350 lines
12 KiB
Rust
350 lines
12 KiB
Rust
//! 灵感相关命令
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use std::sync::Arc;
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use serde::Deserialize;
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use tauri::State;
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use df_ai::provider::LlmProvider;
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use df_types::types::{new_id, Priority};
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use df_ideas::capture::Idea;
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use df_storage::models::{IdeaRecord, ProjectRecord};
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use crate::state::AppState;
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use super::{err_str, now_millis};
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/// 创建灵感入参
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#[derive(Debug, Deserialize)]
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pub struct CreateIdeaInput {
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pub title: String,
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#[serde(default)]
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pub description: String,
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#[serde(default = "default_priority")]
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pub priority: i32,
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/// 标签 JSON 数组字符串
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pub tags: Option<String>,
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pub source: Option<String>,
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}
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fn default_priority() -> i32 {
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1
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}
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/// 列出灵感，可选按 status 过滤（指定状态走 query 走白名单索引列，否则全量）
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#[tauri::command]
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pub async fn list_ideas(
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state: State<'_, AppState>,
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status: Option<String>,
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) -> Result<Vec<IdeaRecord>, String> {
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match status {
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Some(s) => state.ideas.query("status", &s).await.map_err(err_str),
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None => state.ideas.list_all().await.map_err(err_str),
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}
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}
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/// 创建灵感，返回完整记录
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#[tauri::command]
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pub async fn create_idea(
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state: State<'_, AppState>,
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input: CreateIdeaInput,
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) -> Result<IdeaRecord, String> {
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let now = now_millis();
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let record = IdeaRecord {
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id: new_id(),
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title: input.title,
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description: input.description,
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status: "draft".to_string(),
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priority: input.priority,
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score: None,
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tags: input.tags,
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source: input.source,
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promoted_to: None,
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ai_analysis: None,
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scores: None,
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created_at: now.clone(),
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updated_at: now,
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};
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state
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.ideas
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.insert(record.clone())
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.await
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.map_err(err_str)?;
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Ok(record)
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}
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/// 更新灵感单个字段（字段名走 df-storage 白名单校验）
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#[tauri::command]
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pub async fn update_idea(
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state: State<'_, AppState>,
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id: String,
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field: String,
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value: String,
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) -> Result<bool, String> {
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state
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.ideas
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.update_field(&id, &field, &value)
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.await
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.map_err(err_str)
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}
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/// 删除灵感
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#[tauri::command]
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pub async fn delete_idea(state: State<'_, AppState>, id: String) -> Result<bool, String> {
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state.ideas.delete(&id).await.map_err(err_str)
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}
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/// 将灵感晋升为项目 — 复用 df-project 领域逻辑创建项目，回写灵感 status=promoted/promoted_to
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#[tauri::command]
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pub async fn promote_idea(
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state: State<'_, AppState>,
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id: String,
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) -> Result<df_ideas::promotion::PromotionResult, String> {
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let record = state
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.ideas
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.get_by_id(&id)
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.await
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.map_err(err_str)?
