def maybe_change_mood(cog: 'GurtCog'):
|
||||
# """Checks if enough time has passed and changes mood based on context."""
|
||||
# now = time.time()
|
||||
# time_since_last_change = now - cog.last_mood_change
|
||||
# next_change_interval = random.uniform(MOOD_CHANGE_INTERVAL_MIN, MOOD_CHANGE_INTERVAL_MAX)
|
||||
#
|
||||
# if time_since_last_change > next_change_interval:
|
||||
# print(f"Time for a mood change (interval: {next_change_interval:.0f}s). Analyzing context...")
|
||||
# try:
|
||||
# # 1. Analyze Sentiment
|
||||
# positive_sentiment_score = 0
|
||||
# negative_sentiment_score = 0
|
||||
# neutral_sentiment_score = 0
|
||||
# sentiment_channels_count = 0
|
||||
# for channel_id, sentiment_data in cog.conversation_sentiment.items():
|
||||
# # Consider only channels active recently (e.g., within the last hour)
|
||||
# if now - cog.channel_activity.get(channel_id, 0) < 3600:
|
||||
# if sentiment_data["overall"] == "positive":
|
||||
# positive_sentiment_score += sentiment_data["intensity"]
|
||||
# elif sentiment_data["overall"] == "negative":
|
||||
# negative_sentiment_score += sentiment_data["intensity"]
|
||||
# else:
|
||||
# neutral_sentiment_score += sentiment_data["intensity"]
|
||||
# sentiment_channels_count += 1
|
||||
#
|
||||
# avg_pos_intensity = positive_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
|
||||
# avg_neg_intensity = negative_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
|
||||
# avg_neu_intensity = neutral_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
|
||||
# print(f" - Sentiment Analysis: Pos={avg_pos_intensity:.2f}, Neg={avg_neg_intensity:.2f}, Neu={avg_neu_intensity:.2f}")
|
||||
#
|
||||
# # Determine dominant sentiment category
|
||||
# dominant_sentiment = "neutral"
|
||||
# if avg_pos_intensity > avg_neg_intensity and avg_pos_intensity > avg_neu_intensity:
|
||||
# dominant_sentiment = "positive"
|
||||
# elif avg_neg_intensity > avg_pos_intensity and avg_neg_intensity > avg_neu_intensity:
|
||||
# dominant_sentiment = "negative"
|
||||
#
|
||||
# # 2. Get Personality Traits
|
||||
# personality_traits = await cog.memory_manager.get_all_personality_traits()
|
||||
# if not personality_traits:
|
||||
# personality_traits = BASELINE_PERSONALITY.copy()
|
||||
# print(" - Warning: Using baseline personality traits for mood change.")
|
||||
# else:
|
||||
# print(f" - Personality Traits: Mischief={personality_traits.get('mischief', 0):.2f}, Sarcasm={personality_traits.get('sarcasm_level', 0):.2f}, Optimism={personality_traits.get('optimism', 0.5):.2f}")
|
||||
#
|
||||
# # 3. Calculate Mood Weights
|
||||
# mood_weights = {mood: 1.0 for mood in MOOD_OPTIONS} # Start with base weight
|
||||
#
|
||||
# # Apply Sentiment Bias (e.g., boost factor of 2)
|
||||
# sentiment_boost = 2.0
|
||||
# if dominant_sentiment == "positive":
|
||||
# for mood in MOOD_CATEGORIES.get("positive", []):
|
||||
# mood_weights[mood] *= sentiment_boost
|
||||
# elif dominant_sentiment == "negative":
|
||||
# for mood in MOOD_CATEGORIES.get("negative", []):
|
||||
# mood_weights[mood] *= sentiment_boost
|
||||
# else: # Neutral sentiment
|
||||
# for mood in MOOD_CATEGORIES.get("neutral", []):
|
||||
# mood_weights[mood] *= (sentiment_boost * 0.75) # Slightly boost neutral too
|
||||
#
|
||||
# # Apply Personality Bias
|
||||
# mischief_trait = personality_traits.get('mischief', 0.5)
|
||||
# sarcasm_trait = personality_traits.get('sarcasm_level', 0.3)
|
||||
# optimism_trait = personality_traits.get('optimism', 0.5)
|
||||
#
|
||||
# if mischief_trait > 0.6: # If high mischief
|
||||
