From 19f34f02b0c40e8715ad10cf977fb2a765cfa94a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Cl=C3=A9mence=20Lesn=C3=A9?= <clemence@lesne.pro>
Date: Mon, 4 Mar 2024 17:55:09 +0100
Subject: [PATCH] quality: Avoid followed entries with assistant

---
 helpers/call.py | 17 +++++++++++------
 main.py         | 15 ++++++++++-----
 2 files changed, 21 insertions(+), 11 deletions(-)

diff --git a/helpers/call.py b/helpers/call.py
index d3d42865..010477f9 100644
--- a/helpers/call.py
+++ b/helpers/call.py
@@ -140,13 +140,18 @@ async def handle_play(
     See: https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support?tabs=tts
     """
     if store:
-        call.messages.append(
-            MessageModel(
-                content=text,
-                persona=MessagePersonaEnum.ASSISTANT,
-                style=style,
+        if (
+            call.messages[-1].persona == MessagePersonaEnum.ASSISTANT
+        ):  # If the last message was from the assistant, append to it
+            call.messages[-1].content += f" {text}"
+        else:  # Otherwise, create a new message
+            call.messages.append(
+                MessageModel(
+                    content=text,
+                    persona=MessagePersonaEnum.ASSISTANT,
+                    style=style,
+                )
             )
-        )
 
     # Split text in chunks of max 400 characters, separated by sentence
     chunks = []
diff --git a/main.py b/main.py
index 7a5fb553..cb21b8a0 100644
--- a/main.py
+++ b/main.py
@@ -792,9 +792,15 @@ async def execute_llm_chat(
     4. `CallModel`, the updated model
     """
     _logger.debug("Running LLM chat")
+    content_full = ""
     should_user_answer = True
 
-    async def _buffer_user_callback(
+    async def _tools_callback(text: str, style: MessageStyleEnum) -> None:
+        nonlocal content_full
+        content_full += f" {text}"
+        await user_callback(text, style)
+
+    async def _content_callback(
         buffer: str, style: MessageStyleEnum
     ) -> MessageStyleEnum:
         # Remove tool calls from buffer content and detect style
@@ -850,7 +856,7 @@ async def _tools_cancellation_callback() -> None:
         post_call_next=post_call_next,
         post_call_synthesis=post_call_synthesis,
         search=search,
-        user_callback=user_callback,
+        user_callback=_tools_callback,
     )
 
     tools = []
@@ -862,7 +868,6 @@ async def _tools_cancellation_callback() -> None:
 
     # Execute LLM inference
     content_buffer_pointer = 0
-    content_full = ""
     tool_calls_buffer: dict[int, MessageToolModel] = {}
     try:
         async for delta in completion_stream(
@@ -885,7 +890,7 @@ async def _tools_cancellation_callback() -> None:
                     content_full[content_buffer_pointer:], False
                 ):
                     content_buffer_pointer += len(sentence)
-                    plugins.style = await _buffer_user_callback(sentence, plugins.style)
+                    plugins.style = await _content_callback(sentence, plugins.style)
     except ReadError:
         _logger.warn("Network error", exc_info=True)
         return True, True, should_user_answer, call
@@ -895,7 +900,7 @@ async def _tools_cancellation_callback() -> None:
 
     # Flush the remaining buffer
     if content_buffer_pointer < len(content_full):
-        plugins.style = await _buffer_user_callback(
+        plugins.style = await _content_callback(
             content_full[content_buffer_pointer:], plugins.style
         )