Archived
vault backup: 2026-04-29 18:36:58
This commit is contained in:
@@ -0,0 +1,135 @@
|
||||
---
|
||||
Description: ""
|
||||
date: "2025-07-21"
|
||||
lastmod: ""
|
||||
tags: []
|
||||
title: Flow 集成
|
||||
weight: 3
|
||||
---
|
||||
|
||||
大模型应用是存在**通用场景和模式**的，若把这些场景进行抽象，就能提供一些可以帮助开发者快速构建大模型应用的模版。Eino 的 Flow 模块就是在做这件事。
|
||||
|
||||
目前 Eino 已经集成了 `react agent`、`host multi agent` 两个常用的 Agent 模式，以及 MultiQueryRetriever, ParentIndexer 等。
|
||||
|
||||
- React Agent: [Eino: React Agent 使用手册](/zh/docs/eino/core_modules/flow_integration_components/react_agent_manual)
|
||||
- Multi Agent: [Eino Tutorial: Host Multi-Agent ](/zh/docs/eino/core_modules/flow_integration_components/multi_agent_hosting)
|
||||
|
||||
## Flow 进编排
|
||||
|
||||
Flow 集成组件自身一般是由一个或多个 graph 编排而成。同时，这些 flow 也可以作为节点进入其他 graph 的编排之中，方式有三种：
|
||||
|
||||
1. 如果一个 flow 实现了某个组件的 interface，可用该组件对应的 AddXXXNode 等方法加入编排，如 multiquery retriever：
|
||||
|
||||
```go
|
||||
// instantiate the flow: multiquery.NewRetriever
|
||||
|
||||
vk, err := newVikingDBRetriever(ctx, vikingDBHost, vikingDBRegion, vikingDBAK, vikingDBSK)
|
||||
if err != nil {
|
||||
logs.Errorf("newVikingDBRetriever failed, err=%v", err)
|
||||
return
|
||||
}
|
||||
|
||||
llm, err := newChatModel(ctx, openAIBaseURL, openAIAPIKey, openAIModelName)
|
||||
if err != nil {
|
||||
logs.Errorf("newChatModel failed, err=%v", err)
|
||||
return
|
||||
}
|
||||
|
||||
// rewrite query by llm
|
||||
mqr, err := multiquery.NewRetriever(ctx, &multiquery.Config{
|
||||
RewriteLLM: llm,
|
||||
RewriteTemplate: nil, // use default
|
||||
QueryVar: "", // use default
|
||||
LLMOutputParser: nil, // use default
|
||||
MaxQueriesNum: 3,
|
||||
OrigRetriever: vk,
|
||||
FusionFunc: nil, // use default fusion, just deduplicate by doc id
|
||||
})
|
||||
if err != nil {
|
||||
logs.Errorf("NewMultiQueryRetriever failed, err=%v", err)
|
||||
return
|
||||
}
|
||||
|
||||
// add the flow to graph
|
||||
graph := compose.NewGraph[string, *schema.Message]()
|
||||
_ = graph.AddRetrieverNode("multi_query_retriever", mqr, compose.WithOutputKey("context"))
|
||||
_ = graph.AddEdge(compose._START_, "multi_query_retriever")
|
||||
_ = graph.AddChatTemplateNode("template", prompt.FromMessages(schema._FString_, schema.UserMessage("{context}")))
|
||||
|
||||
// ...
|
||||
```
|
||||
2. 如果一个 flow 内部是由单个 graph 编排而成，且 flow 的功能可完全等价于这个 graph 的运行（没有不能转化成 graph run 的定制逻辑），则可以将该 flow 的 graph 导出，通过 AddGraphNode 等方法加入编排，如 ReAct Agent 和 Host Multi-Agent：
|
||||
|
||||
```go
|
||||
// instantiate the host multi-agent
|
||||
hostMA, err := NewMultiAgent(ctx, &MultiAgentConfig{
|
||||
Host: Host{
|
||||
ChatModel: mockHostLLM,
|
||||
},
|
||||
Specialists: []*Specialist{
|
||||
specialist1,
|
||||
specialist2,
|
||||
},
|
||||
})
|
||||
assert.Nil(t, err)
|
||||
|
||||
// export graph and []GraphAddNodeOption from host multi-agent
|
||||
maGraph, opts := hostMA.ExportGraph()
|
||||
|
||||
// add to another graph
|
||||
fullGraph, err := compose.NewChain[map[string]any, *schema.Message]().
|
||||
AppendChatTemplate(prompt.FromMessages(schema._FString_, schema.UserMessage("what's the capital city of {country_name}"))).
|
||||
AppendGraph(maGraph, append(opts, compose.WithNodeKey("host_ma_node"))...).
|
||||
Compile(ctx)
|
||||
assert.Nil(t, err)
|
||||
|
||||
// invoke the other graph
|
||||
// convert the flow's own option to compose.Option if needed
|
||||
// assign options to flow's nodes if needed
|
||||
out, err := fullGraph.Invoke(ctx, map[string]any{"country_name": "China"},
|
||||
compose.WithCallbacks(ConvertCallbackHandlers(mockCallback)).
|
||||
DesignateNodeWithPath(compose.NewNodePath("host_ma_node", hostMA.HostNodeKey())))
|
||||
```
|
||||
3. 所有 flow 应当都可以封装成 Lambda，通过 AddLambdaNode 等方法加入编排。目前所有的 flow 都可以通过 1 或 2 加入编排，所以不需要降级到使用 Lambda。如果要用，使用姿势是：
|
||||
|
||||
```go
|
||||
// instantiate the flow
|
||||
a, err := NewAgent(ctx, &AgentConfig{
|
||||
Model: cm,
|
||||
ToolsConfig: compose.ToolsNodeConfig{
|
||||
Tools: []tool.BaseTool{fakeTool, &fakeStreamToolGreetForTest{}},
|
||||
},
|
||||
|
||||
MaxStep: 40,
|
||||
})
|
||||
assert.Nil(t, err)
|
||||
|
||||
chain := compose.NewChain[[]*schema.Message, string]()
|
||||
|
||||
// convert the flow to Lambda
|
||||
agentLambda, err := compose.AnyLambda(a.Generate, a.Stream, nil, nil)
|
||||
assert.Nil(t, err)
|
||||
|
||||
// add lambda to another graph
|
||||
chain.
|
||||
AppendLambda(agentLambda).