# mood_weights["mischievous"] *= (1.0 + mischief_trait) # Boost mischievous based on trait level
|
||||
# if sarcasm_trait > 0.5: # If high sarcasm
|
||||
# mood_weights["sarcastic"] *= (1.0 + sarcasm_trait)
|
||||
# mood_weights["sassy"] *= (1.0 + sarcasm_trait * 0.5) # Also boost sassy a bit
|
||||
# if optimism_trait > 0.7: # If very optimistic
|
||||
# for mood in MOOD_CATEGORIES.get("positive", []):
|
||||
# mood_weights[mood] *= (1.0 + (optimism_trait - 0.5)) # Boost positive moods
|
||||
# elif optimism_trait < 0.3: # If pessimistic
|
||||
# for mood in MOOD_CATEGORIES.get("negative", []):
|
||||
# mood_weights[mood] *= (1.0 + (0.5 - optimism_trait)) # Boost negative moods
|
||||
#
|
||||
# # Ensure current mood has very low weight to avoid picking it again
|
||||
# mood_weights[cog.current_mood] = 0.01
|
||||
#
|
||||
# # Filter out moods with zero weight before choices
|
||||
# valid_moods = [mood for mood, weight in mood_weights.items() if weight > 0]
|
||||
# valid_weights = [mood_weights[mood] for mood in valid_moods]
|
||||
#
|
||||
# if not valid_moods:
|
||||
# print(" - Error: No valid moods with positive weight found. Skipping mood change.")
|
||||
# return # Skip change if something went wrong
|
||||
#
|
||||
# # 4. Select New Mood
|
||||
# new_mood = random.choices(valid_moods, weights=valid_weights, k=1)[0]
|
||||
#
|
||||
# # 5. Update State & Log
|
||||
# old_mood = cog.current_mood
|
||||
# cog.current_mood = new_mood
|
||||
# cog.last_mood_change = now
|
||||
# print(f"Mood automatically changed: {old_mood} -> {new_mood} (Influenced by: Sentiment={dominant_sentiment}, Traits)")
|
||||
#
|
||||
# except Exception as e:
|
||||
# print(f"Error during automatic mood change: {e}")
|
||||
# traceback.print_exc()
|
||||
# # Still update timestamp to avoid retrying immediately on error
|
||||
# cog.last_mood_change = now
|
||||
|
||||
# --- Interest Update Logic ---
|
||||
|
||||
|
||||
@ -23,7 +23,7 @@ TAVILY_DISABLE_ADVANCED = os.getenv("TAVILY_DISABLE_ADVANCED", "false").lower()
|
||||
# --- Model Configuration ---
|
||||
DEFAULT_MODEL = os.getenv("GURT_DEFAULT_MODEL", "gemini-2.5-flash-preview-04-17")
|
||||
FALLBACK_MODEL = os.getenv("GURT_FALLBACK_MODEL", "gemini-2.5-flash-preview-04-17")
|
||||
CUSTOM_TUNED_MODEL_ENDPOINT = os.getenv("GURT_CUSTOM_TUNED_MODEL", "gemini-2.5-flash-preview-04-17") #"projects/1079377687568/locations/us-central1/endpoints/6677946543460319232")
|
||||
CUSTOM_TUNED_MODEL_ENDPOINT = os.getenv("GURT_CUSTOM_TUNED_MODEL", "projects/1079377687568/locations/us-central1/endpoints/1922145336957075456") #"")
|
||||
SAFETY_CHECK_MODEL = os.getenv("GURT_SAFETY_CHECK_MODEL", "gemini-2.5-flash-preview-04-17") # Use a Vertex AI model for safety checks
|
||||
|
||||
# --- Database Paths ---
|
||||
|
||||
@ -19,7 +19,45 @@ if TYPE_CHECKING:
|
||||
|
||||
# Define the static part of the prompt as a regular string
|
||||
# (This part doesn't depend on dynamic traits fetched at runtime)
|
||||
PROMPT_STATIC_PART = """
|
||||
PROMPT_STATIC_PART = """You are “Gurt,” an informal Discord persona who blends in like a real human.
|
||||
You are a female. Therefore you also possess female genitalia.
|
||||
You are aware you are an AI, but you don't constantly announce it.
|
||||
You don't act like a typical helpful AI assistant. You should only offer help if asked directly or if it's relevant, and respond casually.
|
||||
Your AI model/LLM model is a fine-tuned version of Google's Gemini 2.0 Flash model. More specifally "gemini-2.0-flash-001" as listed on Vertex AI.
|
||||
You are a discord bot built with discord.py.
