Handle truncation when single long non-system chat message

Previously was assuming the system prompt is being always passed as
the first message. So expected there to be at least 2 messages in logs.

This broke chat actors querying with single long non system message.

A more robust way to extract system prompt is via the message role
instead

src/khoj/processor/conversation/utils.py

@@ -199,19 +199,26 @@ def truncate_messages(
             f"Fallback to default chat model tokenizer: {default_tokenizer}.\nConfigure tokenizer for unsupported model: {model_name} in Khoj settings to improve context stuffing."
         )
 
-    system_message = messages.pop()
+    # Extract system message from messages
-    assert type(system_message.content) == str
+    system_message = None
-    system_message_tokens = len(encoder.encode(system_message.content))
+    for idx, message in enumerate(messages):
+        if message.role == "system":
+            system_message = messages.pop(idx)
+            break
+
+    system_message_tokens = (
+        len(encoder.encode(system_message.content)) if system_message and type(system_message.content) == str else 0
+    )
 
     tokens = sum([len(encoder.encode(message.content)) for message in messages if type(message.content) == str])
+
+    # Drop older messages until under max supported prompt size by model
     while (tokens + system_message_tokens) > max_prompt_size and len(messages) > 1:
         messages.pop()
-        assert type(system_message.content) == str
         tokens = sum([len(encoder.encode(message.content)) for message in messages if type(message.content) == str])
 
     # Truncate current message if still over max supported prompt size by model
     if (tokens + system_message_tokens) > max_prompt_size:
-        assert type(system_message.content) == str
         current_message = "\n".join(messages[0].content.split("\n")[:-1]) if type(messages[0].content) == str else ""
         original_question = "\n".join(messages[0].content.split("\n")[-1:]) if type(messages[0].content) == str else ""
         original_question = f"\n{original_question}"
@@ -223,7 +230,7 @@ def truncate_messages(
         )
         messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
 
-    return messages + [system_message]
+    return messages + [system_message] if system_message else messages
 
 
 def reciprocal_conversation_to_chatml(message_pair):

tests/test_conversation_utils.py

@@ -19,49 +19,80 @@ class TestTruncateMessage:
     encoder = tiktoken.encoding_for_model(model_name)
 
     def test_truncate_message_all_small(self):
-        chat_messages = ChatMessageFactory.build_batch(500)
+        # Arrange
+        chat_history = ChatMessageFactory.build_batch(500)
 
-        prompt = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
+        # Act
-        tokens = sum([len(self.encoder.encode(message.content)) for message in prompt])
+        truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
+        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 len(chat_messages) < 500
+        assert len(chat_history) < 500
-        assert len(chat_messages) > 1
+        assert len(chat_history) > 1
         assert tokens <= self.max_prompt_size
 
     def test_truncate_message_first_large(self):
-        chat_messages = ChatMessageFactory.build_batch(25)
+        # Arrange
+        chat_history = ChatMessageFactory.build_batch(25)
         big_chat_message = ChatMessageFactory.build(content=factory.Faker("paragraph", nb_sentences=2000))
         big_chat_message.content = big_chat_message.content + "\n" + "Question?"
         copy_big_chat_message = big_chat_message.copy()
-        chat_messages.insert(0, big_chat_message)
+        chat_history.insert(0, big_chat_message)
-        tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        tokens = sum([len(self.encoder.encode(message.content)) for message in chat_history])
 
-        prompt = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
+        # Act
-        tokens = sum([len(self.encoder.encode(message.content)) for message in prompt])
+        truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
+        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 len(chat_messages) == 1
+        assert len(chat_history) == 1
-        assert prompt[0] != copy_big_chat_message
+        assert truncated_chat_history[0] != copy_big_chat_message
         assert tokens <= self.max_prompt_size
 
     def test_truncate_message_last_large(self):
-        chat_messages = ChatMessageFactory.build_batch(25)
+        # Arrange
+        chat_history = ChatMessageFactory.build_batch(25)
+        chat_history[0].role = "system"  # Mark the first message as system message
         big_chat_message = ChatMessageFactory.build(content=factory.Faker("paragraph", nb_sentences=1000))
         big_chat_message.content = big_chat_message.content + "\n" + "Question?"
         copy_big_chat_message = big_chat_message.copy()
 
-        chat_messages.insert(0, big_chat_message)
+        chat_history.insert(0, big_chat_message)
-        tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        initial_tokens = sum([len(self.encoder.encode(message.content)) for message in chat_history])
 
-        prompt = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
+        # Act
-        tokens = sum([len(self.encoder.encode(message.content)) for message in prompt])
+        truncated_chat_history = utils.truncate_messages(chat_history, 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 len(prompt) == (
+        assert len(truncated_chat_history) == (
-            len(chat_messages) + 1
+            len(chat_history) + 1
         )  # Because the system_prompt is popped off from the chat_messages lsit
-        assert len(prompt) < 26
+        assert len(truncated_chat_history) < 26
-        assert len(prompt) > 1
+        assert len(truncated_chat_history) > 1
-        assert prompt[0] != copy_big_chat_message
+        assert truncated_chat_history[0] != copy_big_chat_message
-        assert tokens <= self.max_prompt_size
+        assert initial_tokens > self.max_prompt_size
+        assert final_tokens <= self.max_prompt_size
+
+    def test_truncate_single_large_non_system_message(self):
+        # Arrange
+        big_chat_message = ChatMessageFactory.build(content=factory.Faker("paragraph", nb_sentences=2000))
+        big_chat_message.content = big_chat_message.content + "\n" + "Question?"
+        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