Merge remote-tracking branch 'upstream/main'

docs/self-hosting/platform/btpanel.mdx

@@ -0,0 +1,43 @@
+---
+title: Deploy LobeChat using aaPanel
+description: Learn how to deploy the LobeChat service using aaPanel-Docker, including installing the Docker container environment and using the command to start the service with one click. Detailed instructions on how to configure environment variables and use proxy addresses.
+tags:
+  - Docker
+  - LobeChat
+  - Deployment guidelines
+---
+
+## Prerequisite
+
+To install aaPanel, go to the [aaPanel](https://www.aapanel.com/new/download.html#install) official website and select the corresponding script to download and install.
+
+## Deployment
+
+1. Log in to aaPanel and click `Docker` in the menu bar
+![install](https://github.com/user-attachments/assets/f67180c2-47ba-4b04-9f12-d274c7821085)
+
+2. The first time you will be prompted to install the `Docker` and `Docker Compose` services, click Install Now. If it is already installed, please ignore it.
+![install2](https://github.com/user-attachments/assets/e269bd27-d323-43ba-811b-c0f5e4137903)
+
+3. After the installation is complete, find `LobeChat` in `One-Click Install` and click `install`  
+![install-LobeChat](https://github.com/user-attachments/assets/9f989104-bb8e-4acd-9721-6b1db1017d2b)
+
+4. configure basic information such as the domain name, OpenAI API key, and port to complete the installation
+   Note:
+   The domain name is optional, if the domain name is filled, it can be managed through [Website]--> [Proxy Project], and you do not need to check [Allow external access] after filling in the domain name, otherwise you need to check it before you can access it through the port
+![addLobeChat](https://github.com/user-attachments/assets/f0b2e72d-9eee-46a8-b094-4834b78764df)
+5. After installation, enter the domain name or IP+ port set in the previous step in the browser to access.
+- Name: application name, default `LobeChat-random characters`
+- Version selection: default `latest`
+- Domain name: If you need to access directly through the domain name, please configure the domain name here and resolve the domain name to the server
+- Allow external access: If you need direct access through `IP+Port`, please check. If you have set up a domain name, please do not check here.
+- Port: Default `3210`, can be modified by yourself
+
+6. After submission, the panel will automatically initialize the application, which will take about `1-3` minutes. It can be accessed after the initialization is completed.
+
+
+## Visit LobeChat
+- If you have set a domain name, please directly enter the domain name in the browser address bar, such as `http://demo.lobechat`, to access the `LobeChat` console.
+- If you choose to access through `IP+Port`, please enter the domain name in the browser address bar to access `http://<aaPanelIP>:3210` to access the `HertzBeat` console.
+![console](https://github.com/user-attachments/assets/a1af5778-f47a-4fdc-baf5-ca2a1e66f48e)
+

