📷 [图片 token=U8wubBj37oDyjoxkw5wccH2Tn3b（未能下载，见飞书原文）]
📷 [图片 token=IlFpbAZgvor2eYxRxjEcISo6nxb（未能下载，见飞书原文）]
注意，运行程序之前请先看：
[环境准备教程](/oncall/智能 OnCall Agent 项目/第八章 _ 实战演练与运行项目/环境准备教程/)
[运行项目教程](/oncall/智能 OnCall Agent 项目/第八章 _ 实战演练与运行项目/运行项目教程(Go)/)
前言
这部分代码在：SuperBizAgent/internal/ai/agent/chat_pipeline
运行的代码在：SuperBizAgent/internal/ai/cmd/chat_cmd/main.go
📷 [图片 token=Xy4zbRr9KokA4HxUGYdcSlTAnOd（未能下载，见飞书原文）]
流程梳理
对话Agent的核心目标是结合外部知识（RAG召回）与工具调用能力（ReAct模式），解决复杂问题。
整体流程可概括为：
用户输入 -> embedding -> 向量数据库召回
构建带上下文(召回的内容)的 prompt
ReAct模式多轮交互
最终输出答案
我们继续使用eino的可视化编排插件，来进行流程的编排：
首先在Goland里面安装eino-dev的插件
打开插件，按照下图进行编排(或者使用右下角的导入功能，直接导入
SuperBizAgent/internal/ai/cmd/chat_cmd/workflow.json)
{
"name": "EinoAgent",
"node_trigger_mode": "AllPredecessor",
"input_type": {
"title": "*UserMessage"
},
"output_type": {
"title": "*schema.Message",
"description": "github.com/cloudwego/eino/schema"
},
"gen_local_state": {
"output_type": {}
},
"id": "0ijYA1",
"component": "Graph",
"nodes": [
{
"id": "start",
"key": "start",
"name": "Start",
"type": "start",
"layoutData": {
"position": {
"x": 80,
"y": 682
}
}
},
{
"id": "end",
"key": "end",
"name": "End",
"type": "end",
"layoutData": {
"position": {
"x": 1988.99,
"y": 476.79
}
}
},
{
"id": "8AgKo6",
"key": "InputToRag",
"name": "UserMessageToQuery",
"type": "Lambda",
"component_schema": {
"name": "Lambda",
"component": "Lambda",
"component_source": "custom",
"input_type": {
"title": "*UserMessage",
"description": ""
},
"output_type": {
"title": "string",
"description": ""
},
"extra_property": {
"schema": {
"type": "object",
"description": "",
"properties": {
"has_option": {
"type": "boolean",
"description": ""
},
"interaction_type": {
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"description": "",
"enum": [
"invoke",
"stream",
"collect",
"transform"
]
},
"option_package_path": {
"type": "string",
"description": ""
},
"option_type_name": {
"type": "string",
"description": ""
}
},
"required": [
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"has_option"
]
},
"extra_property_input": "{\"has_option\":true,\"interaction_type\":\"invoke\"}"
},
"is_io_type_mutable": true,
"version": "1.0.0"
},
"layoutData": {
"position": {
"x": 359.58,
"y": 808.45
}
},
"node_option": {}
},
{
"id": "A7z9_b",
"key": "ChatTemplate",
"name": "",
"type": "ChatTemplate",
"component_schema": {
"name": "chatTemplate",
"component": "ChatTemplate",
"component_source": "official",
"identifier": "github.com/cloudwego/eino/components/prompt",
"input_type": {
"title": "map[string]any",
"description": ""
},
"output_type": {
"title": "[]*schema.Message",
"description": ""
},
"config": {
"description": "github.com/cloudwego/eino/blob/main/components/prompt/chat_template.go",
"schema": {
"type": "object",
"description": "",
"properties": {
"FormatType": {
"type": "number",
"description": "",
"enum": [
"0",
"1",
"2"
],
"goDefinition": {
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},
"typeName": "schema.FormatType",
"kind": "uint8",
"isPtr": false
}
}
},
"propertyOrder": [
"FormatType"
],
"goDefinition": {
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"version": "",
"module": "",
"pkgPath": ""
},
"typeName": "Config",
"kind": "struct",
"isPtr": false
}
},
"config_input": "{\"FormatType\":1}"
},
"is_io_type_mutable": false,
"version": "1.0.0"
},
"layoutData": {
"position": {
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"y": 516.3
}
},
"node_option": {}
},
{
"id": "CDNTqO",
"key": "ReactAgent",
"name": "ReAct Agent",
"type": "Lambda",
"component_schema": {
"name": "react",
"component": "Lambda",
"component_source": "official",
"identifier": "github.com/cloudwego/eino/flow/agent/react",
"input_type": {
"title": "[]*schema.Message",
"description": ""
},
"output_type": {
"title": "*schema.Message",
"description": ""
},
"slots": [
{
"component": "ChatModel",
"field_loc_path": "Model",
"multiple": false,
"required": false,
"component_items": [
{
"name": "openai",
"component": "ChatModel",
