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mcp_server.py

@@ -132,8 +132,8 @@ popularity_weight (default 0.15, rarely needs changing):
 - Lower (0.0): surface niche/rare tags
 
 include_wiki (default False):
-- True: tag meaning matters — disambiguation, explaining tags to user, exploring unfamiliar domains
-- False: prompt generation (wiki irrelevant to downstream), tags already known
+- True: The meaning of the tag is important — disambiguation, explaining tags to users, exploring unfamiliar domains, or when you are unsure of the tag's meaning
+- False: Prompt generation (Wiki is irrelevant to the downstream task), tags are known
 
 ### Quick reference
 
@@ -147,6 +147,8 @@ include_wiki (default False):
 ### Workflow
 
 After search_tags, pass selected tags to get_related_tags to discover complementary tags via co-occurrence (accessories, character features, scene atmosphere).
+Supports chained exploration / iterative loops – take the interesting tags from the returned results as input to call get_related_tags again,
+and use the results from get_related to feed back into a new round of search, enabling multi-hop deep traversal along the co-occurrence graph.
 
 ## Examples
 
@@ -233,6 +235,8 @@ async def get_related_tags(
     """
 Return co-occurrence-based tag recommendations for a given tag list (NPMI scoring).
 Typical workflow: call search_tags first, then pass selected tags here to discover complementary ones.
+Supports chained exploration / iterative loops – take the interesting tags from the returned results as input to call get_related_tags again,
+and use the results from get_related to feed back into a new round of search, enabling multi-hop deep traversal along the co-occurrence graph.
 
 Works well for: clothing accessories, character visual features, theme exploration, multi-tag intersections.
     e.g. tags=["fingerless_gloves"] → returns characters often wearing them

platform_utils.py

@@ -214,6 +214,24 @@ def upload_bytes(
 _MS_WORKDIR = Path('/home/user/app')
 
 
+#  HF Storage Bucket 挂载检测
+
+# HF Storage Buckets 挂载到 Space 时，会映射到容器内的一个本地路径
+# （通常为 /data），文件可直接以本地路径读取，无需 hf_hub_download。
+_HF_BUCKET_MOUNT = Path('/data')
+
+
+def get_hf_bucket_path(relative: str) -> Optional[Path]:
+    """
+    如果 HF Storage Bucket 已挂载且目标文件存在，返回本地绝对路径。
+    否则返回 None（调用方应 fallback 到 hf_hub_download）。
+    """
+    candidate = _HF_BUCKET_MOUNT / relative
+    if candidate.exists():
+        return candidate
+    return None
+
+
 def download_file(
     filename: str,
     *,
@@ -230,7 +248,9 @@ def download_file(
     下载单个引擎数据文件，返回本地绝对路径字符串。
 
     HF 平台：
-        从 Space repo 下载，hf_repo_id 默认读取环境变量 SPACE_ID。
+        优先从挂载的 Storage Bucket（/data）读取本地文件（零延迟）。
+        若 Bucket 未挂载或文件不存在，回退到从 Space repo 下载
+        （hf_repo_id 默认读取环境变量 SPACE_ID）。
 
     MS 平台：
         文件已随 studio repo 部署到容器本地，直接返回工作目录下的路径，
@@ -240,6 +260,13 @@ def download_file(
         直接返回原始路径。
     """
     if PLATFORM == 'hf':
+        # 优先从挂载的 Storage Bucket 读取（本地路径，零延迟）
+        bucket_path = get_hf_bucket_path(filename)
+        if bucket_path is not None:
+            print(f'[PlatformUtils] 从 Storage Bucket 读取: {bucket_path}')
+            return str(bucket_path)
+
+        # Bucket 未挂载或文件不存在，回退到从 Space repo 下载
         from huggingface_hub import hf_hub_download
         repo_id = hf_repo_id or os.environ.get('SPACE_ID')
         if not repo_id: