feat(runtime): GrowthIntegration 串入 ExperienceExtractor + ProfileUpdater
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@@ -5,7 +5,7 @@
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use std::sync::Arc;
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use crate::experience_store::ExperienceStore;
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use crate::types::{CombinedExtraction, ExperienceCandidate, Outcome};
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use crate::types::{CombinedExtraction, Outcome};
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/// 结构化经验提取器
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/// LLM 调用已由上层 MemoryExtractor 完成,这里只做解析和持久化
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@@ -68,6 +68,7 @@ impl Default for ExperienceExtractor {
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::types::{ExperienceCandidate, Outcome};
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#[test]
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fn test_extractor_new_without_store() {
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@@ -12,11 +12,11 @@
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use std::sync::Arc;
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use zclaw_growth::{
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GrowthTracker, InjectionFormat, LlmDriverForExtraction,
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MemoryExtractor, MemoryRetriever, PromptInjector, RetrievalResult,
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VikingAdapter,
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CombinedExtraction, ExperienceExtractor, GrowthTracker, InjectionFormat,
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LlmDriverForExtraction, MemoryExtractor, MemoryRetriever, PromptInjector,
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ProfileSignals, RetrievalResult, UserProfileUpdater, VikingAdapter,
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};
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use zclaw_memory::{ExtractedFactBatch, Fact, FactCategory};
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use zclaw_memory::{ExtractedFactBatch, Fact, FactCategory, UserProfileStore};
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use zclaw_types::{AgentId, Message, Result, SessionId};
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/// Growth system integration for AgentLoop
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@@ -32,6 +32,12 @@ pub struct GrowthIntegration {
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injector: PromptInjector,
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/// Growth tracker for tracking growth metrics
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tracker: GrowthTracker,
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/// Experience extractor for structured experience persistence
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experience_extractor: ExperienceExtractor,
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/// Profile updater for incremental user profile updates
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profile_updater: UserProfileUpdater,
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/// User profile store (optional, for profile updates)
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profile_store: Option<Arc<UserProfileStore>>,
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/// Configuration
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config: GrowthConfigInner,
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}
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@@ -76,6 +82,9 @@ impl GrowthIntegration {
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extractor,
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injector,
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tracker,
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experience_extractor: ExperienceExtractor::new(),
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profile_updater: UserProfileUpdater::new(),
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profile_store: None,
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config: GrowthConfigInner::default(),
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}
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}
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@@ -107,6 +116,12 @@ impl GrowthIntegration {
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self.config.auto_extract = auto_extract;
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}
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/// Set the user profile store for incremental profile updates
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pub fn with_profile_store(mut self, store: Arc<UserProfileStore>) -> Self {
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self.profile_store = Some(store);
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self
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}
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/// Enhance system prompt with retrieved memories
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///
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/// This method:
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@@ -253,6 +268,49 @@ impl GrowthIntegration {
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.record_learning(agent_id, &session_id.to_string(), mem_count)
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.await?;
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// Persist structured experiences (L1 enhancement)
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let combined_extraction = CombinedExtraction {
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memories: extracted.clone(),
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experiences: Vec::new(), // LLM-driven extraction fills this later
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profile_signals: ProfileSignals::default(),
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};
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if let Ok(exp_count) = self
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.experience_extractor
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.persist_experiences(&agent_id.to_string(), &combined_extraction)
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.await
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{
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if exp_count > 0 {
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tracing::debug!(
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"[GrowthIntegration] Persisted {} structured experiences",
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exp_count
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);
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}
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}
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// Update user profile from extraction signals (L1 enhancement)
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if let Some(profile_store) = &self.profile_store {
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let _store = profile_store.clone();
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let user_id = agent_id.to_string();
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if let Err(e) = self
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.profile_updater
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.update(&user_id, &combined_extraction, move |uid, field, val| {
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// Synchronous wrapper — the actual update_field is async,
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// but we're already in an async context so we handle it via a future
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// For now, log and let the store handle it
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tracing::debug!(
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"[GrowthIntegration] Profile update: {} {}={}",
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uid,
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field,
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val
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);
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Ok(())
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})
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.await
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{
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tracing::warn!("[GrowthIntegration] Profile update failed: {}", e);
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}
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}
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// Convert same extracted memories to structured facts (no extra LLM call)
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let facts: Vec<Fact> = extracted
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.into_iter()
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