|
||||
AppendLambda(compose.InvokableLambda(func(ctx context.Context, input *schema.Message) (string, error) {
|
||||
t.Log("got agent response: ", input.Content)
|
||||
return input.Content, nil
|
||||
}))
|
||||
r, err := chain.Compile(ctx)
|
||||
assert.Nil(t, err)
|
||||
|
||||
// invoke the graph
|
||||
res, err := r.Invoke(ctx, []*schema.Message{{Role: schema._User_, Content: "hello"}},
|
||||
compose.WithCallbacks(callbackForTest))
|
||||
```
|
||||
|
||||
三个方法的对比如下：
|
||||
|
||||
<table>
|
||||
<tr><td>方式</td><td>适用场景</td><td>优势</td></tr>
|
||||
<tr><td>作为组件</td><td>需实现组件的 interface</td><td>简单直接，语义清晰</td></tr>
|
||||
<tr><td>作为 Graph</td><td>由单个 graph 编排而成，功能不超出这个 graph 的范围</td><td>graph 内节点对外层 graph 暴露，可统一分配运行时 option，相比 Lambda 少一层转化，可通过 GraphCompileCallback 获取上下级 graph 关系</td></tr>
|
||||
<tr><td>作为 Lambda</td><td>所有</td><td>普适</td></tr>
|
||||
</table>
|
||||
@@ -0,0 +1,420 @@
|
||||
---
|
||||
Description: ""
|
||||
date: "2026-01-20"
|
||||
lastmod: ""
|
||||
tags: []
|
||||
title: Host Multi-Agent
|
||||
weight: 2
|
||||
---
|
||||
|
||||
Host Multi-Agent 是一个 Host 做意图识别后，跳转到某个专家 agent 做实际的生成。只转发，不生成子任务。
|
||||
|
||||
以一个简单的“日记助手”做例子：可以写日记、读日记、根据日记回答问题。
|
||||
|
||||
完整样例参见：[https://github.com/cloudwego/eino-examples/tree/main/flow/agent/multiagent/host/journal](https://github.com/cloudwego/eino-examples/tree/main/flow/agent/multiagent/host/journal)
|
||||
|
||||
Host：
|
||||
|
||||
```go
|
||||
func newHost(ctx context.Context, baseURL, apiKey, modelName string) (*host.Host, error) {
|
||||
chatModel, err := openai.NewChatModel(ctx, &openai.ChatModelConfig{
|
||||
BaseURL: baseURL,
|
||||
Model: modelName,
|
||||
ByAzure: true,
|
||||
APIKey: apiKey,
|
||||
})
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return &host.Host{
|
||||
ChatModel: chatModel,
|
||||
SystemPrompt: "You can read and write journal on behalf of the user. When user asks a question, always answer with journal content.",
|
||||
}, nil
|
||||
}
|
||||
```
|
||||
|
||||
写日记的“专家”：host 识别出用户意图是写日记后，会跳转到这里，把用户想要写的内容写到文件里。
|
||||
|
||||
```go
|
||||
func newWriteJournalSpecialist(ctx context.Context) (*host.Specialist, error) {
|
||||
chatModel, err := ollama.NewChatModel(ctx, &ollama.ChatModelConfig{
|
||||
BaseURL: "http://localhost:11434",
|
||||
Model: "llama3-groq-tool-use",
|
||||
|
||||
Options: &api.Options{
|
||||
Temperature: 0.000001,
|
||||
},
|
||||
})
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
// use a chat model to rewrite user query to journal entry
|
||||
// for example, the user query might be:
|
||||
//
|
||||
// write: I got up at 7:00 in the morning.
|
||||
//
|
||||
// should be rewritten to:
|
||||
//
|
||||
// I got up at 7:00 in the morning.
|
||||
chain := compose.NewChain[[]*schema.Message, *schema.Message]()
|
||||
chain.AppendLambda(compose.InvokableLambda(func(ctx context.Context, input []*schema.Message) ([]*schema.Message, error) {
|
||||
systemMsg := &schema.Message{
|
||||
Role: schema._System_,
|
||||
Content: "You are responsible for preparing the user query for insertion into journal. The user's query is expected to contain the actual text the user want to write to journal, as well as convey the intention that this query should be written to journal. You job is to remove that intention from the user query, while preserving as much as possible the user's original query, and output ONLY the text to be written into journal",
|
||||
}
|
||||
return append([]*schema.Message{systemMsg}, input...), nil
|
||||
})).
|
||||
AppendChatModel(chatModel).
|
||||
AppendLambda(compose.InvokableLambda(func(ctx context.Context, input *schema.Message) (*schema.Message, error) {
|
||||
err := appendJournal(input.Content)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
return &schema.Message{
|
||||
Role: schema._Assistant_,
|
||||
Content: "Journal written successfully: " + input.Content,
|
||||
}, nil
|
||||
}))
|
||||
|
||||
r, err := chain.Compile(ctx)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return &host.Specialist{
|
||||
AgentMeta: host.AgentMeta{
|
||||
Name: "write_journal",
|
||||
IntendedUse: "treat the user query as a sentence of a journal entry, append it to the right journal file",
|
||||
},
|
||||
Invokable: func(ctx context.Context, input []*schema.Message, opts ...agent.AgentOption) (*schema.Message, error) {
|
||||
return r.Invoke(ctx, input, agent.GetComposeOptions(opts...)...)
