主题模式

Agent 平台 ​

概述 ​

本文档介绍如何在各种 Agent 平台中集成和使用 AI Ping 服务，包括 Dify、Coze、LazyLLM 和 LazyCraft 等平台的详细配置和使用方法。

Dify ​

Dify 是一个开源的大语言模型(LLM)应用开发平台。AI Ping 为 Dify 提供了完整的模型接入能力，包括文本模型、嵌入模型、重排序模型以及图片生成工具。

1. 模型篇 ​

AI Ping 为 Dify 提供了文本序列模型、嵌入模型、重排序模型的接入能力。

安装方式 ​

AI Ping 插件支持三种安装方式：

方式一：通过插件市场安装

  1. 进入 Dify 插件市场
  2. 搜索 "AI Ping"
  3. 选择带 Model 标签的插件进行安装

插件市场安装

方式二：通过 GitHub 安装

  1. 点击 Dify 插件页中的 "安装插件"
  2. 选择通过 GitHub 安装
  3. 输入 GitHub URL：
    https://github.com/QingCheng-AI/aiping-dify-plugin

选择安装方式

GitHub 安装

  1. 选择插件版本和模型安装包：
    aiping-dify-plugin-ai.difypkg

选择模型包

方式三：本地离线包安装

  1. 点击 Dify 插件页中 "安装插件"
  2. 选择 "本地插件"
  3. 上传本地离线包（可从仓库获取）：
    https://github.com/QingCheng-AI/aiping-dify-plugin/releases

离线包安装

安装成功

插件安装成功后，您将在插件页看到已安装好的 "AI Ping"：

安装成功

配置 ​

  1. 打开 "设置" 中的 "模型服务商" 配置
  2. 找到 "AI Ping" 插件
  3. 在插件配置页中输入从官网获取的 API Key：
    https://aiping.cn

配置 API Key

工作流测试 ​

  1. 在 Dify 点击 "工作室"，创建工作流

创建工作流

  1. 模型选择 AI Ping 的 LLM，如 DeepSeek-V3-0324

选择模型

对话结果演示

对话演示

知识库测试 ​

步骤 1：创建知识库

创建知识库

步骤 2：上传文档并选择模型

  1. 上传文档
  2. 选择需要的 embedding 和 reranker 模型

选择模型

步骤 3：测试检索

  1. 选择指定知识库进行检索
  2. 点击左侧 "召回测试" 按钮
  3. 输入问题，右侧显示检索到的内容片段

测试检索

插件说明 ​

该插件集成了官网上的所有模型接入，用户可以自定义相关工作流，畅享各模型服务。

2. 工具篇：图片生成工具 ​

AI Ping 为 Dify 提供了图片生成能力，包括文生图（Text to Image）和图生图（Image to Image）功能。

安装方式 ​

方式一：通过插件市场安装

  1. 进入 Dify 插件市场
  2. 搜索 "AI Ping"
  3. 选择带 Tool 标签的插件安装

工具插件安装

方式二：通过 GitHub 安装

  1. 点击 Dify 插件页中的 "安装插件"
  2. 选择通过 GitHub 安装
  3. 输入 GitHub URL：
    https://github.com/QingCheng-AI/aiping-dify-plugin

选择安装方式

GitHub 安装

  1. 选择插件版本和安装包

选择工具包

方式三：本地离线包安装

  1. 点击 Dify 插件页中 "安装插件"
  2. 选择 "本地插件"
  3. 上传本地离线包（可从仓库获取）安装
    https://github.com/QingCheng-AI/aiping-dify-plugin/releases

离线安装

安装成功

工具安装成功

配置 ​

步骤 1：授权验证

授权验证

步骤 2：输入 API Key

输入 API Key

工作流测试 ​

步骤 1：创建工作流

  1. 在 Dify 点击 "工作室(studio)"，创建工作流

创建工作流

  1. 添加工具节点：文生图(text to image)或图生图(image to image)

