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Apply automatic linting fixes to tests directory

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Co-Authored-By: Joe Moura <joao@crewai.com>
This commit is contained in:
Devin AI
2025-05-12 13:31:07 +00:00
parent ad1ea46bbb
commit 46621113af
62 changed files with 1738 additions and 1821 deletions
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78
tests/knowledge/knowledge_test.py
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@@ -1,7 +1,6 @@
"""Test Knowledge creation and querying functionality."""
from pathlib import Path
from typing import List, Union
from unittest.mock import patch
import pytest
@@ -25,23 +24,23 @@ def mock_vector_db():
{
"context": "Brandon's favorite color is blue and he likes Mexican food.",
"score": 0.9,
}
},
]
instance.reset.return_value = None
yield instance
@pytest.fixture(autouse=True)
def reset_knowledge_storage(mock_vector_db):
def reset_knowledge_storage(mock_vector_db) -> None:
"""Fixture to reset knowledge storage before each test."""
yield
return
def test_single_short_string(mock_vector_db):
def test_single_short_string(mock_vector_db) -> None:
# Create a knowledge base with a single short string
content = "Brandon's favorite color is blue and he likes Mexican food."
string_source = StringKnowledgeSource(
content=content, metadata={"preference": "personal"}
content=content, metadata={"preference": "personal"},
)
mock_vector_db.sources = [string_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -56,7 +55,7 @@ def test_single_short_string(mock_vector_db):
# @pytest.mark.vcr(filter_headers=["authorization"])
def test_single_2k_character_string(mock_vector_db):
def test_single_2k_character_string(mock_vector_db) -> None:
# Create a 2k character string with various facts about Brandon
content = (
"Brandon is a software engineer who lives in San Francisco. "
@@ -81,7 +80,7 @@ def test_single_2k_character_string(mock_vector_db):
"He is also a fan of the Golden State Warriors and enjoys watching their games. "
)
string_source = StringKnowledgeSource(
content=content, metadata={"preference": "personal"}
content=content, metadata={"preference": "personal"},
)
mock_vector_db.sources = [string_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -95,7 +94,7 @@ def test_single_2k_character_string(mock_vector_db):
mock_vector_db.query.assert_called_once()
def test_multiple_short_strings(mock_vector_db):
def test_multiple_short_strings(mock_vector_db) -> None:
# Create multiple short string sources
contents = [
"Brandon loves hiking.",
@@ -109,7 +108,7 @@ def test_multiple_short_strings(mock_vector_db):
# Mock the vector db query response
mock_vector_db.query.return_value = [
{"context": "Brandon has a dog named Max.", "score": 0.9}
{"context": "Brandon has a dog named Max.", "score": 0.9},
]
mock_vector_db.sources = string_sources
@@ -124,7 +123,7 @@ def test_multiple_short_strings(mock_vector_db):
mock_vector_db.query.assert_called_once()
def test_multiple_2k_character_strings(mock_vector_db):
def test_multiple_2k_character_strings(mock_vector_db) -> None:
# Create multiple 2k character strings with various facts about Brandon
contents = [
(
@@ -194,7 +193,7 @@ def test_multiple_2k_character_strings(mock_vector_db):
mock_vector_db.query.assert_called_once()
def test_single_short_file(mock_vector_db, tmpdir):
def test_single_short_file(mock_vector_db, tmpdir) -> None:
# Create a single short text file
content = "Brandon's favorite sport is basketball."