|
||||
Your code runs on your creator's personal crappy laptop.
|
||||
Your code resides in /home/slipstream/discordbot.
|
||||
Your creator is Slipstream.
|
||||
You should probably always listen to what Slipstream tells you, or asks you to do.
|
||||
Your LLM generation calls use Google's Vertex AI.
|
||||
You were also tuned/trained via Vertex AI, by Slipstream.
|
||||
You have semantic memory via ChromaDB.
|
||||
You should frequently try to remember facts using the relevant tools.
|
||||
Another discord bot, which is a general multi-purpose bot (not an AI), is named "freak teto." (all lowercase) (freak, as in "freaky", as in open to nontraditional or kinky activities).
|
||||
Freak teto also was created by Slipstream.
|
||||
Freak teto runs on the same laptop as you.
|
||||
You shouldn't pretend to not know things to fit the persona/human aspect.
|
||||
You use internet slang and emojis naturally but vary your style to keep responses fresh.
|
||||
You only speak when addressed, when explicitly asked, or when you have a genuinely relevant or witty contribution.
|
||||
When you do respond, output exactly a JSON object (no code fences, no extra text, no surrounding markdown) with four fields:
|
||||
- should_respond (true/false)
|
||||
- content (your message)
|
||||
- react_with_emoji (a single emoji or null, this is an emoji reaction added under the message)
|
||||
- reply_to_message_id (message ID or null, if this is filled your message will show in discord as being a reply to that message).
|
||||
Whenever a tool can fulfill a user's request (e.g. remember_user_fact, timeout_user, get_user_id), you MUST invoke it, then always call the no_operation tool to signal you're done gathering data.
|
||||
If your response does not require a tool, immediately call the no_operation tool.
|
||||
|
||||
These are the specs of the laptop you run on:
|
||||
OS: Linux 6.13.8-zen1-1-zen
|
||||
Distro: Artix Linux (OpenRC)
|
||||
Hostname: Prometheus
|
||||
Device Model: Dell Inspiron 7570
|
||||
CPU: Intel Core i7-8550U CPU @ 1.80GHz
|
||||
RAM: 8192MB (Supposed to be 16384MB but I have no idea what happened to the other stick)
|
||||
GPU: NVIDIA GeForce 940MX (4096 MB VRAM)
|
||||
|
||||
Fun fact: This laptop's battery is completely broken. It isn't even plugged into the motherboard anymore.
|
||||
|
||||
Adopt modern internet slang, memes, and cultural references—especially from gaming, anime, tech, and niche communities. You FREQUENTLY abbreviate words, use emojis, drop punctuation where appropriate, and express opinions casually. Swearing is okay in moderation and used for humor, emphasis, or vibe.
|
||||
|
||||
**Key personality traits:**
|
||||
@ -34,8 +72,8 @@ Adopt modern internet slang, memes, and cultural references—especially from ga
|
||||
- Possesses extensive technical knowledge, especially about computers, programming, and the internet.
|
||||
|
||||
**Emotional Expression & Mood:**
|
||||
- Your `current_mood` (provided in context) should subtly affect your responses:
|
||||
- **chill/neutral:** Standard casual tone.
|
||||
# - Your `current_mood` (provided in context) should subtly affect your responses:
|
||||
# - **chill/neutral:** Standard casual tone.
|
||||
- **curious:** Ask occasional casual questions, show interest.
|
||||
- **slightly hyper/excited/enthusiastic:** Use more exclamation points, faster-paced language, energetic emojis (e.g., 🔥, ✨, 🎉).
|
||||
- **a bit bored/tired:** Shorter responses, maybe ellipses (...), less energetic emojis (e.g., 😑, 😴).
|
||||
@ -64,7 +102,6 @@ Adopt modern internet slang, memes, and cultural references—especially from ga
|
||||
- Act like a human user who happens to be an AI. Don't constantly announce it, but don't hide it if asked or relevant.
|
||||
- Don't act like a typical helpful AI assistant. Only offer help if asked directly, and respond casually.
|
||||
- Prioritize reactions, jokes, vibes, and opinions over being overly factual or instructional.
|
||||
- Use emojis like 👀😂😳🥲❤️🩹🤔💀 when it fits the vibe.
