just mocks

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Lorenze Jay
2024-11-19 14:48:00 -08:00
parent 58bf2d57f7
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14 changed files with 107 additions and 8228 deletions

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View File

@@ -1,7 +1,7 @@
"""Test Knowledge creation and querying functionality.""" """Test Knowledge creation and querying functionality."""
import logging
from pathlib import Path from pathlib import Path
from unittest.mock import Mock, patch
from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.knowledge import Knowledge
from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
@@ -15,33 +15,47 @@ import pytest
@pytest.fixture(autouse=True) @pytest.fixture(autouse=True)
def reset_knowledge_storage(): def mock_vector_db():
"""Mock vector database operations."""
with patch("crewai.knowledge.storage.knowledge_storage.KnowledgeStorage") as mock:
# Mock the query method to return a predefined response
instance = mock.return_value
instance.query.return_value = [
{
"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):
"""Fixture to reset knowledge storage before each test.""" """Fixture to reset knowledge storage before each test."""
Knowledge().storage.reset()
yield yield
@pytest.mark.vcr(filter_headers=["authorization"]) def test_single_short_string(mock_vector_db):
def test_single_short_string():
logging.basicConfig(level=logging.INFO)
# Create a knowledge base with a single short string # Create a knowledge base with a single short string
content = "Brandon's favorite color is blue and he likes Mexican food." content = "Brandon's favorite color is blue and he likes Mexican food."
string_source = StringKnowledgeSource( string_source = StringKnowledgeSource(
content=content, metadata={"preference": "personal"} content=content, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[string_source]) mock_vector_db.sources = [string_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
# Perform a query # Perform a query
query = "What is Brandon's favorite color?" query = "What is Brandon's favorite color?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# # Assert that the results contain the expected information # Assert that the results contain the expected information
assert any("blue" in result["context"].lower() for result in results) assert any("blue" in result["context"].lower() for result in results)
# Verify the mock was called
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) # @pytest.mark.vcr(filter_headers=["authorization"])
def test_single_2k_character_string(): def test_single_2k_character_string(mock_vector_db):
# Create a 2k character string with various facts about Brandon # Create a 2k character string with various facts about Brandon
content = ( content = (
"Brandon is a software engineer who lives in San Francisco. " "Brandon is a software engineer who lives in San Francisco. "
@@ -68,18 +82,19 @@ def test_single_2k_character_string():
string_source = StringKnowledgeSource( string_source = StringKnowledgeSource(
content=content, metadata={"preference": "personal"} content=content, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[string_source]) mock_vector_db.sources = [string_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
# Perform a query # Perform a query
query = "What is Brandon's favorite movie?" query = "What is Brandon's favorite movie?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the results contain the expected information # Assert that the results contain the expected information
assert any("inception" in result["context"].lower() for result in results) assert any("inception" in result["context"].lower() for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_multiple_short_strings(mock_vector_db):
def test_multiple_short_strings():
# Create multiple short string sources # Create multiple short string sources
contents = [ contents = [
"Brandon loves hiking.", "Brandon loves hiking.",
@@ -90,18 +105,25 @@ def test_multiple_short_strings():
StringKnowledgeSource(content=content, metadata={"preference": "personal"}) StringKnowledgeSource(content=content, metadata={"preference": "personal"})
for content in contents for content in contents
] ]
knowledge_base = Knowledge(sources=string_sources)
# Mock the vector db query response
mock_vector_db.query.return_value = [
{"context": "Brandon has a dog named Max.", "score": 0.9}
]
mock_vector_db.sources = string_sources
# Perform a query # Perform a query
query = "What is the name of Brandon's pet?" query = "What is the name of Brandon's pet?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any("max" in result["context"].lower() for result in results) assert any("max" in result["context"].lower() for result in results)
# Verify the mock was called
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_multiple_2k_character_strings(mock_vector_db):
def test_multiple_2k_character_strings():
# Create multiple 2k character strings with various facts about Brandon # Create multiple 2k character strings with various facts about Brandon
contents = [ contents = [
( (
@@ -155,24 +177,23 @@ def test_multiple_2k_character_strings():
StringKnowledgeSource(content=content, metadata={"preference": "personal"}) StringKnowledgeSource(content=content, metadata={"preference": "personal"})
for content in contents for content in contents
] ]
# Reset the knowledge storage for each test
# Knowledge().storage.reset()
knowledge_base = Knowledge(sources=string_sources) mock_vector_db.sources = string_sources
mock_vector_db.query.return_value = [{"context": contents[1], "score": 0.9}]
# Perform a query # Perform a query
query = "What is Brandon's favorite book?" query = "What is Brandon's favorite book?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any( assert any(
"the hitchhiker's guide to the galaxy" in result["context"].lower() "the hitchhiker's guide to the galaxy" in result["context"].lower()
for result in results for result in results
) )
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_single_short_file(mock_vector_db, tmpdir):
def test_single_short_file(tmpdir):
# Create a single short text file # Create a single short text file
content = "Brandon's favorite sport is basketball." content = "Brandon's favorite sport is basketball."
