288 lines
5.6 KiB
Markdown
288 lines
5.6 KiB
Markdown
# lingma-ipc-proxy
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[English](./README.md) | [简体中文](./README.zh-CN.md)
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`lingma-ipc-proxy` 是一个独立的 Go 后端，通过 Lingma 本地 pipe 或 websocket 传输与其通信，并对外暴露：
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- `GET /v1/models`
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- `POST /v1/messages`
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- `POST /v1/chat/completions`
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当前范围：
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- 支持非流式与流式响应
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- 单次只处理一个请求
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- 支持 Windows named pipe 传输，也支持本地 websocket 传输
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- 直接走 Lingma IPC，不依赖 DOM/CDP
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## 运行
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```powershell
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cd C:\Workspace\Personal\lingma-ipc-proxy
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go run .\cmd\lingma-ipc-proxy
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```
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## 配置文件
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代理现在支持 JSON 配置文件，这样就不用每次都带一长串启动参数。
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默认会尝试读取：
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```text
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./lingma-ipc-proxy.json
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```
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也可以显式指定：
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```powershell
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.\dist\lingma-ipc-proxy.exe --config .\config.example.json
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```
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参数解析优先级：
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- 内置默认值
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- JSON 配置文件
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- 环境变量
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- 命令行参数
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仓库里附带了一份示例配置：
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- `config.example.json`
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比较实用的方式是先复制成 `lingma-ipc-proxy.json`，改好一次，后面直接启动代理，不再重复拼长参数。
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推荐结构：
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```json
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{
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"host": "127.0.0.1",
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"port": 8095,
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"transport": "auto",
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"mode": "chat",
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"session_mode": "reuse",
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"timeout": 120,
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"cwd": "C:/Workspace/Personal/lingma-ipc-proxy",
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"shell_type": "powershell",
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"current_file_path": "",
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"pipe": "",
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"websocket_url": ""
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}
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```
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## 构建
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构建 Windows 可执行文件：
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```powershell
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cd C:\Workspace\Personal\lingma-ipc-proxy
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.\scripts\build.ps1
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```
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默认输出：
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```text
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dist\lingma-ipc-proxy.exe
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```
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等价的 Go 构建命令：
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```powershell
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$env:CGO_ENABLED = "0"
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$env:GOOS = "windows"
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$env:GOARCH = "amd64"
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go build -trimpath -ldflags "-s -w" -o .\dist\lingma-ipc-proxy.exe .\cmd\lingma-ipc-proxy
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```
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运行构建后的二进制：
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```powershell
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.\dist\lingma-ipc-proxy.exe --host 127.0.0.1 --port 8095 --session-mode auto
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.\dist\lingma-ipc-proxy.exe --transport websocket --ws-url ws://127.0.0.1:36510 --port 8095
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```
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## Windows 服务
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这个项目正确的部署形态是本机进程，不是 Docker。原因很直接：代理需要通过本地 pipe 或 websocket 与 Lingma 通信，所以必须和 Lingma 跑在同一台主机上。
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### NSSM
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先构建：
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```powershell
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.\scripts\build.ps1
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```
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再用 NSSM 安装：
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```powershell
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.\scripts\install-nssm-service.ps1 -NssmPath C:\Tools\nssm\nssm.exe
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```
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它等价于执行：
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```powershell
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nssm.exe install LingmaIpcProxy C:\Workspace\Personal\lingma-ipc-proxy\dist\lingma-ipc-proxy.exe --host 127.0.0.1 --port 8095 --session-mode auto
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nssm.exe set LingmaIpcProxy AppDirectory C:\Workspace\Personal\lingma-ipc-proxy
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nssm.exe start LingmaIpcProxy
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```
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### WinSW
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先准备可执行文件：
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```powershell
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.\scripts\build.ps1
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```
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把 WinSW 二进制放到：
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```text
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dist\WinSW-x64.exe
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```
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然后生成服务包装文件：
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```powershell
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.\scripts\install-winsw-service.ps1
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```
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脚本会生成：
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- `LingmaIpcProxy.exe`
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- `LingmaIpcProxy.xml`
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然后安装并启动：
