Add Docling corpus pipeline, retrieval evaluation and hybrid search with reranking

- prepare_corpus.py: split double-page scans (split_spreads.py), re-OCR with
  OCRmyPDF/Tesseract only where the existing text layer is worse, Docling
  layout extraction with heading hierarchy, QA report per PDF
- docling_loader.py: cleanup (margin noise, split paragraphs, footnote marks,
  OCR comma/period repair), page mapping back to the original PDF, chunk
  headers with author, title and section
- local_rag.py: bge-m3 embeddings, configurable chunking and loader, index
  settings check (index_info.json), hybrid/union BM25 search with Reciprocal
  Rank Fusion and bge-reranker-v2-m3 cross-encoder; query instruction text
  removed after it lowered recall
- eval_retrieval.py: CLI options, same retrieval path as the chat, tolerant
  file name matching; goldsets and evaluation reports for the Kafka corpus
- grobid_test.py, grobid_qa.py, extract_docling.py, compare_extractors.py:
  extractor tests and quality checks
- .gitignore: corpus PDFs, vector stores and extractor output stay local
- server.py: uses ./vectorstore_docling_hdr

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

server.py

@@ -13,7 +13,9 @@ from local_rag import LocalRAG
 
 OLLAMA_MODEL = "gpt-oss:20b"
 OPENAI_MODEL = "gpt-5-mini"
-VECTORSTORE_PATH = "./vectorstore"
+# Kafka corpus: Docling pipeline (split double pages) + chunk headers, bge-m3, hybrid search + reranker
+# built with: add_pdfs.py --data data --index ./vectorstore_docling_hdr --loader docling
+VECTORSTORE_PATH = "./vectorstore_docling_hdr"
 
 # Dual RAG instances for on-the-fly provider switching
 rag_ollama = LocalRAG(