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Merge branch 'master' of http://8.155.144.6:23011/qinggang.deng/wiki_crawler
添加docker部署
2025-12-28 10:33:47 +08:00 · 2025-12-23 10:28:18 +08:00 · 2025-12-23 10:28:15 +08:00
9 changed files with 1169 additions and 9 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 __pycache__/
-.venv
+.venv
 wiki_backend.tar
--- a/40
+++ b/40
@@ -0,0 +1,40 @@
 # 1. 使用官方 uv 镜像
 FROM ghcr.io/astral-sh/uv:latest AS uv_setup
 # 2. 运行环境
 FROM python:3.12-slim
 WORKDIR /app
 # 复制 uv 命令
 COPY --from=uv_setup /uv /uvx /bin/
 # 安装系统依赖
 RUN apt-get update && apt-get install -y --no-install-recommends \
    libpq-dev \
    build-essential \
    && rm -rf /var/lib/apt/lists/*
 # 复制依赖描述文件
 COPY pyproject.toml uv.lock ./
 # 【核心修复】使用 uv sync 
 # --frozen: 强制要求 uv.lock 必须是最新的
 # --no-dev: 不安装开发依赖（如 pytest 等），减小体积
 # --no-install-project: 先不安装当前项目代码，只装依赖（优化缓存）
 RUN uv sync --frozen --no-dev --no-install-project
 # 复制项目代码
 COPY . .
 # 【关键点】将虚拟环境的 bin 目录加入系统路径
 # 这样你运行 python 或 uvicorn 时，系统会自动使用 uv 准备好的那个环境
 ENV PATH="/app/.venv/bin:$PATH"
 ENV PYTHONPATH=/app
 # 暴露端口
 EXPOSE 8000
 # 启动命令
 # 现在直接调用 uvicorn 即可，它会自动找到 .venv 里的版本
 CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,16 @@
 version: '3.8'
 services:
  api:
    build: .  # 自动寻找当前目录下的 Dockerfile
    image: wiki-backend:latest
    container_name: wiki_backend
    ports:
      - "8000:8000"
    environment:
      - DB_USER=postgres
      - DB_PASS=DXC_welcome001
      - DB_HOST=8.155.144.6
      - DB_PORT=25432
      - DB_NAME=wiki_crawler
    restart: always
--- a/nodes/parse_and_add_urls.json
+++ b/nodes/parse_and_add_urls.json
@@ -0,0 +1,957 @@
 {
  "files": [],
  "json": [
    {
      "links": [
        "https://docs.dify.ai",
        "https://docs.dify.ai/zh/use-dify/nodes/start",
        "https://docs.dify.ai/zh/self-host/troubleshooting/weaviate-v4-migration",
        "https://docs.dify.ai/en/use-dify/debug/variable-inspect",
        "https://docs.dify.ai/api-reference/metadata-&-tags/modify-knowledge-base-type-tag-name",
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        "https://docs.dify.ai/zh/use-dify/nodes/loop",
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        "https://docs.dify.ai/api-reference/models/get-available-embedding-models",
        "https://docs.dify.ai/ja/self-host/troubleshooting/weaviate-v4-migration",
        "https://docs.dify.ai/en/use-dify/nodes/trigger/webhook-trigger",
        "https://docs.dify.ai/zh/develop-plugin/dev-guides-and-walkthroughs/creating-new-model-provider",
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        "https://docs.dify.ai/ja/develop-plugin/dev-guides-and-walkthroughs/agent-strategy-plugin",
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        "https://docs.dify.ai/ja/use-dify/knowledge/knowledge-and-documents-maintenance/introduction",
        "https://docs.dify.ai/ja/use-dify/nodes/answer",
        "https://docs.dify.ai/ja/develop-plugin/dev-guides-and-walkthroughs/cheatsheet",
        "https://docs.dify.ai/zh/use-dify/workspace/plugins",
        "https://docs.dify.ai/zh/use-dify/knowledge/knowledge-pipeline/create-knowledge-pipeline",
        "https://docs.dify.ai/ja/use-dify/monitor/integrations/integrate-aliyun",
        "https://docs.dify.ai/api-reference/データセット/ナレッジベースリストを取得",
        "https://docs.dify.ai/api-reference/データセット/ナレッジベース詳細を取得",
        "https://docs.dify.ai/api-reference/データセット/空のナレッジベースを作成",
        "https://docs.dify.ai/api-reference/ドキュメント/テキストからドキュメントを作成",
        "https://docs.dify.ai/api-reference/ドキュメント/テキストでドキュメントを更新",
