refactor: v2 search and update examples (#3508)

docs/examples/email_processing.mdx

@@ -112,7 +112,6 @@ class EmailProcessor:
             query=query,
             user_id=user_id,
             categories=["email"],
-            output_format="v1.1",
             version="v2"
         )
         
@@ -136,8 +135,7 @@ class EmailProcessor:
         
         thread = self.client.get_all(
             version="v2",
-            filters=filters,
-            output_format="v1.1"
+            filters=filters
         )
         
         return thread

docs/examples/mem0-agentic-tool.mdx

@@ -112,7 +112,7 @@ async def search_memory(
         query: The search query.
     """
     user_id = context.context.user_id or "default_user"
-    memories = await client.search(query, user_id=user_id, output_format="v1.1")
+    memories = await client.search(query, user_id=user_id)
     results = '\n'.join([result["memory"] for result in memories["results"]])
     return str(results)
 ```
@@ -126,7 +126,7 @@ async def get_all_memory(
 ) -> str:
     """Retrieve all memories from Mem0"""
     user_id = context.context.user_id or "default_user"
-    memories = await client.get_all(user_id=user_id, output_format="v1.1")
+    memories = await client.get_all(user_id=user_id)
     results = '\n'.join([result["memory"] for result in memories["results"]])
     return str(results)
 ```

docs/examples/mem0-google-adk-healthcare-assistant.mdx

@@ -80,8 +80,7 @@ def retrieve_patient_info(query: str) -> dict:
         query,
         user_id=USER_ID,
         limit=5,
-        threshold=0.7,  # Higher threshold for more relevant results
-        output_format="v1.1"
+        threshold=0.7  # Higher threshold for more relevant results
     )
 
     # Format and return the results

docs/examples/personalized-search-tavily-mem0.mdx

@@ -76,7 +76,7 @@ def setup_user_history(user_id):
     ]
 
     for conversation in conversations:
-        mem0_client.add(conversation, user_id=user_id, output_format="v1.1")
+        mem0_client.add(conversation, user_id=user_id)
 ```
 This gives the agent a baseline understanding of the user’s lifestyle and needs.