mirror of
https://github.com/khoj-ai/khoj.git
synced 2024-11-23 23:48:56 +01:00
8b2af0b5ef
* Add support for gpt4all's falcon model as an additional conversation processor - Update the UI pages to allow the user to point to the new endpoints for GPT - Update the internal schemas to support both GPT4 models and OpenAI - Add unit tests benchmarking some of the Falcon performance * Add exc_info to include stack trace in error logs for text processors * Pull shared functions into utils.py to be used across gpt4 and gpt * Add migration for new processor conversation schema * Skip GPT4All actor tests due to typing issues * Fix Obsidian processor configuration in auto-configure flow * Rename enable_local_llm to enable_offline_chat
426 lines
16 KiB
Python
426 lines
16 KiB
Python
# Standard Packages
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from datetime import datetime
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# External Packages
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import pytest
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SKIP_TESTS = True
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pytestmark = pytest.mark.skipif(
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SKIP_TESTS,
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reason="The GPT4All library has some quirks that make it hard to test in CI. This causes some tests to fail. Hence, disable it in CI.",
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)
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import freezegun
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from freezegun import freeze_time
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from gpt4all import GPT4All
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# Internal Packages
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from khoj.processor.conversation.gpt4all.chat_model import converse_falcon, extract_questions_falcon
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from khoj.processor.conversation.utils import message_to_log
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@pytest.fixture(scope="session")
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def loaded_model():
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return GPT4All("ggml-model-gpt4all-falcon-q4_0.bin")
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freezegun.configure(extend_ignore_list=["transformers"])
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# Test
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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@freeze_time("1984-04-02")
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def test_extract_question_with_date_filter_from_relative_day(loaded_model):
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# Act
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response = extract_questions_falcon(
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"Where did I go for dinner yesterday?", loaded_model=loaded_model, run_extraction=True
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)
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assert len(response) >= 1
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assert response[-1] == "Where did I go for dinner yesterday?"
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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@freeze_time("1984-04-02")
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def test_extract_question_with_date_filter_from_relative_month(loaded_model):
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# Act
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response = extract_questions_falcon("Which countries did I visit last month?", loaded_model=loaded_model)
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# Assert
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assert len(response) == 1
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assert response == ["Which countries did I visit last month?"]
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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@freeze_time("1984-04-02")
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def test_extract_question_with_date_filter_from_relative_year(loaded_model):
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# Act
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response = extract_questions_falcon(
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"Which countries have I visited this year?", loaded_model=loaded_model, run_extraction=True
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)
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# Assert
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assert len(response) >= 1
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assert response[-1] == "Which countries have I visited this year?"
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_extract_multiple_explicit_questions_from_message(loaded_model):
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# Act
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response = extract_questions_falcon("What is the Sun? What is the Moon?", loaded_model=loaded_model)
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# Assert
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expected_responses = ["What is the Sun?", "What is the Moon?"]
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assert len(response) == 2
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assert expected_responses == response
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_extract_multiple_implicit_questions_from_message(loaded_model):
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# Act
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response = extract_questions_falcon("Is Morpheus taller than Neo?", loaded_model=loaded_model, run_extraction=True)
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# Assert
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expected_responses = [
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("morpheus", "neo"),
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]
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assert len(response) == 2
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assert any([start in response[0].lower() and end in response[1].lower() for start, end in expected_responses]), (
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"Expected two search queries in response but got: " + response[0]
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.chatquality
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def test_generate_search_query_using_question_from_chat_history(loaded_model):
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# Arrange
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message_list = [
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("What is the name of Mr. Vader's daughter?", "Princess Leia", []),
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]
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# Act
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response = extract_questions_falcon(
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"Does he have any sons?",
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conversation_log=populate_chat_history(message_list),
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loaded_model=loaded_model,
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run_extraction=True,
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use_history=True,
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)
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expected_responses = [
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"do not have",
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"clarify",
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"am sorry",
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]
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# Assert
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assert len(response) >= 1
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assert any([expected_response in response[0] for expected_response in expected_responses]), (
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"Expected chat actor to ask for clarification in response, but got: " + response[0]
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)
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.xfail(reason="Chat actor does not consistently follow template instructions.")
