LLM SDK文档
--- title: "LLM SDK文档" description: "编写全面的LLM SDK文档，帮助开发者快速集成和使用" tags: ["SDK文档", "开发工具", "集成指南"] category: "llm" icon: "🧠"
LLM SDK文档
SDK文档概述
SDK文档是为开发者提供的软件开发工具包使用指南，包含SDK的安装、配置、API参考、示例代码和最佳实践。良好的SDK文档可以显著简化集成过程，提高开发效率，降低学习成本。
SDK文档结构
1. 文档架构设计
class LLMSDKDocumentation:
def __init__(self):
self.document_structure = {
"概述": {
"SDK简介": "SDK的功能和用途",
"支持平台": "支持的编程语言和平台",
"系统要求": "最低系统配置要求",
"版本信息": "当前版本和更新历史"
},
"快速开始": {
"安装指南": "各平台安装方法",
"配置说明": "SDK配置和初始化",
"第一个程序": "Hello World示例",
"常见问题": "安装和配置常见问题"
},
"核心功能": {
"客户端管理": "SDK客户端创建和配置",
"认证管理": "API密钥和身份验证",
"请求处理": "发送请求和处理响应",
"错误处理": "异常捕获和错误恢复"
},
"高级特性": {
"流式处理": "实时流式响应",
"异步操作": "异步请求处理",
"批量处理": "批量操作支持",
"连接池": "连接管理和优化"
},
"API参考": {
"类参考": "所有类的详细说明",
"方法参考": "所有方法的详细说明",
"类型定义": "数据类型和接口定义",
"常量定义": "常量和枚举值"
},
"示例": {
"基础示例": "常见使用场景",
"高级示例": "复杂业务场景",
"最佳实践": "性能优化建议",
"故障排除": "常见问题解决方案"
}
}
def generate_document_outline(self):
"""生成文档大纲"""
outline = []
chapter_num = 1
for chapter, sections in self.document_structure.items():
chapter_info = {
"chapter": chapter_num,
"title": chapter,
"sections": []
}
section_num = 1
for section, description in sections.items():
section_info = {
"section": section_num,
"title": section,
"description": description
}
chapter_info["sections"].append(section_info)
section_num += 1
outline.append(chapter_info)
chapter_num += 1
return outline
2. 安装和配置指南
为不同平台提供详细的安装指南：
class InstallationGuide:
def __init__(self):
self.platforms = ["Python", "Node.js", "Go", "Java", "C#"]
def generate_installation_guide(self):
"""生成安装指南"""
guide = {
"Python": self.generate_python_guide(),
"Node.js": self.generate_nodejs_guide(),
"Go": self.generate_go_guide(),
"Java": self.generate_java_guide(),
"C#": self.generate_csharp_guide()
}
return guide
def generate_python_guide(self):
"""生成Python安装指南"""
guide = {
"安装方式": {
"pip安装": "pip install llm-sdk",
"conda安装": "conda install -c conda-forge llm-sdk",
"源码安装": "git clone && pip install -e ."
},
"系统要求": {
"Python版本": "3.7+",
"操作系统": "Windows, macOS, Linux",
"依赖包": "requests, aiohttp"
},
"配置说明": """
# 基本配置
import os
from llm_sdk import LLMClient
# 使用环境变量
client = LLMClient(
api_key=os.environ.get("LLM_API_KEY"),
base_url=os.environ.get("LLM_BASE_URL", "https://api.llm.com"),
timeout=30,
max_retries=3
)
# 使用配置文件
client = LLMClient.from_config("config.yaml")
""",
"验证安装": """
# 验证安装
python -c "import llm_sdk; print(llm_sdk.__version__)"
# 运行测试
python -m llm_sdk.test
"""
}
return guide
def generate_nodejs_guide(self):
"""生成Node.js安装指南"""
guide = {
"安装方式": {
"npm安装": "npm install @llm/sdk",
"yarn安装": "yarn add @llm/sdk",
"pnpm安装": "pnpm add @llm/sdk"
},
"系统要求": {
"Node.js版本": "14+",
"npm版本": "6+",
"操作系统": "Windows, macOS, Linux"
},
"配置说明": """
const { LLMClient } = require("@llm/sdk");
// 基本配置
const client = new LLMClient({
apiKey: process.env.LLM_API_KEY,
baseUrl: process.env.LLM_BASE_URL || "https://api.llm.com",
timeout: 30000,
maxRetries: 3
});
// 使用配置文件
const client = LLMClient.fromConfig("config.json");
""",
"TypeScript支持": """
// TypeScript类型支持
import { LLMClient, GenerateRequest, GenerateResponse } from "@llm/sdk";
const client: LLMClient = new LLMClient({
apiKey: process.env.LLM_API_KEY
});
async function generateText(prompt: string): Promise<GenerateResponse> {
const request: GenerateRequest = {
prompt,
model: "llm-base",
maxTokens: 100
};
return await client.generateText(request);
}
"""
}
return guide
