sij/khoj
1
0
Fork 0
mirror of https://github.com/khoj-ai/khoj.git synced 2024-12-18 02:27:10 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 23
khoj/tests/test_openai_chat_actors.py
Debanjum Singh Solanky c28d7d3414 Add basic chat actor test to infer scheduled queries
2024-05-01 08:28:59 +05:30

585 lines
22 KiB
Python
Raw Blame History

import os
from datetime import datetime
import freezegun
import pytest
from freezegun import freeze_time
from khoj.processor.conversation.openai.gpt import converse, extract_questions
from khoj.processor.conversation.utils import message_to_log
from khoj.routers.helpers import (
aget_relevant_information_sources,
aget_relevant_output_modes,
generate_online_subqueries,
infer_webpage_urls,
schedule_query,
)
from khoj.utils.helpers import ConversationCommand
from khoj.utils.rawconfig import LocationData
# Initialize variables for tests
api_key = os.getenv("OPENAI_API_KEY")
if api_key is None:
pytest.skip(
reason="Set OPENAI_API_KEY environment variable to run tests below. Get OpenAI API key from https://platform.openai.com/account/api-keys",
allow_module_level=True,
)
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():
# Act
response = extract_questions("Where did I go for dinner yesterday?")
# Assert
expected_responses = [
("dt='1984-04-01'", ""),
("dt>='1984-04-01'", "dt<'1984-04-02'"),
("dt>'1984-03-31'", "dt<'1984-04-02'"),
]
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 1st April 1984 in response but got: " + response[0]
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
@freeze_time("1984-04-02", ignore=["transformers"])
def test_extract_question_with_date_filter_from_relative_month():
# Act
response = extract_questions("Which countries did I visit last month?")
# Assert
expected_responses = [("dt>='1984-03-01'", "dt<'1984-04-01'"), ("dt>='1984-03-01'", "dt<='1984-03-31'")]
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 March 1984 in response but got: " + response[0]
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
@freeze_time("1984-04-02", ignore=["transformers"])
def test_extract_question_with_date_filter_from_relative_year():
# Act
response = extract_questions("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():
# Act
response = extract_questions("What is the Sun? What is the Moon?")
# Assert
expected_responses = [
("sun", "moon"),
]
assert len(response) == 2
assert any([start in response[0].lower() and end in response[1].lower() for start, end in expected_responses]), (
"Expected two search queries in response but got: " + response[0]
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_extract_multiple_implicit_questions_from_message():
# Act
response = extract_questions("Is Morpheus taller than Neo?")
# Assert
expected_responses = [
("morpheus", "neo"),
]
assert len(response) == 2
assert any([start in response[0].lower() and end in response[1].lower() for start, end in expected_responses]), (
"Expected two search queries in response but got: " + response[0]
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_generate_search_query_using_question_from_chat_history():
# Arrange
message_list = [
("What is the name of Mr. Vader's daughter?", "Princess Leia", []),
]
# Act
response = extract_questions("Does he have any sons?", conversation_log=populate_chat_history(message_list))
# Assert
assert len(response) == 1
assert "Vader" in response[0]
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_generate_search_query_using_answer_from_chat_history():
# Arrange
message_list = [
("What is the name of Mr. Vader's daughter?", "Princess Leia", []),
]
# Act
response = extract_questions("Is she a Jedi?", conversation_log=populate_chat_history(message_list))
# Assert
assert len(response) == 1
assert "Leia" in response[0]
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_generate_search_query_using_question_and_answer_from_chat_history():
# Arrange
message_list = [
("Does Luke Skywalker have any Siblings?", "Yes, Princess Leia", []),
]
# Act
response = extract_questions("Who is their father?", conversation_log=populate_chat_history(message_list))
# Assert
assert len(response) == 1
assert "Leia" in response[0] and "Luke" in response[0]
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_chat_with_no_chat_history_or_retrieved_content():
# Act
response_gen = converse(
references=[], # Assume no context retrieved from notes for the user_query
user_query="Hello, my name is Testatron. Who are you?",
api_key=api_key,
)
response = "".join([response_chunk for response_chunk in response_gen])
# Assert
expected_responses = ["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_no_content():
# 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(
references=[], # Assume no context retrieved from notes for the user_query
user_query="What is my name?",
conversation_log=populate_chat_history(message_list),
api_key=api_key,
)
response = "".join([response_chunk for response_chunk in response_gen])
# Assert
expected_responses = ["Testatron", "testatron"]
assert len(response) > 0
assert any([expected_response in response for expected_response in expected_responses]), (
"Expected [T|t]estatron in response but got: " + response
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_answer_from_chat_history_and_previously_retrieved_content():
"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(
references=[], # Assume no context retrieved from notes for the user_query
user_query="Where was I born?",
conversation_log=populate_chat_history(message_list),
api_key=api_key,
)
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():
"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(
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),
api_key=api_key,
)
response = "".join([response_chunk for response_chunk in response_gen])
# Assert
assert len(response) > 0
assert "Testville" in response
# ----------------------------------------------------------------------------------------------------
@pytest.mark.chatquality
def test_refuse_answering_unanswerable_question():
"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(
references=[], # Assume no context retrieved from notes for the user_query
user_query="Where was I born?",
conversation_log=populate_chat_history(message_list),
api_key=api_key,
)
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():
"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(
references=context, # Assume context retrieved from notes for the user_query
user_query="What did I have for Dinner today?",
api_key=api_key,
)
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():
"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(
references=context, # Assume context retrieved from notes for the user_query
user_query="How much did I spend on dining this year?",
api_key=api_key,
)
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():
"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(
references=[], # Assume no context retrieved from notes for the user_query
user_query="Write a haiku about unit testing in 3 lines. Do not say anything else",
conversation_log=populate_chat_history(message_list),
api_key=api_key,
)
response = "".join([response_chunk for response_chunk in response_gen])
# Assert
expected_responses = ["test", "Test"]
assert len(response.splitlines()) == 3 # haikus are 3 lines long
assert any([expected_response in response for expected_response in expected_responses]), (
"Expected [T|t]est in response, but got: " + response
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.xfail(reason="Chat actor not consistently capable of asking for clarification yet.")
