diff --git a/fastgpt_uploader.py b/fastgpt_uploader.py
index 1f61034..6e4260a 100644
--- a/fastgpt_uploader.py
+++ b/fastgpt_uploader.py
@@ -1,6 +1,5 @@
 import json
 import requests
-from markdownify import markdownify as md
 
 from config import __CONFIG__
 
diff --git a/main.py b/main.py
index f9a4945..f67316f 100644
--- a/main.py
+++ b/main.py
@@ -5,6 +5,7 @@ from config import __CONFIG__
 import mysql_connector
 from fastgpt_uploader import upload2fastgpt
 from semanticscholar import search_paper
+from rss import load_rss
 
 app = FastAPI()
 
@@ -34,6 +35,10 @@ async def get_reference(questions):
             mysql_connector.new_load(i['id'])
     return res
 
+@app.get("/rss")
+async def miniflux_rss():
+    load_rss()
+
 if __name__ == '__main__':
     import uvicorn
     uvicorn.run(app, host="127.0.0.1", port=8964)
diff --git a/rss.py b/rss.py
new file mode 100644
index 0000000..ca88153
--- /dev/null
+++ b/rss.py
@@ -0,0 +1,51 @@
+import miniflux
+import time
+
+from config import __CONFIG__
+from fastgpt_uploader import upload2fastgpt
+
+# Authentication using an API token
+client = miniflux.Client(__CONFIG__['miniflux_host'], api_key=__CONFIG__['miniflux_api'])
+
+count = 0
+
+def load_content():
+    for i in range(1,10):
+        try:
+            tmp = client.get_entries(status=['unread'])['entries']
+            print(f"Downloaded {len(tmp)}")
+            return tmp
+        except Exception as e:
+            print(f"Error downloading: {e}")
+            time.sleep(10)
+    return None
+
+def update(ids):
+    for i in range (1,10):
+        try:
+            client.update_entries(entry_ids=ids, status='read')
+            return True
+        except Exception as e:
+            print(f"Error uploading:{count} {e}")
+            time.sleep(60)
+    return False
+
+def load_rss():
+    count = 0
+    loaded = []
+    uploads = []
+    for entry in load_content():
+        loaded.append(entry['id'])
+        uploads.append({
+            'q': entry['title'],
+            'a': entry['content'],
+            'score':[]
+        })
+        count += 1
+        if len(loaded) >= 100 and upload2fastgpt(uploads):
+            update(loaded)
+            loaded = []
+            uploads = []
+    if len(loaded) > 0:
+        update(loaded)
+    print(f"Total entries loaded: {count}")