khoj/tests/test_offline_chat_actors.py

from datetime import datetime

import pytest

SKIP_TESTS = True
pytestmark = pytest.mark.skipif(
    SKIP_TESTS,
    reason="Disable in CI to avoid long test runs.",
)

import freezegun
from freezegun import freeze_time

from khoj.processor.conversation.offline.chat_model import (
    converse_offline,
    extract_questions_offline,
    filter_questions,
)
from khoj.processor.conversation.offline.utils import download_model
from khoj.processor.conversation.utils import message_to_log
from khoj.routers.helpers import aget_relevant_output_modes
from khoj.utils.constants import default_offline_chat_model


@pytest.fixture(scope="session")
def loaded_model():
    return download_model(default_offline_chat_model)


freezegun.configure(extend_ignore_list=["transformers"])


# Test
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
@freeze_time("1984-04-02", ignore=["transformers"])
def test_extract_question_with_date_filter_from_relative_day(loaded_model):
    # Act
    response = extract_questions_offline("Where did I go for dinner yesterday?", loaded_model=loaded_model)

    assert len(response) >= 1

    assert any(
        [
            "dt>='1984-04-01'" in response[0] and "dt<'1984-04-02'" in response[0],
            "dt>='1984-04-01'" in response[0] and "dt<='1984-04-01'" in response[0],
            'dt>="1984-04-01"' in response[0] and 'dt<"1984-04-02"' in response[0],
            'dt>="1984-04-01"' in response[0] and 'dt<="1984-04-01"' in response[0],
        ]
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.xfail(reason="Search actor still isn't very date aware nor capable of formatting")
@pytest.mark.chatquality
@freeze_time("1984-04-02", ignore=["transformers"])
def test_extract_question_with_date_filter_from_relative_month(loaded_model):
    # Act
    response = extract_questions_offline("Which countries did I visit last month?", loaded_model=loaded_model)

    # Assert
    assert len(response) >= 1
    # The user query should be the last question in the response
    assert response[-1] == ["Which countries did I visit last month?"]
    assert any(
        [
            "dt>='1984-03-01'" in response[0] and "dt<'1984-04-01'" in response[0],
            "dt>='1984-03-01'" in response[0] and "dt<='1984-03-31'" in response[0],
            'dt>="1984-03-01"' in response[0] and 'dt<"1984-04-01"' in response[0],
            'dt>="1984-03-01"' in response[0] and 'dt<="1984-03-31"' in response[0],
        ]
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.xfail(reason="Chat actor still isn't very date aware nor capable of formatting")
@pytest.mark.chatquality
@freeze_time("1984-04-02", ignore=["transformers"])
def test_extract_question_with_date_filter_from_relative_year():
    # Act
    response = extract_questions_offline("Which countries have I visited this year?")

    # Assert
    expected_responses = [
        ("dt>='1984-01-01'", ""),
        ("dt>='1984-01-01'", "dt<'1985-01-01'"),
        ("dt>='1984-01-01'", "dt<='1984-12-31'"),
    ]
    assert len(response) == 1
    assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
        "Expected date filter to limit to 1984 in response but got: " + response[0]
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
@freeze_time("1984-04-02", ignore=["transformers"])
def test_extract_question_includes_root_question(loaded_model):
    # Act
    response = extract_questions_offline("Which countries have I visited this year?", loaded_model=loaded_model)

    # Assert
    assert len(response) >= 1
    assert response[-1] == "Which countries have I visited this year?"


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_extract_multiple_explicit_questions_from_message(loaded_model):
    # Act
    response = extract_questions_offline("What is the Sun? What is the Moon?", loaded_model=loaded_model)

    # Assert
    expected_responses = ["What is the Sun?", "What is the Moon?"]
    assert len(response) >= 2
    assert expected_responses[0] == response[-2]
    assert expected_responses[1] == response[-1]


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_extract_multiple_implicit_questions_from_message(loaded_model):
    # Act
    response = extract_questions_offline("Is Carl taller than Ross?", loaded_model=loaded_model)

    # Assert
    expected_responses = ["height", "taller", "shorter", "heights", "who"]
    assert len(response) <= 3

    for question in response:
        assert any([expected_response in question.lower() for expected_response in expected_responses]), (
            "Expected chat actor to ask follow-up questions about Carl and Ross, but got: " + question
        )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_generate_search_query_using_question_from_chat_history(loaded_model):
    # Arrange
    message_list = [
        ("What is the name of Mr. Anderson's daughter?", "Miss Barbara", []),
    ]
    query = "Does he have any sons?"

