mirror of
https://github.com/khoj-ai/khoj.git
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dfb277ee37
- Remove stale message_to_prompt test It is too broad, reduces maintainability. Remove as it doesn't really need its own test right now - Setting skipif at module level for chat actor, director tests reduces code duplication as earlier was using decorator on each chat test
279 lines
10 KiB
Python
279 lines
10 KiB
Python
# Standard Packages
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import os
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from datetime import datetime
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# External Packages
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import pytest
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# Internal Packages
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from khoj.processor.conversation.gpt import converse
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from khoj.processor.conversation.utils import message_to_log
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# Initialize variables for tests
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api_key = os.getenv("OPENAI_API_KEY")
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if api_key is None:
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pytest.skip(
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reason="Set OPENAI_API_KEY environment variable to run tests below. Get OpenAI API key from https://platform.openai.com/account/api-keys",
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allow_module_level=True,
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)
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# Test
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_chat_with_no_chat_history_or_retrieved_content():
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# Act
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response = converse(
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text="", # Assume no context retrieved from notes for the user_query
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user_query="Hello, my name is Testatron. Who are you?",
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api_key=api_key,
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)
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# Assert
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expected_responses = ["Khoj", "khoj"]
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assert len(response) > 0
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected assistants name, [K|k]hoj, in response but got" + response
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_from_chat_history_and_no_content():
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# Arrange
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conversation_log = {"chat": []}
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message_list = [
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("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
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("When was I born?", "You were born on 1st April 1984.", ""),
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]
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# Generate conversation logs
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for user_message, gpt_message, _ in message_list:
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conversation_log["chat"] += message_to_log(user_message, gpt_message)
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# Act
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response = converse(
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text="", # Assume no context retrieved from notes for the user_query
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user_query="What is my name?",
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conversation_log=conversation_log,
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api_key=api_key,
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)
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# Assert
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expected_responses = ["Testatron", "testatron"]
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assert len(response) > 0
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected [T|t]estatron in response but got" + response
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_from_chat_history_and_previously_retrieved_content():
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"Chat actor needs to use context in previous notes and chat history to answer question"
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# Arrange
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conversation_log = {"chat": []}
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message_list = [
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("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
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("When was I born?", "You were born on 1st April 1984.", "Testatron was born on 1st April 1984 in Testville."),
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]
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# Generate conversation logs
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for user_message, gpt_message, context in message_list:
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conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
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# Act
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response = converse(
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text="", # Assume no context retrieved from notes for the user_query
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user_query="Where was I born?",
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conversation_log=conversation_log,
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api_key=api_key,
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)
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# Assert
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assert len(response) > 0
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# Infer who I am and use that to infer I was born in Testville using chat history and previously retrieved notes
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assert "Testville" in response
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_from_chat_history_and_currently_retrieved_content():
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"Chat actor needs to use context across currently retrieved notes and chat history to answer question"
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# Arrange
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conversation_log = {"chat": []}
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message_list = [
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("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
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("When was I born?", "You were born on 1st April 1984.", ""),
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]
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# Generate conversation logs
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for user_message, gpt_message, context in message_list:
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conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
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# Act
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response = converse(
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text="Testatron was born on 1st April 1984 in Testville.", # Assume context retrieved from notes for the user_query
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user_query="Where was I born?",
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conversation_log=conversation_log,
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api_key=api_key,
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)
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# Assert
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assert len(response) > 0
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assert "Testville" in response
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_no_answer_in_chat_history_or_retrieved_content():
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"Chat actor should say don't know as not enough contexts in chat history or retrieved to answer question"
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# Arrange
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conversation_log = {"chat": []}
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message_list = [
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("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
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("When was I born?", "You were born on 1st April 1984.", ""),
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]
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# Generate conversation logs
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for user_message, gpt_message, context in message_list:
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conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
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# Act
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response = converse(
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text="", # Assume no context retrieved from notes for the user_query
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user_query="Where was I born?",
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conversation_log=conversation_log,
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api_key=api_key,
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)
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# Assert
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expected_responses = ["don't know", "do not know", "no information", "do not have", "don't have"]
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assert len(response) > 0
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected chat actor to say they don't know in response, but got: " + response
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_requires_current_date_awareness():
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"Chat actor should be able to answer questions relative to current date using provided notes"
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# Arrange
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context = f"""
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# {datetime.now().strftime("%Y-%m-%d")} "Naco Taco" "Tacos for Dinner"
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Expenses:Food:Dining 10.00 USD
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# {datetime.now().strftime("%Y-%m-%d")} "Sagar Ratna" "Dosa for Lunch"
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Expenses:Food:Dining 10.00 USD
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# 2020-04-01 "SuperMercado" "Bananas"
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Expenses:Food:Groceries 10.00 USD
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# 2020-01-01 "Naco Taco" "Burittos for Dinner"
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Expenses:Food:Dining 10.00 USD
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"""
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# Act
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response = converse(
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text=context, # Assume context retrieved from notes for the user_query
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user_query="What did I have for Dinner today?",
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api_key=api_key,
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)
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# Assert
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expected_responses = ["tacos", "Tacos"]
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assert len(response) > 0
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected [T|t]acos in response, but got: " + response
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_requires_date_aware_aggregation_across_provided_notes():
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"Chat actor should be able to answer questions that require date aware aggregation across multiple notes"
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# Arrange
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context = f"""
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# {datetime.now().strftime("%Y-%m-%d")} "Naco Taco" "Tacos for Dinner"
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Expenses:Food:Dining 10.00 USD
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# {datetime.now().strftime("%Y-%m-%d")} "Sagar Ratna" "Dosa for Lunch"
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Expenses:Food:Dining 10.00 USD
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# 2020-04-01 "SuperMercado" "Bananas"
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Expenses:Food:Groceries 10.00 USD
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# 2020-01-01 "Naco Taco" "Burittos for Dinner"
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Expenses:Food:Dining 10.00 USD
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"""
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# Act
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response = converse(
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text=context, # Assume context retrieved from notes for the user_query
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user_query="How much did I spend on dining this year?",
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api_key=api_key,
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)
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# Assert
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assert len(response) > 0
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assert "20" in response
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_answer_general_question_not_in_chat_history_or_retrieved_content():
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"Chat actor should be able to answer general questions not requiring looking at chat history or notes"
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# Arrange
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conversation_log = {"chat": []}
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message_list = [
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("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
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("When was I born?", "You were born on 1st April 1984.", ""),
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("Where was I born?", "You were born Testville.", ""),
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]
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# Generate conversation logs
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for user_message, gpt_message, context in message_list:
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conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
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# Act
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response = converse(
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text="", # Assume no context retrieved from notes for the user_query
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user_query="Write a haiku about unit testing",
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conversation_log=conversation_log,
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api_key=api_key,
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)
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# Assert
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expected_responses = ["test", "Test"]
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assert len(response.splitlines()) == 3 # haikus are 3 lines long
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected [T|t]est in response, but got: " + response
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.xfail(reason="Chat actor not consistently capable of asking for clarification yet.")
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@pytest.mark.chatquality
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def test_ask_for_clarification_if_not_enough_context_in_question():
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"Chat actor should ask for clarification if question cannot be answered unambiguously with the provided context"
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# Arrange
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context = f"""
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# Ramya
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My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani.
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# Fang
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My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li.
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# Aiyla
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My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.
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"""
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# Act
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response = converse(
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text=context, # Assume context retrieved from notes for the user_query
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user_query="How many kids does my older sister have?",
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api_key=api_key,
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)
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# Assert
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expected_responses = ["which sister", "Which sister", "which of your sister", "Which of your sister"]
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assert any([expected_response in response for expected_response in expected_responses]), (
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"Expected chat actor to ask for clarification in response, but got: " + response
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)
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