v0.1.5

Added

• Semantic chunking — all-MiniLM-L6-v2 embedding-based boundary detection in HybridChunker (optional via use_semantic_chunking).
• Cross-reference resolution — Highly efficient $O(N)$ resolution for explicit ("Figure 3") and implicit ("the table below") references via spatial proximity.
• Summary chunks — Asynchronous ARQ background worker (enrich_summaries_job) to auto-generate LLM section summaries for hierarchical RAG retrieval.
• Chunk quality scorer — Zero-ML, heuristic-based chunk scoring using block token confidences, Dictionary Word Coverage (/usr/share/dict/words), and fastText Lang-ID validation.
• PII redaction — Hybrid approach using fast Regex+Luhn (Emails, Phones, SSNs, CCs, IPs) and optional spaCy NER (en_core_web_sm) for names, organizations, and locations. Preserves original values in secure block metadata for HITL.

Changed

• Bumped marker-pdf version support in dependencies.
• Added ner optional dependency group (spacy>=3.7.0) in pyproject.toml.
• Expanded ChunkingConfig and ProcessingConfig with new semantic, summary, and PII toggle options.
• Marked Phase 1 as officially complete in Roadmap.

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