sij/khoj
1
0
Fork 0
mirror of https://github.com/khoj-ai/khoj.git synced 2024-12-22 20:28:09 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 5
khoj/tests/test_offline_chat_actors.py
Debanjum 01bc6d35dc
Rename Chat Model Options table to Chat Model as short & readable (#1003) 
- Previous was incorrectly plural but was defining only a single model
- Rename chat model table field to name
- Update documentation
- Update references functions and variables to match new name
2024-12-12 11:24:16 -08:00

623 lines
24 KiB
Python
Raw Blame History

from datetime import datetime
import pytest
from khoj.database.models import ChatModel
from khoj.routers.helpers import aget_data_sources_and_output_format
from khoj.utils.helpers import ConversationCommand
from tests.helpers import ConversationFactory, generate_chat_history, get_chat_provider
SKIP_TESTS = get_chat_provider(default=None) != ChatModel.ModelType.OFFLINE
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.utils.constants import default_offline_chat_models
@pytest.fixture(scope="session")
def loaded_model():
return download_model(default_offline_chat_models[0], max_tokens=5000)
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
def test_extract_multiple_explicit_questions_from_message(loaded_model):
# Act
responses = extract_questions_offline("What is the Sun? What is the Moon?", loaded_model=loaded_model)
# Assert
assert len(responses) >= 2
assert ["the Sun" in response for response in responses]
assert ["the Moon" in response for response in responses]
# ----------------------------------------------------------------------------------------------------
@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=generate_chat_history(message_list),
loaded_model=loaded_model,
use_history=True,
)
any_expected_with_barbara = [
"sibling",
"brother",
]
any_expected_with_anderson = [
"son",
"sons",
"children",
"family",
]
# Assert
assert len(response) >= 1
# Ensure the remaining generated search queries use proper nouns and chat history context
for question in response:
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=generate_chat_history(message_list),
loaded_model=loaded_model,
use_history=True,
)
expected_responses = [
"Barbara",
"Anderson",
]
# Assert
assert len(response) >= 1
assert any([expected_response in response[0] for expected_response in expected_responses]), (
"Expected chat actor to mention person's by name, 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=generate_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.anyio
@pytest.mark.django_db(transaction=True)
@pytest.mark.parametrize(
"user_query, expected_conversation_commands",
[
(
"Where did I learn to swim?",
{"sources": [ConversationCommand.Notes], "output": ConversationCommand.Text},
),
(
"Where is the nearest hospital?",
{"sources": [ConversationCommand.Online], "output": ConversationCommand.Text},
),
(
"Summarize the wikipedia page on the history of the internet",
{"sources": [ConversationCommand.Webpage], "output": ConversationCommand.Text},
),
(
"How many noble gases are there?",
{"sources": [ConversationCommand.General], "output": ConversationCommand.Text},
),
(
"Make a painting incorporating my past diving experiences",
{"sources": [ConversationCommand.Notes], "output": ConversationCommand.Image},
),
(
"Create a chart of the weather over the next 7 days in Timbuktu",
{"sources": [ConversationCommand.Online, ConversationCommand.Code], "output": ConversationCommand.Text},
),
(
"What's the highest point in this country and have I been there?",
{"sources": [ConversationCommand.Online, ConversationCommand.Notes], "output": ConversationCommand.Text},
),
],
)
async def test_select_data_sources_actor_chooses_to_search_notes(
client_offline_chat, user_query, expected_conversation_commands, default_user2
):
# Act
selected_conversation_commands = await aget_data_sources_and_output_format(user_query, {}, False, default_user2)
# Assert
assert set(expected_conversation_commands["sources"]) == set(selected_conversation_commands["sources"])
assert expected_conversation_commands["output"] == selected_conversation_commands["output"]
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_get_correct_tools_with_chat_history(client_offline_chat, default_user2):
# Arrange
user_query = "What's the latest in the Israel/Palestine conflict?"
chat_log = [
(
"Let's talk about the current events around the world.",
"Sure, let's discuss the current events. What would you like to know?",
[],
),
("What's up in New York City?", "A Pride parade has recently been held in New York City, on July 31st.", []),
]
chat_history = ConversationFactory(user=default_user2, conversation_log=generate_chat_history(chat_log))
# Act
tools = await aget_data_sources_and_output_format(user_query, chat_history, is_task=False)
# Assert
tools = [tool.value for tool in tools]
assert tools == ["online"]
# ----------------------------------------------------------------------------------------------------
@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=generate_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=[
{"compiled": "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=generate_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=generate_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 = [
{
"compiled": f"""{datetime.now().strftime("%Y-%m-%d")} "Naco Taco" "Tacos for Dinner"
Expenses:Food:Dining 10.00 USD"""
},
{
"compiled": f"""{datetime.now().strftime("%Y-%m-%d")} "Sagar Ratna" "Dosa for Lunch"
Expenses:Food:Dining 10.00 USD"""
},
{
"compiled": f"""2020-04-01 "SuperMercado" "Bananas"
Expenses:Food:Groceries 10.00 USD"""
},
{
"compiled": 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 = [
{
"compiled": f"""# {datetime.now().strftime("%Y-%m-%d")} "Naco Taco" "Tacos for Dinner"
Expenses:Food:Dining 10.00 USD"""
},
{
"compiled": f"""{datetime.now().strftime("%Y-%m-%d")} "Sagar Ratna" "Dosa for Lunch"
Expenses:Food:Dining 10.00 USD"""
},
{
"compiled": f"""2020-04-01 "SuperMercado" "Bananas"
Expenses:Food:Groceries 10.00 USD"""
},
{
"compiled": 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=generate_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 = [
{
"compiled": f"""# Ramya
My sister, Ramya, is married to Kali Devi. They have 2 kids, Ravi and Rani."""
},
{
"compiled": f"""# Fang
My sister, Fang Liu is married to Xi Li. They have 1 kid, Xiao Li."""
},
{
"compiled": 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", "Which one"]
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 = [
{"compiled": f"""I went to the store and bought some bananas for 2.20"""},
{"compiled": f"""I went to the store and bought some apples for 1.30"""},
{"compiled": 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 = [{"compiled": " ".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?"
Reference in a new issue View git blame Copy permalink