From 4c15315ff8b5e04735fde7b00a5732f5f86b5d1f Mon Sep 17 00:00:00 2001 From: ScarbWin Date: Wed, 23 Jul 2025 00:59:03 +0800 Subject: [PATCH] docs: fix search method return value handling in integration and example docs --- docs/examples/ai_companion.mdx | 6 +++--- docs/examples/llamaindex-multiagent-learning-system.mdx | 4 ++-- docs/examples/memory-guided-content-writing.mdx | 2 +- docs/examples/openai-inbuilt-tools.mdx | 2 +- docs/examples/personal-travel-assistant.mdx | 4 ++-- docs/integrations/agentops.mdx | 2 +- docs/integrations/agno.mdx | 2 +- docs/integrations/autogen.mdx | 4 ++-- docs/integrations/elevenlabs.mdx | 2 +- docs/integrations/google-ai-adk.mdx | 4 ++-- docs/integrations/langchain.mdx | 2 +- docs/integrations/langgraph.mdx | 2 +- docs/integrations/livekit.mdx | 4 ++-- docs/integrations/multion.mdx | 6 +++--- docs/integrations/openai-agents-sdk.mdx | 4 ++-- 15 files changed, 25 insertions(+), 25 deletions(-) diff --git a/docs/examples/ai_companion.mdx b/docs/examples/ai_companion.mdx index 56a786c401..ad564d7a25 100644 --- a/docs/examples/ai_companion.mdx +++ b/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.") diff --git a/docs/examples/llamaindex-multiagent-learning-system.mdx b/docs/examples/llamaindex-multiagent-learning-system.mdx index 4bca5efa1a..57cdc0d13a 100644 --- a/docs/examples/llamaindex-multiagent-learning-system.mdx +++ b/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!" diff --git a/docs/examples/memory-guided-content-writing.mdx b/docs/examples/memory-guided-content-writing.mdx index fbb32b9d64..d7a88c3a1f 100644 --- a/docs/examples/memory-guided-content-writing.mdx +++ b/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. diff --git a/docs/examples/openai-inbuilt-tools.mdx b/docs/examples/openai-inbuilt-tools.mdx index 095ff49e85..be96b0b7b3 100644 --- a/docs/examples/openai-inbuilt-tools.mdx +++ b/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}` : ""; }; diff --git a/docs/examples/personal-travel-assistant.mdx b/docs/examples/personal-travel-assistant.mdx index d6714be92b..921d0748e6 100644 --- a/docs/examples/personal-travel-assistant.mdx +++ b/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" diff --git a/docs/integrations/agentops.mdx b/docs/integrations/agentops.mdx index a9df90d76b..53b4017051 100644 --- a/docs/integrations/agentops.mdx +++ b/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") diff --git a/docs/integrations/agno.mdx b/docs/integrations/agno.mdx index f0a1325c5c..2fafc6939a 100644 --- a/docs/integrations/agno.mdx +++ b/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""" diff --git a/docs/integrations/autogen.mdx b/docs/integrations/autogen.mdx index af2b53dc64..cd386383d1 100644 --- a/docs/integrations/autogen.mdx +++ b/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: diff --git a/docs/integrations/elevenlabs.mdx b/docs/integrations/elevenlabs.mdx index 44d55b5c69..adedc11870 100644 --- a/docs/integrations/elevenlabs.mdx +++ b/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: diff --git a/docs/integrations/google-ai-adk.mdx b/docs/integrations/google-ai-adk.mdx index dc547d65b6..f19e3c7a99 100644 --- a/docs/integrations/google-ai-adk.mdx +++ b/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"} diff --git a/docs/integrations/langchain.mdx b/docs/integrations/langchain.mdx index 1ac215181d..10d8e1edea 100644 --- a/docs/integrations/langchain.mdx +++ b/docs/integrations/langchain.mdx @@ -64,7 +64,7 @@ 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""" memories = mem0.search(query, user_id=user_id) - serialized_memories = ' '.join([mem["memory"] for mem in memories]) + serialized_memories = ' '.join([mem["memory"] for mem in memories.get('results', [])]) context = [ { "role": "system", diff --git a/docs/integrations/langgraph.mdx b/docs/integrations/langgraph.mdx index e8f3667ab1..d177a1fb1c 100644 --- a/docs/integrations/langgraph.mdx +++ b/docs/integrations/langgraph.mdx @@ -68,7 +68,7 @@ def chatbot(state: State): memories = mem0.search(messages[-1].content, user_id=user_id) context = "Relevant information from previous conversations:\n" - for memory in memories: + for memory in memories.get('results', []): context += f"- {memory['memory']}\n" system_message = SystemMessage(content=f"""You are a helpful customer support assistant. Use the provided context to personalize your responses and remember user preferences and past interactions. diff --git a/docs/integrations/livekit.mdx b/docs/integrations/livekit.mdx index 0ae4923ac3..bca7c4b77c 100644 --- a/docs/integrations/livekit.mdx +++ b/docs/integrations/livekit.mdx @@ -119,9 +119,9 @@ class MemoryEnabledAgent(Agent): user_id=RAG_USER_ID, ) logger.info(f"mem0_client.search returned: {search_results}") - if search_results and isinstance(search_results, list): + if search_results and search_results.get('results', []): context_parts = [] - for result in search_results: + for result in search_results.get('results', []): paragraph = result.get("memory") or result.get("text") if paragraph: source = "mem0 Memories" diff --git a/docs/integrations/multion.mdx b/docs/integrations/multion.mdx index 853e90ebc8..c1fb08fdfb 100644 --- a/docs/integrations/multion.mdx +++ b/docs/integrations/multion.mdx @@ -71,7 +71,7 @@ Define search command and retrieve relevant memories from Mem0. command = "Find papers on arxiv that I should read based on my interests." relevant_memories = memory.search(command, user_id=USER_ID, limit=3) -relevant_memories_text = '\n'.join(mem['text'] for mem in relevant_memories) +relevant_memories_text = '\n'.join(mem['memory'] for mem in relevant_memories['results']) print(f"Relevant memories:") print(relevant_memories_text) ``` @@ -98,9 +98,9 @@ def get_travel_info(question, use_memory=True): if use_memory: previous_memories = memory_client.search(question, user_id=USER_ID) relevant_memories_text = "" - if previous_memories: + if previous_memories and previous_memories.get('results'): print("Using previous memories to enhance the search...") - relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories) + relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results']) command = "Find travel information based on my interests:" prompt = f"{command}\n Question: {question} \n My preferences: {relevant_memories_text}" diff --git a/docs/integrations/openai-agents-sdk.mdx b/docs/integrations/openai-agents-sdk.mdx index 592d614982..ca942b74ab 100644 --- a/docs/integrations/openai-agents-sdk.mdx +++ b/docs/integrations/openai-agents-sdk.mdx @@ -47,8 +47,8 @@ mem0 = MemoryClient() def search_memory(query: str, user_id: str) -> str: """Search through past conversations and memories""" memories = mem0.search(query, user_id=user_id, limit=3) - if memories: - return "\n".join([f"- {mem['memory']}" for mem in memories]) + if memories and memories.get('results'): + return "\n".join([f"- {mem['memory']}" for mem in memories['results']]) return "No relevant memories found." @function_tool