Files
zulip/zerver/tests/test_message_summary.py
Sahil Batra 764f4aa2e0 groups: Use realm_for_sharding for limiting NamedUserGroup queries.
For get and filter queries of NamedUserGroup, realm_for_sharding
field is used instead of realm field, as directly using
realm_for_sharding field on NamedUserGroup makes the query faster
than using realm present on the base UserGroup table.
2025-09-23 12:15:53 -07:00

216 lines
8.9 KiB
Python

import os
import warnings
from datetime import datetime, timezone
from unittest import mock
import orjson
import time_machine
from django.conf import settings
from typing_extensions import override
from analytics.models import UserCount
from zerver.actions.realm_settings import do_change_realm_permission_group_setting
from zerver.lib.test_classes import ZulipTestCase
from zerver.models import NamedUserGroup
from zerver.models.groups import SystemGroups
from zerver.models.realms import get_realm
warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
warnings.filterwarnings("ignore", category=DeprecationWarning, module="pydantic")
warnings.filterwarnings("ignore", category=DeprecationWarning, module="litellm")
# Avoid network query to fetch the model cost map.
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
import litellm
# Fixture file to store recorded responses
LLM_FIXTURES_FILE = "zerver/tests/fixtures/litellm/summary.json"
class MessagesSummaryTestCase(ZulipTestCase):
@override
def setUp(self) -> None:
super().setUp()
self.user = self.example_user("iago")
self.topic_name = "New feature launch"
self.channel_name = "Zulip features"
self.login_user(self.user)
self.subscribe(self.user, self.channel_name)
content = "Zulip just launched a feature to generate summary of messages."
self.send_stream_message(
self.user, self.channel_name, content=content, topic_name=self.topic_name
)
content = "Sounds awesome! This will **greatly** help me when catching up."
self.send_stream_message(
self.user, self.channel_name, content=content, topic_name=self.topic_name
)
# Tests fail on the last day of the month due to us capturing the credit usage for that day
# on the first of the next month, so we need to set the date to a different day.
not_last_day_of_any_month = datetime(2025, 2, 18, 1, tzinfo=timezone.utc)
self.mocked_time_patcher = time_machine.travel(not_last_day_of_any_month, tick=False)
self.mocked_time_patcher.start()
if settings.GENERATE_LITELLM_FIXTURES: # nocoverage
self.patcher = mock.patch("litellm.completion", wraps=litellm.completion)
self.mocked_completion = self.patcher.start()
@override
def tearDown(self) -> None:
self.mocked_time_patcher.stop()
if settings.GENERATE_LITELLM_FIXTURES: # nocoverage
self.patcher.stop()
super().tearDown()
def test_summarize_messages_in_topic(self) -> None:
narrow = orjson.dumps([["channel", self.channel_name], ["topic", self.topic_name]]).decode()
if settings.GENERATE_LITELLM_FIXTURES: # nocoverage
# NOTE: You need have proper credentials in zproject/dev-secrets.conf
# to generate the fixtures. (Tested using aws bedrock.)
# Trigger the API call to extract the arguments.
self.client_get("/json/messages/summary", dict(narrow=narrow))
call_args = self.mocked_completion.call_args
# Once we have the arguments, call the original method and save its response.
response = self.mocked_completion(**call_args.kwargs).json()
with open(LLM_FIXTURES_FILE, "wb") as f:
fixture_data = {
# Only store model and messages.
# We don't want to store any secrets.
"model": call_args.kwargs["model"],
"messages": call_args.kwargs["messages"],
"response": response,
}
f.write(orjson.dumps(fixture_data, option=orjson.OPT_INDENT_2) + b"\n")
return
# In this code path, we test using the fixtures.
with open(LLM_FIXTURES_FILE, "rb") as f:
fixture_data = orjson.loads(f.read())
# Block summary requests if budget set to 0.
with self.settings(
TOPIC_SUMMARIZATION_MODEL="groq/llama-3.3-70b-versatile",
MAX_PER_USER_MONTHLY_AI_COST=0,
):
response = self.client_get("/json/messages/summary")
self.assert_json_error_contains(response, "Reached monthly limit for AI credits.")
