"""Test Knowledge creation and querying functionality.""" from pathlib import Path from unittest.mock import patch import pytest from crewai.knowledge.source.crew_docling_source import CrewDoclingSource 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 @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 = [ { "content": "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 = [{"content": 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["content"].lower() for result in results) # Verify the mock was called mock_vector_db.query.assert_called_once() # @pytest.mark.vcr() 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 = [{"content": 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["content"].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 = [ {"content": "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["content"].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 = [{"content": 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["content"].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_paths=[file_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [file_source] mock_vector_db.query.return_value = [{"content": 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["content"].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_paths=[file_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [file_source] mock_vector_db.query.return_value = [{"content": 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["content"].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_paths=[path], metadata=metadata) for path, metadata in file_paths ] mock_vector_db.sources = file_sources mock_vector_db.query.return_value = [ {"content": "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["content"].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_paths=[path], metadata={"preference": "personal"}) for path in file_paths ] mock_vector_db.sources = file_sources mock_vector_db.query.return_value = [ { "content": "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["content"].lower() for result in results ) mock_vector_db.query.assert_called_once() @pytest.mark.vcr() 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_paths=[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 = [{"content": 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["content"].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_paths=[pdf_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [pdf_source] mock_vector_db.query.return_value = [ {"content": "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["content"].lower() for result in results ) mock_vector_db.query.assert_called_once() @pytest.mark.vcr() 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_paths=[csv_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [csv_source] mock_vector_db.query.return_value = [ {"content": "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["content"] 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_paths=[json_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [json_source] mock_vector_db.query.return_value = [ {"content": "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["content"].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 # type: ignore[import-untyped] 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_paths=[excel_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [excel_source] mock_vector_db.query.return_value = [ {"content": "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["content"] for result in results) mock_vector_db.query.assert_called_once() @pytest.mark.vcr def test_docling_source(mock_vector_db): docling_source = CrewDoclingSource( file_paths=[ "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", ], ) mock_vector_db.sources = [docling_source] mock_vector_db.query.return_value = [ { "content": "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?" results = mock_vector_db.query(query) assert any("reward hacking" in result["content"].lower() for result in results) mock_vector_db.query.assert_called_once() @pytest.mark.vcr 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/", ] docling_source = CrewDoclingSource(file_paths=urls) assert docling_source.file_paths == urls assert docling_source.content is not None def test_file_path_validation(): """Test file path validation for knowledge sources.""" current_dir = Path(__file__).parent pdf_path = current_dir / "crewai_quickstart.pdf" # Test valid single file_path source = PDFKnowledgeSource(file_path=pdf_path) assert source.safe_file_paths == [pdf_path] # Test valid file_paths list source = PDFKnowledgeSource(file_paths=[pdf_path]) assert source.safe_file_paths == [pdf_path] # Test both file_path and file_paths provided (should use file_paths) source = PDFKnowledgeSource(file_path=pdf_path, file_paths=[pdf_path]) assert source.safe_file_paths == [pdf_path] # Test neither file_path nor file_paths provided with pytest.raises( ValueError, match="file_path/file_paths must be a Path, str, or a list of these types", ): PDFKnowledgeSource() def test_hash_based_id_generation_without_doc_id(mock_vector_db): """Test that documents without doc_id generate hash-based IDs. Duplicates are deduplicated before upsert.""" import hashlib import json from crewai.rag.chromadb.utils import _prepare_documents_for_chromadb from crewai.rag.types import BaseRecord documents: list[BaseRecord] = [ {"content": "First document content", "metadata": {"source": "test1", "category": "research"}}, {"content": "Second document content", "metadata": {"source": "test2", "category": "research"}}, {"content": "Third document content"}, # No metadata ] result = _prepare_documents_for_chromadb(documents) assert len(result.ids) == 3 # Unique documents should get 64-character hex hashes (no suffix) for doc_id in result.ids: assert len(doc_id) == 64, f"ID should be 64 characters: {doc_id}" assert all(c in "0123456789abcdef" for c in doc_id), f"ID should be hex: {doc_id}" # Different documents should have different hashes assert result.ids[0] != result.ids[1] != result.ids[2] # Verify hashes match expected values expected_hash_1 = hashlib.sha256( f"First document content|{json.dumps({'category': 'research', 'source': 'test1'}, sort_keys=True)}".encode() ).hexdigest() assert result.ids[0] == expected_hash_1, "First document hash should match expected" expected_hash_3 = hashlib.sha256("Third document content".encode()).hexdigest() assert result.ids[2] == expected_hash_3, "Third document hash should match expected" # Test that duplicate documents are deduplicated (same ID, only one sent) duplicate_documents: list[BaseRecord] = [ {"content": "Same content", "metadata": {"source": "test"}}, {"content": "Same content", "metadata": {"source": "test"}}, {"content": "Same content", "metadata": {"source": "test"}}, ] duplicate_result = _prepare_documents_for_chromadb(duplicate_documents) # Duplicates should be deduplicated - only one ID should remain assert len(duplicate_result.ids) == 1, "Duplicate documents should be deduplicated" assert len(duplicate_result.ids[0]) == 64, "Deduplicated ID should be clean hash" # Verify it's the expected hash expected_hash = hashlib.sha256( f"Same content|{json.dumps({'source': 'test'}, sort_keys=True)}".encode() ).hexdigest() assert duplicate_result.ids[0] == expected_hash, "Deduplicated ID should match expected hash" def test_hash_based_id_generation_with_doc_id_in_metadata(mock_vector_db): """Test that documents with doc_id in metadata use the doc_id directly, not hash-based.""" from crewai.rag.chromadb.utils import _prepare_documents_for_chromadb from crewai.rag.types import BaseRecord documents_with_doc_id: list[BaseRecord] = [ {"content": "First document", "metadata": {"doc_id": "custom-id-1", "source": "test1"}}, {"content": "Second document", "metadata": {"doc_id": "custom-id-2"}}, ] documents_without_doc_id: list[BaseRecord] = [ {"content": "First document", "metadata": {"source": "test1"}}, {"content": "Second document"}, ] result_with_doc_id = _prepare_documents_for_chromadb(documents_with_doc_id) result_without_doc_id = _prepare_documents_for_chromadb(documents_without_doc_id) assert result_with_doc_id.ids == ["custom-id-1", "custom-id-2"] assert len(result_without_doc_id.ids) == 2 # Unique documents get 64-character hashes for doc_id in result_without_doc_id.ids: assert len(doc_id) == 64, "ID should be 64 characters" assert all(c in "0123456789abcdef" for c in doc_id), "ID should be hex"