feat(db): Add migrations for documents, FTS5, and embeddings

Three new migrations establish the search infrastructure:

- 007_documents: Creates the `documents` table as the central search
  unit. Each document is a rendered text blob derived from an issue,
  MR, or discussion. Includes `dirty_queue` table for tracking which
  entities need document regeneration after ingestion changes.

- 008_fts5: Creates FTS5 virtual table `documents_fts` with content
  sync triggers. Uses `unicode61` tokenizer with `remove_diacritics=2`
  for broad language support. Automatic insert/update/delete triggers
  keep the FTS index synchronized with the documents table.

- 009_embeddings: Creates `embeddings` table for storing vector
  chunks produced by Ollama. Uses `doc_id * 1000 + chunk_index`
  rowid encoding to support multi-chunk documents while enabling
  efficient doc-level deduplication in vector search results.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

migrations/009_embeddings.sql

@@ -0,0 +1,54 @@
+-- Migration 009: Embeddings (Gate B)
+-- Schema version: 9
+-- Adds sqlite-vec vector storage and embedding metadata for semantic search
+-- Requires sqlite-vec extension to be loaded before applying
+
+-- NOTE: sqlite-vec vec0 virtual tables cannot participate in FK cascades.
+-- We must use an explicit trigger to delete orphan embeddings when documents
+-- are deleted. See documents_embeddings_ad trigger below.
+
+-- sqlite-vec virtual table for vector search
+-- Storage rule: embeddings.rowid = document_id * 1000 + chunk_index
+-- This encodes (document_id, chunk_index) into a single integer rowid.
+-- Supports up to 1000 chunks per document (32M chars at 32k/chunk).
+CREATE VIRTUAL TABLE embeddings USING vec0(
+  embedding float[768]
+);
+
+-- Embedding provenance + change detection (one row per chunk)
+-- NOTE: Two hash columns serve different purposes:
+--   document_hash: SHA-256 of full documents.content_text (staleness detection)
+--   chunk_hash: SHA-256 of this individual chunk's text (debug/provenance)
+-- Pending detection uses document_hash (not chunk_hash) because staleness is
+-- a document-level condition: if the document changed, ALL chunks need re-embedding.
+CREATE TABLE embedding_metadata (
+  document_id INTEGER NOT NULL REFERENCES documents(id) ON DELETE CASCADE,
+  chunk_index INTEGER NOT NULL DEFAULT 0,   -- 0-indexed position within document
+  model TEXT NOT NULL,           -- 'nomic-embed-text'
+  dims INTEGER NOT NULL,         -- 768
+  document_hash TEXT NOT NULL,   -- SHA-256 of full documents.content_text (staleness)
+  chunk_hash TEXT NOT NULL,      -- SHA-256 of this chunk's text (provenance)
+  created_at INTEGER NOT NULL,   -- ms epoch UTC
+  last_error TEXT,               -- error message from last failed attempt
+  attempt_count INTEGER NOT NULL DEFAULT 0,
+  last_attempt_at INTEGER,       -- ms epoch UTC
+  PRIMARY KEY(document_id, chunk_index)
+);
+
+CREATE INDEX idx_embedding_metadata_errors
+  ON embedding_metadata(last_error) WHERE last_error IS NOT NULL;
+CREATE INDEX idx_embedding_metadata_doc ON embedding_metadata(document_id);
+
+-- CRITICAL: Delete ALL chunk embeddings when a document is deleted.
+-- vec0 virtual tables don't support FK ON DELETE CASCADE, so we need this trigger.
+-- embedding_metadata has ON DELETE CASCADE, so only vec0 needs explicit cleanup.
+-- Range: [document_id * 1000, document_id * 1000 + 999]
+CREATE TRIGGER documents_embeddings_ad AFTER DELETE ON documents BEGIN
+  DELETE FROM embeddings
+    WHERE rowid >= old.id * 1000
+      AND rowid < (old.id + 1) * 1000;
+END;
+
+-- Update schema version
+INSERT INTO schema_version (version, applied_at, description)
+VALUES (9, strftime('%s', 'now') * 1000, 'Embeddings vec0 table, metadata, orphan cleanup trigger');