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.ok_or_else(|| format!("灵感不存在: {id}"))?;
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if let Some(promoted_to) = &record.promoted_to {
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return Err(format!("灵感已立项: {}", promoted_to));
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}
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// 复用 df-project 领域逻辑构造项目实体（create_from_idea）
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let project = df_project::manager::ProjectManager::create_from_idea(
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record.title.clone(),
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record.description.clone(),
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id.clone(),
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);
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let project_id = project.id.clone();
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let now = now_millis();
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let project_record = ProjectRecord {
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id: project_id.clone(),
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name: project.name,
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description: project.description,
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status: "planning".to_string(),
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idea_id: Some(id.clone()),
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path: None,
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stack: None,
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created_at: now.clone(),
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updated_at: now.clone(),
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};
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state
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.projects
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.insert(project_record)
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.await
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.map_err(err_str)?;
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// 回写灵感：status=promoted + promoted_to（update_full 单事务覆盖可变字段）
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// 补偿删除:第二步失败时回滚第一步已建的 project,保证最终一致性(非原子,但防项目存留而
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// 灵感状态未变的数据不一致)。Repository 方法各自持锁不支持跨 repo 共享事务对象,故选补偿
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// 删除而非真事务(改动最小,工程投入产出比最高)。
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let updated = IdeaRecord {
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status: "promoted".to_string(),
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promoted_to: Some(project_id.clone()),
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updated_at: now,
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..record
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};
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if let Err(e) = state.ideas.update_full(&updated).await {
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// 回写失败:补偿删除已建项目,避免悬空项目(idea.promoted_to 仍空,可重试立项)
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tracing::error!("灵感 {id} 回写失败,补偿删除已建项目 {project_id}: {e}");
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if let Err(del_err) = state.projects.purge_with_descendants(&project_id).await {
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tracing::error!("补偿删除项目 {project_id} 也失败(需人工清理): {del_err}");
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}
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return Err(format!("灵感立项回写失败(已回滚项目创建): {}", e));
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}
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Ok(df_ideas::promotion::PromotionResult {
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idea_id: id,
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project_id: project_id,
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promoted: true,
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reason: "手动立项".to_string(),
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})
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}
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// ============================================================
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// 灵感评估 — 多维评分 + 对抗式评估
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// ============================================================
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/// 评估灵感：多维评分 + 对抗式评估，结果写回 scores/score/ai_analysis，状态置 pending_review，返回更新后的记录
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#[tauri::command]
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pub async fn evaluate_idea(
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state: State<'_, AppState>,
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id: String,
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) -> Result<IdeaRecord, String> {
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// 取出灵感
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let record = state
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.ideas
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.get_by_id(&id)
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.await
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.map_err(err_str)?
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.ok_or_else(|| format!("灵感不存在: {id}"))?;
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let idea = record_to_idea(&record);
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// 多维评分（0-10，IPC 层 *10 缩放为 0-100）
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let scores = df_ideas::scoring::ScoringEngine::compute_default(&idea);
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// 对抗式评估（构造注入：从 DB 读默认 provider 装配 LLM，无 provider/构造失败 → 启发式兜底）
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// F-01 阶段5: 透传 model_configs 池,evaluate_with_llm 经路由选模型(池空兜底 default_model)。
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let provider = build_default_provider(&state).await;
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let engine = match provider {
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Some((p, pool)) => {
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df_ideas::adversarial::AdversarialEngine::with_pool(Arc::from(p), pool)
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}
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None => df_ideas::adversarial::AdversarialEngine::heuristic(),
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};
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let eval = engine.evaluate(&idea).await.map_err(err_str)?;
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// 组装前端扁平结构（与 Ideas.vue 的 AdversarialEval interface 对齐）
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let positive_strength = eval.positive.confidence;
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let negative_strength = eval.negative.confidence;
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let net_sentiment = positive_strength - negative_strength;
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let recommendation = recommendation_str(&eval.recommendation).to_string();
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let final_score = eval.final_score;
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let analyst_summary = eval.analyst.summary.clone();
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let action_items = action_items_for(&eval.recommendation);
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let positive = serde_json::json!({
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"thesis": eval.positive.thesis,
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"evidence": eval.positive.evidence,
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});
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let negative = serde_json::json!({
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"thesis": eval.negative.thesis,
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"evidence": eval.negative.evidence,
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});
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let ai_analysis = serde_json::json!({
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"positive_strength": positive_strength,
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"negative_strength": negative_strength,
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"net_sentiment": net_sentiment,
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"recommendation": recommendation,
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"final_score": final_score,
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"summary": analyst_summary,
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"action_items": action_items,
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"positive": positive,
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"negative": negative,
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"analyst": { "summary": analyst_summary },
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})
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.to_string();
