khoj/tests/test_helpers.py
sabaimran b782683e60
Scrape results from Serper results using Olostep (#627)
* Initailize changes to incporate web scraping logic after getting SERP results
- Do some minor refactors to pass a symptom prompt to the openai model when making a query
- integrate Olostep in order to perform the webscraping
* Fix truncation error with new line, fix typing in olostep code
* Use the authorization header for the token
* Add a small hint/indicator for how to use Khojs other modalities in the welcome prompt
* Add more detailed error message if Olostep query fails
* Add unit tests which invoke Olostep in chat director
* Add test for olostep tool
2024-01-29 14:16:50 +05:30

99 lines
3.5 KiB
Python

import os
import secrets
import numpy as np
import psutil
import pytest
from scipy.stats import linregress
from khoj.processor.embeddings import EmbeddingsModel
from khoj.processor.tools.online_search import search_with_olostep
from khoj.utils import helpers
def test_get_from_null_dict():
# null handling
assert helpers.get_from_dict(dict()) == dict()
assert helpers.get_from_dict(dict(), None) == None
# key present in nested dictionary
# 1-level dictionary
assert helpers.get_from_dict({"a": 1, "b": 2}, "a") == 1
assert helpers.get_from_dict({"a": 1, "b": 2}, "c") == None
# 2-level dictionary
assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a") == {"a_a": 1}
assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a", "a_a") == 1
# key not present in nested dictionary
# 2-level_dictionary
assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "b", "b_a") == None
def test_merge_dicts():
# basic merge of dicts with non-overlapping keys
assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"b": 2}) == {"a": 1, "b": 2}
# use default dict items when not present in priority dict
assert helpers.merge_dicts(priority_dict={}, default_dict={"b": 2}) == {"b": 2}
# do not override existing key in priority_dict with default dict
assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"a": 2}) == {"a": 1}
def test_lru_cache():
# Test initializing cache
cache = helpers.LRU({"a": 1, "b": 2}, capacity=2)
assert cache == {"a": 1, "b": 2}
# Test capacity overflow
cache["c"] = 3
assert cache == {"b": 2, "c": 3}
# Test delete least recently used item from LRU cache on capacity overflow
cache["b"] # accessing 'b' makes it the most recently used item
cache["d"] = 4 # so 'c' is deleted from the cache instead of 'b'
assert cache == {"b": 2, "d": 4}
@pytest.mark.skip(reason="Memory leak exists on GPU, MPS devices")
def test_encode_docs_memory_leak():
# Arrange
iterations = 50
batch_size = 20
embeddings_model = EmbeddingsModel()
memory_usage_trend = []
device = f"{helpers.get_device()}".upper()
# Act
# Encode random strings repeatedly and record memory usage trend
for iteration in range(iterations):
random_docs = [" ".join(secrets.token_hex(5) for _ in range(10)) for _ in range(batch_size)]
a = [embeddings_model.embed_documents(random_docs)]
memory_usage_trend += [psutil.Process().memory_info().rss / (1024 * 1024)]
print(f"{iteration:02d}, {memory_usage_trend[-1]:.2f}", flush=True)
# Calculate slope of line fitting memory usage history
memory_usage_trend = np.array(memory_usage_trend)
slope, _, _, _, _ = linregress(np.arange(len(memory_usage_trend)), memory_usage_trend)
print(f"Memory usage increased at ~{slope:.2f} MB per iteration on {device}")
# Assert
# If slope is positive memory utilization is increasing
# Positive threshold of 2, from observing memory usage trend on MPS vs CPU device
assert slope < 2, f"Memory leak suspected on {device}. Memory usage increased at ~{slope:.2f} MB per iteration"
@pytest.mark.skipif(os.getenv("OLOSTEP_API_KEY") is None, reason="OLOSTEP_API_KEY is not set")
def test_olostep_api():
# Arrange
website = "https://en.wikipedia.org/wiki/Great_Chicago_Fire"
# Act
response = search_with_olostep(website)
# Assert
assert (
"An alarm sent from the area near the fire also failed to register at the courthouse where the fire watchmen were"
in response
)