3. 核心功能文档
详细说明SDK的核心功能：
class CoreFeaturesDocumentation:
def __init__(self):
self.core_features = [
"客户端管理",
"认证管理",
"请求处理",
"响应解析",
"错误处理"
]
def document_client_management(self):
"""文档化客户端管理"""
doc = """
# 客户端管理
## 创建客户端
SDK支持多种方式创建客户端实例：
### 基本创建
```python
from llm_sdk import LLMClient
# 使用API密钥
client = LLMClient(api_key="your-api-key")
# 使用环境变量
client = LLMClient() # 自动读取LLM_API_KEY环境变量
高级配置
client = LLMClient(
api_key="your-api-key",
base_url="https://api.llm.com",
timeout=30,
max_retries=3,
retry_delay=1.0,
verify_ssl=True,
proxy="http://proxy.example.com:8080"
)
使用配置文件
# config.yaml
api_key: "your-api-key"
base_url: "https://api.llm.com"
timeout: 30
client = LLMClient.from_config("config.yaml")
客户端生命周期
# 使用上下文管理器
with LLMClient(api_key="your-api-key") as client:
response = client.generate_text("Hello")
# 自动清理资源
# 手动管理生命周期
client = LLMClient(api_key="your-api-key")
try:
response = client.generate_text("Hello")
finally:
client.close()
""" return doc
def document_request_handling(self):
"""文档化请求处理"""
doc = """
请求处理
同步请求
from llm_sdk import LLMClient
client = LLMClient(api_key="your-api-key")
# 文本生成
response = client.generate_text(
prompt="请介绍一下人工智能",
model="llm-base",
max_tokens=500,
temperature=0.7
)
print(response.text)
print(response.usage)
异步请求
import asyncio
from llm_sdk import AsyncLLMClient
async def main():
async with AsyncLLMClient(api_key="your-api-key") as client:
# 异步文本生成
response = await client.generate_text(
prompt="请介绍一下人工智能",
model="llm-base",
max_tokens=500
)
print(response.text)
asyncio.run(main())
流式请求
from llm_sdk import LLMClient
client = LLMClient(api_key="your-api-key")
# 流式生成
for chunk in client.generate_text_stream(
prompt="请介绍一下人工智能",
model="llm-base",
max_tokens=500
):
print(chunk.text, end="", flush=True)
print() # 换行
# 异步流式生成
async def stream_generation():
async with AsyncLLMClient(api_key="your-api-key") as client:
async for chunk in client.generate_text_stream(
prompt="请介绍一下人工智能",
model="llm-base"
):
print(chunk.text, end="", flush=True)
asyncio.run(stream_generation())
""" return doc
## API参考文档
### 1. 类参考
为SDK中的所有类提供详细的参考文档：
```python
class ClassReference:
def __init__(self):
self.classes = {
"LLMClient": {
"描述": "同步LLM客户端",
"构造函数": {
"参数": {
"api_key": "API密钥",
"base_url": "API基础URL",
"timeout": "请求超时时间(秒)",
"max_retries": "最大重试次数"
}
},
"方法": {
"generate_text": "生成文本响应",
"generate_text_stream": "流式生成文本",
"analyze_text": "分析文本内容",
"close": "关闭客户端连接"
}
},
"AsyncLLMClient": {
"描述": "异步LLM客户端",
"构造函数": "同LLMClient",
"方法": {
"generate_text": "异步生成文本响应",
"generate_text_stream": "异步流式生成文本",
"analyze_text": "异步分析文本内容",
"close": "异步关闭客户端连接"
}
}
}
def generate_class_reference(self, class_name):
"""生成类参考文档"""
class_info = self.classes.get(class_name)
reference = f"""
# {class_name}
## 描述
{class_info['描述']}
## 构造函数
```python
{class_name}(
api_key: str = None,
base_url: str = "https://api.llm.com",
timeout: int = 30,
max_retries: int = 3
)
参数说明
| 参数 | 类型 | 必填 | 默认值 | 描述 |
|---|---|---|---|---|
| """ |
for param, description in class_info['构造函数']['参数'].items():
required = "是" if param == "api_key" else "否"
default = "环境变量" if param == "api_key" else "见上表"
reference += f"| {param} | str/int | {required} | {default} | {description} |\n"
reference += f"""
方法
"""
for method, description in class_info['方法'].items():
reference += f"### {method}\n\n{description}\n\n"
return reference
### 2. 方法参考
为每个方法提供详细的参考文档：