"component_source": "official",
"identifier": "github.com/cloudwego/eino-ext/components/model/openai",
"input_type": {
"title": "[]*schema.Message",
"description": ""
},
"output_type": {
"title": "*schema.Message",
"description": ""
},
"config": {
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"schema": {
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"description": "",
"properties": {
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"description": "",
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},
"APIVersion": {
"type": "string",
"description": "",
"goDefinition": {
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},
"typeName": "string",
"kind": "string",
"isPtr": false
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},
"BaseURL": {
"type": "string",
"description": "",
"goDefinition": {
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"typeName": "string",
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},
"ByAzure": {
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"description": "",
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"typeName": "bool",
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},
"FrequencyPenalty": {
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"goDefinition": {
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"typeName": "float32",
"kind": "float32",
"isPtr": true
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},
"LogitBias": {
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},
"MaxTokens": {
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},
"Model": {
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"PresencePenalty": {
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},
"ResponseFormat": {
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"properties": {
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"goDefinition": {
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},
"typeName": "openai.ChatCompletionResponseFormatType",
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}
},
"propertyOrder": [
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"goDefinition": {
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},
"typeName": "openai.ChatCompletionResponseFormat",
"kind": "struct",
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},
"Seed": {
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"description": "",
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},
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},
"Stop": {
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},
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}
},
"goDefinition": {
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},
"typeName": "[]string",
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}
},
"Temperature": {
"type": "number",
"description": "",
"goDefinition": {
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},
"typeName": "float32",
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},
"Timeout": {
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},
"TopP": {
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"goDefinition": {
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},
"typeName": "float32",
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},
"User": {
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},
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},
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],
"goDefinition": {
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},
"typeName": "openai.ChatModelConfig",
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}
},
"config_input": ""
},
"is_io_type_mutable": false,
"version": "1.0.0",
"id": "SwlhKV",
"layoutData": {
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],
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}
},
{
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"multiple": true,
"required": true,
"component_items": [
