From fd1f31ee76fc29384b8fbb39667beaa876868897 Mon Sep 17 00:00:00 2001
From: Victor Giers <mail@victorgiers.com>
Date: Mon, 15 Jun 2026 15:50:45 +0200
Subject: [PATCH] Refactor knowledge search handler to use multiprocessing for
 isolated context retrieval and improved stability.

---
 backend/agent/tools/knowledge.py | 54 +++++++++++++++++++++++++++-----
 1 file changed, 47 insertions(+), 7 deletions(-)

diff --git a/backend/agent/tools/knowledge.py b/backend/agent/tools/knowledge.py
index 6a61fb7..4fc1c02 100644
--- a/backend/agent/tools/knowledge.py
+++ b/backend/agent/tools/knowledge.py
@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import asyncio
+import multiprocessing
+import time
 from typing import Any, Dict
 
 from ...local_rag import LibraryContextRequest, library_context
@@ -22,13 +24,51 @@ def _knowledge_source(source: Dict[str, Any], library_slug: str) -> Dict[str, An
     }
 
 
-async def knowledge_search_handler(arguments: Dict[str, Any], _context: ToolExecutionContext) -> Dict[str, Any]:
-    request = LibraryContextRequest(
-        prompt=arguments["prompt"],
-        top_k=arguments.get("top_k", 5),
-        embed_model=arguments.get("embedding_model"),
-    )
-    payload = await asyncio.to_thread(library_context, arguments["library_slug"], request)
+def _knowledge_search_worker(connection, arguments: Dict[str, Any]) -> None:
+    try:
+        request = LibraryContextRequest(
+            prompt=arguments["prompt"],
+            top_k=arguments.get("top_k", 5),
+            embed_model=arguments.get("embedding_model"),
+        )
+        connection.send(("ok", library_context(arguments["library_slug"], request)))
+    except BaseException as exc:
+        detail = getattr(exc, "detail", None) or str(exc)
+        connection.send(("error", f"{type(exc).__name__}: {detail}"))
+    finally:
+        connection.close()
+
+
+async def _isolated_library_context(arguments: Dict[str, Any], context: ToolExecutionContext) -> Dict[str, Any]:
+    process_context = multiprocessing.get_context("spawn")
+    parent_connection, child_connection = process_context.Pipe(duplex=False)
+    process = process_context.Process(target=_knowledge_search_worker, args=(child_connection, arguments), daemon=True)
+    process.start()
+    child_connection.close()
+    deadline = time.monotonic() + 170
+    try:
+        while True:
+            if context.cancellation_event.is_set():
+                raise asyncio.CancelledError()
+            if parent_connection.poll():
+                status, value = parent_connection.recv()
+                if status == "ok":
+                    return value
+                raise RuntimeError(value)
+            if not process.is_alive():
+                raise RuntimeError(f"Local retrieval process exited unexpectedly (exit code {process.exitcode}).")
+            if time.monotonic() >= deadline:
+                raise TimeoutError("Local retrieval exceeded its process timeout.")
+            await asyncio.sleep(0.05)
+    finally:
+        parent_connection.close()
+        if process.is_alive():
+            process.terminate()
+        process.join(timeout=2)
+
+
+async def knowledge_search_handler(arguments: Dict[str, Any], context: ToolExecutionContext) -> Dict[str, Any]:
+    payload = await _isolated_library_context(arguments, context)
     raw_hits = (payload.get("result") or {}).get("sources") or []
     budget = int(arguments.get("context_character_budget") or 12_000)
     context_block = str(payload.get("context_block") or "")[:budget]