digital-avatar-app/backend/routers/knowledge.py

@@ -17,7 +17,8 @@ UPLOAD_DIR = os.path.abspath(os.getenv("UPLOAD_DIR", os.path.join(BASE_DIR, "upl
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 
 ALLOWED_EXT = {".md", ".txt", ".pdf", ".doc", ".docx", ".xlsx"}
-MAX_UPLOAD_BYTES = 20 * 1024 * 1024
+MAX_UPLOAD_BYTES = 100 * 1024 * 1024
+UPLOAD_CHUNK_BYTES = 1024 * 1024
 
 
 class QAIn(BaseModel):
@@ -80,17 +81,25 @@ async def upload_doc(avatar_id: str, file: UploadFile = File(...), authorization
     os.makedirs(avatar_dir, exist_ok=True)
     stored = f"{uuid.uuid4().hex}{ext}"
     path = os.path.join(avatar_dir, stored)
-    content = await file.read()
-    if len(content) > MAX_UPLOAD_BYTES:
-        return fail("文件不能超过 20MB", code=400)
+    file_size = 0
+    try:
+        # Stream large files to disk so a 100MB upload does not occupy 100MB RAM.
         with open(path, "wb") as f:
-        f.write(content)
+            while chunk := await file.read(UPLOAD_CHUNK_BYTES):
+                file_size += len(chunk)
+                if file_size > MAX_UPLOAD_BYTES:
+                    raise ValueError("文件不能超过 100MB")
+                f.write(chunk)
+    except ValueError as exc:
+        if os.path.exists(path):
+            os.remove(path)
+        return fail(str(exc), code=400)
     doc = KnowledgeDoc(
         id=uuid.uuid4().hex,
         avatar_id=avatar_id,
         filename=file.filename,
         file_type=ext.lstrip("."),
-        file_size=len(content),
+        file_size=file_size,
         file_url=f"/api/files/{avatar_id}/{stored}",
         status="parsing",
     )

digital-avatar-app/backend/tests/test_knowledge_storage.py

@@ -64,6 +64,29 @@ def test_upload_returns_before_background_vectorization(
         db.close()
 
 
+def test_upload_rejects_oversize_file_before_queuing_indexing(
+    tmp_path: Path,
+    authorization_context,
+):
+    context = authorization_context
+    with (
+        patch("routers.knowledge.UPLOAD_DIR", str(tmp_path)),
+        patch("routers.knowledge.MAX_UPLOAD_BYTES", 4),
+        patch("routers.knowledge.knowledge_vectorizer.enqueue") as enqueue,
+    ):
+        response = client.post(
+            f"/api/avatar/{context['avatar'].id}/knowledge/docs",
+            headers=context["owner_headers"],
+            files={"file": ("oversize.md", b"12345", "text/markdown")},
+        )
+
+    payload = response.json()
+    assert payload["code"] == 400
+    assert payload["message"] == "文件不能超过 100MB"
+    enqueue.assert_not_called()
+    assert not list((tmp_path / context["avatar"].id).glob("*"))
+
+
 def test_background_vectorizer_commits_ready_document_and_chunks_together(
     tmp_path: Path,
     authorization_context,

digital-avatar-app/docs/H5_PRODUCTION_DEPLOYMENT.md

@@ -117,7 +117,7 @@ location /api/ {
     proxy_set_header X-Forwarded-Proto $scheme;
     proxy_buffering off;
     proxy_read_timeout 300s;
-    client_max_body_size 20m;
+    client_max_body_size 100m;
 }
 ```
 

digital-avatar-app/nginx.conf

@@ -26,7 +26,7 @@ http {
         # Keep the application gateway aligned with the production edge gateway.
         # Without this Nginx rejects ordinary PDF uploads with HTTP 413 before
         # FastAPI can return its user-facing file-size validation message.
-        client_max_body_size 20m;
+        client_max_body_size 100m;
 
         # SPA 兜底（hash 路由下深链接也可正常加载）
         location / {