添加工具节点

步骤 2：配置工具参数

在工具配置页面中设置相关参数。"直接回复(Answer)" 节点可以选择输出结果为 files 或 text 格式。建议选择 files 格式，方便在页面上直观、快速查看生成的图片。

配置参数

文生图示例 ​

步骤 1：配置参数

点击选择的工具节点，进行参数配置：

  • 设定 prompt 输入来源
  • 选择需要使用的生图模型

文生图配置

步骤 2：预览测试

点击 "预览（Preview）"，进行工作流测试。预览页直接输入提示词：

预览测试

步骤 3：生成效果展示

文生图效果

图生图示例 ​

步骤 1：配置参数

点击选择的工具节点，进行参数配置：

  • 设定 prompt 输入来源
  • 上传参考图片
  • 选择需要使用的生图模型

图生图配置

步骤 2：预览测试

点击 "预览（Preview）"，进行工作流测试：

  1. 在预览页 "Manage" 中，设置允许 "File Upload"
  2. 选择 URL 或本地文件上传图片
  3. 添加提示词

预览页设置

文件上传设置

上传图片

步骤 3：生成效果展示

图生图效果

3. 完整功能包 ​

完整功能包包含所有模型和工具，仅支持本地部署的 Dify使用。

获取方式 ​

完整功能包下载地址：

https://github.com/QingCheng-AI/aiping-dify-plugin/releases

安装方式 ​

GitHub 安装

使用 GitHub 方式安装时，选择安装包：

aiping-dify-plugin-full.difypkg

完整功能包安装

离线安装

使用本地离线安装时，选择安装包：

aiping-dify-plugin-full.difypkg

4. 其他 ​

安装说明 ​

更多安装说明请参考插件 GitHub 仓库：

https://github.com/QingCheng-AI/aiping-dify-plugin

常见问题 ​

问题 1：图生图上传文件后出现错误

如下图，在图生图上传文件后出现的错误。这是 Dify 的 bug（非插件 bug），在 URL 正确情况下也会偶发。

错误示例

解决方案：

问题 2：完整功能包安装报错

如果在 Dify 官网通过 GitHub 或离线包方式安装 AI Ping 完整功能包，出现以下报错，这是因为 Dify 官网不支持完整功能包安装导致。

安装报错

说明：

  • 只有本地部署的 Dify，才支持完整功能包的安装
  • Dify 官网不支持完整功能包安装

Coze ​

Coze 是字节跳动推出的 AI 应用开发平台。AI Ping 提供了完整的 Coze 插件，支持多种模型调用。

1. 安装 ​

进入 Coze 插件市场，搜索 AI Ping-official-plugin。

💡 提示：点击插件详情页可以进行 "收藏"，便于后续使用。

搜索插件

2. 收藏 ​

收藏后可以在后续应用中便捷使用该插件：

收藏插件

收藏列表

3. 示例展示 ​

创建工作流 ​

  1. 创建工作流，点击 "添加节点"
  2. 选择 "插件"

添加节点

添加插件 ​

  1. 搜索 "AI Ping-official-plugin"
  2. 添加到当前工作流
  3. 或者从 "收藏" 中选择后添加到工作流

插件中可能包含多个工具，选择需要的工具添加到工作流中使用即可。本例选用 LLM_VLM：

选择工具

添加到工作流

文本模型使用 ​

步骤 1：配置参数

配置插件需要的相关设置：

  • api-key
  • model
  • input 等参数

配置参数

步骤 2：配置输出

结束节点可以选择输出想要的字段：

配置输出

步骤 3：对话结果展示

对话结果

生图模型使用（文生图、图生图） ​

说明：

  • 提供了通用生图工具
  • 提供了热门生图模型工具

以文生图为例：

  1. 在 AIPing-official-plugin 插件中
  2. 选择需要的生图工具，加入到工作流中
  3. 填写插件需要的相关参数设置：
    • api_key
    • model
    • prompt 等

