docs: fix search method return value handling in integration and example docs (#3208)

Author: HScarb

docs/examples/ai_companion.mdx

@@ -87,8 +87,8 @@ class Companion:
 
         previous_memories = self.memory.search(question, user_id=user_id_to_use)
         relevant_memories_text = ""
-        if previous_memories:
-            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
+        if previous_memories and previous_memories.get('results'):
+            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results'])
 
         prompt = f"User input: {question}\nPrevious {check_answer} memories: {relevant_memories_text}"
 
@@ -146,7 +146,7 @@ def print_memories(user_id, label):
     memories = ai_companion.get_memories(user_id=user_id)
     if memories:
         for m in memories:
-            print(f"- {m['text']}")
+            print(f"- {m['memory']}")
     else:
         print("No memories found.")
 

docs/examples/llamaindex-multiagent-learning-system.mdx

@@ -213,8 +213,8 @@ class MultiAgentLearningSystem:
                 query="learning machine learning"
             )
 
-            if memories and len(memories):
-                history = "\n".join(f"- {m['memory']}" for m in memories)
+            if memories and memories.get('results'):
+                history = "\n".join(f"- {m['memory']}" for m in memories['results'])
                 return history
             else:
                 return "No learning history found yet. Let's start building your profile!"

docs/examples/memory-guided-content-writing.mdx

@@ -86,7 +86,7 @@ def apply_writing_style(original_content):
         print("No preferences found.")
         return None
 
-    preferences = "\n".join(r["memory"] for r in results)
+    preferences = "\n".join(r["memory"] for r in results.get('results', []))
 
     system_prompt = f"""
 You are a writing assistant.

docs/examples/openai-inbuilt-tools.mdx

@@ -225,7 +225,7 @@ async function addSampleMemories() {
 
 const getMemoryString = (memories) => {
     const MEMORY_STRING_PREFIX = "These are the memories I have stored. Give more weightage to the question by users and try to answer that first. You have to modify your answer based on the memories I have provided. If the memories are irrelevant you can ignore them. Also don't reply to this section of the prompt, or the memories, they are only for your reference. The MEMORIES of the USER are: \n\n";
-    const memoryString = memories.map((mem) => `${mem.memory}`).join("\n") ?? "";
+    const memoryString = (memories?.results || memories).map((mem) => `${mem.memory}`).join("\n") ?? "";
     return memoryString.length > 0 ? `${MEMORY_STRING_PREFIX}${memoryString}` : "";
 };
 

docs/examples/personal-travel-assistant.mdx

@@ -155,11 +155,11 @@ class PersonalTravelAssistant:
 
     def get_memories(self, user_id):
         memories = self.memory.get_all(user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories.get('results', [])]
 
     def search_memories(self, query, user_id):
         memories = self.memory.search(query, user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories.get('results', [])]
 
 # Usage example
 user_id = "traveler_123"

docs/integrations/agentops.mdx

@@ -104,7 +104,7 @@ def demonstrate_sync_memory(local_config, sample_messages, sample_preferences, u
             results = memory.search(query, user_id=user_id)
 
             if results and "results" in results:
-                for j, result in enumerate(results): 
+                for j, result in enumerate(results['results']): 
                     print(f"Result {j+1}: {result.get('memory', 'N/A')}")
             else:
                 print("No results found")

docs/integrations/agno.mdx

@@ -128,7 +128,7 @@ def chat_user(
     if user_input:
         # Search for relevant memories
         memories = client.search(user_input, user_id=user_id)
-        memory_context = "\n".join(f"- {m['memory']}" for m in memories)
+        memory_context = "\n".join(f"- {m['memory']}" for m in memories.get('results', []))
 
         # Construct the prompt
         prompt = f"""

docs/integrations/autogen.mdx

@@ -68,7 +68,7 @@ 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)
-    context = "\n".join([m["memory"] for m in relevant_memories])
+    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
     Previous interactions:
@@ -100,7 +100,7 @@ manager = ConversableAgent(
 
 def escalate_to_manager(question):
     relevant_memories = memory_client.search(question, user_id=USER_ID)
-    context = "\n".join([m["memory"] for m in relevant_memories])
+    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""
     Context from previous interactions:

docs/integrations/elevenlabs.mdx

@@ -152,7 +152,7 @@ Define the two key memory functions that will be registered as tools:
         )
 
         # Extract and join the memory texts
-        memories = ' '.join([result["memory"] for result in results])
+        memories = ' '.join([result["memory"] for result in results.get('results', [])])
         print("[ Memories ]", memories)
 
         if memories:

docs/integrations/google-ai-adk.mdx

@@ -49,8 +49,8 @@ mem0 = MemoryClient()
 def search_memory(query: str, user_id: str) -> dict:
     """Search through past conversations and memories"""
     memories = mem0.search(query, user_id=user_id)
-    if memories:
-        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories])
+    if memories.get('results', []):
+        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories.get('results', [])])
         return {"status": "success", "memories": memory_context}
     return {"status": "no_memories", "message": "No relevant memories found"}