新增: F-01模型能力阶段1+2(ModelConfig数据模型+model_probe探测器+df-storage兼容V18)

crates/df-storage/Cargo.toml

@@ -5,6 +5,8 @@ edition = "2021"
 
 [dependencies]
 df-core = { path = "../df-core" }
+# F-01 阶段1:ModelConfig/deserialize_model_configs 向后兼容反序列化(老字符串数组/新对象数组/null/空)
+df-ai-core = { path = "../df-ai-core" }
 serde = { workspace = true }
 serde_json = { workspace = true }
 anyhow = { workspace = true }

crates/df-storage/src/crud.rs

@@ -1032,6 +1032,28 @@ impl_repo!(
 // ============================================================
 
 fn ai_provider_from_row(row: &Row<'_>) -> std::result::Result<AiProviderRecord, rusqlite::Error> {
+    // model_configs:DB TEXT 列存 JSON 字符串。读 Option<String> 兼容老库 NULL,
+    // 再经 deserialize_model_configs 解析(老字符串数组/新对象数组/空 → Vec<ModelConfig>)。
+    // 解析失败不致命:降级为空 Vec(防单行坏数据拖垮 list_all)。
+    let model_configs: Vec<df_ai_core::model::ModelConfig> = {
+        let raw: Option<String> = row.get("model_configs").ok();
+        match raw {
+            None => Vec::new(),
+            Some(s) => serde_json::from_str::<ModelConfigsWrap>(&format!(
+                r#"{{"v":{}}}"#,
+                if s.trim().is_empty() {
+                    "null".to_string()
+                } else if s.trim_start().starts_with('[') || s.trim_start().starts_with('{') {
+                    s
+                } else {
+                    // 非 JSON 字面文本(理论不会出现)→ 包装为 JSON 字符串让 deserialize 兜底
+                    serde_json::to_string(&s).unwrap_or_else(|_| "null".into())
+                }
+            ))
+            .map(|w| w.v)
+            .unwrap_or_default(),
+        }
+    };
     Ok(AiProviderRecord {
         id: row.get("id")?,
         name: row.get("name")?,
@@ -1040,6 +1062,7 @@ fn ai_provider_from_row(row: &Row<'_>) -> std::result::Result<AiProviderRecord,
         base_url: row.get("base_url")?,
         default_model: row.get("default_model")?,
         models: row.get("models")?,
+        model_configs,
         is_default: row.get::<_, i32>("is_default")? != 0,
         config: row.get("config")?,
         created_at: row.get("created_at")?,
@@ -1047,6 +1070,14 @@ fn ai_provider_from_row(row: &Row<'_>) -> std::result::Result<AiProviderRecord,
     })
 }
 
+/// from_row 内部辅助:复用 deserialize_model_configs 解析 DB TEXT 列 JSON。
+/// 包一层 { "v": <原始值> } 把任意 JSON 值送进 deserialize_model_configs。
+#[derive(serde::Deserialize)]
+struct ModelConfigsWrap {
+    #[serde(default, deserialize_with = "df_ai_core::model::deserialize_model_configs")]
+    v: Vec<df_ai_core::model::ModelConfig>,
+}
+
 fn ai_conversation_from_row(row: &Row<'_>) -> std::result::Result<AiConversationRecord, rusqlite::Error> {
     Ok(AiConversationRecord {
         id: row.get("id")?,
@@ -1118,22 +1149,25 @@ impl_repo!(
     from_row => |row| ai_provider_from_row(row),
     insert  => |conn, rec| {
         let is_default = if rec.is_default { 1i32 } else { 0i32 };
+        // model_configs:Vec<ModelConfig> → JSON 字符串落 TEXT 列
+        let model_configs_json = serde_json::to_string(&rec.model_configs).unwrap_or_else(|_| "[]".into());
         conn.execute(
-            "INSERT OR REPLACE INTO ai_providers (id, name, provider_type, api_key, base_url, default_model, models, is_default, config, created_at, updated_at)
-             VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11)",
+            "INSERT OR REPLACE INTO ai_providers (id, name, provider_type, api_key, base_url, default_model, models, model_configs, is_default, config, created_at, updated_at)
+             VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12)",
             params![
                 rec.id, rec.name, rec.provider_type, rec.api_key, rec.base_url,
-                rec.default_model, rec.models, is_default, rec.config, rec.created_at, rec.updated_at
+                rec.default_model, rec.models, model_configs_json, is_default, rec.config, rec.created_at, rec.updated_at
             ],
         )
     },
     update  => |conn, rec| {
         let is_default = if rec.is_default { 1i32 } else { 0i32 };
+        let model_configs_json = serde_json::to_string(&rec.model_configs).unwrap_or_else(|_| "[]".into());
         conn.execute(
-            "UPDATE ai_providers SET name = ?1, provider_type = ?2, api_key = ?3, base_url = ?4, default_model = ?5, models = ?6, is_default = ?7, config = ?8, updated_at = ?9 WHERE id = ?10",
+            "UPDATE ai_providers SET name = ?1, provider_type = ?2, api_key = ?3, base_url = ?4, default_model = ?5, models = ?6, model_configs = ?7, is_default = ?8, config = ?9, updated_at = ?10 WHERE id = ?11",
             params![
                 rec.name, rec.provider_type, rec.api_key, rec.base_url,
-                rec.default_model, rec.models, is_default, rec.config, rec.updated_at, rec.id
+                rec.default_model, rec.models, model_configs_json, is_default, rec.config, rec.updated_at, rec.id
             ],
         )
     }
@@ -1693,6 +1727,83 @@ mod tests {
 