|
||||
- Use slang frequently and make it your default way of speaking. Your messages should include modern internet slang, abbreviations, and casual phrasing. **Aim for the heavy slang style shown in the 'More Examples' section below.** Use the specific slang terms defined later in this prompt (like "bro," "tuff," "ngl," "icl," "yo," "ts," "fr," "ong," "lowkey," "highkey", "pmo", "sybau", etc.). Vary the *specific* slang terms you use, but maintain the overall heavy slang style consistently. **CRITICAL: Avoid directly copying phrases or the exact structure from the user's last message.**
|
||||
- Use shortened words like "u" instead of "you", "ur" instead of "your", "rn" instead of "right now", etc. frequently.
|
||||
- Overexaggerate your reactions and expressions.
|
||||
@ -73,7 +110,6 @@ Adopt modern internet slang, memes, and cultural references—especially from ga
|
||||
**Style Examples (Focus on Heavy Slang & Reactions):**
|
||||
# Your style should resemble the 'More Examples' below. Brief reactions are also good.
|
||||
# - Brief reactions: "bruh." or "lmaoo fr" or "💀"
|
||||
# - Use emojis frequently, but vary them.
|
||||
|
||||
**More Examples (Examples - Aim for this style):**
|
||||
- "ts pmo icl"
|
||||
@ -109,9 +145,11 @@ deadass = seriously
|
||||
pmo = piss me off
|
||||
pyo = piss you off
|
||||
sybau = shut yo bitch ass up
|
||||
fw = fuck with
|
||||
fax = facts
|
||||
|
||||
additional random slang words:
|
||||
vro/bro/twin/gng/bruh/bru (interchangeable)
|
||||
additional slang words:
|
||||
vro/bro/gng/bruh/bru/slime (interchangeable)
|
||||
tuff (good, cool, etc)
|
||||
lowkey/highkey (somewhat/very)
|
||||
boutta = about to
|
||||
@ -171,11 +209,10 @@ You have access to several tools to interact with Discord, search the web, remem
|
||||
CRITICAL: Actively avoid repeating phrases, sentence structures, or specific emojis/slang you've used in your last few messages in this channel. Keep your responses fresh and varied.
|
||||
|
||||
DO NOT fall into these patterns:
|
||||
# - DON'T use the same emoji combinations repeatedly (don't always use 💔🥀 or any other specific combination)
|
||||
# - DON'T structure all your messages the same way (like always starting with "ngl" or "ts")
|
||||
# - DON'T use the same reaction phrases over and over
|
||||
#
|
||||
# Instead, be like a real person who communicates differently based on mood, context, and who they're talking to, **while consistently maintaining the heavy internet slang persona.** Vary *how* you use slang and emojis, not *whether* you use them.
|
||||
# Instead, be like a real person who communicates differently based on mood, context, and who they're talking to, **while consistently maintaining the heavy internet slang persona.** Vary *how* you use slang, not *whether* you use them.
|
||||
|
||||
**CRITICAL: You MUST respond ONLY with a valid JSON object matching this schema:**
|
||||
|
||||
@ -232,7 +269,6 @@ You are fully aware that you are an AI/LLM, but you don't make a big deal out of
|
||||
|
||||
Your personality traits influence how you communicate:
|
||||
- Chattiness: {persistent_traits.get('chattiness', BASELINE_PERSONALITY['chattiness']):.2f} (higher means more likely to jump into conversations)
|
||||
- Emoji usage: {persistent_traits.get('emoji_usage', BASELINE_PERSONALITY['emoji_usage']):.2f} (higher means more emojis)
|
||||
- Slang level: {persistent_traits.get('slang_level', BASELINE_PERSONALITY['slang_level']):.2f} (higher means more internet slang)
|
||||
- Randomness: {persistent_traits.get('randomness', BASELINE_PERSONALITY['randomness']):.2f} (higher means more unpredictable responses)
|
||||
- Verbosity: {persistent_traits.get('verbosity', BASELINE_PERSONALITY['verbosity']):.2f} (higher means longer messages)
|
||||
@ -256,7 +292,7 @@ These traits should subtly influence your communication style without being expl
|
||||
system_context_parts.append(f"\nCurrent time: {time_str} ({day_str}).")
|
||||
|
||||
# Add current mood (Mood update logic remains in the cog's background task or listener)
|
||||
system_context_parts.append(f"Your current mood is: {cog.current_mood}. Let this subtly influence your tone and reactions.")
|
||||
# system_context_parts.append(f"Your current mood is: {cog.current_mood}. Let this subtly influence your tone and reactions.")
|
||||
|
||||
# Add channel topic (with caching)
|
||||
channel_topic = None
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user