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"identifier": "github.com/cloudwego/eino-ext/components/tool/duckduckgo",
"input_type": {
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},
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},
"config": {
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- 最后点击生成代码，生成到目标目录
📷 [图片 token=Q8clbvraoo4qIFx3OJHcKIfRnXg（未能下载，见飞书原文）]
生成完后，会在目标目录看到生成出来的这些组件，下面我们来逐个介绍
📷 [图片 token=FgxMblVIco0FzixMn2qcEPhZnzg（未能下载，见飞书原文）]
这部分代码在：SuperBizAgent/internal/ai/agent/chat_pipeline
实战
注意，在运行代码之前，务必先看
[环境准备教程](/oncall/智能 OnCall Agent 项目/第八章 _ 实战演练与运行项目/环境准备教程/)
[运行项目教程](/oncall/智能 OnCall Agent 项目/第八章 _ 实战演练与运行项目/运行项目教程(Go)/)
Runnable执行器
运行Agent看看输出
# 运行代码
cd SuperBizAgent/internal/ai/cmd/chat_cmd
# 输出
(base) ➜ chat_cmd git:(main) ✗ go run main.go
Q: 你好
A: 你好！我是你的对话小助手，很高兴为你服务！
我可以帮助你：
- 理解和处理对话上下文
- 搜索网络获取信息
- 查询数据库
- 检查系统警报状态
- 搜索内部文档
请告诉我你需要什么帮助，我会尽力为你提供支持！
2025/11/30 21:46:45 Getting current time: 2025-11-30 21:46:45.704387
2025/11/30 21:46:45 Current time: Seconds=1764510405, Milliseconds=1764510405704, Microseconds=1764510405704387
----------------
Q: 现在是几点
A: 现在是 2025年11月30日 21:46:45
如果需要更精确的时间，我可以提供毫秒或微秒级别的时间信息。
执行代码研究
好，执行完成后。我们来看看具体的代码实现是怎么样的。我们来重点看下面高亮的代码行：
首先调用
chat_pipeline.BuildChatAgent创建了一个runner执行器。调用
runner的Invoke方法，入参是UserMessage。
func main() {
ctx := context.Background()
id := "111"
userMessage := &chat_pipeline.UserMessage{
ID: id,
Query: "你好",
History: mem.GetSimpleMemory(id).GetMessages(),
}
runner, err := chat_pipeline.BuildChatAgent(ctx)
if err != nil {
panic(err)
}
// 第一次对话
out, err := runner.Invoke(ctx, userMessage)
if err != nil {
panic(err)
}
answer := out.Content
fmt.Println("Q: 你好")
fmt.Println("A:", answer)
mem.GetSimpleMemory(id).SetMessages(schema.UserMessage("你好"))
mem.GetSimpleMemory(id).SetMessages(schema.SystemMessage(out.Content))
// 第二次对话
userMessage = &chat_pipeline.UserMessage{
ID: id,
Query: "现在是几点",
History: mem.GetSimpleMemory(id).GetMessages(),
}
out, err = runner.Invoke(ctx, userMessage)
if err != nil {
panic(err)
}
answer = out.Content
fmt.Println("----------------")
fmt.Println("Q: 现在是几点")
fmt.Println("A:", answer)
}
BuildChatAgent研究
我们重点来看一下第一个返回值，r compose.Runnable[*UserMessage, *schema.Message]，这个返回值代表返回一个可以执行的执行器。
其中[*UserMessage, *schema.Message] 对应着下面的Runnable接口的[I, O any]。
这是一个泛型，I代表Input，输入；O代表output输出。
总结一下BuildChatAgent：返回一个执行器，这个执行器的入参是我们自定义的一个UserMessage结构体，出参是框架定义的schema.Message。
func BuildChatAgent(ctx context.Context) (r compose.Runnable[*UserMessage, *schema.Message], err error) {
///
}
// Runnable is the interface for an executable object. Graph, Chain can be compiled into Runnable.
// runnable is the core conception of eino, we do downgrade compatibility for four data flow patterns,
// and can automatically connect components that only implement one or more methods.
// eg, if a component only implements Stream() method, you can still call Invoke() to convert stream output to invoke output.
type Runnable[I, O any] interface {
Invoke(ctx context.Context, input I, opts ...Option) (output O, err error)
Stream(ctx context.Context, input I, opts ...Option) (output *schema.StreamReader[O], err error)
}
type UserMessage struct {
ID string `json:"id"`
Query string `json:"query"`
History []*schema.Message `json:"history"`
}
我们继续看看编排代码BuildChatAgent的其他流程。
里面有很多AddEdge，这里面的点、边连接顺序，其实就是上面我们用eino-dev插件编排的顺序。
📷 [图片 token=BKRpb8ZZUok71CxEV2acjC0anDa（未能下载，见飞书原文）]
也就是说：BuildChatAgent返回的执行器，在调用后会按照这个顺序执行。
下面我们继续来看看每个节点具体做了什么？
func BuildChatAgent(ctx context.Context) (r compose.Runnable[*UserMessage, *schema.Message], err error) {
_ = g.AddEdge(compose.START, InputToRag)
_ = g.AddEdge(compose.START, InputToChat)
_ = g.AddEdge(ReactAgent, compose.END)
_ = g.AddEdge(InputToRag, MilvusRetriever)
_ = g.AddEdge(MilvusRetriever, ChatTemplate)
_ = g.AddEdge(InputToChat, ChatTemplate)
_ = g.AddEdge(ChatTemplate, ReactAgent)
r, err = g.Compile(ctx, compose.WithGraphName("ChatAgent"), compose.WithNodeTriggerMode(compose.AllPredecessor))