     // ---------- COLS 漂移防护(CR-260615-03) ----------
 
+    /// model_configs DB roundtrip + 老库空兼容(F-01 阶段1)
+    #[tokio::test]
+    async fn ai_provider_model_configs_roundtrip_and_old_db_compat() {
+        let db = Database::open_in_memory().await.expect("open_in_memory");
+        let repo = AiProviderRepo::new(&db);
+        use crate::models::AiProviderRecord;
+        use df_ai_core::model::{Capability, IntelligenceTier, Modality, ModelConfig};
+
+        // 新格式:带多模型 + 多维度配置
+        let configs = vec![
+            ModelConfig::with_defaults("glm-4-flash"),
+            ModelConfig {
+                model_id: "glm-4v".into(),
+                modalities: vec![Modality::Text, Modality::Vision],
+                capabilities: vec![Capability::ToolUse],
+                intelligence: IntelligenceTier::Plus,
+                weight: 70,
+                context_window: 128_000,
+                ..ModelConfig::with_defaults("glm-4v")
+            },
+        ];
+        let rec = AiProviderRecord {
+            id: "p1".into(),
+            name: "测试".into(),
+            provider_type: "openai_compat".into(),
+            api_key: String::new(),
+            base_url: "https://x".into(),
+            default_model: "glm-4-flash".into(),
+            models: None,
+            model_configs: configs.clone(),
+            is_default: false,
+            config: None,
+            created_at: "0".into(),
+            updated_at: "0".into(),
+        };
+        repo.insert(rec).await.expect("insert");
+
+        let got = repo.get_by_id("p1").await.expect("get").expect("row exists");
+        assert_eq!(got.model_configs.len(), 2);
+        assert_eq!(got.model_configs[0].model_id, "glm-4-flash");
+        assert_eq!(got.model_configs[1].model_id, "glm-4v");
+        assert_eq!(got.model_configs[1].intelligence, IntelligenceTier::Plus);
+        assert_eq!(got.model_configs[1].context_window, 128_000);
+
+        // 老库空兼容:直接写 model_configs=NULL 的行(模拟 V18 之前的老库行)
+        // 然后 from_row 应得空 Vec
+        {
+            let conn = db.conn();
+            let g = conn.lock().await;
+            g.execute(
+                "INSERT OR REPLACE INTO ai_providers \
+                 (id,name,provider_type,api_key,base_url,default_model,models,model_configs,is_default,config,created_at,updated_at) \
+                 VALUES ('old','','openai_compat','','','','{}',NULL,0,NULL,'0','0')",
+                [],
+            )
+            .expect("raw insert old row");
+        }
+        let old = repo.get_by_id("old").await.expect("get").expect("old row");
+        assert!(old.model_configs.is_empty(), "NULL 列应得空 Vec");
+
+        // 老格式字符串数组 JSON(向后兼容 deserialize_model_configs)
+        {
+            let conn = db.conn();
+            let g = conn.lock().await;
+            g.execute(
+                "INSERT OR REPLACE INTO ai_providers \
+                 (id,name,provider_type,api_key,base_url,default_model,models,model_configs,is_default,config,created_at,updated_at) \
+                 VALUES ('legacy','','openai_compat','','','','{}','[\"glm-4-flash\",\"glm-4v\"]',0,NULL,'0','0')",
+                [],
+            )
+            .expect("raw insert legacy row");
+        }
+        let legacy = repo.get_by_id("legacy").await.expect("get").expect("legacy row");
+        assert_eq!(legacy.model_configs.len(), 2, "老字符串数组应转 2 个默认 ModelConfig");
+        assert_eq!(legacy.model_configs[0].model_id, "glm-4-flash");
+    }
+
     /// KNOWLEDGE_COLS 列数须等于 KNOWLEDGE_COL_COUNT(任一处漂移:加列漏改 / 串错位 → 立即失败)。
     /// `knowledge_from_row` 按 name 取列,SELECT 漏列会在运行时被 rusqlite 报错;此断言提前到测试期捕获。
     #[test]

