2023-06-06 03:58:29 +02:00
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import factory
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import tiktoken
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2023-12-28 13:34:02 +01:00
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from langchain.schema import ChatMessage
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from khoj.processor.conversation import utils
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2023-06-06 03:58:29 +02:00
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2023-06-06 04:29:23 +02:00
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2023-06-06 03:58:29 +02:00
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class ChatMessageFactory(factory.Factory):
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class Meta:
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model = ChatMessage
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content = factory.Faker("paragraph")
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role = factory.Faker("name")
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2023-06-06 03:58:29 +02:00
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class TestTruncateMessage:
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max_prompt_size = 4096
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model_name = "gpt-3.5-turbo"
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encoder = tiktoken.encoding_for_model(model_name)
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def test_truncate_message_all_small(self):
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# Arrange
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chat_history = ChatMessageFactory.build_batch(500)
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# Act
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truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
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tokens = sum([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
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# Assert
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# The original object has been modified. Verify certain properties
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assert len(chat_history) < 500
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assert len(chat_history) > 1
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assert tokens <= self.max_prompt_size
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def test_truncate_message_first_large(self):
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# Arrange
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chat_history = ChatMessageFactory.build_batch(25)
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2023-06-14 01:32:47 +02:00
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big_chat_message = ChatMessageFactory.build(content=factory.Faker("paragraph", nb_sentences=2000))
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big_chat_message.content = big_chat_message.content + "\n" + "Question?"
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copy_big_chat_message = big_chat_message.copy()
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chat_history.insert(0, big_chat_message)
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tokens = sum([len(self.encoder.encode(message.content)) for message in chat_history])
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# Act
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truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
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tokens = sum([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
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# Assert
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# The original object has been modified. Verify certain properties
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assert len(chat_history) == 1
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assert truncated_chat_history[0] != copy_big_chat_message
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assert tokens <= self.max_prompt_size
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def test_truncate_message_last_large(self):
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# Arrange
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chat_history = ChatMessageFactory.build_batch(25)
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chat_history[0].role = "system" # Mark the first message as system message
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big_chat_message = ChatMessageFactory.build(content=factory.Faker("paragraph", nb_sentences=1000))
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big_chat_message.content = big_chat_message.content + "\n" + "Question?"
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copy_big_chat_message = big_chat_message.copy()
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chat_history.insert(0, big_chat_message)
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initial_tokens = sum([len(self.encoder.encode(message.content)) for message in chat_history])
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# Act
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truncated_chat_history = utils.truncate_messages(chat_history, self.max_prompt_size, self.model_name)
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final_tokens = sum([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
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# Assert
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2023-08-01 18:25:52 +02:00
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# The original object has been modified. Verify certain properties.
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assert len(truncated_chat_history) == (
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len(chat_history) + 1
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) # Because the system_prompt is popped off from the chat_messages lsit
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assert len(truncated_chat_history) < 26
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assert len(truncated_chat_history) > 1
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assert truncated_chat_history[0] != copy_big_chat_message
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assert initial_tokens > self.max_prompt_size
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assert final_tokens <= self.max_prompt_size
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def test_truncate_single_large_non_system_message(self):
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# Arrange
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big_chat_message = ChatMessageFactory.build(content=factory.Faker("paragraph", nb_sentences=2000))
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big_chat_message.content = big_chat_message.content + "\n" + "Question?"
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big_chat_message.role = "user"
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copy_big_chat_message = big_chat_message.copy()
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chat_messages = [big_chat_message]
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initial_tokens = sum([len(self.encoder.encode(message.content)) for message in chat_messages])
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# Act
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truncated_chat_history = utils.truncate_messages(chat_messages, self.max_prompt_size, self.model_name)
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final_tokens = sum([len(self.encoder.encode(message.content)) for message in truncated_chat_history])
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# Assert
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# The original object has been modified. Verify certain properties
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assert initial_tokens > self.max_prompt_size
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assert final_tokens <= self.max_prompt_size
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assert len(chat_messages) == 1
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assert truncated_chat_history[0] != copy_big_chat_message
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