Merge branch 'main' into codex/update-parameters-in-xcontrol-server-and-cli
This commit is contained in:
shenlan
GitHub
commit
e1abcd4a1e
2
.gitignore
vendored
2
.gitignore
vendored
@ -1 +1,3 @@
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models/
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hf_cache/
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server/server/
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@ -1,69 +1,154 @@
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#!/usr/bin/env python3
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"""
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离线模型下载器（Hugging Face Hub + SOCKS5 自动支持）
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models_downloading.py
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- 优先级：CN 镜像 (hf-mirror.com) -> 代理(PROXY) -> 官方直连
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- 统一缓存：HF_HOME=./hf_cache（可被环境变量覆盖）
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- 进度可见：启用 huggingface_hub 的 tqdm 进度
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- 幂等安全：本地目录已有关键文件则跳过下载
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默认参数：
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- MODEL_ID="BAAI/bge-m3"
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- MODEL_DIR="./models/bge-m3"
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- PROXY="socks5://127.0.0.1:1080"
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可用环境变量覆盖：
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- export MODEL_ID="你的模型ID"
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- export MODEL_DIR="/保存路径"
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- export PROXY="socks5h://ip:port" # 为空表示直连
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可选环境变量：
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- MODEL_ID 默认 "BAAI/bge-m3"
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- MODEL_DIR 默认 "models/bge-m3"
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- HF_HOME 默认 "./hf_cache"
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- PROXY 默认 "socks5h://127.0.0.1:1081"（留空表示不走代理）
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- HF_ENDPOINT 手动指定镜像时可设置（脚本也会自动探测 cn mirror）
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"""
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import os
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import sys
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from pathlib import Path
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# ==== 自动安装 SOCKS 依赖 ====
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try:
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import socks # PySocks
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except ImportError:
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print("📦 Installing SOCKS proxy support (requests[socks])...")
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os.system(f"{sys.executable} -m pip install -U 'requests[socks]'")
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import socks
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# ---------- 配置 ----------
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MODEL_ID = os.getenv("MODEL_ID", "BAAI/bge-m3")
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MODEL_DIR = Path(os.getenv("MODEL_DIR", "models/bge-m3"))
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HF_HOME = Path(os.getenv("HF_HOME", Path.cwd() / "hf_cache"))
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# ==== 自动安装 huggingface_hub ====
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CN_MIRROR = "https://hf-mirror.com"
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PROXY = os.getenv("PROXY", "socks5h://127.0.0.1:1081")
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# ---------- 提前设置缓存目录（在 import 前） ----------
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os.environ["HF_HOME"] = str(HF_HOME)
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# ---------- 依赖安装 ----------
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def _install(pkgs: str):
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os.system(f"{sys.executable} -m pip install -U {pkgs}")
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try:
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import requests
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except ImportError:
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_install("requests")
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import requests
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# 若走 socks 代理需要 PySocks
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if PROXY and "socks" in PROXY:
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try:
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import socks # noqa: F401
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except ImportError:
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_install("'requests[socks]'")
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# ---------- 选择网络模式（镜像 → 代理 → 官方） ----------
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def set_network_mode():
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# 若外部已设置 HF_ENDPOINT，尊重外部配置
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if os.getenv("HF_ENDPOINT"):
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print(f"🌏 Using custom HF endpoint: {os.getenv('HF_ENDPOINT')}")
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return
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# 1) 尝试 CN 镜像
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try:
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r = requests.get(CN_MIRROR, timeout=2)
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if r.status_code == 200:
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os.environ["HF_ENDPOINT"] = CN_MIRROR
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print(f"🌏 Using Hugging Face CN mirror: {CN_MIRROR}")
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return
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except Exception:
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pass
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# 2) 走代理
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if PROXY:
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os.environ["HTTP_PROXY"] = PROXY
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os.environ["HTTPS_PROXY"] = PROXY
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print(f"🌐 Using proxy: {PROXY}")
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return
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# 3) 官方直连
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print("⚠️ No mirror or proxy, using official huggingface.co")
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set_network_mode()
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# 现在再导入 huggingface_hub，确保拿到正确的 endpoint/proxy 设置
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try:
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from huggingface_hub import snapshot_download
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except ImportError:
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print("📦 Installing huggingface_hub...")
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os.system(f"{sys.executable} -m pip install -U huggingface_hub")
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_install("'huggingface_hub[tqdm]'")
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from huggingface_hub import snapshot_download
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# ==== 默认配置 ====
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DEFAULT_MODEL_ID = "BAAI/bge-m3"
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DEFAULT_MODEL_DIR = "models/bge-m3"
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DEFAULT_PROXY = "socks5://127.0.0.1:1080"
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# ==== 从环境变量读取 ====
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MODEL_ID = os.environ.get("MODEL_ID", DEFAULT_MODEL_ID)
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MODEL_DIR = Path(os.environ.get("MODEL_DIR", DEFAULT_MODEL_DIR))
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PROXY = os.environ.get("PROXY", DEFAULT_PROXY)
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# ==== 设置代理 ====
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if PROXY:
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os.environ["HTTP_PROXY"] = PROXY
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os.environ["HTTPS_PROXY"] = PROXY
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print(f"🌐 Using proxy: {PROXY}")
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else:
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print("🚫 No proxy configured, direct connection.")
