feat(knowledge): Phase A 知识库可见性隔离 + 结构化数据源 + 蒸馏Worker

- knowledge_items 增加 visibility(public/private) + account_id 字段
- 新建 structured_sources + structured_rows 表 (Excel JSONB 行级存储)
- 结构化数据源 CRUD API (5 路由: list/get/rows/delete/query)
- 安全查询: JSONB GIN 索引 + 可见性过滤 + 行数限制
- 蒸馏 Worker: 复用 Provider Key Pool 调 DeepSeek/Qwen API
- L0 质量过滤: 长度/隐私检测
- create_item 增加 is_admin 参数控制可见性默认值
- generate_embedding: extract_keywords_from_text 改为 pub 复用

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

crates/zclaw-saas/src/workers/distill_knowledge.rs

@@ -0,0 +1,253 @@
+//! 知识蒸馏 Worker
+//!
+//! 通过 LLM API 直调生成行业知识条目。
+//! 问题来源：知识缺口 API + 行业关键词 + Self-Instruct
+//! 质量过滤：L0 自动过滤（长度/关键词/隐私检测）
+//!
+//! 成本极低：DeepSeek V3 约 ¥0.001/条，120 条种子知识约 ¥0.5
+
+use async_trait::async_trait;
+use serde::{Deserialize, Serialize};
+use sqlx::PgPool;
+use crate::error::SaasResult;
+use super::Worker;
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct DistillKnowledgeArgs {
+    /// 要蒸馏的问题列表
+    pub questions: Vec<String>,
+    /// 目标行业 ID（可选）
+    pub industry_id: Option<String>,
+    /// 目标知识分类 ID
+    pub category_id: String,
+    /// Provider ID（如 "deepseek"）
+    pub provider_id: String,
+    /// 模型 ID（如 "deepseek-chat"）
+    pub model_id: String,
+}
+
+pub struct DistillationWorker {
+    /// TOTP/API Key 加密密钥（用于解密 provider key）
+    enc_key_bytes: [u8; 32],
+}
+
+impl DistillationWorker {
+    pub fn new(enc_key: [u8; 32]) -> Self {
+        Self { enc_key_bytes: enc_key }
+    }
+}
+
+#[async_trait]
+impl Worker for DistillationWorker {
+    type Args = DistillKnowledgeArgs;
+
+    fn name(&self) -> &str {
+        "distill_knowledge"
+    }
+
+    async fn perform(&self, db: &PgPool, args: Self::Args) -> SaasResult<()> {
+        tracing::info!(
+            "DistillKnowledge: starting {} questions for category '{}'",
+            args.questions.len(),
+            args.category_id,
+        );
+
+        // 1. 获取 provider 信息（base_url）
+        let provider: Option<(String,)> = sqlx::query_as(
+            "SELECT base_url FROM providers WHERE id = $1"
+        )
+        .bind(&args.provider_id)
+        .fetch_optional(db)
+        .await?;
+
+        let base_url = match provider {
+            Some((url,)) => url.trim_end_matches('/').to_string(),
+            None => {
+                tracing::error!("DistillKnowledge: provider '{}' not found", args.provider_id);
+                return Ok(());
+            }
+        };
+
+        // 2. 获取可用 API Key
+        let selection = crate::relay::key_pool::select_best_key(
+            db, &args.provider_id, &self.enc_key_bytes,
+        ).await?;
+
+        let api_key = selection.key.key_value.clone();
+        let client = reqwest::Client::new();
+
+        // 3. 逐条蒸馏
+        let mut success_count = 0u32;
+        let mut skip_count = 0u32;
+
+        for question in &args.questions {
+            match distill_single(&client, &base_url, &api_key, &args.model_id, question).await {
+                Some(answer) => {
+                    // L0 质量过滤
+                    if passes_l0_filter(&answer) {
+                        // 入库
+                        match insert_distilled_item(db, &args, question, &answer).await {
+                            Ok(()) => success_count += 1,
+                            Err(e) => tracing::warn!("DistillKnowledge: insert failed: {}", e),
+                        }
+                    } else {
+                        skip_count += 1;
+                        tracing::debug!("DistillKnowledge: L0 filtered: {}", &question[..question.len().min(50)]);
+                    }
+                }
+                None => {
+                    tracing::warn!("DistillKnowledge: no answer for: {}", &question[..question.len().min(50)]);
+                }
+            }
+        }
+
+        tracing::info!(
+            "DistillKnowledge: completed — {} success, {} filtered, {} total",
+            success_count, skip_count, args.questions.len(),
+        );
+
+        Ok(())
+    }
+}
+
+/// 调用 LLM API 获取单个回答
+async fn distill_single(
+    client: &reqwest::Client,
+    base_url: &str,
+    api_key: &str,
+    model: &str,
+    question: &str,
+) -> Option<String> {
+    let url = format!("{}/chat/completions", base_url);
+
+    let body = serde_json::json!({
+        "model": model,
+        "messages": [
+            {
+                "role": "system",
+                "content": "你是行业知识工程师。请用中文简洁回答问题，回答要准确、实用、不超过500字。只提供事实性内容，不做猜测。"
+            },
+            {
+                "role": "user",
+                "content": question
+            }
+        ],
+        "temperature": 0.3,
+        "max_tokens": 1000,
+    });
+
+    let response = client
+        .post(&url)
+        .header("Authorization", format!("Bearer {}", api_key))
+        .header("Content-Type", "application/json")
+        .json(&body)
+        .timeout(std::time::Duration::from_secs(30))
+        .send()
+        .await
+        .ok()?;
+
+    if !response.status().is_success() {
+        tracing::warn!("DistillKnowledge: API error status: {}", response.status());
+        return None;
+    }
+
+    let json: serde_json::Value = response.json().await.ok()?;
+
+    // 提取回答文本
+    json.get("choices")?
+        .get(0)?
+        .get("message")?
+        .get("content")?
+        .as_str()
+        .map(|s| s.to_string())
+}
+
+/// L0 质量过滤：自动过滤低质量内容
+fn passes_l0_filter(content: &str) -> bool {
+    // 最短长度（至少 20 字符的有效回答）
+    if content.len() < 20 {
+        return false;
+    }
+
+    // 最长限制（100KB 数据库限制，蒸馏内容应远小于此）
+    if content.len() > 50_000 {
+        return false;
+    }
+
+    // 简单隐私检测：不应包含明显敏感信息模式
+    let privacy_patterns = [
+        "身份证号", "银行卡号", "密码是", "社保号",
+    ];
+    for pattern in &privacy_patterns {
+        if content.contains(pattern) {
+            return false;
+        }
+    }
+
+    true
+}
+
+/// 将蒸馏结果插入知识库
+async fn insert_distilled_item(
+    db: &PgPool,
+    args: &DistillKnowledgeArgs,
+    question: &str,
+    answer: &str,
+) -> SaasResult<()> {
+    let id = uuid::Uuid::new_v4().to_string();
+    let title = if question.len() > 100 {
+        format!("{}...", &question[..97])
+    } else {
+        question.to_string()
+    };
+
+    // 从回答中提取关键词
+    let mut keywords = Vec::new();
+    super::generate_embedding::extract_keywords_from_text(answer, &mut keywords);
+    // 也加入问题中的关键词
+    super::generate_embedding::extract_keywords_from_text(question, &mut keywords);
+    keywords.truncate(30);
+
+    // 构建完整内容
+    let content = format!("## {}\n\n{}", question, answer);
+
+    // 插入知识条目
+    sqlx::query(
+        "INSERT INTO knowledge_items \
+         (id, category_id, title, content, keywords, priority, status, source, tags, \
+          visibility, account_id, created_by) \
+         VALUES ($1, $2, $3, $4, $5, 0, 'active', 'distillation', '{}', \
+                 'public', NULL, 'system')"
+    )
+    .bind(&id)
+    .bind(&args.category_id)
+    .bind(&title)
+    .bind(&content)
+    .bind(&keywords)
+    .execute(db)
+    .await?;
+
+    // 触发分块（复用 embedding worker 的分块逻辑）
+    // 注意：这里不用 worker dispatch（避免递归），直接分块
+    let chunks = crate::knowledge::service::chunk_content(&content, 512, 64);
+    for (idx, chunk) in chunks.iter().enumerate() {
+        let chunk_id = uuid::Uuid::new_v4().to_string();
+        let mut chunk_keywords = keywords.clone();
+        super::generate_embedding::extract_keywords_from_text(chunk, &mut chunk_keywords);
+        chunk_keywords.truncate(50);
+
+        sqlx::query(
+            "INSERT INTO knowledge_chunks (id, item_id, chunk_index, content, keywords, created_at) \
+             VALUES ($1, $2, $3, $4, $5, NOW())"
+        )
+        .bind(&chunk_id)
+        .bind(&id)
+        .bind(idx as i32)
+        .bind(chunk)
+        .bind(&chunk_keywords)
+        .execute(db)
+        .await?;
+    }
+
+    Ok(())
+}

