新增: 媒体引用化存储与消息溯源
- media_store 模块: sha256 去重落盘 + 阿里云 OSS 双写(签名手写), OSS 失败降级本机引用 - 飞书图片入站: image 消息下载装配 vision 片, 3 次退避+120s 超时+残件清理防线 - 出站回传: 报告 [交付] 路径行自动上传飞书原生附件, 凭据/密钥名拦截兜底 - 溯源 V52: ai_messages.source 列, 飞书 msg_id/chat_id/发送者随消息落库 - spawn offload: 桌面粘贴图 base64 统一转落盘引用, SQLite 不进大字符串 - 引用统一 url 字段(https/file://)不自造 scheme, image_ref 发送前按需展开
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@@ -47,6 +47,7 @@ fn ai_message_from_row(row: &Row<'_>) -> std::result::Result<AiMessageRecord, ru
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prompt_cache_miss_tokens: row.get("prompt_cache_miss_tokens")?,
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reasoning_tokens: row.get("reasoning_tokens")?,
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is_estimated: row.get("is_estimated")?,
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source: row.get("source")?,
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})
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}
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@@ -87,8 +88,8 @@ impl AiMessageRepo {
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(id, conversation_id, seq, role, content, parts, tool_call_id,
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tool_calls, model, status, reasoning_content, timestamp, created_at,
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prompt_tokens, completion_tokens,
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated)
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VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19)",
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated, source)
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VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20)",
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)
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.map_err(storage_err)?;
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for rec in &records {
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@@ -97,7 +98,7 @@ impl AiMessageRepo {
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rec.parts, rec.tool_call_id, rec.tool_calls, rec.model, rec.status,
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rec.reasoning_content, rec.timestamp, rec.created_at,
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rec.prompt_tokens, rec.completion_tokens,
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rec.prompt_cache_hit_tokens, rec.prompt_cache_miss_tokens, rec.reasoning_tokens, rec.is_estimated
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rec.prompt_cache_hit_tokens, rec.prompt_cache_miss_tokens, rec.reasoning_tokens, rec.is_estimated, rec.source
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])
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.map_err(storage_err)?;
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}
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@@ -124,7 +125,7 @@ impl AiMessageRepo {
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tool_calls, model, status, reasoning_content, timestamp, created_at,
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prompt_tokens, completion_tokens,
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens,
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is_estimated
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is_estimated, source
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FROM ai_messages WHERE conversation_id = ?1 ORDER BY seq ASC",
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)
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.map_err(storage_err)?;
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@@ -171,13 +172,13 @@ impl AiMessageRepo {
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"SELECT id, conversation_id, seq, role, content, parts, tool_call_id,
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tool_calls, model, status, reasoning_content, timestamp, created_at,
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prompt_tokens, completion_tokens,
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated, source
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FROM ai_messages WHERE conversation_id = ?1 AND seq < ?2 ORDER BY seq DESC LIMIT ?3"
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} else {
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"SELECT id, conversation_id, seq, role, content, parts, tool_call_id,
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tool_calls, model, status, reasoning_content, timestamp, created_at,
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prompt_tokens, completion_tokens,
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated, source
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FROM ai_messages WHERE conversation_id = ?1 ORDER BY seq DESC LIMIT ?2"
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};
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let mut stmt = guard.prepare(sql).map_err(storage_err)?;
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@@ -296,8 +297,8 @@ impl AiMessageRepo {
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(id, conversation_id, seq, role, content, parts, tool_call_id,
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tool_calls, model, status, reasoning_content, timestamp, created_at,
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prompt_tokens, completion_tokens,
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated)
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VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19)",
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prompt_cache_hit_tokens, prompt_cache_miss_tokens, reasoning_tokens, is_estimated, source)
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VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20)",
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)
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.map_err(storage_err)?;
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for rec in &records {
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@@ -306,7 +307,7 @@ impl AiMessageRepo {
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rec.parts, rec.tool_call_id, rec.tool_calls, rec.model, rec.status,
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rec.reasoning_content, rec.timestamp, rec.created_at,
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rec.prompt_tokens, rec.completion_tokens,
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rec.prompt_cache_hit_tokens, rec.prompt_cache_miss_tokens, rec.reasoning_tokens, rec.is_estimated
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rec.prompt_cache_hit_tokens, rec.prompt_cache_miss_tokens, rec.reasoning_tokens, rec.is_estimated, rec.source
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])
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.map_err(storage_err)?;
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}
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@@ -381,6 +382,7 @@ mod tests {
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prompt_cache_miss_tokens: None,
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reasoning_tokens: None,
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is_estimated: None,
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source: None,
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}