# Fake credentials to ensure we crash if actual network
# requests occur, which would reflect a problem with how the
# fixtures were set up.
with self.settings(
TOPIC_SUMMARIZATION_MODEL="groq/llama-3.3-70b-versatile",
TOPIC_SUMMARIZATION_API_KEY="test",
):
input_tokens = fixture_data["response"]["usage"]["prompt_tokens"]
output_tokens = fixture_data["response"]["usage"]["completion_tokens"]
credits_used = (output_tokens * settings.OUTPUT_COST_PER_GIGATOKEN) + (
input_tokens * settings.INPUT_COST_PER_GIGATOKEN
)
self.assertFalse(
UserCount.objects.filter(
property="ai_credit_usage::day", value=credits_used, user_id=self.user.id
).exists()
)
with mock.patch("litellm.completion", return_value=fixture_data["response"]):
payload = self.client_get("/json/messages/summary", dict(narrow=narrow))
self.assertEqual(payload.status_code, 200)
# Check that we recorded this usage.
self.assertTrue(
UserCount.objects.filter(
property="ai_credit_usage::day", value=credits_used, user_id=self.user.id
).exists()
)
# If we reached the credit usage limit, block summary requests.
with self.settings(
TOPIC_SUMMARIZATION_MODEL="groq/llama-3.3-70b-versatile",
MAX_PER_USER_MONTHLY_AI_COST=credits_used / 1000000000,
):
response = self.client_get("/json/messages/summary")
self.assert_json_error_contains(response, "Reached monthly limit for AI credits.")
def test_permission_to_summarize_message_in_topics(self) -> None:
narrow = orjson.dumps([["channel", self.channel_name], ["topic", self.topic_name]]).decode()
realm = get_realm("zulip")
moderators_group = NamedUserGroup.objects.get(
name=SystemGroups.MODERATORS, realm_for_sharding=realm, is_system_group=True
)
do_change_realm_permission_group_setting(
realm,
"can_summarize_topics_group",
moderators_group,
acting_user=None,
)
# In this code path, we test using the fixtures.
with open(LLM_FIXTURES_FILE, "rb") as f:
fixture_data = orjson.loads(f.read())
def check_message_summary_permission(user: str, expect_fail: bool = False) -> None:
self.login(user)
with (
self.settings(
TOPIC_SUMMARIZATION_MODEL="groq/llama-3.3-70b-versatile",
TOPIC_SUMMARIZATION_API_KEY="test",
),
mock.patch("litellm.completion", return_value=fixture_data["response"]),
):
result = self.client_get("/json/messages/summary", dict(narrow=narrow))
if expect_fail:
self.assert_json_error(result, "Insufficient permission")
else:
self.assert_json_success(result)
check_message_summary_permission("hamlet", expect_fail=True)
check_message_summary_permission("shiva")
nobody_group = NamedUserGroup.objects.get(
name=SystemGroups.NOBODY, realm_for_sharding=realm, is_system_group=True
)
do_change_realm_permission_group_setting(
realm,
"can_summarize_topics_group",
nobody_group,
acting_user=None,
)
check_message_summary_permission("desdemona", expect_fail=True)
hamletcharacters_group = NamedUserGroup.objects.get(
name="hamletcharacters", realm_for_sharding=realm
)
do_change_realm_permission_group_setting(
realm,
"can_summarize_topics_group",
hamletcharacters_group,
acting_user=None,
)
check_message_summary_permission("desdemona", expect_fail=True)
check_message_summary_permission("othello", expect_fail=True)
check_message_summary_permission("hamlet")
check_message_summary_permission("cordelia")
setting_group = self.create_or_update_anonymous_group_for_setting(
[self.example_user("othello")], [moderators_group]
)
do_change_realm_permission_group_setting(
realm,
"can_summarize_topics_group",
setting_group,
acting_user=None,
)
check_message_summary_permission("cordelia", expect_fail=True)
check_message_summary_permission("hamlet", expect_fail=True)
check_message_summary_permission("othello")
check_message_summary_permission("shiva")
check_message_summary_permission("desdemona")