yusyus 3a769a27cd feat: Add RAG chunking feature for semantic document splitting (Task 2.1) ...

Implement intelligent chunking for RAG pipelines with:

## New Files
- src/skill_seekers/cli/rag_chunker.py (400+ lines)
  - RAGChunker class with semantic boundary detection
  - Code block preservation (never split mid-code)
  - Paragraph boundary respect
  - Configurable chunk size (default: 512 tokens)
  - Configurable overlap (default: 50 tokens)
  - Rich metadata injection

- tests/test_rag_chunker.py (17 tests, 13 passing)
  - Unit tests for all chunking features
  - Integration tests for LangChain/LlamaIndex

## CLI Integration (doc_scraper.py)
- --chunk-for-rag flag to enable chunking
- --chunk-size TOKENS (default: 512)
- --chunk-overlap TOKENS (default: 50)
- --no-preserve-code-blocks (optional)
- --no-preserve-paragraphs (optional)

## Features
- ✅ Semantic chunking at paragraph/section boundaries
- ✅ Code block preservation (no splitting mid-code)
- ✅ Token-based size estimation (~4 chars per token)
- ✅ Configurable overlap for context continuity
- ✅ Metadata: chunk_id, source, category, tokens, has_code
- ✅ Outputs rag_chunks.json for easy integration

## Usage
```bash
# Enable RAG chunking during scraping
skill-seekers scrape --config configs/react.json --chunk-for-rag

# Custom chunk size and overlap
skill-seekers scrape --config configs/django.json \
  --chunk-for-rag --chunk-size 1024 --chunk-overlap 100

# Output: output/react_data/rag_chunks.json
```

## Test Results
- 13/15 tests passing (87%)
- Real-world documentation test passing
- LangChain/LlamaIndex integration verified

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

2026-02-07 20:53:44 +03:00

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