```python
class MethodReference:
def __init__(self):
self.methods = {
"generate_text": {
"描述": "生成文本响应",
"语法": "client.generate_text(**kwargs) -> GenerateResponse",
"参数": {
"prompt": {"类型": "str", "必填": True, "描述": "输入提示文本"},
"model": {"类型": "str", "必填": False, "默认值": "llm-base", "描述": "模型名称"},
"max_tokens": {"类型": "int", "必填": False, "默认值": 1000, "描述": "最大生成token数"},
"temperature": {"类型": "float", "必填": False, "默认值": 0.7, "描述": "生成温度"}
},
"返回值": {
"类型": "GenerateResponse",
"属性": {
"text": "生成的文本",
"usage": "token使用统计",
"model": "使用的模型",
"finish_reason": "完成原因"
}
},
"异常": [
"AuthenticationError: 认证失败",
"RateLimitError: 请求频率超限",
"InvalidRequestError: 请求参数错误"
]
}
}
def generate_method_reference(self, method_name):
"""生成方法参考文档"""
method_info = self.methods.get(method_name)
reference = f"""
# {method_name}
## 描述
{method_info['描述']}
## 语法
```python
{method_info['语法']}
参数
| 参数 | 类型 | 必填 | 默认值 | 描述 |
|---|---|---|---|---|
| """ |
for param, info in method_info['参数'].items():
required = "是" if info['必填'] else "否"
default = info.get('默认值', '无')
reference += f"| {param} | {info['类型']} | {required} | {default} | {info['描述']} |\n"
reference += f"""
返回值
类型: {method_info['返回值']['类型']}
| 属性 | 类型 | 描述 |
|---|---|---|
| """ |
for attr, description in method_info['返回值']['属性'].items():
reference += f"| {attr} | str/int/object | {description} |\n"
reference += """
异常
"""
for exception in method_info['异常']:
reference += f"- {exception}\n"
return reference
## 示例代码
### 1. 基础示例
```python
class BasicExamples:
def __init__(self):
self.examples = {
"文本生成": self.text_generation_example(),
"文本分析": self.text_analysis_example(),
"对话管理": self.conversation_example()
}
def text_generation_example(self):
"""文本生成示例"""
example = """
## 文本生成示例
### 基本文本生成
```python
from llm_sdk import LLMClient
client = LLMClient(api_key="your-api-key")
response = client.generate_text(
prompt="请用三句话介绍人工智能",
model="llm-base",
max_tokens=200,
temperature=0.7
)
print("生成的文本:")
print(response.text)
print(f"\\n使用token数: {response.usage.total_tokens}")
流式文本生成
from llm_sdk import LLMClient
client = LLMClient(api_key="your-api-key")
print("流式生成文本:")
for chunk in client.generate_text_stream(
prompt="请写一首关于春天的诗",
model="llm-base",
max_tokens=300
):
print(chunk.text, end="", flush=True)
print() # 换行
""" return example
def conversation_example(self):
"""对话管理示例"""
example = """
对话管理示例
多轮对话
from llm_sdk import LLMClient
client = LLMClient(api_key="your-api-key")
# 初始化对话历史
conversation_history = []
def chat(user_message):
# 添加用户消息到历史
conversation_history.append({
"role": "user",
"content": user_message
})
# 生成响应
response = client.generate_text(
prompt=user_message,
model="llm-base",
max_tokens=500,
temperature=0.7,
context=conversation_history
)
# 添加助手响应到历史
conversation_history.append({
"role": "assistant",
"content": response.text
})
return response.text
# 示例对话
print("用户: 你好！")
print("助手:", chat("你好！"))
print("\\n用户: 你能做什么？")
print("助手:", chat("你能做什么？"))
print("\\n用户: 请告诉我更多关于机器学习的知识")
print("助手:", chat("请告诉我更多关于机器学习的知识"))
""" return example
## 最佳实践
### 1. 性能优化
- **连接复用**：复用客户端实例，避免频繁创建
- **异步处理**：使用异步API提高并发性能
- **批量请求**：使用批量API减少网络开销
- **缓存策略**：合理使用缓存减少重复请求
### 2. 错误处理
- **异常捕获**：捕获并处理所有可能的异常
- **重试机制**：实现指数退避重试策略
- **降级处理**：实现优雅降级，保证服务可用性
- **日志记录**：记录详细的错误日志
### 3. 安全实践
- **密钥管理**：使用环境变量或密钥管理服务
- **输入验证**：验证所有用户输入
- **输出过滤**：过滤敏感信息
- **审计日志**：记录所有API调用
通过编写全面、清晰的LLM SDK文档，可以帮助开发者快速集成和使用SDK，提高开发效率，降低技术支持成本。