fix: address project review bugs

2026-06-12 17:25:30 +08:00
parent f9e55578b3
commit ad4bca31b1
11 changed files with 209 additions and 124 deletions
@@ -1,8 +1,5 @@
 import { buildApiUrl, buildAuthHeaders } from "./serverConnection";

-const TEXT_MODELS = ["qwen-max", "qwen-plus", "qwen-turbo"];
-const VISION_MODELS = ["qwen3.7-plus", "qwen-vl-plus", "qwen-vl-max"];
-
 type AbortSignalConstructorWithAny = typeof AbortSignal & {
  any?: (signals: AbortSignal[]) => AbortSignal;
 };
@@ -110,11 +107,45 @@ export interface ComplianceCheck {
  allow_video_generation: boolean;
 }

+function findJsonSlice(raw: string): string {
+  const start = raw.search(/[\[{]/);
+  if (start < 0) return raw;
+
+  const stack: string[] = [];
+  let inString = false;
+  let escaped = false;
+
+  for (let index = start; index < raw.length; index += 1) {
+    const char = raw[index];
+
+    if (inString) {
+      if (escaped) {
+        escaped = false;
+      } else if (char === "\\") {
+        escaped = true;
+      } else if (char === "\"") {
+        inString = false;
+      }
+      continue;
+    }
+
+    if (char === "\"") {
+      inString = true;
+    } else if (char === "{" || char === "[") {
+      stack.push(char === "{" ? "}" : "]");
+    } else if (char === "}" || char === "]") {
+      if (stack.pop() !== char) break;
+      if (stack.length === 0) return raw.slice(start, index + 1);
+    }
+  }
+
+  return raw.slice(start);
+}
+
 function extractJson(text: string): unknown {
  const fenced = text.match(/```(?:json)?\s*([\s\S]*?)```/);
  const raw = fenced ? fenced[1].trim() : text.trim();
-  const start = raw.search(/[[{]/);
-  const slice = start >= 0 ? raw.slice(start) : raw;
+  const slice = findJsonSlice(raw);
  try {
    return JSON.parse(slice);
  } catch {
@@ -122,9 +153,16 @@ function extractJson(text: string): unknown {
  }
 }

+type ChatContent =
+  | string
+  | Array<
+      | { type: "image_url"; image_url: { url: string } }
+      | { type: "text"; text: string }
+    >;
+
 interface ChatMessage {
  role: "system" | "user";
-  content: string;
+  content: ChatContent;
 }

 const MAX_RETRIES = 3;
@@ -171,43 +209,32 @@ async function chat(
  userContent: string,
  options?: { model?: string; signal?: AbortSignal },
 ): Promise<string> {
-  const candidateModels = options?.model ? [options.model] : TEXT_MODELS;
-  let lastError: Error | null = null;
+  return retryOnTransient(async () => {
+    const messages: ChatMessage[] = [
+      { role: "system", content: systemPrompt },
+      { role: "user", content: userContent },
+    ];
+    const timeoutSignal = AbortSignal.timeout(CHAT_TIMEOUT_MS);
+    const combinedSignal = combineAbortSignals(options?.signal, timeoutSignal);
+    const body: Record<string, unknown> = { messages, stream: false, temperature: 0.4 };
+    if (options?.model) body.model = options.model;

-  for (const model of candidateModels) {
-    try {
-      return await retryOnTransient(async () => {
-        const messages: ChatMessage[] = [
-          { role: "system", content: systemPrompt },
-          { role: "user", content: userContent },
-        ];
-        const timeoutSignal = AbortSignal.timeout(CHAT_TIMEOUT_MS);
-        const combinedSignal = combineAbortSignals(options?.signal, timeoutSignal);
-        const res = await fetch(buildApiUrl("ai/chat"), {
-          method: "POST",
-          headers: buildAuthHeaders(),
-          body: JSON.stringify({ model, messages, stream: false, temperature: 0.4 }),
-          signal: combinedSignal,
-        });
-        if (!res.ok) {
-          const errBody = await res.text().catch(() => "");
-          throw new Error(`AI 调用失败 (${res.status})${errBody ? `: ${errBody.slice(0, 120)}` : ""}`);
-        }
-        const payload = await res.json();
-        const content: string =
-          payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
-        if (!content) throw new Error("模型未返回有效内容");
-        return content;
-      }, options?.signal);
-    } catch (err) {
-      lastError = err instanceof Error ? err : new Error(String(err));
-      if (options?.signal?.aborted) throw lastError;
-      // If user pinned a specific model, don't fall back to others
-      if (options?.model) throw lastError;
-      // Try next model in fallback chain
+    const res = await fetch(buildApiUrl("ai/chat"), {
+      method: "POST",
+      headers: buildAuthHeaders(),
+      body: JSON.stringify(body),
+      signal: combinedSignal,
+    });
+    if (!res.ok) {
+      const errBody = await res.text().catch(() => "");
+      throw new Error(`AI 调用失败 (${res.status})${errBody ? `: ${errBody.slice(0, 120)}` : ""}`);
    }
-  }
-  throw lastError ?? new Error("所有候选模型均不可用");
+    const payload = await res.json();
+    const content: string =
+      payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
+    if (!content) throw new Error("模型未返回有效内容");
+    return content;
+  }, options?.signal);
 }

 async function visionChat(
@@ -216,50 +243,36 @@ async function visionChat(
  imageUrls: string[],
  signal?: AbortSignal,
 ): Promise<string> {
-  const content = [
-    ...imageUrls.map((url) => ({ type: "image_url", image_url: { url } })),
+  const content: ChatContent = [
+    ...imageUrls.map((url) => ({ type: "image_url" as const, image_url: { url } })),
    { type: "text", text },
  ];
  const messages = [
    { role: "system", content: systemPrompt },
    { role: "user", content },
-  ];
+  ] satisfies ChatMessage[];

-  let lastError: Error | null = null;
-  for (const model of VISION_MODELS) {
+  return retryOnTransient(async () => {
    const timeoutSignal = AbortSignal.timeout(CHAT_TIMEOUT_MS);
    const combinedSignal = combineAbortSignals(signal, timeoutSignal);
-    try {
-      const out = await retryOnTransient(async () => {
-        const res = await fetch(buildApiUrl("ai/chat"), {
-          method: "POST",
-          headers: buildAuthHeaders(),
-          body: JSON.stringify({ model, messages, stream: false, temperature: 0.3 }),
-          signal: combinedSignal,
-        });
-        if (!res.ok) {
-          const errBody = await res.text().catch(() => "");
-          if (errBody.includes("image format")) throw new Error("IMAGE_FORMAT_FALLBACK");
-          throw new Error(`图片理解调用失败 (${res.status})${errBody ? `: ${errBody.slice(0, 120)}` : ""}`);
-        }
-        const payload = await res.json();
-        const result: string =
-          payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
-        if (!result) throw new Error("图片理解未返回有效内容");
-        return result;
-      }, signal);
-      return out;
-    } catch (err) {
-      lastError = err instanceof Error ? err : new Error(String(err));
-      if (signal?.aborted) throw lastError;
-      // Continue trying next vision model on transient failures, image format errors, or upstream errors
-      if (lastError.message === "IMAGE_FORMAT_FALLBACK") continue;
-      if (lastError.message.includes("图片理解调用失败")) continue;
-      if (isTransientError(lastError)) continue;
-      throw lastError;
+
+    const res = await fetch(buildApiUrl("ai/chat"), {
+      method: "POST",
+      headers: buildAuthHeaders(),
+      body: JSON.stringify({ messages, stream: false, temperature: 0.3 }),
+      signal: combinedSignal,
+    });
+    if (!res.ok) {
+      const errBody = await res.text().catch(() => "");
+      if (errBody.includes("image format")) throw new Error("图片格式不受支持，请更换图片后重试");
+      throw new Error(`图片理解调用失败 (${res.status})${errBody ? `: ${errBody.slice(0, 120)}` : ""}`);
    }
-  }
-  throw lastError ?? new Error("图片理解调用失败，所有模型均不可用");
+    const payload = await res.json();
+    const result: string =
+      payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
+    if (!result) throw new Error("图片理解未返回有效内容");
+    return result;
+  }, signal);
 }

 const IMAGE_UNDERSTANDING_PROMPT = `你是电商产品图片分析专家。请分析用户提供的产品图片，识别产品主体、外观、颜色、材质、形状、尺寸感、品牌标识、关键部件、可视化卖点和适合展示的镜头角度。请用简洁的中文段落描述，不要编造图片中看不到的信息。`;
@@ -12,7 +12,7 @@ import type { WebGenerationPreviewTask } from "../types";
 export interface ImageGenInput {
  projectId?: string;
  conversationId?: number;
-  model: string;
+  model?: string;
  prompt: string;
  ratio?: string;
  quality?: string;
@@ -210,18 +210,18 @@ function getStoredSessionRole(): string {
 }

 function emitImageRouteDebug(label: string, payload: Record<string, unknown>): void {
-  // Only emit console logs for admin users — hides enterprise routing details
-  if (getStoredSessionRole() === "admin") {
-    const entry: ImageRouteDebugEntry = {
-      at: new Date().toISOString(),
-      label,
-      ...payload,
-    };
-    try {
-      console.log(`${label} ${JSON.stringify(entry)}`);
-    } catch {
-      console.log(label, entry);
-    }
+  // Only emit route debug for admin users; provider routing is operational data.
+  if (getStoredSessionRole() !== "admin") return;
+
+  const entry: ImageRouteDebugEntry = {
+    at: new Date().toISOString(),
+    label,
+    ...payload,
+  };
+  try {
+    console.log(`${label} ${JSON.stringify(entry)}`);
+  } catch {
+    console.log(label, entry);
  }

  if (typeof window === "undefined") return;
@@ -229,7 +229,6 @@ function emitImageRouteDebug(label: string, payload: Record<string, unknown>): v
  const previousEntries = Array.isArray(debugWindow.__OMNIAI_IMAGE_ROUTE_DEBUG__)
    ? debugWindow.__OMNIAI_IMAGE_ROUTE_DEBUG__
    : [];
-  const entry: ImageRouteDebugEntry = { at: new Date().toISOString(), label, ...payload };
  debugWindow.__OMNIAI_IMAGE_ROUTE_DEBUG__ = [...previousEntries.slice(-19), entry];
 }

@@ -161,7 +161,11 @@ export function clearAllUserStorage(): void {
      "omniai-web-profile-ui",
      "omniai:more-recent-tools",
      "omniai:generation-queue",
+      "omniai:generation-records.pending",
+      "omniai:ecommerce-video-workspace",
      "omniai-canvas-saved-assets",
+      "omniai.clone-ai.",
+      "omniai.ecommerce.",
    ];
    for (let i = window.localStorage.length - 1; i >= 0; i--) {
      const key = window.localStorage.key(i);
@@ -50,7 +50,6 @@ export const webGenerationGateway = {
        const result = await aiGenerationClient.createImageTask({
          projectId: params?.projectId,
          conversationId: params?.conversationId,
-          model: "gpt-image-2",
          prompt,
          ratio: params?.ratio || "16:9",
          quality: params?.quality || "1K",