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- 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）
155 lines
3.3 KiB
Markdown
155 lines
3.3 KiB
Markdown
# 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
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```
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## 运行测试
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```bash
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# 单元测试
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go test ./internal/... -count=1
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# 集成测试
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go test ./test/integration/ -count=1
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# 全部测试
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go test ./internal/... -count=1 && \
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go test ./test/integration/ -count=1 && \
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go test ./test/security/ -count=1 && \
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go test ./test/compatibility/ -count=1 && \
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go test ./test/performance/ -count=1 && \
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go test ./test/chaos/ -count=1
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```
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