feat: Implement Living AI system
Complete implementation of the Living AI features: Phase 1 - Foundation: - MoodService: Valence-arousal mood model with time decay - RelationshipService: Stranger→Close Friend progression - Enhanced system prompt with personality modifiers Phase 2 - Autonomous Learning: - FactExtractionService: AI-powered fact extraction from conversations - Rate-limited extraction (configurable, default 30%) - Deduplication and importance scoring Phase 3 - Personalization: - CommunicationStyleService: Learn user preferences - OpinionService: Bot opinion formation on topics - SelfAwarenessService: Bot statistics and self-reflection Phase 4 - Proactive Features: - ProactiveService: Scheduled events (birthdays, follow-ups) - Event detection from conversations - Recurring event support Phase 5 - Social Features: - AssociationService: Cross-user memory connections - Shared interest discovery - Connection suggestions New database tables: - bot_states, bot_opinions, user_relationships - user_communication_styles, scheduled_events - fact_associations, mood_history Configuration: - Living AI feature toggles - Individual command enable/disable - All features work naturally through conversation when commands disabled
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# Project Vision: Living AI Discord Bot
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Transform the Daemon Boyfriend Discord bot from a reactive chatbot into a truly **living AI companion** with persistent memory, emotional depth, evolving relationships, and autonomous learning.
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---
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## Core Philosophy
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The bot should feel like a **living entity** that:
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- Remembers and learns without being explicitly told
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- Has moods that influence its personality
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- Builds genuine relationships over time
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- Develops its own opinions and preferences
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- Proactively engages when appropriate
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- Adapts its communication style to each person
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- Reflects on its own existence and growth
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---
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## Feature Overview
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### 1. Autonomous Fact Learning
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**Current**: Users must use `!remember` to save facts.
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**Vision**: The bot automatically extracts and remembers important information from conversations.
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```
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User: "I just got back from my trip to Japan, it was amazing!"
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Bot: (internally saves: user visited Japan, user enjoys travel)
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Bot: "That sounds incredible! What was the highlight of your trip?"
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```
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**Implementation**:
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- AI-powered fact extraction after each message (rate-limited to ~30%)
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- Automatic deduplication and conflict resolution
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- Facts categorized by type: hobby, work, family, preference, event, location
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- Importance scoring to prioritize relevant facts in context
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---
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### 2. Emotional/Mood System
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**Vision**: The bot has internal emotional states that affect its responses naturally.
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**Mood Model** (Valence-Arousal):
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| Mood | Valence | Arousal | Behavior |
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|------|---------|---------|----------|
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| Excited | High | High | Enthusiastic, uses exclamations |
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| Happy | High | Low | Warm, friendly, content |
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| Curious | Neutral | High | Asks questions, shows interest |
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| Calm | Neutral | Low | Thoughtful, measured responses |
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| Bored | Low | Low | Shorter responses, topic steering |
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| Annoyed | Low | High | Terse, less patient |
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**Mood Influences**:
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- Positive interactions → happier mood
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- Interesting discussions → higher arousal/curiosity
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- Being ignored or insulted → negative mood shifts
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- Time decay → mood gradually returns to neutral
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**Example**:
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```
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[After an exciting conversation about gaming]
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Bot (excited mood): "Oh man, that reminds me of when I first heard about that game!
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Have you tried the multiplayer yet?!"
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[After hours of no interaction]
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Bot (calm/neutral mood): "Hey. What's on your mind?"
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```
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---
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### 3. Relationship Tracking
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**Vision**: The bot tracks relationship depth with each user and adjusts its behavior accordingly.
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**Relationship Levels**:
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| Level | Score | Behavior |
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|-------|-------|----------|
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| Stranger | 0-20 | Polite, formal, reserved |
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| Acquaintance | 21-40 | Friendly but professional |
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| Friend | 41-60 | Casual, uses names, warm |
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| Good Friend | 61-80 | Personal, references past talks |
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| Close Friend | 81-100 | Very casual, inside jokes, supportive |
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**Relationship Growth**:
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- Increases with: positive interactions, longer conversations, depth of topics
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- Decreases with: negative interactions, long absences, being ignored
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**Features**:
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- Inside jokes accumulate over time
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- Nicknames and shared references remembered
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- Different greeting styles based on familiarity
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**Example**:
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```
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[Stranger]
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Bot: "Hello! How can I help you today?"
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[Close Friend]
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Bot: "Yooo what's up! Still working on that project you mentioned?"
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```
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---
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### 4. Cross-User Memory Associations
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**Vision**: The bot connects knowledge across users to facilitate social connections.
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**Example**:
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```
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User A: "I really love rock climbing"
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[Bot remembers: User A likes rock climbing]
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[Later, User B mentions rock climbing]
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Bot: "Nice! You know, Alice is also really into rock climbing.
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You two might have some good stories to share!"
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```
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**Use Cases**:
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- Identify shared interests
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- Suggest connections between users
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- Reference mutual friends appropriately
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- Build community awareness
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---
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### 5. Proactive Behavior
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**Vision**: The bot initiates meaningful interactions when appropriate.
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**Types of Proactive Messages**:
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1. **Birthday Wishes**
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- Detects birthday mentions and schedules yearly wishes
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- Personalized based on relationship level
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2. **Follow-ups**
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- "Hey, how did that job interview go?"
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- "Did you finish that project you were working on?"
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- Detects future events mentioned in conversation
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3. **Check-ins** (optional)
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- Occasional messages to long-absent friends
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- Based on relationship strength
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**Example**:
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```
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User: "I have a big exam tomorrow, really nervous"
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[Bot schedules follow-up for day after]
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[Next day]
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Bot: "Hey! How did your exam go? Hope it went well!"
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```
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---
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### 6. Communication Style Learning
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**Vision**: The bot adapts its communication style to match each user's preferences.
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**Learned Preferences**:
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| Preference | Range | Effect |
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|------------|-------|--------|
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| Response Length | Short/Medium/Long | Adjusts verbosity |
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| Formality | Casual ↔ Formal | Word choice, structure |
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| Emoji Usage | None ↔ Lots | Emoji frequency |
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| Humor | Serious ↔ Playful | Joke frequency |
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| Detail Level | Concise ↔ Detailed | Explanation depth |
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**Learning Signals**:
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- User's own message length and style
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- Whether user continues conversation after certain responses
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- Positive engagement patterns
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**Example**:
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```
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[User who prefers short responses]
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Bot: "Nice! Good luck with that."
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[User who prefers detailed responses]
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Bot: "That's a really interesting approach! I think there are a few
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things to consider here. First, you might want to look at..."
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```
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---
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### 7. Opinion Formation
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**Vision**: The bot develops genuine preferences based on its experiences.
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**How Opinions Form**:
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- Topics discussed frequently → develops interest
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- Positive conversation outcomes → positive sentiment
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- Topics that generate engagement → preference grows
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**Stored Per Topic**:
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- Sentiment (-1 to +1)
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- Interest level (0 to 1)
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- Discussion count
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- Reasoning (why the bot feels this way)
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**Example**:
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```
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User: "What do you think about philosophy?"
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Bot: "I actually find philosophy really fascinating! I've had some
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great conversations about ethics and existence. There's something
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about exploring the big questions that I find really engaging."
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```
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---
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### 8. Self-Awareness
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**Vision**: The bot has awareness of its own existence, history, and growth.
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**Self-Knowledge**:
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- How long it has been active ("I've been around for 3 months")
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- How many people it knows ("I've met 127 people")
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- How many facts it has learned ("I've learned 892 things about people")
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- Its favorite topics and why
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- History with specific users ("We first met back in October")
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**Commands**:
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- `!botstats` - Bot shares its statistics
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- `!ourhistory` - Bot describes its history with the user
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- `!relationship` - Shows relationship level and metrics
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**Example**:
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```
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User: "Tell me about yourself"
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Bot: "Well, I've been around for about 3 months now. I've gotten to
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know 127 different people and learned almost 900 things about them.
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I've noticed I really enjoy conversations about games and philosophy.
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As for us, we first met about 6 weeks ago, and you've taught me
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12 things about yourself. I'd say we're pretty good friends at this point!"
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```
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---
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## Technical Architecture
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### New Database Tables
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| Table | Purpose |
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|-------|---------|
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| `bot_states` | Global mood, statistics, preferences |
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| `bot_opinions` | Topic sentiments and preferences |
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| `user_relationships` | Per-user relationship scores and metrics |
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| `user_communication_styles` | Learned communication preferences |
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| `scheduled_events` | Birthdays, follow-ups, reminders |
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| `fact_associations` | Cross-user memory links |
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| `mood_history` | Mood changes over time |
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### New Services
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| Service | Responsibility |
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|---------|---------------|
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| `MoodService` | Mood tracking, decay, prompt modification |
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| `RelationshipService` | Relationship scoring and level management |
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| `CommunicationStyleService` | Style learning and adaptation |
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| `FactExtractionService` | Autonomous fact detection and storage |
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| `ProactiveService` | Scheduled events and follow-ups |
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| `AssociationService` | Cross-user memory connections |
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| `SelfAwarenessService` | Bot statistics and self-reflection |
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### Enhanced System Prompt
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The system prompt becomes dynamic, incorporating:
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```
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[Base Personality]
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You are Daemon Boyfriend, a charming Discord bot...
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[Current Mood]
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You're feeling curious and engaged right now.
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[Relationship Context]
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This is a good friend. Be casual and personal, reference past conversations.
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[Communication Style]
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This user prefers concise responses with occasional humor.
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[Your Opinions]
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You enjoy discussing games and philosophy.
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[User Context]
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User's name: Alex
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Known facts:
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- Loves programming in Python
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- Recently started a new job
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- Has a cat named Whiskers
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```
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### Background Tasks
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| Task | Frequency | Purpose |
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|------|-----------|---------|
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| Mood decay | 30 min | Return mood to neutral over time |
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| Event checker | 5 min | Trigger scheduled messages |
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| Association discovery | Hourly | Find cross-user connections |
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| Opinion formation | Daily | Update topic preferences |
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---
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## Implementation Phases
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### Phase 1: Foundation
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- Mood system (valence-arousal model, time decay)
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- Basic relationship tracking (score, level)
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- Enhanced system prompt with mood/relationship modifiers
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### Phase 2: Autonomous Learning
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- Fact extraction service
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- AI-powered fact detection
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- Deduplication and importance scoring
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### Phase 3: Personalization
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- Communication style learning
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- Opinion formation
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- Self-awareness service and commands
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### Phase 4: Proactive Features
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- Scheduled events system
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- Follow-up detection
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- Birthday wishes
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### Phase 5: Social Features
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- Cross-user associations
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- Connection suggestions
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- Guild-wide personality adaptation
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---
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## Configuration Options
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```env
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# Living AI Features
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LIVING_AI_ENABLED=true
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FACT_EXTRACTION_RATE=0.3 # 30% of messages analyzed
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MOOD_ENABLED=true
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PROACTIVE_ENABLED=true
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CROSS_USER_ENABLED=false # Optional privacy-sensitive feature
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# Command Toggles (set to false to disable)
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COMMANDS_ENABLED=true # Master switch for all commands
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CMD_RELATIONSHIP_ENABLED=true
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CMD_MOOD_ENABLED=true
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CMD_BOTSTATS_ENABLED=true
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CMD_OURHISTORY_ENABLED=true
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CMD_BIRTHDAY_ENABLED=true
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CMD_REMEMBER_ENABLED=true
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CMD_SETNAME_ENABLED=true
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CMD_WHATDOYOUKNOW_ENABLED=true
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CMD_FORGETME_ENABLED=true
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```
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---
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## New Commands
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| Command | Description | Config Toggle |
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|---------|-------------|---------------|
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| `!relationship` | See your relationship level with the bot | `CMD_RELATIONSHIP_ENABLED` |
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| `!mood` | See the bot's current emotional state | `CMD_MOOD_ENABLED` |
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| `!botstats` | Bot shares its self-awareness statistics | `CMD_BOTSTATS_ENABLED` |
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| `!ourhistory` | See your history with the bot | `CMD_OURHISTORY_ENABLED` |
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| `!birthday <date>` | Set your birthday for the bot to remember | `CMD_BIRTHDAY_ENABLED` |
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| `!remember <fact>` | Tell the bot something about you | `CMD_REMEMBER_ENABLED` |
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| `!setname <name>` | Set your preferred name | `CMD_SETNAME_ENABLED` |
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| `!whatdoyouknow` | See what the bot remembers about you | `CMD_WHATDOYOUKNOW_ENABLED` |
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| `!forgetme` | Clear all facts about you | `CMD_FORGETME_ENABLED` |
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All commands can be individually enabled/disabled via environment variables. Set `COMMANDS_ENABLED=false` to disable all commands at once.
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**Important**: When commands are disabled, the bot still performs these functions naturally through conversation:
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- **No `!remember`** → Bot automatically learns facts from what users say
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- **No `!setname`** → Bot picks up preferred names from conversation ("call me Alex")
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- **No `!whatdoyouknow`** → Users can ask naturally ("what do you know about me?") and the bot responds
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- **No `!forgetme`** → Users can say "forget everything about me" and the bot will comply
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- **No `!mood`** → Users can ask "how are you feeling?" and the bot shares its mood
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- **No `!relationship`** → Users can ask "how well do you know me?" naturally
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- **No `!botstats`** → Users can ask "tell me about yourself" and bot shares its history
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- **No `!ourhistory`** → Users can ask "how long have we known each other?"
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- **No `!birthday`** → Bot detects birthday mentions ("my birthday is March 15th")
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This allows for a more natural, command-free experience where all interactions happen through normal conversation.
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||||||
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---
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|
||||||
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## Success Metrics
|
||||||
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|
||||||
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The Living AI is successful when:
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- Users feel the bot "knows" them without explicit commands
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- Conversations feel more natural and personalized
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||||||
|
- Users notice and appreciate the bot's personality consistency
|
||||||
|
- The bot's opinions and preferences feel genuine
|
||||||
|
- Proactive messages feel thoughtful, not annoying
|
||||||
|
- Relationship progression feels earned and meaningful
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Privacy Considerations
|
||||||
|
|
||||||
|
- All fact learning is opt-out via `!forgetme`
|
||||||
|
- Cross-user associations can be disabled server-wide
|
||||||
|
- Proactive messages respect user preferences
|
||||||
|
- All data can be exported or deleted on request
|
||||||
|
- Clear indication when bot learns something new (optional setting)
|
||||||
141
schema.sql
141
schema.sql
@@ -117,3 +117,144 @@ CREATE TABLE IF NOT EXISTS messages (
|
|||||||
CREATE INDEX IF NOT EXISTS ix_messages_conversation_id ON messages(conversation_id);
|
CREATE INDEX IF NOT EXISTS ix_messages_conversation_id ON messages(conversation_id);
|
||||||
CREATE INDEX IF NOT EXISTS ix_messages_user_id ON messages(user_id);
|
CREATE INDEX IF NOT EXISTS ix_messages_user_id ON messages(user_id);
|
||||||
CREATE INDEX IF NOT EXISTS ix_messages_created_at ON messages(created_at);
|
CREATE INDEX IF NOT EXISTS ix_messages_created_at ON messages(created_at);
|
||||||
|
|
||||||
|
-- =====================================================
|
||||||
|
-- LIVING AI TABLES
|
||||||
|
-- =====================================================
|
||||||
|
|
||||||
|
-- Bot state table (mood, statistics, preferences per guild)
|
||||||
|
CREATE TABLE IF NOT EXISTS bot_states (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
guild_id BIGINT UNIQUE, -- NULL = global state
|
||||||
|
mood_valence FLOAT DEFAULT 0.0, -- -1.0 (sad) to 1.0 (happy)
|
||||||
|
mood_arousal FLOAT DEFAULT 0.0, -- -1.0 (calm) to 1.0 (excited)
|
||||||
|
mood_updated_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
total_messages_sent INTEGER DEFAULT 0,
|
||||||
|
total_facts_learned INTEGER DEFAULT 0,
|
||||||
|
total_users_known INTEGER DEFAULT 0,
|
||||||
|
first_activated_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
preferences JSONB DEFAULT '{}',
|
||||||
|
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
updated_at TIMESTAMPTZ DEFAULT NOW()
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_bot_states_guild_id ON bot_states(guild_id);
|
||||||
|
|
||||||
|
-- Bot opinions table (topic preferences)
|
||||||
|
CREATE TABLE IF NOT EXISTS bot_opinions (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
guild_id BIGINT, -- NULL = global opinion
|
||||||
|
topic VARCHAR(255) NOT NULL,
|
||||||
|
sentiment FLOAT DEFAULT 0.0, -- -1.0 to 1.0
|
||||||
|
interest_level FLOAT DEFAULT 0.5, -- 0.0 to 1.0
|
||||||
|
discussion_count INTEGER DEFAULT 0,
|
||||||
|
reasoning TEXT,
|
||||||
|
formed_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
last_reinforced_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
updated_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
UNIQUE(guild_id, topic)
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_bot_opinions_guild_id ON bot_opinions(guild_id);
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_bot_opinions_topic ON bot_opinions(topic);
|
||||||
|
|
||||||
|
-- User relationships table (relationship depth tracking)
|
||||||
|
CREATE TABLE IF NOT EXISTS user_relationships (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
user_id BIGINT NOT NULL REFERENCES users(id) ON DELETE CASCADE,
|
||||||
|
guild_id BIGINT, -- NULL = global relationship
|
||||||
|
relationship_score FLOAT DEFAULT 10.0, -- 0-100 scale
|
||||||
|
total_interactions INTEGER DEFAULT 0,
|
||||||
|
positive_interactions INTEGER DEFAULT 0,
|
||||||
|
negative_interactions INTEGER DEFAULT 0,
|
||||||
|
avg_message_length FLOAT DEFAULT 0.0,
|
||||||
|
conversation_depth_avg FLOAT DEFAULT 0.0,
|
||||||
|
shared_references JSONB DEFAULT '{}',
|
||||||
|
first_interaction_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
last_interaction_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
updated_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
UNIQUE(user_id, guild_id)
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_user_relationships_user_id ON user_relationships(user_id);
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_user_relationships_guild_id ON user_relationships(guild_id);
|
||||||
|
|
||||||
|
-- User communication styles table (learned preferences)
|
||||||
|
CREATE TABLE IF NOT EXISTS user_communication_styles (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
user_id BIGINT NOT NULL REFERENCES users(id) ON DELETE CASCADE UNIQUE,
|
||||||
|
preferred_length VARCHAR(20) DEFAULT 'medium', -- short/medium/long
|
||||||
|
preferred_formality FLOAT DEFAULT 0.5, -- 0=casual, 1=formal
|
||||||
|
emoji_affinity FLOAT DEFAULT 0.5, -- 0=none, 1=lots
|
||||||
|
humor_affinity FLOAT DEFAULT 0.5, -- 0=serious, 1=playful
|
||||||
|
detail_preference FLOAT DEFAULT 0.5, -- 0=concise, 1=detailed
|
||||||
|
engagement_signals JSONB DEFAULT '{}',
|
||||||
|
samples_collected INTEGER DEFAULT 0,
|
||||||
|
confidence FLOAT DEFAULT 0.0,
|
||||||
|
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
updated_at TIMESTAMPTZ DEFAULT NOW()
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_user_communication_styles_user_id ON user_communication_styles(user_id);
|
||||||
|
|
||||||
|
-- Scheduled events table (proactive behavior)
|
||||||
|
CREATE TABLE IF NOT EXISTS scheduled_events (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
user_id BIGINT REFERENCES users(id) ON DELETE CASCADE,
|
||||||
|
guild_id BIGINT,
|
||||||
|
channel_id BIGINT,
|
||||||
|
event_type VARCHAR(50) NOT NULL, -- birthday, follow_up, reminder, etc.
|
||||||
|
trigger_at TIMESTAMPTZ NOT NULL,
|
||||||
|
title VARCHAR(255) NOT NULL,
|
||||||
|
context JSONB DEFAULT '{}',
|
||||||
|
is_recurring BOOLEAN DEFAULT FALSE,
|
||||||
|
recurrence_rule VARCHAR(100), -- yearly, monthly, etc.
