fix: harden AI analysis provider responses

modules/ai/server/analysis.ts

@@ -30,6 +30,7 @@ function createChatModel(config: { baseUrl: string; apiKey: string; chatModel: s
   return new ChatOpenAI({
     apiKey: config.apiKey,
     model: config.chatModel,
+    maxTokens: 1800,
     temperature: 0.2,
     configuration,
   });
@@ -241,6 +242,7 @@ export async function generateElderAiAnalysis(
     "输出 JSON 字段：overallRiskLevel(low|medium|high|critical|unknown), summary, keyFindings, recommendations, dataGaps, citations, confidence, modelSummary。",
     "keyFindings[] 字段：category, severity(info|warning|critical), evidence, citationIds。",
     "recommendations[] 字段：title, priority(low|normal|high|urgent), rationale, suggestedNextStep, citationIds。",
+    "citations[] 只返回你实际引用过的 citation 对象，citationIds 必须来自可用 citations。",
     knowledgeUnavailableReason ? `知识库检索不可用：${knowledgeUnavailableReason}。请在 dataGaps 中包含“知识库检索不可用，分析未使用内部知识库”。` : "",
     `modelSummary 固定为 {"provider":"openai-compatible","chatModel":"${runtimeConfig.config.chatModel}","embeddingModel":"${runtimeConfig.config.embeddingModel}"}。`,
     `可用 citations：${JSON.stringify(citations)}`,
@@ -250,7 +252,9 @@ export async function generateElderAiAnalysis(
 
   let rawOutput: unknown;
   try {
-    const response = await model.invoke(prompt);
+    const response = await model.invoke(prompt, {
+      response_format: { type: "json_object" },
+    });
     const content = Array.isArray(response.content)
       ? response.content.map((item) => (typeof item === "string" ? item : "")).join("\n")
       : String(response.content);

modules/ai/server/knowledge.ts

@@ -125,8 +125,12 @@ async function buildKnowledgeChunks(input: KnowledgeEntryInput): Promise<Service
   }
 
   const chunks = chunkKnowledgeText(input);
-  const vectors = await embeddingsResult.embeddings.embedDocuments(chunks);
-  return { success: true, data: { chunks, vectors } };
+  try {
+    const vectors = await embeddingsResult.embeddings.embedDocuments(chunks);
+    return { success: true, data: { chunks, vectors } };
+  } catch {
+    return { success: false, reason: "知识库向量生成失败", status: 500 };
+  }
 }
 
 export async function listKnowledgeEntries(context: AuthContext): Promise<KnowledgeEntry[]> {
@@ -214,15 +218,15 @@ async function updateEntryWithChunks(
     const rows = await transaction
       .update(aiKnowledgeEntries)
       .set({
-      scope: input.scope,
-      organizationId,
-      title: input.title,
-      category: input.category,
-      tags: input.tags,
-      body: input.body,
-      status: input.status,
-      updatedByAccountId: context.account.id,
-      updatedAt: new Date(),
+        scope: input.scope,
+        organizationId,
+        title: input.title,
+        category: input.category,
+        tags: input.tags,
+        body: input.body,
+        status: input.status,
+        updatedByAccountId: context.account.id,
+        updatedAt: new Date(),
       })
       .where(eq(aiKnowledgeEntries.id, id))
       .returning();
@@ -322,7 +326,8 @@ export async function retrieveKnowledge(
     return { success: false, reason: embeddingsResult.reason, status: 500 };
   }
 
-  const [embedding] = await embeddingsResult.embeddings.embedDocuments([query]);
+  const vectors = await embeddingsResult.embeddings.embedDocuments([query]).catch(() => []);
+  const embedding = vectors[0];
   if (!embedding) {
     return { success: false, reason: "知识库向量生成失败", status: 500 };
   }