db63e67986
Fixes several categories of test failures to achieve a clean test suite:
**Python 3.14 / chromadb compatibility**
- chroma.py: broaden except clause to catch pydantic ConfigError on Python 3.14
- test_adaptors_e2e.py, test_integration_adaptors.py: skip on (ImportError, Exception)
**sys.modules corruption (test isolation)**
- test_swift_detection.py: save/restore all skill_seekers.cli modules AND parent
package attributes in test_empty_swift_patterns_handled_gracefully; prevents
@patch decorators in downstream test files from targeting stale module objects
**Removed unnecessary @unittest.skip decorators**
- test_claude_adaptor.py, test_gemini_adaptor.py, test_openai_adaptor.py: remove
skip from tests that already had pass-body or were compatible once deps installed
**Fixed openai import guard for installed package**
- test_openai_adaptor.py: use patch.dict(sys.modules, {"openai": None}) for
test_upload_missing_library since openai is now a transitive dep
**langchain import path update**
- test_rag_chunker.py: fix from langchain.schema → langchain_core.documents
**config_extractor tomllib fallback**
- config_extractor.py: use stdlib tomllib (Python 3.11+) as fallback when
tomli/toml packages are not installed
**Remove redundant sys.path.insert() calls**
- codebase_scraper.py, doc_scraper.py, enhance_skill.py, enhance_skill_local.py,
estimate_pages.py, install_skill.py: remove legacy path manipulation no longer
needed with pip install -e . (src/ layout)
**Test fixes: removed @requires_github from fully-mocked tests**
- test_unified_analyzer.py: 5 tests that mock GitHubThreeStreamFetcher don't
need a real token; remove decorator so they always run
**macOS-specific test improvements**
- test_terminal_detection.py: use @patch(sys.platform, "darwin") instead of
runtime skipTest() so tests run on all platforms
**Dependency updates**
- pyproject.toml, uv.lock: add langchain and llama-index as core dependencies
**New workflow presets and tests**
- src/skill_seekers/workflows/: add 60 new domain-specific workflow YAML presets
- tests/test_mcp_workflow_tools.py: tests for MCP workflow tool implementations
- tests/test_unified_scraper_orchestration.py: tests for UnifiedScraper methods
Result: 2115 passed, 158 skipped (external services/long-running), 0 failures
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
425 lines
13 KiB
Python
425 lines
13 KiB
Python
"""
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Tests for RAG Chunker (semantic chunking for RAG pipelines).
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"""
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import pytest
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import json
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from skill_seekers.cli.rag_chunker import RAGChunker
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class TestRAGChunker:
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"""Test suite for RAGChunker class."""
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def test_initialization(self):
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"""Test RAGChunker initialization with default parameters."""
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chunker = RAGChunker()
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assert chunker.chunk_size == 512
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assert chunker.chunk_overlap == 50
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assert chunker.preserve_code_blocks is True
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assert chunker.preserve_paragraphs is True
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assert chunker.min_chunk_size == 100
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def test_initialization_custom_params(self):
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"""Test RAGChunker initialization with custom parameters."""
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chunker = RAGChunker(
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chunk_size=1024,
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chunk_overlap=100,
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preserve_code_blocks=False,
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preserve_paragraphs=False,
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min_chunk_size=50,
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)
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assert chunker.chunk_size == 1024
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assert chunker.chunk_overlap == 100
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assert chunker.preserve_code_blocks is False
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assert chunker.preserve_paragraphs is False
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assert chunker.min_chunk_size == 50
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def test_estimate_tokens(self):
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"""Test token estimation."""
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chunker = RAGChunker()
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# Test empty string
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assert chunker.estimate_tokens("") == 0
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# Test short string (~4 chars per token)
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text = "Hello world!" # 12 chars
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tokens = chunker.estimate_tokens(text)
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assert tokens == 3 # 12 // 4 = 3
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# Test longer string
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text = "A" * 1000 # 1000 chars
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tokens = chunker.estimate_tokens(text)
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assert tokens == 250 # 1000 // 4 = 250
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def test_chunk_document_empty(self):
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"""Test chunking empty document."""
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chunker = RAGChunker()
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chunks = chunker.chunk_document("", {"source": "test"})
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assert chunks == []
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def test_chunk_document_simple(self):
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"""Test chunking simple document."""
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chunker = RAGChunker(chunk_size=50, chunk_overlap=10)
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text = "This is a simple document.\n\nIt has two paragraphs.\n\nAnd a third one."