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```powershell
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.\LingmaIpcProxy.exe install
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.\LingmaIpcProxy.exe start
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```
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WinSW 模板文件位置：
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- `scripts\lingma-ipc-proxy.xml.template`
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## 启动参数
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```powershell
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go run .\cmd\lingma-ipc-proxy --port 8095 --session-mode auto
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```
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- `--host`
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- `--port`
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- `--transport`
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- `--pipe`
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- `--ws-url`
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- `--cwd`
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- `--current-file-path`
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- `--mode`
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- `--shell-type`
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- `--session-mode`
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- `reuse`：持续复用 sticky Lingma 会话
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- `fresh`：为本次请求创建临时会话，结束后自动删除
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- `auto`：单轮请求复用会话；带 system/history 的请求走临时 fresh 会话并在结束后自动删除
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- `--timeout`
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## 环境变量
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- `LINGMA_PROXY_TRANSPORT`
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- `LINGMA_IPC_PIPE`
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- `LINGMA_PROXY_WS_URL`
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- `LINGMA_PROXY_HOST`
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- `LINGMA_PROXY_PORT`
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- `LINGMA_PROXY_CWD`
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- `LINGMA_PROXY_CURRENT_FILE_PATH`
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- `LINGMA_PROXY_MODE`
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- `LINGMA_PROXY_SHELL_TYPE`
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- `LINGMA_PROXY_SESSION_MODE`
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- `LINGMA_PROXY_TIMEOUT_SECONDS`
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## 示例
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Anthropic 非流式：
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```powershell
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$body = @{
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model = "dashscope_qwen3_coder"
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messages = @(
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@{ role = "user"; content = "请只回复：ANTHROPIC_OK" }
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)
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stream = $false
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} | ConvertTo-Json -Depth 8
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Invoke-RestMethod `
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-Method Post `
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-Uri http://127.0.0.1:8095/v1/messages `
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-ContentType "application/json" `
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-Body $body
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```
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Anthropic 流式：
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```powershell
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$body = @{
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model = "dashscope_qwen3_coder"
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messages = @(
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@{ role = "user"; content = "请只回复：ANTHROPIC_STREAM_OK" }
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)
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stream = $true
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} | ConvertTo-Json -Depth 8
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curl.exe -N `
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-H "Content-Type: application/json" `
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-d $body `
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http://127.0.0.1:8095/v1/messages
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```
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OpenAI 非流式：
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```powershell
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$body = @{
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model = "dashscope_qwen3_coder"
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messages = @(
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@{ role = "user"; content = "请只回复：OPENAI_OK" }
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)
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stream = $false
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} | ConvertTo-Json -Depth 8
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Invoke-RestMethod `
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-Method Post `
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-Uri http://127.0.0.1:8095/v1/chat/completions `
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-ContentType "application/json" `
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-Body $body
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```
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OpenAI 流式：
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```powershell
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$body = @{
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model = "dashscope_qwen3_coder"
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messages = @(
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@{ role = "user"; content = "请只回复：OPENAI_STREAM_OK" }
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)
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stream = $true
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} | ConvertTo-Json -Depth 8
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curl.exe -N `
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-H "Content-Type: application/json" `
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-d $body `
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http://127.0.0.1:8095/v1/chat/completions
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```
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## 流式返回形状
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Anthropic 流式响应会输出与 `messages` API 兼容的 SSE 事件：
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- `message_start`
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- `content_block_start`
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- `content_block_delta`
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- `content_block_stop`
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- `message_delta`
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- `message_stop`
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OpenAI 流式响应会输出 `chat.completion.chunk` 形状的 `data:` 行，并以：
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- `data: [DONE]`
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结束。
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