# 使用 OpenAI 部署您自己的知识库聊天机器人 ## 概述 [AutoFlow](https://github.com/pingcap/autoflow) 是一个基于图形 RAG 的对话式知识库聊天助手，使用 TiDB 向量存储和 LlamaIndex 构建，开源且免费使用。 + 在本实验中，您将学习如何： + 将您自己的知识（Word/PDF/TXT/Markdown 等）导入到知识库中。 + 使用聊天机器人在知识库中搜索信息。 + 连接并在为您提供的 TiDB Cloud Serverless 集群上运行查询。该集群同时作为向量存储和图形数据库。 + 部署您自己的知识库聊天机器人，类似于 [这个](https://tidb.ai)： ![70%](https://lab-static.pingcap.com/quick-demo/taa-example-does-tidb-support-fk.jpeg) ### 实验时长 + 本实验大约需要 30 分钟完成。 ### 任务 + 使用 [pingcap/autoflow](https://github.com/pingcap/autoflow) 仓库部署知识库应用程序。 + 配置您的知识库应用程序。 + 与 AI 聊天机器人进行随机聊天，看看它是否能使用知识库中的信息回答您的问题。 ### 先决条件 + 本次实验将依赖您自己的 OpenAI API Key。 > [!TIP] > 点击 **开始** 启动实验环境。 > 环境准备就绪后，点击底部工具栏中的 **前进** 进入 **模块 1**。 > 然后，按照实验指南的步骤连接到实践环境并开始你的任务。 ## 模块 1：准备运行环境并初始化 AutoFlow 服务 ### 步骤 1. 登录应用服务器虚拟机 + 点击这里 - [终端 1](ssh://monitor1-ssh#1) 打开一个新的终端，连接到为你的实践实验分配的虚拟机。 + 登录后显示的信息如下： ```output ubuntu@ip-10-90-1-154:~$ ``` ### 步骤 2. 安装必要的软件包，如 Docker、Docker Compose、MySQL 客户端 + 卸载旧版本的 Docker 和 Docker Compose。 ```bash for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc; do sudo apt-get remove $pkg; done ``` ```output Reading package lists... Done Building dependency tree... Done Reading state information... Done . . . Reading state information... Done Package 'runc' is not installed, so not removed 0 upgraded, 0 newly installed, 0 to remove and 30 not upgraded. ``` + 添加 Docker 的官方 GPG 密钥。 ```bash sudo apt-get update -y sudo apt-get install ca-certificates curl -y sudo install -m 0755 -d /etc/apt/keyrings sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc sudo chmod a+r /etc/apt/keyrings/docker.asc ``` ```output Hit:1 http://us-west-2.ec2.archive.ubuntu.com/ubuntu noble InRelease Get:2 http://us-west-2.ec2.archive.ubuntu.com/ubuntu noble-updates InRelease [126 kB] . . . Get:15 http://us-west-2.ec2.archive.ubuntu.com/ubuntu noble-updates/main Translation-en [151 kB] Fetched 30.6 MB in 5s (6133 kB/s) Reading package lists... Done Reading package lists... Done Building dependency tree... Done Reading state information... Done ca-certificates is already the newest version (20240203). ca-certificates set to manually installed. curl is already the newest version (8.5.0-2ubuntu10.4). curl set to manually installed. 0 upgraded, 0 newly installed, 0 to remove and 33 not upgraded. ``` + 将仓库添加到 `apt` 源。 ```bash echo \ "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \ $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \ sudo tee /etc/apt/sources.list.d/docker.list > /dev/null sudo apt-get update -y ``` ```output Reading package lists... Done . . . Get:6 https://download.docker.com/linux/ubuntu noble/stable amd64 Packages [15.3 kB] Fetched 64.2 kB in 1s (79.9 kB/s) Reading package lists... Done ``` + 安装 Docker 软件包。 ```bash sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin ``` ```output Reading package lists... Done Building dependency tree... Done Reading state information... Done . . . No VM guests are running outdated hypervisor (qemu) binaries on this host. ``` + 通过运行 `hello-world` 镜像验证 Docker 是否正确安装。 ```bash sudo docker run hello-world ``` ```output Unable to find image 'hello-world:latest' locally latest: Pulling from library/hello-world c1ec31eb5944: Pull complete Digest: sha256:d211f485f2dd1dee407a80973c8f129f00d54604d2c90732e8e320e5038a0348 Status: Downloaded newer image for hello-world:latest Hello from Docker! . . . To try something more ambitious, you can run an Ubuntu container with: $ docker run -it ubuntu bash Share images, automate workflows, and more with a free Docker ID: https://hub.docker.com/ For more examples and ideas, visit: https://docs.docker.com/get-started/ ``` + 安装 MySQL 客户端。 ```bash sudo apt-get install -y mysql-client-core-8.0 ``` ```output Reading package lists... Done Building dependency tree... Done Reading state information... Done The following NEW packages will be installed: mysql-client-core-8.0 0 upgraded, 1 newly installed, 0 to remove and 33 not upgraded. Need to get 2794 kB of archives. After this operation, 61.6 MB of additional disk space will be used. Get:1 http://us-west-2.ec2.archive.ubuntu.com/ubuntu noble-updates/main amd64 mysql-client-core-8.0 amd64 8.0.39-0ubuntu0.24.04.2 [2794 kB] Fetched 2794 kB in 0s (34.7 MB/s) Selecting previously unselected package mysql-client-core-8.0. (Reading database ... 68102 files and directories currently installed.) Preparing to unpack .../mysql-client-core-8.0_8.0.39-0ubuntu0.24.04.2_amd64.deb ... Unpacking mysql-client-core-8.0 (8.0.39-0ubuntu0.24.04.2) ... Setting up mysql-client-core-8.0 (8.0.39-0ubuntu0.24.04.2) ... Processing triggers for man-db (2.12.0-4build2) ... Scanning processes... Scanning linux images... Running kernel seems to be up-to-date. No services need to be restarted. No containers need to be restarted. No user sessions are running outdated binaries. No VM guests are running outdated hypervisor (qemu) binaries on this host. ``` ### 步骤 3. 克隆并配置 AutoFlow + 克隆 pingcap/autoflow 仓库并导航到 `autoflow` 目录。 ```bash git clone https://github.com/pingcap/autoflow; cd autoflow; git switch --detach adb5f5483b83c5af98868b8a5f080b3a6f4f4116; ``` ```output Cloning into 'autoflow'... remote: Enumerating objects: 12041, done. remote: Counting objects: 100% (538/538), done. remote: Compressing objects: 100% (264/264), done. remote: Total 12041 (delta 312), reused 446 (delta 268), pack-reused 11503 (from 1) Receiving objects: 100% (12041/12041), 4.33 MiB | 22.07 MiB/s, done. Resolving deltas: 100% (7431/7431), done. ``` + 使用以下值更新 `.env` 文件： ```bash cat > .env <<'EOF' ENVIRONMENT=production SECRET_KEY="some_secret_key_that_is_at_least_32_characters_long" TIDB_HOST=serverless_host TIDB_PORT=serverless_port TIDB_USER='serverless_username' TIDB_PASSWORD='serverless_password' TIDB_DATABASE=tidbai_test TIDB_SSL=true EMBEDDING_DIMS=1536 EMBEDDING_MAX_TOKENS=8191 EOF cat .env ``` ```output ENVIRONMENT=production SECRET_KEY="some_secret_key_that_is_at_least_32_characters_long" TIDB_HOST=serverless_host TIDB_PORT=serverless_port TIDB_USER='serverless_username' TIDB_PASSWORD='serverless_password' TIDB_DATABASE=tidbai_test TIDB_SSL=true EMBEDDING_DIMS=1536 EMBEDDING_MAX_TOKENS=8191 ``` ### 步骤 4. 确保您的数据库干净并准备好进行下一步 + 使用以下命令连接到 TiDB Cloud Serverless 集群。 ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/ssl/certs/ca-certificates.crt ``` ```output mysql: [Warning] Using a password on the command line interface can be insecure. Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 2168969 Server version: 5.7.28-TiDB-Serverless TiDB Server (Apache License 2.0) Community Edition, MySQL 5.7 compatible Copyright (c) 2000, 2024, Oracle and/or its affiliates. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql> ``` + 删除现有数据库并创建一个新的。 ```bash DROP DATABASE IF EXISTS tidbai_test; CREATE DATABASE tidbai_test; EXIT; ``` ```output mysql> DROP DATABASE IF EXISTS tidbai_test; Query OK, 0 rows affected (0.00 sec) mysql> CREATE DATABASE tidbai_test; Query OK, 0 rows affected (0.12 sec) mysql> EXIT; Bye ``` ### 步骤 5. 创建架构 + 运行数据迁移以创建表架构并设置初始管理员用户。请注意输出中红色行中生成的管理员密码。 ```bash sudo docker compose run backend /bin/sh -c "alembic upgrade head"; sudo docker compose run backend /bin/sh -c "python bootstrap.py"; ``` ```output [+] Creating 1/0 ✔ Container tidb-ai-redis-1 Running 0.0s INFO [alembic.runtime.migration] Context impl MySQLImpl. INFO [alembic.runtime.migration] Will assume non-transactional DDL. INFO [alembic.runtime.migration] Running upgrade -> 2fc10c21bf88, empty message INFO [alembic.runtime.migration] Running upgrade 2fc10c21bf88 -> 94b198e20946, empty message INFO [alembic.runtime.migration] Running upgrade 94b198e20946 -> 04d4f05116ed, empty message INFO [alembic.runtime.migration] Running upgrade 04d4f05116ed -> 197bc8be72d1, empty message INFO [alembic.runtime.migration] Running upgrade 197bc8be72d1 -> ac6e4d58580d, empty message INFO [alembic.runtime.migration] Running upgrade ac6e4d58580d -> a8c79553c9f6, empty message INFO [alembic.runtime.migration] Running upgrade a8c79553c9f6 -> bd17a4ebccc5, empty message INFO [alembic.runtime.migration] Running upgrade bd17a4ebccc5 -> e32f1e546eec, empty message INFO [alembic.runtime.migration] Running upgrade e32f1e546eec -> 04d81be446c3, empty message INFO [alembic.runtime.migration] Running upgrade 04d81be446c3 -> 041fbef26e3a, empty message INFO [alembic.runtime.migration] Running upgrade 041fbef26e3a -> 10f36e8a25c4, empty message INFO [alembic.runtime.migration] Running upgrade 10f36e8a25c4 -> 00534dc350db, empty message INFO [alembic.runtime.migration] Running upgrade 00534dc350db -> eb0b85608c0a, empty message INFO [alembic.runtime.migration] Running upgrade eb0b85608c0a -> dfee070b8abd, empty message WARN[0000] Found orphan containers ([tidb-ai-backend-run-d4d35b6b5909]) for this project. If you removed or renamed this service in your compose file, you can run this command with the --remove-orphans flag to clean it up. [+] Creating 1/0 ✔ Container tidb-ai-redis-1 Running 0.0s Bootstrapping the application... /usr/local/lib/python3.11/site-packages/pydantic/_internal/_config.py:341: UserWarning: Valid config keys have changed in V2: * 'orm_mode' has been renamed to 'from_attributes' warnings.warn(message, UserWarning) User 019309f5-2a8f-7e9d-b848-cc81312e598a has registered. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! [IMPORTANT] Admin user created with email: admin@example.com and password: !