feat(rag): hybrid FTS+vector retrieval and multi-format ingestion — name-your-tool questions find the right document

app/models.py

@@ -2,7 +2,7 @@
 
 Data model — see ``.agent/PLAN.md`` §Data Model:
 
-* ``documents`` — one row per ``*.md`` file (full content, path, sha256 hash).
+* ``documents`` — one row per imported A9 file (full content, path, sha256 hash).
 * ``chunks``    — retrieval units; each chunk points at its parent document
                   via ``document_id``. This is how an embedding maps back to
                   a document path (the "feed the whole document" requirement).
@@ -74,6 +74,9 @@ class QueryLog(Base):
     id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     question: Mapped[str] = mapped_column(Text)
     top_score: Mapped[float] = mapped_column(Float, default=0.0)  # best cosine similarity
+    #: Lexical (FTS) candidates matched — the OR-tsquery hit count (A8). NULL
+    #: for pre-hybrid rows (migration 0002).
+    fts_hits: Mapped[int | None] = mapped_column(Integer)
     chunk_hits: Mapped[int] = mapped_column(Integer, default=0)
     deflected: Mapped[bool] = mapped_column(Boolean, default=False)  # True = honest "no idea"
     sources: Mapped[str] = mapped_column(Text, default="")  # comma-joined source paths