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Knowledge (#1567)
* initial knowledge * WIP * Adding core knowledge sources * Improve types and better support for file paths * added additional sources * fix linting * update yaml to include optional deps * adding in lorenze feedback * ensure embeddings are persisted * improvements all around Knowledge class * return this * properly reset memory * properly reset memory+knowledge * consolodation and improvements * linted * cleanup rm unused embedder * fix test * fix duplicate * generating cassettes for knowledge test * updated default embedder * None embedder to use default on pipeline cloning * improvements * fixed text_file_knowledge * mypysrc fixes * type check fixes * added extra cassette * just mocks * linted * mock knowledge query to not spin up db * linted * verbose run * put a flag * fix * adding docs * better docs * improvements from review * more docs * linted * rm print * more fixes * clearer docs * added docstrings and type hints for cli --------- Co-authored-by: João Moura <joaomdmoura@gmail.com> Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
committed by
GitHub
GitHub
parent
fde1ee45f9
commit
14a36d3f5e
@@ -10,10 +10,11 @@ from crewai import Agent, Crew, Task
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from crewai.agents.cache import CacheHandler
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from crewai.agents.crew_agent_executor import CrewAgentExecutor
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from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
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from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
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from crewai.llm import LLM
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from crewai.tools import tool
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from crewai.tools.tool_calling import InstructorToolCalling
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from crewai.tools.tool_usage import ToolUsage
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from crewai.tools import tool
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from crewai.tools.tool_usage_events import ToolUsageFinished
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from crewai.utilities import RPMController
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from crewai.utilities.events import Emitter
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@@ -1574,3 +1575,42 @@ def test_agent_execute_task_with_ollama():
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result = agent.execute_task(task)
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assert len(result.split(".")) == 2
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assert "AI" in result or "artificial intelligence" in result.lower()
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_with_knowledge_sources():
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# Create a knowledge source with some content
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content = "Brandon's favorite color is blue and he likes Mexican food."
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string_source = StringKnowledgeSource(
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content=content, metadata={"preference": "personal"}
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)
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with patch('crewai.knowledge.storage.knowledge_storage.KnowledgeStorage') as MockKnowledge:
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mock_knowledge_instance = MockKnowledge.return_value
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mock_knowledge_instance.sources = [string_source]
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mock_knowledge_instance.query.return_value = [{
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"content": content,
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"metadata": {"preference": "personal"}
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}]
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agent = Agent(
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role="Information Agent",
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goal="Provide information based on knowledge sources",
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backstory="You have access to specific knowledge sources.",
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llm=LLM(model="gpt-4o-mini"),
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)
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# Create a task that requires the agent to use the knowledge
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task = Task(
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description="What is Brandon's favorite color?",
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expected_output="Brandon's favorite color.",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task])
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result = crew.kickoff()
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# Assert that the agent provides the correct information
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assert "blue" in result.raw.lower()
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115
tests/cassettes/test_agent_with_knowledge_sources.yaml
Normal file
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Normal file
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0
tests/knowledge/__init__.py
Normal file
0
tests/knowledge/__init__.py
Normal file
BIN
tests/knowledge/crewai_quickstart.pdf
Normal file
BIN
tests/knowledge/crewai_quickstart.pdf
Normal file
Binary file not shown.
545
tests/knowledge/knowledge_test.py
Normal file
545
tests/knowledge/knowledge_test.py
Normal file
@@ -0,0 +1,545 @@
|
||||
"""Test Knowledge creation and querying functionality."""
|
||||
|
||||
from pathlib import Path
|
||||
from unittest.mock import patch
|
||||
|
||||
from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
|
||||
from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
|
||||
from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
|
||||
from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
|
||||
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
|
||||
from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
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."""
|
||||
yield
|
||||
|
||||
|
||||
def test_single_short_string(mock_vector_db):
|
||||
# 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"}
|
||||
)
|
||||
mock_vector_db.sources = [string_source]
|
||||
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
|
||||
# Perform a query
|
||||
query = "What is Brandon's favorite color?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the results contain the expected information
|
||||
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"])
|
||||
def test_single_2k_character_string(mock_vector_db):
|
||||
# Create a 2k character string with various facts about Brandon
|
||||
content = (
|
||||
"Brandon is a software engineer who lives in San Francisco. "
|
||||
"He enjoys hiking and often visits the trails in the Bay Area. "
|
||||
"Brandon has a pet dog named Max, who is a golden retriever. "
|
||||
"He loves reading science fiction books, and his favorite author is Isaac Asimov. "
|
||||
"Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
|
||||
"He is also a fan of Mexican cuisine, especially tacos and burritos. "
|
||||
"Brandon plays the guitar and often performs at local open mic nights. "
|
||||
"He is learning French and plans to visit Paris next year. "
|
||||
"Brandon is passionate about technology and often attends tech meetups in the city. "
|
||||
"He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
|
||||
"Brandon's favorite color is blue, and he often wears blue shirts. "
|
||||
"He enjoys cooking and often tries new recipes on weekends. "
|
||||
"Brandon is a morning person and likes to start his day with a run in the park. "
|
||||
"He is also a coffee enthusiast and enjoys trying different coffee blends. "
|
||||
"Brandon is a member of a local book club and enjoys discussing books with fellow members. "
|
||||
"He is also a fan of board games and often hosts game nights at his place. "
|
||||
"Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
|
||||
"He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
|
||||
"Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
|
||||
"He is also a fan of the Golden State Warriors and enjoys watching their games. "
|
||||
)
|
||||
string_source = StringKnowledgeSource(
|
||||
content=content, metadata={"preference": "personal"}
|
||||
)
|
||||
mock_vector_db.sources = [string_source]
|
||||
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
|
||||
|
||||
# Perform a query
|
||||
query = "What is Brandon's favorite movie?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the results contain the expected information
|
||||
assert any("inception" in result["context"].lower() for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_multiple_short_strings(mock_vector_db):
|
||||
# Create multiple short string sources
|
||||
contents = [
|
||||
"Brandon loves hiking.",
|
||||
"Brandon has a dog named Max.",
|
||||
"Brandon enjoys painting landscapes.",
|
||||
]
|
||||
string_sources = [
|
||||
StringKnowledgeSource(content=content, metadata={"preference": "personal"})
|
||||
for content in contents
|
||||
]
|
||||
|
||||
# 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
|
||||
query = "What is the name of Brandon's pet?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any("max" in result["context"].lower() for result in results)
|
||||
# Verify the mock was called
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_multiple_2k_character_strings(mock_vector_db):
|
||||
# Create multiple 2k character strings with various facts about Brandon
|
||||
contents = [
|
||||
(
|
||||
"Brandon is a software engineer who lives in San Francisco. "
|
||||
"He enjoys hiking and often visits the trails in the Bay Area. "
|
||||
"Brandon has a pet dog named Max, who is a golden retriever. "
|
||||
"He loves reading science fiction books, and his favorite author is Isaac Asimov. "
|
||||
"Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
|
||||
"He is also a fan of Mexican cuisine, especially tacos and burritos. "
|
||||
"Brandon plays the guitar and often performs at local open mic nights. "
|
||||
"He is learning French and plans to visit Paris next year. "
|
||||
"Brandon is passionate about technology and often attends tech meetups in the city. "
|
||||
"He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
|
||||
"Brandon's favorite color is blue, and he often wears blue shirts. "
|
||||
"He enjoys cooking and often tries new recipes on weekends. "
|
||||
"Brandon is a morning person and likes to start his day with a run in the park. "
|
||||
"He is also a coffee enthusiast and enjoys trying different coffee blends. "
|
||||
"Brandon is a member of a local book club and enjoys discussing books with fellow members. "
|
||||
"He is also a fan of board games and often hosts game nights at his place. "
|
||||
"Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
|
||||
"He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
|
||||
"Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
|
||||
"He is also a fan of the Golden State Warriors and enjoys watching their games. "
|
||||
)
|
||||
* 2, # Repeat to ensure it's 2k characters
|
||||
(
|
||||
"Brandon loves traveling and has visited over 20 countries. "
|
||||
"He is fluent in Spanish and often practices with his friends. "
|
||||
"Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. "
|
||||
"He is a foodie and loves trying new cuisines, with a particular fondness for sushi. "
|
||||
"Brandon is an avid cyclist and participates in local cycling events. "
|
||||
"He is also a photographer and enjoys capturing landscapes and cityscapes. "
|
||||
"Brandon is a tech enthusiast and follows the latest trends in gadgets and software. "
|
||||
"He is also a fan of virtual reality and owns a VR headset. "
|
||||
"Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. "
|
||||
"He enjoys watching documentaries and learning about history and science. "
|
||||
"Brandon is a coffee lover and has a collection of coffee mugs from different countries. "
|
||||
"He is also a fan of jazz music and often attends live performances. "
|
||||
"Brandon is a member of a local running club and participates in marathons. "
|
||||
"He is also a volunteer at a local animal shelter and helps with dog walking. "
|
||||
"Brandon's favorite holiday is Christmas, and he enjoys decorating his home. "
|
||||
"He is also a fan of classic movies and has a collection of DVDs. "
|
||||
"Brandon is a mentor for young professionals and enjoys giving career advice. "
|
||||
"He is also a fan of puzzles and enjoys solving them in his free time. "
|
||||
"Brandon's favorite sport is soccer, and he often plays with his friends. "
|
||||
"He is also a fan of FC Barcelona and enjoys watching their matches. "
|
||||
)
|
||||
* 2, # Repeat to ensure it's 2k characters
|
||||
]
|
||||
string_sources = [
|
||||
StringKnowledgeSource(content=content, metadata={"preference": "personal"})
|
||||
for content in contents
|
||||
]
|
||||
|
||||
mock_vector_db.sources = string_sources
|
||||
mock_vector_db.query.return_value = [{"context": contents[1], "score": 0.9}]
|
||||
|
||||
# Perform a query
|
||||
query = "What is Brandon's favorite book?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any(
|
||||
"the hitchhiker's guide to the galaxy" in result["context"].lower()
|
||||
for result in results
|
||||
)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_single_short_file(mock_vector_db, tmpdir):
|
||||
# Create a single short text file
|
||||
content = "Brandon's favorite sport is basketball."
|
||||
file_path = Path(tmpdir.join("short_file.txt"))
|
||||
with open(file_path, "w") as f:
|
||||
f.write(content)
|
||||
|
||||
file_source = TextFileKnowledgeSource(
|
||||
file_path=file_path, metadata={"preference": "personal"}
|
||||
)
|
||||
mock_vector_db.sources = [file_source]
|
||||
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
|
||||
# Perform a query
|
||||
query = "What sport does Brandon like?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the results contain the expected information
|
||||
assert any("basketball" in result["context"].lower() for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_single_2k_character_file(mock_vector_db, tmpdir):
|
||||
# Create a single 2k character text file with various facts about Brandon
|
||||
content = (
|
||||
"Brandon is a software engineer who lives in San Francisco. "
|
||||
"He enjoys hiking and often visits the trails in the Bay Area. "
|
||||
"Brandon has a pet dog named Max, who is a golden retriever. "
|
||||
"He loves reading science fiction books, and his favorite author is Isaac Asimov. "
|
||||
"Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
|
||||
"He is also a fan of Mexican cuisine, especially tacos and burritos. "
|
||||
"Brandon plays the guitar and often performs at local open mic nights. "
|
||||
"He is learning French and plans to visit Paris next year. "
|
||||
"Brandon is passionate about technology and often attends tech meetups in the city. "
|
||||
"He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
|
||||
"Brandon's favorite color is blue, and he often wears blue shirts. "
|
||||
"He enjoys cooking and often tries new recipes on weekends. "
|
||||
"Brandon is a morning person and likes to start his day with a run in the park. "
|
||||
"He is also a coffee enthusiast and enjoys trying different coffee blends. "
|
||||
"Brandon is a member of a local book club and enjoys discussing books with fellow members. "
|
||||
"He is also a fan of board games and often hosts game nights at his place. "
|
||||
"Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
|
||||
"He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
|
||||
"Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
|
||||
"He is also a fan of the Golden State Warriors and enjoys watching their games. "
|
||||
) * 2 # Repeat to ensure it's 2k characters
|
||||
file_path = Path(tmpdir.join("long_file.txt"))
|
||||
with open(file_path, "w") as f:
|
||||
f.write(content)
|
||||
|
||||
file_source = TextFileKnowledgeSource(
|
||||
file_path=file_path, metadata={"preference": "personal"}
|
||||
)
|
||||
mock_vector_db.sources = [file_source]
|
||||
mock_vector_db.query.return_value = [{"context": content, "score": 0.9}]
|
||||
# Perform a query
|
||||
query = "What is Brandon's favorite movie?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the results contain the expected information
|
||||
assert any("inception" in result["context"].lower() for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_multiple_short_files(mock_vector_db, tmpdir):
|
||||
# Create multiple short text files
|
||||
contents = [
|
||||
{
|
||||
"content": "Brandon works as a software engineer.",
|
||||
"metadata": {"category": "profession", "source": "occupation"},
|
||||
},
|
||||
{
|
||||
"content": "Brandon lives in New York.",
|
||||
"metadata": {"category": "city", "source": "personal"},
|
||||
},
|
||||
{
|
||||
"content": "Brandon enjoys cooking Italian food.",
|
||||
"metadata": {"category": "hobby", "source": "personal"},
|
||||
},
|
||||
]
|
||||
file_paths = []
|
||||
for i, item in enumerate(contents):
|
||||
file_path = Path(tmpdir.join(f"file_{i}.txt"))
|
||||
with open(file_path, "w") as f:
|
||||
f.write(item["content"])
|
||||
file_paths.append((file_path, item["metadata"]))
|
||||
|
||||
file_sources = [
|
||||
TextFileKnowledgeSource(file_path=path, metadata=metadata)
|
||||
for path, metadata in file_paths
|
||||
]
|
||||
mock_vector_db.sources = file_sources
|
||||
mock_vector_db.query.return_value = [
|
||||
{"context": "Brandon lives in New York.", "score": 0.9}
|
||||
]
|
||||
# Perform a query
|
||||
query = "What city does he reside in?"
|
||||
results = mock_vector_db.query(query)
|
||||
# Assert that the correct information is retrieved
|
||||
assert any("new york" in result["context"].lower() for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_multiple_2k_character_files(mock_vector_db, tmpdir):
|
||||
# Create multiple 2k character text files with various facts about Brandon
|
||||
contents = [
|
||||
(
|
||||
"Brandon loves traveling and has visited over 20 countries. "
|
||||
"He is fluent in Spanish and often practices with his friends. "
|
||||
"Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. "
|
||||
"He is a foodie and loves trying new cuisines, with a particular fondness for sushi. "
|
||||
"Brandon is an avid cyclist and participates in local cycling events. "
|
||||
"He is also a photographer and enjoys capturing landscapes and cityscapes. "
|
||||
"Brandon is a tech enthusiast and follows the latest trends in gadgets and software. "
|
||||
"He is also a fan of virtual reality and owns a VR headset. "
|
||||
"Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. "
|
||||
"He enjoys watching documentaries and learning about history and science. "
|
||||
"Brandon is a coffee lover and has a collection of coffee mugs from different countries. "
|
||||
"He is also a fan of jazz music and often attends live performances. "
|
||||
"Brandon is a member of a local running club and participates in marathons. "
|
||||
"He is also a volunteer at a local animal shelter and helps with dog walking. "
|
||||
"Brandon's favorite holiday is Christmas, and he enjoys decorating his home. "
|
||||
"He is also a fan of classic movies and has a collection of DVDs. "
|
||||
"Brandon is a mentor for young professionals and enjoys giving career advice. "
|
||||
"He is also a fan of puzzles and enjoys solving them in his free time. "
|
||||
"Brandon's favorite sport is soccer, and he often plays with his friends. "
|
||||
"He is also a fan of FC Barcelona and enjoys watching their matches. "
|
||||
)
|
||||
* 2, # Repeat to ensure it's 2k characters
|
||||
(
|
||||
"Brandon is a software engineer who lives in San Francisco. "
|
||||
"He enjoys hiking and often visits the trails in the Bay Area. "
|
||||
"Brandon has a pet dog named Max, who is a golden retriever. "
|
||||
"He loves reading science fiction books, and his favorite author is Isaac Asimov. "
|
||||
"Brandon's favorite movie is Inception, and he enjoys watching it with his friends. "
|
||||
"He is also a fan of Mexican cuisine, especially tacos and burritos. "
|
||||
"Brandon plays the guitar and often performs at local open mic nights. "
|
||||
"He is learning French and plans to visit Paris next year. "
|
||||
"Brandon is passionate about technology and often attends tech meetups in the city. "
|
||||
"He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. "
|
||||
"Brandon's favorite color is blue, and he often wears blue shirts. "
|
||||
"He enjoys cooking and often tries new recipes on weekends. "
|
||||
"Brandon is a morning person and likes to start his day with a run in the park. "
|
||||
"He is also a coffee enthusiast and enjoys trying different coffee blends. "
|
||||
"Brandon is a member of a local book club and enjoys discussing books with fellow members. "
|
||||
"He is also a fan of board games and often hosts game nights at his place. "
|
||||
"Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. "
|
||||
"He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. "
|
||||
"Brandon's favorite sport is basketball, and he often plays with his friends on weekends. "
|
||||
"He is also a fan of the Golden State Warriors and enjoys watching their games. "
|
||||
)
|
||||
* 2, # Repeat to ensure it's 2k characters
|
||||
]
|
||||
file_paths = []
|
||||
for i, content in enumerate(contents):
|
||||
file_path = Path(tmpdir.join(f"long_file_{i}.txt"))
|
||||
with open(file_path, "w") as f:
|
||||
f.write(content)
|
||||
file_paths.append(file_path)
|
||||
|
||||
file_sources = [
|
||||
TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
|
||||
for path in file_paths
|
||||
]
|
||||
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
|
||||
query = "What is Brandon's favorite book?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any(
|
||||
"the hitchhiker's guide to the galaxy" in result["context"].lower()
|
||||
for result in results
|
||||
)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_hybrid_string_and_files(mock_vector_db, tmpdir):
|
||||
# Create string sources
|
||||
string_contents = [
|
||||
"Brandon is learning French.",
|
||||
"Brandon visited Paris last summer.",
|
||||
]
|
||||
string_sources = [
|
||||
StringKnowledgeSource(content=content, metadata={"preference": "personal"})
|
||||
for content in string_contents
|
||||
]
|
||||
|
||||
# Create file sources
|
||||
file_contents = [
|
||||
"Brandon prefers tea over coffee.",
|
||||
"Brandon's favorite book is 'The Alchemist'.",
|
||||
]
|
||||
file_paths = []
|
||||
for i, content in enumerate(file_contents):
|
||||
file_path = Path(tmpdir.join(f"file_{i}.txt"))
|
||||
with open(file_path, "w") as f:
|
||||
f.write(content)
|
||||
file_paths.append(file_path)
|
||||
|
||||
file_sources = [
|
||||
TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"})
|
||||
for path in file_paths
|
||||
]
|
||||
|
||||
# Combine string and 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
|
||||
query = "What is Brandon's favorite book?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any("the alchemist" in result["context"].lower() for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_pdf_knowledge_source(mock_vector_db):
|
||||
# Get the directory of the current file
|
||||
current_dir = Path(__file__).parent
|
||||
# Construct the path to the PDF file
|
||||
pdf_path = current_dir / "crewai_quickstart.pdf"
|
||||
|
||||
# Create a PDFKnowledgeSource
|
||||
pdf_source = PDFKnowledgeSource(
|
||||
file_path=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}
|
||||
]
|
||||
|
||||
# Perform a query
|
||||
query = "How do you create a crew?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any(
|
||||
"crewai create crew latest-ai-development" in result["context"].lower()
|
||||
for result in results
|
||||
)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_csv_knowledge_source(mock_vector_db, tmpdir):
|
||||
"""Test CSVKnowledgeSource with a simple CSV file."""
|
||||
|
||||
# Create a CSV file with sample data
|
||||
csv_content = [
|
||||
["Name", "Age", "City"],
|
||||
["Brandon", "30", "New York"],
|
||||
["Alice", "25", "Los Angeles"],
|
||||
["Bob", "35", "Chicago"],
|
||||
]
|
||||
csv_path = Path(tmpdir.join("data.csv"))
|
||||
with open(csv_path, "w", encoding="utf-8") as f:
|
||||
for row in csv_content:
|
||||
f.write(",".join(row) + "\n")
|
||||
|
||||
# Create a CSVKnowledgeSource
|
||||
csv_source = CSVKnowledgeSource(
|
||||
file_path=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}
|
||||
]
|
||||
|
||||
# Perform a query
|
||||
query = "How old is Brandon?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any("30" in result["context"] for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_json_knowledge_source(mock_vector_db, tmpdir):
|
||||
"""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:
|
||||
import json
|
||||
|
||||
json.dump(json_data, f)
|
||||
|
||||
# Create a JSONKnowledgeSource
|
||||
json_source = JSONKnowledgeSource(
|
||||
file_path=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}
|
||||
]
|
||||
|
||||
# Perform a query
|
||||
query = "Where does Alice reside?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any("los angeles" in result["context"].lower() for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
|
||||
|
||||
def test_excel_knowledge_source(mock_vector_db, tmpdir):
|
||||
"""Test ExcelKnowledgeSource with a simple Excel file."""
|
||||
|
||||
# Create an Excel file with sample data
|
||||
import pandas as pd
|
||||
|
||||
excel_data = {
|
||||
"Name": ["Brandon", "Alice", "Bob"],
|
||||
"Age": [30, 25, 35],
|
||||
"City": ["New York", "Los Angeles", "Chicago"],
|
||||
}
|
||||
df = pd.DataFrame(excel_data)
|
||||
excel_path = Path(tmpdir.join("data.xlsx"))
|
||||
df.to_excel(excel_path, index=False)
|
||||
|
||||
# Create an ExcelKnowledgeSource
|
||||
excel_source = ExcelKnowledgeSource(
|
||||
file_path=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}
|
||||
]
|
||||
|
||||
# Perform a query
|
||||
query = "What is Brandon's age?"
|
||||
results = mock_vector_db.query(query)
|
||||
|
||||
# Assert that the correct information is retrieved
|
||||
assert any("30" in result["context"] for result in results)
|
||||
mock_vector_db.query.assert_called_once()
|
||||
@@ -38,6 +38,7 @@ def mock_crew_factory():
|
||||
|
||||
crew = MockCrew()
|
||||
crew.name = name
|
||||
crew.knowledge = None
|
||||
|
||||
task_output = TaskOutput(
|
||||
description="Test task", raw="Task output", agent="Test Agent"
|
||||
@@ -67,6 +68,7 @@ def mock_crew_factory():
|
||||
crew.process = Process.sequential
|
||||
crew.config = None
|
||||
crew.cache = True
|
||||
crew.embedder = None
|
||||
|
||||
# Add non-empty agents and tasks
|
||||
mock_agent = MagicMock(spec=Agent)
|
||||
|
||||
Reference in New Issue
Block a user