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/query_understanding.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import os
 import json
 import logging
 import uuid
@@ -13,8 +12,6 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_LLM_MODEL = os.environ.get('SEARCH_LLM_MODEL', 'ollama/deepseek-v4-flash')
-
 QUERY_ANALYZE_SYSTEM = (
     "Du bist ein Query-Analyzer fuer ein CRM. "
     "Analysiere die Suchanfrage und gib JSON zurueck: "
@@ -27,33 +24,9 @@ RESULT_AGGREGATE_SYSTEM = (
     '{"summary": str, "facets": {"types": {}, "dates": {}, "people": []}, "suggestions": [str]}'
 )
 
-
-async def _get_api_credentials(
-    db: AsyncSession | None, tenant_id: uuid.UUID | None
-) -> tuple[str | None, str | None, str | None]:
-    """Get API key, base_url and provider_type from the default AI provider in DB.
-
-    Falls back to API_KEY_OLLAMA_CLOUD env var.
-    Returns (api_key, base_url, provider_type).
-    """
-    if db and tenant_id:
-        try:
-            from app.plugins.builtins.ai_assistant.contracts import get_default_provider
-            provider = await get_default_provider(db, tenant_id)
-            if provider and provider.api_key:
-                return provider.api_key, provider.base_url, provider.provider_type
-        except Exception:
-            logger.debug("Failed to get provider from DB, falling back to env")
-    env_key = os.environ.get('API_KEY_OLLAMA_CLOUD', '')
-    return (env_key if env_key else None), None, None
-
-
-def _build_model(model: str, provider_type: str | None) -> str:
-    """Build litellm model string with provider prefix."""
-    if provider_type:
-        model_parts = model.split("/", 1)
-        return f"{provider_type}/{model_parts[-1]}"
-    return model
+# Sensible default model used when no provider-specific model is configured.
+# llm_complete() resolves credentials/model prefix from the central config.
+DEFAULT_LLM_MODEL = "ollama/deepseek-v4-flash"
 
 
 def _fallback_query_analysis(query: str) -> dict[str, Any]:
@@ -74,6 +47,16 @@ def _fallback_aggregate(results: list[dict], query: str) -> dict[str, Any]:
     }
 
 
+def _parse_json_content(content: str) -> dict[str, Any]:
+    """Parse LLM JSON response, stripping markdown code fences if present."""
+    content = content.strip()
+    if content.startswith("```"):
+        content = content.split("\n", 1)[-1] if "\n" in content else content[3:]
+    if content.endswith("```"):
+        content = content[:-3].strip()
+    return json.loads(content)
+
+
 async def llm_analyze_query(
     query: str,
     db: AsyncSession | None = None,
@@ -84,11 +67,8 @@ async def llm_analyze_query(
     Falls back to a simple dict if LLM fails.
     """
     try:
-        api_key, api_base, provider_type = await _get_api_credentials(db, tenant_id)
-        model = _build_model(DEFAULT_LLM_MODEL, provider_type)
-
         result = await llm_complete(
-            model=model,
+            model=DEFAULT_LLM_MODEL,
             messages=[
                 {"role": "system", "content": QUERY_ANALYZE_SYSTEM},
                 {"role": "user", "content": query},
@@ -96,17 +76,10 @@ async def llm_analyze_query(
             temperature=0.1,
             max_tokens=500,
             response_format={"type": "json_object"},
-            api_key=api_key,
-            api_base=api_base,
+            db=db,
+            tenant_id=tenant_id,
         )
-        content = result["content"]
-        # Strip markdown code fences if present
-        content = content.strip()
-        if content.startswith("```"):
-            content = content.split("\n", 1)[-1] if "\n" in content else content[3:]
-        if content.endswith("```"):
-            content = content[:-3].strip()
-        return json.loads(content)
+        return _parse_json_content(result["content"])
     except Exception:
         logger.warning("LLM query analysis failed, using fallback", exc_info=True)
         return _fallback_query_analysis(query)
@@ -125,9 +98,6 @@ async def llm_aggregate_results(
     if not results:
         return _fallback_aggregate(results, query)
     try:
-        api_key, api_base, provider_type = await _get_api_credentials(db, tenant_id)
-        model = _build_model(DEFAULT_LLM_MODEL, provider_type)
-
         # Truncate results to avoid token overflow
         compact = [
             {"entity_type": r.get("entity_type"), "title": r.get("title", "")[:100]}
@@ -136,7 +106,7 @@ async def llm_aggregate_results(
         user_msg = json.dumps({"query": query, "results": compact})
 
         result = await llm_complete(
-            model=model,
+            model=DEFAULT_LLM_MODEL,
             messages=[
                 {"role": "system", "content": RESULT_AGGREGATE_SYSTEM},
                 {"role": "user", "content": user_msg},
@@ -144,17 +114,10 @@ async def llm_aggregate_results(
             temperature=0.1,
             max_tokens=1000,
             response_format={"type": "json_object"},
-            api_key=api_key,
-            api_base=api_base,
+            db=db,
+            tenant_id=tenant_id,
         )
-        content = result["content"]
-        # Strip markdown code fences if present
-        content = content.strip()
-        if content.startswith("```"):
-            content = content.split("\n", 1)[-1] if "\n" in content else content[3:]
-        if content.endswith("```"):
-            content = content[:-3].strip()
-        return json.loads(content)
+        return _parse_json_content(result["content"])
     except Exception:
         logger.warning("LLM result aggregation failed, using fallback", exc_info=True)
         return _fallback_aggregate(results, query)