使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/dhruvanbhalara/skills --skill dart-genkit开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
AI Engineering with Genkit Dart
Build structured, type-safe, and observable AI-powered workflows and agents using the Genkit Dart SDK.
Contents
Project Setup
- Add the Genkit package to your Dart project's
pubspec.yaml:
yaml
dependencies:
genkit: ^0.1.0-preview # Replace with the latest version
google_generative_ai: ^0.4.0- Set up your API credentials (e.g., Gemini API key) in your environment:
bash
export GEMINI_API_KEY="your_api_key_here"Defining Prompts & Models
Genkit separates model invocation and prompt structure from core application logic using structured prompts.
dart
import 'package:genkit/genkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';
void main() async {
// Initialize Genkit
final ai = Genkit(
model: 'gemini-1.5-flash',
apiKey: String.fromEnvironment('GEMINI_API_KEY'),
);
// Invoke the model with a simple prompt
final response = await ai.generate(
prompt: 'Explain the concept of monads in Dart.',
);
print(response.text);
}Creating Custom Tools
Genkit agents utilize Tools to execute tasks (e.g., database queries, web scraping, mathematical calculations).
dart
import 'package:genkit/genkit.dart';
// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
'a': Schema.number(description: 'First number'),
'b': Schema.number(description: 'Second number'),
});
// 2. Define the tool
final addTool = ai.defineTool(
name: 'addNumbers',
description: 'Adds two numbers together.',
inputSchema: additionInputSchema,
action: (input) async {
final a = input['a'] as num;
final b = input['b'] as num;
return {'result': a + b};
},
);Orchestrating Flows
Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.
dart
import 'package:genkit/genkit.dart';
// Define the input and output schemas
final jokeRequestSchema = Schema.object({
'topic': Schema.string(description: 'The topic for the joke'),
});
final jokeResponseSchema = Schema.object({
'setup': Schema.string(),
'punchline': Schema.string(),
});
// Define the Flow
final jokeFlow = ai.defineFlow(
name: 'jokeFlow',
inputSchema: jokeRequestSchema,
outputSchema: jokeResponseSchema,
action: (input) async {
final topic = input['topic'] as String;
final response = await ai.generate(
prompt: 'Tell me a structured joke about $topic.',
responseSchema: jokeResponseSchema,
);
return response.structuredOutput!;
},
);
void main() async {
// Run the flow
final result = await jokeFlow.run({'topic': 'coding'});
print('Setup: ${result['setup']}');
print('Punchline: ${result['punchline']}');
}Best Practices
- Exhaustive Schema Definitions: Always define explicit
inputSchemaandoutputSchemafor tools and flows to ensure the LLM generates correctly structured arguments. - Environment Isolation: Do not hardcode API keys. Use environment variables or secure credential storage.
- Trace Observability: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
- Error Handling: Wrap tool execution in standard
try-catchblocks and return error details gracefully so the agent can self-correct.
