feat(intelligence): complete Phase 2-3 migration to Rust
Phase 2 - Core Engines: - Heartbeat Engine: Periodic proactive checks with quiet hours support - Context Compactor: Token estimation and message summarization - CJK character handling (1.5 tokens per char) - Rule-based summary generation Phase 3 - Advanced Features: - Reflection Engine: Pattern analysis and improvement suggestions - Agent Identity: SOUL.md/AGENTS.md/USER.md management - Proposal-based changes (requires user approval) - Snapshot history for rollback All modules include: - Tauri commands for frontend integration - Unit tests - Re-exported types via mod.rs Reference: docs/plans/INTELLIGENCE-LAYER-MIGRATION.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
iven
568
desktop/src-tauri/src/intelligence/reflection.rs
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568
desktop/src-tauri/src/intelligence/reflection.rs
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//! Reflection Engine - Agent self-improvement through conversation analysis
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//!
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//! Periodically analyzes recent conversations to:
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//! - Identify behavioral patterns (positive and negative)
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//! - Generate improvement suggestions
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//! - Propose identity file changes (with user approval)
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//! - Create meta-memories about agent performance
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//!
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//! Phase 3 of Intelligence Layer Migration.
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//! Reference: ZCLAW_AGENT_INTELLIGENCE_EVOLUTION.md §6.4.2
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use chrono::{DateTime, Utc};
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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// === Types ===
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/// Reflection configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ReflectionConfig {
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#[serde(default = "default_trigger_conversations")]
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pub trigger_after_conversations: usize,
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#[serde(default = "default_trigger_hours")]
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pub trigger_after_hours: u64,
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#[serde(default)]
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pub allow_soul_modification: bool,
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#[serde(default = "default_require_approval")]
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pub require_approval: bool,
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#[serde(default = "default_use_llm")]
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pub use_llm: bool,
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#[serde(default = "default_llm_fallback")]
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pub llm_fallback_to_rules: bool,
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}
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fn default_trigger_conversations() -> usize { 5 }
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fn default_trigger_hours() -> u64 { 24 }
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fn default_require_approval() -> bool { true }
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fn default_use_llm() -> bool { true }
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fn default_llm_fallback() -> bool { true }
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impl Default for ReflectionConfig {
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fn default() -> Self {
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Self {
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trigger_after_conversations: 5,
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trigger_after_hours: 24,
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allow_soul_modification: false,
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require_approval: true,
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use_llm: true,
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llm_fallback_to_rules: true,
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}
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}
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}
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/// Observed pattern from analysis
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct PatternObservation {
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pub observation: String,
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pub frequency: usize,
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pub sentiment: Sentiment,
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pub evidence: Vec<String>,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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#[serde(rename_all = "lowercase")]
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pub enum Sentiment {
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Positive,
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Negative,
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Neutral,
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}
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/// Improvement suggestion
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ImprovementSuggestion {
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pub area: String,
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pub suggestion: String,
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pub priority: Priority,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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#[serde(rename_all = "lowercase")]
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pub enum Priority {
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High,
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Medium,
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Low,
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}
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/// Identity change proposal
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct IdentityChangeProposal {
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pub agent_id: String,
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pub field: String,
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pub current_value: String,
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pub proposed_value: String,
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pub reason: String,
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}
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/// Result of reflection
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ReflectionResult {
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pub patterns: Vec<PatternObservation>,
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pub improvements: Vec<ImprovementSuggestion>,
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pub identity_proposals: Vec<IdentityChangeProposal>,
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pub new_memories: usize,
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pub timestamp: String,
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}
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/// Reflection state
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ReflectionState {
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pub conversations_since_reflection: usize,
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pub last_reflection_time: Option<String>,
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pub last_reflection_agent_id: Option<String>,
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}
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impl Default for ReflectionState {
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fn default() -> Self {
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Self {
