重构: ai_node.rs 拆分(836→423,抽 AiSelfReviewNode,REFACTOR-260619-06)
纯结构拆分,逻辑等价,Node trait/AiNode API 不变: - 抽 ai_self_review_node.rs(447,AiSelfReviewNode 结构体+Node impl+build_review_prompt, 符合 crate 一节点一文件约定:human_node/script_node/task_advance_node) - ai_node.rs 瘦身至 423 行(保留 AiNode+测试,params/self_review 纯逻辑已在 ai_node_helpers.rs) - 测试共置(9 自审测试移 ai_self_review_node::tests) - state.rs 路径引用更新(ai_node::AiSelfReviewNode → ai_self_review_node::AiSelfReviewNode) - 注册键 "ai_self_review" 字符串 + node_type() 不变 注:任务清单标 1107 行实为 836(标注有误),params/self_review 已在 ai_node_helpers(298) 自验: df-nodes 82 passed + workspace EXIT 0
This commit is contained in:
447
crates/df-nodes/src/ai_self_review_node.rs
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447
crates/df-nodes/src/ai_self_review_node.rs
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@@ -0,0 +1,447 @@
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//! AI 自审节点(决策 a 步骤③)— AiSelfReviewNode
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//!
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//! 从 ai_node.rs 拆出的独立节点(纯结构搬运,逻辑等价)。复用 AiNode 的 provider
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//! 调用链(resolve_and_parse + provider_from_params),差异在 prompt 模板/JSON 解析/
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//! verdict 闸门。provider 解析与自审 JSON 解析等纯逻辑见 `ai_node_helpers`。
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//!
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//! 节点类型注册 key:`ai_self_review`(state.rs build_registry)。
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//! 模板对齐:task_workflow_templates.rs testing 模板 `ai_self_review` 节点 + gate:true。
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use std::sync::Arc;
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use async_trait::async_trait;
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use df_ai::provider::{ChatMessage, CompletionRequest, LlmProvider};
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use df_storage::crud::TaskRepo;
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use df_storage::db::Database;
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use df_workflow::node::{Node, NodeContext, NodeOutput, NodeResult, NodeSchema};
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// 抽离的纯函数/类型(与 AiNode 共用)。
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use crate::ai_node_helpers::{
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gate_should_block, parse_review_json, provider_from_params, resolve_and_parse,
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REVIEW_SYSTEM_PROMPT,
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};
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// AiSelfReviewNode 节点 — AI 自审闭环(决策 a 步骤③)
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//
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// 复用 AiNode 的 provider 调用逻辑(parse_params + build_provider + complete),差异:
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// 1. prompt 固定四维度审查模板(需求符合度/完整性/正确性/边界),不从 config/inputs 取 prompt
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// 2. system_prompt 强约束「只输出 JSON」+ temperature=0
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// 3. LLM 输出按结构化 JSON 解析,失败兜底 verdict=unknown(防 LLM 不按要求输出)
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// 4. 自审输入:task.description(需求,经 ctx.config["task_id"] 读 task) + task.output_json.text(产出)
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// 5. 自审结果写回 task.output_json 加 review 子字段(不覆盖 text,保留对照)
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// 6. review 摘要塞 NodeOutput.data,供下游 human_review 经 DAG inputs["ai_self_review"] 读
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//
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// verdict 处理(首版保守):自审是辅助,最终决策权在人。verdict=fail 不主动 Err,
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// 节点返 Ok 让 human_review 继续,人在审批卡看到 fail 结论再定(human reject 走 Err→工作流
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// failed→②-4 回调退回 in_review)。
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/// AI 自审节点(决策 a 步骤③)
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pub struct AiSelfReviewNode {
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db: Arc<Database>,
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}
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impl AiSelfReviewNode {
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pub fn new(db: Arc<Database>) -> Self {
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Self { db }
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}
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/// 拼装自审 user prompt:任务需求 + 待审产出 + 四维度审查要求 + 输出格式。
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/// description / output_text 缺失时给占位(不报错,信任调用方注入合法 task_id)。
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fn build_review_prompt(description: &str, output_text: &str) -> String {
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format!(
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"\
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## 任务需求
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{description}
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## 待审产出
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{output_text}
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## 审查维度
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1. 需求符合度:产出是否覆盖需求描述的所有要点
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2. 产出完整性:是否有遗漏、未完成的部分
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3. 正确性:逻辑/事实/语法是否正确
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4. 边界处理:异常输入、空值、错误路径是否考虑
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## 输出格式(严格 JSON)
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{{\"verdict\":\"pass\"|\"fail\",\"dimensions\":{{\"requirement_fit\":{{\"score\":0-10,\"issues\":[\"...\"]}},\"completeness\":{{\"score\":0-10,\"issues\":[\"...\"]}},\"correctness\":{{\"score\":0-10,\"issues\":[\"...\"]}},\"boundary\":{{\"score\":0-10,\"issues\":[\"...\"]}}}},\"summary\":\"一句话总结\",\"suggestions\":[\"改进建议\"]}}
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verdict=fail 当且仅当任一维度 score<6 或有阻断性 issue。"