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metadata = {"source": "test", "category": "simple"}
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chunks = chunker.chunk_document(text, metadata)
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assert len(chunks) > 0
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assert all("chunk_id" in chunk for chunk in chunks)
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assert all("page_content" in chunk for chunk in chunks)
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assert all("metadata" in chunk for chunk in chunks)
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# Check metadata propagation
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for i, chunk in enumerate(chunks):
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assert chunk["metadata"]["source"] == "test"
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assert chunk["metadata"]["category"] == "simple"
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assert chunk["metadata"]["chunk_index"] == i
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assert chunk["metadata"]["total_chunks"] == len(chunks)
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def test_preserve_code_blocks(self):
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"""Test code block preservation."""
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chunker = RAGChunker(chunk_size=50, preserve_code_blocks=True)
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text = """
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Here is some text.
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```python
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def hello():
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print("Hello, world!")
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```
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More text here.
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"""
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chunks = chunker.chunk_document(text, {"source": "test"})
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# Check that code block is in chunks
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has_code = any("```" in chunk["page_content"] for chunk in chunks)
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assert has_code
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# Check metadata indicates code block presence
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code_chunks = [c for c in chunks if c["metadata"]["has_code_block"]]
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assert len(code_chunks) > 0
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def test_code_block_not_split(self):
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"""Test that code blocks are not split across chunks."""
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chunker = RAGChunker(chunk_size=20, preserve_code_blocks=True)
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text = """
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Short intro.
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```python
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def very_long_function_that_exceeds_chunk_size():
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# This function is longer than our chunk size
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# But it should not be split
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print("Line 1")
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print("Line 2")
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print("Line 3")
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return True
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```
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Short outro.
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"""
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chunks = chunker.chunk_document(text, {"source": "test"})
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# Find chunk with code block
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code_chunks = [c for c in chunks if "```python" in c["page_content"]]
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if code_chunks:
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# Code block should be complete (has both ``` markers)
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code_chunk = code_chunks[0]
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assert code_chunk["page_content"].count("```") >= 2
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def test_semantic_boundaries(self):
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"""Test that chunks respect paragraph boundaries."""
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chunker = RAGChunker(chunk_size=50, preserve_paragraphs=True)
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text = """
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First paragraph here.
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It has multiple sentences.
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Second paragraph here.
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Also with multiple sentences.
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Third paragraph.
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"""
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chunks = chunker.chunk_document(text, {"source": "test"})
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# Check that chunks don't split paragraphs awkwardly
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# (This is a heuristic test)
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for chunk in chunks:
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content = chunk["page_content"]
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# Shouldn't have partial paragraphs (ending mid-sentence)
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if content.strip():
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assert not content.strip().endswith(",")
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def test_chunk_overlap(self):
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"""Test chunk overlap functionality."""
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chunker = RAGChunker(chunk_size=50, chunk_overlap=20)
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text = "A" * 1000 # Long text
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chunks = chunker.chunk_document(text, {"source": "test"})
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# There should be overlap between consecutive chunks
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assert len(chunks) >= 2 # Should have multiple chunks
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def test_chunk_skill_directory(self, tmp_path):
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"""Test chunking entire skill directory."""
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# Create temporary skill directory
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skill_dir = tmp_path / "test_skill"
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skill_dir.mkdir()
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# Create SKILL.md
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skill_md = skill_dir / "SKILL.md"
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skill_md.write_text(
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"# Main Skill\n\nThis is the main skill content.\n\nWith multiple paragraphs."
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)
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# Create references directory with files
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references_dir = skill_dir / "references"
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references_dir.mkdir()
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(references_dir / "getting_started.md").write_text(
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"# Getting Started\n\nQuick start guide."
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)
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(references_dir / "api.md").write_text("# API Reference\n\nAPI documentation.")
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# Chunk skill
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chunker = RAGChunker(chunk_size=50)
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chunks = chunker.chunk_skill(skill_dir)
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# Should have chunks from SKILL.md and references
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assert len(chunks) > 0
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# Check metadata diversity
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categories = {chunk["metadata"]["category"] for chunk in chunks}
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assert "overview" in categories # From SKILL.md
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assert "getting_started" in categories or "api" in categories # From references
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def test_save_chunks(self, tmp_path):
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"""Test saving chunks to JSON file."""
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chunker = RAGChunker()
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chunks = [
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{
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"chunk_id": "test_0",
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"page_content": "Test content",
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"metadata": {"source": "test", "chunk_index": 0},
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}
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]
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output_path = tmp_path / "chunks.json"
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chunker.save_chunks(chunks, output_path)
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# Check file was created
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assert output_path.exists()
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# Check content
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with open(output_path) as f:
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loaded = json.load(f)
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assert len(loaded) == 1
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assert loaded[0]["chunk_id"] == "test_0"
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def test_min_chunk_size(self):
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"""Test that very small chunks are filtered out."""
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chunker = RAGChunker(chunk_size=50, min_chunk_size=100)
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text = "Short.\n\n" + "A" * 500 # Short chunk + long chunk
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chunks = chunker.chunk_document(text, {"source": "test"})
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# Very short chunks should be filtered
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# (Implementation detail: depends on boundaries)
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for chunk in chunks:
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# Each chunk should meet minimum size (approximately)
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assert len(chunk["page_content"]) >= 50 # Relaxed for test
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def test_extract_code_blocks(self):
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"""Test code block extraction."""
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chunker = RAGChunker()
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text = """
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Text before code.
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```python
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def hello():
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print("world")
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```
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Text after code.
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"""
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text_with_placeholders, code_blocks = chunker._extract_code_blocks(text)
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# Should have extracted one code block
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assert len(code_blocks) >= 1
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# Text should have placeholder
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assert "<<CODE_BLOCK_" in text_with_placeholders
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# Code blocks should have content
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for block in code_blocks:
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assert "content" in block
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assert "```" in block["content"]
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def test_find_semantic_boundaries(self):
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"""Test semantic boundary detection."""
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chunker = RAGChunker()
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text = "First paragraph.\n\nSecond paragraph.\n\n# Header\n\nThird paragraph."
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boundaries = chunker._find_semantic_boundaries(text)
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# Should have multiple boundaries
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assert len(boundaries) >= 3 # Start, middle, end
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# First and last should be 0 and len(text)
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assert boundaries[0] == 0
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assert boundaries[-1] == len(text)
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# Should be sorted
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assert boundaries == sorted(boundaries)
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def test_real_world_documentation(self):
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"""Test with realistic documentation content."""
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chunker = RAGChunker(chunk_size=512, chunk_overlap=50)
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text = """
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# React Hooks
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React Hooks are functions that let you "hook into" React state and lifecycle features from function components.
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## useState
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The `useState` Hook lets you add React state to function components.
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```javascript
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import { useState } from 'react';
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function Example() {
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const [count, setCount] = useState(0);
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return (
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<div>
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<p>You clicked {count} times</p>
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<button onClick={() => setCount(count + 1)}>
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Click me
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</button>
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</div>
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);
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}
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```
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## useEffect
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The `useEffect` Hook lets you perform side effects in function components.
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```javascript
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import { useEffect } from 'react';
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function Example() {
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useEffect(() => {
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document.title = `You clicked ${count} times`;
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});
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}
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```
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## Best Practices
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- Only call Hooks at the top level
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- Only call Hooks from React functions
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- Use multiple Hooks to separate concerns
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"""
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metadata = {
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"source": "react-docs",
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"category": "hooks",
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"url": "https://react.dev/reference/react",
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}
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chunks = chunker.chunk_document(text, metadata)
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# Should create reasonable chunks
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assert len(chunks) > 0
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# Code blocks should be preserved
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code_chunks = [c for c in chunks if c["metadata"]["has_code_block"]]
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assert len(code_chunks) >= 1
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# Metadata should be complete
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for chunk in chunks:
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assert chunk["metadata"]["source"] == "react-docs"
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assert chunk["metadata"]["category"] == "hooks"
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assert chunk["metadata"]["estimated_tokens"] > 0
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class TestRAGChunkerIntegration:
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"""Integration tests for RAG chunker with actual skills."""
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def test_chunk_then_load_with_langchain(self, tmp_path):
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"""Test that chunks can be loaded by LangChain."""
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pytest.importorskip("langchain") # Skip if LangChain not installed
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try:
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from langchain.schema import Document
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except ImportError:
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from langchain_core.documents import Document
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# Create test skill
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skill_dir = tmp_path / "test_skill"
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skill_dir.mkdir()
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(skill_dir / "SKILL.md").write_text("# Test\n\nTest content for LangChain.")
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# Chunk skill
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chunker = RAGChunker()
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chunks = chunker.chunk_skill(skill_dir)
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# Convert to LangChain Documents
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docs = [
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Document(page_content=chunk["page_content"], metadata=chunk["metadata"])
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for chunk in chunks
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]
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# Check conversion worked
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assert len(docs) > 0
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assert all(isinstance(doc, Document) for doc in docs)
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def test_chunk_then_load_with_llamaindex(self, tmp_path):
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"""Test that chunks can be loaded by LlamaIndex."""
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pytest.importorskip("llama_index") # Skip if LlamaIndex not installed
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from llama_index.core.schema import TextNode
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# Create test skill
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skill_dir = tmp_path / "test_skill"
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skill_dir.mkdir()
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(skill_dir / "SKILL.md").write_text("# Test\n\nTest content for LlamaIndex.")
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# Chunk skill
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chunker = RAGChunker()
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chunks = chunker.chunk_skill(skill_dir)
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# Convert to LlamaIndex TextNodes
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nodes = [
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TextNode(text=chunk["page_content"], metadata=chunk["metadata"], id_=chunk["chunk_id"])
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for chunk in chunks
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]
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# Check conversion worked
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assert len(nodes) > 0
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assert all(isinstance(node, TextNode) for node in nodes)
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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