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conversations_since_reflection: 0,
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last_reflection_time: None,
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last_reflection_agent_id: None,
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}
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}
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}
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// === Memory Entry (simplified for analysis) ===
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct MemoryEntryForAnalysis {
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pub memory_type: String,
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pub content: String,
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pub importance: usize,
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pub access_count: usize,
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pub tags: Vec<String>,
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}
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// === Reflection Engine ===
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pub struct ReflectionEngine {
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config: ReflectionConfig,
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state: ReflectionState,
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history: Vec<ReflectionResult>,
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}
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impl ReflectionEngine {
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pub fn new(config: Option<ReflectionConfig>) -> Self {
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Self {
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config: config.unwrap_or_default(),
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state: ReflectionState::default(),
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history: Vec::new(),
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}
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}
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/// Record that a conversation happened
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pub fn record_conversation(&mut self) {
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self.state.conversations_since_reflection += 1;
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}
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/// Check if it's time for reflection
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pub fn should_reflect(&self) -> bool {
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// Conversation count trigger
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if self.state.conversations_since_reflection >= self.config.trigger_after_conversations {
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return true;
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}
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// Time-based trigger
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if let Some(last_time) = &self.state.last_reflection_time {
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if let Ok(last) = DateTime::parse_from_rfc3339(last_time) {
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let elapsed = Utc::now().signed_duration_since(last);
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let hours_since = elapsed.num_hours() as u64;
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if hours_since >= self.config.trigger_after_hours {
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return true;
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}
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}
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} else {
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// Never reflected before, trigger after initial conversations
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return self.state.conversations_since_reflection >= 3;
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}
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false
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}
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/// Execute reflection cycle
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pub fn reflect(&mut self, agent_id: &str, memories: &[MemoryEntryForAnalysis]) -> ReflectionResult {
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// 1. Analyze memory patterns
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let patterns = self.analyze_patterns(memories);
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// 2. Generate improvement suggestions
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let improvements = self.generate_improvements(&patterns, memories);
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// 3. Propose identity changes if patterns warrant it
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let identity_proposals: Vec<IdentityChangeProposal> = if self.config.allow_soul_modification {
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self.propose_identity_changes(agent_id, &patterns)
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} else {
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vec![]
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};
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// 4. Count new memories that would be saved
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let new_memories = patterns.iter()
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.filter(|p| p.frequency >= 3)
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.count()
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+ improvements.iter()
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.filter(|i| matches!(i.priority, Priority::High))
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.count();
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// 5. Build result
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let result = ReflectionResult {
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patterns,
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improvements,
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identity_proposals,
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new_memories,
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timestamp: Utc::now().to_rfc3339(),
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};
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// 6. Update state
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self.state.conversations_since_reflection = 0;
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self.state.last_reflection_time = Some(result.timestamp.clone());
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self.state.last_reflection_agent_id = Some(agent_id.to_string());
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// 7. Store in history
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self.history.push(result.clone());
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if self.history.len() > 20 {
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self.history = self.history.split_off(10);
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}
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result
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}
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/// Analyze patterns in memories
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fn analyze_patterns(&self, memories: &[MemoryEntryForAnalysis]) -> Vec<PatternObservation> {
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let mut patterns = Vec::new();
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// Analyze memory type distribution
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let mut type_counts: HashMap<String, usize> = HashMap::new();
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for m in memories {
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*type_counts.entry(m.memory_type.clone()).or_insert(0) += 1;
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}
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// Pattern: Too many tasks accumulating
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let task_count = *type_counts.get("task").unwrap_or(&0);