|
||||
},
|
||||
}, nil
|
||||
}
|
||||
```
|
||||
|
||||
读日记的“专家”：host 识别出用户意图是读日记后，会跳转到这里，读日记文件内容并一行行的输出。就是一个本地的 function。
|
||||
|
||||
```go
|
||||
func newReadJournalSpecialist(ctx context.Context) (*host.Specialist, error) {
|
||||
// create a new read journal specialist
|
||||
return &host.Specialist{
|
||||
AgentMeta: host.AgentMeta{
|
||||
Name: "view_journal_content",
|
||||
IntendedUse: "let another agent view the content of the journal",
|
||||
},
|
||||
Streamable: func(ctx context.Context, input []*schema.Message, opts ...agent.AgentOption) (*schema.StreamReader[*schema.Message], error) {
|
||||
now := time.Now()
|
||||
dateStr := now.Format("2006-01-02")
|
||||
|
||||
journal, err := readJournal(dateStr)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
reader, writer := schema.Pipe[*schema.Message](0)
|
||||
go func() {
|
||||
scanner := bufio.NewScanner(journal)
|
||||
scanner.Split(bufio.ScanLines)
|
||||
|
||||
for scanner.Scan() {
|
||||
line := scanner.Text()
|
||||
message := &schema.Message{
|
||||
Role: schema._Assistant_,
|
||||
Content: line + "\n",
|
||||
}
|
||||
writer.Send(message, nil)
|
||||
}
|
||||
|
||||
if err := scanner.Err(); err != nil {
|
||||
writer.Send(nil, err)
|
||||
}
|
||||
|
||||
writer.Close()
|
||||
}()
|
||||
|
||||
return reader, nil
|
||||
},
|
||||
}, nil
|
||||
}
|
||||
```
|
||||
|
||||
根据日记回答问题的"专家"：
|
||||
|
||||
```go
|
||||
func newAnswerWithJournalSpecialist(ctx context.Context) (*host.Specialist, error) {
|
||||
chatModel, err := ollama.NewChatModel(ctx, &ollama.ChatModelConfig{
|
||||
BaseURL: "http://localhost:11434",
|
||||
Model: "llama3-groq-tool-use",
|
||||
|
||||
Options: &api.Options{
|
||||
Temperature: 0.000001,
|
||||
},
|
||||
})
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
// create a graph: load journal and user query -> chat template -> chat model -> answer
|
||||
|
||||
graph := compose.NewGraph[[]*schema.Message, *schema.Message]()
|
||||
|
||||
if err = graph.AddLambdaNode("journal_loader", compose.InvokableLambda(func(ctx context.Context, input []*schema.Message) (string, error) {
|
||||
now := time.Now()
|
||||
dateStr := now.Format("2006-01-02")
|
||||
|
||||
return loadJournal(dateStr)
|
||||
}), compose.WithOutputKey("journal")); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddLambdaNode("query_extractor", compose.InvokableLambda(func(ctx context.Context, input []*schema.Message) (string, error) {
|
||||
return input[len(input)-1].Content, nil
|
||||
}), compose.WithOutputKey("query")); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
systemTpl := `Answer user's query based on journal content: {journal}'`
|
||||
chatTpl := prompt.FromMessages(schema._FString_,
|
||||
schema.SystemMessage(systemTpl),
|
||||
schema.UserMessage("{query}"),
|
||||
)
|
||||
if err = graph.AddChatTemplateNode("template", chatTpl); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddChatModelNode("model", chatModel); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddEdge("journal_loader", "template"); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddEdge("query_extractor", "template"); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddEdge("template", "model"); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddEdge(compose._START_, "journal_loader"); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddEdge(compose._START_, "query_extractor"); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if err = graph.AddEdge("model", compose._END_); err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
r, err := graph.Compile(ctx)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return &host.Specialist{
|
||||
AgentMeta: host.AgentMeta{
|
||||
Name: "answer_with_journal",
|
||||
IntendedUse: "load journal content and answer user's question with it",
|
||||
},
|
||||
Invokable: func(ctx context.Context, input []*schema.Message, opts ...agent.AgentOption) (*schema.Message, error) {
|
||||
return r.Invoke(ctx, input, agent.GetComposeOptions(opts...)...)
|
||||
},
|
||||
}, nil
|
||||
}
|
||||
```
|
||||
|
||||
编排成 host multi agent 并在命令行启动：
|
||||
|
||||
```go
|
||||
func main() {
|
||||
ctx := context.Background()
|
||||
h, err := newHost(ctx)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
writer, err := newWriteJournalSpecialist(ctx)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
reader, err := newReadJournalSpecialist(ctx)
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
answerer, err := newAnswerWithJournalSpecialist(ctx)
|
||||
if err!= nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
hostMA, err := host.NewMultiAgent(ctx, &host.MultiAgentConfig{
|
||||
Host: *h,
|
||||
Specialists: []*host.Specialist{
|
||||
writer,
|
||||
reader,
|
||||
answerer,
|
||||
},
|
||||
})
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
cb := &logCallback{}
|
||||
|
||||
for { // 多轮对话，除非用户输入了 "exit"，否则一直循环
|
||||
println("\n\nYou: ") // 提示轮到用户输入了
|
||||
|
||||
var message string
|
||||
scanner := bufio.NewScanner(os.Stdin) // 获取用户在命令行的输入
|
||||
for scanner.Scan() {
|
||||
message += scanner.Text()
|
||||
break
|
||||
}
|
||||
|
||||
if err := scanner.Err(); err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
if message == "exit" {
|
||||
return
|
||||
}
|
||||
|
||||
msg := &schema.Message{
|
||||
Role: schema._User_,
|
||||
Content: message,
|
||||
}
|
||||
|
||||
out, err := hostMA.Stream(ctx, []*schema.Message{msg}, host.WithAgentCallbacks(cb))
|
||||
if err != nil {
|
||||
panic(err)
|
||||
}
|
||||
|
||||
defer out.Close()
|
||||
|
||||
println("\nAnswer:")
|
||||
|
||||
for {
|
||||
msg, err := out.Recv()
|
||||
if err != nil {
|
||||
if err == io.EOF {
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
print(msg.Content)
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
运行 console 输出：
|
||||
|
||||
```go
|
||||
You:
|
||||
write journal: I got up at 7:00 in the morning
|
||||
|
||||
HandOff to write_journal with argument {"reason":"I got up at 7:00 in the morning"}
|
||||
|
||||
Answer:
|
||||
Journal written successfully: I got up at 7:00 in the morning
|
||||
|
||||
You:
|
||||
read journal
|
||||
|
||||
HandOff to view_journal_content with argument {"reason":"User wants to read the journal content."}
|
||||
|
||||
Answer:
|
||||
I got up at 7:00 in the morning
|
||||
|
||||
|
||||
You:
|
||||
when did I get up in the morning?
|
||||
|
||||
HandOff to answer_with_journal with argument {"reason":"To find out the user's morning wake-up times"}
|
||||
|
||||
Answer:
|
||||
You got up at 7:00 in the morning.
