refactor: v2 search and update examples (#3508)

docs/integrations/agno.mdx

@@ -125,7 +125,7 @@ def chat_user(
 
     if user_input:
         # Search for relevant memories
-        memories = client.search(user_input, user_id=user_id, output_format='v1.1')
+        memories = client.search(user_input, user_id=user_id)
         memory_context = "\n".join(f"- {m['memory']}" for m in memories['results'])
 
         # Construct the prompt

docs/integrations/autogen.mdx

@@ -62,7 +62,7 @@ conversation = [
     {"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
 ]
 
-memory_client.add(messages=conversation, user_id=USER_ID, output_format="v1.1")
+memory_client.add(messages=conversation, user_id=USER_ID)
 print("Conversation added to memory.")
 ```
 
@@ -72,7 +72,7 @@ Create a function to get context-aware responses based on user's question and pr
 
 ```python
 def get_context_aware_response(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
+    relevant_memories = memory_client.search(question, user_id=USER_ID)
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
@@ -104,7 +104,7 @@ manager = ConversableAgent(
 )
 
 def escalate_to_manager(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
+    relevant_memories = memory_client.search(question, user_id=USER_ID)
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""

docs/integrations/google-ai-adk.mdx

@@ -50,7 +50,7 @@ mem0 = MemoryClient()
 # Define memory function tools
 def search_memory(query: str, user_id: str) -> dict:
     """Search through past conversations and memories"""
-    memories = mem0.search(query, user_id=user_id, output_format='v1.1')
+    memories = mem0.search(query, user_id=user_id)
     if memories.get('results', []):
         memory_list = memories['results']
         memory_context = "\n".join([f"- {mem['memory']}" for mem in memory_list])
@@ -60,7 +60,7 @@ def search_memory(query: str, user_id: str) -> dict:
 def save_memory(content: str, user_id: str) -> dict:
     """Save important information to memory"""
     try:
-        result = mem0.add([{"role": "user", "content": content}], user_id=user_id, output_format='v1.1')
+        result = mem0.add([{"role": "user", "content": content}], user_id=user_id)
         return {"status": "success", "message": "Information saved to memory", "result": result}
     except Exception as e:
         return {"status": "error", "message": f"Failed to save memory: {str(e)}"}

docs/integrations/langchain.mdx

@@ -65,7 +65,7 @@ Create functions to handle context retrieval, response generation, and addition
 def retrieve_context(query: str, user_id: str) -> List[Dict]:
     """Retrieve relevant context from Mem0"""
     try:
-        memories = mem0.search(query, user_id=user_id, output_format='v1.1')
+        memories = mem0.search(query, user_id=user_id)
         memory_list = memories['results']
         
         serialized_memories = ' '.join([mem["memory"] for mem in memory_list])
@@ -107,7 +107,7 @@ def save_interaction(user_id: str, user_input: str, assistant_response: str):
                 "content": assistant_response
             }
         ]
-        result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+        result = mem0.add(interaction, user_id=user_id)
         print(f"Memory saved successfully: {len(result.get('results', []))} memories added")
     except Exception as e:
         print(f"Error saving interaction: {e}")

docs/integrations/langgraph.mdx

@@ -67,7 +67,7 @@ def chatbot(state: State):
 
     try:
         # Retrieve relevant memories
-        memories = mem0.search(messages[-1].content, user_id=user_id, output_format='v1.1')
+        memories = mem0.search(messages[-1].content, user_id=user_id)
         
         # Handle dict response format
         memory_list = memories['results']
@@ -94,7 +94,7 @@ def chatbot(state: State):
                     "content": response.content
                 }
             ]
-            result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+            result = mem0.add(interaction, user_id=user_id)
             print(f"Memory saved: {len(result.get('results', []))} memories added")
         except Exception as e:
             print(f"Error saving memory: {e}")