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feat: add services

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- Introduced Convex, an open-source reactive database, with README and environment variable configurations.
- Added Chinese translation for Convex documentation.
- Created docker-compose configuration for Convex services.
- Introduced llama-swap, a model swapping proxy for OpenAI/Anthropic compatible servers, with comprehensive README and example configuration.
- Added Chinese translation for llama-swap documentation.
- Included example environment file and docker-compose setup for llama-swap.
- Configured health checks and resource limits for both Convex and llama-swap services.
This commit is contained in:
Sun-ZhenXing
2026-03-09 09:27:06 +08:00
parent 64c9b251c3
commit fbd0c9b7f4
14 changed files with 1260 additions and 2 deletions
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2
README.md
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@@ -37,6 +37,7 @@ These services require building custom Docker images from source.
| [Clash](./src/clash) | 1.18.0 |
| [ClickHouse](./src/clickhouse) | 24.11.1 |
| [Conductor](./src/conductor) | latest |
| [Convex](./src/convex) | 33cef775 |
| [DeepTutor](./apps/deeptutor) | latest |
| [Dify](./apps/dify) | 0.18.2 |
| [DNSMasq](./src/dnsmasq) | 2.91 |
@@ -77,6 +78,7 @@ These services require building custom Docker images from source.
| [LibreOffice](./src/libreoffice) | latest |
| [libSQL Server](./src/libsql) | latest |
| [LiteLLM](./src/litellm) | main-stable |
| [llama-swap](./src/llama-swap) | cpu |
| [llama.cpp](./src/llama.cpp) | server |
| [LMDeploy](./src/lmdeploy) | v0.11.1 |
| [Logstash](./src/logstash) | 8.16.1 |

2
README.zh.md
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@@ -37,6 +37,7 @@ Compose Anything 通过提供一组高质量的 Docker Compose 配置文件，
| [Clash](./src/clash) | 1.18.0 |
| [ClickHouse](./src/clickhouse) | 24.11.1 |
| [Conductor](./src/conductor) | latest |
| [Convex](./src/convex) | 33cef775 |
| [DeepTutor](./apps/deeptutor) | latest |
| [Dify](./apps/dify) | 0.18.2 |
| [DNSMasq](./src/dnsmasq) | 2.91 |
@@ -77,6 +78,7 @@ Compose Anything 通过提供一组高质量的 Docker Compose 配置文件，
| [LibreOffice](./src/libreoffice) | latest |
| [libSQL Server](./src/libsql) | latest |
| [LiteLLM](./src/litellm) | main-stable |
| [llama-swap](./src/llama-swap) | cpu |
| [llama.cpp](./src/llama.cpp) | server |
| [LMDeploy](./src/lmdeploy) | v0.11.1 |
| [Logstash](./src/logstash) | 8.16.1 |

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apps/opik/docker-compose.yaml
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@@ -182,6 +182,7 @@ services:
minio-init:
<<: *defaults
image: ${GLOBAL_REGISTRY:-}minio/mc:${MINIO_MC_VERSION:-RELEASE.2025-03-12T17-29-24Z}
restart: on-failure
depends_on:
minio:
condition: service_healthy

3
builds/mineru/Dockerfile
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@@ -1,7 +1,6 @@
# Use the official vllm image for gpu with Ampere、Ada Lovelace、Hopper architecture (8.0 <= Compute Capability <= 9.0)
# Compute Capability version query (https://developer.nvidia.com/cuda-gpus)
# only support x86_64 architecture
FROM vllm/vllm-openai:v0.10.1.1
FROM vllm/vllm-openai:v0.10.2
# Use the official vllm image for gpu with Volta、Turing、Blackwell architecture (7.0 < Compute Capability < 8.0 or Compute Capability >= 10.0)
# support x86_64 architecture and ARM(AArch64) architecture

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# Use DaoCloud mirrored vllm image for China region for gpu with Ampere、Ada Lovelace、Hopper architecture (8.0 <= Compute Capability <= 9.0)
# Compute Capability version query (https://developer.nvidia.com/cuda-gpus)
FROM docker.m.daocloud.io/vllm/vllm-openai:v0.10.2
# Use DaoCloud mirrored vllm image for China region for gpu with Volta、Turing、Blackwell architecture (7.0 < Compute Capability < 8.0 or Compute Capability >= 10.0)
# support x86_64 architecture and ARM(AArch64) architecture
# FROM docker.m.daocloud.io/vllm/vllm-openai:v0.11.0
# Install libgl for opencv support & Noto fonts for Chinese characters
RUN apt-get update && \
apt-get install -y \
fonts-noto-core \
fonts-noto-cjk \
fontconfig \
libgl1 && \
fc-cache -fv && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install mineru latest
RUN python3 -m pip install -U 'mineru[core]>=2.7.0' -i https://mirrors.aliyun.com/pypi/simple --break-system-packages && \
python3 -m pip cache purge
# Download models and update the configuration file
RUN /bin/bash -c "mineru-models-download -s modelscope -m all"
# Set the entry point to activate the virtual environment and run the command line tool
ENTRYPOINT ["/bin/bash", "-c", "export MINERU_MODEL_SOURCE=local && exec \"$@\"", "--"]

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src/convex/.env.example Normal file
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# Convex Configuration
# =============================================================================
# Versions
# =============================================================================
CONVEX_BACKEND_VERSION=33cef775a8a6228cbacee4a09ac2c4073d62ed13
CONVEX_DASHBOARD_VERSION=33cef775a8a6228cbacee4a09ac2c4073d62ed13
POSTGRES_VERSION=17-alpine
# =============================================================================
# Port Configuration
# =============================================================================
CONVEX_BACKEND_PORT_OVERRIDE=3210
CONVEX_SITE_PROXY_PORT_OVERRIDE=3211
CONVEX_DASHBOARD_PORT_OVERRIDE=6791
# =============================================================================
# Instance Configuration
# =============================================================================
# Name of your Convex instance
INSTANCE_NAME=convex-self-hosted