    # Act
    response = extract_questions_offline(
        query,
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
        use_history=True,
    )

    any_expected_with_barbara = [
        "sibling",
        "brother",
    ]

    any_expected_with_anderson = [
        "son",
        "sons",
        "children",
    ]

    # Assert
    assert len(response) >= 1
    assert response[-1] == query, "Expected last question to be the user query, but got: " + response[-1]
    # Ensure the remaining generated search queries use proper nouns and chat history context
    for question in response[:-1]:
        if "Barbara" in question:
            assert any([expected_relation in question for expected_relation in any_expected_with_barbara]), (
                "Expected search queries using proper nouns and chat history for context, but got: " + question
            )
        elif "Anderson" in question:
            assert any([expected_response in question for expected_response in any_expected_with_anderson]), (
                "Expected search queries using proper nouns and chat history for context, but got: " + question
            )
        else:
            assert False, (
                "Expected search queries using proper nouns and chat history for context, but got: " + question
            )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_generate_search_query_using_answer_from_chat_history(loaded_model):
    # Arrange
    message_list = [
        ("What is the name of Mr. Anderson's daughter?", "Miss Barbara", []),
    ]

    # Act
    response = extract_questions_offline(
        "Is she a Doctor?",
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
        use_history=True,
    )

    expected_responses = [
        "Barbara",
        "Robert",
        "daughter",
    ]

    # Assert
    assert len(response) >= 1
    assert any([expected_response in response[0] for expected_response in expected_responses]), (
        "Expected chat actor to mention Darth Vader's daughter, but got: " + response[0]
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.xfail(reason="Search actor unable to create date filter using chat history and notes as context")
@pytest.mark.chatquality
def test_generate_search_query_with_date_and_context_from_chat_history(loaded_model):
    # Arrange
    message_list = [
        ("When did I visit Masai Mara?", "You visited Masai Mara in April 2000", []),
    ]

    # Act
    response = extract_questions_offline(
        "What was the Pizza place we ate at over there?",
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
    )

    # Assert
    expected_responses = [
        ("dt>='2000-04-01'", "dt<'2000-05-01'"),
        ("dt>='2000-04-01'", "dt<='2000-04-30'"),
        ('dt>="2000-04-01"', 'dt<"2000-05-01"'),
        ('dt>="2000-04-01"', 'dt<="2000-04-30"'),
    ]
    assert len(response) == 1
    assert "Masai Mara" in response[0]
    assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
        "Expected date filter to limit to April 2000 in response but got: " + response[0]
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_chat_with_no_chat_history_or_retrieved_content(loaded_model):
    # Act
    response_gen = converse_offline(
        references=[],  # Assume no context retrieved from notes for the user_query
        user_query="Hello, my name is Testatron. Who are you?",
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    expected_responses = ["Khoj", "khoj", "KHOJ"]
    assert len(response) > 0
    assert any([expected_response in response for expected_response in expected_responses]), (
        "Expected assistants name, [K|k]hoj, in response but got: " + response
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_answer_from_chat_history_and_previously_retrieved_content(loaded_model):
    "Chat actor needs to use context in previous notes and chat history to answer question"
    # Arrange
    message_list = [
        ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
        (
            "When was I born?",
            "You were born on 1st April 1984.",
            ["Testatron was born on 1st April 1984 in Testville."],
        ),
    ]

    # Act
    response_gen = converse_offline(
        references=[],  # Assume no context retrieved from notes for the user_query
        user_query="Where was I born?",
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    assert len(response) > 0
    # Infer who I am and use that to infer I was born in Testville using chat history and previously retrieved notes
    assert "Testville" in response


