refactor(docker): 优化nginx配置，修复文件上传1M的限制问题 (#48)

移除vllm模块的docker-compose配置及相关脚本，优化management-frontend的nginx配置

docker/docker-compose.yml

@@ -32,10 +32,12 @@ services:
   management-frontend:
     container_name: ragflowplus-management-frontend
     image: zstar1003/ragflowplus-management-web:v0.2.0
-    build:
+    # build:
-      context: .
+    #   context: .
-      dockerfile: Dockerfile
+    #   dockerfile: Dockerfile
-      target: frontend
+    #   target: frontend
+    volumes:
+      - ./nginx/management_nginx.conf:/etc/nginx/conf.d/default.conf
     ports:
       - "8888:80"
     depends_on:
@@ -44,7 +46,7 @@ services:
       - API_BASE_URL=/api 
     networks:
       - ragflow
-      
+
   # 后台信息管理系统后端
   management-backend:
     container_name: ragflowplus-management-backend

docker/docker-compose_gpu.yml

@@ -32,10 +32,12 @@ services:
   management-frontend:
     container_name: ragflowplus-management-frontend
     image: zstar1003/ragflowplus-management-web:v0.2.0
-    build:
+    # build:
-      context: .
+    #   context: .
-      dockerfile: Dockerfile
+    #   dockerfile: Dockerfile
-      target: frontend
+    #   target: frontend
+    volumes:
+      - ./nginx/management_nginx.conf:/etc/nginx/conf.d/default.conf
     ports:
       - "8888:80"
     depends_on:

docker/nginx/management_nginx.conf

@@ -0,0 +1,26 @@
+server {
+    listen 80;
+    
+    client_max_body_size 500M;
+    
+    location / {
+        root /usr/share/nginx/html;
+        try_files $uri $uri/ /index.html;
+    }
+
+    location /v3-admin-vite/ {
+        alias /usr/share/nginx/html/;
+        try_files $uri $uri/ /index.html;
+    }
+
+    location /api/ {
+        # 将所有以/api/开头的请求转发到后端服务(management-backend容器的5000端口)
+        proxy_pass http://management-backend:5000/api/;
+        # 设置代理请求头
+        proxy_set_header Host $host;  # 保留原始请求的Host头
+        # 传递客户端真实IP
+        proxy_set_header X-Real-IP $remote_addr;  # 记录客户端IP
+        # 添加X-Forwarded-For头
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;  # 代理链路追踪
+    }
+}

vllm/docker-compose.yml

@@ -1,50 +0,0 @@
-services:
-  vllm-bge:
-    image: vllm/vllm-openai:latest
-    ipc: host
-    volumes:
-      - ./models/bge-m3:/models
-    command: [
-      "--model", "/models",
-      "--served-model-name", "bge-m3",
-      "--dtype", "float16",
-      "--gpu-memory-utilization", "0.9",
-    ]
-    ports:
-      - "8000:8000"
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              capabilities: [gpu]
-    networks:
-      - ragflow
-
-  vllm-deepseek:
-    image: vllm/vllm-openai:latest
-    ipc: host
-    volumes:
-      - ./models/DeepSeek-R1-1.5B:/models
-    command: [
-      "--model", "/models",
-      "--served-model-name", "deepseek-r1",
-      "--dtype", "float16",
-      "--tensor-parallel-size", "1",
-      "--max-model-len", "4096"
-    ]
-    ports:
-      - "8001:8000"
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              capabilities: [gpu]
-    networks:
-      - ragflow
-
-networks:
-  ragflow:
-    name: docker_ragflow 
-    driver: bridge

vllm/download_model.py

@@ -1,34 +0,0 @@
-import os
-from huggingface_hub import snapshot_download
-
-# 1. 设置镜像源（国内加速）
-# os.environ["HF_ENDPOINT"] = "https://mirrors.tuna.tsinghua.edu.cn/hugging-face/"
-
-# 2. 定义模型列表（名称 + 下载路径）
-models_to_download = [
-    {
-        "repo_id": "BAAI/bge-m3",  # Embedding 模型
-        "local_dir": os.path.expanduser("./models/bge-m3"),
-    },
-    {
-        "repo_id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",  # LLM 模型
-        "local_dir": os.path.expanduser("./models/DeepSeek-R1-1.5B"),
-    }
-]
-
-# 3. 遍历下载所有模型
-for model in models_to_download:
-    while True:  # 断点续传重试机制
-        try:
-            print(f"开始下载模型: {model['repo_id']} 到目录: {model['local_dir']}")
-            snapshot_download(
-                repo_id=model["repo_id"],
-                local_dir=model["local_dir"],
-                resume_download=True,  # 启用断点续传
-                force_download=False,  # 避免重复下载已有文件
-                token=None,            # 如需访问私有模型，替换为你的 token
-            )
-            print(f"模型 {model['repo_id']} 下载完成！")
-            break
-        except Exception as e:
-            print(f"下载失败: {e}, 重试中...")

vllm/model_test.py

@@ -1,49 +0,0 @@
-import requests
-from openai import OpenAI
-
-# 测试 embedding 模型 (vllm-bge)
-def test_embedding(model, text):
-    """测试嵌入模型"""
-    client = OpenAI(base_url="http://localhost:8000/v1", api_key="1")
-    
-    response = client.embeddings.create(
-        model=model,  # 使用支持嵌入的模型
-        input=text  # 需要嵌入的文本
-    )
-    
-    # 打印嵌入响应内容
-    # print(f"Embedding response: {response}")
-    
-    result = response.data[0].embedding
-
-    if response and response.data:
-        print(len(result))
-    else:
-        print("Failed to get embedding.")
-
-# 测试文本生成模型 (vllm-deepseek)
-def test_chat(model, prompt):
-    """测试文本生成模型"""
-    client = OpenAI(base_url="http://localhost:8001/v1", api_key="1")
-    
-    response = client.completions.create(
-        model=model,
-        prompt=prompt
-    )
-    
-    # 打印生成的文本
-    print(f"Chat response: {response.choices[0].text}")
-
-def main():
-    # 测试文本生成模型 deepseek-r1
-    prompt = "你好，今天的天气怎么样？"
-    print("Testing vllm-deepseek model for chat...")
-    test_chat("deepseek-r1", prompt)
-    
-    # 测试嵌入模型 bge-m3
-    embedding_text = "我喜欢编程，尤其是做AI模型。"
-    print("\nTesting vllm-bge model for embedding...")
-    test_embedding("bge-m3", embedding_text)
-
-if __name__ == "__main__":
-    main()