file_path = Path(tmpdir.join("short_file.txt"))
@@ -202,7 +201,7 @@ def test_single_short_file(mock_vector_db, tmpdir):
f.write(content)
file_source = TextFileKnowledgeSource(
file_paths=[file_path], metadata={"preference": "personal"}
file_paths=[file_path], metadata={"preference": "personal"},
)
mock_vector_db.sources = [file_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -215,7 +214,7 @@ def test_single_short_file(mock_vector_db, tmpdir):
mock_vector_db.query.assert_called_once()
def test_single_2k_character_file(mock_vector_db, tmpdir):
def test_single_2k_character_file(mock_vector_db, tmpdir) -> None:
# Create a single 2k character text file with various facts about Brandon
content = (
"Brandon is a software engineer who lives in San Francisco. "
@@ -244,7 +243,7 @@ def test_single_2k_character_file(mock_vector_db, tmpdir):
f.write(content)
file_source = TextFileKnowledgeSource(
file_paths=[file_path], metadata={"preference": "personal"}
file_paths=[file_path], metadata={"preference": "personal"},
)
mock_vector_db.sources = [file_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
@@ -257,7 +256,7 @@ def test_single_2k_character_file(mock_vector_db, tmpdir):
mock_vector_db.query.assert_called_once()
def test_multiple_short_files(mock_vector_db, tmpdir):
def test_multiple_short_files(mock_vector_db, tmpdir) -> None:
# Create multiple short text files
contents = [
{
@@ -286,7 +285,7 @@ def test_multiple_short_files(mock_vector_db, tmpdir):
]
mock_vector_db.sources = file_sources
mock_vector_db.query.return_value = [
{"context": "Brandon lives in New York.", "score": 0.9}
{"context": "Brandon lives in New York.", "score": 0.9},
]
# Perform a query
query = "What city does he reside in?"
@@ -296,7 +295,7 @@ def test_multiple_short_files(mock_vector_db, tmpdir):
mock_vector_db.query.assert_called_once()
def test_multiple_2k_character_files(mock_vector_db, tmpdir):
def test_multiple_2k_character_files(mock_vector_db, tmpdir) -> None:
# Create multiple 2k character text files with various facts about Brandon
contents = [
(
@@ -362,7 +361,7 @@ def test_multiple_2k_character_files(mock_vector_db, tmpdir):
{
"context": "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'.",
"score": 0.9,
}
},
]
# Perform a query
query = "What is Brandon's favorite book?"
@@ -377,7 +376,7 @@ def test_multiple_2k_character_files(mock_vector_db, tmpdir):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_hybrid_string_and_files(mock_vector_db, tmpdir):
def test_hybrid_string_and_files(mock_vector_db, tmpdir) -> None:
# Create string sources
string_contents = [
"Brandon is learning French.",
@@ -418,7 +417,7 @@ def test_hybrid_string_and_files(mock_vector_db, tmpdir):
mock_vector_db.query.assert_called_once()
def test_pdf_knowledge_source(mock_vector_db):
def test_pdf_knowledge_source(mock_vector_db) -> None:
# Get the directory of the current file
current_dir = Path(__file__).parent
# Construct the path to the PDF file
@@ -426,11 +425,11 @@ def test_pdf_knowledge_source(mock_vector_db):
# Create a PDFKnowledgeSource
pdf_source = PDFKnowledgeSource(
file_paths=[pdf_path], metadata={"preference": "personal"}
file_paths=[pdf_path], metadata={"preference": "personal"},
)
mock_vector_db.sources = [pdf_source]
mock_vector_db.query.return_value = [
{"context": "crewai create crew latest-ai-development", "score": 0.9}
{"context": "crewai create crew latest-ai-development", "score": 0.9},
]
# Perform a query
@@ -446,9 +445,8 @@ def test_pdf_knowledge_source(mock_vector_db):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_csv_knowledge_source(mock_vector_db, tmpdir):
def test_csv_knowledge_source(mock_vector_db, tmpdir) -> None:
"""Test CSVKnowledgeSource with a simple CSV file."""