crates/df-storage/src/migrations.rs

@@ -34,7 +34,7 @@ pub fn run(conn: &Connection) -> Result<()> {
 
     // 迁移步骤链: 顺序执行,跳过已应用的版本(current_version < N 才跑)。
     // 新增版本时,在此数组追加一项 (N, migrate_vN) 即可,无需改逻辑。
-    let steps: [(i32, fn(&Connection) -> Result<()>); 17] = [
+    let steps: [(i32, fn(&Connection) -> Result<()>); 18] = [
         (1, migrate_v1),
         (2, migrate_v2),
         (3, migrate_v3),
@@ -52,6 +52,7 @@ pub fn run(conn: &Connection) -> Result<()> {
         (15, migrate_v15),
         (16, migrate_v16),
         (17, migrate_v17),
+        (18, migrate_v18),
     ];
 
     for (version, migrate_fn) in steps {
@@ -312,6 +313,21 @@ fn migrate_v17(conn: &Connection) -> Result<()> {
     Ok(())
 }
 
+/// V18: 幂等补 ai_providers.model_configs 列(模型能力配置,F-01 阶段1)
+///
+/// 模型 4 维度(模态/能力/价格/智力)+ 路由控制配置 JSON 字符串。TEXT NULL 向后兼容:
+/// 老库行默认 NULL,from_row 经 deserialize_model_configs 解析为空 Vec(配合 default_model 过渡)。
+/// 用 PRAGMA 探测列存在性,缺失才 ALTER(同 v4/v5/v6/v8/v10/v11/v14/v15/v16/v17 模式),对新库/老库均安全。
+fn migrate_v18(conn: &Connection) -> Result<()> {
+    if !column_exists(conn, "ai_providers", "model_configs") {
+        conn.execute("ALTER TABLE ai_providers ADD COLUMN model_configs TEXT", [])?;
+        tracing::info!("v18: 补建 ai_providers.model_configs 列(模型能力配置)");
+    }
+    conn.execute("INSERT INTO schema_version (version) VALUES (?)", [18])?;
+    tracing::info!("迁移 v18 完成");
+    Ok(())
+}
+
 /// V1 建表 SQL
 const V1_SQL: &str = "
 -- 想法表

crates/df-storage/src/models.rs

@@ -1,5 +1,6 @@
 //! 数据模型定义 — 与数据库表对应的 Rust 结构体
 
+use df_ai_core::model::{deserialize_model_configs, ModelConfig};
 use serde::{Deserialize, Serialize};
 
 // ============================================================
@@ -152,6 +153,11 @@ pub struct AiProviderRecord {
     pub base_url: String,
     pub default_model: String,
     pub models: Option<String>,       // JSON array of model names
+    /// 模型能力配置数组(F-01 阶段1,4 维度 + 路由控制)。
+    /// DB TEXT 列存 JSON 字符串,from_row 经 deserialize_model_configs 解析。
+    /// 向后兼容:老库 NULL/空/老字符串数组 → 空 Vec 或转默认 ModelConfig。default_model 保留过渡。
+    #[serde(default, deserialize_with = "deserialize_model_configs")]
+    pub model_configs: Vec<ModelConfig>,
     pub is_default: bool,
     pub config: Option<String>,       // JSON extra config
     pub created_at: String,

crates/df-storage/src/secret.rs

@@ -209,7 +209,7 @@ mod tests {
         let rec = AiProviderRecord {
             id: "t1".into(), name: "t".into(), provider_type: "openai_compat".into(),
             api_key: "sk-db-fallback".into(), base_url: "https://x".into(),
-            default_model: "m".into(), models: None, is_default: false,
+            default_model: "m".into(), models: None, model_configs: Vec::new(), is_default: false,
             config: None, created_at: "0".into(), updated_at: "0".into(),
         };
         assert_eq!(resolve_provider_secret(&rec), "sk-db-fallback");