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)
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}
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}
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#[async_trait]
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impl Node for AiSelfReviewNode {
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async fn execute(&self, ctx: NodeContext) -> NodeResult {
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tracing::info!("AiSelfReviewNode 执行: node_id={}", ctx.node_id);
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// FR-S1 注入链：provider 经 df_storage::secret 在 AiNode 内存解析，api_key 不进 config。
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let p = resolve_and_parse(&self.db, &ctx.config, &ctx.inputs).await?;
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// ── 读任务(需求 + 产出) ──
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// task_id 必填(自审对象是具体任务的产出);缺失报错而非静默跳过。
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let task_id = ctx
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.config
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.get("task_id")
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.and_then(|v| v.as_str())
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.ok_or_else(|| anyhow::anyhow!("AiSelfReviewNode 缺少必填参数: task_id"))?;
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let repo = TaskRepo::new(&self.db);
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let task = repo
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.get_by_id(task_id)
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.await
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.map_err(|e| anyhow::anyhow!("自审读任务失败: {}", e))?
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.ok_or_else(|| anyhow::anyhow!("自审任务 {} 不存在", task_id))?;
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// output_json 可能无(ai_execute 未跑或失败) → 待审产出占位提示,不报错。
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let output_text = task
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.output_json
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.as_deref()
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.and_then(|s| serde_json::from_str::<serde_json::Value>(s).ok())
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.and_then(|v| v.get("text").and_then(|t| t.as_str()).map(String::from))
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.unwrap_or_else(|| "(无产出,仅依据需求审查)".to_string());
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let prompt = Self::build_review_prompt(&task.description, &output_text);
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let provider: Box<dyn LlmProvider> = provider_from_params(&p);
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let request = CompletionRequest {
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model: p.model.clone(),
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messages: vec![
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ChatMessage::system(REVIEW_SYSTEM_PROMPT),
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ChatMessage::user(prompt),
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],
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temperature: Some(0.0), // 强约束确定性输出
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max_tokens: p.max_tokens,
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stream: false,
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tools: None,
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tool_choice: None,
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reasoning_content: None,
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};
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tracing::info!(
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"AiSelfReviewNode 调用 LLM: model={}, base_url={}",
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p.provider.default_model,
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p.provider.base_url
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);
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let response = provider.complete(request).await?;
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// ── 解析自审 JSON(失败兜底 verdict=unknown) ──
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let review = parse_review_json(&response.text);
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let verdict = review
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.get("verdict")
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.and_then(|v| v.as_str())
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.unwrap_or("unknown")
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.to_string();
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// summary / suggestions 在 move review 前先取走(供 NodeOutput.data 平铺)
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let summary = review
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.get("summary")
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.and_then(|v| v.as_str())
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.unwrap_or("")
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.to_string();
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let suggestions = review
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.get("suggestions")
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.cloned()
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.unwrap_or(serde_json::json!([]));
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tracing::info!(
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"AiSelfReviewNode 完成: verdict={}, model={}",
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verdict,
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response.model
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);
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// ── 写回 task.output_json 加 review 子字段(不覆盖 text) ──
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// 读现有 output_json(若有),合并 review 子字段后整体写回。
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let mut output_obj: serde_json::Map<String, serde_json::Value> = task
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.output_json