# Secret key for instance authentication (generate a strong random string)
# Example: openssl rand -hex 32
INSTANCE_SECRET=
# =============================================================================
# Origins
# =============================================================================
# URL where the Convex backend is accessible
CONVEX_CLOUD_ORIGIN=http://127.0.0.1:3210
# URL where the Convex site proxy is accessible
CONVEX_SITE_ORIGIN=http://127.0.0.1:3211
# URL for the dashboard to connect to the backend
NEXT_PUBLIC_DEPLOYMENT_URL=http://127.0.0.1:3210
# =============================================================================
# Database Configuration
# =============================================================================
# PostgreSQL password (change in production)
POSTGRES_PASSWORD=convex
# Full PostgreSQL connection URL (optional, constructed from above if not set)
# POSTGRES_URL=postgresql://postgres:convex@postgres:5432/convex
# MySQL URL (alternative to PostgreSQL, leave empty to use PostgreSQL)
# MYSQL_URL=
# =============================================================================
# Application Limits
# =============================================================================
# Maximum concurrent mutations
APPLICATION_MAX_CONCURRENT_MUTATIONS=16
# Maximum concurrent Node.js actions
APPLICATION_MAX_CONCURRENT_NODE_ACTIONS=16
# Maximum concurrent queries
APPLICATION_MAX_CONCURRENT_QUERIES=16
# Maximum concurrent V8 actions
APPLICATION_MAX_CONCURRENT_V8_ACTIONS=16
# User action timeout in seconds (empty for default)
ACTIONS_USER_TIMEOUT_SECS=
# =============================================================================
# SSL/TLS Settings
# =============================================================================
# Set to false to require SSL (recommended for production)
DO_NOT_REQUIRE_SSL=true
# =============================================================================
# Data Retention
# =============================================================================
# Document retention delay in seconds (default: 2 days)
DOCUMENT_RETENTION_DELAY=172800
# =============================================================================
# Telemetry and Metrics
# =============================================================================
# Disable telemetry beacon (set to true to disable)
DISABLE_BEACON=false
# Enable Prometheus-compatible /metrics endpoint
DISABLE_METRICS_ENDPOINT=true
# =============================================================================
# Logging
# =============================================================================
# Rust log level (error, warn, info, debug, trace)
RUST_LOG=info
# Enable Rust backtrace (1, full, or empty)
RUST_BACKTRACE=
# Redact logs sent to clients
REDACT_LOGS_TO_CLIENT=
# HTTP server timeout in seconds
HTTP_SERVER_TIMEOUT_SECONDS=
# =============================================================================
# AWS S3 Configuration (Optional - for external storage)
# =============================================================================
# AWS_ACCESS_KEY_ID=
# AWS_SECRET_ACCESS_KEY=
# AWS_REGION=
# AWS_SESSION_TOKEN=
# S3_ENDPOINT_URL=
# S3_STORAGE_EXPORTS_BUCKET=
# S3_STORAGE_FILES_BUCKET=
# S3_STORAGE_MODULES_BUCKET=
# S3_STORAGE_SEARCH_BUCKET=
# S3_STORAGE_SNAPSHOT_IMPORTS_BUCKET=
# AWS_S3_DISABLE_CHECKSUMS=
# AWS_S3_DISABLE_SSE=
# AWS_S3_FORCE_PATH_STYLE=
# =============================================================================
# Development Settings
# =============================================================================
# Development version override
CONVEX_RELEASE_VERSION_DEV=
# Load Monaco editor internally in dashboard
NEXT_PUBLIC_LOAD_MONACO_INTERNALLY=
# =============================================================================
# Timezone
# =============================================================================
TZ=UTC
# =============================================================================
# Resource Limits - Convex Backend
# =============================================================================
CONVEX_BACKEND_CPU_LIMIT=2.0
CONVEX_BACKEND_CPU_RESERVATION=0.5
CONVEX_BACKEND_MEMORY_LIMIT=2G
CONVEX_BACKEND_MEMORY_RESERVATION=512M
# =============================================================================
# Resource Limits - Convex Dashboard
# =============================================================================
CONVEX_DASHBOARD_CPU_LIMIT=0.5
CONVEX_DASHBOARD_CPU_RESERVATION=0.25
CONVEX_DASHBOARD_MEMORY_LIMIT=256M
CONVEX_DASHBOARD_MEMORY_RESERVATION=128M
# =============================================================================
# Resource Limits - PostgreSQL
# =============================================================================
POSTGRES_CPU_LIMIT=1.0
POSTGRES_CPU_RESERVATION=0.25
POSTGRES_MEMORY_LIMIT=1G
POSTGRES_MEMORY_RESERVATION=256M

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# Convex
Convex is an open-source reactive database designed to make life easy for web app developers, whether human or LLM.
## Features
- **Reactive Queries**: Queries automatically update when underlying data changes
- **Real-time Subscriptions**: Live UI updates without manual polling
- **Serverless Functions**: Write backend logic in TypeScript/JavaScript
- **Automatic Caching**: Built-in intelligent caching for optimal performance
- **Type Safety**: Full TypeScript support with generated types
- **Scalable Architecture**: Designed to handle high-throughput applications
## Quick Start
1. Copy `.env.example` to `.env`:
```bash
cp .env.example .env
```
2. Generate an instance secret (required for production):
```bash
openssl rand -hex 32
```
Then set `INSTANCE_SECRET` in your `.env` file.
