feat(ebook): add phrase metadata tables for protected phrase matching

Introduce four ORM models and their Alembic migration to support
phrase-based query matching in the ebook RAG engine:

- EbookCandidatePhrase: high-recall phrase candidates extracted per book,
  with source flags (ngram/yake/spacy/capitalized/metadata), scoring, and
  LLM judge results.
- EbookProtectedPhrase: phrases accepted by the LLM judge, with canonical
  id, importance, and nesting controls.
- EbookPhraseAlias: normalized aliases mapping to protected phrases.
- EbookChunkPhraseMention: precomputed phrase occurrences within chunks.

Export the new models from python.orm.richie and add a JSON_DOCUMENT
helper (JSON with JSONB postgres variant) for storing sample contexts.

python/orm/richie/__init__.py

@@ -12,12 +12,16 @@ from python.orm.richie.contact import (
     RelationshipType,
 )
 from python.orm.richie.ebook import (
+    EbookCandidatePhrase,
     EbookChapter,
     EbookChunk,
     EbookChunkEmbedding1024,
     EbookChunkEmbedding2560,
     EbookChunkEmbedding4096,
+    EbookChunkPhraseMention,
     EbookEmbeddingModel,
+    EbookPhraseAlias,
+    EbookProtectedPhrase,
     EbookSource,
 )
 
@@ -28,12 +32,16 @@ __all__ = [
     "Contact",
     "ContactNeed",
     "ContactRelationship",
+    "EbookCandidatePhrase",
     "EbookChapter",
     "EbookChunk",
     "EbookChunkEmbedding1024",
     "EbookChunkEmbedding2560",
     "EbookChunkEmbedding4096",
+    "EbookChunkPhraseMention",
     "EbookEmbeddingModel",
+    "EbookPhraseAlias",
+    "EbookProtectedPhrase",
     "EbookSource",
     "Need",
     "RelationshipType",