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.as_deref()
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.and_then(|s| serde_json::from_str::<serde_json::Value>(s).ok())
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.and_then(|v| v.as_object().cloned())
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.unwrap_or_default();
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// review 子字段:自审结论 + reviewed_at + reviewer_model(便于前端展示与人对照)
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let mut review_with_meta = match review {
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serde_json::Value::Object(m) => m,
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other => {
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let mut m = serde_json::Map::new();
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m.insert("verdict".into(), other);
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m
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}
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};
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review_with_meta.insert(
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"reviewed_at".into(),
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serde_json::Value::Number(df_types::now_millis().into()),
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);
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review_with_meta.insert("reviewer_model".into(), serde_json::json!(response.model));
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output_obj.insert("review".into(), serde_json::Value::Object(review_with_meta));
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let merged = serde_json::Value::Object(output_obj);
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if let Ok(json_str) = serde_json::to_string(&merged) {
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if let Err(e) = repo.update_field(task_id, "output_json", &json_str).await {
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tracing::warn!(
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task_id = task_id,
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error = %e,
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"AiSelfReviewNode 落 review 子字段失败(摘要已在 NodeOutput,节点继续)"
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);
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}
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}
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// ── NodeOutput.data 塞 review 摘要,供下游 human_review 经 inputs["ai_self_review"] 读 ──
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// human_review(HumanNode)从 config/inputs 拼 description;此处把 verdict/summary/suggestions
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// 平铺进 data,HumanNode 零改动即可透传(前端/模板层可据此拼审批卡描述)。
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let output = NodeOutput::from_value(serde_json::json!({
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"verdict": verdict,
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"summary": summary,
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"suggestions": suggestions,
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"review": merged.get("review").cloned().unwrap_or(serde_json::Value::Null),
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"model": response.model,
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}));
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// ── 阶段3: 自审闸门(F-260616-07 决策 a 步骤③) ──
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// config["gate"]==true 时,AiSelfReviewNode 从「自审辅助」升级为「DAG 节点闸门」:
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// verdict="fail" → 返回 Err → executor first_err 中止后续层(下游 human_review 不跑)
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// → 工作流 failed → ②-4 回调退回 in_review(对齐工作流失败语义)
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// verdict="unknown" → 保持 Ok(LLM 输出不可靠时不阻断,人定权)
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// verdict="pass" → Ok 放行下游
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//
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// 落库在前(已 update_field output_json),故闸门 fail 时 task.output_json.review
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// 已含结论,前端/二次工作流可读自审详情(不丢失审查痕迹)。
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//
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// 设计选型(三方案对比,见 commit/设计文档):
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// A) AiNode 内部门控(本方案):1 处 return Err,复用 executor first_err + ②-4 回调,
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// 零 executor 核心改动,零依赖暂缓的条件引擎(T-260614-11)。最简。
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// B) DAG edges 条件 + ConditionEngine 求值:依赖 T-260614-11(暂缓),executor 当前
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// 完全不评估 edge.condition(topological_layers 无条件收录所有边),须先做条件
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// 引擎 Phase1+2 才能用 → 拆波。
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// C) executor 闸门检查钩子(节点 execute 后 executor 读 output.verdict):改 DagExecutor
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// 核心循环,牵动所有节点,风险/范围不符「最简不破坏」。
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// 选 A:语义最贴近「自审结果作为闸门」(自审节点自行决定放行/阻断),且 gate 可按节点
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// config 开关(默认 false = 阶段2 行为不变,模板/前端零强制改动,向后兼容)。
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let gate_enabled = ctx
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.config
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.get("gate")
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.and_then(|v| v.as_bool())
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.unwrap_or(false);
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if gate_should_block(gate_enabled, &verdict) {
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tracing::info!(
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node_id = %ctx.node_id,
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task_id,
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"AiSelfReviewNode 闸门触发: verdict=fail, 阻断下游(工作流将 failed)"
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);
