服务相关配置文件

This commit is contained in:
BBIT-Kai
2025-11-10 18:09:34 +08:00
parent 625d185f69
commit 17b500d4e0
8 changed files with 1213 additions and 7 deletions
+2 -1
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@@ -11,4 +11,5 @@ bbit_ai/test/ocr/
bbit_ai/ce_pybackend.tar bbit_ai/ce_pybackend.tar
ktor/.kotlin/ ktor/.kotlin/
*.mp3 *.mp3
*.log *.log
*.pt
+33
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@@ -0,0 +1,33 @@
services:
# ---------- Bot Server ----------
bot_server:
container_name: ce_bot_server
image: ai.ronsunny.cn:13011/bbit_ai/ce_bot_server:latest
ports:
- "8000:8000" # ws服务端
- "8003:8003" # http服务的端口,用于简单OTA接口(单服务部署),以及视觉分析接口
volumes: # 在本文件所在目录运行
- ./config/bot:/app/data # 配置文件目录
- ./config/models/SenseVoiceSmall/model.pt:/app/models/SenseVoiceSmall/model.pt # 模型文件
depends_on:
- bot_mcp
networks:
- ce_network
# ---------- Bot MCP ----------
bot_mcp:
container_name: ce_bot_mcp
image: ai.ronsunny.cn:13011/bbit_ai/ce_bot_mcp:latest
networks:
- ce_network
restart: unless-stopped
ports:
- "8004:8004"
volumes:
# 配置文件目录
- ./config/bot:/app/data
# ---------- 网络 ----------
networks:
ce_network:
external: true
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@@ -0,0 +1,25 @@
# 如果你只想轻量化安装xiaozhi-server,只使用本地的配置文件,不需要理会这个文件,不需要改动本文件任何东西
# 如果你想从manager-api获取配置,请往下看:
# 请将本文件复制到xiaozhi-server/data目录下,没有data目录,请创建一个,并将复制过去的文件命名为.config.yaml
# 注意如果data目录有.config.yaml文件,请先删除它
# 先启动manager-api和manager-web,注册一个账号,第一个注册的账号为管理员
# 使用管理员,进入【参数管理】页面,找到【server.secret】,复制它到参数值,注意每次从零部署,server.secret都会变化
# 打开本data目录下的.config.yaml文件,修改manager-api.secret为刚才复制出来的server.secret
server:
ip: 0.0.0.0
port: 8000
# http服务的端口,用于视觉分析接口
http_port: 8003
# 视觉分析接口地址
# 向设备发送的视觉分析的接口地址
# 如果按下面默认的写法,系统会自动生成视觉识别地址,并输出在启动日志里,这个地址你可以直接用浏览器访问确认一下
# 当你使用docker部署或使用公网部署(使用ssl、域名)时,不一定准确
# 所以如果你使用docker部署时,将vision_explain设置成局域网地址
# 如果你使用公网部署时,将vision_explain设置成公网地址
vision_explain: http://你的ip或者域名:端口号/mcp/vision/explain
manager-api:
# 你的manager-api的地址,最好使用局域网ip
# 如果使用docker部署,请使用填写成 http://xiaozhi-esp32-server-web:8002/xiaozhi
url: http://127.0.0.1:8002/xiaozhi
# 你的manager-api的token,就是刚才复制出来的server.secret
secret: 你的server.secret值
@@ -0,0 +1,22 @@
[server]
host = 0.0.0.0
port = 8004
debug = false
log_level = INFO
key = 3d880556c4f3470b8b2242d0db0971f9
[websocket]
max_connections = 1000
ping_interval = 30
ping_timeout = 10
close_timeout = 10
[security]
allowed_origins = *
enable_cors = true
[logging]
log_file = logs/mcp_server.log
max_file_size = 10MB
backup_count = 5
@@ -0,0 +1,56 @@
{
"des": [
"在data目录下创建.mcp_server_settings.json文件,可以选择下面的MCP服务,也可以自行添加新的MCP服务。",
"后面不断测试补充好用的mcp服务,欢迎大家一起补充。",
"记得删除注释行,des属性仅为说明,不会被解析。",
"des和link属性,仅为说明安装方式,方便大家查看原始链接,不是必须项。",
"当前支持三种传输模式:stdio(标准输入输出), sse(Server-Sent Events), streamable-http(流式HTTP)。"
],
"mcpServers": {
"Home Assistant": {
"command": "mcp-proxy",
"args": [
"http://YOUR_HA_HOST/mcp_server/sse"
],
"env": {
"API_ACCESS_TOKEN": "YOUR_API_ACCESS_TOKEN"
}
},
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/username/Desktop",
"/path/to/other/allowed/dir"
],
"link":"https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem"
},
"playwright": {
"command": "npx",
"args": ["-y", "@executeautomation/playwright-mcp-server"],
"des" : "run 'npx playwright install' first",
"link": "https://github.com/executeautomation/mcp-playwright"
