docs: 添加使用示范和启动指南
- examples/demo_usage.py: Python 使用示范(openai SDK + requests) - examples/demo_usage.go: Go 使用示范(net/http) - run.md: 完整启动指南(前置条件、3种启动方式、API Key 配置、验证命令)
This commit is contained in:
@@ -0,0 +1,141 @@
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// Edge AI Gateway 使用示范 (Go 版本)
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//
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// 演示 Go 应用如何调用 Edge AI Gateway 的 OpenAI 兼容 API。
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//
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// 运行:go run examples/demo_usage.go
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package main
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import (
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"bufio"
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"encoding/json"
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"fmt"
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"io"
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"net/http"
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"strings"
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)
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const (
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BaseURL = "http://localhost:8080"
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APIKey = "test-key"
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)
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func main() {
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// === 1. 非流式对话 ===
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fmt.Println("=== 非流式对话 ===")
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resp := doPost(BaseURL+"/v1/chat/completions", map[string]any{
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"model": "general-chat",
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"messages": []map[string]string{{"role": "user", "content": "你好,介绍一下你自己"}},
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"stream": false,
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})
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fmt.Printf("回复: %s\n", resp["choices"].([]any)[0].(map[string]any)["message"].(map[string]any)["content"])
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fmt.Printf("模型: %s\n", resp["actual_model"])
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fmt.Println()
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// === 2. 流式对话 (SSE) ===
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fmt.Println("=== 流式对话 ===")
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doStream(BaseURL+"/v1/chat/completions", map[string]any{
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"model": "fast-chat",
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"messages": []map[string]string{{"role": "user", "content": "写一个Go hello world"}},
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"stream": true,
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})
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fmt.Println()
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// === 3. 查看模型列表 ===
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fmt.Println("=== 可用模型 ===")
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resp2 := doGet(BaseURL + "/v1/models")
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for _, m := range resp2["data"].([]any) {
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fmt.Printf(" - %s\n", m.(map[string]any)["id"])
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}
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fmt.Println()
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// === 4. 会话管理 ===
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fmt.Println("=== 会话管理 ===")
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sess := doPost(BaseURL+"/v1/sessions", map[string]any{
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"application_id": "demo-app",
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"user_id": "user1",
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})
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sessionID := sess["session_id"].(string)
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fmt.Printf("创建会话: %s\n\n", sessionID)
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// === 5. 错误处理 ===
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fmt.Println("=== 错误处理 ===")
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// 无认证
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resp3, _ := http.Get(BaseURL + "/v1/models")
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fmt.Printf("无认证: HTTP %d\n", resp3.StatusCode)
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resp3.Body.Close()
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// 缺少 model
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resp4 := doPost(BaseURL+"/v1/chat/completions", map[string]any{
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"messages": []map[string]string{{"role": "user", "content": "hi"}},
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})
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if err, ok := resp4["error"]; ok {
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fmt.Printf("缺少model: %s\n", err.(map[string]any)["code"])
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}
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fmt.Println("\n✅ 示范完成!")
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}
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func doPost(url string, body map[string]any) map[string]any {
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b, _ := json.Marshal(body)
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req, _ := http.NewRequest("POST", url, strings.NewReader(string(b)))
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req.Header.Set("Content-Type", "application/json")
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req.Header.Set("Authorization", "Bearer "+APIKey)
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resp, err := http.DefaultClient.Do(req)
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if err != nil {
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fmt.Printf("请求失败: %v\n", err)
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return nil
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}
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defer resp.Body.Close()
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data, _ := io.ReadAll(resp.Body)
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var result map[string]any
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json.Unmarshal(data, &result)
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return result
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}
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func doGet(url string) map[string]any {
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req, _ := http.NewRequest("GET", url, nil)
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req.Header.Set("Authorization", "Bearer "+APIKey)
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resp, err := http.DefaultClient.Do(req)
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if err != nil {
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fmt.Printf("请求失败: %v\n", err)
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return nil
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}
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defer resp.Body.Close()
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data, _ := io.ReadAll(resp.Body)
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var result map[string]any
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json.Unmarshal(data, &result)
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return result
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}
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func doStream(url string, body map[string]any) {
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b, _ := json.Marshal(body)
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req, _ := http.NewRequest("POST", url, strings.NewReader(string(b)))
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req.Header.Set("Content-Type", "application/json")
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req.Header.Set("Authorization", "Bearer "+APIKey)
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resp, err := http.DefaultClient.Do(req)
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if err != nil {
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fmt.Printf("请求失败: %v\n", err)
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return
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}
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defer resp.Body.Close()
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scanner := bufio.NewScanner(resp.Body)
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for scanner.Scan() {
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line := scanner.Text()
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if !strings.HasPrefix(line, "data: ") {
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continue
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}
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data := strings.TrimPrefix(line, "data: ")
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if data == "[DONE]" {
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return
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}
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var chunk map[string]any
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json.Unmarshal([]byte(data), &chunk)
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choices := chunk["choices"].([]any)
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delta := choices[0].(map[string]any)["delta"].(map[string]any)
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if content, ok := delta["content"]; ok {
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fmt.Print(content.(string))
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}
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}
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if err := scanner.Err(); err != nil {
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fmt.Printf("\nstream error: %v\n", err)
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}
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}
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@@ -0,0 +1,184 @@
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#!/usr/bin/env python3
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"""
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Edge AI Gateway 使用示范
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本文件演示其他应用如何通过 OpenAI 兼容 API 调用 Edge AI Gateway。
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支持两种方式:
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1. 使用 openai SDK(推荐)
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2. 使用 requests 库直接调用
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运行前请确保:
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- Edge AI Gateway 已启动 (默认 http://localhost:8080)
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- 已配置有效的 API Key
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"""
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# ============================================================
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# 方式一:使用 openai SDK(推荐)
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# ============================================================
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# pip install openai
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8080/v1",
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api_key="test-key", # 替换为你的 API Key
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)
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# --- 非流式对话 ---
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print("=== 非流式对话 ===")
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response = client.chat.completions.create(
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model="general-chat", # 逻辑模型名,网关自动路由到实际模型
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messages=[
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{"role": "system", "content": "你是一个简洁的助手"},
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{"role": "user", "content": "你好,介绍一下你自己"},
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],
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stream=False,
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)
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print(f"回复: {response.choices[0].message.content}")
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print(f"Token: 输入={response.usage.prompt_tokens}, 输出={response.usage.completion_tokens}")
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print()
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# --- 流式对话 (SSE) ---
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print("=== 流式对话 ===")
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stream = client.chat.completions.create(
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model="fast-chat",
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messages=[
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{"role": "user", "content": "写一个 Python 的 hello world"},
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],
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stream=True,
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)
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for chunk in stream:
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delta = chunk.choices[0].delta.content
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if delta:
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print(delta, end="", flush=True)
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print("\n")
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# --- 带优先级的请求 (P0 最高) ---
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print("=== 高优先级请求 ===")
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response = client.chat.completions.create(
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model="general-chat",
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messages=[{"role": "user", "content": "1+1=?"}],
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extra_headers={"X-Priority": "P0"}, # 通过 header 传递优先级
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)
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print(f"回复: {response.choices[0].message.content}")
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print()
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# --- 查看可用模型 ---
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print("=== 可用模型 ===")
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models = client.models.list()
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for m in models.data:
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print(f" - {m.id}")
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print()
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# ============================================================
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# 方式二:使用 requests 库直接调用
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# ============================================================
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# pip install requests
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import requests
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BASE_URL = "http://localhost:8080"
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API_KEY = "test-key"
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HEADERS = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json",
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}
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# --- 非流式 ---
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print("=== requests: 非流式 ===")
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resp = requests.post(
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f"{BASE_URL}/v1/chat/completions",
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headers=HEADERS,
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json={
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"model": "general-chat",
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"messages": [{"role": "user", "content": "你好"}],
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"stream": False,
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},
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)
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data = resp.json()
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print(f"状态: {data.get('status')}")
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print(f"回复: {data['choices'][0]['message']['content']}")
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print(f"模型: {data.get('actual_model')}")
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print()
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# --- 流式 (SSE) ---
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print("=== requests: 流式 ===")
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resp = requests.post(
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f"{BASE_URL}/v1/chat/completions",
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headers=HEADERS,
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json={
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"model": "fast-chat",
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"messages": [{"role": "user", "content": "讲个笑话"}],
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"stream": True,
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},
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stream=True,
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)
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for line in resp.iter_lines():
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if line:
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line = line.decode("utf-8")
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if line.startswith("data: ") and line != "data: [DONE]":
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import json
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chunk = json.loads(line[6:])
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delta = chunk["choices"][0]["delta"].get("content", "")
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if delta:
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print(delta, end="", flush=True)
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print("\n")
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# --- 会话管理 ---
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print("=== 会话管理 ===")
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# 创建会话
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resp = requests.post(
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f"{BASE_URL}/v1/sessions",
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headers=HEADERS,
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json={"application_id": "my-app", "user_id": "user123"},
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)
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session = resp.json()
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session_id = session["session_id"]
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print(f"创建会话: {session_id}")
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# 查询会话
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resp = requests.get(
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f"{BASE_URL}/v1/sessions/{session_id}",
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headers=HEADERS,
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)
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print(f"会话信息: {resp.json()}")
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# 删除会话
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resp = requests.delete(
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f"{BASE_URL}/v1/sessions/{session_id}",
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headers=HEADERS,
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)
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print(f"删除会话: HTTP {resp.status_code}")
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print()
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# --- 错误处理 ---
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print("=== 错误处理 ===")
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# 无认证
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resp = requests.get(f"{BASE_URL}/v1/models")
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print(f"无认证: {resp.status_code} - {resp.json()['error']['code']}")
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# 无效 key
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resp = requests.get(
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f"{BASE_URL}/v1/models",
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headers={"Authorization": "Bearer invalid-key"},
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)
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print(f"无效key: {resp.status_code} - {resp.json()['error']['code']}")
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# 缺少 model
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resp = requests.post(
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f"{BASE_URL}/v1/chat/completions",
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headers=HEADERS,
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json={"messages": [{"role": "user", "content": "hi"}]},
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)
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print(f"缺少model: {resp.status_code} - {resp.json()['error']['code']}")
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# 未知 model
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resp = requests.post(
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f"{BASE_URL}/v1/chat/completions",
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headers=HEADERS,
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json={"model": "nonexistent", "messages": [{"role": "user", "content": "hi"}]},
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)
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print(f"未知model: {resp.status_code} - {resp.json()['error']['code']}")
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print("\n✅ 示范完成!")