文生图配置

结束节点配置：

方式一：默认返回

返回类型：Array<String>

默认返回

方式二：图片在线预览

将图片返回格式改成 Array<Image>

设置图片格式

配置图片返回

运行结果：

（图片返回格式改成 Array<Image>）

生图结果

LazyLLM ​

LazyLLM 是由商汤 LazyAGI 团队开发的一款开源低代码大模型应用开发工具，提供从应用搭建、数据准备、模型部署、微调到评测的一站式工具支持，以极低的成本快速构建 AI 应用，持续迭代优化效果。

环境准备 ​

拉取最新仓库代码：

# 优先拉取 fork 仓库最新代码：
https://github.com/MichaelHuang7119/LazyLLM.git

# 官方仓库待 mr 合入：
https://github.com/LazyAGI/LazyLLM.git

安装依赖：

bash
# cd 项目路径
cd LazyLLM
# 安装依赖
pip install -r requirements.txt
pip install .

1. 对话和图片识别 ​

文本问答演示 ​

填好 api_key 后，运行下面代码可以迅速调用模型并生成一个问答形式的前端界面：

python
from lazyllm import OnlineChatModule, WebModule

api_key = "QC-xxx"  # 替换成申请的api
# 测试chat模块
llm = OnlineChatModule(source="aiping", api_key=api_key, stream=False, model="DeepSeek-R1")
w = WebModule(llm, port=8846, title="aiping")
w.start().wait()

我们询问 "什么是 LazyLLM"，运行结果如下：

文本问答演示

多模态问答演示 ​

在输入中通过 lazyllm_files 参数传入一张图片，并询问图片的内容，就可以实现多模态的问答。

python
from lazyllm import OnlineChatModule

api_key = "QC-xxx"  # 替换成申请的api
llm = OnlineChatModule(source="aiping", api_key=api_key, model="Qwen2.5-VL-72B-Instruct")
print(llm("你好，这是什么？", lazyllm_files=["./df9e91c1-49c5-4038-ada6-e24133319b5f12345.png"]))

测试图片：

测试图片

终端回答结果：

多模态回答

2. 文生图 ​

使用 OnlineMultiModalModule 进行文生图，运行后会输出生成的文件路径：

python
from lazyllm import OnlineMultiModalModule

api_key = "QC-xxx"
# 测试文生图 function=text2image
llm = OnlineMultiModalModule(
    source="aiping", api_key=api_key, function="text2image"
)
print(llm("生成一个可爱的小狗"))

运行结果：

查看生成的图片（在有些系统中，生成图片可能是 "xxxpng"名称，需要重命名为 ".png" 后缀的文件）：

文生图效果

3. 知识库问答 ​

实现 Embed 和 Rerank 功能 ​

运行下面代码，使用 OnlineEmbeddingModule 进行向量化嵌入；设置 type='rerank' 调用重排序模型。

python
from lazyllm import OnlineEmbeddingModule

api_key = "QC-xxx"

# 测试embed模块
llm = OnlineEmbeddingModule(source="aiping", api_key=api_key)
print(llm("苹果"))

# 测试rerank模块
llm = OnlineEmbeddingModule(source="aiping", api_key=api_key, type="rerank")
print(llm(input="苹果", documents=["苹果", "香蕉", "橘子", "梨"], top_n=2))

向量化结果：

plain
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词相似性分数：

python
[(0, 0.9947530031204224), (2, 0.09633564949035645)]

LazyCraft ​

LazyCraft 是基于 LazyLLM 搭建的应用编排平台，它支持大模型调用和自定义代码块，具备强大的资源管理和数据集成能力，并配合 RAG 技术，支持自定义离线解算和在线调用。在 LazyCraft 中使用 AI Ping 服务，结合 AI Ping 模型丰富、速度快的优势，快速构建多样化的 AI 应用。

