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aa081913bf
- Cache last anthropic message. Given research mode now uses function calling paradigm and not the old research mode structure. - Cache tool definitions passed to anthropic models - Stop dropping first message if by assistant as seems like Anthropic API doesn't complain about it any more. - Drop tool result when tool call is truncated as invalid state - Do not truncate tool use message content, just drop the whole tool use message. AI model APIs need tool use assistant message content in specific form (e.g with thinking etc.). So dropping content items breaks expected tool use message content format. Handle tool use scenarios where iteration query isn't set for retry
232 lines
11 KiB
Python
232 lines
11 KiB
Python
from copy import deepcopy
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import tiktoken
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from langchain_core.messages.chat import ChatMessage
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from khoj.processor.conversation import utils
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class TestTruncateMessage:
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max_prompt_size = 40
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model_name = "gpt-4o-mini"
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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 = generate_chat_history(50)
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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([utils.count_tokens(message.content, self.encoder) 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) < 50
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assert len(chat_history) > 5
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assert tokens <= self.max_prompt_size
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def test_truncate_message_only_oldest_big(self):
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# Arrange
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chat_history = generate_chat_history(5)
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big_chat_message = ChatMessage(role="user", content=generate_content(100, suffix="Question?"))
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chat_history.append(big_chat_message)
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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([utils.count_tokens(message.content, self.encoder) 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) == 5
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assert tokens <= self.max_prompt_size
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def test_truncate_message_with_image(self):
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# Arrange
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image_content_item = {"type": "image_url", "image_url": {"url": "placeholder"}}
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content_list = [{"type": "text", "text": f"{index}"} for index in range(100)]
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content_list += [image_content_item, {"type": "text", "text": "Question?"}]
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big_chat_message = ChatMessage(role="user", content=content_list)
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copy_big_chat_message = deepcopy(big_chat_message)
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chat_history = [big_chat_message]
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initial_tokens = sum([utils.count_tokens(message.content, self.encoder) 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([utils.count_tokens(message.content, self.encoder) 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 truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
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assert truncated_chat_history[0].content[-1]["text"] == "Question?", "Query should be preserved"
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assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
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assert final_tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
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def test_truncate_message_with_content_list(self):
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# Arrange
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chat_history = generate_chat_history(5)
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content_list = [{"type": "text", "text": f"{index}"} for index in range(100)]
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content_list += [{"type": "text", "text": "Question?"}]
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big_chat_message = ChatMessage(role="user", content=content_list)
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copy_big_chat_message = deepcopy(big_chat_message)
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chat_history.insert(0, big_chat_message)
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initial_tokens = sum([utils.count_tokens(message.content, self.encoder) 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([utils.count_tokens(message.content, self.encoder) 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 (
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len(chat_history) == 1
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), "Only most recent message should be present as it itself is larger than context size"
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assert len(truncated_chat_history[0].content) < len(
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copy_big_chat_message.content
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), "message content list should be modified"
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assert truncated_chat_history[0].content[-1]["text"] == "Question?", "Query should be preserved"
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assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
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assert final_tokens <= self.max_prompt_size, "Truncated message should be within 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 = generate_chat_history(5)
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big_chat_message = ChatMessage(role="user", content=generate_content(100, suffix="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([utils.count_tokens(message.content, self.encoder) 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([utils.count_tokens(message.content, self.encoder) 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 (
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len(chat_history) == 1
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), "Only most recent message should be present as it itself is larger than context size"
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assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
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assert truncated_chat_history[0].content[0]["text"].endswith("\nQuestion?"), "Query should be preserved"
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assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
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assert final_tokens <= self.max_prompt_size, "Truncated message should be within max prompt size"
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def test_truncate_message_large_system_message_first(self):
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# Arrange
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chat_history = generate_chat_history(5)
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chat_history[0].role = "system" # Mark the first message as system message
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big_chat_message = ChatMessage(role="user", content=generate_content(100, suffix="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([utils.count_tokens(message.content, self.encoder) 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([utils.count_tokens(message.content, self.encoder) 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 (
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len(truncated_chat_history) == len(chat_history) + 1
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) # Because the system_prompt is popped off from the chat_messages list
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assert len(truncated_chat_history) < 10
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assert len(truncated_chat_history) > 1
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assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
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assert truncated_chat_history[0].content[0]["text"].endswith("\nQuestion?"), "Query should be preserved"
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assert initial_tokens > self.max_prompt_size, "Initial tokens should be greater than max prompt size"
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assert final_tokens <= self.max_prompt_size, "Final tokens should be within 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 = ChatMessage(role="user", content=generate_content(100, suffix="Question?"))
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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([utils.count_tokens(message.content, self.encoder) 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([utils.count_tokens(message.content, self.encoder) 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, "Initial tokens should be greater than max prompt size"
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assert final_tokens <= self.max_prompt_size, "Final tokens should be within max prompt size"
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assert (
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len(chat_messages) == 1
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), "Only most recent message should be present as it itself is larger than context size"
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assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
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assert truncated_chat_history[0].content[0]["text"].endswith("\nQuestion?"), "Query should be preserved"
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def test_truncate_single_large_question(self):
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# Arrange
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big_chat_message_content = [{"type": "text", "text": " ".join(["hi"] * (self.max_prompt_size + 1))}]
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big_chat_message = ChatMessage(role="user", content=big_chat_message_content)
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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([utils.count_tokens(message.content, self.encoder) 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([utils.count_tokens(message.content, self.encoder) 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, "Initial tokens should be greater than max prompt size"
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assert final_tokens <= self.max_prompt_size, "Final tokens should be within max prompt size"
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assert (
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len(chat_messages) == 1
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), "Only most recent message should be present as it itself is larger than context size"
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assert truncated_chat_history[0] != copy_big_chat_message, "Original message should be modified"
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class TestLoadComplexJson:
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def test_load_complex_raw_json_string(self):
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# Arrange
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raw_json = r"""{"key": "value with unescaped " and unescaped \' and escaped \" and escaped \\'"}"""
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expected_json = {"key": "value with unescaped \" and unescaped \\' and escaped \" and escaped \\'"}
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# Act
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parsed_json = utils.load_complex_json(raw_json)
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# Assert
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assert parsed_json == expected_json
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def test_load_complex_json_with_python_code(self):
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# Arrange
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raw_json = r"""{"python": "import os\nvalue = \"\"\"\nfirst line of "text"\nsecond line of 'text'\n\"\"\"\nprint(value)"}"""
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expected_json = {
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"python": 'import os\nvalue = """\nfirst line of "text"\nsecond line of \'text\'\n"""\nprint(value)'
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}
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# Act
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parsed_json = utils.load_complex_json(raw_json)
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# Assert
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assert parsed_json == expected_json
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def test_load_complex_json_inline(self):
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# Arrange
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raw_json = """
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{"key1": "value1", "key2": "value2"}plain text suffix
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"""
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expected_json = {
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"key1": "value1",
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"key2": "value2",
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}
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# Act
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parsed_json = utils.load_complex_json(raw_json)
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# Assert
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assert parsed_json == expected_json
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def generate_content(count, suffix=""):
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return [{"type": "text", "text": " ".join([f"{index}" for index, _ in enumerate(range(count))]) + "\n" + suffix}]
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def generate_chat_history(count):
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return [
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ChatMessage(role="user" if index % 2 == 0 else "assistant", content=[{"type": "text", "text": f"{index}"}])
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for index, _ in enumerate(range(count))
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]
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