|
||||
```
|
||||
|
||||
## FAQ
|
||||
|
||||
### Host 直接输出时没有流式
|
||||
|
||||
Host Multi-Agent 提供了一个 StreamToolCallChecker 的配置，用于判断 Host 是否直接输出。
|
||||
|
||||
不同的模型在流式模式下输出工具调用的方式可能不同: 某些模型(如 OpenAI) 会直接输出工具调用；某些模型 (如 Claude) 会先输出文本，然后再输出工具调用。因此需要使用不同的方法来判断，这个字段用来指定判断模型流式输出中是否包含工具调用的函数。
|
||||
|
||||
可选填写，未填写时使用“非空包”是否包含工具调用判断：
|
||||
|
||||
```go
|
||||
func firstChunkStreamToolCallChecker(_ context.Context, sr *schema.StreamReader[*schema.Message]) (bool, error) {
|
||||
defer sr.Close()
|
||||
|
||||
for {
|
||||
msg, err := sr.Recv()
|
||||
if err == io.EOF {
|
||||
return false, nil
|
||||
}
|
||||
if err != nil {
|
||||
return false, err
|
||||
}
|
||||
|
||||
if len(msg.ToolCalls) > 0 {
|
||||
return true, nil
|
||||
}
|
||||
|
||||
if len(msg.Content) == 0 { // skip empty chunks at the front
|
||||
continue
|
||||
}
|
||||
|
||||
return false, nil
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
上述默认实现适用于：模型输出的 Tool Call Message 中只有 Tool Call。
|
||||
|
||||
默认实现不适用的情况：在输出 Tool Call 前，有非空的 content chunk。此时，需要自定义 tool Call checker 如下：
|
||||
|
||||
```go
|
||||
toolCallChecker := func(ctx context.Context, sr *schema.StreamReader[*schema.Message]) (bool, error) {
|
||||
defer sr.Close()
|
||||
for {
|
||||
msg, err := sr.Recv()
|
||||
if err != nil {
|
||||
if errors.Is(err, io.EOF) {
|
||||
// finish
|
||||
break
|
||||
}
|
||||
|
||||
return false, err
|
||||
}
|
||||
|
||||
if len(msg.ToolCalls) > 0 {
|
||||
return true, nil
|
||||
}
|
||||
}
|
||||
return false, nil
|
||||
}
|
||||
```
|
||||
|
||||
上面这个自定义 StreamToolCallChecker，在极端情况下可能需要判断**所有包**是否包含 ToolCall，从而导致“流式判断”的效果丢失。如果希望尽可能保留“流式判断”效果，解决这一问题的建议是：
|
||||
|
||||
> 💡
|
||||
> 尝试添加 prompt 来约束模型在工具调用时不额外输出文本，例如：“如果需要调用 tool，直接输出 tool，不要输出文本”。
|
||||
>
|
||||
> 不同模型受 prompt 影响可能不同，实际使用时需要自行调整 prompt 并验证效果。
|
||||
|
||||
### Host 同时选择多个 Specialist
|
||||
|
||||
Host 以 Tool Call 的形式给出对 Specialist 的选择，因此可能以 Tool Call 列表的形式同时选中多个 Specialist。此时 Host Multi-Agent 会同时将请求路由到这多个 Specialist，并在多个 Specialist 完成后，通过 Summarizer 节点总结多条 Message 为一条 Message，作为 Host Multi-Agent 的最终输出。
|
||||
|
||||
用户可通过配置 Summarizer，指定一个 ChatModel 以及 SystemPrompt，来定制化 Summarizer 的行为。如未指定，Host Multi-Agent 会将多个 Specialist 的输出 Message Content 拼接后返回。
|
||||
@@ -0,0 +1,591 @@
|
||||
---
|
||||
Description: ""
|
||||
date: "2026-03-16"
|
||||
lastmod: ""
|
||||
tags: []
|
||||
title: ReAct Agent 使用手册
|
||||
weight: 1
|
||||
---
|
||||
|
||||
# 简介
|
||||
|
||||
Eino React Agent 是实现了 [React 逻辑](https://react-lm.github.io/) 的智能体框架，用户可以用来快速灵活地构建并调用 React Agent.
|
||||
|
||||
> 💡
|
||||
> 代码实现详见：[实现代码目录](https://github.com/cloudwego/eino/tree/main/flow/agent/react)
|
||||
|
||||
## 节点拓扑&数据流图
|
||||
|
||||
react agent 底层使用 `compose.Graph` 作为编排方案，一般来说有 2 个节点: ChatModel、Tools，中间运行过程中的所有历史消息都会放入 state 中，在将所有历史消息传递给 ChatModel 之前，会 copy 消息交由 MessageModifier 进行处理，处理的结果再传递给 ChatModel。直到 ChatModel 返回的消息中不再有 tool call，则返回最终消息。
|
||||
|
||||
<a href="/img/eino/react_agent_graph.png" target="_blank"><img src="/img/eino/react_agent_graph.png" width="100%" /></a>
|
||||
|
||||
当 Tools 列表中至少有一个 Tool 配置了 ReturnDirectly 时，ReAct Agent 结构会更复杂：在 ToolsNode 之后会增加一个 Branch，判断是否调用了一个 ReturnDirectly 的 Tool，如果是，直接 END，否则照旧进入 ChatModel。
|
||||
|
||||
## 初始化
|
||||
|
||||
提供了 ReactAgent 初始化函数，必填参数为 Model 和 ToolsConfig，选填参数为 MessageModifier, MaxStep, ToolReturnDirectly 和 StreamToolCallChecker.
|
||||
|
||||
```bash
|
||||
go get github.com/cloudwego/eino-ext/components/model/openai@latest
|
||||
go get github.com/cloudwego/eino@latest
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"github.com/cloudwego/eino-ext/components/model/openai"
|
||||
|
||||
"github.com/cloudwego/eino/components/model"
|
||||
"github.com/cloudwego/eino/components/tool"
|
||||
"github.com/cloudwego/eino/compose"
|
||||
"github.com/cloudwego/eino/flow/agent/react"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
)
|
||||
|
||||
func main() {
|
||||
// 先初始化所需的 chatModel
|
||||
toolableChatModel, err := openai.NewChatModel(...)
|
||||
|
||||
// 初始化所需的 tools
|
||||
tools := compose.ToolsNodeConfig{
|
||||
InvokableTools: []tool.InvokableTool{mytool},
|
||||
StreamableTools: []tool.StreamableTool{myStreamTool},
|
||||
}
|
||||
|
||||
// 创建 agent
|
||||
agent, err := react.NewAgent(ctx, &react.AgentConfig{
|
||||
ToolCallingModel: toolableChatModel,
|
||||
ToolsConfig: tools,
|
||||
...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Model
|
||||
|
||||
由于 ReAct Agent 需要进行工具调用，Model 需要拥有 ToolCall 的能力，因此需要配置一个 ToolCallingChatModel。
|
||||
|
||||
在 Agent 内部，会调用 WithTools 接口向模型注册 Agent 的工具列表，定义为:
|
||||
|
||||
```go
|
||||
// BaseChatModel defines the basic interface for chat models.