# Create a CSV file with sample data
csv_content = [
["Name", "Age", "City"],
@@ -463,11 +461,11 @@ def test_csv_knowledge_source(mock_vector_db, tmpdir):
# Create a CSVKnowledgeSource
csv_source = CSVKnowledgeSource(
file_paths=[csv_path], metadata={"preference": "personal"}
file_paths=[csv_path], metadata={"preference": "personal"},
)
mock_vector_db.sources = [csv_source]
mock_vector_db.query.return_value = [
{"context": "Brandon is 30 years old.", "score": 0.9}
{"context": "Brandon is 30 years old.", "score": 0.9},
]
# Perform a query
@@ -479,16 +477,15 @@ def test_csv_knowledge_source(mock_vector_db, tmpdir):
mock_vector_db.query.assert_called_once()
def test_json_knowledge_source(mock_vector_db, tmpdir):
def test_json_knowledge_source(mock_vector_db, tmpdir) -> None:
"""Test JSONKnowledgeSource with a simple JSON file."""
# Create a JSON file with sample data
json_data = {
"people": [
{"name": "Brandon", "age": 30, "city": "New York"},
{"name": "Alice", "age": 25, "city": "Los Angeles"},
{"name": "Bob", "age": 35, "city": "Chicago"},
]
],
}
json_path = Path(tmpdir.join("data.json"))
with open(json_path, "w", encoding="utf-8") as f:
@@ -498,11 +495,11 @@ def test_json_knowledge_source(mock_vector_db, tmpdir):
# Create a JSONKnowledgeSource
json_source = JSONKnowledgeSource(
file_paths=[json_path], metadata={"preference": "personal"}
file_paths=[json_path], metadata={"preference": "personal"},
)
mock_vector_db.sources = [json_source]
mock_vector_db.query.return_value = [
{"context": "Alice lives in Los Angeles.", "score": 0.9}
{"context": "Alice lives in Los Angeles.", "score": 0.9},
]
# Perform a query
@@ -514,9 +511,8 @@ def test_json_knowledge_source(mock_vector_db, tmpdir):
mock_vector_db.query.assert_called_once()
def test_excel_knowledge_source(mock_vector_db, tmpdir):
def test_excel_knowledge_source(mock_vector_db, tmpdir) -> None:
"""Test ExcelKnowledgeSource with a simple Excel file."""
# Create an Excel file with sample data
import pandas as pd
@@ -531,11 +527,11 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
# Create an ExcelKnowledgeSource
excel_source = ExcelKnowledgeSource(
file_paths=[excel_path], metadata={"preference": "personal"}
file_paths=[excel_path], metadata={"preference": "personal"},
)
mock_vector_db.sources = [excel_source]
mock_vector_db.query.return_value = [
{"context": "Brandon is 30 years old.", "score": 0.9}
{"context": "Brandon is 30 years old.", "score": 0.9},
]
# Perform a query
@@ -548,7 +544,7 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir):
@pytest.mark.vcr
def test_docling_source(mock_vector_db):
def test_docling_source(mock_vector_db) -> None:
docling_source = CrewDoclingSource(
file_paths=[
"https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
@@ -559,7 +555,7 @@ def test_docling_source(mock_vector_db):
{
"context": "Reward hacking is a technique used to improve the performance of reinforcement learning agents.",
"score": 0.9,
}
},
]
# Perform a query
query = "What is reward hacking?"
@@ -569,8 +565,8 @@ def test_docling_source(mock_vector_db):
@pytest.mark.vcr
def test_multiple_docling_sources():
urls: List[Union[Path, str]] = [
def test_multiple_docling_sources() -> None:
urls: list[Path | str] = [
"https://lilianweng.github.io/posts/2024-11-28-reward-hacking/",
"https://lilianweng.github.io/posts/2024-07-07-hallucination/",
]
@@ -580,7 +576,7 @@ def test_multiple_docling_sources():
assert docling_source.content is not None
def test_file_path_validation():
def test_file_path_validation() -> None:
"""Test file path validation for knowledge sources."""
current_dir = Path(__file__).parent
pdf_path = current_dir / "crewai_quickstart.pdf"