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if task_count >= 5 {
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let evidence: Vec<String> = memories
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.iter()
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.filter(|m| m.memory_type == "task")
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.take(3)
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.map(|m| m.content.clone())
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.collect();
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patterns.push(PatternObservation {
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observation: format!("积累了 {} 个待办任务，可能存在任务管理不善", task_count),
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frequency: task_count,
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sentiment: Sentiment::Negative,
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evidence,
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});
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}
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// Pattern: Strong preference accumulation
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let pref_count = *type_counts.get("preference").unwrap_or(&0);
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if pref_count >= 5 {
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let evidence: Vec<String> = memories
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.iter()
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.filter(|m| m.memory_type == "preference")
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.take(3)
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.map(|m| m.content.clone())
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.collect();
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patterns.push(PatternObservation {
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observation: format!("已记录 {} 个用户偏好，对用户习惯有较好理解", pref_count),
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frequency: pref_count,
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sentiment: Sentiment::Positive,
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evidence,
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});
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}
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// Pattern: Many lessons learned
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let lesson_count = *type_counts.get("lesson").unwrap_or(&0);
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if lesson_count >= 5 {
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let evidence: Vec<String> = memories
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.iter()
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.filter(|m| m.memory_type == "lesson")
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.take(3)
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.map(|m| m.content.clone())
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.collect();
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patterns.push(PatternObservation {
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observation: format!("积累了 {} 条经验教训，知识库在成长", lesson_count),
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frequency: lesson_count,
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sentiment: Sentiment::Positive,
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evidence,
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});
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}
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// Pattern: High-importance items being accessed frequently
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let high_access: Vec<_> = memories
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.iter()
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.filter(|m| m.access_count >= 5 && m.importance >= 7)
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.collect();
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if high_access.len() >= 3 {
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let evidence: Vec<String> = high_access.iter().take(3).map(|m| m.content.clone()).collect();
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patterns.push(PatternObservation {
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observation: format!("有 {} 条高频访问的重要记忆，核心知识正在形成", high_access.len()),
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frequency: high_access.len(),
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sentiment: Sentiment::Positive,
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evidence,
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});
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}
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// Pattern: Low-importance memories accumulating
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let low_importance_count = memories.iter().filter(|m| m.importance <= 3).count();
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if low_importance_count > 20 {
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patterns.push(PatternObservation {
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observation: format!("有 {} 条低重要性记忆，建议清理", low_importance_count),
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frequency: low_importance_count,
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sentiment: Sentiment::Neutral,
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evidence: vec![],
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});
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}
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// Pattern: Tag analysis - recurring topics
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let mut tag_counts: HashMap<String, usize> = HashMap::new();
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for m in memories {
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for tag in &m.tags {
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if tag != "auto-extracted" {
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*tag_counts.entry(tag.clone()).or_insert(0) += 1;
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}
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}
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}
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let mut frequent_tags: Vec<_> = tag_counts
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.iter()
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.filter(|(_, count)| **count >= 5)
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.map(|(tag, count)| (tag.clone(), *count))
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.collect();
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frequent_tags.sort_by(|a, b| b.1.cmp(&a.1));
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if !frequent_tags.is_empty() {
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let tag_str: Vec<String> = frequent_tags
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.iter()
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.take(5)
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.map(|(tag, count)| format!("{}({}次)", tag, count))
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.collect();
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patterns.push(PatternObservation {
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observation: format!("反复出现的主题: {}", tag_str.join(", ")),
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frequency: frequent_tags[0].1,
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sentiment: Sentiment::Neutral,
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evidence: frequent_tags.iter().take(5).map(|(t, _)| t.clone()).collect(),
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});
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}
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patterns
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}
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/// Generate improvement suggestions
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fn generate_improvements(
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&self,
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patterns: &[PatternObservation],