3. Start Convex:
```bash
docker compose up -d
```
4. Wait for services to be healthy (check with `docker compose ps`)
5. Access the Dashboard at `http://localhost:6791`
6. Backend API is available at `http://localhost:3210`
## Default Configuration
| Service | Port | Description |
| -------------- | ---- | ------------------------------- |
| Convex Backend | 3210 | Main API and WebSocket endpoint |
| Site Proxy | 3211 | Site hosting proxy |
| Dashboard | 6791 | Web UI for managing Convex |
| PostgreSQL | 5432 | Database (internal) |
**Authentication**: Set `INSTANCE_SECRET` for production use.
## Environment Variables
Key environment variables (see `.env.example` for full list):
| Variable | Description | Default |
| --------------------------------- | --------------------------------- | ----------------------- |
| `CONVEX_BACKEND_PORT_OVERRIDE` | Host port for backend API | `3210` |
| `CONVEX_SITE_PROXY_PORT_OVERRIDE` | Host port for site proxy | `3211` |
| `CONVEX_DASHBOARD_PORT_OVERRIDE` | Host port for dashboard | `6791` |
| `INSTANCE_NAME` | Name of the Convex instance | `convex-self-hosted` |
| `INSTANCE_SECRET` | Secret key for authentication | (required) |
| `CONVEX_CLOUD_ORIGIN` | URL for backend access | `http://127.0.0.1:3210` |
| `CONVEX_SITE_ORIGIN` | URL for site proxy access | `http://127.0.0.1:3211` |
| `POSTGRES_PASSWORD` | PostgreSQL password | `convex` |
| `RUST_LOG` | Log level (error/warn/info/debug) | `info` |
| `TZ` | Timezone | `UTC` |
## Resource Requirements
**Minimum**:
- CPU: 1 core
- RAM: 1GB
- Disk: 5GB
**Recommended**:
- CPU: 2+ cores
- RAM: 2GB+
- Disk: 20GB+
## Volumes
- `convex_data`: Convex backend data storage
- `postgres_data`: PostgreSQL database data
## Using with Your Application
To use this self-hosted Convex backend with your application:
1. Set the `CONVEX_SELF_HOSTED_URL` environment variable in your app:
```bash
CONVEX_SELF_HOSTED_URL=http://localhost:3210
```
2. Set the `CONVEX_SELF_HOSTED_ADMIN_KEY` environment variable:
```bash
CONVEX_SELF_HOSTED_ADMIN_KEY=your-instance-secret
```
3. Deploy your Convex functions:
```bash
npx convex dev
```
For more details, see the [Convex Self-Hosting Documentation](https://stack.convex.dev/self-hosted-develop-and-deploy).
## Security Notes
- **Always set a strong `INSTANCE_SECRET`** in production
- Enable SSL/TLS by setting `DO_NOT_REQUIRE_SSL=false` and using a reverse proxy
- Use strong database passwords
- Restrict network access to Convex services
- Consider using AWS S3 for external storage in production
## License
Apache-2.0 (<https://github.com/get-convex/convex-backend/blob/main/LICENSE>)

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# Convex
Convex 是一个开源的响应式数据库，旨在让 Web 应用开发者（无论是人类还是 LLM）的生活更加轻松。
## 功能特性
- **响应式查询**：当底层数据变化时，查询会自动更新
- **实时订阅**：无需手动轮询即可实现实时 UI 更新
- **无服务器函数**：使用 TypeScript/JavaScript 编写后端逻辑
- **自动缓存**：内置智能缓存以获得最佳性能
- **类型安全**：完整的 TypeScript 支持，并生成类型定义
- **可扩展架构**：专为高吞吐量应用而设计
## 快速开始
1. 复制 `.env.example` 到 `.env`：
```bash
cp .env.example .env
```
2. 生成实例密钥（生产环境必需）：
```bash
openssl rand -hex 32
```
然后在 `.env` 文件中设置 `INSTANCE_SECRET`。
3. 启动 Convex：
```bash
docker compose up -d
```
4. 等待服务健康（使用 `docker compose ps` 检查）
5. 访问 Dashboard：`http://localhost:6791`
6. 后端 API 地址：`http://localhost:3210`
## 默认配置
| 服务 | 端口 | 说明 |
| -------------- | ---- | ------------------------ |
| Convex Backend | 3210 | 主 API 和 WebSocket 端点 |
| Site Proxy | 3211 | 站点托管代理 |
| Dashboard | 6791 | 管理 Convex 的 Web UI |
| PostgreSQL | 5432 | 数据库（内部） |
**认证**：生产环境请设置 `INSTANCE_SECRET`。
## 环境变量
关键环境变量（完整列表请参见 `.env.example`）：
| 变量 | 说明 | 默认值 |
| --------------------------------- | --------------------------------- | ----------------------- |
| `CONVEX_BACKEND_PORT_OVERRIDE` | 后端 API 的主机端口 | `3210` |
| `CONVEX_SITE_PROXY_PORT_OVERRIDE` | 站点代理的主机端口 | `3211` |
| `CONVEX_DASHBOARD_PORT_OVERRIDE` | Dashboard 的主机端口 | `6791` |
| `INSTANCE_NAME` | Convex 实例名称 | `convex-self-hosted` |
| `INSTANCE_SECRET` | 认证密钥 | （必需） |
| `CONVEX_CLOUD_ORIGIN` | 后端访问 URL | `http://127.0.0.1:3210` |
| `CONVEX_SITE_ORIGIN` | 站点代理访问 URL | `http://127.0.0.1:3211` |
| `POSTGRES_PASSWORD` | PostgreSQL 密码 | `convex` |
| `RUST_LOG` | 日志级别（error/warn/info/debug） | `info` |
| `TZ` | 时区 | `UTC` |
## 资源需求
**最低配置**：
- CPU：1 核
- 内存：1GB
- 磁盘：5GB
**推荐配置**：
- CPU：2+ 核
- 内存：2GB+
- 磁盘：20GB+
## 数据卷
- `convex_data`：Convex 后端数据存储
- `postgres_data`：PostgreSQL 数据库数据
## 在应用中使用
要将此自托管 Convex 后端与您的应用一起使用：
1. 在应用中设置 `CONVEX_SELF_HOSTED_URL` 环境变量：
```bash
CONVEX_SELF_HOSTED_URL=http://localhost:3210
```
2. 设置 `CONVEX_SELF_HOSTED_ADMIN_KEY` 环境变量：
```bash
CONVEX_SELF_HOSTED_ADMIN_KEY=your-instance-secret
```
3. 部署您的 Convex 函数：
```bash
npx convex dev
```
更多详情，请参阅 [Convex 自托管文档](https://stack.convex.dev/self-hosted-develop-and-deploy)。
## 安全说明
- **生产环境务必设置强 `INSTANCE_SECRET`**
- 通过设置 `DO_NOT_REQUIRE_SSL=false` 并使用反向代理来启用 SSL/TLS
- 使用强数据库密码
- 限制对 Convex 服务的网络访问
- 生产环境考虑使用 AWS S3 进行外部存储
## 许可证
Apache-2.0（<https://github.com/get-convex/convex-backend/blob/main/LICENSE>）

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# Convex - Open-source Reactive Database