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used. Default chat engine created. Bootstrapping completed. ``` ### 步骤 6. 启动知识库应用程序 + 通过运行以下 `docker compose` 命令启动应用程序。 ```bash sudo docker compose up -d ``` ```output [+] Running 43/33 ✔ backend Pulled 51.9s ✔ background Pulled 51.9s ✔ redis Pulled 6.5s ✔ frontend Pulled 36.8s [+] Running 5/5 ✔ Network tidb-ai_default Created 0.3s ✔ Container tidb-ai-redis-1 Started 35.0s ✔ Container tidb-ai-backend-1 Started 0.7s ✔ Container tidb-ai-background-1 Started 0.7s ✔ Container tidb-ai-frontend-1 Started 0.9s ``` ## 模块 2：配置 OpenAI 服务 ### 步骤 1. 访问应用程序 + 通过在浏览器中访问 [http://monitor1_address:3000](http://monitor1_address:3000) 来访问和配置知识库应用程序。 + 使用默认管理员用户 `admin@example.com` 和您在 模块 1，步骤 5 中收到的密码登录。 ![60%](https://lab-static.pingcap.com/quick-demo/taa-app-credentials.jpeg) ### 步骤 2. 配置 LLM + 点击 Models 然后点击 LLMs 标签，然后点击 New LLM 按钮。 + 输入以下信息，然后点击 Create LLM 按钮： + 名称：my-openai-llm + 提供商：OpenAI + 模型：gpt-4o + OpenAI API 密钥：${YOUR_OPENAI_API_KEY} + 是否为默认 LLM：是 ![60%](https://lab-static.pingcap.com/quick-demo/taa-default-llm.jpeg) ![60%](https://lab-static.pingcap.com/quick-demo/taa-setup-progress.jpeg) ### 步骤 3. 配置嵌入模型 + 点击 Models 然后点击 Embedding Models 标签，然后点击 New Embedding Model 按钮。 + 输入以下信息，然后点击 Create Embedding Model 按钮： + 名称：my-openai-embedding + 提供商：OpenAI + 模型：text-embedding-3-small + OpenAI API 密钥：${YOUR_OPENAI_API_KEY} + 向量维度：1536 ![60%](https://lab-static.pingcap.com/quick-demo/setup-embedding-model-start.jpeg) ![60%](https://lab-static.pingcap.com/quick-demo/setup-embedding-model-done.png) ### 步骤 4. 创建知识库 + 点击 Knowledge Base 标签，然后点击 New Knowledge Base 按钮。 + 输入以下信息，然后点击 Create 按钮： + 名称：tidb overview + 描述：tidb overview + LLM：my-openai-llm + 嵌入模型：my-openai-embedding + 知识图谱索引：是 ### 步骤 5. 创建数据源 + 在知识库页面，点击 Data Sources 标签，然后点击 Web Pages 按钮。 + 在页面 URL 部分下点击 New Item 按钮，并输入以下信息，然后点击 Create 按钮： + 页面 URL：https://docs.pingcap.com/tidb/stable/overview + 数据源名称：tidb overview ![60%](https://lab-static.pingcap.com/quick-demo/setup-datasource-start.jpeg) ![60%](https://lab-static.pingcap.com/quick-demo/setup-datasource-done.jpeg) ### 步骤 6. 观察索引进度 + 点击 Index Progress 标签导航到索引进度状态页面，检查索引任务的进度。 + 您即将完成！现在应用程序正在运行后台任务下载您提供的 URL 中的内容。 + 如果圆圈颜色为蓝色，表示正在下载内容。 + 如果圆圈颜色为绿色，表示内容已成功下载并导入（已建立索引），这意味着您可以开始与 AI 聊天机器人聊天了。 ![60%](https://lab-static.pingcap.com/quick-demo/build-index-done.jpeg) ### 步骤 7. 配置默认聊天引擎 + 点击 Chat Engines 标签，然后点击 default 聊天引擎。 + 点击 Retrieval 标签，然后选择您刚刚创建的知识库，然后点击 Save 按钮。 ### 步骤 8. 使用您刚刚构建的聊天机器人 + 在浏览器中打开 [http://monitor1_address:3000](http://monitor1_address:3000) 或点击 Home 开始与 AI 聊天机器人聊天！只需问："什么是 TiDB？" + 享受与您自己的 AI 聊天机器人聊天的乐趣！ ![60%](https://lab-static.pingcap.com/quick-demo/chat-start.jpeg) ![60%](https://lab-static.pingcap.com/quick-demo/chat-done.jpeg) ## 结论 在本实验中，您学习了如何部署自己的知识库聊天机器人，类似于 [https://tidb.ai](https://tidb.ai)，您可以将自己的知识导入到知识库中，并使用聊天机器人在知识库中搜索信息。 但我们必须提醒您，此实验环境中存储的数据不能保证持久性。它将在实验会话结束后被释放。 如果您想在工作环境中使用这个 AI 聊天机器人，您需要在自己的服务器上部署它，您需要： + 在 [pingcap/autoflow](https://github.com/pingcap/autoflow) 获取源代码。 + 在 [https://tidbcloud.com](https://tidbcloud.com) 获取您自己的 TiDB Cloud Serverless 集群。 + 在 [https://platform.openai.com](https://platform.openai.com) 获取您自己的 OpenAI API 密钥。