feat: update app_mentioned_callback to not require conversations history

Author: mwbrooks

listeners/assistant/assistant.py

@@ -73,7 +73,7 @@ def respond_in_assistant_thread(
         user_message = payload["text"]
 
         set_status(
-            status="Drafting...",
+            status="thinking...",
             loading_messages=[
                 "Teaching the hamsters to type faster…",
                 "Untangling the internet cables…",

listeners/events/app_mentioned.py

@@ -1,11 +1,9 @@
 from logging import Logger
 from slack_sdk import WebClient
 from slack_bolt import Say
-from typing import List, Dict
 
 from ..llm_caller import call_llm
 from ..views.feedback_block import create_feedback_block
-from ..listeners_constants import loading_messages
 
 """
 Handles the event when the app is mentioned in a Slack channel, retrieves the conversation context,
@@ -15,36 +13,35 @@
 
 def app_mentioned_callback(client: WebClient, event: dict, logger: Logger, say: Say):
     try:
-
         channel_id = event.get("channel")
-        thread_ts = event.get("thread_ts")
-        user_id = event.get("user")
         team_id = event.get("team")
         text = event.get("text")
-
-        if thread_ts:
-            conversation_context = client.conversations_replies(channel=channel_id, ts=thread_ts, limit=10)
-        else:
-            conversation_context = client.conversations_history(channel=channel_id, limit=50)
-            thread_ts = event["ts"]
-
-        messages_in_thread: List[Dict[str, str]] = []
-        for message in conversation_context["messages"]:
-            role = "user" if message.get("bot_id") is None else "assistant"
-            messages_in_thread.append({"role": role, "content": message["text"]})
-        if text:
-            returned_message = call_llm(messages_in_thread)
+        thread_ts = event.get("thread_ts") or event.get("ts")
+        user_id = event.get("user")
 
         client.assistant_threads_setStatus(
-            channel_id=channel_id, thread_ts=thread_ts, status="Bolt is typing", loading_messages=loading_messages
+            channel_id=channel_id,
+            thread_ts=thread_ts,
+            status="thinking...",
+            loading_messages=[
+                "Teaching the hamsters to type faster…",
+                "Untangling the internet cables…",
+                "Consulting the office goldfish…",
+                "Polishing up the response just for you…",
+                "Convincing the AI to stop overthinking…",
+            ],
         )
+
+        returned_message = call_llm([{"role": "user", "content": text}])
+
         stream_response = client.chat_startStream(
             channel=channel_id, recipient_team_id=team_id, recipient_user_id=user_id, thread_ts=thread_ts
         )
 
         stream_ts = stream_response["ts"]
 
-        # use of this for loop is specific to openai response method
+        # Loop over OpenAI response stream
+        # https://platform.openai.com/docs/api-reference/responses/create
         for event in returned_message:
             if event.type == "response.output_text.delta":
                 client.chat_appendStream(channel=channel_id, ts=stream_ts, markdown_text=f"{event.delta}")