# https://github.com/get-convex/convex-backend
#
# Convex is an open-source reactive database designed to make life easy for
# web app developers. It provides real-time data synchronization, automatic
# caching, and a powerful query language.
#
# Key Features:
# - Reactive queries that automatically update when data changes
# - Real-time subscriptions for live UI updates
# - Built-in authentication and authorization
# - Serverless functions with TypeScript/JavaScript
# - Automatic scaling and caching
#
# Default Credentials:
# - Dashboard at http://localhost:6791
# - Backend API at http://localhost:3210
# - Site proxy at http://localhost:3211
#
# Security Notes:
# - Set a strong INSTANCE_SECRET in production
# - Enable SSL/TLS in production
# - Use strong database passwords
# - Restrict network access to Convex services
#
# License: Apache-2.0 (https://github.com/get-convex/convex-backend/blob/main/LICENSE)
x-defaults: &defaults
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: '3'
services:
convex-backend:
<<: *defaults
image: ${GHCR_REGISTRY:-ghcr.io/}get-convex/convex-backend:${CONVEX_BACKEND_VERSION:-33cef775a8a6228cbacee4a09ac2c4073d62ed13}
stop_grace_period: 10s
stop_signal: SIGINT
ports:
- '${CONVEX_BACKEND_PORT_OVERRIDE:-3210}:3210'
- '${CONVEX_SITE_PROXY_PORT_OVERRIDE:-3211}:3211'
volumes:
- convex_data:/convex/data
environment:
# Instance configuration
- INSTANCE_NAME=${INSTANCE_NAME:-convex-self-hosted}
- INSTANCE_SECRET=${INSTANCE_SECRET}
# Origins
- CONVEX_CLOUD_ORIGIN=${CONVEX_CLOUD_ORIGIN:-http://127.0.0.1:3210}
- CONVEX_SITE_ORIGIN=${CONVEX_SITE_ORIGIN:-http://127.0.0.1:3211}
# Database configuration (PostgreSQL)
- POSTGRES_URL=${POSTGRES_URL:-postgresql://postgres:${POSTGRES_PASSWORD:-convex}@postgres:5432/convex}
# Application limits
- APPLICATION_MAX_CONCURRENT_MUTATIONS=${APPLICATION_MAX_CONCURRENT_MUTATIONS:-16}
- APPLICATION_MAX_CONCURRENT_NODE_ACTIONS=${APPLICATION_MAX_CONCURRENT_NODE_ACTIONS:-16}
- APPLICATION_MAX_CONCURRENT_QUERIES=${APPLICATION_MAX_CONCURRENT_QUERIES:-16}
- APPLICATION_MAX_CONCURRENT_V8_ACTIONS=${APPLICATION_MAX_CONCURRENT_V8_ACTIONS:-16}
# Actions timeout
- ACTIONS_USER_TIMEOUT_SECS=${ACTIONS_USER_TIMEOUT_SECS:-}
# SSL/TLS settings
- DO_NOT_REQUIRE_SSL=${DO_NOT_REQUIRE_SSL:-true}
# Document retention (default 2 days in seconds)
- DOCUMENT_RETENTION_DELAY=${DOCUMENT_RETENTION_DELAY:-172800}
# Metrics and beacon
- DISABLE_BEACON=${DISABLE_BEACON:-false}
- DISABLE_METRICS_ENDPOINT=${DISABLE_METRICS_ENDPOINT:-true}
# Logging
- RUST_LOG=${RUST_LOG:-info}
- RUST_BACKTRACE=${RUST_BACKTRACE:-}
- REDACT_LOGS_TO_CLIENT=${REDACT_LOGS_TO_CLIENT:-}
# HTTP server timeout
- HTTP_SERVER_TIMEOUT_SECONDS=${HTTP_SERVER_TIMEOUT_SECONDS:-}
# AWS S3 configuration (optional, for external storage)
- AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID:-}
- AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY:-}
- AWS_REGION=${AWS_REGION:-}
- AWS_SESSION_TOKEN=${AWS_SESSION_TOKEN:-}
- S3_ENDPOINT_URL=${S3_ENDPOINT_URL:-}
- S3_STORAGE_EXPORTS_BUCKET=${S3_STORAGE_EXPORTS_BUCKET:-}
- S3_STORAGE_FILES_BUCKET=${S3_STORAGE_FILES_BUCKET:-}
- S3_STORAGE_MODULES_BUCKET=${S3_STORAGE_MODULES_BUCKET:-}
- S3_STORAGE_SEARCH_BUCKET=${S3_STORAGE_SEARCH_BUCKET:-}
- S3_STORAGE_SNAPSHOT_IMPORTS_BUCKET=${S3_STORAGE_SNAPSHOT_IMPORTS_BUCKET:-}
- AWS_S3_DISABLE_CHECKSUMS=${AWS_S3_DISABLE_CHECKSUMS:-}
- AWS_S3_DISABLE_SSE=${AWS_S3_DISABLE_SSE:-}
- AWS_S3_FORCE_PATH_STYLE=${AWS_S3_FORCE_PATH_STYLE:-}
# MySQL URL (alternative to PostgreSQL)
- MYSQL_URL=${MYSQL_URL:-}
# Development settings
- CONVEX_RELEASE_VERSION_DEV=${CONVEX_RELEASE_VERSION_DEV:-}
# Timezone
- TZ=${TZ:-UTC}
depends_on:
postgres:
condition: service_healthy
healthcheck:
test:
- CMD
- curl
- -f
- http://localhost:3210/version
interval: 5s
timeout: 5s
retries: 5
start_period: 30s
deploy:
resources:
limits:
cpus: '${CONVEX_BACKEND_CPU_LIMIT:-2.0}'
memory: '${CONVEX_BACKEND_MEMORY_LIMIT:-2G}'
reservations:
cpus: '${CONVEX_BACKEND_CPU_RESERVATION:-0.5}'
memory: '${CONVEX_BACKEND_MEMORY_RESERVATION:-512M}'
convex-dashboard:
<<: *defaults
image: ${GHCR_REGISTRY:-ghcr.io/}get-convex/convex-dashboard:${CONVEX_DASHBOARD_VERSION:-33cef775a8a6228cbacee4a09ac2c4073d62ed13}
stop_grace_period: 10s
stop_signal: SIGINT
ports:
- '${CONVEX_DASHBOARD_PORT_OVERRIDE:-6791}:6791'
environment:
- NEXT_PUBLIC_DEPLOYMENT_URL=${NEXT_PUBLIC_DEPLOYMENT_URL:-http://127.0.0.1:3210}
- NEXT_PUBLIC_LOAD_MONACO_INTERNALLY=${NEXT_PUBLIC_LOAD_MONACO_INTERNALLY:-}
depends_on:
convex-backend:
condition: service_healthy
healthcheck:
test:
- CMD
- wget
- --quiet
- --tries=1
- --spider
- http://localhost:6791
interval: 10s
timeout: 5s
retries: 3
start_period: 10s
deploy:
resources:
limits:
cpus: '${CONVEX_DASHBOARD_CPU_LIMIT:-0.5}'
memory: '${CONVEX_DASHBOARD_MEMORY_LIMIT:-256M}'
reservations:
cpus: '${CONVEX_DASHBOARD_CPU_RESERVATION:-0.25}'
memory: '${CONVEX_DASHBOARD_MEMORY_RESERVATION:-128M}'
postgres:
<<: *defaults
image: ${GLOBAL_REGISTRY:-}postgres:${POSTGRES_VERSION:-17-alpine}
environment:
- POSTGRES_DB=convex
- POSTGRES_USER=postgres
- POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-convex}
- POSTGRES_INITDB_ARGS=--encoding=UTF8
- TZ=${TZ:-UTC}
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test:
- CMD-SHELL
- pg_isready -U postgres
interval: 5s
timeout: 5s
retries: 5
start_period: 10s
deploy:
resources:
limits:
cpus: '${POSTGRES_CPU_LIMIT:-1.0}'
memory: '${POSTGRES_MEMORY_LIMIT:-1G}'
reservations:
cpus: '${POSTGRES_CPU_RESERVATION:-0.25}'
memory: '${POSTGRES_MEMORY_RESERVATION:-256M}'
volumes:
convex_data:
postgres_data:

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# =============================================================================
# llama-swap Configuration
# https://github.com/mostlygeek/llama-swap
# Reliable model swapping for any local OpenAI/Anthropic compatible server
# =============================================================================
# -----------------------------------------------------------------------------
# General Settings
# -----------------------------------------------------------------------------
# Timezone for the container (default: UTC)
TZ=UTC
# GitHub Container Registry prefix (default: ghcr.io/)
GHCR_REGISTRY=ghcr.io/
# -----------------------------------------------------------------------------
# Image Variants
# -----------------------------------------------------------------------------
# CPU-only image version tag (default: cpu)
# Available: cpu, cuda, vulkan, rocm, intel, musa
# Tagged releases example: v197-cuda-b8193
LLAMA_SWAP_VERSION=cpu
# NVIDIA CUDA image version tag (used with the `gpu` profile)
LLAMA_SWAP_CUDA_VERSION=cuda
# AMD GPU image version tag (used with the `gpu-amd` profile)
# Options: vulkan (Vulkan/AMD), rocm (ROCm/AMD)
LLAMA_SWAP_AMD_VERSION=vulkan
# -----------------------------------------------------------------------------
# Network Settings
# -----------------------------------------------------------------------------
# Host port override for the llama-swap API (default: 9292)
# The Web UI and OpenAI-compatible API are both served on this port
LLAMA_SWAP_PORT_OVERRIDE=9292
# -----------------------------------------------------------------------------
# GPU Settings (used with `gpu` profile)
# -----------------------------------------------------------------------------
# Number of NVIDIA GPUs to use (default: 1)
LLAMA_SWAP_GPU_COUNT=1
# -----------------------------------------------------------------------------
# Resource Limits
# -----------------------------------------------------------------------------
# CPU limit (in cores)
LLAMA_SWAP_CPU_LIMIT=4.0
# CPU reservation (in cores)
LLAMA_SWAP_CPU_RESERVATION=2.0
# Memory limit (e.g., 8G, 16G)
LLAMA_SWAP_MEMORY_LIMIT=8G
# Memory reservation (e.g., 4G, 8G)
LLAMA_SWAP_MEMORY_RESERVATION=4G

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# llama-swap
[llama-swap](https://github.com/mostlygeek/llama-swap) is a lightweight reverse proxy that provides reliable on-demand model swapping for any local OpenAI/Anthropic-compatible inference server (e.g., llama.cpp, vllm). Only one model is loaded at a time, and it is automatically swapped out when a different model is requested, making it easy to work with many models on a single machine.
See also: [README.zh.md](./README.zh.md)
## Features
- **On-demand model swapping**: Automatically load/unload models based on API requests with zero manual intervention.
- **OpenAI/Anthropic compatible**: Drop-in replacement for any client that uses the OpenAI or Anthropic chat completion API.
- **Multi-backend support**: Works with llama.cpp (llama-server), vllm, and any OpenAI-compatible server.
- **Real-time Web UI**: Built-in interface for monitoring logs, inspecting requests, and manually managing models.
- **TTL-based unloading**: Models can be configured to unload automatically after a period of inactivity.
- **HuggingFace model downloads**: Reference HuggingFace models directly in `config.yaml` and they are downloaded on first use.
- **Multi-GPU support**: Works with NVIDIA CUDA, AMD ROCm/Vulkan, Intel, and CPU-only setups.
## Quick Start
1. Copy the example environment file:
```bash
cp .env.example .env
```
2. Edit `config.yaml` to add your models. The provided `config.yaml` includes a commented example for a local GGUF model and a HuggingFace download. See [Configuration](#configuration) for details.
3. Start the service (CPU-only by default):
```bash
docker compose up -d
```
4. For NVIDIA GPU support:
```bash
docker compose --profile gpu up -d
```
5. For AMD GPU support (Vulkan):
```bash
docker compose --profile gpu-amd up -d
```
The API and Web UI are available at: `http://localhost:9292`
## Services
| Service | Profile | Description |
| ----------------- | ----------- | --------------------------------- |
| `llama-swap` | _(default)_ | CPU-only inference |
| `llama-swap-cuda` | `gpu` | NVIDIA CUDA GPU inference |
| `llama-swap-amd` | `gpu-amd` | AMD GPU inference (Vulkan / ROCm) |
> **Note**: Only start one service at a time. All three services bind to the same host port (`LLAMA_SWAP_PORT_OVERRIDE`).