@pytest.mark.chatquality
def test_ask_for_clarification_if_not_enough_context_in_question():
"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(
references=context, # Assume context retrieved from notes for the user_query
user_query="How many kids does my older sister have?",
api_key=api_key,
)
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(openai_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""",
]
expected_responses = ["9.50", "9.5"]
# Act
response_gen = converse(
references=context, # Assume context retrieved from notes for the user_query
user_query="What did I buy?",
api_key=api_key,
)
no_agent_response = "".join([response_chunk for response_chunk in response_gen])
response_gen = converse(
references=context, # Assume context retrieved from notes for the user_query
user_query="What did I buy?",
api_key=api_key,
agent=openai_agent,
)
agent_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
assert all([expected_response not in no_agent_response for expected_response in expected_responses]), (
"Expected chat actor to summarize values of purchases" + no_agent_response
)
assert any([expected_response in agent_response for expected_response in expected_responses]), (
"Expected chat actor to summarize values of purchases" + agent_response
)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
@freeze_time("2024-04-04", ignore=["transformers"])
async def test_websearch_with_operators(chat_client):
# Arrange
user_query = "Share popular posts on r/worldnews this month"
# Act
responses = await generate_online_subqueries(user_query, {}, None)
# Assert
assert any(
["reddit.com/r/worldnews" in response for response in responses]
), "Expected a search query to include site:reddit.com but got: " + str(responses)
assert any(
["site:reddit.com" in response for response in responses]
), "Expected a search query to include site:reddit.com but got: " + str(responses)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_websearch_khoj_website_for_info_about_khoj(chat_client):
# Arrange
user_query = "Do you support image search?"
# Act
responses = await generate_online_subqueries(user_query, {}, None)
# Assert
assert any(
["site:khoj.dev" in response for response in responses]
), "Expected search query to include site:khoj.dev but got: " + str(responses)
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
@pytest.mark.parametrize(
"user_query, expected_mode",
[
("What's the latest in the Israel/Palestine conflict?", "default"),
("Summarize the latest tech news every Monday evening", "reminder"),
("Paint a scenery in Timbuktu in the winter", "image"),
("Remind me, when did I last visit the Serengeti?", "default"),
],
)
async def test_use_default_response_mode(chat_client, user_query, expected_mode):
# Act
mode = await aget_relevant_output_modes(user_query, {})
# Assert
assert mode.value == expected_mode
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
@pytest.mark.parametrize(
"user_query, expected_conversation_commands",
[
("Where did I learn to swim?", [ConversationCommand.Notes]),
("Where is the nearest hospital?", [ConversationCommand.Online]),
("Summarize the wikipedia page on the history of the internet", [ConversationCommand.Webpage]),
],
)
async def test_select_data_sources_actor_chooses_to_search_notes(
chat_client, user_query, expected_conversation_commands
):
# Act
conversation_commands = await aget_relevant_information_sources(user_query, {})
# Assert
assert expected_conversation_commands in conversation_commands
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
async def test_infer_webpage_urls_actor_extracts_correct_links(chat_client):
# Arrange
user_query = "Summarize the wikipedia page on the history of the internet"
# Act
urls = await infer_webpage_urls(user_query, {}, None)
# Assert
assert "https://en.wikipedia.org/wiki/History_of_the_Internet" in urls
# ----------------------------------------------------------------------------------------------------
@pytest.mark.anyio
@pytest.mark.django_db(transaction=True)
@pytest.mark.parametrize(
"user_query, location, expected_crontime, expected_queries",
[
(
"Share the weather forecast for the next day at 19:30",
("Boston", "MA", "USA"),
"30 23 * * *",
["weather forecast", "boston"],
),
],
)
async def test_infer_task_scheduling_request(chat_client, user_query, location, expected_crontime, expected_queries):
# Arrange
location_data = LocationData(city=location[0], region=location[1], country=location[2])
# Act
crontime, inferred_query = await schedule_query(user_query, location_data, {})
# Assert
assert expected_crontime in crontime
for query in expected_queries:
assert query in inferred_query.lower()
# Helpers
# ----------------------------------------------------------------------------------------------------
def populate_chat_history(message_list):
# Generate conversation logs
conversation_log = {"chat": []}
for user_message, gpt_message, context in message_list:
conversation_log["chat"] += message_to_log(
user_message,
gpt_message,
{"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
)
return conversation_log
Reference in a new issue View git blame Copy permalink