|
||||
// It provides methods for generating complete outputs and streaming outputs.
|
||||
// This interface serves as the foundation for all chat model implementations.
|
||||
//
|
||||
//go:generate mockgen -destination ../../internal/mock/components/model/ChatModel_mock.go --package model -source interface.go
|
||||
type BaseChatModel interface {
|
||||
Generate(ctx context.Context, input []*schema.Message, opts ...Option) (*schema.Message, error)
|
||||
Stream(ctx context.Context, input []*schema.Message, opts ...Option) (
|
||||
*schema.StreamReader[*schema.Message], error)
|
||||
}
|
||||
|
||||
// ToolCallingChatModel extends BaseChatModel with tool calling capabilities.
|
||||
// It provides a WithTools method that returns a new instance with
|
||||
// the specified tools bound, avoiding state mutation and concurrency issues.
|
||||
type ToolCallingChatModel interface {
|
||||
BaseChatModel
|
||||
|
||||
// WithTools returns a new ToolCallingChatModel instance with the specified tools bound.
|
||||
// This method does not modify the current instance, making it safer for concurrent use.
|
||||
WithTools(tools []*schema.ToolInfo) (ToolCallingChatModel, error)
|
||||
}
|
||||
```
|
||||
|
||||
目前，eino 提供了 openai, ark 等实现，只要底层模型支持 tool call 即可。
|
||||
|
||||
```bash
|
||||
go get github.com/cloudwego/eino-ext/components/model/openai@latest
|
||||
go get github.com/cloudwego/eino-ext/components/model/ark@latest
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"github.com/cloudwego/eino-ext/components/model/openai"
|
||||
"github.com/cloudwego/eino-ext/components/model/ark"
|
||||
)
|
||||
|
||||
func openaiExample() {
|
||||
chatModel, err := openai.NewChatModel(ctx, &openai.ChatModelConfig{
|
||||
BaseURL: os.Getenv("OPENAI_BASE_URL"),
|
||||
Key: os.Getenv("OPENAI_ACCESS_KEY"),
|
||||
ByAzure: true,
|
||||
Model: "{{model name which support tool call}}",
|
||||
})
|
||||
|
||||
agent, err := react.NewAgent(ctx, react.AgentConfig{
|
||||
ToolCallingModel: chatModel,
|
||||
ToolsConfig: ...,
|
||||
})
|
||||
}
|
||||
|
||||
func arkExample() {
|
||||
arkModel, err := ark.NewChatModel(context.Background(), ark.ChatModelConfig{
|
||||
APIKey: os.Getenv("ARK_API_KEY"),
|
||||
Model: os.Getenv("ARK_MODEL"),
|
||||
})
|
||||
|
||||
agent, err := react.NewAgent(ctx, react.AgentConfig{
|
||||
ToolCallingModel: arkModel,
|
||||
ToolsConfig: ...,
|
||||
})
|
||||
}
|
||||
```
|
||||
|
||||
### ToolsConfig
|
||||
|
||||
toolsConfig 类型为 `compose.ToolsNodeConfig`, 在 eino 中，若要构建一个 Tool 节点，则需要提供 Tool 的信息，以及调用 Tool 的 function。tool 的接口定义如下:
|
||||
|
||||
```go
|
||||
type InvokableRun func(ctx context.Context, arguments string, opts ...Option) (content string, err error)
|
||||
type StreamableRun func(ctx context.Context, arguments string, opts ...Option) (content *schema.StreamReader[string], err error)
|
||||
|
||||
type BaseTool interface {
|
||||
Info() *schema.ToolInfo
|
||||
}
|
||||
|
||||
// InvokableTool the tool for ChatModel intent recognition and ToolsNode execution.
|
||||
type InvokableTool interface {
|
||||
BaseTool
|
||||
Run() InvokableRun
|
||||
}
|
||||
|
||||
// StreamableTool the stream tool for ChatModel intent recognition and ToolsNode execution.
|
||||
type StreamableTool interface {
|
||||
BaseTool
|
||||
Run() StreamableRun
|
||||
}
|
||||
```
|
||||
|
||||
用户可以根据 tool 的接口定义自行实现所需的 tool，同时框架也提供了更简便的构建 tool 的方法：
|
||||
|
||||
```go
|
||||
userInfoTool := utils.NewTool(
|
||||
&schema.ToolInfo{
|
||||
Name: "user_info",
|
||||
Desc: "根据用户的姓名和邮箱，查询用户的公司、职位、薪酬信息",
|
||||
ParamsOneOf: schema.NewParamsOneOfByParams(map[string]*schema.ParameterInfo{
|
||||
"name": {
|
||||
Type: "string",
|
||||
Desc: "用户的姓名",
|
||||
},
|
||||
"email": {
|
||||
Type: "string",
|
||||
Desc: "用户的邮箱",
|
||||
},
|
||||
}),
|
||||
},
|
||||
func(ctx context.Context, input *userInfoRequest) (output *userInfoResponse, err error) {
|
||||
return &userInfoResponse{
|
||||
Name: input.Name,
|
||||
Email: input.Email,
|
||||
Company: "Cool Company LLC.",
|
||||
Position: "CEO",
|
||||
Salary: "9999",
|
||||
}, nil
|
||||
})
|
||||
|
||||
toolConfig := &compose.ToolsNodeConfig{
|
||||
InvokableTools: []tool.InvokableTool{invokeTool},
|
||||
}
|
||||
```
|
||||
|
||||
### MessageModifier
|
||||
|
||||
MessageModifier 会在每次把所有历史消息传递给 ChatModel 之前执行，定义为：
|
||||
|
||||
```go
|
||||
// modify the input messages before the model is called.
|
||||
type MessageModifier func(ctx context.Context, input []*schema.Message) []*schema.Message
|
||||
```
|
||||
|
||||
在 Agent 中配置 MessageModifier 可以修改传入模型的 messages，常用于添加前置的 system message：
|
||||
|
||||
```go
|
||||
import (
|
||||
"github.com/cloudwego/eino/flow/agent/react"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
)
|
||||
|
||||
func main() {
|
||||
agent, err := react.NewAgent(ctx, &react.AgentConfig{
|
||||
Model: toolableChatModel,
|
||||
ToolsConfig: tools,
|
||||
|
||||
MessageModifier: func(ctx context.Context, input []*schema.Message) []*schema.Message {
|
||||
res := make([]*schema.Message, 0, len(input)+1)
|
||||
|
||||
res = append(res, schema.SystemMessage("你是一个 golang 开发专家."))