## Configuration
### `config.yaml`
The `config.yaml` file defines the models llama-swap manages. It is mounted read-only at `/app/config.yaml` inside the container. Edit the provided `config.yaml` to add your models.
Minimal example:
```yaml
healthCheckTimeout: 300
models:
my-model:
cmd: /app/llama-server --port ${PORT} --model /root/.cache/llama.cpp/model.gguf --ctx-size 4096
proxy: 'http://localhost:${PORT}'
ttl: 900
```
- `${PORT}` is automatically assigned by llama-swap.
- `ttl` (seconds): unload the model after this many seconds of inactivity.
- `cmd`: the command to start the inference server.
- `proxy`: the address llama-swap forwards requests to.
For downloading models from HuggingFace on first use:
```yaml
models:
Qwen2.5-7B:
cmd: /app/llama-server --port ${PORT} -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M --ctx-size 8192 --n-gpu-layers 99
proxy: 'http://localhost:${PORT}'
```
See the [official configuration documentation](https://github.com/mostlygeek/llama-swap/blob/main/docs/config.md) for all options including `groups`, `hooks`, `macros`, `aliases`, `filters`, and more.
### Models Volume
The named volume `llama_swap_models` is mounted to `/root/.cache/llama.cpp` inside the container. To place local GGUF model files inside the volume, you can use:
```bash
# Copy a model into the named volume
docker run --rm -v llama_swap_models:/data -v /path/to/model.gguf:/src/model.gguf alpine cp /src/model.gguf /data/model.gguf
```
Alternatively, change the volume definition in `docker-compose.yaml` to use a host path:
```yaml
volumes:
llama_swap_models:
driver: local
driver_opts:
type: none
o: bind
device: /path/to/your/models
```
## Environment Variables
| Variable | Default | Description |
| ------------------------------- | ---------- | -------------------------------------------------- |
| `TZ` | `UTC` | Container timezone |
| `GHCR_REGISTRY` | `ghcr.io/` | GitHub Container Registry prefix |
| `LLAMA_SWAP_VERSION` | `cpu` | Image tag for the default CPU service |
| `LLAMA_SWAP_CUDA_VERSION` | `cuda` | Image tag for the CUDA service |
| `LLAMA_SWAP_AMD_VERSION` | `vulkan` | Image tag for the AMD service (`vulkan` or `rocm`) |
| `LLAMA_SWAP_PORT_OVERRIDE` | `9292` | Host port for the API and Web UI |
| `LLAMA_SWAP_GPU_COUNT` | `1` | Number of NVIDIA GPUs to use (CUDA profile) |
| `LLAMA_SWAP_CPU_LIMIT` | `4.0` | CPU limit in cores |
| `LLAMA_SWAP_CPU_RESERVATION` | `2.0` | CPU reservation in cores |
| `LLAMA_SWAP_MEMORY_LIMIT` | `8G` | Memory limit |
| `LLAMA_SWAP_MEMORY_RESERVATION` | `4G` | Memory reservation |
## Default Ports
| Port | Description |
| ------ | ------------------------------------------ |
| `9292` | OpenAI/Anthropic-compatible API and Web UI |
## API Usage
llama-swap exposes an OpenAI-compatible API. Use any OpenAI client by pointing it to `http://localhost:9292`:
```bash
# List available models
curl http://localhost:9292/v1/models
# Chat completion (automatically loads the model if not running)
curl http://localhost:9292/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "my-model",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
The Web UI is available at `http://localhost:9292` and provides real-time log streaming, request inspection, and manual model management.
## NVIDIA GPU Setup
Requires the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
```bash
docker compose --profile gpu up -d
```
For non-root security hardening, use the `cuda-non-root` image tag:
```yaml
LLAMA_SWAP_CUDA_VERSION=cuda-non-root
```
## AMD GPU Setup
Requires the `/dev/dri` and `/dev/dri` devices to be accessible on the host.
```bash
docker compose --profile gpu-amd up -d
```
Use `rocm` instead of `vulkan` for full ROCm support:
```bash
LLAMA_SWAP_AMD_VERSION=rocm docker compose --profile gpu-amd up -d
```
## Security Notes
- By default, the container runs as root. Use the `cuda-non-root` or `rocm-non-root` image tags for improved security on GPU deployments.
- The `config.yaml` is mounted read-only (`ro`).
- Consider placing llama-swap behind a reverse proxy (e.g., Nginx, Caddy) when exposing it beyond localhost.
## References
- [llama-swap GitHub](https://github.com/mostlygeek/llama-swap)
- [Configuration Documentation](https://github.com/mostlygeek/llama-swap/blob/main/docs/config.md)
- [Container Security](https://github.com/mostlygeek/llama-swap/blob/main/docs/container-security.md)
- [Docker Compose Wiki](https://github.com/mostlygeek/llama-swap/wiki/Docker-Compose-Example)
## License
llama-swap is released under the MIT License. See the [LICENSE](https://github.com/mostlygeek/llama-swap/blob/main/LICENSE) file for details.