1. 在线平台 ​

LazyCraft 目前没有在线平台。

2. 本地部署 ​

按照仓库文档进行部署（自行构建镜像启动服务）：

# 优先拉取 fork 仓库最新代码：
https://github.com/MichaelHuang7119/LazyCraft.git

# 官方仓库待 mr 合入：
https://github.com/LazyAGI/LazyCraft.git

# 备注：自行构建镜像启动服务时，后端api会报错有些表缺失，已经向官方仓库提 issue 了；自行解决的话可以在相应数据库中补充下缺失的表

3. AI Ping 服务使用 ​

配置 ​

  1. 进入 LazyCraft "推理服务" 页面
  2. 点击 "云服务"
  3. 选择服务商为 "AIPing"
  4. 点击 "key" 后，输入从官网获取的 API Key：
    https://aiping.cn/api/v1
  5. 进行验证，验证成功后即可开启 AI Ping 服务

配置服务商

输入 API Key

效果示例 ​

具体使用请参考 LazyCraft 官方使用文档说明，这里给出一些简单案例展示：

LazyCraft 界面

应用示例 1：大模型对话应用 ​

步骤 1：创建应用

  1. 在应用商店中点击 "新建应用"
  2. "创建空白应用"，创建一个新应用

创建应用

步骤 2：添加 LLM 节点

  1. 在应用画布中搭建工作流
  2. 添加 LLM 节点

添加 LLM 节点

  1. 配置 LLM 相关参数：
    • 选择模型来源为 "在线服务"
    • 选择服务提供商为 "AIPing"
    • 选择需要使用的模型如 "DeepSeek-V3.1"
    • 提示词模板可以自由选择并编辑

配置 LLM 参数

步骤 3：连接节点

  1. 配置 "开始" 节点参数 query 为 str 类型
  2. 将其输入连接到 "大模型" 节点

连接开始节点

  1. "大模型" 节点中配置好：
    • 输入参数（连接到上一个节点输出）
    • 输出参数（连接到下一个节点输入）

配置大模型节点

  1. "结束" 节点配置好参数，并连接到上一节点

配置结束节点

步骤 4：测试运行

  1. 搭建好工作流后，先点击 "启用调试"
  2. 然后执行 "运行"

启用调试

  1. 发送问题后，点击开始运行，效果如下：

运行效果 1

运行效果 2

步骤 5：发布

  1. 点击右上角 "发布" 按钮
  2. 在弹出窗中点击 "发布" 或 "更新" 以发布应用

发布应用

应用示例 2：其他场景应用 ​

其他类型的应用可以参考前述的 "大模型对话应用示例" 以及 LazyCraft 官方使用指南。

应用场景

VLM 示例：

上传一张图片，提问 "图中内容"：

VLM 示例

文生图示例：

文生图示例

Embedding 示例：

  1. 将 "大模型对话应用示例" 工作流中的大模型节点换成 "RAG召唤器"

  2. 在左侧导航栏中：

    • 选择 "资源控件" -> "文档管理"
    • 选择在 "资源库" 中创建的知识库

资源管理

  1. "文档管理"：需要配置 "内置节点组"：
    • 选择在线模型
    • 选择服务提供商
    • 选择模型名

配置文档管理

  1. "RAG 召唤器" 中可以配置：
    • 知识库
    • "文档切片组名"
    • "输出格式" 等

配置 RAG 召唤器

  1. 效果展示：

Embedding 效果

Reranker 示例：

  1. 注意：此时需要将工作流中 "RAG重排器" 节点的上一节点 "RAG召回器" 的输出格式设置为 "节点"，否则 LazyCraft 将报错

配置输出格式

  1. RAG重排器配置：
    • 选择重排算法类型
    • 模型来源
    • 选择模型
    • 输出格式

配置重排器 1

配置重排器 2

  1. 效果展示：

Reranker 效果