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// scores JSON：中文维度 key + 0-100 值（前端雷达图直接当百分比用）
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let scores_json = serde_json::json!({
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"可行性": (scores.feasibility * 10.0).round() as i64,
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"影响力": (scores.impact * 10.0).round() as i64,
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"紧急度": (scores.urgency * 10.0).round() as i64,
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"综合": (scores.overall * 10.0).round() as i64,
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})
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.to_string();
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let score_value = (scores.overall * 10.0).round() as i64;
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// 构造完整记录后单次原子写回（update_full 保留 id 与 created_at）
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let updated = IdeaRecord {
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scores: Some(scores_json),
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ai_analysis: Some(ai_analysis),
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score: Some(score_value as f64),
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status: "pending_review".to_string(),
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updated_at: now_millis(),
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..record
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};
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state
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.ideas
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.update_full(&updated)
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.await
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.map_err(err_str)?;
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Ok(updated)
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}
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/// 从 DB 读取默认 provider 配置（is_default 优先，否则首个）+ build_provider 构造实例。
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///
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/// 返回 `None` 的两种情况（统一走启发式评估兜底）：
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/// - DB 未配置任何 provider（`list_all` 空或全无 is_default 且无首条）
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/// - provider 密钥不可用（keyring 无记录 / 纯空白），`build_provider_for` 返 Err
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///
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/// 复用 `commands::ai::secret::build_provider_for`（resolve→ensure→build 三步），
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/// 与 AI Chat / 项目扫描的 provider 构造路径统一（FR-S1 密钥解析一致）。
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///
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/// 返回 (provider, model_pool):model_pool = 选中 provider 的 model_configs(F-01 阶段5,
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/// 供对抗评估路由)。池空(用户未拉取)→ 调用方兜底 default_model。
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async fn build_default_provider(
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state: &State<'_, AppState>,
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) -> Option<(Box<dyn LlmProvider>, Vec<df_ai::df_ai_core::model::ModelConfig>)> {
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let providers = state.ai_providers.list_all().await.ok()?;
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let pc = providers
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.iter()
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.find(|p| p.is_default)
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.cloned()
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.or_else(|| providers.into_iter().next())?;
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match crate::commands::ai::secret::build_provider_for(&pc) {
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Ok(p) => Some((p, pc.model_configs.clone())),
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Err(e) => {
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// 密钥不可用:启发式兜底,不阻断评估（与 evaluate_idea LLM 失败降级语义一致）
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tracing::warn!("默认 provider 密钥不可用,对抗评估走启发式: {e}");
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None
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}
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}
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}
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/// IdeaRecord → df_ideas::Idea（评估用，status/time 不影响评分）
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fn record_to_idea(record: &IdeaRecord) -> Idea {
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let tags: Vec<String> = match record.tags.as_deref() {
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Some(t) => match serde_json::from_str::<Vec<String>>(t) {
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Ok(v) => v,
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Err(e) => {
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tracing::warn!(error = %e, idea_id = %record.id, "[ideas] tags JSON 解析失败,降级空 tags 继续评估");
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Vec::new()
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}
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},
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None => Vec::new(),
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};
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Idea {
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id: record.id.clone(),
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title: record.title.clone(),
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description: record.description.clone(),
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status: df_types::types::IdeaStatus::Draft,
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priority: priority_from_i32(record.priority),
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scores: None,
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tags,
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source: record.source.clone(),
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related_ids: Vec::new(),
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created_at: chrono::Utc::now(),
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updated_at: chrono::Utc::now(),
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}
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}
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/// i32 优先级 → Priority 枚举（与 df-types 枚举值一致：Low=0/Medium=1/High=2/Critical=3）
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fn priority_from_i32(p: i32) -> Priority {
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match p {
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0 => Priority::Low,
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2 => Priority::High,
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x if x >= 3 => Priority::Critical,
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_ => Priority::Medium,
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}
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}
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/// Recommendation → 前端 assessmentLabel 期望的全小写空格分隔（匹配 map key）
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fn recommendation_str(r: &df_ideas::adversarial::Recommendation) -> &'static str {
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use df_ideas::adversarial::Recommendation::*;
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match r {
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ImmediateAction => "immediate action",
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Soon => "soon",
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WithResources => "with resources",
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ResearchMore => "research more",
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Monitor => "monitor",
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}
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}
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/// 行动建议 — 按推荐等级返回
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fn action_items_for(r: &df_ideas::adversarial::Recommendation) -> Vec<String> {
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use df_ideas::adversarial::Recommendation::*;
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match r {
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ImmediateAction => vec!["立即组建项目团队".into(), "制定详细执行计划".into(), "分配必要资源".into()],
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Soon => vec!["下周启动项目".into(), "准备资源需求".into(), "制定时间表".into()],
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WithResources => vec!["确认资源预算".into(), "评估 ROI".into(), "制定风险预案".into()],
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ResearchMore => vec!["进行市场调研".into(), "收集用户反馈".into(), "验证技术可行性".into()],
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Monitor => vec!["持续跟踪相关指标".into(), "定期评估进展".into(), "等待更好时机".into()],
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}
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}
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