        "https://docs.dify.ai/api-reference/ドキュメント/ドキュメントを削除",
        "https://docs.dify.ai/api-reference/ドキュメント/ドキュメントステータスを更新",
        "https://docs.dify.ai/api-reference/ドキュメント/ドキュメント埋め込みステータス（進捗）を取得",
        "https://docs.dify.ai/api-reference/ドキュメント/ドキュメント詳細を取得",
        "https://docs.dify.ai/api-reference/ドキュメント/ナレッジベースのドキュメントリストを取得",
        "https://docs.dify.ai/api-reference/ドキュメント/ファイルからドキュメントを作成",
        "https://docs.dify.ai/api-reference/ドキュメント/ファイルでドキュメントを更新",
        "https://docs.dify.ai/api-reference/ファイル操作-ワークフロー/ファイルアップロード-ワークフロー用",
        "https://docs.dify.ai/api-reference/ファイル操作/ファイルプレビュー",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/データセットとナレッジベースタイプタグのバインドを解除",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/データセットにバインドされたタグをクエリ",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/データセットをナレッジベースタイプタグにバインド",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/ナレッジベースタイプタグを削除",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/ナレッジベースタイプタグを取得",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/ナレッジベースタイプタグ名を変更",
        "https://docs.dify.ai/api-reference/メタデータ・タグ/新しいナレッジベースタイプタグを作成",
        "https://docs.dify.ai/api-reference/メッセージフィードバック/メッセージフィードバック",
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  "text": ""
 }
--- a/nodes/parse_and_add_urls.py
+++ b/nodes/parse_and_add_urls.py
@@ -0,0 +1,106 @@
 import requests
 import json
 import math
 from concurrent.futures import ThreadPoolExecutor, as_completed
 def parse_urls(map_json: list[dict]):
    """
    解析 Firecrawl 返回的数据
    """
    if not map_json:
        return []
    map_obj = map_json[0]
    # 稍微做个容错，防止有些时候结构不一样
    if not map_obj.get("success", False):
        # 如果不是必须抛异常，可以打印日志并返回空
        print(f"Firecrawl Map节点返回失败或无数据：{map_obj}")
        return []
    urls = map_obj.get("links", [])
    return urls
 def send_batch_request(urls_batch: list[str], task_id: int, BASE_URL: str):
    """
    发送单个批次的请求
    """
    try:
        # 设置 timeout 是好习惯，防止卡死
        # 因为我们不关心返回值，只要发出去就行，timeout设置短一点
        res = requests.post(
            f"{BASE_URL}/add_urls", 
            json={
                "task_id": task_id,
                "urls": urls_batch
            },
            timeout=10 # 10秒没发完就拉倒，防止拖累主进程
        )
        if res.status_code == 200:
            return True
        else:
            print(f"Batch failed with status {res.status_code}: {res.text[:100]}")
            return False
    except Exception as e:
        print(f"Batch request error: {e}")
        return False
 def main(map_json: list[dict], BASE_URL: str, task_id: float):
    # 1. 解析 URL
    all_urls = parse_urls(map_json)
    total_count = len(all_urls)
    if total_count == 0:
        return {"msg": "没有解析到URL"}
    # ================= 配置区 =================
    BATCH_SIZE = 50       # 每一批发送 50 个 URL (根据你后端性能调整)
    MAX_WORKERS = 10      # 同时开 10 个线程并发发送
    # ==========================================
    # 2. 将 URL 切片 (分批)
    # 比如 1000 个 URL，切成 20 个 batch
    batches = [all_urls[i:i + BATCH_SIZE] for i in range(0, total_count, BATCH_SIZE)]
    print(f"总共 {total_count} 个URL，分为 {len(batches)} 批发送，并发数: {MAX_WORKERS}")
    # 3. 多线程并发发送
    # 在 Dify/Lambda 等环境中，必须等待线程池执行完才能退出 main，
    # 否则请求还没发出去进程就被杀掉了。但因为是并发，速度会非常快。
    success_batches = 0
    with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
        # 提交所有任务
        futures = [
            executor.submit(send_batch_request, batch, int(task_id), BASE_URL) 
            for batch in batches
        ]
        # 等待完成 (as_completed)
        for future in as_completed(futures):
            if future.result():