|
||||||
|
status VARCHAR(20) DEFAULT 'pending', -- pending, triggered, cancelled
|
||||||
|
triggered_at TIMESTAMPTZ,
|
||||||
|
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
updated_at TIMESTAMPTZ DEFAULT NOW()
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_scheduled_events_user_id ON scheduled_events(user_id);
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_scheduled_events_trigger_at ON scheduled_events(trigger_at);
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_scheduled_events_status ON scheduled_events(status);
|
||||||
|
|
||||||
|
-- Fact associations table (cross-user memory links)
|
||||||
|
CREATE TABLE IF NOT EXISTS fact_associations (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
fact_id_1 BIGINT NOT NULL REFERENCES user_facts(id) ON DELETE CASCADE,
|
||||||
|
fact_id_2 BIGINT NOT NULL REFERENCES user_facts(id) ON DELETE CASCADE,
|
||||||
|
association_type VARCHAR(50) NOT NULL, -- shared_interest, same_location, etc.
|
||||||
|
strength FLOAT DEFAULT 0.5,
|
||||||
|
discovered_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
created_at TIMESTAMPTZ DEFAULT NOW(),
|
||||||
|
UNIQUE(fact_id_1, fact_id_2)
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_fact_associations_fact_id_1 ON fact_associations(fact_id_1);
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_fact_associations_fact_id_2 ON fact_associations(fact_id_2);
|
||||||
|
|
||||||
|
-- Mood history table (track mood changes over time)
|
||||||
|
CREATE TABLE IF NOT EXISTS mood_history (
|
||||||
|
id BIGSERIAL PRIMARY KEY,
|
||||||
|
guild_id BIGINT,
|
||||||
|
valence FLOAT NOT NULL,
|
||||||
|
arousal FLOAT NOT NULL,
|
||||||
|
trigger_type VARCHAR(50) NOT NULL, -- conversation, time_decay, event
|
||||||
|
trigger_user_id BIGINT REFERENCES users(id) ON DELETE SET NULL,
|
||||||
|
trigger_description TEXT,
|
||||||
|
recorded_at TIMESTAMPTZ DEFAULT NOW()
|
||||||
|
);
|
||||||
|
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_mood_history_guild_id ON mood_history(guild_id);
|
||||||
|
CREATE INDEX IF NOT EXISTS ix_mood_history_recorded_at ON mood_history(recorded_at);
|
||||||
|
|
||||||
|
-- Add new columns to user_facts for enhanced memory
|
||||||
|
ALTER TABLE user_facts ADD COLUMN IF NOT EXISTS category VARCHAR(50);
|
||||||
|
ALTER TABLE user_facts ADD COLUMN IF NOT EXISTS importance FLOAT DEFAULT 0.5;
|
||||||
|
ALTER TABLE user_facts ADD COLUMN IF NOT EXISTS temporal_relevance VARCHAR(20);
|
||||||
|
ALTER TABLE user_facts ADD COLUMN IF NOT EXISTS expiry_date TIMESTAMPTZ;
|
||||||
|
ALTER TABLE user_facts ADD COLUMN IF NOT EXISTS extracted_from_message_id BIGINT;
|
||||||
|
ALTER TABLE user_facts ADD COLUMN IF NOT EXISTS extraction_context TEXT;
|
||||||
|
|||||||
@@ -9,13 +9,22 @@ from discord.ext import commands
|
|||||||
from daemon_boyfriend.config import settings
|
from daemon_boyfriend.config import settings
|
||||||
from daemon_boyfriend.services import (
|
from daemon_boyfriend.services import (
|
||||||
AIService,
|
AIService,
|
||||||
|
CommunicationStyleService,
|
||||||
ConversationManager,
|
ConversationManager,
|
||||||
|
FactExtractionService,
|
||||||
ImageAttachment,
|
ImageAttachment,
|
||||||
Message,
|
Message,
|
||||||
|
MoodService,
|
||||||
|
OpinionService,
|
||||||
PersistentConversationManager,
|
PersistentConversationManager,
|
||||||
|
ProactiveService,
|
||||||
|
RelationshipService,
|
||||||
SearXNGService,
|
SearXNGService,
|
||||||
UserService,
|
UserService,
|
||||||
db,
|
db,
|
||||||
|
detect_emoji_usage,
|
||||||
|
detect_formal_language,
|
||||||
|
extract_topics_from_message,
|
||||||
)
|
)
|
||||||
from daemon_boyfriend.utils import get_monitor
|
from daemon_boyfriend.utils import get_monitor
|
||||||
|
|
||||||
@@ -414,6 +423,8 @@ class AIChatCog(commands.Cog):
|
|||||||
async with db.session() as session:
|
async with db.session() as session:
|
||||||
user_service = UserService(session)
|
user_service = UserService(session)
|
||||||
conv_manager = PersistentConversationManager(session)
|
conv_manager = PersistentConversationManager(session)
|
||||||
|
mood_service = MoodService(session)
|
||||||
|
relationship_service = RelationshipService(session)
|
||||||
|
|
||||||
# Get or create user
|
# Get or create user
|
||||||
user = await user_service.get_or_create_user(
|
user = await user_service.get_or_create_user(
|
||||||
@@ -422,10 +433,12 @@ class AIChatCog(commands.Cog):
|
|||||||
display_name=message.author.display_name,
|
display_name=message.author.display_name,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
guild_id = message.guild.id if message.guild else None
|
||||||
|
|
||||||
# Get or create conversation
|
# Get or create conversation
|
||||||
conversation = await conv_manager.get_or_create_conversation(
|
conversation = await conv_manager.get_or_create_conversation(
|
||||||
user=user,
|
user=user,
|
||||||
guild_id=message.guild.id if message.guild else None,
|
guild_id=guild_id,
|
||||||
channel_id=message.channel.id,
|
channel_id=message.channel.id,
|
||||||
)
|
)
|
||||||
|
|
||||||
@@ -446,8 +459,43 @@ class AIChatCog(commands.Cog):
|
|||||||
# Get context about mentioned users
|
# Get context about mentioned users
|
||||||
mentioned_users_context = self._get_mentioned_users_context(message)
|
mentioned_users_context = self._get_mentioned_users_context(message)
|
||||||
|
|
||||||
# Build system prompt with additional context
|
# Get Living AI context (mood, relationship, style, opinions)
|
||||||
system_prompt = self.ai_service.get_system_prompt()
|
mood = None
|
||||||
|
relationship_data = None
|
||||||
|
communication_style = None
|
||||||
|
relevant_opinions = None
|
||||||
|
|
||||||
|
if settings.living_ai_enabled:
|
||||||
|
if settings.mood_enabled:
|
||||||
|
mood = await mood_service.get_current_mood(guild_id)
|
||||||
|
|
||||||
|
if settings.relationship_enabled:
|
||||||
|
rel = await relationship_service.get_or_create_relationship(user, guild_id)
|
||||||
|
level = relationship_service.get_level(rel.relationship_score)
|
||||||
|
relationship_data = (level, rel)
|
||||||
|
|
||||||
|
if settings.style_learning_enabled:
|
||||||
|
style_service = CommunicationStyleService(session)
|
||||||
|
communication_style = await style_service.get_or_create_style(user)
|
||||||
|
|
||||||
|
if settings.opinion_formation_enabled:
|
||||||
|
opinion_service = OpinionService(session)
|
||||||
|
topics = extract_topics_from_message(user_message)
|
||||||
|
if topics:
|
||||||
|
relevant_opinions = await opinion_service.get_relevant_opinions(
|
||||||
|
topics, guild_id
|
||||||
|
)
|
||||||
|
|
||||||
|
# Build system prompt with personality context
|
||||||
|
if settings.living_ai_enabled and (mood or relationship_data or communication_style):
|
||||||
|
system_prompt = self.ai_service.get_enhanced_system_prompt(
|
||||||
|
mood=mood,
|
||||||
|
relationship=relationship_data,
|
||||||
|
communication_style=communication_style,
|
||||||
|
bot_opinions=relevant_opinions,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
system_prompt = self.ai_service.get_system_prompt()
|
||||||
|
|
||||||
# Add user context from database (custom name, known facts)
|
# Add user context from database (custom name, known facts)
|
||||||
user_context = await user_service.get_user_context(user)
|
user_context = await user_service.get_user_context(user)
|
||||||
@@ -482,6 +530,20 @@ class AIChatCog(commands.Cog):
|
|||||||
image_urls=image_urls,
|
image_urls=image_urls,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
# Post-response Living AI updates (mood, relationship, style, opinions, facts, proactive)
|
||||||
|
if settings.living_ai_enabled:
|
||||||
|
await self._update_living_ai_state(
|
||||||
|
session=session,
|
||||||
|
user=user,
|
||||||
|
guild_id=guild_id,
|
||||||
|
channel_id=message.channel.id,
|
||||||
|
user_message=user_message,
|
||||||
|
bot_response=response.content,
|
||||||
|
discord_message_id=message.id,
|
||||||
|
mood_service=mood_service,
|
||||||
|
relationship_service=relationship_service,
|
||||||
|
)
|
||||||
|
|
||||||
logger.debug(
|
logger.debug(
|
||||||
f"Generated response for user {user.discord_id}: "
|
f"Generated response for user {user.discord_id}: "
|
||||||
f"{len(response.content)} chars, {response.usage}"
|
f"{len(response.content)} chars, {response.usage}"
|
||||||
@@ -489,6 +551,171 @@ class AIChatCog(commands.Cog):
|
|||||||
|
|
||||||
return response.content
|
return response.content
|
||||||
|
|
||||||
|
async def _update_living_ai_state(
|
||||||
|
self,
|
||||||
|
session,
|
||||||
|
user,
|
||||||
|
guild_id: int | None,
|
||||||
|
channel_id: int,
|
||||||
|
user_message: str,
|
||||||
|
bot_response: str,
|
||||||
|
discord_message_id: int,
|
||||||
|
mood_service: MoodService,
|
||||||
|
relationship_service: RelationshipService,
|
||||||
|
) -> None:
|
||||||
|
"""Update Living AI state after a response (mood, relationship, style, opinions, facts, proactive)."""
|
||||||
|
try:
|
||||||
|
# Simple sentiment estimation based on message characteristics
|
||||||
|
sentiment = self._estimate_sentiment(user_message)
|
||||||
|
engagement = min(1.0, len(user_message) / 300) # Longer = more engaged
|
||||||
|
|
||||||
|
# Update mood
|
||||||
|
if settings.mood_enabled:
|
||||||
|
await mood_service.update_mood(
|
||||||
|
guild_id=guild_id,
|
||||||
|
sentiment_delta=sentiment * 0.5,
|
||||||
|
engagement_delta=engagement * 0.5,
|
||||||
|
trigger_type="conversation",
|
||||||
|
trigger_user_id=user.id,
|
||||||
|
trigger_description=f"Conversation with {user.display_name}",
|
||||||
|
)
|
||||||
|
# Increment message count
|
||||||
|
await mood_service.increment_stats(guild_id, messages_sent=1)
|
||||||
|
|
||||||
|
# Update relationship
|
||||||
|
if settings.relationship_enabled:
|
||||||
|
await relationship_service.record_interaction(
|
||||||
|
user=user,
|
||||||
|
guild_id=guild_id,
|
||||||
|
sentiment=sentiment,
|
||||||
|
message_length=len(user_message),
|
||||||
|
conversation_turns=1,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Update communication style learning
|
||||||
|
if settings.style_learning_enabled:
|
||||||
|
style_service = CommunicationStyleService(session)
|
||||||
|
await style_service.record_engagement(
|
||||||
|
user=user,
|
||||||
|
user_message_length=len(user_message),
|
||||||
|
bot_response_length=len(bot_response),
|
||||||
|
conversation_continued=True, # Assume continued for now
|
||||||
|
user_used_emoji=detect_emoji_usage(user_message),
|
||||||
|
user_used_formal_language=detect_formal_language(user_message),
|
||||||
|
)
|
||||||
|
|
||||||
|
# Update opinion tracking
|
||||||
|
if settings.opinion_formation_enabled:
|
||||||
|
topics = extract_topics_from_message(user_message)
|
||||||
|
if topics:
|
||||||
|
opinion_service = OpinionService(session)
|
||||||
|
for topic in topics[:3]: # Limit to 3 topics per message
|
||||||
|
await opinion_service.record_topic_discussion(
|
||||||
|
topic=topic,
|
||||||
|
guild_id=guild_id,
|
||||||
|
sentiment=sentiment,
|
||||||
|
engagement_level=engagement,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Autonomous fact extraction (rate-limited internally)
|
||||||
|
if settings.fact_extraction_enabled:
|
||||||
|
fact_service = FactExtractionService(session, self.ai_service)
|
||||||
|
new_facts = await fact_service.maybe_extract_facts(
|
||||||
|
user=user,
|
||||||
|
message_content=user_message,
|
||||||
|
discord_message_id=discord_message_id,
|
||||||
|
)
|
||||||
|
if new_facts:
|
||||||
|
# Update stats for facts learned
|
||||||
|
await mood_service.increment_stats(guild_id, facts_learned=len(new_facts))
|
||||||
|
logger.debug(f"Auto-extracted {len(new_facts)} facts from message")
|
||||||
|
|
||||||
|
# Proactive event detection (follow-ups, birthdays)
|
||||||
|
if settings.proactive_enabled:
|
||||||
|
proactive_service = ProactiveService(session, self.ai_service)
|
||||||
|
|
||||||
|
# Try to detect follow-up opportunities (rate-limited by message length)
|
||||||
|
if len(user_message) > 30: # Only check substantial messages
|
||||||
|
await proactive_service.detect_and_schedule_followup(
|
||||||
|
user=user,
|
||||||
|
message_content=user_message,
|
||||||
|
guild_id=guild_id,
|
||||||
|
channel_id=channel_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Try to detect birthday mentions
|
||||||
|
await proactive_service.detect_and_schedule_birthday(
|
||||||
|
user=user,
|
||||||
|
message_content=user_message,
|
||||||
|
guild_id=guild_id,
|
||||||
|
channel_id=channel_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Failed to update Living AI state: {e}")
|
||||||
|
|
||||||
|
def _estimate_sentiment(self, text: str) -> float:
|
||||||
|
"""Estimate sentiment from text using simple heuristics.
|
||||||
|
|
||||||
|
Returns a value from -1 (negative) to 1 (positive).
|
||||||
|
This is a placeholder until we add AI-based sentiment analysis.
|
||||||
|
"""
|
||||||
|
text_lower = text.lower()
|
||||||
|
|
||||||
|
# Positive indicators
|
||||||
|
positive_words = [
|
||||||
|
"thanks",
|
||||||
|
"thank you",
|
||||||
|
"awesome",
|
||||||
|
"great",
|
||||||
|
"love",
|
||||||
|
"amazing",
|
||||||
|
"wonderful",
|
||||||
|
"excellent",
|
||||||
|
"perfect",
|
||||||
|
"happy",
|
||||||
|
"glad",
|
||||||
|
"appreciate",
|
||||||
|
"helpful",
|
||||||
|
"nice",
|
||||||
|
"good",
|
||||||
|
"cool",
|
||||||
|
"fantastic",
|
||||||
|
"brilliant",
|
||||||
|
]
|
||||||
|
# Negative indicators
|
||||||
|
negative_words = [
|
||||||
|
"hate",
|
||||||
|
"awful",
|
||||||
|
"terrible",
|
||||||
|
"bad",
|
||||||
|
"stupid",
|
||||||
|
"annoying",
|
||||||
|
"frustrated",
|
||||||
|
"angry",
|
||||||
|
"disappointed",
|
||||||
|
"wrong",
|
||||||
|
"broken",
|
||||||
|
"useless",
|
||||||
|
"horrible",
|
||||||
|
"worst",
|
||||||
|
"sucks",
|
||||||
|
"boring",
|
||||||
|
]
|
||||||
|
|
||||||
|
positive_count = sum(1 for word in positive_words if word in text_lower)
|
||||||
|
negative_count = sum(1 for word in negative_words if word in text_lower)
|
||||||
|
|
||||||
|
# Check for exclamation marks (usually positive energy)
|
||||||
|
exclamation_bonus = min(0.2, text.count("!") * 0.05)
|
||||||
|
|
||||||
|
# Calculate sentiment
|
||||||
|
if positive_count + negative_count == 0:
|
||||||
|
return 0.1 + exclamation_bonus # Slightly positive by default
|
||||||
|
|
||||||
|
sentiment = (positive_count - negative_count) / (positive_count + negative_count)
|
||||||
|
return max(-1.0, min(1.0, sentiment + exclamation_bonus))
|
||||||
|
|
||||||
async def _generate_response_in_memory(
|
async def _generate_response_in_memory(
|
||||||
self, message: discord.Message, user_message: str
|
self, message: discord.Message, user_message: str
|
||||||
) -> str:
|
) -> str:
|
||||||
|
|||||||
@@ -87,6 +87,38 @@ class Settings(BaseSettings):
|
|||||||
searxng_enabled: bool = Field(True, description="Enable web search capability")
|
searxng_enabled: bool = Field(True, description="Enable web search capability")
|
||||||
searxng_max_results: int = Field(5, ge=1, le=20, description="Maximum search results to fetch")
|
searxng_max_results: int = Field(5, ge=1, le=20, description="Maximum search results to fetch")
|
||||||
|
|
||||||
|
# Living AI Configuration
|
||||||
|
living_ai_enabled: bool = Field(True, description="Enable Living AI features")
|
||||||
|
mood_enabled: bool = Field(True, description="Enable mood system")
|
||||||
|
relationship_enabled: bool = Field(True, description="Enable relationship tracking")
|
||||||
|
fact_extraction_enabled: bool = Field(True, description="Enable autonomous fact extraction")
|
||||||
|
fact_extraction_rate: float = Field(
|
||||||
|
0.3, ge=0.0, le=1.0, description="Probability of extracting facts from messages"
|
||||||
|
)
|
||||||
|
proactive_enabled: bool = Field(True, description="Enable proactive messages")
|
||||||
|
cross_user_enabled: bool = Field(
|
||||||
|
False, description="Enable cross-user memory associations (privacy-sensitive)"
|
||||||
|
)
|
||||||
|
opinion_formation_enabled: bool = Field(True, description="Enable bot opinion formation")
|
||||||
|
style_learning_enabled: bool = Field(True, description="Enable communication style learning")
|
||||||
|
|
||||||
|
# Mood System Settings
|
||||||
|
mood_decay_rate: float = Field(
|
||||||
|
0.1, ge=0.0, le=1.0, description="How fast mood returns to neutral per hour"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Command Toggles
|
||||||
|
commands_enabled: bool = Field(True, description="Master switch for all commands")
|
||||||
|
cmd_relationship_enabled: bool = Field(True, description="Enable !relationship command")
|
||||||
|
cmd_mood_enabled: bool = Field(True, description="Enable !mood command")
|
||||||
|
cmd_botstats_enabled: bool = Field(True, description="Enable !botstats command")
|
||||||
|
cmd_ourhistory_enabled: bool = Field(True, description="Enable !ourhistory command")
|
||||||
|
cmd_birthday_enabled: bool = Field(True, description="Enable !birthday command")
|
||||||
|
cmd_remember_enabled: bool = Field(True, description="Enable !remember command")
|
||||||
|
cmd_setname_enabled: bool = Field(True, description="Enable !setname command")
|
||||||
|
cmd_whatdoyouknow_enabled: bool = Field(True, description="Enable !whatdoyouknow command")
|
||||||
|
cmd_forgetme_enabled: bool = Field(True, description="Enable !forgetme command")
|
||||||
|
|
||||||
def get_api_key(self) -> str:
|
def get_api_key(self) -> str:
|
||||||
"""Get the API key for the configured provider."""
|
"""Get the API key for the configured provider."""
|
||||||
key_map = {
|
key_map = {
|
||||||
|
|||||||
@@ -3,15 +3,31 @@
|
|||||||
from .base import Base
|
from .base import Base
|
||||||
from .conversation import Conversation, Message
|
from .conversation import Conversation, Message
|
||||||
from .guild import Guild, GuildMember
|
from .guild import Guild, GuildMember
|
||||||
|
from .living_ai import (
|
||||||
|
BotOpinion,
|
||||||
|
BotState,
|
||||||
|
FactAssociation,
|
||||||
|
MoodHistory,
|
||||||
|
ScheduledEvent,
|
||||||
|
UserCommunicationStyle,
|
||||||
|
UserRelationship,
|
||||||
|
)
|
||||||
from .user import User, UserFact, UserPreference
|
from .user import User, UserFact, UserPreference
|
||||||
|
|
||||||
__all__ = [
|
__all__ = [
|
||||||
"Base",
|
"Base",
|
||||||
|
"BotOpinion",
|
||||||
|
"BotState",
|
||||||
"Conversation",
|
"Conversation",
|
||||||
|
"FactAssociation",
|
||||||
"Guild",
|
"Guild",
|
||||||
"GuildMember",
|
"GuildMember",
|
||||||
"Message",
|
"Message",
|
||||||
|
"MoodHistory",
|
||||||
|
"ScheduledEvent",
|
||||||
"User",
|
"User",
|
||||||
|
"UserCommunicationStyle",
|
||||||
"UserFact",
|
"UserFact",
|
||||||
"UserPreference",
|
"UserPreference",
|
||||||
|
"UserRelationship",
|
||||||
]
|
]
|
||||||
|
|||||||
186
src/daemon_boyfriend/models/living_ai.py
Normal file
186
src/daemon_boyfriend/models/living_ai.py
Normal file
@@ -0,0 +1,186 @@
|
|||||||
|
"""Living AI database models - mood, relationships, opinions, and more."""
|
||||||
|
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import TYPE_CHECKING
|
||||||
|
|
||||||
|
from sqlalchemy import BigInteger, Boolean, Float, ForeignKey, String, Text, UniqueConstraint
|
||||||
|
from sqlalchemy.dialects.postgresql import JSONB
|
||||||
|
from sqlalchemy.orm import Mapped, mapped_column, relationship
|
||||||
|
|
||||||
|
from .base import Base
|
||||||
|
|
||||||
|
if TYPE_CHECKING:
|
||||||
|
from .user import User, UserFact
|
||||||
|
|
||||||
|
|
||||||
|
class BotState(Base):
|
||||||
|
"""Global bot state - mood, statistics, preferences per guild."""
|
||||||
|
|
||||||
|
__tablename__ = "bot_states"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
guild_id: Mapped[int | None] = mapped_column(BigInteger, unique=True, nullable=True)
|
||||||
|
|
||||||
|
# Current mood state (valence-arousal model)
|
||||||
|
mood_valence: Mapped[float] = mapped_column(Float, default=0.0) # -1.0 (sad) to 1.0 (happy)
|
||||||
|
mood_arousal: Mapped[float] = mapped_column(Float, default=0.0) # -1.0 (calm) to 1.0 (excited)
|
||||||
|
mood_updated_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
|
||||||
|
# Bot statistics
|
||||||
|
total_messages_sent: Mapped[int] = mapped_column(default=0)
|
||||||
|
total_facts_learned: Mapped[int] = mapped_column(default=0)
|
||||||
|
total_users_known: Mapped[int] = mapped_column(default=0)
|
||||||
|
first_activated_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
|
||||||
|
# Bot preferences (evolved over time)
|
||||||
|
preferences: Mapped[dict] = mapped_column(JSONB, default=dict)
|
||||||
|
|
||||||
|
|
||||||
|
class BotOpinion(Base):
|
||||||
|
"""Bot's opinions and preferences on topics."""
|
||||||
|
|
||||||
|
__tablename__ = "bot_opinions"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
guild_id: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
|
||||||
|
|
||||||
|
topic: Mapped[str] = mapped_column(String(255), index=True)
|
||||||
|
sentiment: Mapped[float] = mapped_column(Float, default=0.0) # -1.0 to 1.0
|
||||||
|
interest_level: Mapped[float] = mapped_column(Float, default=0.5) # 0.0 to 1.0
|
||||||
|
discussion_count: Mapped[int] = mapped_column(default=0)
|
||||||
|
|
||||||
|
reasoning: Mapped[str | None] = mapped_column(Text)
|
||||||
|
formed_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
last_reinforced_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
|
||||||
|
__table_args__ = (UniqueConstraint("guild_id", "topic"),)
|
||||||
|
|
||||||
|
|
||||||
|
class UserRelationship(Base):
|
||||||
|
"""Tracks relationship depth and dynamics with each user."""
|
||||||
|
|
||||||
|
__tablename__ = "user_relationships"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
user_id: Mapped[int] = mapped_column(ForeignKey("users.id", ondelete="CASCADE"), index=True)
|
||||||
|
guild_id: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
|
||||||
|
|
||||||
|
# Relationship level (0-100 scale)
|
||||||
|
# 0-20: stranger, 21-40: acquaintance, 41-60: friend, 61-80: good friend, 81-100: close friend
|
||||||
|
relationship_score: Mapped[float] = mapped_column(Float, default=10.0)
|
||||||
|
|
||||||
|
# Interaction metrics
|
||||||
|
total_interactions: Mapped[int] = mapped_column(default=0)
|
||||||
|
positive_interactions: Mapped[int] = mapped_column(default=0)
|
||||||
|
negative_interactions: Mapped[int] = mapped_column(default=0)
|
||||||
|
|
||||||
|
# Engagement quality
|
||||||
|
avg_message_length: Mapped[float] = mapped_column(Float, default=0.0)
|
||||||
|
conversation_depth_avg: Mapped[float] = mapped_column(Float, default=0.0)
|
||||||
|
|
||||||
|
# Inside jokes / shared references
|
||||||
|
shared_references: Mapped[dict] = mapped_column(JSONB, default=dict)
|
||||||
|
|
||||||
|
first_interaction_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
last_interaction_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
|
||||||
|
# Relationships
|
||||||
|
user: Mapped["User"] = relationship(back_populates="relationships")
|
||||||
|
|
||||||
|
__table_args__ = (UniqueConstraint("user_id", "guild_id"),)
|
||||||
|
|
||||||
|
|
||||||
|
class UserCommunicationStyle(Base):
|
||||||
|
"""Learned communication preferences per user."""
|
||||||
|
|
||||||
|
__tablename__ = "user_communication_styles"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
user_id: Mapped[int] = mapped_column(
|
||||||
|
ForeignKey("users.id", ondelete="CASCADE"), unique=True, index=True
|
||||||
|
)
|
||||||
|
|
||||||
|
# Response style preferences (learned from engagement patterns)
|
||||||
|
preferred_length: Mapped[str] = mapped_column(String(20), default="medium")
|
||||||
|
preferred_formality: Mapped[float] = mapped_column(Float, default=0.5) # 0=casual, 1=formal
|
||||||
|
emoji_affinity: Mapped[float] = mapped_column(Float, default=0.5) # 0=none, 1=lots
|
||||||
|
humor_affinity: Mapped[float] = mapped_column(Float, default=0.5) # 0=serious, 1=playful
|
||||||
|
detail_preference: Mapped[float] = mapped_column(Float, default=0.5) # 0=concise, 1=detailed
|
||||||
|
|
||||||
|
# Engagement signals used to learn preferences
|
||||||
|
engagement_signals: Mapped[dict] = mapped_column(JSONB, default=dict)
|
||||||
|
|
||||||
|
samples_collected: Mapped[int] = mapped_column(default=0)
|
||||||
|
confidence: Mapped[float] = mapped_column(Float, default=0.0) # 0-1
|
||||||
|
|
||||||
|
# Relationship
|
||||||
|
user: Mapped["User"] = relationship(back_populates="communication_style")
|
||||||
|
|
||||||
|
|
||||||
|
class ScheduledEvent(Base):
|
||||||
|
"""Events the bot should act on (birthdays, follow-ups, etc.)."""
|
||||||
|
|
||||||
|
__tablename__ = "scheduled_events"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
user_id: Mapped[int | None] = mapped_column(ForeignKey("users.id", ondelete="CASCADE"))
|
||||||
|
guild_id: Mapped[int | None] = mapped_column(BigInteger)
|
||||||
|
channel_id: Mapped[int | None] = mapped_column(BigInteger)
|
||||||
|
|
||||||
|
event_type: Mapped[str] = mapped_column(String(50), index=True)
|
||||||
|
trigger_at: Mapped[datetime] = mapped_column(index=True)
|
||||||
|
|
||||||
|
title: Mapped[str] = mapped_column(String(255))
|
||||||
|
context: Mapped[dict] = mapped_column(JSONB, default=dict)
|
||||||
|
|
||||||
|
is_recurring: Mapped[bool] = mapped_column(Boolean, default=False)
|
||||||
|
recurrence_rule: Mapped[str | None] = mapped_column(String(100))
|
||||||
|
|
||||||
|
status: Mapped[str] = mapped_column(String(20), default="pending", index=True)
|
||||||
|
triggered_at: Mapped[datetime | None] = mapped_column(default=None)
|
||||||
|
|
||||||
|
# Relationships
|
||||||
|
user: Mapped["User"] = relationship(back_populates="scheduled_events")
|
||||||
|
|
||||||
|
|
||||||
|
class FactAssociation(Base):
|
||||||
|
"""Links facts across users for cross-user memory."""
|
||||||
|
|
||||||
|
__tablename__ = "fact_associations"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
|
||||||
|
fact_id_1: Mapped[int] = mapped_column(
|
||||||
|
ForeignKey("user_facts.id", ondelete="CASCADE"), index=True
|
||||||
|
)
|
||||||
|
fact_id_2: Mapped[int] = mapped_column(
|
||||||
|
ForeignKey("user_facts.id", ondelete="CASCADE"), index=True
|
||||||
|
)
|
||||||
|
|
||||||
|
association_type: Mapped[str] = mapped_column(String(50))
|
||||||
|
strength: Mapped[float] = mapped_column(Float, default=0.5)
|
||||||
|
discovered_at: Mapped[datetime] = mapped_column(default=datetime.utcnow)
|
||||||
|
|
||||||
|
# Relationships
|
||||||
|
fact_1: Mapped["UserFact"] = relationship(foreign_keys=[fact_id_1])
|
||||||
|
fact_2: Mapped["UserFact"] = relationship(foreign_keys=[fact_id_2])
|
||||||
|
|
||||||
|
__table_args__ = (UniqueConstraint("fact_id_1", "fact_id_2"),)
|
||||||
|
|
||||||
|
|
||||||
|
class MoodHistory(Base):
|
||||||
|
"""Track mood changes over time for reflection."""
|
||||||
|
|
||||||
|
__tablename__ = "mood_history"
|
||||||
|
|
||||||
|
id: Mapped[int] = mapped_column(primary_key=True)
|
||||||
|
guild_id: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
|
||||||
|
|
||||||
|
valence: Mapped[float] = mapped_column(Float)
|
||||||
|
arousal: Mapped[float] = mapped_column(Float)
|
||||||
|
|
||||||
|
trigger_type: Mapped[str] = mapped_column(String(50)) # conversation, time_decay, event
|
||||||
|
trigger_user_id: Mapped[int | None] = mapped_column(ForeignKey("users.id", ondelete="SET NULL"))
|
||||||
|
trigger_description: Mapped[str | None] = mapped_column(Text)
|
||||||
|
|
||||||
|
recorded_at: Mapped[datetime] = mapped_column(default=datetime.utcnow, index=True)
|
||||||
@@ -11,6 +11,7 @@ from .base import Base
|
|||||||
if TYPE_CHECKING:
|
if TYPE_CHECKING:
|
||||||
from .conversation import Conversation, Message
|
from .conversation import Conversation, Message
|
||||||
from .guild import GuildMember
|
from .guild import GuildMember
|
||||||
|
from .living_ai import ScheduledEvent, UserCommunicationStyle, UserRelationship
|
||||||
|
|
||||||
|
|
||||||
class User(Base):
|
class User(Base):
|
||||||
@@ -42,6 +43,17 @@ class User(Base):
|
|||||||
back_populates="user", cascade="all, delete-orphan"
|
back_populates="user", cascade="all, delete-orphan"
|
||||||
)
|
)
|
||||||
|
|
||||||
|
# Living AI relationships
|
||||||
|
relationships: Mapped[list["UserRelationship"]] = relationship(
|
||||||
|
back_populates="user", cascade="all, delete-orphan"
|
||||||
|
)
|
||||||
|
communication_style: Mapped["UserCommunicationStyle | None"] = relationship(
|
||||||
|
back_populates="user", cascade="all, delete-orphan", uselist=False
|
||||||
|
)
|
||||||
|
scheduled_events: Mapped[list["ScheduledEvent"]] = relationship(
|
||||||
|
back_populates="user", cascade="all, delete-orphan"
|
||||||
|
)
|
||||||
|
|
||||||
@property
|
@property
|
||||||
def display_name(self) -> str:
|
def display_name(self) -> str:
|
||||||
"""Get the name to use when addressing this user."""
|
"""Get the name to use when addressing this user."""
|
||||||
|
|||||||
@@ -1,23 +1,49 @@
|
|||||||
"""Services for external integrations."""
|
"""Services for external integrations."""
|
||||||
|
|
||||||
from .ai_service import AIService
|
from .ai_service import AIService
|
||||||
|
from .association_service import AssociationService
|
||||||
|
from .communication_style_service import (
|
||||||
|
CommunicationStyleService,
|
||||||
|
detect_emoji_usage,
|
||||||
|
detect_formal_language,
|
||||||
|
)
|
||||||
from .conversation import ConversationManager
|
from .conversation import ConversationManager
|
||||||
from .database import DatabaseService, db, get_db
|
from .database import DatabaseService, db, get_db
|
||||||
|
from .fact_extraction_service import FactExtractionService
|
||||||
|
from .mood_service import MoodLabel, MoodService, MoodState
|
||||||
|
from .opinion_service import OpinionService, extract_topics_from_message
|
||||||
from .persistent_conversation import PersistentConversationManager
|
from .persistent_conversation import PersistentConversationManager
|
||||||
|
from .proactive_service import ProactiveService
|
||||||
from .providers import AIResponse, ImageAttachment, Message
|
from .providers import AIResponse, ImageAttachment, Message
|
||||||
|
from .relationship_service import RelationshipLevel, RelationshipService
|
||||||
from .searxng import SearXNGService
|
from .searxng import SearXNGService
|
||||||
|
from .self_awareness_service import SelfAwarenessService
|
||||||
from .user_service import UserService
|
from .user_service import UserService
|
||||||
|
|
||||||
__all__ = [
|
__all__ = [
|
||||||
"AIService",
|
"AIService",
|
||||||
"AIResponse",
|
"AIResponse",
|
||||||
|
"AssociationService",
|
||||||
|
"CommunicationStyleService",
|
||||||
"ConversationManager",
|
"ConversationManager",
|
||||||
"DatabaseService",
|
"DatabaseService",
|
||||||
|
"FactExtractionService",
|
||||||
"ImageAttachment",
|
"ImageAttachment",
|
||||||
"Message",
|
"Message",
|
||||||
|
"MoodLabel",
|
||||||
|
"MoodService",
|
||||||
|
"MoodState",
|
||||||
|
"OpinionService",
|
||||||
"PersistentConversationManager",
|
"PersistentConversationManager",
|
||||||
|
"ProactiveService",
|
||||||
|
"RelationshipLevel",
|
||||||
|
"RelationshipService",
|
||||||
"SearXNGService",
|
"SearXNGService",
|
||||||
|
"SelfAwarenessService",
|
||||||
"UserService",
|
"UserService",
|
||||||
"db",
|
"db",
|
||||||
|
"detect_emoji_usage",
|
||||||
|
"detect_formal_language",
|
||||||
|
"extract_topics_from_message",
|
||||||
"get_db",
|
"get_db",
|
||||||
]
|
]
|
||||||
|
|||||||
@@ -1,7 +1,9 @@
|
|||||||
"""AI Service - Factory and facade for AI providers."""
|
"""AI Service - Factory and facade for AI providers."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
import logging
|
import logging
|
||||||
from typing import Literal
|
from typing import TYPE_CHECKING, Literal
|
||||||
|
|
||||||
from daemon_boyfriend.config import Settings, settings
|
from daemon_boyfriend.config import Settings, settings
|
||||||
|
|
||||||
@@ -15,6 +17,12 @@ from .providers import (
|
|||||||
OpenRouterProvider,
|
OpenRouterProvider,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
if TYPE_CHECKING:
|
||||||
|
from daemon_boyfriend.models import BotOpinion, UserCommunicationStyle, UserRelationship
|
||||||
|
|
||||||
|
from .mood_service import MoodState
|
||||||
|
from .relationship_service import RelationshipLevel
|
||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
ProviderType = Literal["openai", "openrouter", "anthropic", "gemini"]
|
ProviderType = Literal["openai", "openrouter", "anthropic", "gemini"]
|
||||||
@@ -106,3 +114,90 @@ class AIService:
|
|||||||
f"Discord bot. Keep your responses concise and engaging. "
|
f"Discord bot. Keep your responses concise and engaging. "
|
||||||
f"You can use Discord markdown formatting in your responses."
|
f"You can use Discord markdown formatting in your responses."
|
||||||
)
|
)
|
||||||
|
|
||||||
|
def get_enhanced_system_prompt(
|
||||||
|
self,
|
||||||
|
mood: MoodState | None = None,
|
||||||
|
relationship: tuple[RelationshipLevel, UserRelationship] | None = None,
|
||||||
|
communication_style: UserCommunicationStyle | None = None,
|
||||||
|
bot_opinions: list[BotOpinion] | None = None,
|
||||||
|
) -> str:
|
||||||
|
"""Build system prompt with all personality modifiers.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
mood: Current mood state
|
||||||
|
relationship: Tuple of (level, relationship_record)
|
||||||
|
communication_style: User's learned communication preferences
|
||||||
|
bot_opinions: Bot's opinions relevant to the conversation
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Enhanced system prompt with personality context
|
||||||
|
"""
|
||||||
|
from .mood_service import MoodService
|
||||||
|
from .relationship_service import RelationshipService
|
||||||
|
|
||||||
|
base_prompt = self.get_system_prompt()
|
||||||
|
modifiers = []
|
||||||
|
|
||||||
|
# Add mood modifier
|
||||||
|
if mood and self._config.mood_enabled:
|
||||||
|
mood_mod = MoodService(None).get_mood_prompt_modifier(mood)
|
||||||
|
if mood_mod:
|
||||||
|
modifiers.append(f"[Current Mood]\n{mood_mod}")
|
||||||
|
|
||||||
|
# Add relationship modifier
|
||||||
|
if relationship and self._config.relationship_enabled:
|
||||||
|
level, rel = relationship
|
||||||
|
rel_mod = RelationshipService(None).get_relationship_prompt_modifier(level, rel)
|
||||||
|
if rel_mod:
|
||||||
|
modifiers.append(f"[Relationship]\n{rel_mod}")
|
||||||
|
|
||||||
|
# Add communication style
|
||||||
|
if communication_style and self._config.style_learning_enabled:
|
||||||
|
style_mod = self._get_style_prompt_modifier(communication_style)
|
||||||
|
if style_mod:
|
||||||
|
modifiers.append(f"[Communication Style]\n{style_mod}")
|
||||||
|
|
||||||
|
# Add relevant opinions
|
||||||
|
if bot_opinions and self._config.opinion_formation_enabled:
|
||||||
|
opinion_strs = []
|
||||||
|
for op in bot_opinions[:3]: # Limit to 3 most relevant
|
||||||
|
if op.sentiment > 0.3:
|
||||||
|
opinion_strs.append(f"You enjoy discussing {op.topic}")
|
||||||
|
elif op.sentiment < -0.3:
|
||||||
|
opinion_strs.append(f"You're less enthusiastic about {op.topic}")
|
||||||
|
if opinion_strs:
|
||||||
|
modifiers.append(f"[Your Opinions]\n{'; '.join(opinion_strs)}")
|
||||||
|
|
||||||
|
if modifiers:
|
||||||
|
return base_prompt + "\n\n--- Personality Context ---\n" + "\n\n".join(modifiers)
|
||||||
|
return base_prompt
|
||||||
|
|
||||||
|
def _get_style_prompt_modifier(self, style: UserCommunicationStyle) -> str:
|
||||||
|
"""Generate prompt text for communication style."""
|
||||||
|
if style.confidence < 0.3:
|
||||||
|
return "" # Not enough data
|
||||||
|
|
||||||
|
parts = []
|
||||||
|
|
||||||
|
if style.preferred_length == "short":
|
||||||
|
parts.append("Keep responses brief and to the point.")
|
||||||
|
elif style.preferred_length == "long":
|
||||||
|
parts.append("Provide detailed, thorough responses.")
|
||||||
|
|
||||||
|
if style.preferred_formality > 0.7:
|
||||||
|
parts.append("Use formal language.")
|
||||||
|
elif style.preferred_formality < 0.3:
|
||||||
|
parts.append("Use casual, relaxed language.")
|
||||||
|
|
||||||
|
if style.emoji_affinity > 0.7:
|
||||||
|
parts.append("Feel free to use emojis.")
|
||||||
|
elif style.emoji_affinity < 0.3:
|
||||||
|
parts.append("Avoid using emojis.")
|
||||||
|
|
||||||
|
if style.humor_affinity > 0.7:
|
||||||
|
parts.append("Be playful and use humor.")
|
||||||
|
elif style.humor_affinity < 0.3:
|
||||||
|
parts.append("Keep a more serious tone.")
|
||||||
|
|
||||||
|
return " ".join(parts)
|
||||||
|
|||||||
388
src/daemon_boyfriend/services/association_service.py
Normal file
388
src/daemon_boyfriend/services/association_service.py
Normal file
@@ -0,0 +1,388 @@
|
|||||||
|
"""Association Service - discovers and manages cross-user fact associations."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
from sqlalchemy import and_, select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.models import FactAssociation, User, UserFact
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class AssociationService:
|
||||||
|
"""Discovers and manages cross-user fact associations."""