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# llama-swap
[llama-swap](https://github.com/mostlygeek/llama-swap) 是一个轻量级反向代理，为任何本地 OpenAI/Anthropic 兼容的推理服务器（如 llama.cpp、vllm 等）提供可靠的按需模型切换功能。同一时间只加载一个模型，当收到对不同模型的请求时，llama-swap 会自动切换，让你可以在单台机器上轻松使用多个模型。
参见：[README.md](./README.md)
## 功能特性
- **按需模型切换**：根据 API 请求自动加载/卸载模型，无需手动干预。
- **兼容 OpenAI/Anthropic**：可直接替代任何使用 OpenAI 或 Anthropic 聊天补全 API 的客户端。
- **多后端支持**：适用于 llama.cpp（llama-server）、vllm 及任何 OpenAI 兼容服务器。
- **实时 Web UI**：内置界面，可监控日志、检查请求、手动管理模型。
- **基于 TTL 的自动卸载**：可配置模型在闲置一段时间后自动卸载。
- **HuggingFace 模型下载**：在 `config.yaml` 中直接引用 HuggingFace 模型，首次使用时自动下载。
- **多 GPU 支持**：支持 NVIDIA CUDA、AMD ROCm/Vulkan、Intel 及纯 CPU 部署。
## 快速开始
1. 复制环境变量示例文件：
```bash
cp .env.example .env
```
2. 编辑 `config.yaml`，添加你的模型配置。提供的 `config.yaml` 包含本地 GGUF 模型和 HuggingFace 下载的注释示例。详见[配置说明](#配置说明)。
3. 启动服务（默认仅使用 CPU）：
```bash
docker compose up -d
```
4. 启用 NVIDIA GPU 支持：
```bash
docker compose --profile gpu up -d
```
5. 启用 AMD GPU 支持（Vulkan）：
```bash
docker compose --profile gpu-amd up -d
```
API 和 Web UI 地址：`http://localhost:9292`
## 服务说明
| 服务名称 | Profile | 说明 |
| ----------------- | ---------- | ----------------------------- |
| `llama-swap` | _（默认）_ | 纯 CPU 推理 |
| `llama-swap-cuda` | `gpu` | NVIDIA CUDA GPU 推理 |
| `llama-swap-amd` | `gpu-amd` | AMD GPU 推理（Vulkan / ROCm） |
> **注意**：每次只启动一个服务，三个服务均绑定到同一主机端口（`LLAMA_SWAP_PORT_OVERRIDE`）。
## 配置说明
### `config.yaml`
`config.yaml` 文件定义了 llama-swap 管理的模型列表，以只读方式挂载到容器内的 `/app/config.yaml`。编辑提供的 `config.yaml` 即可添加你的模型。
最简示例：
```yaml
healthCheckTimeout: 300
models:
my-model:
cmd: /app/llama-server --port ${PORT} --model /root/.cache/llama.cpp/model.gguf --ctx-size 4096
proxy: 'http://localhost:${PORT}'
ttl: 900
```
- `${PORT}` 由 llama-swap 自动分配。
- `ttl`（秒）：模型闲置超过该时长后自动卸载。
- `cmd`：启动推理服务器的命令。
- `proxy`：llama-swap 转发请求的地址。
直接使用 HuggingFace 模型（首次使用时自动下载）：
```yaml
models:
Qwen2.5-7B:
cmd: /app/llama-server --port ${PORT} -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M --ctx-size 8192 --n-gpu-layers 99
proxy: 'http://localhost:${PORT}'
```
完整配置选项（包括 `groups`、`hooks`、`macros`、`aliases`、`filters` 等）请参阅[官方配置文档](https://github.com/mostlygeek/llama-swap/blob/main/docs/config.md)。
### 模型卷
命名卷 `llama_swap_models` 挂载到容器内的 `/root/.cache/llama.cpp`。可以通过以下方式将本地 GGUF 模型文件放入卷中：
```bash
# 将模型文件复制到命名卷
docker run --rm -v llama_swap_models:/data -v /path/to/model.gguf:/src/model.gguf alpine cp /src/model.gguf /data/model.gguf
```
或者将 `docker-compose.yaml` 中的卷定义改为主机路径绑定：
```yaml
volumes:
llama_swap_models:
driver: local
driver_opts:
type: none
o: bind
device: /path/to/your/models
```
## 环境变量
| 变量名 | 默认值 | 说明 |
| ------------------------------- | ---------- | -------------------------------------- |
| `TZ` | `UTC` | 容器时区 |
| `GHCR_REGISTRY` | `ghcr.io/` | GitHub 容器镜像仓库前缀 |
| `LLAMA_SWAP_VERSION` | `cpu` | 默认 CPU 服务镜像标签 |
| `LLAMA_SWAP_CUDA_VERSION` | `cuda` | CUDA 服务镜像标签 |
| `LLAMA_SWAP_AMD_VERSION` | `vulkan` | AMD 服务镜像标签（`vulkan` 或 `rocm`） |
| `LLAMA_SWAP_PORT_OVERRIDE` | `9292` | API 和 Web UI 的主机端口 |
| `LLAMA_SWAP_GPU_COUNT` | `1` | 使用的 NVIDIA GPU 数量（gpu profile） |
| `LLAMA_SWAP_CPU_LIMIT` | `4.0` | CPU 上限（核心数） |
| `LLAMA_SWAP_CPU_RESERVATION` | `2.0` | CPU 预留（核心数） |
| `LLAMA_SWAP_MEMORY_LIMIT` | `8G` | 内存上限 |
| `LLAMA_SWAP_MEMORY_RESERVATION` | `4G` | 内存预留 |
## 默认端口
| 端口 | 说明 |
| ------ | ----------------------------------- |
| `9292` | OpenAI/Anthropic 兼容 API 及 Web UI |
## API 使用示例
llama-swap 暴露 OpenAI 兼容 API。将任何 OpenAI 客户端指向 `http://localhost:9292` 即可使用：
```bash
# 列出可用模型
curl http://localhost:9292/v1/models
# 聊天补全（若模型未运行则自动加载）
curl http://localhost:9292/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "my-model",
"messages": [{"role": "user", "content": "你好！"}]
}'
```
Web UI 可通过 `http://localhost:9292` 访问，提供实时日志流、请求检查和手动模型管理功能。
## NVIDIA GPU 配置
需要安装 [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)。
```bash
docker compose --profile gpu up -d
```
如需非 root 安全加固，可使用 `cuda-non-root` 镜像标签：
```bash
LLAMA_SWAP_CUDA_VERSION=cuda-non-root docker compose --profile gpu up -d
```
## AMD GPU 配置
需要主机上 `/dev/dri` 和 `/dev/kfd` 设备可访问。
```bash
docker compose --profile gpu-amd up -d
```
如需完整 ROCm 支持，可使用 `rocm` 替代 `vulkan`：
```bash
LLAMA_SWAP_AMD_VERSION=rocm docker compose --profile gpu-amd up -d
```
## 安全说明
- 默认情况下容器以 root 用户运行。GPU 部署时建议使用 `cuda-non-root` 或 `rocm-non-root` 镜像标签提升安全性。
- `config.yaml` 以只读方式（`ro`）挂载。
- 若需在 localhost 之外暴露服务，建议在 llama-swap 前部署反向代理（如 Nginx、Caddy）。
## 参考链接
- [llama-swap GitHub](https://github.com/mostlygeek/llama-swap)
- [配置文档](https://github.com/mostlygeek/llama-swap/blob/main/docs/config.md)
- [容器安全文档](https://github.com/mostlygeek/llama-swap/blob/main/docs/container-security.md)
- [Docker Compose Wiki](https://github.com/mostlygeek/llama-swap/wiki/Docker-Compose-Example)
## 许可证
llama-swap 使用 MIT 许可证发布。详情请参阅 [LICENSE](https://github.com/mostlygeek/llama-swap/blob/main/LICENSE) 文件。

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# llama-swap configuration file