docs/self-hosting/platform/btpanel.zh-CN.mdx

@@ -0,0 +1,49 @@
+---
+title: 通过 宝塔面板Docker应用商店 部署 LobeChat
+description: 学习如何使用 宝塔面板Docker应用 部署 LobeChat 服务，包括安装 Docker 容器环境和使用指令一键启动服务。详细说明如何配置环境变量和使用代理地址。
+tags:
+  - Docker
+  - LobeChat
+  - 部署指引
+---
+
+# 宝塔面板部署
+
+## 前提
+- 仅适用于宝塔面板9.2.0及以上版本
+- 安装宝塔面板，前往[宝塔面板](https://www.bt.cn/new/download.html)官网，选择正式版的脚本下载安装
+
+
+## 部署
+
+1. 登录宝塔面板，在左侧菜单栏中点击 `Docker`
+![Docker](https://github.com/user-attachments/assets/15d92756-92f0-45da-8f95-bfe725d13003) 
+
+2. 首次会提示安装`Docker`和`Docker Compose`服务，点击立即安装，若已安装请忽略。
+![安装环境](https://github.com/user-attachments/assets/6e383b75-09e3-42d1-8a6c-5fb7cf558f00)
+
+3. 安装完成后在`Docker-应用商店-AI/大模型`中找到 `LobeChat`，点击`安装`
+![安装](https://github.com/user-attachments/assets/db59a5e7-32ed-49d7-a791-8f8ee6618c01)
+
+4. 设置域名等基本信息，点击`确定`
+![设置](https://github.com/user-attachments/assets/37251adf-949b-4aec-bc49-bf4647e119da)
+- 名称：应用名称，默认`lobechat_随机字符`
+- 版本选择：默认`latest`
+- 域名：如您需要通过域名访问，请在此处填写您的域名
+- 允许外部访问：如您需通过`IP+Port`直接访问，请勾选，如您已经设置了域名，请不要勾选此处
+- 端口：默认`3210`，可自行修改
+- 访问密码：默认随机生成
+- OpenAI API 密钥：请输入您的 Open API 密钥
+- OpenAI 代理 URL：默认为官方地址
+- OpenAI 模型列表：输入使用的模型
+- CPU 核心数限制：0为不限制，根据实际需要设置
+- 内存限制：0为不限制，根据实际需要设置
+
+
+5. 提交后面板会自动进行应用初始化，大概需要`1-3`分钟，初始化完成后即可访问。
+
+
+## 访问 LobeChat
+- 如果您填写域名，请在浏览器输入您的域名访问，如`http://demo.lobechat`，即可访问 `LobeChat` 页面。
+- 请在浏览器地址栏中输入域名访问 `http://<宝塔面板IP>:3210`，即可访问 `LobeChat` 页面。
+![LobeChat](https://github.com/user-attachments/assets/808f8849-5738-4a60-8ccf-01e300b0dc88)

src/app/(backend)/webapi/assistant/store/route.ts

@@ -1,20 +1,34 @@
+import { NextResponse } from 'next/server';
+
 import { DEFAULT_LANG } from '@/const/locale';
 import { AssistantStore } from '@/server/modules/AssistantStore';
 
 export const runtime = 'edge';
 
 export const GET = async (req: Request) => {
-  const locale = new URL(req.url).searchParams.get('locale');
+  try {
+    const locale = new URL(req.url).searchParams.get('locale');
 
-  const market = new AssistantStore();
+    const market = new AssistantStore();
 
-  let res: Response;
+    let res: Response;
 
-  res = await fetch(market.getAgentIndexUrl(locale as any));
+    res = await fetch(market.getAgentIndexUrl(locale as any));
 
-  if (res.status === 404) {
-    res = await fetch(market.getAgentIndexUrl(DEFAULT_LANG));
-  }
+    if (res.status === 404) {
+      res = await fetch(market.getAgentIndexUrl(DEFAULT_LANG));
+    }
 
-  return res;
+    const data = await res.json();
+    return NextResponse.json(data);
+  } catch (e) {
+    console.error(e);
+    return new Response(`failed to fetch agent market index`, {
+      headers: {
+        'Access-Control-Allow-Origin': '*',
+        'Content-Type': 'application/json',
+      },
+      status: 500,
+    });
+  }
 };

src/database/server/schemas/lobechat/agent.ts

@@ -19,7 +19,7 @@ export const agents = pgTable('agents', {
     .$defaultFn(() => idGenerator('agents'))
     .notNull(),
   slug: varchar('slug', { length: 100 })
-    .$defaultFn(() => randomSlug())
+    .$defaultFn(() => randomSlug(4))
     .unique(),
   title: text('title'),
   description: text('description'),

src/database/server/utils/idGenerator.ts

@@ -22,6 +22,6 @@ export const idGenerator = (namespace: keyof typeof prefixes, size = 12) => {
 
   return `${prefix}_${hash()}`;
 };
-export const randomSlug = () => (generate(2) as string[]).join('-');
+export const randomSlug = (count = 2) => (generate(count) as string[]).join('-');
 
 export const inboxSessionId = (userId: string) => `ssn_inbox_${userId}`;

src/libs/agent-runtime/BaseAI.ts

@@ -5,7 +5,7 @@ import { ChatModelCard } from '@/types/llm';
 import {
   ChatCompetitionOptions,
   ChatStreamPayload,
-  EmbeddingItem,
+  Embeddings,
   EmbeddingsOptions,
   EmbeddingsPayload,
   TextToImagePayload,
@@ -17,7 +17,7 @@ export interface LobeRuntimeAI {
   baseURL?: string;
   chat(payload: ChatStreamPayload, options?: ChatCompetitionOptions): Promise<Response>;
 
