Send a notification to users when proactive notification apps exceed … (

#1307)

…the prompts limit

backend/utils/plugins.py

@@ -20,39 +20,39 @@ def get_github_docs_content(repo="BasedHardware/omi", path="docs/docs"):
     """
     Recursively retrieves content from GitHub docs folder and subfolders using GitHub API.
     Returns a dict mapping file paths to their raw content.
-    
+
     If cached, returns cached content. (24 hours)
     So any changes to the docs will take 24 hours to be reflected.
     """
     if cached := get_generic_cache(f'get_github_docs_content_{repo}_{path}'):
         return cached
     docs_content = {}
     headers = {"Authorization": f"token {os.getenv('GITHUB_TOKEN')}"}
-    
+
     def get_contents(path):
         url = f"https://api.github.com/repos/{repo}/contents/{path}"
         response = requests.get(url, headers=headers)
-        
+
         if response.status_code != 200:
             print(f"Failed to fetch contents for {path}: {response.status_code}")
             return
-            
+
         contents = response.json()
-        
+
         if not isinstance(contents, list):
             return
-            
+
         for item in contents:
             if item["type"] == "file" and (item["name"].endswith(".md") or item["name"].endswith(".mdx")):
                 # Get raw content for documentation files
                 raw_response = requests.get(item["download_url"], headers=headers)
                 if raw_response.status_code == 200:
                     docs_content[item["path"]] = raw_response.text
-            
+
             elif item["type"] == "dir":
                 # Recursively process subfolders
                 get_contents(item["path"])
-    
+
     get_contents(path)
     set_generic_cache(f'get_github_docs_content_{repo}_{path}', docs_content, 60 * 24*7)
     return docs_content
@@ -270,6 +270,7 @@ def _single(plugin: Plugin):
                             send_plugin_notification(token, plugin.name, plugin.id, message)
                             results[plugin.id] = message
                     elif len(prompt) > 5000:
+                        send_plugin_notification(token, plugin.name, plugin.id, f"Prompt too long: {len(prompt)}/5000 characters. Please shorten.")
                         print(f"Plugin {plugin.id} prompt too long, length: {len(prompt)}/5000")
 
         except Exception as e:

plugins/example/basic/mentor.py

@@ -20,11 +20,14 @@ def normalize(text):
 
     session_id = data.session_id
     segments = get_upsert_segment_to_transcript_plugin('mentor-01', session_id, data.segments)
-    scan_segment = scan_segment_session[session_id] if session_id in scan_segment_session and len(segments) > len(data.segments) else 0
+    if len(segments) <= len(data.segments) or session_id not in scan_segment_session:
+        scan_segment_session[session_id] = 0
+    scan_segment = scan_segment_session[session_id]
 
     # 1. Detect codewords. You could either use a simple regexp or call LLMs to trigger the step 2.
     codewords = ['hey Omi what do you think']
     scan_segments = segments[scan_segment:]
+    print(session_id, "scan_segment", len(scan_segments), scan_segment)
     if len(scan_segments) == 0:
         return {}
     text_lower = normalize(" ".join([segment.text for segment in scan_segments]))
@@ -50,7 +53,7 @@ def normalize(text):
     The following is a {user_name}'s conversation, with the transcripts, that {user_name} had during the meeting.
     {user_name} wants to get the call-to-action advice to move faster during the meetting based on the conversation.
 
-    First, identify the topics or problems that {user_name} is discussing or trying to resolve during the meeting, and then provide advice specific to those topics or problems. If you cannot find the topic or problem of the meeting, respond with an empty message.
+    First, identify the topics or problems that {user_name} is discussing or trying to resolve during the meeting, and then provide advice specific to those topics or problems.
 
     The advice must focus on the specific object mentioned in the conversation. The object could be a product, a person, or an event.
 
@@ -62,6 +65,8 @@ def normalize(text):
     Respond in at most 100 words.
 
     Output your response in plain text, without markdown.
+
+    If you cannot find the topic or problem of the meeting, respond 'Nah 🤷 ~'.
     ```
     ${transcript}
     ```