fix(ollama): stop infinite subprocess spawning from useEffect re-render loop (#1716)

* fix(graphiti): migrate graphiti_memory imports to canonical paths

The `graphiti_memory.py` shim was removed during the backend refactor
(commit 11fcdf42) but consumers were never updated. This caused all
`from graphiti_memory import ...` to fail silently (caught by
try/except ImportError), preventing the Graphiti memory system from
initializing for any project.

Migrate the 3 remaining import sites to use the canonical module path
`integrations.graphiti.memory` instead of the deleted shim.

Closes #1220

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

* style: combine docstring import example into single line

Address Gemini Code Assist review suggestion to merge two import
examples from the same module into one line.

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

* fix(ollama): stop infinite subprocess spawning from useEffect re-render loop

OllamaModelSelector's checkInstalledModels was a plain async function used
as a useEffect dependency. Since the function reference changed on every
render, the effect fired continuously — each iteration spawning 2-3 Python
subprocesses via executeOllamaDetector, causing hundreds of processes.

- Wrap checkInstalledModels in useCallback with [baseUrl] dependency so
  the useEffect only re-runs when baseUrl actually changes
- Add a 2s deduplication cache to executeOllamaDetector as a safety net:
  identical command+baseUrl calls within the TTL return the same promise
  instead of spawning a new subprocess

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

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Andy <119136210+AndyMik90@users.noreply.github.com>

apps/frontend/src/main/ipc-handlers/memory-handlers.ts

@@ -201,6 +201,10 @@ function getOllamaInstallCommand(): string {
  * Spawns a subprocess to run Ollama detection/management commands with a 10-second timeout.
  * Used to check Ollama status, list models, and manage downloads.
  *
+ * Includes deduplication: identical command+baseUrl requests within 2s return the cached
+ * result/promise instead of spawning a new subprocess. This prevents runaway subprocess
+ * spawning from React re-render loops.
+ *
  * Supported commands:
  * - 'check-status': Verify Ollama service is running
  * - 'list-models': Get all available models
@@ -212,9 +216,43 @@ function getOllamaInstallCommand(): string {
  * @param {string} [baseUrl] - Optional Ollama API base URL (defaults to http://localhost:11434)
  * @returns {Promise<{success, data?, error?}>} Result object with success flag and data/error
  */
+// Deduplication cache to prevent rapid-fire subprocess spawning (e.g., from React re-render loops)
+const ollamaDetectorCache = new Map<string, { promise: Promise<{ success: boolean; data?: unknown; error?: string }>; timestamp: number }>();
+const OLLAMA_CACHE_TTL_MS = 2000; // Cache results for 2 seconds
+
 async function executeOllamaDetector(
   command: string,
   baseUrl?: string
+): Promise<{ success: boolean; data?: unknown; error?: string }> {
+  // Deduplication: return cached promise for identical requests within TTL
+  const cacheKey = `${command}:${baseUrl || 'default'}`;
+  const cached = ollamaDetectorCache.get(cacheKey);
+  if (cached && Date.now() - cached.timestamp < OLLAMA_CACHE_TTL_MS) {
+    if (process.env.DEBUG) {
+      console.log('[OllamaDetector] Returning cached result for:', command);
+    }
+    return cached.promise;
+  }
+
+  const promise = executeOllamaDetectorImpl(command, baseUrl);
+  ollamaDetectorCache.set(cacheKey, { promise, timestamp: Date.now() });
+
+  // Clean up cache entry after TTL
+  promise.finally(() => {
+    setTimeout(() => {
+      const entry = ollamaDetectorCache.get(cacheKey);
+      if (entry && entry.promise === promise) {
+        ollamaDetectorCache.delete(cacheKey);
+      }
+    }, OLLAMA_CACHE_TTL_MS);
+  });
+
+  return promise;
+}
+
+async function executeOllamaDetectorImpl(
+  command: string,
+  baseUrl?: string
 ): Promise<{ success: boolean; data?: unknown; error?: string }> {
   // Use configured Python path (venv if ready, otherwise bundled/system)
   // Note: ollama_model_detector.py doesn't require dotenv, but using venv is safer

apps/frontend/src/renderer/components/onboarding/OllamaModelSelector.tsx

@@ -1,4 +1,4 @@
-import { useState, useEffect, useRef } from 'react';
+import { useState, useEffect, useRef, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
   Check,
@@ -133,7 +133,7 @@ export function OllamaModelSelector({
    * @param {AbortSignal} [abortSignal] - Optional abort signal to cancel the request
    * @returns {Promise<void>}
    */
-  const checkInstalledModels = async (abortSignal?: AbortSignal) => {
+  const checkInstalledModels = useCallback(async (abortSignal?: AbortSignal) => {
     setIsLoading(true);
     setError(null);
     setOllamaState('checking');
@@ -217,7 +217,7 @@ export function OllamaModelSelector({
         setIsLoading(false);
       }
     }
-  };
+  }, [baseUrl]);
 
   /**
    * Install Ollama by opening terminal with the official install command.