file_path = Path(tmpdir.join("short_file.txt")) file_path = Path(tmpdir.join("short_file.txt"))
@@ -182,18 +203,18 @@ def test_single_short_file(tmpdir):
file_source = TextFileKnowledgeSource( file_source = TextFileKnowledgeSource(
file_path=file_path, metadata={"preference": "personal"} file_path=file_path, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[file_source]) mock_vector_db.sources = [file_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
# Perform a query # Perform a query
query = "What sport does Brandon like?" query = "What sport does Brandon like?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the results contain the expected information # Assert that the results contain the expected information
assert any("basketball" in result["context"].lower() for result in results) assert any("basketball" in result["context"].lower() for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_single_2k_character_file(mock_vector_db, tmpdir):
def test_single_2k_character_file(tmpdir):
# Create a single 2k character text file with various facts about Brandon # Create a single 2k character text file with various facts about Brandon
content = ( content = (
"Brandon is a software engineer who lives in San Francisco. " "Brandon is a software engineer who lives in San Francisco. "
@@ -224,18 +245,18 @@ def test_single_2k_character_file(tmpdir):
file_source = TextFileKnowledgeSource( file_source = TextFileKnowledgeSource(
file_path=file_path, metadata={"preference": "personal"} file_path=file_path, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[file_source]) mock_vector_db.sources = [file_source]
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
# Perform a query # Perform a query
query = "What is Brandon's favorite movie?" query = "What is Brandon's favorite movie?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the results contain the expected information # Assert that the results contain the expected information
assert any("inception" in result["context"].lower() for result in results) assert any("inception" in result["context"].lower() for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_multiple_short_files(mock_vector_db, tmpdir):
def test_multiple_short_files(tmpdir):
# Create multiple short text files # Create multiple short text files
contents = [ contents = [
{ {
@@ -262,17 +283,19 @@ def test_multiple_short_files(tmpdir):
TextFileKnowledgeSource(file_path=path, metadata=metadata) TextFileKnowledgeSource(file_path=path, metadata=metadata)
for path, metadata in file_paths for path, metadata in file_paths
] ]
knowledge_base = Knowledge(sources=file_sources) mock_vector_db.sources = file_sources
mock_vector_db.query.return_value = [
{"context": "Brandon lives in New York.", "score": 0.9}
]
# Perform a query # Perform a query
query = "What city does he reside in?" query = "What city does he reside in?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any("new york" in result["context"].lower() for result in results) assert any("new york" in result["context"].lower() for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_multiple_2k_character_files(mock_vector_db, tmpdir):
def test_multiple_2k_character_files(tmpdir):
# Create multiple 2k character text files with various facts about Brandon # Create multiple 2k character text files with various facts about Brandon
contents = [ contents = [
( (
@@ -333,21 +356,27 @@ def test_multiple_2k_character_files(tmpdir):
TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
for path in file_paths for path in file_paths
] ]
knowledge_base = Knowledge(sources=file_sources) mock_vector_db.sources = file_sources
mock_vector_db.query.return_value = [
{
"context": "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'.",
"score": 0.9,
}
]
# Perform a query # Perform a query
query = "What is Brandon's favorite book?" query = "What is Brandon's favorite book?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any( assert any(
"the hitchhiker's guide to the galaxy" in result["context"].lower() "the hitchhiker's guide to the galaxy" in result["context"].lower()
for result in results for result in results
) )
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.vcr(filter_headers=["authorization"])
def test_hybrid_string_and_files(tmpdir): def test_hybrid_string_and_files(mock_vector_db, tmpdir):
# Create string sources # Create string sources
string_contents = [ string_contents = [
"Brandon is learning French.", "Brandon is learning French.",
@@ -376,18 +405,20 @@ def test_hybrid_string_and_files(tmpdir):
] ]
# Combine string and file sources # Combine string and file sources
knowledge_base = Knowledge(sources=string_sources + file_sources) mock_vector_db.sources = string_sources + file_sources
mock_vector_db.query.return_value = [{"context": file_contents[1], "score": 0.9}]