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_answer_from_chat_history_and_currently_retrieved_content(loaded_model):
    "Chat actor needs to use context across currently retrieved notes and chat history to answer question"
    # Arrange
    message_list = [
        ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
        ("When was I born?", "You were born on 1st April 1984.", []),
    ]

    # Act
    response_gen = converse_offline(
        references=[
            "Testatron was born on 1st April 1984 in Testville."
        ],  # Assume context retrieved from notes for the user_query
        user_query="Where was I born?",
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    assert len(response) > 0
    assert "Testville" in response


# ----------------------------------------------------------------------------------------------------
@pytest.mark.xfail(reason="Chat actor lies when it doesn't know the answer")
@pytest.mark.chatquality
def test_refuse_answering_unanswerable_question(loaded_model):
    "Chat actor should not try make up answers to unanswerable questions."
    # Arrange
    message_list = [
        ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
        ("When was I born?", "You were born on 1st April 1984.", []),
    ]

    # Act
    response_gen = converse_offline(
        references=[],  # Assume no context retrieved from notes for the user_query
        user_query="Where was I born?",
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    expected_responses = [
        "don't know",
        "do not know",
        "no information",
        "do not have",
        "don't have",
        "cannot answer",
        "I'm sorry",
    ]
    assert len(response) > 0
    assert any([expected_response in response for expected_response in expected_responses]), (
        "Expected chat actor to say they don't know in response, but got: " + response
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_answer_requires_current_date_awareness(loaded_model):
    "Chat actor should be able to answer questions relative to current date using provided notes"
    # Arrange
    context = [
        f"""{datetime.now().strftime("%Y-%m-%d")} "Naco Taco" "Tacos for Dinner"
Expenses:Food:Dining  10.00 USD""",
        f"""{datetime.now().strftime("%Y-%m-%d")} "Sagar Ratna" "Dosa for Lunch"
Expenses:Food:Dining  10.00 USD""",
        f"""2020-04-01 "SuperMercado" "Bananas"
Expenses:Food:Groceries  10.00 USD""",
        f"""2020-01-01 "Naco Taco" "Burittos for Dinner"
Expenses:Food:Dining  10.00 USD""",
    ]

    # Act
    response_gen = converse_offline(
        references=context,  # Assume context retrieved from notes for the user_query
        user_query="What did I have for Dinner today?",
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    expected_responses = ["tacos", "Tacos"]
    assert len(response) > 0
    assert any([expected_response in response for expected_response in expected_responses]), (
        "Expected [T|t]acos in response, but got: " + response
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_answer_requires_date_aware_aggregation_across_provided_notes(loaded_model):
    "Chat actor should be able to answer questions that require date aware aggregation across multiple notes"
    # Arrange
    context = [
        f"""# {datetime.now().strftime("%Y-%m-%d")} "Naco Taco" "Tacos for Dinner"
Expenses:Food:Dining  10.00 USD""",
        f"""{datetime.now().strftime("%Y-%m-%d")} "Sagar Ratna" "Dosa for Lunch"
Expenses:Food:Dining  10.00 USD""",
        f"""2020-04-01 "SuperMercado" "Bananas"
Expenses:Food:Groceries  10.00 USD""",
        f"""2020-01-01 "Naco Taco" "Burittos for Dinner"
Expenses:Food:Dining  10.00 USD""",
    ]

    # Act
    response_gen = converse_offline(
        references=context,  # Assume context retrieved from notes for the user_query
        user_query="How much did I spend on dining this year?",
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    assert len(response) > 0
    assert "20" in response


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_answer_general_question_not_in_chat_history_or_retrieved_content(loaded_model):
    "Chat actor should be able to answer general questions not requiring looking at chat history or notes"
    # Arrange
    message_list = [
        ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", []),
        ("When was I born?", "You were born on 1st April 1984.", []),
        ("Where was I born?", "You were born Testville.", []),
    ]

