Embedding status tests were creating search_vector_chunks inline using
SQLite-only DDL (AUTOINCREMENT). Added Postgres DDL constants to
models/search.py and wired them into the test fixture so both backends
create the table at setup time — matching what the Alembic migration
does in production.
Also fixed stub search_vector_embeddings to use chunk_id (Postgres
column name) instead of rowid, and added inter-test cleanup to prevent
ordering-dependent failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Add EmbeddingStatus model to project_info schemas and wire it into
ProjectInfoResponse. ProjectService.get_embedding_status() queries
vector tables for chunk/embedding counts, detects orphaned chunks
and missing embeddings, and recommends reindex when appropriate.
Handles both SQLite and Postgres backends. 🔍
Includes 6 unit tests covering: disabled search, missing vector tables,
entities without chunks, orphaned chunks, healthy state, and integration
with get_project_info().
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>