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@@ -0,0 +1,154 @@
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# Edge AI Gateway 启动指南
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## 前置条件
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### 1. 安装 Go
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```bash
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# macOS
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brew install go
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# Linux
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wget https://go.dev/dl/go1.23.0.linux-amd64.tar.gz
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sudo tar -C /usr/local -xzf go1.23.0.linux-amd64.tar.gz
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export PATH=$PATH:/usr/local/go/bin
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```
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### 2. 安装 Ollama(本地推理引擎)
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```bash
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# macOS
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brew install ollama
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# Linux
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curl -fsSL https://ollama.com/install.sh | sh
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# 拉取模型
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ollama pull deepseek-r1:1.5b
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```
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### 3. 安装 SQLite
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```bash
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# macOS(自带)
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# Linux
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sudo apt install sqlite3
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```
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## 启动方式
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### 方式一:直接运行
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```bash
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# 1. 创建数据目录
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mkdir -p /tmp/edgeai-data
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# 2. 启动 Ollama(另一个终端)
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ollama serve
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# 3. 启动 Edge AI Gateway
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EDGEAI_DB_PATH=/tmp/edgeai-data go run ./cmd/gateway/ --config configs/config.yaml
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```
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服务器将在 `http://0.0.0.0:8080` 启动。
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### 方式二:编译后运行
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```bash
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# 1. 编译
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go build -o bin/gateway ./cmd/gateway/
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# 2. 启动
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EDGEAI_DB_PATH=/tmp/edgeai-data ./bin/gateway --config configs/config.yaml
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```
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### 方式三:Docker Compose
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```bash
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cd deploy
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docker-compose up -d
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```
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服务端口:
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- Gateway: http://localhost:8080
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- Ollama: http://localhost:11434
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- Prometheus: http://localhost:9090
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- Grafana: http://localhost:3000
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## 配置 API Key
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首次启动后需要创建 API Key:
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```bash
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# 1. 计算 key 的 SHA-256 哈希
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HASH=$(echo -n "your-api-key" | shasum -a 256 | awk '{print $1}')
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# 2. 插入数据库
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sqlite3 /tmp/edgeai-data/auth.db "INSERT INTO api_keys (app_id, tenant_id, name, key_hash, allowed_models, allowed_priorities, is_admin, enabled) VALUES ('my-app', 'my-tenant', 'my-key', '$HASH', '[]', '[]', 1, 1);"
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# 3. 重启服务器使 key 生效
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```
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## 环境变量
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| 变量 | 默认值 | 说明 |
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|------|--------|------|
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| `EDGEAI_DB_PATH` | `/var/lib/edgeai` | 数据库目录 |
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| `EDGEAI_SERVER_PORT` | `8080` | 服务端口 |
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| `EDGEAI_LOG_LEVEL` | `info` | 日志级别 |
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| `EDGEAI_CONFIG_PATH` | `configs/config.yaml` | 配置文件路径 |
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## 验证
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```bash
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# 健康检查
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curl http://localhost:8080/health
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# 查看模型(需 API Key)
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curl -H "Authorization: Bearer your-api-key" http://localhost:8080/v1/models
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# 非流式对话
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curl -X POST http://localhost:8080/v1/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer your-api-key" \
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-d '{"model":"general-chat","messages":[{"role":"user","content":"你好"}]}'
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# 流式对话
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curl -N -X POST http://localhost:8080/v1/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer your-api-key" \
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-d '{"model":"fast-chat","messages":[{"role":"user","content":"你好"}],"stream":true}'
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# Prometheus 指标
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curl http://localhost:8080/metrics
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```
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## 可选:启动 vLLM
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||||
|
||||
```bash
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||||
# 安装 vLLM
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||||
pip install vllm
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# 启动 vLLM 服务(端口 8000)
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vllm serve --model deepseek-ai/deepseek-r1-1.5b --port 8000
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|
||||
# 然后在 Gateway 配置中使用 vllm-chat 模型
|
||||
```
|
||||
|
||||
## 运行测试
|
||||
|
||||
```bash
|
||||
# 单元测试
|
||||
go test ./internal/... -count=1
|
||||
|
||||
# 集成测试
|
||||
go test ./test/integration/ -count=1
|
||||
|
||||
# 全部测试
|
||||
go test ./internal/... -count=1 && \
|
||||
go test ./test/integration/ -count=1 && \
|
||||
go test ./test/security/ -count=1 && \
|
||||
go test ./test/compatibility/ -count=1 && \
|
||||
go test ./test/performance/ -count=1 && \
|
||||
go test ./test/chaos/ -count=1
|
||||
```
|
||||
Reference in New Issue
Block a user