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return Err(anyhow::anyhow!(
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"AI 自审闸门未通过(verdict=fail): {}",
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if summary.is_empty() { "自审结论 fail" } else { &summary }
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));
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}
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Ok(output)
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}
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fn schema(&self) -> NodeSchema {
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NodeSchema {
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params: serde_json::json!({
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"type": "object",
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"properties": {
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"task_id": { "type": "string", "description": "自审目标任务 ID(必填)" },
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"provider_id": { "type": "string", "description": "AI Provider ID(FR-S1:密钥经 secret 解析不进 config;留空走默认 provider)" },
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"model": { "type": "string", "description": "模型名(可选,留空用 record.default_model)" },
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"max_tokens": { "type": "integer" },
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"gate": { "type": "boolean", "description": "阶段3 闸门开关:false(默认)=自审辅助,verdict 仅透传展示;true=自审结果作 DAG 闸门,verdict=fail 返回 Err 阻断下游(工作流 failed → ②-4 退回),verdict=unknown/pass 放行" }
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},
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"required": ["task_id", "provider_id"]
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}),
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output: serde_json::json!({
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"type": "object",
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"properties": {
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"verdict": { "type": "string", "enum": ["pass", "fail", "unknown"] },
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"summary": { "type": "string" },
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"suggestions": { "type": "array", "items": { "type": "string" } },
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"review": { "type": "object" },
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"model": { "type": "string" }
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}
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}),
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}
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}
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fn node_type(&self) -> &str {
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"ai_self_review"
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::ai_node_helpers::{gate_should_block, parse_review_json, truncate_for_summary};
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use df_storage::crud::{ProjectRepo, TaskRepo};
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use df_storage::db::Database;
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use df_storage::models::{ProjectRecord, TaskRecord};
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use serde_json::json;
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// ============================================================
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// 决策 a 自审闭环单测 — output_json 写回 + review JSON 解析兜底
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// ============================================================
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/// 内存 DB 构造 helper:插父项目 + 指定 task,返回 (db, task_id)。
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async fn setup_task_db(task_output_json: Option<&str>) -> (Database, String) {
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let db = Database::open_in_memory().await.expect("open_in_memory");
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ProjectRepo::new(&db)
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.insert(ProjectRecord {
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id: "p1".to_string(),
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name: "proj".to_string(),
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description: "".to_string(),
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status: "planning".to_string(),
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idea_id: None,
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path: None,
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stack: None,
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created_at: "0".to_string(),
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updated_at: "0".to_string(),
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})
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.await
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.expect("insert project");
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TaskRepo::new(&db)
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.insert(TaskRecord {
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id: "t1".to_string(),
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project_id: "p1".to_string(),
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title: "t1".to_string(),
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description: "实现登录接口".to_string(),
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status: "testing".to_string(),
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priority: 2,
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branch_name: None,
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assignee: None,
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workflow_def_id: None,
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base_branch: None,
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review_rounds: 0,
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output_json: task_output_json.map(String::from),