crates/zclaw-saas/src/workers/generate_embedding.rs

@@ -78,7 +78,7 @@ impl Worker for GenerateEmbeddingWorker {
             let chunk_id = uuid::Uuid::new_v4().to_string();
 
             let mut chunk_keywords = keywords.clone();
-            extract_chunk_keywords(chunk, &mut chunk_keywords);
+            extract_keywords_from_text(chunk, &mut chunk_keywords);
 
             sqlx::query(
                 "INSERT INTO knowledge_chunks (id, item_id, chunk_index, content, keywords, created_at)
@@ -112,10 +112,8 @@ impl Worker for GenerateEmbeddingWorker {
     }
 }
 
-/// 从 chunk 内容中提取高频中文词组作为补充关键词
-///
-/// 简单策略：提取 2-4 字的连续中文字符段，取出现频率 > 1 的
-fn extract_chunk_keywords(content: &str, keywords: &mut Vec<String>) {
+/// 从 chunk 内容中提取高频中文词组作为补充关键词（公开，供 distill_knowledge worker 复用）
+pub fn extract_keywords_from_text(content: &str, keywords: &mut Vec<String>) {
     let chars: Vec<char> = content.chars().collect();
     let mut i = 0;
 

crates/zclaw-saas/src/workers/mod.rs

@@ -251,6 +251,7 @@ pub mod update_last_used;
 pub mod record_usage;
 pub mod aggregate_usage;
 pub mod generate_embedding;
+pub mod distill_knowledge;
 
 // 便捷导出
 pub use log_operation::LogOperationWorker;
@@ -259,3 +260,4 @@ pub use cleanup_refresh_tokens::CleanupRefreshTokensWorker;
 pub use update_last_used::UpdateLastUsedWorker;
 pub use record_usage::RecordUsageWorker;
 pub use aggregate_usage::AggregateUsageWorker;
+pub use distill_knowledge::DistillationWorker;