feat: make RAG conditional on useCustomEmbed toggle; filter embed model list to only show embedding models

app/api/ai/models/route.js

@@ -3,7 +3,7 @@ import { NextResponse } from 'next/server';
 // 通用模型列表拉取 — 支持 OpenAI 兼容格式和 Gemini 原生格式
 export async function POST(request) {
     try {
-        const { apiKey, baseUrl, provider } = await request.json();
+        const { apiKey, baseUrl, provider, embedOnly } = await request.json();
 
         if (!apiKey) {
             return NextResponse.json(
@@ -14,11 +14,11 @@ export async function POST(request) {
 
         // Gemini 原生格式
         if (provider === 'gemini-native') {
-            return await fetchGeminiModels(apiKey, baseUrl);
+            return await fetchGeminiModels(apiKey, baseUrl, embedOnly);
         }
 
         // OpenAI 兼容格式（适用于所有其他供应商）
-        return await fetchOpenAIModels(apiKey, baseUrl);
+        return await fetchOpenAIModels(apiKey, baseUrl, embedOnly);
 
     } catch (error) {
         console.error('拉取模型列表错误:', error);
@@ -30,7 +30,7 @@ export async function POST(request) {
 }
 
 // Gemini 原生格式拉取模型
-async function fetchGeminiModels(apiKey, baseUrl) {
+async function fetchGeminiModels(apiKey, baseUrl, embedOnly) {
     const base = (baseUrl || 'https://generativelanguage.googleapis.com/v1beta').replace(/\/$/, '');
     const url = `${base}/models?key=${apiKey}`;
 
@@ -44,19 +44,25 @@ async function fetchGeminiModels(apiKey, baseUrl) {
     }
 
     const data = await response.json();
-    const models = (data.models || [])
-        .filter(m => m.supportedGenerationMethods?.includes('generateContent') || m.supportedGenerationMethods?.includes('embedContent'))
-        .map(m => ({
-            id: m.name?.replace('models/', '') || m.name,
-            displayName: m.displayName || m.name,
-        }))
+    let models = (data.models || []);
+
+    if (embedOnly) {
+        models = models.filter(m => m.supportedGenerationMethods?.includes('embedContent'));
+    } else {
+        models = models.filter(m => m.supportedGenerationMethods?.includes('generateContent') || m.supportedGenerationMethods?.includes('embedContent'));
+    }
+
+    models = models.map(m => ({
+        id: m.name?.replace('models/', '') || m.name,
+        displayName: m.displayName || m.name,
+    }))
         .sort((a, b) => a.id.localeCompare(b.id));
 
     return NextResponse.json({ models });
 }
 
 // OpenAI 兼容格式拉取模型（/v1/models）
-async function fetchOpenAIModels(apiKey, baseUrl) {
+async function fetchOpenAIModels(apiKey, baseUrl, embedOnly) {
     const base = (baseUrl || '').replace(/\/$/, '');
     if (!base) {
         return NextResponse.json(
@@ -80,12 +86,16 @@ async function fetchOpenAIModels(apiKey, baseUrl) {
     }
 
     const data = await response.json();
-    // OpenAI 格式返回 { data: [{id, ...}, ...] }
-    const models = (data.data || [])
-        .map(m => ({
-            id: m.id,
-            displayName: m.id,
-        }))
+    let models = (data.data || []);
+
+    if (embedOnly) {
+        models = models.filter(m => /embed/i.test(m.id));
+    }
+
+    models = models.map(m => ({
+        id: m.id,
+        displayName: m.id,
+    }))
         .sort((a, b) => a.id.localeCompare(b.id));
 
     return NextResponse.json({ models });

app/components/SettingsPanel.js

@@ -550,7 +550,7 @@ function ApiConfigForm({ data, onChange }) {
         try {
             const embedKey = data.embedApiKey || data.apiKey;
             const embedBase = data.embedBaseUrl || data.baseUrl;
-            const res = await fetch('/api/ai/models', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ apiKey: embedKey, baseUrl: embedBase, provider: data.embedProvider }) });
+            const res = await fetch('/api/ai/models', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ apiKey: embedKey, baseUrl: embedBase, provider: data.embedProvider, embedOnly: true }) });
             const result = await res.json();
             if (result.error) { setFetchedEmbedModels(null); setTestStatus({ success: false, error: t('apiConfig.embedApiPrefix') + result.error }); }
             else { setFetchedEmbedModels(result.models || []); }

app/lib/context-engine.js

@@ -203,14 +203,14 @@ export async function buildContext(activeChapterId, selectedText, selectedIds =
         }
     }
 
-    // --- RAG 自动检索 ---
+    // --- RAG 自动检索（仅当开启嵌入功能时） ---
     let autoRetrievedNodes = [];
     const queryText = (selectedText || '').trim();
-    if (queryText && unselectedItemNodes.length > 0) {
+    const { apiConfig } = getProjectSettings();
+    if (apiConfig.useCustomEmbed && queryText && unselectedItemNodes.length > 0) {
         try {
             // 获取 Query 的 Embedding
             // 此处的文本可以是光标前的一小段文字，为了简便使用 selectedText
-            const { apiConfig } = getProjectSettings();
             // TODO: 这里如果是空文本，可能需要补当前章节最近的内容
             let ragSourceText = queryText;
             if (ragSourceText.length < 50 && currentChapter) {

app/lib/settings.js

@@ -490,9 +490,11 @@ export async function addSettingsNode({ name, type, category, parentId, icon, co
     };
 
     if (node.type === 'item') {
-        const textToEmbed = extractTextForEmbedding(node);
         const { apiConfig } = getProjectSettings();
-        node.embedding = await getEmbedding(textToEmbed, apiConfig);
+        if (apiConfig.useCustomEmbed) {
+            const textToEmbed = extractTextForEmbedding(node);
+            node.embedding = await getEmbedding(textToEmbed, apiConfig);
+        }
     }
 
     nodes.push(node);
@@ -514,12 +516,12 @@ export async function updateSettingsNode(id, updates) {
         delete updates.parentId;
     }
 
-    // 如果名称或内容发生改变，且是条目，重新计算 embedding
+    // 如果名称或内容发生改变，且是条目，且开启了嵌入功能，重新计算 embedding
     const nodeType = updates.type || nodes[idx].type;
-    if (nodeType === 'item' && (updates.name !== undefined || updates.content !== undefined)) {
+    const { apiConfig } = getProjectSettings();
+    if (nodeType === 'item' && apiConfig.useCustomEmbed && (updates.name !== undefined || updates.content !== undefined)) {
         const tempNode = { ...nodes[idx], ...updates };
         const textToEmbed = extractTextForEmbedding(tempNode);
-        const { apiConfig } = getProjectSettings();
         updates.embedding = await getEmbedding(textToEmbed, apiConfig);
     }