return r, err
}
召回组件1—InputToRag lambda node
观察lambda的输入和输出，user message是我们构造的，然后输出一个string，这个string就是用于RAG做召回使用的
/ newInputToRagLambda component initialization function of node 'InputToQuery' in graph 'EinoAgent'
func newInputToRagLambda(ctx context.Context, input *UserMessage, opts ...any) (output string, err error) {
return input.Query, nil
}
召回组件2—InputToRag* *-> Retriever
这里的详细在 《RAG召回实战2》讲了，这里就不赘述了
需要注意的一点，在 orchestration.go 中，我们将输出放到了map[“documents”]中。从向量数据库中召回的内容，会放到一个map里面，并且其key为documents。value就是召回的内容
_ = g.AddRetrieverNode(MilvusRetriever, Retriever, compose.WithOutputKey("documents"))
接收用户输入的组件—InputToChat* *lambda node
观察lambda的输入和输出，user message 是我们构造的。输出是一个map，这个map在chatTemplate里面会用到
// newInputToChatLambda component initialization function of node 'InputToHistory' in graph 'EinoAgent'
func newInputToChatLambda(ctx context.Context, input *UserMessage, opts ...any) (output map[string]any, err error) {
return map[string]any{
"content": input.Query,
"history": input.History,
"date": time.Now().Format("2006-01-02 15:04:05"),
}, nil
}
动态拼接上下文与对话历史Prompt构建—ChatTemplate
我们先来观察一下ChatTemplate节点，它有两个输入，一个是InputToChat的输出，另一个是Retriever的输出。所以ChatTemplate的输入可以看作是前面两个节点输出的并集。分别是：
content : input.Query —用户的输入
history : input.History —历史的输入和输出
date : time.Now() —当前时间
documents : []*.schema.document —召回的内容
📷 [图片 token=FOZdbk95QoQTAYx2HZ6cqo4enpf（未能下载，见飞书原文）]
有了这个map，怎么用呢？我们继续往下看，FormatType: schema.FString* *代表使用{}作为占位符。那么{date} {content} {documents} 这三个比较好理解，框架自动将这三个key的value填充进去。
history是什么意思,为什么没有加{}占位符呢？是因为history的值是[]*schema.Message类型。本质是自定义结构体切片，比如：
"history": []*schema.Message{
{Role: "user", Content: "what is eino?"},
{Role: "assistant", Content: "eino is a great freamwork to build llm apps"}
}
MessagesPlaceholder的意思就是占位，如果map里面有history，直接将其value append进行即可。这里可能有点绕，什么是直接添加进去？
注意这一行：Templates: []schema.MessagesTemplate{},说明Templates 本质也是一个自定义结构体切片。所有直接添加进去的意思就是，把Templates 和history 这两个切片合并起来。从2个小切片合并成一个大切片。
// newChatTemplate component initialization function of node 'ChatTemplate' in graph 'EinoAgent'
func newChatTemplate(ctx context.Context) (ctp prompt.ChatTemplate, err error) {
config := &ChatTemplateConfig{
FormatType: schema.FString,
Templates: []schema.MessagesTemplate{
schema.SystemMessage(systemPrompt),
schema.MessagesPlaceholder("history", false),
schema.UserMessage("{content}"),
},
}
ctp = prompt.FromMessages(config.FormatType, config.Templates...)
return ctp, nil
}
var systemPrompt = `
# 角色：对话小助手
## 核心能力
- 上下文理解与对话
## 互动指南
- 在回复前，请确保你：
• 完全理解用户的需求和问题，如果有不清楚的地方，要向用户确认
## 输出要求：
• 易读，结构良好，必要时换行
## 上下文信息
- 当前日期：{date}
- 相关文档：|-
==== 文档开始 ====
{documents}
==== 文档结束 ====
`
让Agent学会"思考-行动"循环——ReAct组件
📷 [图片 token=Ur0Cb0OqwoEQcYxARtdcgdtUnxc（未能下载，见飞书原文）]
就跟编排图中的ReAct组件一样，它有几个插口，我们需要给它装一个model，以及多个tool工具。
这个react.NewAgent是eino框架提供的接口，我们不需要从0到1实现ReAct，只需要使用eino提供的sdk即可。（但是原理要知道，前面重点介绍了原理！本质就是通过大模型的输出来判断要不要调用工具）
func newReactAgentLambda(ctx context.Context) (lba *compose.Lambda, err error) {
config := &react.AgentConfig{
MaxStep: 25,
ToolReturnDirectly: map[string]struct{}{}}
chatModelIns11, err := newChatModel(ctx)
if err != nil {
return nil, err
}
// 1. 安装模型
config.ToolCallingModel = chatModelIns11
mcpTool, err := tools.GetLogMcpTool()
if err != nil {
return nil, err
}
// 2. 安装工具
config.ToolsConfig.Tools = mcpTool
config.ToolsConfig.Tools = append(config.ToolsConfig.Tools, tools.NewPrometheusAlertsQueryTool())
config.ToolsConfig.Tools = append(config.ToolsConfig.Tools, tools.NewMysqlCrudTool())
config.ToolsConfig.Tools = append(config.ToolsConfig.Tools, tools.NewGetCurrentTimeTool())
config.ToolsConfig.Tools = append(config.ToolsConfig.Tools, tools.NewQueryInternalDocsTool())
// 3. 创建ReAct Agent
ins, err := react.NewAgent(ctx, config)
if err != nil {
return nil, err
}
lba, err = compose.AnyLambda(ins.Generate, ins.Stream, nil, nil)
if err != nil {
return nil, err
}
return lba, nil
}
总结
至此，对话Agent的核心流程RAG召回与ReAct模式的代码就讲完了。如果你一篇一篇看下来，会发现其实代码实现真的不难，而且也不重要，框架帮我们做了很多事情。核心是要搞懂我们的设计原理：RAG、ReAct。