#!/usr/bin/env python3 -m pytest import asyncio import io import os import shutil import pytest import httpx from markitdown import AsyncMarkItDown skip_remote = ( True if os.environ.get("GITHUB_ACTIONS") else False ) # Don't run these tests in CI skip_exiftool = shutil.which("exiftool") is None TEST_FILES_DIR = os.path.join(os.path.dirname(__file__), "test_files") JPG_TEST_EXIFTOOL = { "Author": "AutoGen Authors", "Title": "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation", "Description": "AutoGen enables diverse LLM-based applications", "ImageSize": "1615x1967", "DateTimeOriginal": "2024:03:14 22:10:00", } PDF_TEST_URL = "https://arxiv.org/pdf/2308.08155v2.pdf" PDF_TEST_STRINGS = [ "While there is contemporaneous exploration of multi-agent approaches" ] YOUTUBE_TEST_URL = "https://www.youtube.com/watch?v=V2qZ_lgxTzg" YOUTUBE_TEST_STRINGS = [ "## AutoGen FULL Tutorial with Python (Step-By-Step)", "This is an intermediate tutorial for installing and using AutoGen locally", "PT15M4S", "the model we're going to be using today is GPT 3.5 turbo", # From the transcript ] XLSX_TEST_STRINGS = [ "## 09060124-b5e7-4717-9d07-3c046eb", "6ff4173b-42a5-4784-9b19-f49caff4d93d", "affc7dad-52dc-4b98-9b5d-51e65d8a8ad0", ] DOCX_TEST_STRINGS = [ "314b0a30-5b04-470b-b9f7-eed2c2bec74a", "49e168b7-d2ae-407f-a055-2167576f39a1", "## d666f1f7-46cb-42bd-9a39-9a39cf2a509f", "# Abstract", "# Introduction", "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation", ] PPTX_TEST_STRINGS = [ "2cdda5c8-e50e-4db4-b5f0-9722a649f455", "04191ea8-5c73-4215-a1d3-1cfb43aaaf12", "44bf7d06-5e7a-4a40-a2e1-a2e42ef28c8a", "1b92870d-e3b5-4e65-8153-919f4ff45592", "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation", ] BLOG_TEST_URL = "https://microsoft.github.io/autogen/blog/2023/04/21/LLM-tuning-math" BLOG_TEST_STRINGS = [ "Large language models (LLMs) are powerful tools that can generate natural language texts for various applications, such as chatbots, summarization, translation, and more. GPT-4 is currently the state of the art LLM in the world. Is model selection irrelevant? What about inference parameters?", "an example where high cost can easily prevent a generic complex", ] WIKIPEDIA_TEST_URL = "https://en.wikipedia.org/wiki/Microsoft" WIKIPEDIA_TEST_STRINGS = [ "Microsoft entered the operating system (OS) business in 1980 with its own version of [Unix]", 'Microsoft was founded by [Bill Gates](/wiki/Bill_Gates "Bill Gates")', ] WIKIPEDIA_TEST_EXCLUDES = [ "You are encouraged to create an account and log in", "154 languages", "move to sidebar", ] SERP_TEST_URL = "https://www.bing.com/search?q=microsoft+wikipedia" SERP_TEST_STRINGS = [ "](https://en.wikipedia.org/wiki/Microsoft", "Microsoft Corporation is **an American multinational corporation and technology company headquartered** in Redmond", "1995–2007: Foray into the Web, Windows 95, Windows XP, and Xbox", ] SERP_TEST_EXCLUDES = [ "https://www.bing.com/ck/a?!&&p=", "data:image/svg+xml,%3Csvg%20width%3D", ] @pytest.mark.asyncio @pytest.mark.skipif( skip_remote, reason="do not run tests that query external urls", ) async def test_async_markitdown_remote(): """Test async remote file conversion.""" async with AsyncMarkItDown() as markitdown: # Test URL conversion result = await markitdown.convert(PDF_TEST_URL) for test_string in PDF_TEST_STRINGS: assert test_string in result.text_content # Test Wikipedia conversion result = await markitdown.convert( os.path.join(TEST_FILES_DIR, "test_wikipedia.html"), url=WIKIPEDIA_TEST_URL ) text_content = result.text_content.replace("\\", "") for test_string in WIKIPEDIA_TEST_EXCLUDES: assert test_string not in text_content for test_string in