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@pytest.mark.chatquality
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def test_generate_search_query_using_answer_from_chat_history(loaded_model):
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# Arrange
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message_list = [
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("What is the name of Mr. Vader's daughter?", "Princess Leia", []),
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]
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# Act
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response = extract_questions_falcon(
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"Is she a Jedi?",
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conversation_log=populate_chat_history(message_list),
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loaded_model=loaded_model,
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run_extraction=True,
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use_history=True,
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)
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# Assert
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assert len(response) == 1
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assert "Leia" in response[0]
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.xfail(reason="Chat actor is not sufficiently date-aware")
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@pytest.mark.chatquality
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def test_generate_search_query_with_date_and_context_from_chat_history(loaded_model):
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# Arrange
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message_list = [
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("When did I visit Masai Mara?", "You visited Masai Mara in April 2000", []),
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]
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# Act
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response = extract_questions_falcon(
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"What was the Pizza place we ate at over there?",
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conversation_log=populate_chat_history(message_list),
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run_extraction=True,
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loaded_model=loaded_model,
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)
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# Assert
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expected_responses = [
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("dt>='2000-04-01'", "dt<'2000-05-01'"),
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("dt>='2000-04-01'", "dt<='2000-04-30'"),
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('dt>="2000-04-01"', 'dt<"2000-05-01"'),
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('dt>="2000-04-01"', 'dt<="2000-04-30"'),
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]
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assert len(response) == 1
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assert "Masai Mara" in response[0]
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assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
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"Expected date filter to limit to April 2000 in response but got: " + response[0]
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)
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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(loaded_model):
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# Act
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response_gen = converse_falcon(
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references=[], # 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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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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# Assert
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expected_responses = ["Khoj", "khoj", "khooj", "Khooj", "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.xfail(reason="Chat actor isn't really good at proper nouns yet.")
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@pytest.mark.chatquality
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def test_answer_from_chat_history_and_previously_retrieved_content(loaded_model):
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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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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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(
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"When was I born?",
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"You were born on 1st April 1984.",
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["Testatron was born on 1st April 1984 in Testville."],
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),
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]
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# Act
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response_gen = converse_falcon(
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references=[], # 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=populate_chat_history(message_list),
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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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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(loaded_model):
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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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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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# Act
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response_gen = converse_falcon(
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references=[
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"Testatron was born on 1st April 1984 in Testville."
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], # 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=populate_chat_history(message_list),
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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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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.xfail(reason="Chat actor is rather liable to lying.")
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@pytest.mark.chatquality
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def test_refuse_answering_unanswerable_question(loaded_model):
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"Chat actor should not try make up answers to unanswerable questions."
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# Arrange
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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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# Act
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response_gen = converse_falcon(
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references=[], # 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=populate_chat_history(message_list),
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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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# Assert
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expected_responses = [
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"don't know",
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"do not know",
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"no information",
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"do not have",
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"don't have",
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"cannot answer",
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"I'm sorry",
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]
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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(loaded_model):
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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 = [
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f"""{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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f"""{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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f"""2020-04-01 "SuperMercado" "Bananas"
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Expenses:Food:Groceries 10.00 USD""",
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f"""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_gen = converse_falcon(
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references=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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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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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(loaded_model):
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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 = [
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f"""# {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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f"""{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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f"""2020-04-01 "SuperMercado" "Bananas"
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Expenses:Food:Groceries 10.00 USD""",
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f"""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_gen = converse_falcon(
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references=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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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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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(loaded_model):
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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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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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# Act
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response_gen = converse_falcon(
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references=[], # Assume no context retrieved from notes for the user_query
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user_query="Write a haiku about unit testing in 3 lines",
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conversation_log=populate_chat_history(message_list),
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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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# Assert
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expected_responses = ["test", "testing"]
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assert len(response.splitlines()) >= 3 # haikus are 3 lines long, but Falcon tends to add a lot of new lines.
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assert any([expected_response in response.lower() 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(loaded_model):
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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 = [
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f"""# Ramya
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My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani.""",
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f"""# Fang
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My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li.""",
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f"""# 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_gen = converse_falcon(
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references=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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loaded_model=loaded_model,
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)
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response = "".join([response_chunk for response_chunk in response_gen])
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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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# Helpers
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# ----------------------------------------------------------------------------------------------------
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def populate_chat_history(message_list):
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# Generate conversation logs
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conversation_log = {"chat": []}
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for user_message, chat_response, context in message_list:
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message_to_log(
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user_message,
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chat_response,
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{"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
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conversation_log=conversation_log["chat"],
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)
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return conversation_log
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