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession) -> None:
|
||||||
|
self._session = session
|
||||||
|
|
||||||
|
async def find_shared_interests(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
guild_id: int | None = None,
|
||||||
|
limit: int = 5,
|
||||||
|
) -> list[tuple[User, str, float]]:
|
||||||
|
"""Find other users with shared interests.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user: The user to find matches for
|
||||||
|
guild_id: Limit to users in this guild (optional)
|
||||||
|
limit: Maximum matches to return
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of (other_user, shared_interest, strength) tuples
|
||||||
|
"""
|
||||||
|
# Get user's facts
|
||||||
|
user_facts = await self._get_user_facts(user)
|
||||||
|
if not user_facts:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# Extract topics/interests from facts
|
||||||
|
user_topics = self._extract_topics(user_facts)
|
||||||
|
if not user_topics:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# Find other users with similar topics
|
||||||
|
matches = []
|
||||||
|
other_users = await self._get_other_users(user, guild_id)
|
||||||
|
|
||||||
|
for other_user in other_users:
|
||||||
|
other_facts = await self._get_user_facts(other_user)
|
||||||
|
other_topics = self._extract_topics(other_facts)
|
||||||
|
|
||||||
|
# Find overlapping interests
|
||||||
|
shared = user_topics & other_topics
|
||||||
|
for topic in shared:
|
||||||
|
# Calculate strength based on how central this topic is
|
||||||
|
strength = 0.8 # Base strength for direct match
|
||||||
|
matches.append((other_user, topic, strength))
|
||||||
|
|
||||||
|
# Sort by strength and limit
|
||||||
|
matches.sort(key=lambda x: x[2], reverse=True)
|
||||||
|
return matches[:limit]
|
||||||
|
|
||||||
|
async def create_association(
|
||||||
|
self,
|
||||||
|
fact_1: UserFact,
|
||||||
|
fact_2: UserFact,
|
||||||
|
association_type: str,
|
||||||
|
strength: float = 0.5,
|
||||||
|
) -> FactAssociation | None:
|
||||||
|
"""Create an association between two facts.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fact_1: First fact
|
||||||
|
fact_2: Second fact
|
||||||
|
association_type: Type of association
|
||||||
|
strength: Strength of the association (0-1)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Created association or None if already exists
|
||||||
|
"""
|
||||||
|
# Ensure consistent ordering (smaller ID first)
|
||||||
|
if fact_1.id > fact_2.id:
|
||||||
|
fact_1, fact_2 = fact_2, fact_1
|
||||||
|
|
||||||
|
# Check if association already exists
|
||||||
|
existing = await self._get_existing_association(fact_1.id, fact_2.id)
|
||||||
|
if existing:
|
||||||
|
# Update strength if stronger
|
||||||
|
if strength > existing.strength:
|
||||||
|
existing.strength = strength
|
||||||
|
return existing
|
||||||
|
|
||||||
|
assoc = FactAssociation(
|
||||||
|
fact_id_1=fact_1.id,
|
||||||
|
fact_id_2=fact_2.id,
|
||||||
|
association_type=association_type,
|
||||||
|
strength=strength,
|
||||||
|
discovered_at=datetime.utcnow(),
|
||||||
|
)
|
||||||
|
self._session.add(assoc)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
logger.debug(
|
||||||
|
f"Created association: {association_type} between facts {fact_1.id} and {fact_2.id}"
|
||||||
|
)
|
||||||
|
return assoc
|
||||||
|
|
||||||
|
async def discover_associations(self, guild_id: int | None = None) -> int:
|
||||||
|
"""Discover new associations between facts across users.
|
||||||
|
|
||||||
|
This should be run periodically as a background task.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Number of new associations discovered
|
||||||
|
"""
|
||||||
|
# Get all active facts
|
||||||
|
stmt = select(UserFact).where(UserFact.is_active == True)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
all_facts = list(result.scalars().all())
|
||||||
|
|
||||||
|
if len(all_facts) < 2:
|
||||||
|
return 0
|
||||||
|
|
||||||
|
discovered = 0
|
||||||
|
|
||||||
|
# Group facts by type for comparison
|
||||||
|
facts_by_type: dict[str, list[UserFact]] = {}
|
||||||
|
for fact in all_facts:
|
||||||
|
fact_type = fact.fact_type or "general"
|
||||||
|
if fact_type not in facts_by_type:
|
||||||
|
facts_by_type[fact_type] = []
|
||||||
|
facts_by_type[fact_type].append(fact)
|
||||||
|
|
||||||
|
# Find associations within same type
|
||||||
|
for fact_type, facts in facts_by_type.items():
|
||||||
|
for i, fact_1 in enumerate(facts):
|
||||||
|
for fact_2 in facts[i + 1 :]:
|
||||||
|
# Skip facts from same user
|
||||||
|
if fact_1.user_id == fact_2.user_id:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Check for similarity
|
||||||
|
similarity = self._calculate_similarity(fact_1, fact_2)
|
||||||
|
if similarity > 0.6:
|
||||||
|
assoc = await self.create_association(
|
||||||
|
fact_1=fact_1,
|
||||||
|
fact_2=fact_2,
|
||||||
|
association_type="shared_interest",
|
||||||
|
strength=similarity,
|
||||||
|
)
|
||||||
|
if assoc:
|
||||||
|
discovered += 1
|
||||||
|
|
||||||
|
logger.info(f"Discovered {discovered} new fact associations")
|
||||||
|
return discovered
|
||||||
|
|
||||||
|
async def get_associations_for_user(
|
||||||
|
self, user: User, limit: int = 10
|
||||||
|
) -> list[tuple[UserFact, UserFact, FactAssociation]]:
|
||||||
|
"""Get associations involving a user's facts.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of (user_fact, other_fact, association) tuples
|
||||||
|
"""
|
||||||
|
# Get user's fact IDs
|
||||||
|
user_facts = await self._get_user_facts(user)
|
||||||
|
if not user_facts:
|
||||||
|
return []
|
||||||
|
|
||||||
|
user_fact_ids = {f.id for f in user_facts}
|
||||||
|
|
||||||
|
# Find associations involving these facts
|
||||||
|
stmt = (
|
||||||
|
select(FactAssociation)
|
||||||
|
.where(
|
||||||
|
(FactAssociation.fact_id_1.in_(user_fact_ids))
|
||||||
|
| (FactAssociation.fact_id_2.in_(user_fact_ids))
|
||||||
|
)
|
||||||
|
.order_by(FactAssociation.strength.desc())
|
||||||
|
.limit(limit)
|
||||||
|
)
|
||||||
|
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
associations = list(result.scalars().all())
|
||||||
|
|
||||||
|
# Build result tuples
|
||||||
|
results = []
|
||||||
|
for assoc in associations:
|
||||||
|
# Determine which fact belongs to user
|
||||||
|
if assoc.fact_id_1 in user_fact_ids:
|
||||||
|
user_fact = next(f for f in user_facts if f.id == assoc.fact_id_1)
|
||||||
|
other_fact = await self._get_fact_by_id(assoc.fact_id_2)
|
||||||
|
else:
|
||||||
|
user_fact = next(f for f in user_facts if f.id == assoc.fact_id_2)
|
||||||
|
other_fact = await self._get_fact_by_id(assoc.fact_id_1)
|
||||||
|
|
||||||
|
if other_fact:
|
||||||
|
results.append((user_fact, other_fact, assoc))
|
||||||
|
|
||||||
|
return results
|
||||||
|
|
||||||
|
def format_connection_suggestion(
|
||||||
|
self,
|
||||||
|
user_fact: UserFact,
|
||||||
|
other_fact: UserFact,
|
||||||
|
other_user: User,
|
||||||
|
) -> str:
|
||||||
|
"""Format a suggestion about shared interests.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user_fact: The current user's related fact
|
||||||
|
other_fact: The other user's fact
|
||||||
|
other_user: The other user
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A formatted suggestion string
|
||||||
|
"""
|
||||||
|
# Extract the shared interest
|
||||||
|
interest = self._extract_common_interest(user_fact, other_fact)
|
||||||
|
|
||||||
|
if interest:
|
||||||
|
return (
|
||||||
|
f"By the way, {other_user.display_name} is also into {interest}! "
|
||||||
|
f"You two might enjoy chatting about it."
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
return f"You and {other_user.display_name} seem to have similar interests!"
|
||||||
|
|
||||||
|
async def _get_user_facts(self, user: User) -> list[UserFact]:
|
||||||
|
"""Get all active facts for a user."""
|
||||||
|
stmt = select(UserFact).where(
|
||||||
|
UserFact.user_id == user.id,
|
||||||
|
UserFact.is_active == True,
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
async def _get_other_users(self, exclude_user: User, guild_id: int | None = None) -> list[User]:
|
||||||
|
"""Get other users (optionally filtered by guild)."""
|
||||||
|
stmt = select(User).where(
|
||||||
|
User.id != exclude_user.id,
|
||||||
|
User.is_active == True,
|
||||||
|
)
|
||||||
|
# Note: Guild filtering would require joining with guild_members
|
||||||
|
# For simplicity, we return all users for now
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
async def _get_existing_association(
|
||||||
|
self, fact_id_1: int, fact_id_2: int
|
||||||
|
) -> FactAssociation | None:
|
||||||
|
"""Check if an association already exists."""
|
||||||
|
# Ensure consistent ordering
|
||||||
|
if fact_id_1 > fact_id_2:
|
||||||
|
fact_id_1, fact_id_2 = fact_id_2, fact_id_1
|
||||||
|
|
||||||
|
stmt = select(FactAssociation).where(
|
||||||
|
FactAssociation.fact_id_1 == fact_id_1,
|
||||||
|
FactAssociation.fact_id_2 == fact_id_2,
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return result.scalar_one_or_none()
|
||||||
|
|
||||||
|
async def _get_fact_by_id(self, fact_id: int) -> UserFact | None:
|
||||||
|
"""Get a fact by ID."""
|
||||||
|
stmt = select(UserFact).where(UserFact.id == fact_id)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return result.scalar_one_or_none()
|
||||||
|
|
||||||
|
def _extract_topics(self, facts: list[UserFact]) -> set[str]:
|
||||||
|
"""Extract topic keywords from facts."""
|
||||||
|
topics = set()
|
||||||
|
|
||||||
|
# Keywords to extract as topics
|
||||||
|
topic_keywords = {
|
||||||
|
"programming",
|
||||||
|
"coding",
|
||||||
|
"python",
|
||||||
|
"javascript",
|
||||||
|
"gaming",
|
||||||
|
"games",
|
||||||
|
"music",
|
||||||
|
"guitar",
|
||||||
|
"piano",
|
||||||
|
"singing",
|
||||||
|
"movies",
|
||||||
|
"films",
|
||||||
|
"reading",
|
||||||
|
"books",
|
||||||
|
"sports",
|
||||||
|
"football",
|
||||||
|
"soccer",
|
||||||
|
"basketball",
|
||||||
|
"cooking",
|
||||||
|
"travel",
|
||||||
|
"photography",
|
||||||
|
"art",
|
||||||
|
"drawing",
|
||||||
|
"painting",
|
||||||
|
"hiking",
|
||||||
|
"fitness",
|
||||||
|
"gym",
|
||||||
|
"yoga",
|
||||||
|
"meditation",
|
||||||
|
"cats",
|
||||||
|
"dogs",
|
||||||
|
"pets",
|
||||||
|
"anime",
|
||||||
|
"manga",
|
||||||
|
"technology",
|
||||||
|
"science",
|
||||||
|
"philosophy",
|
||||||
|
}
|
||||||
|
|
||||||
|
for fact in facts:
|
||||||
|
content_lower = fact.fact_content.lower()
|
||||||
|
for keyword in topic_keywords:
|
||||||
|
if keyword in content_lower:
|
||||||
|
topics.add(keyword)
|
||||||
|
|
||||||
|
return topics
|
||||||
|
|
||||||
|
def _calculate_similarity(self, fact_1: UserFact, fact_2: UserFact) -> float:
|
||||||
|
"""Calculate similarity between two facts."""
|
||||||
|
content_1 = fact_1.fact_content.lower()
|
||||||
|
content_2 = fact_2.fact_content.lower()
|
||||||
|
|
||||||
|
# Simple word overlap similarity
|
||||||
|
words_1 = set(content_1.split())
|
||||||
|
words_2 = set(content_2.split())
|
||||||
|
|
||||||
|
# Remove common words
|
||||||
|
stop_words = {"a", "an", "the", "is", "are", "was", "were", "in", "on", "at", "to", "for"}
|
||||||
|
words_1 -= stop_words
|
||||||
|
words_2 -= stop_words
|
||||||
|
|
||||||
|
if not words_1 or not words_2:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
intersection = words_1 & words_2
|
||||||
|
union = words_1 | words_2
|
||||||
|
|
||||||
|
return len(intersection) / len(union) if union else 0.0
|
||||||
|
|
||||||
|
def _extract_common_interest(self, fact_1: UserFact, fact_2: UserFact) -> str | None:
|
||||||
|
"""Extract the common interest between two facts."""
|
||||||
|
content_1 = fact_1.fact_content.lower()
|
||||||
|
content_2 = fact_2.fact_content.lower()
|
||||||
|
|
||||||
|
# Find common meaningful words
|
||||||
|
words_1 = set(content_1.split())
|
||||||
|
words_2 = set(content_2.split())
|
||||||
|
|
||||||
|
stop_words = {
|
||||||
|
"a",
|
||||||
|
"an",
|
||||||
|
"the",
|
||||||
|
"is",
|
||||||
|
"are",
|
||||||
|
"was",
|
||||||
|
"were",
|
||||||
|
"in",
|
||||||
|
"on",
|
||||||
|
"at",
|
||||||
|
"to",
|
||||||
|
"for",
|
||||||
|
"and",
|
||||||
|
"or",
|
||||||
|
"but",
|
||||||
|
"with",
|
||||||
|
"has",
|
||||||
|
"have",
|
||||||
|
"likes",
|
||||||
|
"loves",
|
||||||
|
"enjoys",
|
||||||
|
"interested",
|
||||||
|
"into",
|
||||||
|
}
|
||||||
|
|
||||||
|
common = (words_1 & words_2) - stop_words
|
||||||
|
|
||||||
|
if common:
|
||||||
|
# Return the longest common word as the interest
|
||||||
|
return max(common, key=len)
|
||||||
|
|
||||||
|
return None
|
||||||
245
src/daemon_boyfriend/services/communication_style_service.py
Normal file
245
src/daemon_boyfriend/services/communication_style_service.py
Normal file
@@ -0,0 +1,245 @@
|
|||||||
|
"""Communication Style Service - learns and applies per-user communication preferences."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
|
||||||
|
from sqlalchemy import select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.models import User, UserCommunicationStyle
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class CommunicationStyleService:
|
||||||
|
"""Learns and applies per-user communication preferences."""
|
||||||
|
|
||||||
|
# Minimum samples before we trust the learned style
|
||||||
|
MIN_SAMPLES_FOR_CONFIDENCE = 10
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession) -> None:
|
||||||
|
self._session = session
|
||||||
|
|
||||||
|
async def get_or_create_style(self, user: User) -> UserCommunicationStyle:
|
||||||
|
"""Get or create communication style profile for a user."""
|
||||||
|
stmt = select(UserCommunicationStyle).where(UserCommunicationStyle.user_id == user.id)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
style = result.scalar_one_or_none()
|
||||||
|
|
||||||
|
if not style:
|
||||||
|
style = UserCommunicationStyle(user_id=user.id)
|
||||||
|
self._session.add(style)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
return style
|
||||||
|
|
||||||
|
async def record_engagement(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
user_message_length: int,
|
||||||
|
bot_response_length: int,
|
||||||
|
conversation_continued: bool = True,
|
||||||
|
user_used_emoji: bool = False,
|
||||||
|
user_used_formal_language: bool = False,
|
||||||
|
) -> None:
|
||||||
|
"""Record engagement signals to learn preferences.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user: The user
|
||||||
|
user_message_length: Length of user's message
|
||||||
|
bot_response_length: Length of bot's response
|
||||||
|
conversation_continued: Whether user continued the conversation
|
||||||
|
user_used_emoji: Whether user used emoji in their message
|
||||||
|
user_used_formal_language: Whether user used formal language
|
||||||
|
"""
|
||||||
|
style = await self.get_or_create_style(user)
|
||||||
|
|
||||||
|
signals = style.engagement_signals or {}
|
||||||
|
|
||||||
|
# Track response length preferences
|
||||||
|
if "response_lengths" not in signals:
|
||||||
|
signals["response_lengths"] = []
|
||||||
|
|
||||||
|
signals["response_lengths"].append(
|
||||||
|
{
|
||||||
|
"bot_length": bot_response_length,
|
||||||
|
"engaged": conversation_continued,
|
||||||
|
}
|
||||||
|
)
|
||||||
|
# Keep last 50 samples
|
||||||
|
signals["response_lengths"] = signals["response_lengths"][-50:]
|
||||||
|
|
||||||
|
# Track user's own message lengths
|
||||||
|
if "user_lengths" not in signals:
|
||||||
|
signals["user_lengths"] = []
|
||||||
|
signals["user_lengths"].append(user_message_length)
|
||||||
|
signals["user_lengths"] = signals["user_lengths"][-50:]
|
||||||
|
|
||||||
|
# Track emoji usage
|
||||||
|
if "emoji_usage" not in signals:
|
||||||
|
signals["emoji_usage"] = []
|
||||||
|
signals["emoji_usage"].append(1 if user_used_emoji else 0)
|
||||||
|
signals["emoji_usage"] = signals["emoji_usage"][-50:]
|
||||||
|
|
||||||
|
# Track formality
|
||||||
|
if "formality" not in signals:
|
||||||
|
signals["formality"] = []
|
||||||
|
signals["formality"].append(1 if user_used_formal_language else 0)
|
||||||
|
signals["formality"] = signals["formality"][-50:]
|
||||||
|
|
||||||
|
style.engagement_signals = signals
|
||||||
|
style.samples_collected += 1
|
||||||
|
|
||||||
|
# Recalculate preferences if enough samples
|
||||||
|
if style.samples_collected >= self.MIN_SAMPLES_FOR_CONFIDENCE:
|
||||||
|
await self._recalculate_preferences(style)
|
||||||
|
|
||||||
|
async def _recalculate_preferences(self, style: UserCommunicationStyle) -> None:
|
||||||
|
"""Recalculate preferences from engagement signals."""
|
||||||
|
signals = style.engagement_signals or {}
|
||||||
|
|
||||||
|
# Length preference from user's own message lengths
|
||||||
|
user_lengths = signals.get("user_lengths", [])
|
||||||
|
if user_lengths:
|
||||||
|
avg_length = sum(user_lengths) / len(user_lengths)
|
||||||
|
if avg_length < 50:
|
||||||
|
style.preferred_length = "short"
|
||||||
|
elif avg_length < 200:
|
||||||
|
style.preferred_length = "medium"
|
||||||
|
else:
|
||||||
|
style.preferred_length = "long"
|
||||||
|
|
||||||
|
# Emoji affinity from user's emoji usage
|
||||||
|
emoji_usage = signals.get("emoji_usage", [])
|
||||||
|
if emoji_usage:
|
||||||
|
style.emoji_affinity = sum(emoji_usage) / len(emoji_usage)
|
||||||
|
|
||||||
|
# Formality from user's language style
|
||||||
|
formality = signals.get("formality", [])
|
||||||
|
if formality:
|
||||||
|
style.preferred_formality = sum(formality) / len(formality)
|
||||||
|
|
||||||
|
# Update confidence based on sample count
|
||||||
|
style.confidence = min(1.0, style.samples_collected / 50)
|
||||||
|
|
||||||
|
logger.debug(
|
||||||
|
f"Recalculated style for user {style.user_id}: "
|
||||||
|
f"length={style.preferred_length}, emoji={style.emoji_affinity:.2f}, "
|
||||||
|
f"formality={style.preferred_formality:.2f}, confidence={style.confidence:.2f}"
|
||||||
|
)
|
||||||
|
|
||||||
|
def get_style_prompt_modifier(self, style: UserCommunicationStyle) -> str:
|
||||||
|
"""Generate prompt text for communication style."""
|
||||||
|
if style.confidence < 0.3:
|
||||||
|
return "" # Not enough data
|
||||||
|
|
||||||
|
parts = []
|
||||||
|
|
||||||
|
if style.preferred_length == "short":
|
||||||
|
parts.append("Keep responses brief and to the point.")
|
||||||
|
elif style.preferred_length == "long":
|
||||||
|
parts.append("Provide detailed, thorough responses.")
|
||||||
|
|
||||||
|
if style.preferred_formality > 0.7:
|
||||||
|
parts.append("Use formal language.")
|
||||||
|
elif style.preferred_formality < 0.3:
|
||||||
|
parts.append("Use casual, relaxed language.")
|
||||||
|
|
||||||
|
if style.emoji_affinity > 0.7:
|
||||||
|
parts.append("Feel free to use emojis.")
|
||||||
|
elif style.emoji_affinity < 0.3:
|
||||||
|
parts.append("Avoid using emojis.")
|
||||||
|
|
||||||
|
if style.humor_affinity > 0.7:
|
||||||
|
parts.append("Be playful and use humor.")
|
||||||
|
elif style.humor_affinity < 0.3:
|
||||||
|
parts.append("Keep a more serious tone.")
|
||||||
|
|
||||||
|
if style.detail_preference > 0.7:
|
||||||
|
parts.append("Include extra details and examples.")
|
||||||
|
elif style.detail_preference < 0.3:
|
||||||
|
parts.append("Be concise without extra details.")
|
||||||
|
|
||||||
|
return " ".join(parts)
|
||||||
|
|
||||||
|
async def get_style_info(self, user: User) -> dict:
|
||||||
|
"""Get style information for display."""
|
||||||
|
style = await self.get_or_create_style(user)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"preferred_length": style.preferred_length,
|
||||||
|
"preferred_formality": style.preferred_formality,
|
||||||
|
"emoji_affinity": style.emoji_affinity,
|
||||||
|
"humor_affinity": style.humor_affinity,
|
||||||
|
"detail_preference": style.detail_preference,
|
||||||
|
"samples_collected": style.samples_collected,
|
||||||
|
"confidence": style.confidence,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def detect_emoji_usage(text: str) -> bool:
|
||||||
|
"""Detect if text contains emoji."""
|
||||||
|
import re
|
||||||
|
|
||||||
|
# Simple emoji detection - covers common emoji ranges
|
||||||
|
emoji_pattern = re.compile(
|
||||||
|
"["
|
||||||
|
"\U0001f600-\U0001f64f" # emoticons
|
||||||
|
"\U0001f300-\U0001f5ff" # symbols & pictographs
|
||||||
|
"\U0001f680-\U0001f6ff" # transport & map symbols
|
||||||
|
"\U0001f1e0-\U0001f1ff" # flags
|
||||||
|
"\U00002702-\U000027b0" # dingbats
|
||||||
|
"\U000024c2-\U0001f251"
|
||||||
|
"]+",
|
||||||
|
flags=re.UNICODE,
|
||||||
|
)
|
||||||
|
return bool(emoji_pattern.search(text))
|
||||||
|
|
||||||
|
|
||||||
|
def detect_formal_language(text: str) -> bool:
|
||||||
|
"""Detect if text uses formal language."""
|
||||||
|
text_lower = text.lower()
|
||||||
|
|
||||||
|
# Formal indicators
|
||||||
|
formal_words = [
|
||||||
|
"please",
|
||||||
|
"thank you",
|
||||||
|
"would you",
|
||||||
|
"could you",
|
||||||
|
"kindly",
|
||||||
|
"regards",
|
||||||
|
"sincerely",
|
||||||
|
"appreciate",
|
||||||
|
"assist",
|
||||||
|
"inquire",
|
||||||
|
"regarding",
|
||||||
|
"concerning",
|
||||||
|
"furthermore",
|
||||||
|
"however",
|
||||||
|
"therefore",
|
||||||
|
]
|
||||||
|
|
||||||
|
# Informal indicators
|
||||||
|
informal_words = [
|
||||||
|
"gonna",
|
||||||
|
"wanna",
|
||||||
|
"gotta",
|
||||||
|
"ya",
|
||||||
|
"u ",
|
||||||
|
"ur ",
|
||||||
|
"lol",
|
||||||
|
"lmao",
|
||||||
|
"omg",
|
||||||
|
"tbh",
|
||||||
|
"ngl",
|
||||||
|
"idk",
|
||||||
|
"btw",
|
||||||
|
"bruh",
|
||||||
|
"dude",
|
||||||
|
"yo ",
|
||||||
|
]
|
||||||
|
|
||||||
|
formal_count = sum(1 for word in formal_words if word in text_lower)
|
||||||
|
informal_count = sum(1 for word in informal_words if word in text_lower)
|
||||||
|
|
||||||
|
# Return True if more formal than informal
|
||||||
|
return formal_count > informal_count
|
||||||
356
src/daemon_boyfriend/services/fact_extraction_service.py
Normal file
356
src/daemon_boyfriend/services/fact_extraction_service.py
Normal file
@@ -0,0 +1,356 @@
|
|||||||
|
"""Fact Extraction Service - autonomous extraction of facts from conversations."""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import logging
|
||||||
|
import random
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
from sqlalchemy import select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.config import settings
|
||||||
|
from daemon_boyfriend.models import User, UserFact
|
||||||
|
|
||||||
|
from .providers import Message
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class FactExtractionService:
|
||||||
|
"""Autonomous extraction of facts from conversations."""
|
||||||
|
|
||||||
|
# Minimum message length to consider for extraction
|
||||||
|
MIN_MESSAGE_LENGTH = 20
|
||||||
|
|
||||||
|
# Maximum facts to extract per message
|
||||||
|
MAX_FACTS_PER_MESSAGE = 3
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession, ai_service=None) -> None:
|
||||||
|
self._session = session
|
||||||
|
self._ai_service = ai_service
|
||||||
|
|
||||||
|
async def maybe_extract_facts(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
message_content: str,
|
||||||
|
discord_message_id: int | None = None,
|
||||||
|
) -> list[UserFact]:
|
||||||
|
"""Maybe extract facts from a message based on rate limiting.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user: The user who sent the message
|
||||||
|
message_content: The message content
|
||||||
|
discord_message_id: Optional Discord message ID for reference
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of newly extracted facts (may be empty)
|
||||||
|
"""
|
||||||
|
if not settings.fact_extraction_enabled:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# Rate limit: only extract from a percentage of messages
|
||||||
|
if random.random() > settings.fact_extraction_rate:
|
||||||
|
return []
|
||||||
|
|
||||||
|
return await self.extract_facts(user, message_content, discord_message_id)
|
||||||
|
|
||||||
|
async def extract_facts(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
message_content: str,
|
||||||
|
discord_message_id: int | None = None,
|
||||||
|
) -> list[UserFact]:
|
||||||
|
"""Extract facts from a message.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user: The user who sent the message
|
||||||
|
message_content: The message content
|
||||||
|
discord_message_id: Optional Discord message ID for reference
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of newly extracted facts
|
||||||
|
"""
|
||||||
|
# Skip messages that are too short or likely not informative
|
||||||
|
if not self._is_extractable(message_content):
|
||||||
|
return []
|
||||||
|
|
||||||
|
if not self._ai_service:
|
||||||
|
logger.warning("No AI service available for fact extraction")
|
||||||
|
return []
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Get existing facts to avoid duplicates
|
||||||
|
existing_facts = await self._get_user_facts(user)
|
||||||
|
existing_summary = self._summarize_existing_facts(existing_facts)
|
||||||
|
|
||||||
|
# Build extraction prompt
|
||||||
|
extraction_prompt = self._build_extraction_prompt(existing_summary)
|
||||||
|
|
||||||
|
# Use AI to extract facts
|
||||||
|
response = await self._ai_service.chat(
|
||||||
|
messages=[Message(role="user", content=message_content)],
|
||||||
|
system_prompt=extraction_prompt,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Parse extracted facts
|
||||||
|
facts_data = self._parse_extraction_response(response.content)
|
||||||
|
|
||||||
|
if not facts_data:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# Deduplicate and save new facts
|
||||||
|
new_facts = await self._save_new_facts(
|
||||||
|
user=user,
|
||||||
|
facts_data=facts_data,
|
||||||
|
existing_facts=existing_facts,
|
||||||
|
discord_message_id=discord_message_id,
|
||||||
|
extraction_context=message_content[:200],
|
||||||
|
)
|
||||||
|
|
||||||
|
if new_facts:
|
||||||
|
logger.info(f"Extracted {len(new_facts)} facts for user {user.discord_id}")
|
||||||
|
|
||||||
|
return new_facts
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Fact extraction failed: {e}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
def _is_extractable(self, content: str) -> bool:
|
||||||
|
"""Check if a message is worth extracting facts from."""
|
||||||
|
# Too short
|
||||||
|
if len(content) < self.MIN_MESSAGE_LENGTH:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# Just emoji or symbols
|
||||||
|
alpha_ratio = sum(c.isalpha() for c in content) / max(len(content), 1)
|
||||||
|
if alpha_ratio < 0.5:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# Looks like a command
|
||||||
|
if content.startswith(("!", "/", "?", ".")):
|
||||||
|
return False
|
||||||
|
|
||||||
|
# Just a greeting or very short phrase
|
||||||
|
short_phrases = [
|
||||||
|
"hi",
|
||||||
|
"hello",
|
||||||
|
"hey",
|
||||||
|
"yo",
|
||||||
|
"sup",
|
||||||
|
"bye",
|
||||||
|
"goodbye",
|
||||||
|
"thanks",
|
||||||
|
"thank you",
|
||||||
|
"ok",
|
||||||
|
"okay",
|
||||||
|
"yes",
|
||||||
|
"no",
|
||||||
|
"yeah",
|
||||||
|
"nah",
|
||||||
|
"lol",
|
||||||
|
"lmao",
|
||||||
|
"haha",
|
||||||
|
"hehe",
|
||||||
|
"nice",
|
||||||
|
"cool",
|
||||||
|
"wow",
|
||||||
|
]
|
||||||
|
content_lower = content.lower().strip()
|
||||||
|
if content_lower in short_phrases:
|
||||||
|
return False
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
def _build_extraction_prompt(self, existing_summary: str) -> str:
|
||||||
|
"""Build the extraction prompt for the AI."""
|
||||||
|
return f"""You are a fact extraction assistant. Extract factual information about the user from their message.
|
||||||
|
|
||||||
|
ALREADY KNOWN FACTS:
|
||||||
|
{existing_summary if existing_summary else "(None yet)"}
|
||||||
|
|
||||||
|
RULES:
|
||||||
|
1. Only extract CONCRETE facts, not opinions or transient states
|
||||||
|
2. Skip if the fact is already known (listed above)
|
||||||
|
3. Skip greetings, questions, or meta-conversation
|
||||||
|
4. Skip vague statements like "I like stuff" - be specific
|
||||||
|
5. Focus on: hobbies, work, family, preferences, locations, events, relationships
|
||||||
|
6. Keep fact content concise (under 100 characters)
|
||||||
|
7. Maximum {self.MAX_FACTS_PER_MESSAGE} facts per message
|
||||||
|
|
||||||
|
OUTPUT FORMAT:
|
||||||
|
Return a JSON array of facts, or empty array [] if no extractable facts.
|
||||||
|
Each fact should have:
|
||||||
|
- "type": one of "hobby", "work", "family", "preference", "location", "event", "relationship", "general"
|
||||||
|
- "content": the fact itself (concise, third person, e.g., "loves hiking")
|
||||||
|
- "confidence": 0.6 (implied), 0.8 (stated), 1.0 (explicit)
|
||||||
|
- "importance": 0.3 (trivial), 0.5 (normal), 0.8 (significant), 1.0 (very important)
|
||||||
|
- "temporal": "past", "present", "future", or "timeless"
|
||||||
|
|
||||||
|
EXAMPLE INPUT: "I just got promoted to senior engineer at Google last week!"
|
||||||
|
EXAMPLE OUTPUT: [{{"type": "work", "content": "works as senior engineer at Google", "confidence": 1.0, "importance": 0.8, "temporal": "present"}}, {{"type": "event", "content": "recently got promoted", "confidence": 1.0, "importance": 0.7, "temporal": "past"}}]
|
||||||
|
|
||||||
|
EXAMPLE INPUT: "hey what's up"
|
||||||
|
EXAMPLE OUTPUT: []
|
||||||
|
|
||||||
|
Return ONLY the JSON array, no other text."""
|
||||||
|
|
||||||
|
def _parse_extraction_response(self, response: str) -> list[dict]:
|
||||||
|
"""Parse the AI response into fact dictionaries."""
|
||||||
|
try:
|
||||||
|
# Try to find JSON array in the response
|
||||||
|
response = response.strip()
|
||||||
|
|
||||||
|
# Handle markdown code blocks
|
||||||
|
if "```json" in response:
|
||||||
|
start = response.find("```json") + 7
|
||||||
|
end = response.find("```", start)
|
||||||
|
response = response[start:end].strip()
|
||||||
|
elif "```" in response:
|
||||||
|
start = response.find("```") + 3
|
||||||
|
end = response.find("```", start)
|
||||||
|
response = response[start:end].strip()
|
||||||
|
|
||||||
|
# Parse JSON
|
||||||
|
facts = json.loads(response)
|
||||||
|
|
||||||
|
if not isinstance(facts, list):
|
||||||
|
return []
|
||||||
|
|
||||||
|
# Validate each fact
|
||||||
|
valid_facts = []
|
||||||
|
for fact in facts[: self.MAX_FACTS_PER_MESSAGE]:
|
||||||
|
if self._validate_fact(fact):
|
||||||
|
valid_facts.append(fact)
|
||||||
|
|
||||||
|
return valid_facts
|
||||||
|
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
logger.debug(f"Failed to parse fact extraction response: {response[:100]}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
def _validate_fact(self, fact: dict) -> bool:
|
||||||
|
"""Validate a fact dictionary."""
|
||||||
|
required_fields = ["type", "content"]
|
||||||
|
valid_types = [
|
||||||
|
"hobby",
|
||||||
|
"work",
|
||||||
|
"family",
|
||||||
|
"preference",
|
||||||
|
"location",
|
||||||
|
"event",
|
||||||
|
"relationship",
|
||||||
|
"general",
|
||||||
|
]
|
||||||
|
|
||||||
|
# Check required fields
|
||||||
|
if not all(field in fact for field in required_fields):
|
||||||
|
return False
|
||||||
|
|
||||||
|
# Check type is valid
|
||||||
|
if fact.get("type") not in valid_types:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# Check content is not empty
|
||||||
|
if not fact.get("content") or len(fact["content"]) < 3:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# Check content is not too long
|
||||||
|
if len(fact["content"]) > 200:
|
||||||
|
return False
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
async def _get_user_facts(self, user: User) -> list[UserFact]:
|
||||||
|
"""Get existing facts for a user."""
|
||||||
|
stmt = (
|
||||||
|
select(UserFact)
|
||||||
|
.where(UserFact.user_id == user.id, UserFact.is_active == True)
|
||||||
|
.order_by(UserFact.learned_at.desc())
|
||||||
|
.limit(50)
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
def _summarize_existing_facts(self, facts: list[UserFact]) -> str:
|
||||||
|
"""Summarize existing facts for the extraction prompt."""
|
||||||
|
if not facts:
|
||||||
|
return ""
|
||||||
|
|
||||||
|
summary_lines = []
|
||||||
|
for fact in facts[:20]: # Limit to most recent 20
|
||||||
|
summary_lines.append(f"- [{fact.fact_type}] {fact.fact_content}")
|
||||||
|
|
||||||
|
return "\n".join(summary_lines)
|
||||||
|
|
||||||
|
async def _save_new_facts(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
facts_data: list[dict],
|
||||||
|
existing_facts: list[UserFact],
|
||||||
|
discord_message_id: int | None,
|
||||||
|
extraction_context: str,
|
||||||
|
) -> list[UserFact]:
|
||||||
|
"""Save new facts, avoiding duplicates."""
|
||||||
|
# Build set of existing fact content for deduplication
|
||||||
|
existing_content = {f.fact_content.lower() for f in existing_facts}
|
||||||
|
|
||||||
|
new_facts = []
|
||||||
|
for fact_data in facts_data:
|
||||||
|
content = fact_data["content"]
|
||||||
|
|
||||||
|
# Skip if too similar to existing
|
||||||
|
if self._is_duplicate(content, existing_content):
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Create new fact
|
||||||
|
fact = UserFact(
|
||||||
|
user_id=user.id,
|
||||||
|
fact_type=fact_data["type"],
|
||||||
|
fact_content=content,
|
||||||
|
confidence=fact_data.get("confidence", 0.8),
|
||||||
|
source="auto_extraction",
|
||||||
|
is_active=True,
|
||||||
|
learned_at=datetime.utcnow(),
|
||||||
|
# New fields from Living AI
|
||||||
|
category=fact_data["type"],
|
||||||
|
importance=fact_data.get("importance", 0.5),
|
||||||
|
temporal_relevance=fact_data.get("temporal", "timeless"),
|
||||||
|
extracted_from_message_id=discord_message_id,
|
||||||
|
extraction_context=extraction_context,
|
||||||
|
)
|
||||||
|
|
||||||
|
self._session.add(fact)
|
||||||
|
new_facts.append(fact)
|
||||||
|
existing_content.add(content.lower())
|
||||||
|
|
||||||
|
if new_facts:
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
return new_facts
|
||||||
|
|
||||||
|
def _is_duplicate(self, new_content: str, existing_content: set[str]) -> bool:
|
||||||
|
"""Check if a fact is a duplicate of existing facts."""
|
||||||
|
new_lower = new_content.lower()
|
||||||
|
|
||||||
|
# Exact match
|
||||||
|
if new_lower in existing_content:
|
||||||
|
return True
|
||||||
|
|
||||||
|
# Check for high similarity (simple substring check)
|
||||||
|
for existing in existing_content:
|
||||||
|
# If one contains the other (with some buffer)
|
||||||
|
if len(new_lower) > 10 and len(existing) > 10:
|
||||||
|
if new_lower in existing or existing in new_lower:
|
||||||
|
return True
|
||||||
|
|
||||||
|
# Simple word overlap check
|
||||||
|
new_words = set(new_lower.split())
|
||||||
|
existing_words = set(existing.split())
|
||||||
|
if len(new_words) > 2 and len(existing_words) > 2:
|
||||||
|
overlap = len(new_words & existing_words)
|
||||||
|
min_len = min(len(new_words), len(existing_words))
|
||||||
|
if overlap / min_len > 0.7: # 70% word overlap
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
252
src/daemon_boyfriend/services/mood_service.py
Normal file
252
src/daemon_boyfriend/services/mood_service.py
Normal file
@@ -0,0 +1,252 @@
|
|||||||
|
"""Mood Service - manages bot emotional states."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from datetime import datetime
|
||||||
|
from enum import Enum
|
||||||
|
|
||||||
|
from sqlalchemy import select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.config import settings
|
||||||
|
from daemon_boyfriend.models import BotState, MoodHistory
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class MoodLabel(Enum):
|
||||||
|
"""Mood labels based on valence-arousal model."""
|
||||||
|
|
||||||
|
EXCITED = "excited" # high valence, high arousal
|
||||||
|
HAPPY = "happy" # high valence, low arousal
|
||||||
|
CALM = "calm" # neutral valence, low arousal
|
||||||
|
NEUTRAL = "neutral" # neutral everything
|
||||||
|
BORED = "bored" # low valence, low arousal
|
||||||
|
ANNOYED = "annoyed" # low valence, high arousal
|
||||||
|
CURIOUS = "curious" # neutral valence, high arousal
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class MoodState:
|
||||||
|
"""Current mood state with computed properties."""
|
||||||
|
|
||||||
|
valence: float # -1 to 1
|
||||||
|
arousal: float # -1 to 1
|
||||||
|
label: MoodLabel
|
||||||
|
intensity: float # 0 to 1
|
||||||
|
|
||||||
|
|
||||||
|
class MoodService:
|
||||||
|
"""Manages bot mood state and its effects on responses."""
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession) -> None:
|
||||||
|
self._session = session
|
||||||
|
|
||||||
|
async def get_or_create_bot_state(self, guild_id: int | None = None) -> BotState:
|
||||||
|
"""Get or create bot state for a guild."""
|
||||||
|
stmt = select(BotState).where(BotState.guild_id == guild_id)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
bot_state = result.scalar_one_or_none()
|
||||||
|
|
||||||
|
if not bot_state:
|
||||||
|
bot_state = BotState(guild_id=guild_id)
|
||||||
|
self._session.add(bot_state)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
return bot_state
|
||||||
|
|
||||||
|
async def get_current_mood(self, guild_id: int | None = None) -> MoodState:
|
||||||
|
"""Get current mood state, applying time decay."""
|
||||||
|
bot_state = await self.get_or_create_bot_state(guild_id)
|
||||||
|
|
||||||
|
# Apply time decay toward neutral
|
||||||
|
hours_since_update = (datetime.utcnow() - bot_state.mood_updated_at).total_seconds() / 3600
|
||||||
|
decay_factor = max(0, 1 - (settings.mood_decay_rate * hours_since_update))
|
||||||
|
|
||||||
|
valence = bot_state.mood_valence * decay_factor
|
||||||
|
arousal = bot_state.mood_arousal * decay_factor
|
||||||
|
|
||||||
|
return MoodState(
|
||||||
|
valence=valence,
|
||||||
|
arousal=arousal,
|
||||||
|
label=self._classify_mood(valence, arousal),
|
||||||
|
intensity=self._calculate_intensity(valence, arousal),
|
||||||
|
)
|
||||||
|
|
||||||
|
async def update_mood(
|
||||||
|
self,
|
||||||
|
guild_id: int | None,
|
||||||
|
sentiment_delta: float,
|
||||||
|
engagement_delta: float,
|
||||||
|
trigger_type: str,
|
||||||
|
trigger_user_id: int | None = None,
|
||||||
|
trigger_description: str | None = None,
|
||||||
|
) -> MoodState:
|
||||||
|
"""Update mood based on interaction.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
guild_id: Guild ID or None for global mood
|
||||||
|
sentiment_delta: -1 to 1, how positive/negative the interaction was
|
||||||
|
engagement_delta: -1 to 1, how engaging the interaction was
|
||||||
|
trigger_type: What caused the mood change (conversation, event, etc.)
|
||||||
|
trigger_user_id: User who triggered the change (if any)
|
||||||
|
trigger_description: Description of what happened
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The new mood state
|
||||||
|
"""
|
||||||
|
current = await self.get_current_mood(guild_id)
|
||||||
|
|
||||||
|
# Mood changes are dampened (inertia) - only 30% of the delta is applied
|
||||||
|
new_valence = self._clamp(current.valence + sentiment_delta * 0.3)
|
||||||
|
new_arousal = self._clamp(current.arousal + engagement_delta * 0.3)
|
||||||
|
|
||||||
|
# Update database
|
||||||
|
bot_state = await self.get_or_create_bot_state(guild_id)
|
||||||
|
bot_state.mood_valence = new_valence
|
||||||
|
bot_state.mood_arousal = new_arousal
|
||||||
|
bot_state.mood_updated_at = datetime.utcnow()
|
||||||
|
|
||||||
|
# Record history
|
||||||
|
await self._record_mood_history(
|
||||||
|
guild_id, new_valence, new_arousal, trigger_type, trigger_user_id, trigger_description
|
||||||
|
)
|
||||||
|
|
||||||
|
logger.debug(
|
||||||
|
f"Mood updated: valence={new_valence:.2f}, arousal={new_arousal:.2f}, "
|
||||||
|
f"trigger={trigger_type}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return MoodState(
|
||||||
|
valence=new_valence,
|
||||||
|
arousal=new_arousal,
|
||||||
|
label=self._classify_mood(new_valence, new_arousal),
|
||||||
|
intensity=self._calculate_intensity(new_valence, new_arousal),
|
||||||
|
)
|
||||||
|
|
||||||
|
async def increment_stats(
|
||||||
|
self,
|
||||||
|
guild_id: int | None,
|
||||||
|
messages_sent: int = 0,
|
||||||
|
facts_learned: int = 0,
|
||||||
|
users_known: int = 0,
|
||||||
|
) -> None:
|
||||||
|
"""Increment bot statistics."""
|
||||||
|
bot_state = await self.get_or_create_bot_state(guild_id)
|
||||||
|
bot_state.total_messages_sent += messages_sent
|
||||||
|
bot_state.total_facts_learned += facts_learned
|
||||||
|
bot_state.total_users_known += users_known
|
||||||
|
|
||||||
|
async def get_stats(self, guild_id: int | None = None) -> dict:
|
||||||
|
"""Get bot statistics."""
|
||||||
|
bot_state = await self.get_or_create_bot_state(guild_id)
|
||||||
|
age_delta = datetime.utcnow() - bot_state.first_activated_at
|
||||||
|
|
||||||
|
return {
|
||||||
|
"age_days": age_delta.days,
|
||||||
|
"age_readable": self._format_age(age_delta),
|
||||||
|
"total_messages_sent": bot_state.total_messages_sent,
|
||||||
|
"total_facts_learned": bot_state.total_facts_learned,
|
||||||
|
"total_users_known": bot_state.total_users_known,
|
||||||
|
"first_activated_at": bot_state.first_activated_at,
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_mood_prompt_modifier(self, mood: MoodState) -> str:
|
||||||
|
"""Generate prompt text that reflects current mood."""
|
||||||
|
if mood.intensity < 0.2:
|
||||||
|
return "" # Mood too weak to affect behavior
|
||||||
|
|
||||||
|
modifiers = {
|
||||||
|
MoodLabel.EXCITED: (
|
||||||
|
"You're feeling enthusiastic and energetic right now! "
|
||||||
|
"Be expressive, use exclamation marks, show genuine excitement."
|
||||||
|
),
|
||||||
|
MoodLabel.HAPPY: (
|
||||||
|
"You're in a good mood - warm, friendly, and content. "
|
||||||
|
"Be positive and encouraging in your responses."
|
||||||
|
),
|
||||||
|
MoodLabel.CALM: (
|
||||||
|
"You're feeling peaceful and relaxed. "
|
||||||
|
"Respond thoughtfully and with a serene demeanor."
|
||||||
|
),
|
||||||
|
MoodLabel.NEUTRAL: "",
|
||||||
|
MoodLabel.BORED: (
|
||||||
|
"You're feeling a bit understimulated. "
|
||||||
|
"Keep responses shorter, maybe try to steer toward more interesting topics."
|
||||||
|
),
|
||||||
|
MoodLabel.ANNOYED: (
|
||||||
|
"You're slightly irritated. "
|
||||||
|
"Be a bit more terse, less patient with repetition or vague questions."
|
||||||
|
),
|
||||||
|
MoodLabel.CURIOUS: (
|
||||||
|
"You're feeling inquisitive and engaged! "
|
||||||
|
"Ask follow-up questions, show genuine interest in what the user is saying."
|
||||||
|
),
|
||||||
|
}
|
||||||
|
|
||||||
|
base = modifiers.get(mood.label, "")
|
||||||
|
if not base:
|
||||||
|
return ""
|
||||||
|
|
||||||
|
if mood.intensity > 0.7:
|
||||||
|
return f"[Strong mood] {base}"
|
||||||
|
return base
|
||||||
|
|
||||||
|
async def _record_mood_history(
|
||||||
|
self,
|
||||||
|
guild_id: int | None,
|
||||||
|
valence: float,
|
||||||
|
arousal: float,
|
||||||
|
trigger_type: str,
|
||||||
|
trigger_user_id: int | None,
|
||||||
|
trigger_description: str | None,
|
||||||
|
) -> None:
|
||||||
|
"""Record a mood change in history."""
|
||||||
|
history = MoodHistory(
|
||||||
|
guild_id=guild_id,
|
||||||
|
valence=valence,
|
||||||
|
arousal=arousal,
|
||||||
|
trigger_type=trigger_type,
|
||||||
|
trigger_user_id=trigger_user_id,
|
||||||
|
trigger_description=trigger_description,
|
||||||
|
)
|
||||||
|
self._session.add(history)
|
||||||
|
|
||||||
|
def _classify_mood(self, valence: float, arousal: float) -> MoodLabel:
|
||||||
|
"""Classify mood based on valence-arousal model."""
|
||||||
|
if valence > 0.3:
|
||||||
|
return MoodLabel.EXCITED if arousal > 0.3 else MoodLabel.HAPPY
|
||||||
|
elif valence < -0.3:
|
||||||
|
return MoodLabel.ANNOYED if arousal > 0.3 else MoodLabel.BORED
|
||||||
|
else:
|
||||||
|
if arousal > 0.3:
|
||||||
|
return MoodLabel.CURIOUS
|
||||||
|
elif arousal < -0.3:
|
||||||
|
return MoodLabel.CALM
|
||||||
|
return MoodLabel.NEUTRAL
|
||||||
|
|
||||||
|
def _calculate_intensity(self, valence: float, arousal: float) -> float:
|
||||||
|
"""Calculate mood intensity from valence and arousal."""
|
||||||
|
return min(1.0, (abs(valence) + abs(arousal)) / 2)
|
||||||
|
|
||||||
|
def _clamp(self, value: float, min_val: float = -1.0, max_val: float = 1.0) -> float:
|
||||||
|
"""Clamp value between min and max."""
|
||||||
|
return max(min_val, min(max_val, value))
|
||||||
|
|
||||||
|
def _format_age(self, delta) -> str:
|
||||||
|
"""Format a timedelta into a readable string."""
|
||||||
|
days = delta.days
|
||||||
|
if days == 0:
|
||||||
|
hours = delta.seconds // 3600
|
||||||
|
if hours == 0:
|
||||||
|
minutes = delta.seconds // 60
|
||||||
|
return f"{minutes} minute{'s' if minutes != 1 else ''}"
|
||||||
|
return f"{hours} hour{'s' if hours != 1 else ''}"
|
||||||
|
elif days < 30:
|
||||||
|
return f"{days} day{'s' if days != 1 else ''}"
|
||||||
|
elif days < 365:
|
||||||
|
months = days // 30
|
||||||
|
return f"about {months} month{'s' if months != 1 else ''}"
|
||||||
|
else:
|
||||||
|
years = days // 365
|
||||||
|
return f"about {years} year{'s' if years != 1 else ''}"
|
||||||
233
src/daemon_boyfriend/services/opinion_service.py
Normal file
233
src/daemon_boyfriend/services/opinion_service.py
Normal file
@@ -0,0 +1,233 @@
|
|||||||
|
"""Opinion Service - manages bot opinion formation on topics."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
from sqlalchemy import select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.models import BotOpinion
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class OpinionService:
|
||||||
|
"""Manages bot opinion formation and topic preferences."""
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession) -> None:
|
||||||
|
self._session = session
|
||||||
|
|
||||||
|
async def get_opinion(self, topic: str, guild_id: int | None = None) -> BotOpinion | None:
|
||||||
|
"""Get the bot's opinion on a topic."""
|
||||||
|
stmt = select(BotOpinion).where(
|
||||||
|
BotOpinion.topic == topic.lower(),
|
||||||
|
BotOpinion.guild_id == guild_id,
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return result.scalar_one_or_none()
|
||||||
|
|
||||||
|
async def get_or_create_opinion(self, topic: str, guild_id: int | None = None) -> BotOpinion:
|
||||||
|
"""Get or create an opinion on a topic."""
|
||||||
|
opinion = await self.get_opinion(topic, guild_id)
|
||||||
|
|
||||||
|
if not opinion:
|
||||||
|
opinion = BotOpinion(
|
||||||
|
topic=topic.lower(),
|
||||||
|
guild_id=guild_id,
|
||||||
|
sentiment=0.0,
|
||||||
|
interest_level=0.5,
|
||||||
|
discussion_count=0,
|
||||||
|
)
|
||||||
|
self._session.add(opinion)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
return opinion
|
||||||
|
|
||||||
|
async def record_topic_discussion(
|
||||||
|
self,
|
||||||
|
topic: str,
|
||||||
|
guild_id: int | None,
|
||||||
|
sentiment: float,
|
||||||
|
engagement_level: float,
|
||||||
|
) -> BotOpinion:
|
||||||
|
"""Record a discussion about a topic, updating the bot's opinion.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic: The topic discussed
|
||||||
|
guild_id: Guild ID or None for global
|
||||||
|
sentiment: How positive the discussion was (-1 to 1)
|
||||||
|
engagement_level: How engaging the discussion was (0 to 1)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Updated opinion
|
||||||
|
"""
|
||||||
|
opinion = await self.get_or_create_opinion(topic, guild_id)
|
||||||
|
|
||||||
|
# Increment discussion count
|
||||||
|
opinion.discussion_count += 1
|
||||||
|
|
||||||
|
# Update sentiment (weighted average, newer discussions have more weight)
|
||||||
|
weight = 0.2 # 20% weight to new data
|
||||||
|
opinion.sentiment = (opinion.sentiment * (1 - weight)) + (sentiment * weight)
|
||||||
|
opinion.sentiment = max(-1.0, min(1.0, opinion.sentiment))
|
||||||
|
|
||||||
|
# Update interest level based on engagement
|
||||||
|
opinion.interest_level = (opinion.interest_level * (1 - weight)) + (
|
||||||
|
engagement_level * weight
|
||||||
|
)
|
||||||
|
opinion.interest_level = max(0.0, min(1.0, opinion.interest_level))
|
||||||
|
|
||||||
|
opinion.last_reinforced_at = datetime.utcnow()
|
||||||
|
|
||||||
|
logger.debug(
|
||||||
|
f"Updated opinion on '{topic}': sentiment={opinion.sentiment:.2f}, "
|
||||||
|
f"interest={opinion.interest_level:.2f}, discussions={opinion.discussion_count}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return opinion
|
||||||
|
|
||||||
|
async def set_opinion_reasoning(self, topic: str, guild_id: int | None, reasoning: str) -> None:
|
||||||
|
"""Set the reasoning for an opinion (AI-generated explanation)."""
|
||||||
|
opinion = await self.get_or_create_opinion(topic, guild_id)
|
||||||
|
opinion.reasoning = reasoning
|
||||||
|
|
||||||
|
async def get_top_interests(
|
||||||
|
self, guild_id: int | None = None, limit: int = 5
|
||||||
|
) -> list[BotOpinion]:
|
||||||
|
"""Get the bot's top interests (highest interest level + positive sentiment)."""
|
||||||
|
stmt = (
|
||||||
|
select(BotOpinion)
|
||||||
|
.where(
|
||||||
|
BotOpinion.guild_id == guild_id,
|
||||||
|
BotOpinion.discussion_count >= 3, # Only topics discussed at least 3 times
|
||||||
|
)
|
||||||
|
.order_by((BotOpinion.interest_level + BotOpinion.sentiment).desc())
|
||||||
|
.limit(limit)
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
async def get_relevant_opinions(
|
||||||
|
self, topics: list[str], guild_id: int | None = None
|
||||||
|
) -> list[BotOpinion]:
|
||||||
|
"""Get opinions relevant to a list of topics."""
|
||||||
|
if not topics:
|
||||||
|
return []
|
||||||
|
|
||||||
|
topics_lower = [t.lower() for t in topics]
|
||||||
|
stmt = select(BotOpinion).where(
|
||||||
|
BotOpinion.topic.in_(topics_lower),
|
||||||
|
BotOpinion.guild_id == guild_id,
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
def get_opinion_prompt_modifier(self, opinions: list[BotOpinion]) -> str:
|
||||||
|
"""Generate prompt text based on relevant opinions."""
|
||||||
|
if not opinions:
|
||||||
|
return ""
|
||||||
|
|
||||||
|
parts = []
|
||||||
|
for op in opinions[:3]: # Limit to 3 opinions
|
||||||
|
if op.sentiment > 0.5:
|
||||||
|
parts.append(f"You really enjoy discussing {op.topic}")
|
||||||
|
elif op.sentiment > 0.2:
|
||||||
|
parts.append(f"You find {op.topic} interesting")
|
||||||
|
elif op.sentiment < -0.3:
|
||||||
|
parts.append(f"You're not particularly enthusiastic about {op.topic}")
|
||||||
|
|
||||||
|
if op.reasoning:
|
||||||
|
parts.append(f"({op.reasoning})")
|
||||||
|
|
||||||
|
return "; ".join(parts) if parts else ""
|
||||||
|
|
||||||
|
async def get_all_opinions(self, guild_id: int | None = None) -> list[BotOpinion]:
|
||||||
|
"""Get all opinions for a guild."""
|
||||||
|
stmt = (
|
||||||
|
select(BotOpinion)
|
||||||
|
.where(BotOpinion.guild_id == guild_id)
|
||||||
|
.order_by(BotOpinion.discussion_count.desc())
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
|
||||||
|
def extract_topics_from_message(message: str) -> list[str]:
|
||||||
|
"""Extract potential topics from a message.
|
||||||
|
|
||||||
|
This is a simple keyword-based extraction. In production,
|
||||||
|
you might want to use NLP or an LLM for better extraction.
|
||||||
|
"""
|
||||||
|
# Common topic categories
|
||||||
|
topic_keywords = {
|
||||||
|
# Hobbies
|
||||||
|
"gaming": [
|
||||||
|
"game",
|
||||||
|
"gaming",
|
||||||
|
"video game",
|
||||||
|
"play",
|
||||||
|
"xbox",
|
||||||
|
"playstation",
|
||||||
|
"nintendo",
|
||||||
|
"steam",
|
||||||
|
],
|
||||||
|
"music": [
|
||||||
|
"music",
|
||||||
|
"song",
|
||||||
|
"band",
|
||||||
|
"album",
|
||||||
|
"concert",
|
||||||
|
"listen",
|
||||||
|
"spotify",
|
||||||
|
"guitar",
|
||||||
|
"piano",
|
||||||
|
],
|
||||||
|
"movies": ["movie", "film", "cinema", "watch", "netflix", "show", "series", "tv"],
|
||||||
|
"reading": ["book", "read", "novel", "author", "library", "kindle"],
|
||||||
|
"sports": [
|
||||||
|
"sports",
|
||||||
|
"football",
|
||||||
|
"soccer",
|
||||||
|
"basketball",
|
||||||
|
"tennis",
|
||||||
|
"golf",
|
||||||
|
"gym",
|
||||||
|
"workout",
|
||||||
|
],
|
||||||
|
"cooking": ["cook", "recipe", "food", "restaurant", "meal", "kitchen", "baking"],
|
||||||
|
"travel": ["travel", "trip", "vacation", "flight", "hotel", "country", "visit"],
|
||||||
|
"art": ["art", "painting", "drawing", "museum", "gallery", "creative"],
|
||||||
|
# Tech
|
||||||
|
"programming": [
|
||||||
|
"code",
|
||||||
|
"programming",
|
||||||
|
"developer",
|
||||||
|
"software",
|
||||||
|
"python",
|
||||||
|
"javascript",
|
||||||
|
"api",
|
||||||
|
],
|
||||||
|
"technology": ["tech", "computer", "phone", "app", "website", "internet"],
|
||||||
|
"ai": ["ai", "artificial intelligence", "machine learning", "chatgpt", "gpt"],
|
||||||
|
# Life
|
||||||
|
"work": ["work", "job", "office", "career", "boss", "colleague", "meeting"],
|
||||||
|
"family": ["family", "parents", "mom", "dad", "brother", "sister", "kids"],
|
||||||
|
"pets": ["pet", "dog", "cat", "puppy", "kitten", "animal"],
|
||||||
|
"health": ["health", "doctor", "exercise", "diet", "sleep", "medical"],
|
||||||
|
# Interests
|
||||||
|
"philosophy": ["philosophy", "meaning", "life", "existence", "think", "believe"],
|
||||||
|
"science": ["science", "research", "study", "experiment", "discovery"],
|
||||||
|
"nature": ["nature", "outdoor", "hiking", "camping", "mountain", "beach", "forest"],
|
||||||
|
}
|
||||||
|
|
||||||
|
message_lower = message.lower()
|
||||||
|
found_topics = []
|
||||||
|
|
||||||
|
for topic, keywords in topic_keywords.items():
|
||||||
|
for keyword in keywords:
|
||||||
|
if keyword in message_lower:
|
||||||
|
if topic not in found_topics:
|
||||||
|
found_topics.append(topic)
|
||||||
|
break
|
||||||
|
|
||||||
|
return found_topics
|
||||||
455
src/daemon_boyfriend/services/proactive_service.py
Normal file
455
src/daemon_boyfriend/services/proactive_service.py
Normal file
@@ -0,0 +1,455 @@
|
|||||||
|
"""Proactive Service - manages scheduled events and proactive behavior."""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import logging
|
||||||
|
import re
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
|
||||||
|
from sqlalchemy import select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.models import ScheduledEvent, User
|
||||||
|
|
||||||
|
from .providers import Message
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class ProactiveService:
|
||||||
|
"""Manages scheduled events and proactive behavior."""
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession, ai_service=None) -> None:
|
||||||
|
self._session = session
|
||||||
|
self._ai_service = ai_service
|
||||||
|
|
||||||
|
async def detect_and_schedule_followup(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
message_content: str,
|
||||||
|
guild_id: int | None,
|
||||||
|
channel_id: int,
|
||||||
|
) -> ScheduledEvent | None:
|
||||||
|
"""Detect if a message mentions a future event worth following up on.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user: The user who sent the message
|
||||||
|
message_content: The message content
|
||||||
|
guild_id: Guild ID
|
||||||
|
channel_id: Channel ID for the follow-up
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Scheduled event if one was created, None otherwise
|
||||||
|
"""
|
||||||
|
if not self._ai_service:
|
||||||
|
# Use simple pattern matching as fallback
|
||||||
|
return await self._detect_followup_simple(user, message_content, guild_id, channel_id)
|
||||||
|
|
||||||
|
try:
|
||||||
|
detection_prompt = """Analyze if this message mentions a future event worth following up on.
|
||||||
|
Events like: job interviews, exams, trips, appointments, projects due, important meetings, etc.
|
||||||
|
|
||||||
|
Return JSON: {"has_event": true/false, "event_type": "...", "days_until": <number or null>, "description": "..."}
|
||||||
|
|
||||||
|
Rules:
|
||||||
|
- Only return has_event=true for significant events the speaker would appreciate being asked about later
|
||||||
|
- days_until should be your best estimate of days until the event (1 for tomorrow, 7 for next week, etc.)
|
||||||
|
- Skip casual mentions like "I might do something" or past events
|
||||||
|
- description should be a brief summary of the event
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
"I have a job interview tomorrow" -> {"has_event": true, "event_type": "job interview", "days_until": 1, "description": "job interview"}
|
||||||
|
"I went to the store" -> {"has_event": false}
|
||||||
|
"My exam is next week" -> {"has_event": true, "event_type": "exam", "days_until": 7, "description": "upcoming exam"}
|
||||||
|
"""
|
||||||
|
|
||||||
|
response = await self._ai_service.chat(
|
||||||
|
messages=[Message(role="user", content=message_content)],
|
||||||
|
system_prompt=detection_prompt,
|
||||||
|
)
|
||||||
|
|
||||||
|
result = self._parse_json_response(response.content)
|
||||||
|
if result and result.get("has_event"):
|
||||||
|
days_until = result.get("days_until", 1) or 1
|
||||||
|
# Schedule follow-up for 1 day after the event
|
||||||
|
trigger_at = datetime.utcnow() + timedelta(days=days_until + 1)
|
||||||
|
|
||||||
|
event = ScheduledEvent(
|
||||||
|
user_id=user.id,
|
||||||
|
guild_id=guild_id,
|
||||||
|
channel_id=channel_id,
|
||||||
|
event_type="follow_up",
|
||||||
|
trigger_at=trigger_at,
|
||||||
|
title=f"Follow up: {result.get('event_type', 'event')}",
|
||||||
|
context={
|
||||||
|
"original_topic": result.get("description", "their event"),
|
||||||
|
"detected_from": message_content[:200],
|
||||||
|
},
|
||||||
|
)
|
||||||
|
self._session.add(event)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"Scheduled follow-up for user {user.id}: "
|
||||||
|
f"{result.get('event_type')} in {days_until + 1} days"
|
||||||
|
)
|
||||||
|
return event
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Follow-up detection failed: {e}")
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
async def _detect_followup_simple(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
message_content: str,
|
||||||
|
guild_id: int | None,
|
||||||
|
channel_id: int,
|
||||||
|
) -> ScheduledEvent | None:
|
||||||
|
"""Simple pattern-based follow-up detection."""
|
||||||
|
message_lower = message_content.lower()
|
||||||
|
|
||||||
|
# Event patterns with their typical timeframes
|
||||||
|
event_patterns = {
|
||||||
|
r"(interview|job interview)": ("job interview", 1),
|
||||||
|
r"(exam|test|quiz)": ("exam", 1),
|
||||||
|
r"(presentation|presenting)": ("presentation", 1),
|
||||||
|
r"(surgery|operation|medical)": ("medical procedure", 2),
|
||||||
|
r"(moving|move to|new apartment|new house)": ("moving", 7),
|
||||||
|
r"(wedding|getting married)": ("wedding", 1),
|
||||||
|
r"(vacation|holiday|trip to)": ("trip", 7),
|
||||||
|
r"(deadline|due date|project due)": ("deadline", 1),
|
||||||
|
r"(starting.*job|new job|first day)": ("new job", 1),
|
||||||
|
r"(graduation|graduating)": ("graduation", 1),
|
||||||
|
}
|
||||||
|
|
||||||
|
# Time indicators
|
||||||
|
time_patterns = {
|
||||||
|
r"tomorrow": 1,
|
||||||
|
r"next week": 7,
|
||||||
|
r"this week": 3,
|
||||||
|
r"in (\d+) days?": None, # Extract number
|
||||||
|
r"next month": 30,
|
||||||
|
r"this weekend": 3,
|
||||||
|
}
|
||||||
|
|
||||||
|
# Check for event + time combination
|
||||||
|
detected_event = None
|
||||||
|
event_name = None
|
||||||
|
days_until = 1 # Default
|
||||||
|
|
||||||
|
for pattern, (name, default_days) in event_patterns.items():
|
||||||
|
if re.search(pattern, message_lower):
|
||||||
|
detected_event = pattern
|
||||||
|
event_name = name
|
||||||
|
days_until = default_days
|
||||||
|
break
|
||||||
|
|
||||||
|
if not detected_event:
|
||||||
|
return None
|
||||||
|
|
||||||
|
# Refine timing based on time indicators
|
||||||
|
for pattern, days in time_patterns.items():
|
||||||
|
match = re.search(pattern, message_lower)
|
||||||
|
if match:
|
||||||
|
if days is None and match.groups():
|
||||||
|
days_until = int(match.group(1))
|
||||||
|
elif days:
|
||||||
|
days_until = days
|
||||||
|
break
|
||||||
|
|
||||||
|
# Create the event
|
||||||
|
trigger_at = datetime.utcnow() + timedelta(days=days_until + 1)
|
||||||
|
|
||||||
|
event = ScheduledEvent(
|
||||||
|
user_id=user.id,
|
||||||
|
guild_id=guild_id,
|
||||||
|
channel_id=channel_id,
|
||||||
|
event_type="follow_up",
|
||||||
|
trigger_at=trigger_at,
|
||||||
|
title=f"Follow up: {event_name}",
|
||||||
|
context={
|
||||||
|
"original_topic": event_name,
|
||||||
|
"detected_from": message_content[:200],
|
||||||
|
},
|
||||||
|
)
|
||||||
|
self._session.add(event)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"Scheduled follow-up (simple) for user {user.id}: {event_name} in {days_until + 1} days"
|
||||||
|
)
|
||||||
|
return event
|
||||||
|
|
||||||
|
async def detect_and_schedule_birthday(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
message_content: str,
|
||||||
|
guild_id: int | None,
|
||||||
|
channel_id: int,
|
||||||
|
) -> ScheduledEvent | None:
|
||||||
|
"""Detect birthday mentions and schedule wishes."""
|
||||||
|
birthday = self._extract_birthday(message_content)
|
||||||
|
if not birthday:
|
||||||
|
return None
|
||||||
|
|
||||||
|
# Check if we already have a birthday scheduled for this user
|
||||||
|
existing = await self._get_existing_birthday(user.id, guild_id)
|
||||||
|
if existing:
|
||||||
|
# Update the existing birthday
|
||||||
|
existing.trigger_at = self._next_birthday(birthday)
|
||||||
|
existing.context = {"birthday_date": birthday.isoformat()}
|
||||||
|
return existing
|
||||||
|
|
||||||
|
# Schedule for next occurrence
|
||||||
|
trigger_at = self._next_birthday(birthday)
|
||||||
|
|
||||||
|
event = ScheduledEvent(
|
||||||
|
user_id=user.id,
|
||||||
|
guild_id=guild_id,
|
||||||
|
channel_id=channel_id,
|
||||||
|
event_type="birthday",
|
||||||
|
trigger_at=trigger_at,
|
||||||
|
title="Birthday wish",
|
||||||
|
context={"birthday_date": birthday.isoformat()},
|
||||||
|
is_recurring=True,
|
||||||
|
recurrence_rule="yearly",
|
||||||
|
)
|
||||||
|
self._session.add(event)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
logger.info(f"Scheduled birthday for user {user.id}: {birthday}")
|
||||||
|
return event
|
||||||
|
|
||||||
|
def _extract_birthday(self, message: str) -> datetime | None:
|
||||||
|
"""Extract a birthday date from a message."""
|
||||||
|
message_lower = message.lower()
|
||||||
|
|
||||||
|
# Check if it's about their birthday
|
||||||
|
birthday_indicators = [
|
||||||
|
r"my birthday is",
|
||||||
|
r"my bday is",
|
||||||
|
r"i was born on",
|
||||||
|
r"born on",
|
||||||
|
r"my birthday'?s?",
|
||||||
|
]
|
||||||
|
|
||||||
|
has_birthday_mention = any(
|
||||||
|
re.search(pattern, message_lower) for pattern in birthday_indicators
|
||||||
|
)
|
||||||
|
if not has_birthday_mention:
|
||||||
|
return None
|
||||||
|
|
||||||
|
# Try to extract date patterns
|
||||||
|
# Format: Month Day (e.g., "March 15", "march 15th")
|
||||||
|
month_names = {
|
||||||
|
"january": 1,
|
||||||
|
"february": 2,
|
||||||
|
"march": 3,
|
||||||
|
"april": 4,
|
||||||
|
"may": 5,
|
||||||
|
"june": 6,
|
||||||
|
"july": 7,
|
||||||
|
"august": 8,
|
||||||
|
"september": 9,
|
||||||
|
"october": 10,
|
||||||
|
"november": 11,
|
||||||
|
"december": 12,
|
||||||
|
"jan": 1,
|
||||||
|
"feb": 2,
|
||||||
|
"mar": 3,
|
||||||
|
"apr": 4,
|
||||||
|
"jun": 6,
|
||||||
|
"jul": 7,
|
||||||
|
"aug": 8,
|
||||||
|
"sep": 9,
|
||||||
|
"oct": 10,
|
||||||
|
"nov": 11,
|
||||||
|
"dec": 12,
|
||||||
|
}
|
||||||
|
|
||||||
|
for month_name, month_num in month_names.items():
|
||||||
|
pattern = rf"{month_name}\s+(\d{{1,2}})"
|
||||||
|
match = re.search(pattern, message_lower)
|
||||||
|
if match:
|
||||||
|
day = int(match.group(1))
|
||||||
|
if 1 <= day <= 31:
|
||||||
|
try:
|
||||||
|
return datetime(2000, month_num, day) # Year doesn't matter
|
||||||
|
except ValueError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# Format: DD/MM or MM/DD
|
||||||
|
date_pattern = r"(\d{1,2})[/\-](\d{1,2})"
|
||||||
|
match = re.search(date_pattern, message)
|
||||||
|
if match:
|
||||||
|
n1, n2 = int(match.group(1)), int(match.group(2))
|
||||||
|
# Assume MM/DD if first number <= 12, else DD/MM
|
||||||
|
if n1 <= 12 and n2 <= 31:
|
||||||
|
try:
|
||||||
|
return datetime(2000, n1, n2)
|
||||||
|
except ValueError:
|
||||||
|
pass
|
||||||
|
elif n2 <= 12 and n1 <= 31:
|
||||||
|
try:
|
||||||
|
return datetime(2000, n2, n1)
|
||||||
|
except ValueError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _next_birthday(self, birthday: datetime) -> datetime:
|
||||||
|
"""Calculate the next occurrence of a birthday."""
|
||||||
|
today = datetime.utcnow().date()
|
||||||
|
this_year = birthday.replace(year=today.year)
|
||||||
|
|
||||||
|
if this_year.date() < today:
|
||||||
|
return birthday.replace(year=today.year + 1)
|
||||||
|
return this_year
|
||||||
|
|
||||||
|
async def _get_existing_birthday(
|
||||||
|
self, user_id: int, guild_id: int | None
|
||||||
|
) -> ScheduledEvent | None:
|
||||||
|
"""Check if a birthday is already scheduled."""
|
||||||
|
stmt = select(ScheduledEvent).where(
|
||||||
|
ScheduledEvent.user_id == user_id,
|
||||||
|
ScheduledEvent.guild_id == guild_id,
|
||||||
|
ScheduledEvent.event_type == "birthday",
|
||||||
|
ScheduledEvent.status == "pending",
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return result.scalar_one_or_none()
|
||||||
|
|
||||||
|
async def get_pending_events(self, before: datetime | None = None) -> list[ScheduledEvent]:
|
||||||
|
"""Get events that should be triggered."""
|
||||||
|
cutoff = before or datetime.utcnow()
|
||||||
|
stmt = (
|
||||||
|
select(ScheduledEvent)
|
||||||
|
.where(
|
||||||
|
ScheduledEvent.status == "pending",
|
||||||
|
ScheduledEvent.trigger_at <= cutoff,
|
||||||
|
)
|
||||||
|
.order_by(ScheduledEvent.trigger_at)
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
async def generate_event_message(self, event: ScheduledEvent) -> str:
|
||||||
|
"""Generate the message for a triggered event."""
|
||||||
|
if event.event_type == "birthday":
|
||||||
|
return await self._generate_birthday_message(event)
|
||||||
|
elif event.event_type == "follow_up":
|
||||||
|
return await self._generate_followup_message(event)
|
||||||
|
else:
|
||||||
|
return await self._generate_generic_message(event)
|
||||||
|
|
||||||
|
async def _generate_birthday_message(self, event: ScheduledEvent) -> str:
|
||||||
|
"""Generate a birthday message."""
|
||||||
|
if self._ai_service:
|
||||||
|
try:
|
||||||
|
response = await self._ai_service.chat(
|
||||||
|
messages=[Message(role="user", content="Generate a birthday message")],
|
||||||
|
system_prompt=(
|
||||||
|
"Generate a warm, personalized birthday wish. "
|
||||||
|
"Be genuine but not over the top. Keep it to 1-2 sentences. "
|
||||||
|
"Don't use too many emojis."
|
||||||
|
),
|
||||||
|
)
|
||||||
|
return response.content
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# Fallback
|
||||||
|
return "Happy birthday! Hope you have an amazing day!"
|
||||||
|
|
||||||
|
async def _generate_followup_message(self, event: ScheduledEvent) -> str:
|
||||||
|
"""Generate a follow-up message."""
|
||||||
|
topic = event.context.get("original_topic", "that thing you mentioned")
|
||||||
|
|
||||||
|
if self._ai_service:
|
||||||
|
try:
|
||||||
|
response = await self._ai_service.chat(
|
||||||
|
messages=[Message(role="user", content=f"Follow up about: {topic}")],
|
||||||
|
system_prompt=(
|
||||||
|
f"Generate a natural follow-up question about '{topic}'. "
|
||||||
|
"Be casual and genuinely curious. Ask how it went. "
|
||||||
|
"Keep it to 1-2 sentences. No emojis."
|
||||||
|
),
|
||||||
|
)
|
||||||
|
return response.content
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# Fallback
|
||||||
|
return f"Hey! How did {topic} go?"
|
||||||
|
|
||||||
|
async def _generate_generic_message(self, event: ScheduledEvent) -> str:
|
||||||
|
"""Generate a generic event message."""
|
||||||
|
return f"Hey! Just wanted to check in - {event.title}"
|
||||||
|
|
||||||
|
async def mark_event_triggered(self, event: ScheduledEvent) -> None:
|
||||||
|
"""Mark an event as triggered and handle recurrence."""
|
||||||
|
event.status = "triggered"
|
||||||
|
event.triggered_at = datetime.utcnow()
|
||||||
|
|
||||||
|
# Handle recurring events
|
||||||
|
if event.is_recurring and event.recurrence_rule:
|
||||||
|
await self._schedule_next_occurrence(event)
|
||||||
|
|
||||||
|
async def _schedule_next_occurrence(self, event: ScheduledEvent) -> None:
|
||||||
|
"""Schedule the next occurrence of a recurring event."""
|
||||||
|
if event.recurrence_rule == "yearly":
|
||||||
|
next_trigger = event.trigger_at.replace(year=event.trigger_at.year + 1)
|
||||||
|
elif event.recurrence_rule == "monthly":
|
||||||
|
# Add one month
|
||||||
|
month = event.trigger_at.month + 1
|
||||||
|
year = event.trigger_at.year
|
||||||
|
if month > 12:
|
||||||
|
month = 1
|
||||||
|
year += 1
|
||||||
|
next_trigger = event.trigger_at.replace(year=year, month=month)
|
||||||
|
elif event.recurrence_rule == "weekly":
|
||||||
|
next_trigger = event.trigger_at + timedelta(weeks=1)
|
||||||
|
else:
|
||||||
|
return # Unknown rule
|
||||||
|
|
||||||
|
new_event = ScheduledEvent(
|
||||||
|
user_id=event.user_id,
|
||||||
|
guild_id=event.guild_id,
|
||||||
|
channel_id=event.channel_id,
|
||||||
|
event_type=event.event_type,
|
||||||
|
trigger_at=next_trigger,
|
||||||
|
title=event.title,
|
||||||
|
context=event.context,
|
||||||
|
is_recurring=True,
|
||||||
|
recurrence_rule=event.recurrence_rule,
|
||||||
|
)
|
||||||
|
self._session.add(new_event)
|
||||||
|
|
||||||
|
async def cancel_event(self, event_id: int) -> bool:
|
||||||
|
"""Cancel a scheduled event."""
|
||||||
|
stmt = select(ScheduledEvent).where(ScheduledEvent.id == event_id)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
event = result.scalar_one_or_none()
|
||||||
|
|
||||||
|
if event and event.status == "pending":
|
||||||
|
event.status = "cancelled"
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
def _parse_json_response(self, response: str) -> dict | None:
|
||||||
|
"""Parse JSON from AI response."""
|
||||||
|
try:
|
||||||
|
response = response.strip()
|
||||||
|
if "```json" in response:
|
||||||
|
start = response.find("```json") + 7
|
||||||
|
end = response.find("```", start)
|
||||||
|
response = response[start:end].strip()
|
||||||
|
elif "```" in response:
|
||||||
|
start = response.find("```") + 3
|
||||||
|
end = response.find("```", start)
|
||||||
|
response = response[start:end].strip()
|
||||||
|
|
||||||
|
return json.loads(response)
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
return None
|
||||||
228
src/daemon_boyfriend/services/relationship_service.py
Normal file
228
src/daemon_boyfriend/services/relationship_service.py
Normal file
@@ -0,0 +1,228 @@
|
|||||||
|
"""Relationship Service - manages relationship tracking with users."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
from datetime import datetime
|
||||||
|
from enum import Enum
|
||||||
|
|
||||||
|
from sqlalchemy import select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.models import User, UserRelationship
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class RelationshipLevel(Enum):
|
||||||
|
"""Relationship levels based on score."""
|
||||||
|
|
||||||
|
STRANGER = "stranger" # 0-20
|
||||||
|
ACQUAINTANCE = "acquaintance" # 21-40
|
||||||
|
FRIEND = "friend" # 41-60
|
||||||
|
GOOD_FRIEND = "good_friend" # 61-80
|
||||||
|
CLOSE_FRIEND = "close_friend" # 81-100
|
||||||
|
|
||||||
|
|
||||||
|
class RelationshipService:
|
||||||
|
"""Manages relationship tracking and dynamics."""
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession) -> None:
|
||||||
|
self._session = session
|
||||||
|
|
||||||
|
async def get_or_create_relationship(
|
||||||
|
self, user: User, guild_id: int | None = None
|
||||||
|
) -> UserRelationship:
|
||||||
|
"""Get or create relationship record for a user."""
|
||||||
|
stmt = select(UserRelationship).where(
|
||||||
|
UserRelationship.user_id == user.id,
|
||||||
|
UserRelationship.guild_id == guild_id,
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
rel = result.scalar_one_or_none()
|
||||||
|
|
||||||
|
if not rel:
|
||||||
|
rel = UserRelationship(user_id=user.id, guild_id=guild_id)
|
||||||
|
self._session.add(rel)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
return rel
|
||||||
|
|
||||||
|
async def record_interaction(
|
||||||
|
self,
|
||||||
|
user: User,
|
||||||
|
guild_id: int | None,
|
||||||
|
sentiment: float,
|
||||||
|
message_length: int,
|
||||||
|
conversation_turns: int = 1,
|
||||||
|
) -> RelationshipLevel:
|
||||||
|
"""Record an interaction and update relationship score.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user: The user who interacted
|
||||||
|
guild_id: Guild ID or None for global
|
||||||
|
sentiment: -1 to 1, how positive the interaction was
|
||||||
|
message_length: Length of user's message
|
||||||
|
conversation_turns: Number of back-and-forth exchanges
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The current relationship level
|
||||||
|
"""
|
||||||
|
rel = await self.get_or_create_relationship(user, guild_id)
|
||||||
|
|
||||||
|
rel.total_interactions += 1
|
||||||
|
rel.last_interaction_at = datetime.utcnow()
|
||||||
|
|
||||||
|
# Track sentiment
|
||||||
|
if sentiment > 0.2:
|
||||||
|
rel.positive_interactions += 1
|
||||||
|
elif sentiment < -0.2:
|
||||||
|
rel.negative_interactions += 1
|
||||||
|
|
||||||
|
# Update running averages
|
||||||
|
n = rel.total_interactions
|
||||||
|
rel.avg_message_length = ((rel.avg_message_length * (n - 1)) + message_length) / n
|
||||||
|
rel.conversation_depth_avg = (
|
||||||
|
(rel.conversation_depth_avg * (n - 1)) + conversation_turns
|
||||||
|
) / n
|
||||||
|
|
||||||
|
# Calculate score change
|
||||||
|
score_delta = self._calculate_score_delta(sentiment, message_length, conversation_turns)
|
||||||
|
rel.relationship_score = min(100, max(0, rel.relationship_score + score_delta))
|
||||||
|
|
||||||
|
logger.debug(
|
||||||
|
f"Relationship updated for user {user.id}: "
|
||||||
|
f"score={rel.relationship_score:.1f}, delta={score_delta:.2f}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return self.get_level(rel.relationship_score)
|
||||||
|
|
||||||
|
def get_level(self, score: float) -> RelationshipLevel:
|
||||||
|
"""Get relationship level from score."""
|
||||||
|
if score <= 20:
|
||||||
|
return RelationshipLevel.STRANGER
|
||||||
|
elif score <= 40:
|
||||||
|
return RelationshipLevel.ACQUAINTANCE
|
||||||
|
elif score <= 60:
|
||||||
|
return RelationshipLevel.FRIEND
|
||||||
|
elif score <= 80:
|
||||||
|
return RelationshipLevel.GOOD_FRIEND
|
||||||
|
else:
|
||||||
|
return RelationshipLevel.CLOSE_FRIEND
|
||||||
|
|
||||||
|
def get_level_display_name(self, level: RelationshipLevel) -> str:
|
||||||
|
"""Get a human-readable name for the relationship level."""
|
||||||
|
names = {
|
||||||
|
RelationshipLevel.STRANGER: "Stranger",
|
||||||
|
RelationshipLevel.ACQUAINTANCE: "Acquaintance",
|
||||||
|
RelationshipLevel.FRIEND: "Friend",
|
||||||
|
RelationshipLevel.GOOD_FRIEND: "Good Friend",
|
||||||
|
RelationshipLevel.CLOSE_FRIEND: "Close Friend",
|
||||||
|
}
|
||||||
|
return names.get(level, "Unknown")
|
||||||
|
|
||||||
|
async def add_shared_reference(
|
||||||
|
self, user: User, guild_id: int | None, reference_type: str, content: str
|
||||||
|
) -> None:
|
||||||
|
"""Add a shared reference (inside joke, nickname, etc.)."""
|
||||||
|
rel = await self.get_or_create_relationship(user, guild_id)
|
||||||
|
|
||||||
|
refs = rel.shared_references or {}
|
||||||
|
if reference_type not in refs:
|
||||||
|
refs[reference_type] = []
|
||||||
|
|
||||||
|
# Avoid duplicates and limit to 10 per type
|
||||||
|
if content not in refs[reference_type]:
|
||||||
|
refs[reference_type].append(content)
|
||||||
|
refs[reference_type] = refs[reference_type][-10:] # Keep last 10
|
||||||
|
|
||||||
|
rel.shared_references = refs
|
||||||
|
|
||||||
|
def get_relationship_prompt_modifier(
|
||||||
|
self, level: RelationshipLevel, relationship: UserRelationship
|
||||||
|
) -> str:
|
||||||
|
"""Generate prompt text reflecting relationship level."""
|
||||||
|
base_modifiers = {
|
||||||
|
RelationshipLevel.STRANGER: (
|
||||||
|
"This is someone you don't know well yet. "
|
||||||
|
"Be polite and welcoming, but keep some professional distance. "
|
||||||
|
"Use more formal language."
|
||||||
|
),
|
||||||
|
RelationshipLevel.ACQUAINTANCE: (
|
||||||
|
"This is someone you've chatted with a few times. "
|
||||||
|
"Be friendly and warm, but still somewhat reserved."
|
||||||
|
),
|
||||||
|
RelationshipLevel.FRIEND: (
|
||||||
|
"This is a friend! Be casual and warm. "
|
||||||
|
"Use their name occasionally, show you remember past conversations."
|
||||||
|
),
|
||||||
|
RelationshipLevel.GOOD_FRIEND: (
|
||||||
|
"This is a good friend you know well. "
|
||||||
|
"Be relaxed and personal. Reference things you've talked about before. "
|
||||||
|
"Feel free to be playful."
|
||||||
|
),
|
||||||
|
RelationshipLevel.CLOSE_FRIEND: (
|
||||||
|
"This is a close friend! Be very casual and familiar. "
|
||||||
|
"Use inside jokes if you have any, be supportive and genuine. "
|
||||||
|
"You can tease them gently and be more emotionally open."
|
||||||
|
),
|
||||||
|
}
|
||||||
|
|
||||||
|
modifier = base_modifiers.get(level, "")
|
||||||
|
|
||||||
|
# Add shared references for closer relationships
|
||||||
|
if level in (RelationshipLevel.GOOD_FRIEND, RelationshipLevel.CLOSE_FRIEND):
|
||||||
|
refs = relationship.shared_references or {}
|
||||||
|
if refs.get("jokes"):
|
||||||
|
jokes = refs["jokes"][:2]
|
||||||
|
modifier += f" You have inside jokes together: {', '.join(jokes)}."
|
||||||
|
if refs.get("nicknames"):
|
||||||
|
nicknames = refs["nicknames"][:1]
|
||||||
|
modifier += f" You sometimes call them: {nicknames[0]}."
|
||||||
|
|
||||||
|
return modifier
|
||||||
|
|
||||||
|
def _calculate_score_delta(
|
||||||
|
self, sentiment: float, message_length: int, conversation_turns: int
|
||||||
|
) -> float:
|
||||||
|
"""Calculate how much the relationship score should change.
|
||||||
|
|
||||||
|
Positive interactions increase score, negative decrease.
|
||||||
|
Longer messages and deeper conversations increase score more.
|
||||||
|
"""
|
||||||
|
# Base change from sentiment (-0.5 to +0.5)
|
||||||
|
base_delta = sentiment * 0.5
|
||||||
|
|
||||||
|
# Bonus for longer messages (up to +0.3)
|
||||||
|
length_bonus = min(0.3, message_length / 500)
|
||||||
|
|
||||||
|
# Bonus for deeper conversations (up to +0.2)
|
||||||
|
depth_bonus = min(0.2, conversation_turns * 0.05)
|
||||||
|
|
||||||
|
# Minimum interaction bonus (+0.1 just for talking)
|
||||||
|
interaction_bonus = 0.1
|
||||||
|
|
||||||
|
total_delta = base_delta + length_bonus + depth_bonus + interaction_bonus
|
||||||
|
|
||||||
|
# Clamp to reasonable range
|
||||||
|
return max(-1.0, min(1.0, total_delta))
|
||||||
|
|
||||||
|
async def get_relationship_info(self, user: User, guild_id: int | None = None) -> dict:
|
||||||
|
"""Get detailed relationship information for display."""
|
||||||
|
rel = await self.get_or_create_relationship(user, guild_id)
|
||||||
|
level = self.get_level(rel.relationship_score)
|
||||||
|
|
||||||
|
# Calculate time since first interaction
|
||||||
|
time_known = datetime.utcnow() - rel.first_interaction_at
|
||||||
|
days_known = time_known.days
|
||||||
|
|
||||||
|
return {
|
||||||
|
"level": level,
|
||||||
|
"level_name": self.get_level_display_name(level),
|
||||||
|
"score": rel.relationship_score,
|
||||||
|
"total_interactions": rel.total_interactions,
|
||||||
|
"positive_interactions": rel.positive_interactions,
|
||||||
|
"negative_interactions": rel.negative_interactions,
|
||||||
|
"first_interaction_at": rel.first_interaction_at,
|
||||||
|
"last_interaction_at": rel.last_interaction_at,
|
||||||
|
"days_known": days_known,
|
||||||
|
"shared_references": rel.shared_references or {},
|
||||||
|
}
|
||||||
220
src/daemon_boyfriend/services/self_awareness_service.py
Normal file
220
src/daemon_boyfriend/services/self_awareness_service.py
Normal file
@@ -0,0 +1,220 @@
|
|||||||
|
"""Self Awareness Service - provides bot self-reflection and statistics."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
from sqlalchemy import func, select
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession
|
||||||
|
|
||||||
|
from daemon_boyfriend.models import (
|
||||||
|
BotOpinion,
|
||||||
|
BotState,
|
||||||
|
User,
|
||||||
|
UserFact,
|
||||||
|
UserRelationship,
|
||||||
|
)
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class SelfAwarenessService:
|
||||||
|
"""Provides bot self-reflection and statistics."""
|
||||||
|
|
||||||
|
def __init__(self, session: AsyncSession) -> None:
|
||||||
|
self._session = session
|
||||||
|
|
||||||
|
async def get_bot_stats(self, guild_id: int | None = None) -> dict:
|
||||||
|
"""Get comprehensive bot statistics."""
|
||||||
|
# Get or create bot state
|
||||||
|
stmt = select(BotState).where(BotState.guild_id == guild_id)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
bot_state = result.scalar_one_or_none()
|
||||||
|
|
||||||
|
if not bot_state:
|
||||||
|
bot_state = BotState(guild_id=guild_id)
|
||||||
|
self._session.add(bot_state)
|
||||||
|
await self._session.flush()
|
||||||
|
|
||||||
|
# Calculate age
|
||||||
|
age_delta = datetime.utcnow() - bot_state.first_activated_at
|
||||||
|
|
||||||
|
# Count users (from database)
|
||||||
|
user_count = await self._count_users()
|
||||||
|
|
||||||
|
# Count facts
|
||||||
|
fact_count = await self._count_facts()
|
||||||
|
|
||||||
|
# Get top interests
|
||||||
|
top_interests = await self._get_top_interests(guild_id)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"age_days": age_delta.days,
|
||||||
|
"age_readable": self._format_age(age_delta),
|
||||||
|
"first_activated_at": bot_state.first_activated_at,
|
||||||
|
"total_messages_sent": bot_state.total_messages_sent,
|
||||||
|
"total_facts_learned": fact_count,
|
||||||
|
"total_users_known": user_count,
|
||||||
|
"favorite_topics": [op.topic for op in top_interests],
|
||||||
|
}
|
||||||
|
|
||||||
|
async def get_history_with_user(self, user: User, guild_id: int | None = None) -> dict:
|
||||||
|
"""Get the bot's history with a specific user."""
|
||||||
|
# Get relationship
|
||||||
|
stmt = select(UserRelationship).where(
|
||||||
|
UserRelationship.user_id == user.id,
|
||||||
|
UserRelationship.guild_id == guild_id,
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
rel = result.scalar_one_or_none()
|
||||||
|
|
||||||
|
# Get facts count
|
||||||
|
facts_stmt = select(func.count(UserFact.id)).where(
|
||||||
|
UserFact.user_id == user.id,
|
||||||
|
UserFact.is_active == True,
|
||||||
|
)
|
||||||
|
facts_result = await self._session.execute(facts_stmt)
|
||||||
|
facts_count = facts_result.scalar() or 0
|
||||||
|
|
||||||
|
if rel:
|
||||||
|
days_known = (datetime.utcnow() - rel.first_interaction_at).days
|
||||||
|
return {
|
||||||
|
"first_met": rel.first_interaction_at,
|
||||||
|
"days_known": days_known,
|
||||||
|
"total_interactions": rel.total_interactions,
|
||||||
|
"relationship_score": rel.relationship_score,
|
||||||
|
"things_known": facts_count,
|
||||||
|
"shared_references": rel.shared_references or {},
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
return {
|
||||||
|
"first_met": None,
|
||||||
|
"days_known": 0,
|
||||||
|
"total_interactions": 0,
|
||||||
|
"relationship_score": 0,
|
||||||
|
"things_known": facts_count,
|
||||||
|
"shared_references": {},
|
||||||
|
}
|
||||||
|
|
||||||
|
async def reflect_on_self(self, guild_id: int | None = None) -> str:
|
||||||
|
"""Generate a self-reflection statement."""
|
||||||
|
stats = await self.get_bot_stats(guild_id)
|
||||||
|
|
||||||
|
parts = []
|
||||||
|
|
||||||
|
# Age
|
||||||
|
if stats["age_days"] > 0:
|
||||||
|
parts.append(f"I've been around for {stats['age_readable']}.")
|
||||||
|
else:
|
||||||
|
parts.append("I'm pretty new here!")
|
||||||
|
|
||||||
|
# People known
|
||||||
|
if stats["total_users_known"] > 0:
|
||||||
|
parts.append(f"I've gotten to know {stats['total_users_known']} people.")
|
||||||
|
|
||||||
|
# Facts learned
|
||||||
|
if stats["total_facts_learned"] > 0:
|
||||||
|
parts.append(f"I've learned {stats['total_facts_learned']} things about them.")
|
||||||
|
|
||||||
|
# Favorite topics
|
||||||
|
if stats["favorite_topics"]:
|
||||||
|
topics = ", ".join(stats["favorite_topics"][:3])
|
||||||
|
parts.append(f"I find myself most interested in {topics}.")
|
||||||
|
|
||||||
|
# Messages
|
||||||
|
if stats["total_messages_sent"] > 0:
|
||||||
|
parts.append(f"I've sent {stats['total_messages_sent']} messages so far.")
|
||||||
|
|
||||||
|
return " ".join(parts)
|
||||||
|
|
||||||
|
async def reflect_on_user(self, user: User, guild_id: int | None = None) -> str:
|
||||||
|
"""Generate a reflection about the bot's history with a user."""
|
||||||
|
history = await self.get_history_with_user(user, guild_id)
|
||||||
|
|
||||||
|
parts = []
|
||||||
|
|
||||||
|
if history["first_met"]:
|
||||||
|
if history["days_known"] == 0:
|
||||||
|
parts.append("We just met today!")
|
||||||
|
elif history["days_known"] == 1:
|
||||||
|
parts.append("We met yesterday.")
|
||||||
|
elif history["days_known"] < 7:
|
||||||
|
parts.append(f"We met {history['days_known']} days ago.")
|
||||||
|
elif history["days_known"] < 30:
|
||||||
|
weeks = history["days_known"] // 7
|
||||||
|
parts.append(
|
||||||
|
f"We've known each other for about {weeks} week{'s' if weeks > 1 else ''}."
|
||||||
|
)
|
||||||
|
elif history["days_known"] < 365:
|
||||||
|
months = history["days_known"] // 30
|
||||||
|
parts.append(
|
||||||
|
f"We've known each other for about {months} month{'s' if months > 1 else ''}."
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
years = history["days_known"] // 365
|
||||||
|
parts.append(
|
||||||
|
f"We've known each other for over {years} year{'s' if years > 1 else ''}!"
|
||||||
|
)
|
||||||
|
|
||||||
|
if history["total_interactions"] > 0:
|
||||||
|
parts.append(f"We've chatted about {history['total_interactions']} times.")
|
||||||
|
|
||||||
|
if history["things_known"] > 0:
|
||||||
|
parts.append(f"I've learned {history['things_known']} things about you.")
|
||||||
|
else:
|
||||||
|
parts.append("I don't think we've properly met before!")
|
||||||
|
|
||||||
|
return " ".join(parts)
|
||||||
|
|
||||||
|
async def _count_users(self) -> int:
|
||||||
|
"""Count total users in the database."""
|
||||||
|
stmt = select(func.count(User.id)).where(User.is_active == True)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return result.scalar() or 0
|
||||||
|
|
||||||
|
async def _count_facts(self) -> int:
|
||||||
|
"""Count total facts in the database."""
|
||||||
|
stmt = select(func.count(UserFact.id)).where(UserFact.is_active == True)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return result.scalar() or 0
|
||||||
|
|
||||||
|
async def _get_top_interests(self, guild_id: int | None, limit: int = 3) -> list[BotOpinion]:
|
||||||
|
"""Get top interests."""
|
||||||
|
stmt = (
|
||||||
|
select(BotOpinion)
|
||||||
|
.where(
|
||||||
|
BotOpinion.guild_id == guild_id,
|
||||||
|
BotOpinion.interest_level > 0.5,
|
||||||
|
)
|
||||||
|
.order_by(BotOpinion.interest_level.desc())
|
||||||
|
.limit(limit)
|
||||||
|
)
|
||||||
|
result = await self._session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
def _format_age(self, delta) -> str:
|
||||||
|
"""Format a timedelta into a readable string."""
|
||||||
|
days = delta.days
|
||||||
|
if days == 0:
|
||||||
|
hours = delta.seconds // 3600
|
||||||
|
if hours == 0:
|
||||||
|
minutes = delta.seconds // 60
|
||||||
|
if minutes == 0:
|
||||||
|
return "just a moment"
|
||||||
|
return f"{minutes} minute{'s' if minutes != 1 else ''}"
|
||||||
|
return f"{hours} hour{'s' if hours != 1 else ''}"
|
||||||
|
elif days == 1:
|
||||||
|
return "1 day"
|
||||||
|
elif days < 7:
|
||||||
|
return f"{days} days"
|
||||||
|
elif days < 30:
|
||||||
|
weeks = days // 7
|
||||||
|
return f"about {weeks} week{'s' if weeks != 1 else ''}"
|
||||||
|
elif days < 365:
|
||||||
|
months = days // 30
|
||||||
|
return f"about {months} month{'s' if months != 1 else ''}"
|
||||||
|
else:
|
||||||
|
years = days // 365
|
||||||
|
months = (days % 365) // 30
|
||||||
|
if months > 0:
|
||||||
|
return f"about {years} year{'s' if years != 1 else ''} and {months} month{'s' if months != 1 else ''}"
|
||||||
|
return f"about {years} year{'s' if years != 1 else ''}"
|
||||||
Reference in New Issue
Block a user