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>

requirements.txt

@@ -10,3 +10,5 @@ pypdf
 numpy<2.0
 fastapi
 uvicorn[standard]
+rank_bm25
+snowballstemmer