},
"windows-cli": {
"command": "npx",
"args": ["-y", "@simonb97/server-win-cli"],
"link": "https://github.com/SimonB97/win-cli-mcp-server"
},
"sse-mcp-server": {
"url": "http://localhost:8080/sse",
"headers": {
"Authorization": "Bearer YOUR TOKEN"
},
"des": "使用SSE传输模式(默认)"
},
"streamable-http-mcp-server": {
"url": "http://localhost:8000/mcp",
"transport": "streamable-http",
"headers": {
"Authorization": "Bearer YOUR TOKEN"
},
"des": "使用Streamable HTTP传输模式,适用于生产环境的Web部署"
}
}
}
+28 -1
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@@ -16,8 +16,35 @@ services:
restart: unless-stopped restart: unless-stopped
depends_on: depends_on:
- vue - vue
# ---------- Bot Server ----------
bot_server:
container_name: ce_bot_server
image: ai.ronsunny.cn:13011/bbit_ai/ce_bot_server:latest
ports:
- "8000:8000" # ws服务端
- "8003:8003" # http服务的端口,用于简单OTA接口(单服务部署),以及视觉分析接口
volumes: # 在本文件所在目录运行
- ./config/bot:/app/data # 配置文件目录
- ./config/models/SenseVoiceSmall/model.pt:/app/models/SenseVoiceSmall/model.pt # 模型文件
networks:
- ce_network
# ---------- Bot MCP ----------
bot_mcp:
container_name: ce_bot_mcp
image: ai.ronsunny.cn:13011/bbit_ai/ce_bot_mcp:latest
networks:
- ce_network
restart: unless-stopped
ports:
- "8004:8004"
depends_on:
- vue
volumes: volumes:
- /home/bbit/ssl/ai.ronsunny.cn.pem:/ssl/ai.ronsunny.cn.pem # 配置文件目录
- ./config/bot:/app/data
# ---------- 网络 ---------- # ---------- 网络 ----------
networks: networks:
+46 -5
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@@ -6,17 +6,58 @@ x-kong-config:
KONG_PG_DATABASE: kong KONG_PG_DATABASE: kong
KONG_PG_USER: postgres KONG_PG_USER: postgres
KONG_PG_PASSWORD: 123456 KONG_PG_PASSWORD: 123456
services: services:
fastapi2: pybackend:
build: # 在本文件所在目录运行构建 docker compose build --no-cache # 因为每次build都会用缓存 调试起来极不方便,所以索性在外部先构建 在这里直接运行镜像
context: ../../ # build: # 在本文件所在目录运行构建 docker compose build --no-cache
dockerfile: ./bbit_ai/docker/Dockerfiledev # 开发版 Dockerfile # context: ../../bbit_ai/
container_name: fastapi-dev # dockerfile: ./Dockerfile_ai_lab # 开发版 Dockerfile
container_name: ce_pybackend
image: ce_pybackend:latest
ports: ports:
- "13011:13011" - "13011:13011"
volumes: # 在本文件所在目录运行 volumes: # 在本文件所在目录运行
- ../../bbit_ai/app:/app # 挂载本地代码,实现热更新 - ../../bbit_ai/app:/app # 挂载本地代码,实现热更新
- ../../bbit_ai/docker/:/root/.local/pyzxing # 挂载 jar - ../../bbit_ai/docker/:/root/.local/pyzxing # 挂载 jar
- ./config/bot:/app/data # 配置文件目录
networks:
- ce_network
depends_on:
- milvus
bot_server:
# build: # 在本文件所在目录运行构建 docker compose build --no-cache
# context: ../../bbit_ai/
# dockerfile: ./Dockerfile_bot_server
container_name: ce_bot_server
image: ce_bot_server:latest
ports:
# ws服务端
- "8000:8000"
# http服务的端口,用于简单OTA接口(单服务部署),以及视觉分析接口
- "8003:8003"
volumes: # 在本文件所在目录运行
- ../../bbit_ai/app_bot:/app # 挂载本地代码,实现热更新
# 配置文件目录
- ./config/bot:/app/data
# 模型文件挂接,很重要
- ./config/models/SenseVoiceSmall/model.pt:/app/models/SenseVoiceSmall/model.pt
networks:
- ce_network
bot_mcp:
# build: # 在本文件所在目录运行构建 docker compose build --no-cache
# context: ../../bbit_ai/
# dockerfile: ./Dockerfile_bot_mcp
image: ce_bot_mcp:latest
container_name: ce_bot_mcp
ports:
- "8004:8004"
volumes: # 在本文件所在目录运行
- ../../bbit_ai/app_mcp:/app
# 配置文件目录
- ./config/bot:/app/data
networks: networks:
- ce_network - ce_network