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# ==== 创建保存目录 ====
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MODEL_DIR.parent.mkdir(parents=True, exist_ok=True)
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# ==== 下载模型 ====
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print(f"⬇️ Downloading model from Hugging Face...")
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print(f" Model ID: {MODEL_ID}")
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print(f" Save dir: {MODEL_DIR}")
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snapshot_download(
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repo_id=MODEL_ID,
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local_dir=str(MODEL_DIR),
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local_dir_use_symlinks=False
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# ---------- 工具函数 ----------
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KEY_FILES = (
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"tokenizer.json",
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"config.json",
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"sentencepiece.bpe.model",
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"onnx/model.onnx",
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"pytorch_model.bin",
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"model.safetensors",
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)
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print(f"✅ Model cached to {MODEL_DIR}")
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def has_local_model(root: Path) -> bool:
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if not root.exists():
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return False
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for k in KEY_FILES:
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if any(root.rglob(k)):
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return True
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# 兜底：只要非空也算有内容（对应部分仓库布局）
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return any(root.iterdir())
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# ---------- 主流程 ----------
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def main():
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print("⬇️ Downloading model from Hugging Face…")
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print(f" Model ID : {MODEL_ID}")
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print(f" Save dir : {MODEL_DIR}")
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print(f" HF_HOME : {HF_HOME}")
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if os.getenv("HF_ENDPOINT"):
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print(f" Endpoint : {os.getenv('HF_ENDPOINT')}")
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elif os.getenv("HTTP_PROXY"):
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print(f" Proxy : {os.getenv('HTTP_PROXY')}")
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else:
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print(" Endpoint : official (huggingface.co)")
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MODEL_DIR.parent.mkdir(parents=True, exist_ok=True)
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HF_HOME.mkdir(parents=True, exist_ok=True)
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# 已有可用文件 → 跳过下载
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if has_local_model(MODEL_DIR):
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print(f"📂 Local model exists, skip download: {MODEL_DIR}")
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print("💡 To force re-download, remove the folder and rerun.")
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return
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# 下载（显示进度）
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try:
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snapshot_download(
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repo_id=MODEL_ID,
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local_dir=str(MODEL_DIR),
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local_dir_use_symlinks=False,
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tqdm_class=None, # 使用默认 tqdm 进度条
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)
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except Exception as e:
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# 失败时检查是否已经有部分或全部文件
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if has_local_model(MODEL_DIR):
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print(f"⚠️ Online fetch failed but local files exist: {MODEL_DIR}")
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print(f" Error: {e}")
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else:
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print("❌ Download failed and no local files found.")
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print(f" Error: {e}")
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print("🔁 Try: 1) 切换镜像/代理 2) 检查网络 3) 稍后重试")
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sys.exit(1)
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# 最终确认
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if has_local_model(MODEL_DIR):
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print(f"✅ Model cached to {MODEL_DIR}")
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print("💡 To run offline later, set: export HF_HUB_OFFLINE=1")
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else:
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print("❌ No model files found after download attempt.")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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@ -2,44 +2,134 @@
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import os, sys, numpy as np
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from pathlib import Path
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# 自动装依赖
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# ==== 自动安装依赖 ====
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def install(pkg):
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os.system(f"{sys.executable} -m pip install -U {pkg}")
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try:
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from flask import Flask, request, jsonify
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from fastembed import TextEmbedding
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from huggingface_hub import snapshot_download
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except ImportError:
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os.system(f"{sys.executable} -m pip install -U flask fastembed numpy huggingface_hub")
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install("flask fastembed numpy huggingface_hub")
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from flask import Flask, request, jsonify
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from fastembed import TextEmbedding
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from huggingface_hub import snapshot_download
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# 模型路径
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MODEL_DIR = Path(os.getenv("BGE_M3_DIR", "models/bge-m3"))
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# ==== 配置 ====
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MODEL_ID = os.getenv("MODEL_ID", "BAAI/bge-m3")
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MODEL_DIR = Path(os.getenv("MODEL_DIR", "models/bge-m3"))
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HF_HOME = Path(os.getenv("HF_HOME", Path.cwd() / "hf_cache"))
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# 如果本地无模型，先下载
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if not MODEL_DIR.exists():
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print(f"⬇️ Downloading BGE-M3 to {MODEL_DIR} ...")
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snapshot_download("BAAI/bge-m3", local_dir=str(MODEL_DIR), local_dir_use_symlinks=False)
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# 设置 HF 缓存目录
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os.environ["HF_HOME"] = str(HF_HOME)
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# 离线模式
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os.environ["HF_HOME"] = str(Path.cwd() / "hf_cache")
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# ==== 确保模型已存在（不在这里判断支持性） ====
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if not MODEL_DIR.exists() or not any(MODEL_DIR.iterdir()):
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print(f"⬇️ Downloading model {MODEL_ID} to {MODEL_DIR} ...")