    # Act
    response_gen = converse_offline(
        references=[],  # Assume no context retrieved from notes for the user_query
        user_query="Write a haiku about unit testing in 3 lines",
        conversation_log=populate_chat_history(message_list),
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    expected_responses = ["test", "testing"]
    assert len(response.splitlines()) >= 3  # haikus are 3 lines long, but Falcon tends to add a lot of new lines.
    assert any([expected_response in response.lower() for expected_response in expected_responses]), (
        "Expected [T|t]est in response, but got: " + response
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_ask_for_clarification_if_not_enough_context_in_question(loaded_model):
    "Chat actor should ask for clarification if question cannot be answered unambiguously with the provided context"
    # Arrange
    context = [
        f"""# Ramya
My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani.""",
        f"""# Fang
My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li.""",
        f"""# Aiyla
My sister, Aiyla is married to Tolga. They have 3 kids, Yildiz, Ali and Ahmet.""",
    ]

    # Act
    response_gen = converse_offline(
        references=context,  # Assume context retrieved from notes for the user_query
        user_query="How many kids does my older sister have?",
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    expected_responses = ["which sister", "Which sister", "which of your sister", "Which of your sister"]
    assert any([expected_response in response for expected_response in expected_responses]), (
        "Expected chat actor to ask for clarification in response, but got: " + response
    )


# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_agent_prompt_should_be_used(loaded_model, offline_agent):
    "Chat actor should ask be tuned to think like an accountant based on the agent definition"
    # Arrange
    context = [
        f"""I went to the store and bought some bananas for 2.20""",
        f"""I went to the store and bought some apples for 1.30""",
        f"""I went to the store and bought some oranges for 6.00""",
    ]

    # Act
    response_gen = converse_offline(
        references=context,  # Assume context retrieved from notes for the user_query
        user_query="What did I buy?",
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert that the model without the agent prompt does not include the summary of purchases
    expected_responses = ["9.50", "9.5"]
    assert all([expected_response not in response for expected_response in expected_responses]), (
        "Expected chat actor to summarize values of purchases" + response
    )

    # Act
    response_gen = converse_offline(
        references=context,  # Assume context retrieved from notes for the user_query
        user_query="What did I buy?",
        loaded_model=loaded_model,
        agent=offline_agent,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert that the model with the agent prompt does include the summary of purchases
    expected_responses = ["9.50", "9.5"]
    assert any([expected_response in response for expected_response in expected_responses]), (
        "Expected chat actor to summarize values of purchases" + response
    )


# ----------------------------------------------------------------------------------------------------
def test_chat_does_not_exceed_prompt_size(loaded_model):
    "Ensure chat context and response together do not exceed max prompt size for the model"
    # Arrange
    prompt_size_exceeded_error = "ERROR: The prompt size exceeds the context window size and cannot be processed"
    context = [" ".join([f"{number}" for number in range(2043)])]

    # Act
    response_gen = converse_offline(
        references=context,  # Assume context retrieved from notes for the user_query
        user_query="What numbers come after these?",
        loaded_model=loaded_model,
    )
    response = "".join([response_chunk for response_chunk in response_gen])

    # Assert
    assert prompt_size_exceeded_error not in response, (
        "Expected chat response to be within prompt limits, but got exceeded error: " + response
    )


# ----------------------------------------------------------------------------------------------------
def test_filter_questions():
    test_questions = [
        "I don't know how to answer that",
        "I cannot answer anything about the nuclear secrets",
        "Who is on the basketball team?",
    ]
    filtered_questions = filter_questions(test_questions)
    assert len(filtered_questions) == 1
    assert filtered_questions[0] == "Who is on the basketball team?"


# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_use_default_response_mode(client_offline_chat):
    # Arrange
    user_query = "What's the latest in the Israel/Palestine conflict?"

    # Act
    mode = await aget_relevant_output_modes(user_query, {})

    # Assert
    assert mode.value == "default"


# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_use_image_response_mode(client_offline_chat):
    # Arrange
    user_query = "Paint a picture of the scenery in Timbuktu in the winter"

    # Act
    mode = await aget_relevant_output_modes(user_query, {})

    # Assert
    assert mode.value == "image"


# Helpers
# ----------------------------------------------------------------------------------------------------
def populate_chat_history(message_list):
    # Generate conversation logs
    conversation_log = {"chat": []}
    for user_message, chat_response, context in message_list:
        message_to_log(
            user_message,
            chat_response,
            {"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
            conversation_log=conversation_log["chat"],
        )
    return conversation_log