-  embeddings?(payload: EmbeddingsPayload, options?: EmbeddingsOptions): Promise<EmbeddingItem[]>;
+  embeddings?(payload: EmbeddingsPayload, options?: EmbeddingsOptions): Promise<Embeddings[]>;
 
   models?(): Promise<any>;
 
@@ -40,5 +40,5 @@ export abstract class LobeOpenAICompatibleRuntime {
   abstract embeddings(
     payload: EmbeddingsPayload,
     options?: EmbeddingsOptions,
-  ): Promise<EmbeddingItem[]>;
+  ): Promise<Embeddings[]>;
 }

src/libs/agent-runtime/types/embeddings.ts

@@ -25,19 +25,9 @@ export interface EmbeddingsOptions {
   user?: string;
 }
 
-export interface EmbeddingItem {
-  /**
-   * The embedding vector, which is a list of floats. The length of vector depends on
-   * the model as listed in the
-   * [embedding guide](https://platform.openai.com/docs/guides/embeddings).
-   */
-  embedding: Array<number>;
-  /**
-   * The index of the embedding in the list of embeddings.
-   */
-  index: number;
-  /**
-   * The object type, which is always "embedding".
-   */
-  object: 'embedding';
-}
+/**
+ * The embedding vector, which is a list of floats. The length of vector depends on
+ * the model as listed in the
+ * [embedding guide](https://platform.openai.com/docs/guides/embeddings).
+ */
+export type Embeddings = Array<number>;

src/libs/agent-runtime/utils/openaiCompatibleFactory/index.ts

@@ -10,7 +10,7 @@ import {
   ChatCompetitionOptions,
   ChatCompletionErrorPayload,
   ChatStreamPayload,
-  EmbeddingItem,
+  Embeddings,
   EmbeddingsOptions,
   EmbeddingsPayload,
   TextToImagePayload,
@@ -271,14 +271,14 @@ export const LobeOpenAICompatibleFactory = <T extends Record<string, any> = any>
     async embeddings(
       payload: EmbeddingsPayload,
       options?: EmbeddingsOptions,
-    ): Promise<EmbeddingItem[]> {
+    ): Promise<Embeddings[]> {
       try {
         const res = await this.client.embeddings.create(
           { ...payload, user: options?.user },
           { headers: options?.headers, signal: options?.signal },
         );
 
-        return res.data;
+        return res.data.map((item) => item.embedding);
       } catch (error) {
         throw this.handleError(error);
       }

src/server/routers/async/file.ts

@@ -105,9 +105,9 @@ export const fileRouter = router({
                 console.timeEnd(`任务[${number}]: embeddings`);
 
                 const items: NewEmbeddingsItem[] =
-                  embeddings?.map((e) => ({
-                    chunkId: chunks[e.index].id,
-                    embeddings: e.embedding,
+                  embeddings?.map((e, idx) => ({
+                    chunkId: chunks[idx].id,
+                    embeddings: e,
                     fileId: input.fileId,
                     model: input.model,
                   })) || [];

src/server/routers/async/ragEval.ts

@@ -70,7 +70,7 @@ export const ragEvalRouter = router({
           });
 
           const embeddingId = await ctx.embeddingModel.create({
-            embeddings: embeddings?.[0].embedding,
+            embeddings: embeddings?.[0],
             model: embeddingModel,
           });
 

src/server/routers/lambda/chunk.ts

@@ -120,7 +120,7 @@ export const chunkRouter = router({
       console.timeEnd('embedding');
 
       return ctx.chunkModel.semanticSearch({
-        embedding: embeddings![0].embedding,
+        embedding: embeddings![0],
         fileIds: input.fileIds,
         query: input.query,
       });
@@ -147,7 +147,7 @@ export const chunkRouter = router({
           model: input.model || DEFAULT_EMBEDDING_MODEL,
         });
 
-        embedding = embeddings![0].embedding;
+        embedding = embeddings![0];
         const embeddingsId = await ctx.embeddingModel.create({
           embeddings: embedding,
           model: input.model,