WIKIPEDIA_TEST_STRINGS: assert test_string in text_content # Test Blog conversion result = await markitdown.convert( os.path.join(TEST_FILES_DIR, "test_blog.html"), url=BLOG_TEST_URL ) for test_string in BLOG_TEST_STRINGS: text_content = result.text_content.replace("\\", "") assert test_string in text_content # Test SERP conversion result = await markitdown.convert( os.path.join(TEST_FILES_DIR, "test_serp.html"), url=SERP_TEST_URL ) text_content = result.text_content.replace("\\", "") for test_string in SERP_TEST_EXCLUDES: assert test_string not in text_content for test_string in SERP_TEST_STRINGS: assert test_string in text_content # Test YouTube conversion result = await markitdown.convert(YOUTUBE_TEST_URL) for test_string in YOUTUBE_TEST_STRINGS: assert test_string in result.text_content @pytest.mark.asyncio async def test_async_markitdown_local(): """Test async local file conversion.""" async with AsyncMarkItDown() as markitdown: # Test DOCX conversion result = await markitdown.convert(os.path.join(TEST_FILES_DIR, "test.docx")) for test_string in DOCX_TEST_STRINGS: text_content = result.text_content.replace("\\", "") assert test_string in text_content # Test XLSX conversion result = await markitdown.convert(os.path.join(TEST_FILES_DIR, "test.xlsx")) for test_string in XLSX_TEST_STRINGS: text_content = result.text_content.replace("\\", "") assert test_string in text_content # Test PPTX conversion result = await markitdown.convert(os.path.join(TEST_FILES_DIR, "test.pptx")) for test_string in PPTX_TEST_STRINGS: text_content = result.text_content.replace("\\", "") assert test_string in text_content # Test HTML conversion result = await markitdown.convert( os.path.join(TEST_FILES_DIR, "test_blog.html"), url=BLOG_TEST_URL ) for test_string in BLOG_TEST_STRINGS: text_content = result.text_content.replace("\\", "") assert test_string in text_content # Test Wikipedia conversion result = await markitdown.convert( os.path.join(TEST_FILES_DIR, "test_wikipedia.html"), url=WIKIPEDIA_TEST_URL ) text_content = result.text_content.replace("\\", "") for test_string in WIKIPEDIA_TEST_EXCLUDES: assert test_string not in text_content for test_string in WIKIPEDIA_TEST_STRINGS: assert test_string in text_content # Test SERP conversion result = await markitdown.convert( os.path.join(TEST_FILES_DIR, "test_serp.html"), url=SERP_TEST_URL ) text_content = result.text_content.replace("\\", "") for test_string in SERP_TEST_EXCLUDES: assert test_string not in text_content for test_string in SERP_TEST_STRINGS: assert test_string in text_content @pytest.mark.asyncio @pytest.mark.skipif( skip_exiftool, reason="exiftool not installed", ) async def test_async_markitdown_exiftool(): """Test async image conversion with exiftool.""" async with AsyncMarkItDown() as markitdown: result = await markitdown.convert(os.path.join(TEST_FILES_DIR, "test.jpg")) for k, v in JPG_TEST_EXIFTOOL.items(): assert f"{k}: {v}" in result.text_content @pytest.mark.asyncio async def test_async_markitdown_concurrent(): """Test concurrent async operations.""" async with AsyncMarkItDown() as markitdown: # Create a list of tasks tasks = [] for _ in range(5): tasks.append(markitdown.convert(os.path.join(TEST_FILES_DIR, "test.docx"))) tasks.append(markitdown.convert(os.path.join(TEST_FILES_DIR, "test.xlsx"))) tasks.append(markitdown.convert(os.path.join(TEST_FILES_DIR, "test.pptx"))) # Run all tasks concurrently results = await asyncio.gather(*tasks) # Verify results for result in results: assert result is not None assert isinstance(result.text_content, str) assert len(result.text_content) > 0 if __name__ == "__main__": """Run tests from command line.""" pytest.main([__file__])