developing-genkit-go

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Original

English

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Chinese

Genkit Go

Genkit Go is an AI SDK for Go that provides generation, structured output, streaming, tool calling, prompts, and flows with a unified interface across model providers.

Genkit Go 是面向 Go 语言的 AI SDK，提供内容生成、结构化输出、流式传输、工具调用、提示词管理、工作流能力，对不同模型提供商提供统一的调用接口。

Hello World

package main

import (
	"context"
	"fmt"
	"log"
	"net/http"

	"github.com/genkit-ai/genkit/go/ai"
	"github.com/genkit-ai/genkit/go/genkit"
	"github.com/genkit-ai/genkit/go/plugins/googlegenai"
	"github.com/genkit-ai/genkit/go/plugins/server"
)

func main() {
	ctx := context.Background()
	g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.GoogleAI{}))

	genkit.DefineFlow(g, "jokeFlow", func(ctx context.Context, topic string) (string, error) {
		return genkit.GenerateText(ctx, g,
			ai.WithModelName("googleai/gemini-flash-latest"),
			ai.WithPrompt("Tell me a joke about %s", topic),
		)
	})

	mux := http.NewServeMux()
	for _, f := range genkit.ListFlows(g) {
		mux.HandleFunc("POST /"+f.Name(), genkit.Handler(f))
	}
	log.Fatal(server.Start(ctx, "127.0.0.1:8080", mux))
}

package main

import (
	"context"
	"fmt"
	"log"
	"net/http"

	"github.com/genkit-ai/genkit/go/ai"
	"github.com/genkit-ai/genkit/go/genkit"
	"github.com/genkit-ai/genkit/go/plugins/googlegenai"
	"github.com/genkit-ai/genkit/go/plugins/server"
)

func main() {
	ctx := context.Background()
	g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.GoogleAI{}))

	genkit.DefineFlow(g, "jokeFlow", func(ctx context.Context, topic string) (string, error) {
		return genkit.GenerateText(ctx, g,
			ai.WithModelName("googleai/gemini-flash-latest"),
			ai.WithPrompt("Tell me a joke about %s", topic),
		)
	})

	mux := http.NewServeMux()
	for _, f := range genkit.ListFlows(g) {
		mux.HandleFunc("POST /"+f.Name(), genkit.Handler(f))
	}
	log.Fatal(server.Start(ctx, "127.0.0.1:8080", mux))
}

Core Features

核心功能

Load the appropriate reference based on what you need:

Feature	Reference	When to load
Initialization	references/getting-started.md	Setting up `genkit.Init` , plugins, the `*Genkit` instance pattern
Generation	references/generation.md	`Generate` , `GenerateText` , `GenerateData` , streaming, output formats
Prompts	references/prompts.md	`DefinePrompt` , `DefineDataPrompt` , `.prompt` files, schemas
Tools	references/tools.md	`DefineTool` , tool interrupts, `RestartWith` / `RespondWith`
Flows & HTTP	references/flows-and-http.md	`DefineFlow` , `DefineStreamingFlow` , `genkit.Handler` , HTTP serving
Model Providers	references/providers.md	Google AI, Vertex AI, Anthropic, OpenAI-compatible, Ollama setup

根据你的需求加载对应的参考文档：

功能	参考文档	适用场景
初始化	references/getting-started.md	配置 `genkit.Init` 、插件、 `*Genkit` 实例模式
内容生成	references/generation.md	`Generate` 、 `GenerateText` 、 `GenerateData` 、流式传输、输出格式配置
提示词	references/prompts.md	`DefinePrompt` 、 `DefineDataPrompt` 、 `.prompt` 文件、Schema配置
工具	references/tools.md	`DefineTool` 、工具中断、 `RestartWith` / `RespondWith`
工作流与HTTP	references/flows-and-http.md	`DefineFlow` 、 `DefineStreamingFlow` 、 `genkit.Handler` 、HTTP服务部署
模型提供商	references/providers.md	Google AI、Vertex AI、Anthropic、OpenAI兼容模型、Ollama配置

Genkit CLI

Check if installed:

genkit --version

Installation:

bash

curl -sL cli.genkit.dev | bash

Key commands:

bash

undefined

检查是否安装：

genkit --version

安装方式：

bash

curl -sL cli.genkit.dev | bash

核心命令：

bash

undefined

Start app with Developer UI (tracing, flow testing) at http://localhost:4000

genkit start -- go run . genkit start -o -- go run . # also opens browser

Run a flow directly from the CLI

genkit flow:run myFlow '{"data": "input"}' genkit flow:run myFlow '{"data": "input"}' --stream # with streaming genkit flow:run myFlow '{"data": "input"}' --wait # wait for completion

Look up Genkit documentation

genkit docs:search "streaming" go genkit docs:list go genkit docs:read go/flows.md


See [references/getting-started.md](references/getting-started.md) for full CLI and Developer UI details.

genkit docs:search "streaming" go genkit docs:list go genkit docs:read go/flows.md


完整的CLI和开发者UI详情请查看 [references/getting-started.md](references/getting-started.md)。

Key Guidance

核心使用指引

Pass
g
explicitly. The
```
*Genkit
```
instance returned by
```
genkit.Init
```
is the central registry. Pass it to all Genkit functions rather than storing it as a global. This is a core pattern throughout the SDK.
Wrap AI logic in flows. Flows give you tracing, observability, HTTP deployment via
```
genkit.Handler
```
, and the ability to test from the Developer UI and CLI. Any generation call worth keeping should live in a flow.
Use
jsonschema:"description=..."
struct tags on output types. The model uses these descriptions to understand what each field should contain. Without them, structured output quality drops significantly.
Write good tool descriptions. The model decides which tools to call based on their description string. Vague descriptions lead to missed or incorrect tool calls.
Use
.prompt
files for complex prompts. They separate prompt content from Go code, support Handlebars templating, and can be iterated on without recompilation. Code-defined prompts are better for simple, single-line cases.
Look up the latest model IDs. Model names change frequently. Check provider documentation for current model IDs rather than relying on hardcoded names. See references/providers.md.

显式传递
g
参数。
```
genkit.Init
```
返回的
```
*Genkit
```
实例是核心注册中心，请将其传递给所有 Genkit 函数，而不是将其存储为全局变量，这是整个SDK的核心设计模式。
将AI逻辑封装在工作流中。 工作流为你提供链路追踪、可观测性、通过
```
genkit.Handler
```
快速部署HTTP服务的能力，同时支持从开发者UI和CLI进行测试，所有需要长期维护的生成调用都应该放在工作流中。
在输出类型上使用
jsonschema:"description=..."
结构体标签。模型会通过这些描述理解每个字段应该包含的内容，缺少这些标签会大幅降低结构化输出的质量。
编写清晰的工具描述。 模型会根据工具的描述字符串决定调用哪个工具，模糊的描述会导致漏调用或错调用工具。
复杂提示词使用
.prompt
文件存储。这样可以将提示词内容与Go代码分离，支持Handlebars模板，而且迭代提示词无需重新编译。代码定义的提示词更适合简单的单行场景。
查询最新的模型ID。 模型名称会频繁更新，请参考模型提供商的文档获取当前可用的模型ID，不要依赖硬编码的名称，详情见 references/providers.md。