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idea_id: None,
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created_at: "0".to_string(),
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updated_at: "0".to_string(),
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})
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.await
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.expect("insert task");
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(db, "t1".to_string())
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}
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/// 决策 a 步骤②:AiNode.execute 落产出验证 — 有 task_id 时 output_json 写入。
|
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/// 用 mock provider 避免真调 LLM:自定义 Node 替换不便,改为直接验证 update_field 路径
|
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/// (execute 内部调 provider.complete 需真 LLM,此处降级验证 parse_review_json 兜底 +
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/// 直接 repo.update_field 路径,与 execute 落库逻辑同一条 crud.update_field)。
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#[tokio::test]
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async fn update_field_writes_output_json() {
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// 验证 output_json 在 tasks 白名单(update_field 不报「不允许的字段名」)。
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// 这正是 AiNode.execute / AiSelfReviewNode.execute 落产出的底层路径。
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let (db, task_id) = setup_task_db(None).await;
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let repo = TaskRepo::new(&db);
|
||||
let payload = r#"{"text":"产出内容","model":"glm-4","usage":{}}"#;
|
||||
let ok = repo
|
||||
.update_field(&task_id, "output_json", payload)
|
||||
.await
|
||||
.expect("update_field output_json");
|
||||
assert!(ok, "update_field 应返回 affected>0=true");
|
||||
let after = repo.get_by_id(&task_id).await.unwrap().unwrap();
|
||||
assert_eq!(
|
||||
after.output_json.as_deref(),
|
||||
Some(payload),
|
||||
"output_json 应被写入"
|
||||
);
|
||||
// 原始 text 字段(产出对照)保留
|
||||
let parsed: serde_json::Value = serde_json::from_str(after.output_json.as_deref().unwrap()).unwrap();
|
||||
assert_eq!(parsed["text"], json!("产出内容"));
|
||||
}
|
||||
|
||||
/// 步骤③:parse_review_json 合规 JSON 直通(保留 verdict/dimensions/summary/suggestions)。
|
||||
#[test]
|
||||
fn parse_review_json_valid_passes_through() {
|
||||
let raw = r#"{"verdict":"pass","dimensions":{"requirement_fit":{"score":9,"issues":[]}},"summary":"符合需求","suggestions":["可加单测"]}"#;
|
||||
let v = parse_review_json(raw);
|
||||
assert_eq!(v["verdict"], json!("pass"));
|
||||
assert_eq!(v["summary"], json!("符合需求"));
|
||||
assert_eq!(v["suggestions"], json!(["可加单测"]));
|
||||
assert_eq!(v["dimensions"]["requirement_fit"]["score"], json!(9));
|
||||
}
|
||||
|
||||
/// 步骤③:parse_review_json 非 JSON / 缺 verdict → 兜底 verdict=unknown + summary 含原文。
|
||||
#[test]
|
||||
fn parse_review_json_invalid_falls_back_unknown() {
|
||||
// 非 JSON
|
||||
let v = parse_review_json("这不是 JSON,LLM 没按要求输出");
|
||||
assert_eq!(v["verdict"], json!("unknown"));
|
||||
assert!(v["summary"].as_str().unwrap().contains("不是 JSON"));
|
||||
assert!(v["summary"].as_str().unwrap().contains("解析失败"));
|
||||
|
||||
// JSON 但缺 verdict
|
||||
let v = parse_review_json(r#"{"foo":"bar"}"#);
|
||||
assert_eq!(v["verdict"], json!("unknown"));
|
||||
|
||||
// JSON 非 object(数组)
|
||||
let v = parse_review_json("[1,2,3]");
|
||||
assert_eq!(v["verdict"], json!("unknown"));
|
||||
}
|
||||
|
||||
/// 步骤③:LLM 偶尔包 ```json 代码块,parse_review_json 应剥离后解析成功。
|
||||
#[test]
|
||||
fn parse_review_json_strips_markdown_code_fence() {
|
||||
let raw = "```json\n{\"verdict\":\"fail\",\"summary\":\"缺边界处理\"}\n```";
|
||||
let v = parse_review_json(raw);
|
||||
assert_eq!(v["verdict"], json!("fail"));
|
||||
assert_eq!(v["summary"], json!("缺边界处理"));
|
||||
}
|
||||
|
||||
/// 步骤③:truncate_for_summary 长文截断。
|
||||
#[test]
|
||||
fn truncate_for_summary_long_text() {
|
||||
let short = "短文";
|
||||
assert_eq!(truncate_for_summary(short), "短文");
|
||||
let long: String = "字".repeat(500);
|
||||
let t = truncate_for_summary(&long);
|
||||
assert!(t.ends_with("…"));
|
||||
assert!(t.chars().count() <= 302, "截断后含省略号应 ≈300 字");
|
||||
}
|
||||
|
||||
/// 步骤③:AiSelfReviewNode.build_review_prompt 含需求 + 产出 + 四维度。
|
||||
#[test]
|
||||
fn build_review_prompt_contains_inputs() {
|
||||
let p = AiSelfReviewNode::build_review_prompt("登录接口", "已实现 /login");
|
||||
assert!(p.contains("登录接口"), "prompt 应含需求");
|
||||
assert!(p.contains("已实现 /login"), "prompt 应含产出");
|
||||
assert!(p.contains("需求符合度"), "prompt 应含四维度");
|
||||
assert!(p.contains("边界处理"));
|
||||
assert!(p.contains("verdict"), "prompt 应含输出格式约束");
|
||||
}
|
||||
|
||||
// ============================================================
|
||||
// 阶段3 自审闸门(gate_should_block)单测
|
||||
// ============================================================
|
||||
//
|
||||
// gate 决策矩阵:
|
||||
// gate=false(默认,阶段2 行为) → 任何 verdict 都放行(辅助模式)
|
||||
// gate=true + verdict=pass → 放行
|
||||
// gate=true + verdict=unknown → 放行(LLM 不可靠时不阻断,人定权)
|
||||
// gate=true + verdict=fail → 阻断(返回 Err,工作流 failed)
|
||||
|
||||
#[test]
|
||||
fn gate_disabled_never_blocks_any_verdict() {
|
||||
// gate 默认 false(阶段2 兼容):无论 verdict 如何都不阻断,自审仅辅助展示
|
||||
assert!(!gate_should_block(false, "fail"), "gate 关闭时 fail 也不阻断");
|
||||
assert!(!gate_should_block(false, "pass"), "gate 关闭时 pass 放行");
|
||||
assert!(!gate_should_block(false, "unknown"), "gate 关闭时 unknown 放行");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gate_enabled_blocks_only_on_fail() {
|
||||
// gate 开启:仅 fail 阻断
|
||||
assert!(gate_should_block(true, "fail"), "gate 开 + fail 应阻断");
|
||||
assert!(!gate_should_block(true, "pass"), "gate 开 + pass 放行");
|
||||
assert!(
|
||||
!gate_should_block(true, "unknown"),
|
||||
"gate 开 + unknown 放行(LLM 不可靠时保人定权)"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn gate_unknown_verdict_does_not_block() {
|
||||
// 防御:verdict 非标准值(如解析异常)不应触发阻断(对齐 unknown 保守不阻断语义)
|
||||
assert!(
|
||||
!gate_should_block(true, "weird"),
|
||||
"非标准 verdict 不应阻断(对齐 unknown 保守)"
|
||||
);
|
||||
assert!(!gate_should_block(true, ""), "空 verdict 不应阻断");
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user