docs: new algorithm migration guides + memory evaluation (#4811)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
Co-authored-by: Saket Aryan <saketaryan2002@gmail.com>

docs/vibecoding.mdx

@@ -88,19 +88,19 @@ Sub-50ms retrieval. Dual storage: vector embeddings + graph databases.
   from mem0 import MemoryClient
   client = MemoryClient(api_key="m0-xxx")
   client.add("I prefer dark mode and use VS Code.", user_id="user1")
-  results = client.search("What editor do they use?", user_id="user1")
+  results = client.search("What editor do they use?", filters={"user_id": "user1"})
 
 **Quick Usage (JavaScript Platform):**
   import MemoryClient from 'mem0ai';
   const client = new MemoryClient({ apiKey: 'm0-xxx' });
-  await client.add([{ role: "user", content: "I prefer dark mode." }], { user_id: "user1" });
-  const results = await client.search("What editor?", { user_id: "user1" });
+  await client.add([{ role: "user", content: "I prefer dark mode." }], { userId: "user1" });
+  const results = await client.search("What editor?", { filters: { userId: "user1" } });
 
 **Quick Usage (Python Open Source):**
   from mem0 import Memory
   m = Memory()
   m.add("I prefer dark mode and use VS Code.", user_id="user1")
-  results = m.search("What editor do they use?", user_id="user1")
+  results = m.search("What editor do they use?", filters={"user_id": "user1"})
 
 Help me integrate Mem0 into my project. Start by asking what I'm building,
 what language/framework I'm using, and whether I want managed or self-hosted.