# https://github.com/mostlygeek/llama-swap/blob/main/docs/config.md
#
# This is the main configuration file for llama-swap.
# Mount this file to /app/config.yaml inside the container.
#
# llama-swap will automatically swap models on demand:
# - Only the requested model is loaded at a time.
# - Idle models are unloaded when a new one is requested.
# Maximum time (in seconds) to wait for a model to become healthy.
# A high value is useful when downloading models from HuggingFace.
healthCheckTimeout: 300
# Macro definitions: reusable command snippets for model configuration.
# Reference with $${macro-name} inside cmd fields.
macros:
"llama-server": >
/app/llama-server
--port ${PORT}
# Model definitions
models:
# Example: a local GGUF model stored in the models volume.
# The volume `llama_swap_models` is mounted to /root/.cache/llama.cpp inside
# the container. Place your .gguf files there and reference them with
# /root/.cache/llama.cpp/<filename>.gguf
"my-local-model":
# ${PORT} is automatically assigned by llama-swap
cmd: >
$${llama-server}
--model /root/.cache/llama.cpp/model.gguf
--ctx-size 4096
--n-gpu-layers 0
proxy: "http://localhost:${PORT}"
# Automatically unload the model after 15 minutes of inactivity
ttl: 900
# Example: download a model from HuggingFace on first use (requires internet access)
# "Qwen2.5-7B-Instruct":
# cmd: >
# $${llama-server}
# -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
# --ctx-size 8192
# --n-gpu-layers 99
# proxy: "http://localhost:${PORT}"
# ttl: 900

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# Docker Compose configuration for llama-swap
# https://github.com/mostlygeek/llama-swap
# Reliable model swapping for any local OpenAI/Anthropic compatible server
x-defaults: &defaults
restart: unless-stopped
logging:
driver: json-file
options:
max-size: 100m
max-file: '3'
services:
# llama-swap - CPU variant (default)
llama-swap:
<<: *defaults
image: ${GHCR_REGISTRY:-ghcr.io/}mostlygeek/llama-swap:${LLAMA_SWAP_VERSION:-cpu}
ports:
- '${LLAMA_SWAP_PORT_OVERRIDE:-9292}:8080'
volumes:
- ./config.yaml:/app/config.yaml:ro
- llama_swap_models:/root/.cache/llama.cpp
environment:
- TZ=${TZ:-UTC}
healthcheck:
test:
- CMD
- wget
- --quiet
- --tries=1
- --spider
- 'http://localhost:8080/v1/models'
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
deploy:
resources:
limits:
cpus: ${LLAMA_SWAP_CPU_LIMIT:-4.0}
memory: ${LLAMA_SWAP_MEMORY_LIMIT:-8G}
reservations:
cpus: ${LLAMA_SWAP_CPU_RESERVATION:-2.0}
memory: ${LLAMA_SWAP_MEMORY_RESERVATION:-4G}
# llama-swap - NVIDIA CUDA variant
# Requires NVIDIA Container Toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
llama-swap-cuda:
<<: *defaults
image: ${GHCR_REGISTRY:-ghcr.io/}mostlygeek/llama-swap:${LLAMA_SWAP_CUDA_VERSION:-cuda}
ports:
- '${LLAMA_SWAP_PORT_OVERRIDE:-9292}:8080'
volumes:
- ./config.yaml:/app/config.yaml:ro
- llama_swap_models:/root/.cache/llama.cpp
environment:
- TZ=${TZ:-UTC}
healthcheck:
test:
- CMD
- wget
- --quiet
- --tries=1
- --spider
- 'http://localhost:8080/v1/models'
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
deploy:
resources:
limits:
cpus: ${LLAMA_SWAP_CPU_LIMIT:-4.0}
memory: ${LLAMA_SWAP_MEMORY_LIMIT:-8G}
reservations:
cpus: ${LLAMA_SWAP_CPU_RESERVATION:-2.0}
memory: ${LLAMA_SWAP_MEMORY_RESERVATION:-4G}
devices:
- driver: nvidia
count: ${LLAMA_SWAP_GPU_COUNT:-1}
capabilities: [gpu]
profiles:
- gpu
# llama-swap - AMD ROCm / Vulkan variant (AMD GPU)
# For AMD GPUs, ensure /dev/dri and /dev/kfd are accessible
llama-swap-amd:
<<: *defaults
image: ${GHCR_REGISTRY:-ghcr.io/}mostlygeek/llama-swap:${LLAMA_SWAP_AMD_VERSION:-vulkan}
ports:
- '${LLAMA_SWAP_PORT_OVERRIDE:-9292}:8080'
volumes:
- ./config.yaml:/app/config.yaml:ro
- llama_swap_models:/root/.cache/llama.cpp
devices:
- /dev/dri:/dev/dri
- /dev/kfd:/dev/kfd
group_add:
- video
environment:
- TZ=${TZ:-UTC}
healthcheck:
test:
- CMD
- wget
- --quiet
- --tries=1
- --spider
- 'http://localhost:8080/v1/models'
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
deploy:
resources:
limits:
cpus: ${LLAMA_SWAP_CPU_LIMIT:-4.0}
memory: ${LLAMA_SWAP_MEMORY_LIMIT:-8G}
reservations:
cpus: ${LLAMA_SWAP_CPU_RESERVATION:-2.0}
memory: ${LLAMA_SWAP_MEMORY_RESERVATION:-4G}
profiles:
- gpu-amd
volumes:
llama_swap_models: