Move message truncation logic into a separate function. Add unit tests with factory boy.

pyproject.toml

@@ -56,6 +56,8 @@ dependencies = [
     "aiohttp == 3.8.4",
     "langchain >= 0.0.187",
     "pypdf >= 3.9.0",
+    "factory-boy==3.2.1",
+    "Faker==18.10.1"
 ]
 dynamic = ["version"]
 

src/khoj/processor/conversation/utils.py

@@ -97,23 +97,33 @@ def generate_chatml_messages_with_context(
     messages = user_chatml_message + rest_backnforths + system_chatml_message
 
     # Truncate oldest messages from conversation history until under max supported prompt size by model
+    messages = truncate_message(messages, max_prompt_size[model_name], model_name)
+
+    # Return message in chronological order
+    return messages[::-1]
+
+def truncate_message(messages, max_prompt_size, model_name):
+    """Truncate messages to fit within max prompt size supported by model"""
     encoder = tiktoken.encoding_for_model(model_name)
     tokens = sum([len(encoder.encode(message.content)) for message in messages])
-    while tokens > max_prompt_size[model_name] and len(messages) > 1:
+    logger.info(f"num tokens: {tokens}")
+    while tokens > max_prompt_size and len(messages) > 1:
         messages.pop()
         tokens = sum([len(encoder.encode(message.content)) for message in messages])
 
     # Truncate last message if still over max supported prompt size by model
-    if tokens > max_prompt_size[model_name]:
+    if tokens > max_prompt_size:
-        last_message = messages[-1]
+        last_message = '\n'.join(messages[-1].content.split("\n")[:-1])
-        truncated_message = encoder.decode(encoder.encode(last_message.content))
+        original_question = '\n'.join(messages[-1].content.split("\n")[-1:])
+        original_question_tokens = len(encoder.encode(original_question))
+        remaining_tokens = max_prompt_size - original_question_tokens
+        truncated_message = encoder.decode(encoder.encode(last_message)[:remaining_tokens]).strip()
         logger.debug(
-            f"Truncate last message to fit within max prompt size of {max_prompt_size[model_name]} supported by {model_name} model:\n {truncated_message}"
+            f"Truncate last message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message}"
         )
-        messages = [ChatMessage(content=truncated_message, role=last_message.role)]
+        messages = [ChatMessage(content=truncated_message + original_question, role=messages[-1].role)]
 
-    # Return message in chronological order
+    return messages
-    return messages[::-1]
 
 
 def reciprocal_conversation_to_chatml(message_pair):

tests/test_conversation_utils.py

@@ -0,0 +1,78 @@
+from khoj.processor.conversation import utils
+from langchain.schema import ChatMessage
+import factory
+import logging
+import tiktoken
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+class ChatMessageFactory(factory.Factory):
+    class Meta:
+        model = ChatMessage
+
+    content = factory.Faker('paragraph')
+    role = factory.Faker('name')
+
+class TestTruncateMessage:
+    max_prompt_size = 4096
+    model_name = 'gpt-3.5-turbo'
+    encoder = tiktoken.encoding_for_model(model_name)
+
+    def test_truncate_message_all_small(self):
+        chat_messages = ChatMessageFactory.build_batch(500)
+        assert len(chat_messages) == 500
+        tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        assert tokens > self.max_prompt_size
+
+        prompt = utils.truncate_message(chat_messages, self.max_prompt_size, self.model_name)
+
+        # The original object has been modified. Verify certain properties
+        assert len(chat_messages) < 500
+        assert len(chat_messages) > 1
+        assert prompt == chat_messages
+
+        tokens = sum([len(self.encoder.encode(message.content)) for message in prompt])
+        assert tokens <= self.max_prompt_size
+
+    def test_truncate_message_first_large(self):
+        chat_messages = ChatMessageFactory.build_batch(25)
+        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)
+        assert len(chat_messages) == 26
+        tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        assert tokens > self.max_prompt_size
+
+        prompt = utils.truncate_message(chat_messages, self.max_prompt_size, self.model_name)
+
+        # The original object has been modified. Verify certain properties
+        assert len(chat_messages) < 26
+        assert len(chat_messages) == 1
+        assert prompt[0] != copy_big_chat_message
+
+        tokens = sum([len(self.encoder.encode(message.content)) for message in prompt])
+        assert tokens <= self.max_prompt_size
+
+    def test_truncate_message_last_large(self):
+        chat_messages = ChatMessageFactory.build_batch(25)
+        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.append(big_chat_message)
+        assert len(chat_messages) == 26
+        tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
+        assert tokens > self.max_prompt_size
+
+        prompt = utils.truncate_message(chat_messages, self.max_prompt_size, self.model_name)
+
+        # The original object has been modified. Verify certain properties
+        assert len(chat_messages) < 26
+        assert len(chat_messages) > 1
+        assert prompt[0] != copy_big_chat_message
+
+        tokens = sum([len(self.encoder.encode(message.content)) for message in prompt])
+        assert tokens < self.max_prompt_size
+