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snapshot_download(repo_id=MODEL_ID, local_dir=str(MODEL_DIR), local_dir_use_symlinks=False)
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# ==== 离线模式 ====
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os.environ["HF_HUB_OFFLINE"] = "1"
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# 启动服务
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# ==== 启动 Flask 服务 ====
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app = Flask(__name__)
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model = TextEmbedding(str(MODEL_DIR))
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# 这里用模型 ID 初始化，而不是路径
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model = TextEmbedding(MODEL_ID)
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DIM = 1024 # bge-m3 的维度
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@app.post("/v1/embeddings")
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def embeddings():
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data = request.get_json(force=True) or {}
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texts = [data["input"]] if isinstance(data.get("input"), str) else data.get("input", [])
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vecs = [np.asarray(v, np.float32) / (np.linalg.norm(v) + 1e-12) for v in model.embed(texts)]
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return jsonify({"object": "list", "data": [
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{"object": "embedding", "index": i, "embedding": v.tolist()} for i, v in enumerate(vecs)
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], "model": data.get("model", "BAAI/bge-m3")})
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return jsonify({
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"object": "list",
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"data": [
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{"object": "embedding", "index": i, "embedding": v.tolist()}
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for i, v in enumerate(vecs)
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],
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"model": data.get("model", MODEL_ID)
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})
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@app.get("/healthz")
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def healthz(): return "ok", 200
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def healthz():
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return "ok", 200
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if __name__ == "__main__":
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app.run(host=os.getenv("EMBED_HOST", "0.0.0.0"), port=int(os.getenv("EMBED_PORT", 9000)))
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host = os.getenv("EMBED_HOST", "0.0.0.0")
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port = int(os.getenv("EMBED_PORT", 9000))
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print(f"🚀 Starting embedding server on http://{host}:{port}")
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print(f" Model: {MODEL_ID}")
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print(f" Cache dir: {HF_HOME}")
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app.run(host=host, port=port)
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shenlan@MacBook-Pro-3 XControl % clear
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shenlan@MacBook-Pro-3 XControl %
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shenlan@MacBook-Pro-3 XControl % cat docs/offline_embed_server.py
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#!/usr/bin/env python3
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import os, sys, numpy as np
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from pathlib import Path
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# ==== 自动安装依赖 ====
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def install(pkg):
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os.system(f"{sys.executable} -m pip install -U {pkg}")
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try:
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from flask import Flask, request, jsonify
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from fastembed import TextEmbedding
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from huggingface_hub import snapshot_download
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except ImportError:
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install("flask fastembed numpy huggingface_hub")
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from flask import Flask, request, jsonify
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from fastembed import TextEmbedding
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from huggingface_hub import snapshot_download
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# ==== 配置 ====
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MODEL_ID = os.getenv("MODEL_ID", "BAAI/bge-m3")
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MODEL_DIR = Path(os.getenv("MODEL_DIR", "models/bge-m3"))
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HF_HOME = Path(os.getenv("HF_HOME", Path.cwd() / "hf_cache"))
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# 设置 HF 缓存目录
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os.environ["HF_HOME"] = str(HF_HOME)
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# ==== 确保模型已存在（不在这里判断支持性） ====
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if not MODEL_DIR.exists() or not any(MODEL_DIR.iterdir()):
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print(f"⬇️ Downloading model {MODEL_ID} to {MODEL_DIR} ...")
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snapshot_download(repo_id=MODEL_ID, local_dir=str(MODEL_DIR), local_dir_use_symlinks=False)
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# ==== 离线模式 ====
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os.environ["HF_HUB_OFFLINE"] = "1"
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# ==== 启动 Flask 服务 ====
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app = Flask(__name__)
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# 这里用模型 ID 初始化，而不是路径
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model = TextEmbedding(MODEL_ID)
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DIM = 1024 # bge-m3 的维度
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@app.post("/v1/embeddings")
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def embeddings():
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data = request.get_json(force=True) or {}
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texts = [data["input"]] if isinstance(data.get("input"), str) else data.get("input", [])
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vecs = [np.asarray(v, np.float32) / (np.linalg.norm(v) + 1e-12) for v in model.embed(texts)]
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return jsonify({
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"object": "list",
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"data": [
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{"object": "embedding", "index": i, "embedding": v.tolist()}
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for i, v in enumerate(vecs)
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],
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"model": data.get("model", MODEL_ID)
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})
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@app.get("/healthz")
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def healthz():
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return "ok", 200
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if __name__ == "__main__":
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host = os.getenv("EMBED_HOST", "0.0.0.0")
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port = int(os.getenv("EMBED_PORT", 9000))
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print(f"🚀 Starting embedding server on http://{host}:{port}")
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print(f" Model: {MODEL_ID}")
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print(f" Cache dir: {HF_HOME}")
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app.run(host=host, port=port)
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