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}
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@@ -544,6 +546,7 @@ mod tests {
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prompt_cache_miss_tokens: None,
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reasoning_tokens: None,
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is_estimated: None,
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source: None,
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},
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AiMessageRecord {
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id: "new_1".into(),
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@@ -565,6 +568,7 @@ mod tests {
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prompt_cache_miss_tokens: None,
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reasoning_tokens: None,
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is_estimated: None,
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source: None,
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},
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];
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repo.replace_conversation("conv", records).await.expect("replace");
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@@ -638,6 +642,7 @@ mod tests {
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prompt_cache_miss_tokens: None,
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reasoning_tokens: None,
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is_estimated: None,
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source: None,
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}],
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)
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.await
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@@ -680,6 +685,7 @@ mod tests {
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prompt_cache_miss_tokens: None,
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reasoning_tokens: None,
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is_estimated: None,
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source: None,
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};
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repo.replace_conversation("c", vec![rec()]).await.expect("1st");
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repo.replace_conversation("c", vec![rec()]).await.expect("2nd");
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@@ -64,7 +64,8 @@ pub fn run(conn: &Connection) -> Result<()> {
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// conversation_checkpoints.token_total 语义回填。
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// V51 = 待发箱 ai_outbox 表(定时消息与待发箱持久化 P0 地基):队列/定时统一实体,
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// 调度器按 status+due_at 索引到点派发。纯新增表,可逆=DROP TABLE ai_outbox。
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let steps: [(i32, fn(&Connection) -> Result<()>); 51] = [
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// V52 = ai_messages.source 列(#958 消息溯源:外部渠道原始消息 ID JSON,NULL=桌面本端)。
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let steps: [(i32, fn(&Connection) -> Result<()>); 52] = [
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(1, migrate_v1),
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(2, migrate_v2),
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(3, migrate_v3),
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@@ -122,6 +123,8 @@ pub fn run(conn: &Connection) -> Result<()> {
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(50, migrate_v50),
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// V51 = 待发箱 ai_outbox 表(定时消息与待发箱持久化 P0 地基)。
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(51, migrate_v51),
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// V52 = ai_messages 加 source 列(#958 消息溯源:飞书等外部渠道原始消息 ID)。
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(52, migrate_v52),
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];
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for (version, migrate_fn) in steps {
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@@ -1529,7 +1532,7 @@ fn migrate_v50(conn: &Connection) -> Result<()> {
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/// 调度器启动时 reset_stale_sending 复位为 pending。
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/// 迁移链顶点版本(单一真值来源)：**新增迁移时必须同步 +1**,否则下方迁移测试会失败
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/// (测试用它断言「全量迁移后 schema_version == 顶点」「重跑仍到顶点」——既防链中断,也免多处写死)。
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pub const LATEST_SCHEMA_VERSION: i32 = 51;
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pub const LATEST_SCHEMA_VERSION: i32 = 52;
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fn migrate_v51(conn: &Connection) -> Result<()> {
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if !table_exists(conn, "ai_outbox") {
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@@ -1569,6 +1572,18 @@ fn migrate_v51(conn: &Connection) -> Result<()> {
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Ok(())
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}
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/// V52: ai_messages 加 source 列(#958 消息溯源)。TEXT JSON(MessageSource 序列化),
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/// NULL=桌面本端。纯加列零回退风险(老数据恒 NULL=既有语义)。
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fn migrate_v52(conn: &Connection) -> Result<()> {
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if !column_exists(conn, "ai_messages", "source") {
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conn.execute("ALTER TABLE ai_messages ADD COLUMN source TEXT", [])?;
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tracing::info!("v52: ai_messages 加 source 列(消息溯源)");
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}
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conn.execute("INSERT OR IGNORE INTO schema_version (version) VALUES (?)", [52])?;
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tracing::info!("迁移 v52 完成");
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Ok(())
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}
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/// 回填 conversation_checkpoints.token_total:按 snapshot JSON 内逐条消息的
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/// prompt_tokens+completion_tokens 求真实总量。
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///
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@@ -479,6 +479,9 @@ pub struct AiMessageRecord {
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/// 本轮 prompt 是否估算值(round_usage.prompt_tokens==0 → estimated_prompt 兜底打标)。
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/// reload 逐条回显「估算」标注;老消息 NULL → None(向前兼容)。
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pub is_estimated: Option<bool>,
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/// 消息来源 JSON(#958 溯源,MessageSource 序列化;V52 加列)。
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/// NULL=桌面本端(既有语义);非空=外部渠道(飞书 message_id 等,可跳原消息追溯)
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pub source: Option<String>,
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}
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/// 待发箱记录(ai_outbox 表,V51)。
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