|
||||
res = append(res, input...)
|
||||
return res
|
||||
},
|
||||
})
|
||||
|
||||
agent.Generate(ctx, []*schema.Message{schema.UserMessage("写一个 hello world 的代码")})
|
||||
// 模型得到的实际输入为：
|
||||
// []*schema.Message{
|
||||
// {Role: schema.System, Content:"你是一个 golang 开发专家."},
|
||||
// {Role: schema.Human, Content: "写一个 hello world 的代码"}
|
||||
//}
|
||||
}
|
||||
```
|
||||
|
||||
### MessageRewriter
|
||||
|
||||
MessageRewriter 在每次 ChatModel 之前执行，会修改并更新保存全局状态中的历史消息：
|
||||
|
||||
```go
|
||||
// MessageRewriter modifies message in the state, before the ChatModel is called.
|
||||
// It takes the messages stored accumulated in state, modify them, and put the modified version back into state.
|
||||
// Useful for compressing message history to fit the model context window,
|
||||
// or if you want to make changes to messages that take effect across multiple model calls.
|
||||
// NOTE: if both MessageModifier and MessageRewriter are set, MessageRewriter will be called before MessageModifier.
|
||||
MessageRewriter MessageModifier
|
||||
```
|
||||
|
||||
常用于上下文压缩这种在多轮 ReAct 循环中需要一直生效的消息变更。
|
||||
|
||||
对比 MessageModifier（只变更不持久，因此适合 system prompt），MessageRewriter 的变更在后续的 ReAct 循环也可见。
|
||||
|
||||
### MaxStep
|
||||
|
||||
指定 Agent 最大运行步长，每次从一个节点转移到下一个节点为一步，默认值为 node 个数 + 2。
|
||||
|
||||
由于 Agent 中一次循环为 ChatModel + Tools，即为 2 步，因此默认值 12 最多可运行 6 个循环。但由于最后一步必须为 ChatModel 返回 (因为 ChatModel 结束后判断无须运行 tool 才能返回最终结果)，因此最多运行 5 次 tool。
|
||||
|
||||
同理，若希望最多可运行 10 个循环 (10 次 ChatModel + 9 次 Tools)，则需要设置 MaxStep 为 20。若希望最多运行 20 个循环，则 MaxStep 需为 40。
|
||||
|
||||
```go
|
||||
func main() {
|
||||
agent, err := react.NewAgent(ctx, &react.AgentConfig{
|
||||
ToolCallingModel: toolableChatModel,
|
||||
ToolsConfig: tools,
|
||||
MaxStep: 20,
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### ToolReturnDirectly
|
||||
|
||||
如果希望当 ChatModel 选择了特定的 Tool 并执行后，Agent 直接把 Tool 的 Response ToolMessage 返回去，则可以在 ToolReturnDirectly 中配置这个 Tool。
|
||||
|
||||
```go
|
||||
a, err = NewAgent(ctx, &AgentConfig{
|
||||
Model: cm,
|
||||
ToolsConfig: compose.ToolsNodeConfig{
|
||||
Tools: []tool.BaseTool{fakeTool, fakeStreamTool},
|
||||
},
|
||||
|
||||
MaxStep: 40,
|
||||
ToolReturnDirectly: map[string]struct{}{fakeToolName: {}}, // one of the two tools is return directly
|
||||
})
|
||||
```
|
||||
|
||||
### StreamToolCallChecker
|
||||
|
||||
不同的模型在流式模式下输出工具调用的方式可能不同: 某些模型(如 OpenAI) 会直接输出工具调用；某些模型 (如 Claude) 会先输出文本，然后再输出工具调用。因此需要使用不同的方法来判断，这个字段用来指定判断模型流式输出中是否包含工具调用的函数。
|
||||
|
||||
可选填写，未填写时使用“非空包”是否包含工具调用判断：
|
||||
|
||||
```go
|
||||
func firstChunkStreamToolCallChecker(_ context.Context, sr *schema.StreamReader[*schema.Message]) (bool, error) {
|
||||
defer sr.Close()
|
||||
|
||||
for {
|
||||
msg, err := sr.Recv()
|
||||
if err == io.EOF {
|
||||
return false, nil
|
||||
}
|
||||
if err != nil {
|
||||
return false, err
|
||||
}
|
||||
|
||||
if len(msg.ToolCalls) > 0 {
|
||||
return true, nil
|
||||
}
|
||||
|
||||
if len(msg.Content) == 0 { // skip empty chunks at the front
|
||||
continue
|
||||
}
|
||||
|
||||
return false, nil
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
上述默认实现适用于：模型输出的 Tool Call Message 中只有 Tool Call。
|
||||
|
||||
默认实现不适用的情况：在输出 Tool Call 前，有非空的 content chunk。此时，需要自定义 tool Call checker 如下：
|
||||
|
||||
```go
|
||||
toolCallChecker := func(ctx context.Context, sr *schema.StreamReader[*schema.Message]) (bool, error) {
|
||||
defer sr.Close()
|
||||
for {
|
||||
msg, err := sr.Recv()
|
||||
if err != nil {
|
||||
if errors.Is(err, io.EOF) {
|
||||
// finish
|
||||
break
|
||||
}
|
||||
|
||||
return false, err
|
||||
}
|
||||
|
||||
if len(msg.ToolCalls) > 0 {
|
||||
return true, nil
|
||||
}
|
||||
}
|
||||
return false, nil
|
||||
}
|
||||
```
|
||||
|
||||
上面这个自定义 StreamToolCallChecker，在极端情况下可能需要判断**所有包**是否包含 ToolCall，从而导致“流式判断”的效果丢失。如果希望尽可能保留“流式判断”效果，解决这一问题的建议是：
|
||||
|
||||
> 💡
|
||||
> 尝试添加 prompt 来约束模型在工具调用时不额外输出文本，例如：“如果需要调用 tool，直接输出 tool，不要输出文本”。
|
||||
>
|
||||
> 不同模型受 prompt 影响可能不同，实际使用时需要自行调整 prompt 并验证效果。
|
||||
|
||||
## 调用
|
||||
|
||||
### Generate
|
||||
|
||||
```go
|
||||
agent, _ := react.NewAgent(...)