                success_batches += 1
    return {
        "status": "done",
        "total_urls": total_count,
        "batches_sent": len(batches),
        "success_batches": success_batches,
        "msg": "已使用多线程并发发送数据，忽略详细返回值"
    }
 def test():
    import json
    from time import time
    with open("nodes\parse_and_add_urls.json", "r", encoding="utf-8") as f:
        data = json.load(f)
        map_json = data["json"]
        BASE_URL = "http://47.122.127.178"
        task_id = 6
        start_time = time()
        res = main(map_json, BASE_URL, task_id)
        end_time = time()
        print(f"添加URL耗时：{end_time - start_time}秒")
        print(res)
 test()
--- a/nodes/parse_firecrawl_map.py
+++ b/nodes/parse_firecrawl_map.py
@@ -1,4 +1,4 @@
-def main(map_json: list[dict]):
+def main(map_json: list[dict], BASE_URL: str):
    """
    将Firecrawl Map节点的输出转换为干净的输出，避免杂七杂八的数据干扰
    输入: Firecrawl Map节点的输出，结构如下
--- a/nodes/parse_register.json
+++ b/nodes/parse_register.json
@@ -0,0 +1,14 @@
 {
  "body": "{\"code\":1,\"msg\":\"Success\",\"data\":{\"task_id\":6,\"is_new_task\":false}}",
  "files": [],
  "headers": {
    "cache-status": "5e126c1b3d46;detail=mismatch",
    "connection": "keep-alive",
    "content-length": "67",
    "content-type": "application/json",
    "date": "Tue, 23 Dec 2025 08:30:31 GMT",
    "server": "uvicorn",
    "via": "1.1 5e126c1b3d46 (squid/6.13)"
  },
  "status_code": 200
 }
--- a/nodes/parse_register.py
+++ b/nodes/parse_register.py
@@ -1,23 +1,40 @@
-def check_status(status_code: float, body: str):
+import json
 def parse_response(status_code: float, body: str):
    '''
    检查状态码和约定的返回值
    并且返回正确的body
    '''
    if status_code != 200:
        raise Exception(f"注册任务失败，状态码：{status_code}")
-    if "code" not in body or body["code"] != 1:
+    
    data = json.loads(body)
    if "code" not in data or data["code"] != 1:
        raise Exception(f"注册任务失败，返回值：{body}")
    return data["data"]
 def main(status_code: float, body: str):
    try:
-        check_status(status_code, body)
+        data = parse_response(status_code, body)
    except Exception as e:
        raise e
-    task_id = body["data"]["task_id"]
+    task_id = data["task_id"]
-    is_new_task = body["data"]["is_new_task"]
+    is_new_task = data["is_new_task"]
    return {
        "task_id": task_id,
        "is_new_task": is_new_task
    }
 def test():
    import json
    with open("nodes\parse_register.json", "r") as f:
        data = json.load(f)
        status_code = data["status_code"]
        body = data["body"]
        res = main(status_code, body)
        print(res)
 test()
--- a/scripts/test_apis.py
+++ b/scripts/test_apis.py
@@ -3,7 +3,7 @@ import json
 import random
 # 配置后端地址
-BASE_URL = "http://127.0.0.1:8000"
+BASE_URL = "http://47.122.127.178"
 def log_res(name, response):
    print(f"\n=== 测试接口: {name} ===")
@@ -80,7 +80,16 @@ def run_tests():
    }
    res = requests.post(f"{BASE_URL}/save_results", json=payload)
    log_res("保存结果", res)
-
+    # 5. 测试 /search
    print("\n步骤 5: 测试基于向量的搜索...")
    query = [round(random.uniform(-1, 1), 8) for _ in range(1536)]
    res = requests.post(f"{BASE_URL}/search", json={
        "task_id": None,
        "query_embedding": query,
        "limit": 5
    })
    log_res("基于向量的搜索", res)
    print("\n✅ 所有 API 流程测试完成！")
 if __name__ == "__main__":
Author	SHA1	Message	Date
QingGang	79b3f79c15	aaaa	2025-12-28 10:33:47 +08:00
QingGang	061ce1b2a6	Merge branch 'master' of http://8.155.144.6:23011/qinggang.deng/wiki_crawler	2025-12-23 10:28:18 +08:00
QingGang	0f44ef1338	添加docker部署	2025-12-23 10:28:15 +08:00