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memories: &[MemoryEntryForAnalysis],
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) -> Vec<ImprovementSuggestion> {
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let mut improvements = Vec::new();
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// Suggestion: Clear pending tasks
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if patterns.iter().any(|p| p.observation.contains("待办任务")) {
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improvements.push(ImprovementSuggestion {
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area: "任务管理".to_string(),
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suggestion: "清理已完成的任务记忆，对长期未处理的任务降低重要性或标记为已取消".to_string(),
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priority: Priority::High,
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});
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}
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// Suggestion: Prune low-importance memories
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if patterns.iter().any(|p| p.observation.contains("低重要性")) {
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improvements.push(ImprovementSuggestion {
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area: "记忆管理".to_string(),
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suggestion: "执行记忆清理，移除30天以上未访问且重要性低于3的记忆".to_string(),
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priority: Priority::Medium,
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});
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}
|
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|
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// Suggestion: User profile enrichment
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let pref_count = memories.iter().filter(|m| m.memory_type == "preference").count();
|
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if pref_count < 3 {
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improvements.push(ImprovementSuggestion {
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area: "用户理解".to_string(),
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suggestion: "主动在对话中了解用户偏好（沟通风格、技术栈、工作习惯），丰富用户画像".to_string(),
|
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priority: Priority::Medium,
|
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});
|
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}
|
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|
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// Suggestion: Knowledge consolidation
|
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let fact_count = memories.iter().filter(|m| m.memory_type == "fact").count();
|
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if fact_count > 20 {
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improvements.push(ImprovementSuggestion {
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area: "知识整合".to_string(),
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suggestion: "合并相似的事实记忆，提高检索效率。可将相关事实整合为结构化的项目/用户档案".to_string(),
|
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priority: Priority::Low,
|
||||
});
|
||||
}
|
||||
|
||||
improvements
|
||||
}
|
||||
|
||||
/// Propose identity changes based on patterns
|
||||
fn propose_identity_changes(
|
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&self,
|
||||
agent_id: &str,
|
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patterns: &[PatternObservation],
|
||||
) -> Vec<IdentityChangeProposal> {
|
||||
let mut proposals = Vec::new();
|
||||
|
||||
// If many negative patterns, propose instruction update
|
||||
let negative_patterns: Vec<_> = patterns
|
||||
.iter()
|
||||
.filter(|p| matches!(p.sentiment, Sentiment::Negative))
|
||||
.collect();
|
||||
|
||||
if negative_patterns.len() >= 2 {
|
||||
let additions: Vec<String> = negative_patterns
|
||||
.iter()
|
||||
.map(|p| format!("- 注意: {}", p.observation))
|
||||
.collect();
|
||||
|
||||
proposals.push(IdentityChangeProposal {
|
||||
agent_id: agent_id.to_string(),
|
||||
field: "instructions".to_string(),
|
||||
current_value: "...".to_string(),
|
||||
proposed_value: format!("\n\n## 自我反思改进\n{}", additions.join("\n")),
|
||||
reason: format!(
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||||
"基于 {} 个负面模式观察，建议在指令中增加自我改进提醒",
|
||||
negative_patterns.len()
|
||||
),
|
||||
});
|
||||
}
|
||||
|
||||
proposals
|
||||
}
|
||||
|
||||
/// Get reflection history
|
||||
pub fn get_history(&self, limit: usize) -> Vec<&ReflectionResult> {
|
||||
self.history.iter().rev().take(limit).collect()
|
||||
}
|
||||
|
||||
/// Get last reflection result
|
||||
pub fn get_last_result(&self) -> Option<&ReflectionResult> {
|
||||
self.history.last()
|
||||
}
|
||||
|
||||
/// Get current state
|
||||
pub fn get_state(&self) -> &ReflectionState {
|
||||
&self.state
|
||||
}
|
||||
|
||||
/// Get configuration
|
||||
pub fn get_config(&self) -> &ReflectionConfig {
|
||||
&self.config
|
||||
}
|
||||
|
||||
/// Update configuration
|
||||
pub fn update_config(&mut self, config: ReflectionConfig) {
|
||||
self.config = config;
|
||||
}
|
||||
}
|
||||
|
||||
// === Tauri Commands ===
|
||||
|
||||
use std::sync::Arc;
|
||||
use tokio::sync::Mutex;
|
||||
|
||||
pub type ReflectionEngineState = Arc<Mutex<ReflectionEngine>>;
|
||||
|
||||
/// Initialize reflection engine
|
||||
#[tauri::command]
|
||||
pub async fn reflection_init(
|
||||
config: Option<ReflectionConfig>,
|
||||
) -> Result<ReflectionEngineState, String> {
|
||||
Ok(Arc::new(Mutex::new(ReflectionEngine::new(config))))
|
||||
}
|
||||
|
||||
/// Record a conversation
|
||||
#[tauri::command]
|
||||
pub async fn reflection_record_conversation(
|
||||
state: tauri::State<'_, ReflectionEngineState>,
|
||||
) -> Result<(), String> {
|
||||
let mut engine = state.lock().await;
|
||||
engine.record_conversation();
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Check if reflection should run
|
||||
#[tauri::command]
|
||||
pub async fn reflection_should_reflect(
|
||||
state: tauri::State<'_, ReflectionEngineState>,
|
||||
) -> Result<bool, String> {
|
||||
let engine = state.lock().await;
|
||||
Ok(engine.should_reflect())
|
||||
}
|
||||
|
||||
/// Execute reflection
|
||||
#[tauri::command]
|
||||
pub async fn reflection_reflect(
|
||||
agent_id: String,
|
||||
memories: Vec<MemoryEntryForAnalysis>,
|
||||
state: tauri::State<'_, ReflectionEngineState>,
|
||||
) -> Result<ReflectionResult, String> {
|
||||
let mut engine = state.lock().await;
|
||||
Ok(engine.reflect(&agent_id, &memories))
|
||||
}
|
||||
|
||||
/// Get reflection history
|
||||
#[tauri::command]
|
||||
pub async fn reflection_get_history(
|
||||
limit: Option<usize>,
|
||||
state: tauri::State<'_, ReflectionEngineState>,
|
||||
) -> Result<Vec<ReflectionResult>, String> {
|
||||
let engine = state.lock().await;
|
||||
Ok(engine.get_history(limit.unwrap_or(10)).into_iter().cloned().collect())
|
||||
}
|
||||
|
||||
/// Get reflection state
|
||||
#[tauri::command]
|
||||
pub async fn reflection_get_state(
|
||||
state: tauri::State<'_, ReflectionEngineState>,
|
||||
) -> Result<ReflectionState, String> {
|
||||
let engine = state.lock().await;
|
||||
Ok(engine.get_state().clone())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_should_reflect_initial() {
|
||||
let mut engine = ReflectionEngine::new(None);
|
||||
assert!(!engine.should_reflect());
|
||||
|
||||
// After 3 conversations
|
||||
for _ in 0..3 {
|
||||
engine.record_conversation();
|
||||
}
|
||||
assert!(engine.should_reflect());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_analyze_patterns() {
|
||||
let engine = ReflectionEngine::new(None);
|
||||
let memories = vec![
|
||||
MemoryEntryForAnalysis {
|
||||
memory_type: "task".to_string(),
|
||||
content: "Task 1".to_string(),
|
||||
importance: 7,
|
||||
access_count: 1,
|
||||
tags: vec![],
|
||||
},
|
||||
MemoryEntryForAnalysis {
|
||||
memory_type: "task".to_string(),
|
||||
content: "Task 2".to_string(),
|
||||
importance: 8,
|
||||
access_count: 2,
|
||||
tags: vec![],
|
||||
},
|
||||
];
|
||||
|
||||
let patterns = engine.analyze_patterns(&memories);
|
||||
// Should not trigger (only 2 tasks, threshold is 5)
|
||||
assert!(!patterns.iter().any(|p| p.observation.contains("待办任务")));
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user