Repository: mem0ai/mem0
File: mem0/memory/main.py (Python)

Code before:
            new_retrieved_facts = []

        retrieved_old_memory = []
        new_message_embeddings = {}

        async def process_fact_for_search(new_mem_content):
            embeddings = await asyncio.to_thread(self.embedding_model.embed, new_mem_content, "add")
            new_message_embeddings[new_mem_content] = embeddings
            existing_mems = await asyncio.to_thread(
                self.vector_store.search,
                query=new_mem_content,
                vectors=embeddings,
                limit=5,
                filters=filters,  # 'filters' is query_filters_for_inference
            )
            return [{"id": mem.id, "text": mem.payload["data"]} for mem in existing_mems]

        search_tasks = [process_fact_for_search(fact) for fact in new_retrieved_facts]
        search_results_list = await asyncio.gather(*search_tasks)
        for result_group in search_results_list:
            retrieved_old_memory.extend(result_group)

        unique_data = {}
        for item in retrieved_old_memory:
            unique_data[item["id"]] = item
        retrieved_old_memory = list(unique_data.values())
        logging.info(f"Total existing memories: {len(retrieved_old_memory)}")
        temp_uuid_mapping = {}
        for idx, item in enumerate(retrieved_old_memory):
            temp_uuid_mapping[str(idx)] = item["id"]
            retrieved_old_memory[idx]["id"] = str(idx)

        if new_retrieved_facts:
            function_calling_prompt = get_update_memory_messages(
                retrieved_old_memory, new_retrieved_facts, self.config.custom_update_memory_prompt
            )
            try:
                response = await asyncio.to_thread(
                    self.llm.generate_response,
                    messages=[{"role": "user", "content": function_calling_prompt}],
                    response_format={"type": "json_object"},
                )
            except Exception as e:
                logging.error(f"Error in new memory actions response: {e}")
                response = ""
            try:
                response = remove_code_blocks(response)
                new_memories_with_actions = json.loads(response)
            except Exception as e:
                logging.error(f"Invalid JSON response: {e}")
                new_memories_with_actions = {}

Review comment (suggestion):
**Note**: the early return causes a regression, making it skip the capture_event call at the end of the function. This PR restores that behavior and makes it skip the second llm call as #2744 intended.

Code after:
                        "event": "ADD",
                        "actor_id": actor_name if actor_name else None,
                        "role": message_dict["role"],
                    }
                )
            return returned_memories

        parsed_messages = parse_messages(messages)
        if self.config.custom_fact_extraction_prompt:
            system_prompt = self.config.custom_fact_extraction_prompt
            user_prompt = f"Input:\n{parsed_messages}"
        else:
            system_prompt, user_prompt = get_fact_retrieval_messages(parsed_messages)

        response = await asyncio.to_thread(
            self.llm.generate_response,
            messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}],
            response_format={"type": "json_object"},
        )
        try:
            response = remove_code_blocks(response)
            new_retrieved_facts = json.loads(response)["facts"]
        except Exception as e:
            logging.error(f"Error in new_retrieved_facts: {e}")
            new_retrieved_facts = []
        
        if not new_retrieved_facts:
            logger.debug("No new facts retrieved from input. Skipping memory update LLM call.")

        retrieved_old_memory = []
        new_message_embeddings = {}

        async def process_fact_for_search(new_mem_content):
            embeddings = await asyncio.to_thread(self.embedding_model.embed, new_mem_content, "add")
            new_message_embeddings[new_mem_content] = embeddings
            existing_mems = await asyncio.to_thread(
                self.vector_store.search,
                query=new_mem_content,
                vectors=embeddings,
                limit=5,
                filters=effective_filters,  # 'filters' is query_filters_for_inference
            )
            return [{"id": mem.id, "text": mem.payload["data"]} for mem in existing_mems]

        search_tasks = [process_fact_for_search(fact) for fact in new_retrieved_facts]
        search_results_list = await asyncio.gather(*search_tasks)
        for result_group in search_results_list:
            retrieved_old_memory.extend(result_group)

        unique_data = {}
        for item in retrieved_old_memory: