Handle msg truncation when question is larger than max prompt size

Notice and truncate the question it self at this point

src/khoj/processor/conversation/offline/utils.py

@@ -21,6 +21,7 @@ def download_model(repo_id: str, filename: str = "*Q4_K_M.gguf"):
 
     # Check if the model is already downloaded
     model_path = load_model_from_cache(repo_id, filename)
+    chat_model = None
     try:
         if model_path:
             chat_model = Llama(model_path, **kwargs)

src/khoj/processor/conversation/openai/utils.py

@@ -101,8 +101,3 @@ def llm_thread(g, messages, model_name, temperature, openai_api_key=None, model_
     chat(messages=messages)
 
     g.close()
-
-
-def extract_summaries(metadata):
-    """Extract summaries from metadata"""
-    return "".join([f'\n{session["summary"]}' for session in metadata])

src/khoj/processor/conversation/utils.py

@@ -232,12 +232,17 @@ def truncate_messages(
         original_question = "\n".join(messages[0].content.split("\n")[-1:]) if type(messages[0].content) == str else ""
         original_question = f"\n{original_question}"
         original_question_tokens = len(encoder.encode(original_question))
-        remaining_tokens = max_prompt_size - original_question_tokens - system_message_tokens
-        truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
+        remaining_tokens = max_prompt_size - system_message_tokens
+        if remaining_tokens > original_question_tokens:
+            remaining_tokens -= original_question_tokens
+            truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
+            messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
+        else:
+            truncated_message = encoder.decode(encoder.encode(original_question)[:remaining_tokens]).strip()
+            messages = [ChatMessage(content=truncated_message, role=messages[0].role)]
         logger.debug(
             f"Truncate current message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message}"
         )
-        messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
 
     return messages + [system_message] if system_message else messages
 

tests/test_conversation_utils.py

@@ -96,3 +96,23 @@ class TestTruncateMessage:
         assert final_tokens <= self.max_prompt_size
         assert len(chat_messages) == 1
         assert truncated_chat_history[0] != copy_big_chat_message
+
+    def test_truncate_single_large_question(self):
+        # Arrange
+        big_chat_message_content = " ".join(["hi"] * (self.max_prompt_size + 1))
+        big_chat_message = ChatMessageFactory.build(content=big_chat_message_content)
+        big_chat_message.role = "user"
+        copy_big_chat_message = big_chat_message.copy()
+        chat_messages = [big_chat_message]
+        initial_tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+
+        # Act
+        truncated_chat_history = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
+        final_tokens = sum([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
+
+        # Assert
+        # The original object has been modified. Verify certain properties
+        assert initial_tokens > self.max_prompt_size
+        assert final_tokens <= self.max_prompt_size
+        assert len(chat_messages) == 1
+        assert truncated_chat_history[0] != copy_big_chat_message