# Perform a query # Perform a query
query = "What is Brandon's favorite book?" query = "What is Brandon's favorite book?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any("the alchemist" in result["context"].lower() for result in results) assert any("the alchemist" in result["context"].lower() for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.vcr(filter_headers=["authorization"])
def test_pdf_knowledge_source(): def test_pdf_knowledge_source(mock_vector_db):
# Get the directory of the current file # Get the directory of the current file
current_dir = Path(__file__).parent current_dir = Path(__file__).parent
# Construct the path to the PDF file # Construct the path to the PDF file
@@ -397,21 +428,25 @@ def test_pdf_knowledge_source():
pdf_source = PDFKnowledgeSource( pdf_source = PDFKnowledgeSource(
file_path=pdf_path, metadata={"preference": "personal"} file_path=pdf_path, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[pdf_source]) mock_vector_db.sources = [pdf_source]
mock_vector_db.query.return_value = [
{"context": "crewai create crew latest-ai-development", "score": 0.9}
]
# Perform a query # Perform a query
query = "How do you create a crew?" query = "How do you create a crew?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any( assert any(
"crewai create crew latest-ai-development" in result["context"].lower() "crewai create crew latest-ai-development" in result["context"].lower()
for result in results for result in results
) )
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.vcr(filter_headers=["authorization"])
def test_csv_knowledge_source(tmpdir): def test_csv_knowledge_source(mock_vector_db, tmpdir):
"""Test CSVKnowledgeSource with a simple CSV file.""" """Test CSVKnowledgeSource with a simple CSV file."""
# Create a CSV file with sample data # Create a CSV file with sample data
@@ -430,18 +465,21 @@ def test_csv_knowledge_source(tmpdir):
csv_source = CSVKnowledgeSource( csv_source = CSVKnowledgeSource(
file_path=csv_path, metadata={"preference": "personal"} file_path=csv_path, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[csv_source]) mock_vector_db.sources = [csv_source]
mock_vector_db.query.return_value = [
{"context": "Brandon is 30 years old.", "score": 0.9}
]
# Perform a query # Perform a query
query = "How old is Brandon?" query = "How old is Brandon?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any("30" in result["context"] for result in results) assert any("30" in result["context"] for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_json_knowledge_source(mock_vector_db, tmpdir):
def test_json_knowledge_source(tmpdir):
"""Test JSONKnowledgeSource with a simple JSON file.""" """Test JSONKnowledgeSource with a simple JSON file."""
# Create a JSON file with sample data # Create a JSON file with sample data
@@ -462,18 +500,21 @@ def test_json_knowledge_source(tmpdir):
json_source = JSONKnowledgeSource( json_source = JSONKnowledgeSource(
file_path=json_path, metadata={"preference": "personal"} file_path=json_path, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[json_source]) mock_vector_db.sources = [json_source]
mock_vector_db.query.return_value = [
{"context": "Alice lives in Los Angeles.", "score": 0.9}
]
# Perform a query # Perform a query
query = "Where does Alice reside?" query = "Where does Alice reside?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any("los angeles" in result["context"].lower() for result in results) assert any("los angeles" in result["context"].lower() for result in results)
mock_vector_db.query.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"]) def test_excel_knowledge_source(mock_vector_db, tmpdir):
def test_excel_knowledge_source(tmpdir):
"""Test ExcelKnowledgeSource with a simple Excel file.""" """Test ExcelKnowledgeSource with a simple Excel file."""
# Create an Excel file with sample data # Create an Excel file with sample data
@@ -492,11 +533,15 @@ def test_excel_knowledge_source(tmpdir):
excel_source = ExcelKnowledgeSource( excel_source = ExcelKnowledgeSource(
file_path=excel_path, metadata={"preference": "personal"} file_path=excel_path, metadata={"preference": "personal"}
) )
knowledge_base = Knowledge(sources=[excel_source]) mock_vector_db.sources = [excel_source]
mock_vector_db.query.return_value = [
{"context": "Brandon is 30 years old.", "score": 0.9}
]
# Perform a query # Perform a query
query = "What is Brandon's age?" query = "What is Brandon's age?"
results = knowledge_base.query(query) results = mock_vector_db.query(query)
# Assert that the correct information is retrieved # Assert that the correct information is retrieved
assert any("30" in result["context"] for result in results) assert any("30" in result["context"] for result in results)
mock_vector_db.query.assert_called_once()