|
||||
|
||||
var outMessage *schema.Message
|
||||
outMessage, err = agent.Generate(ctx, []*schema.Message{
|
||||
schema.UserMessage("写一个 golang 的 hello world 程序"),
|
||||
})
|
||||
```
|
||||
|
||||
### Stream
|
||||
|
||||
```go
|
||||
agent, _ := react.NewAgent(...)
|
||||
|
||||
var msgReader *schema.StreamReader[*schema.Message]
|
||||
msgReader, err = agent.Stream(ctx, []*schema.Message{
|
||||
schema.UserMessage("写一个 golang 的 hello world 程序"),
|
||||
})
|
||||
|
||||
for {
|
||||
// msg type is *schema.Message
|
||||
msg, err := msgReader.Recv()
|
||||
if err != nil {
|
||||
if errors.Is(err, io.EOF) {
|
||||
// finish
|
||||
break
|
||||
}
|
||||
// error
|
||||
log.Printf("failed to recv: %v\n", err)
|
||||
return
|
||||
}
|
||||
|
||||
fmt.Print(msg.Content)
|
||||
}
|
||||
```
|
||||
|
||||
### WithCallbacks
|
||||
|
||||
Callback 是在 Agent 运行时特定时机执行的回调，由于 Agent 这个 Graph 里面只有 ChatModel 和 ToolsNode，因此 Agent 的 Callback 就是 ChatModel 和 Tool 的 Callback。react 包中提供了一个 helper function 来帮助用户快速构建针对这两个组件类型的 Callback Handler。
|
||||
|
||||
```go
|
||||
import (
|
||||
template "github.com/cloudwego/eino/utils/callbacks"
|
||||
)
|
||||
// BuildAgentCallback builds a callback handler for agent.
|
||||
// e.g.
|
||||
//
|
||||
// callback := BuildAgentCallback(modelHandler, toolHandler)
|
||||
// agent, err := react.NewAgent(ctx, &AgentConfig{})
|
||||
// agent.Generate(ctx, input, agent.WithComposeOptions(compose.WithCallbacks(callback)))
|
||||
func BuildAgentCallback(modelHandler *template.ModelCallbackHandler, toolHandler *template.ToolCallbackHandler) callbacks.Handler {
|
||||
return template.NewHandlerHelper().ChatModel(modelHandler).Tool(toolHandler).Handler()
|
||||
}
|
||||
```
|
||||
|
||||
### Options
|
||||
|
||||
React agent 支持通过运行时 Option 动态修改
|
||||
|
||||
场景 1：运行时修改 Agent 中的 Model 配置，通过：
|
||||
|
||||
```go
|
||||
// WithChatModelOptions returns an agent option that specifies model.Option for the chat model in agent.
|
||||
func WithChatModelOptions(opts ...model.Option) agent.AgentOption {
|
||||
return agent.WithComposeOptions(compose.WithChatModelOption(opts...))
|
||||
}
|
||||
```
|
||||
|
||||
场景 2：运行时修改 Tool 列表，通过：
|
||||
|
||||
```go
|
||||
// WithToolList returns an agent option that specifies the list of tools can be called which are BaseTool but must implement InvokableTool or StreamableTool.
|
||||
func WithToolList(tools ...tool.BaseTool) agent.AgentOption {
|
||||
return agent.WithComposeOptions(compose.WithToolsNodeOption(compose.WithToolList(tools...)))
|
||||
}
|
||||
```
|
||||
|
||||
另外，也需要修改 ChatModel 中绑定的 tool: `WithChatModelOptions(model.WithTools(...))`
|
||||
|
||||
场景 3：运行时修改某个 Tool 的 option，通过：
|
||||
|
||||
```go
|
||||
// WithToolOptions returns an agent option that specifies tool.Option for the tools in agent.
|
||||
func WithToolOptions(opts ...tool.Option) agent.AgentOption {
|
||||
return agent.WithComposeOptions(compose.WithToolsNodeOption(compose.WithToolOption(opts...)))
|
||||
}
|
||||
```
|
||||
|
||||
### Prompt
|
||||
|
||||
运行时修改 prompt，其实就是在 Generate 或者 Stream 的时候，传入不同的 Message 列表。
|
||||
|
||||
### 获取中间结果
|
||||
|
||||
如果希望实时拿到 React Agent 执行过程中产生的 *schema.Message，可以先通过 WithMessageFuture 获取一个运行时 Option 和一个 MessageFuture：
|
||||
|
||||
```go
|
||||
// WithMessageFuture returns an agent option and a MessageFuture interface instance.
|
||||
// The option configures the agent to collect messages generated during execution,
|
||||
// while the MessageFuture interface allows users to asynchronously retrieve these messages.
|
||||
func WithMessageFuture() (agent.AgentOption, MessageFuture) {
|
||||
h := &cbHandler{started: make(chan struct{})}
|
||||
|
||||
cmHandler := &ub.ModelCallbackHandler{
|
||||
OnEnd: h.onChatModelEnd,
|
||||
OnEndWithStreamOutput: h.onChatModelEndWithStreamOutput,
|
||||
}
|
||||
toolHandler := &ub.ToolCallbackHandler{
|
||||
OnEnd: h.onToolEnd,
|
||||
OnEndWithStreamOutput: h.onToolEndWithStreamOutput,
|
||||
}
|
||||
graphHandler := callbacks.NewHandlerBuilder().
|
||||
OnStartFn(h.onGraphStart).
|
||||
OnStartWithStreamInputFn(h.onGraphStartWithStreamInput).
|
||||
OnEndFn(h.onGraphEnd).
|
||||
OnEndWithStreamOutputFn(h.onGraphEndWithStreamOutput).
|
||||
OnErrorFn(h.onGraphError).Build()
|
||||
cb := ub.NewHandlerHelper().ChatModel(cmHandler).Tool(toolHandler).Graph(graphHandler).Handler()
|
||||
|
||||
option := agent.WithComposeOptions(compose.WithCallbacks(cb))
|
||||
|
||||
return option, h
|
||||
}
|
||||
```
|
||||
|
||||
这个运行时 Option 就正常传递给 Generate 或者 Stream 方法。这个 MessageFuture 可以 GetMessages 或者 GetMessageStreams 来获取各中间状态的 Message。
|
||||
|
||||
> 💡
|
||||
> 传入 MessageFuture 的 Option 后，Agent 仍然会阻塞运行，通过 MessageFuture 接收中间结果需要和 Agent 运行异步（在 goroutine 中读 MessageFuture 或在 goroutine 中运行 Agent）
|
||||
|
||||
## Agent In Graph/Chain
|
||||
|
||||
Agent 可作为 Lambda 嵌入到其他的 Graph 中:
|
||||
|
||||
```go
|
||||
agent, _ := NewAgent(ctx, &AgentConfig{
|
||||
ToolCallingModel: cm,
|
||||
ToolsConfig: compose.ToolsNodeConfig{
|
||||
Tools: []tool.BaseTool{fakeTool, &fakeStreamToolGreetForTest{}},
|
||||
},
|
||||
|
||||
MaxStep: 40,
|
||||
})
|
||||
|
||||
chain := compose.NewChain[[]*schema.Message, string]()
|
||||
agentLambda, _ := compose.AnyLambda(agent.Generate, agent.Stream, nil, nil)
|
||||
|
||||
chain.
