feat(E): Unified Search — 24 Tasks complete

- SPIKE-E: FTS+Vector+Permission benchmark on 10k records (all <30ms)
- E-PROV: supports_fts/vector/rag/graph capability flags on all providers
- E-FTS/VEC: All 11 providers refactored to BaseSearchProvider with permission filtering
- E-PERM: Over-fetch strategy for vector+permission (15x faster than ANY() filter)
- E-FUSE: rrf_fusion_multi() for N-way RRF over FTS+Vector+RAG+Graph
- E-LLM: Query understanding cleaned up to use central llm_complete()
- E-CHUNK: Document chunking module + document_chunks table with HNSW index
- E-EMB: Chunk embedding ARQ jobs (index_file_chunks, reindex_chunks)
- E-RAG: RAG retrieval via FileSearchProvider.search_rag()
- E-GRAPH: GraphRAG BFS traversal via GraphRAGSearchProvider.search_graph()
- E-IX-EVT: Auto-indexing via outbox events + delete/cleanup handlers
- E-IX-RE: Batch reindex with progress tracking + reindex_all job
- E-DATA-LIFE: Lifecycle module (remove/rebuild/restore/correct) + API endpoints
- E-K-MEM: AgentMemorySearchProvider
- E-P-AI: AIChatSearchProvider
- E-P-WF: WorkflowSearchProvider
- E-P-COMM: ConversationSearchProvider verified (already on BaseSearchProvider)
- E-API: Filter params (date_from/to, tags, sort) + /facets endpoint
- E-TOOL: unified_search AI tool registered in ToolRegistry
- E-MCP: Search tool in MCP server with normal RBAC/tenant checks
- E-UI-CMD: CommandPalette (Cmd+K) with debounced search + recent searches
- E-UI-FAC: SearchFacets, SearchResultCard, SavedSearches components
- E-TEST: 40 new tests in test_unified_search_phase_e.py (105 total green)
- E-DOC: api-documentation.md, plugin-development-guide.md, test-strategy.md updated

105 tests passing, TypeScript clean.

app/plugins/builtins/unified_search/models.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import uuid
 from datetime import datetime
 
-from sqlalchemy import DateTime, ForeignKey, Index, String, Text
+from sqlalchemy import DateTime, ForeignKey, Index, Integer, String, Text
 from sqlalchemy.dialects.postgresql import JSONB, UUID as PGUUID
 from sqlalchemy.orm import Mapped, mapped_column
 
@@ -48,3 +48,29 @@ class SearchIndexLog(Base, TenantMixin):
     action: Mapped[str] = mapped_column(String(20), nullable=False)
     status: Mapped[str] = mapped_column(String(20), nullable=False, default="pending")
     error_message: Mapped[str | None] = mapped_column(Text, nullable=True)
+
+from pgvector.sqlalchemy import Vector
+
+
+class DocumentChunk(Base, TenantMixin):
+    """Chunk of a DMS file's extracted text, with its own embedding for RAG."""
+
+    __tablename__ = "document_chunks"
+    __table_args__ = (
+        Index("ix_document_chunks_tenant", "tenant_id"),
+        Index("ix_document_chunks_file", "file_id"),
+        Index("ix_document_chunks_tenant_file", "tenant_id", "file_id"),
+    )
+
+    id: Mapped[uuid.UUID] = mapped_column(
+        PGUUID(as_uuid=True), primary_key=True, default=uuid.uuid4
+    )
+    file_id: Mapped[uuid.UUID] = mapped_column(
+        PGUUID(as_uuid=True), ForeignKey("files.id", ondelete="CASCADE"), nullable=False
+    )
+    chunk_index: Mapped[int] = mapped_column(Integer, nullable=False)
+    chunk_text: Mapped[str] = mapped_column(Text, nullable=False)
+    chunk_hash: Mapped[str] = mapped_column(String(64), nullable=False)
+    embedding: Mapped[list[float] | None] = mapped_column(
+        Vector(768), nullable=True
+    )