feat(growth): ExperienceExtractor + ProfileUpdater — 结构化经验提取和画像增量更新

crates/zclaw-growth/src/experience_extractor.rs

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+//! 结构化经验提取器
+//! 从对话中提取 ExperienceCandidate（pain_pattern → solution_steps → outcome）
+//! 持久化到 ExperienceStore
+
+use std::sync::Arc;
+
+use crate::experience_store::ExperienceStore;
+use crate::types::{CombinedExtraction, ExperienceCandidate, Outcome};
+
+/// 结构化经验提取器
+/// LLM 调用已由上层 MemoryExtractor 完成，这里只做解析和持久化
+pub struct ExperienceExtractor {
+    store: Option<Arc<ExperienceStore>>,
+}
+
+impl ExperienceExtractor {
+    pub fn new() -> Self {
+        Self { store: None }
+    }
+
+    pub fn with_store(mut self, store: Arc<ExperienceStore>) -> Self {
+        self.store = Some(store);
+        self
+    }
+
+    /// 从 CombinedExtraction 中提取经验并持久化
+    /// LLM 调用已由上层完成，这里只做解析和存储
+    pub async fn persist_experiences(
+        &self,
+        agent_id: &str,
+        extraction: &CombinedExtraction,
+    ) -> zclaw_types::Result<usize> {
+        let store = match &self.store {
+            Some(s) => s,
+            None => return Ok(0),
+        };
+
+        let mut count = 0;
+        for candidate in &extraction.experiences {
+            if candidate.confidence < 0.6 {
+                continue;
+            }
+            let outcome_str = match candidate.outcome {
+                Outcome::Success => "success",
+                Outcome::Partial => "partial",
+                Outcome::Failed => "failed",
+            };
+            let exp = crate::experience_store::Experience::new(
+                agent_id,
+                &candidate.pain_pattern,
+                &candidate.context,
+                candidate.solution_steps.clone(),
+                outcome_str,
+            );
+            store.store_experience(&exp).await?;
+            count += 1;
+        }
+        Ok(count)
+    }
+}
+
+impl Default for ExperienceExtractor {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_extractor_new_without_store() {
+        let ext = ExperienceExtractor::new();
+        assert!(ext.store.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_persist_no_store_returns_zero() {
+        let ext = ExperienceExtractor::new();
+        let extraction = CombinedExtraction::default();
+        let count = ext.persist_experiences("agent1", &extraction).await.unwrap();
+        assert_eq!(count, 0);
+    }
+
+    #[tokio::test]
+    async fn test_persist_filters_low_confidence() {
+        let viking = Arc::new(crate::VikingAdapter::in_memory());
+        let store = Arc::new(ExperienceStore::new(viking));
+        let ext = ExperienceExtractor::new().with_store(store);
+
+        let mut extraction = CombinedExtraction::default();
+        extraction.experiences.push(ExperienceCandidate {
+            pain_pattern: "low confidence task".to_string(),
+            context: "should be filtered".to_string(),
+            solution_steps: vec!["step1".to_string()],
+            outcome: Outcome::Success,
+            confidence: 0.3, // 低于 0.6 阈值
+            tools_used: vec![],
+            industry_context: None,
+        });
+        extraction.experiences.push(ExperienceCandidate {
+            pain_pattern: "high confidence task".to_string(),
+            context: "should be stored".to_string(),
+            solution_steps: vec!["step1".to_string(), "step2".to_string()],
+            outcome: Outcome::Success,
+            confidence: 0.9,
+            tools_used: vec!["researcher".to_string()],
+            industry_context: Some("healthcare".to_string()),
+        });
+
+        let count = ext.persist_experiences("agent-1", &extraction).await.unwrap();
+        assert_eq!(count, 1); // 只有 1 个通过置信度过滤
+    }
+}