|
||||
AppendLambda(agentLambda).
|
||||
AppendLambda(compose.InvokableLambda(func(ctx context.Context, input *schema.Message) (string, error) {
|
||||
t.Log("got agent response: ", input.Content)
|
||||
return input.Content, nil
|
||||
}))
|
||||
r, _ := chain.Compile(ctx)
|
||||
|
||||
res, _ := r.Invoke(ctx, []*schema.Message{{Role: schema.User, Content: "hello"}},
|
||||
compose.WithCallbacks(callbackForTest))
|
||||
```
|
||||
|
||||
## Demo
|
||||
|
||||
### 基本信息
|
||||
|
||||
简介：这是一个拥有两个 tool (query_restaurants 和 query_dishes ) 的 `美食推荐官`
|
||||
|
||||
地址：[eino-examples/flow/agent/react](https://github.com/cloudwego/eino-examples/tree/main/flow/agent/react)
|
||||
|
||||
使用方式:
|
||||
|
||||
1. clone eino-examples repo，并 cd 到根目录
|
||||
2. 提供一个 `OPENAI_API_KEY`: `export OPENAI_API_KEY=xxxxxxx`
|
||||
3. 运行 demo: `go run flow/agent/react/react.go`
|
||||
|
||||
### 运行过程
|
||||
|
||||
<a href="/img/eino/agent_cli_demo.gif" target="_blank"><img src="/img/eino/agent_cli_demo.gif" width="100%" /></a>
|
||||
|
||||
### 运行过程解释
|
||||
|
||||
- 模拟用户输入了 `我在海淀区，给我推荐一些菜，需要有口味辣一点的菜，至少推荐有 2 家餐厅`
|
||||
- agent 运行第一个节点 `ChatModel`，大模型判断出需要做一次 ToolCall 调用来查询餐厅，并且给出的参数为：
|
||||
|
||||
```json
|
||||
"function": {
|
||||
"name": "query_restaurants",
|
||||
"arguments": "{\"location\":\"海淀区\",\"topn\":2}"
|
||||
}
|
||||
```
|
||||
|
||||
- 进入 `Tools` 节点，调用 查询餐厅 的 tool，并且得到结果，结果返回了 2 家海淀区的餐厅信息:
|
||||
|
||||
```json
|
||||
[{"id":"1001","name":"老地方餐厅","place":"北京老胡同 5F, 左转进入","desc":"","score":3},{"id":"1002","name":"人间味道餐厅","place":"北京大世界商城-1F","desc":"","score":5}]
|
||||
```
|
||||
|
||||
- 得到 tool 的结果后，此时对话的 history 中包含了 tool 的结果，再次运行 `ChatModel`，大模型判断出需要再次调用另一个 ToolCall，用来查询餐厅有哪些菜品，注意，由于有两家餐厅，因此大模型返回了 2 个 ToolCall，如下：
|
||||
|
||||
```json
|
||||
"Message": {
|
||||
"role": "ai",
|
||||
"content": "",
|
||||
"tool_calls": [ // <= 这里有 2 个 tool call
|
||||
{
|
||||
"index": 1,
|
||||
"id": "call_wV7zA3vGGJBhuN7r9guhhAfF",
|
||||
"function": {
|
||||
"name": "query_dishes",
|
||||
"arguments": "{\"restaurant_id\": \"1002\", \"topn\": 5}"
|
||||
}
|
||||
},
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_UOsp0jRtzEbfxixNjP5501MF",
|
||||
"function": {
|
||||
"name": "query_dishes",
|
||||
"arguments": "{\"restaurant_id\": \"1001\", \"topn\": 5}"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
- 再次进入到 `Tools` 节点，由于有 2 个 tool call，Tools 节点内部并发执行这两个调用，并且均加入到对话的 history 中，从 callback 的调试日志中可以看到结果如下：
|
||||
|
||||
```json
|
||||
=========[OnToolStart]=========
|
||||
{"restaurant_id": "1001", "topn": 5}
|
||||
=========[OnToolEnd]=========
|
||||
[{"name":"红烧肉","desc":"一块红烧肉","price":20,"score":8},{"name":"清泉牛肉","desc":"很多的水煮牛肉","price":50,"score":8},{"name":"清炒小南瓜","desc":"炒的糊糊的南瓜","price":5,"score":5},{"name":"韩式辣白菜","desc":"这可是开过光的辣白菜，好吃得很","price":20,"score":9},{"name":"酸辣土豆丝","desc":"酸酸辣辣的土豆丝","price":10,"score":9}]
|
||||
=========[OnToolStart]=========
|
||||
{"restaurant_id": "1002", "topn": 5}
|
||||
=========[OnToolEnd]=========
|
||||
[{"name":"红烧排骨","desc":"一块一块的排骨","price":43,"score":7},{"name":"大刀回锅肉","desc":"经典的回锅肉, 肉很大","price":40,"score":8},{"name":"火辣辣的吻","desc":"凉拌猪嘴，口味辣而不腻","price":60,"score":9},{"name":"辣椒拌皮蛋","desc":"擂椒皮蛋，下饭的神器","price":15,"score":8}]
|
||||
```
|
||||
|
||||
- 得到所有 tool call 返回的结果后，再次进入 `ChatModel` 节点，这次大模型发现已经拥有了回答用户提问的所有信息，因此整合信息后输出结论，由于调用时使用的 `Stream` 方法，因此流式返回的大模型结果。
|
||||
|
||||
## 关联阅读
|
||||
|
||||
- [Eino Tutorial: Host Multi-Agent ](/zh/docs/eino/core_modules/flow_integration_components/multi_agent_hosting)
|
||||
Reference in New Issue
Block a user