refactor(arch): modular package structure — split monolithic app.py into config/db/auth/memory/search/rag/gpu + routers/

- config.py: all constants, env vars, limits, skill registry, profiles
- db.py: schema init, connection factory, skill state helpers
- security.py: PIN hashing, audit logging, rate limiting, CSRF, request helpers
- auth.py: session management, PIN verify, auth routes
- memory.py: FTS5 CRUD + remember/forget command processing
- search.py: SearXNG integration, perplexity scoring, refusal/hedge detection
- gpu.py: rocm-smi stats
- rag.py: Qdrant vector search + system prompt assembly
- routers/: conversations, memories, models, presets, profile, settings, skills, chat, search
- app.py: slim entry point, middleware, router registration only

Bumps to v1.9.0

routers/search_route.py

@@ -0,0 +1,108 @@
+"""JarvisChat routers - /api/search explicit search endpoint."""
+import json
+import logging
+import uuid
+from datetime import datetime, timezone
+
+import httpx
+from fastapi import APIRouter, HTTPException, Request
+from fastapi.responses import StreamingResponse
+
+from config import DEFAULT_MODEL, LLAMA_SERVER_BASE, MAX_SEARCH_QUERY_CHARS
+from db import get_db
+from search import query_searxng, format_search_results
+from routers.chat import parse_llama_stream_chunk
+from security import read_json_body, log_incident, BODY_LIMIT_CHAT_BYTES
+
+log = logging.getLogger("jarvischat")
+router = APIRouter()
+
+
+@router.post("/api/search")
+async def explicit_search(request: Request):
+    body = await read_json_body(request, BODY_LIMIT_CHAT_BYTES)
+    query = body.get("query", "").strip()
+    if len(query) > MAX_SEARCH_QUERY_CHARS:
+        raise HTTPException(status_code=413, detail="Search query is too long")
+    conv_id = body.get("conversation_id")
+    model = body.get("model", DEFAULT_MODEL)
+
+    if not query:
+        raise HTTPException(status_code=400, detail="Empty query")
+
+    db = get_db()
+    now = datetime.now(timezone.utc).isoformat()
+
+    if not conv_id:
+        conv_id = str(uuid.uuid4())
+        title = f"🔍 {query[:70]}..." if len(query) > 70 else f"🔍 {query}"
+        db.execute("INSERT INTO conversations (id, title, model, created_at, updated_at) VALUES (?, ?, ?, ?, ?)",
+                   (conv_id, title, model, now, now))
+    else:
+        db.execute("UPDATE conversations SET updated_at = ? WHERE id = ?", (now, conv_id))
+
+    db.execute("INSERT INTO messages (conversation_id, role, content, created_at) VALUES (?, ?, ?, ?)",
+               (conv_id, "user", f"🔍 {query}", now))
+    db.commit()
+    db.close()
+
+    async def stream_search():
+        yield f"data: {json.dumps({'conversation_id': conv_id, 'searching': True})}\n\n"
+
+        results = await query_searxng(query, max_results=5)
+
+        if not results:
+            error_msg = "No search results found."
+            yield f"data: {json.dumps({'token': error_msg, 'conversation_id': conv_id})}\n\n"
+            db2 = get_db()
+            db2.execute("INSERT INTO messages (conversation_id, role, content, created_at) VALUES (?, ?, ?, ?)",
+                        (conv_id, "assistant", error_msg, datetime.now(timezone.utc).isoformat()))
+            db2.commit()
+            db2.close()
+            yield f"data: {json.dumps({'done': True, 'conversation_id': conv_id})}\n\n"
+            return
+
+        yield f"data: {json.dumps({'search_results': len(results), 'conversation_id': conv_id})}\n\n"
+
+        search_context = format_search_results(results)
+        messages = [
+            {"role": "system", "content": f"You have access to current web data. Answer directly using ONLY the data below. Be concise. No apologies. No disclaimers.\n\n{search_context}"},
+            {"role": "user", "content": query},
+        ]
+
+        full_response = []
+        async with httpx.AsyncClient() as client:
+            try:
+                async with client.stream(
+                    "POST", f"{LLAMA_SERVER_BASE}/v1/chat/completions",
+                    json={"model": model, "messages": messages, "stream": True},
+                    timeout=httpx.Timeout(300.0, connect=10.0),
+                ) as resp:
+                    async for line in resp.aiter_lines():
+                        if line.strip():
+                            token, done, _ = parse_llama_stream_chunk(line)
+                            if token:
+                                full_response.append(token)
+                                yield f"data: {json.dumps({'token': token, 'conversation_id': conv_id})}\n\n"
+                            if done:
+                                break
+            except Exception as e:
+                incident_key = log_incident("search_summarization_stream",
+                                            message="Stream failure during explicit search summarization",
+                                            request=request, exc=e)
+                yield f"data: {json.dumps({'error': 'Search summarization could not complete right now.', 'error_key': incident_key})}\n\n"
+                return
+
+        summary = "".join(full_response)
+        saved_msg = f"{summary}\n\n---\n*🔍 Web search results*"
+
+        db2 = get_db()
+        db2.execute("INSERT INTO messages (conversation_id, role, content, created_at) VALUES (?, ?, ?, ?)",
+                    (conv_id, "assistant", saved_msg, datetime.now(timezone.utc).isoformat()))
+        db2.commit()
+        db2.close()
+
+        yield f"data: {json.dumps({'raw_results': results, 'conversation_id': conv_id})}\n\n"
+        yield f"data: {json.dumps({'done': True, 'conversation_id': conv_id, 'searched': True})}\n\n"
+
+    return StreamingResponse(stream_search(), media_type="text/event-stream")