Embodied agents have successfully leveraged large language models (LLMs) to better transform human instructions and images into executable task plans. Furthermore, memories of agents can be leveraged to achieve continual self-learning and optimization. However, vector data quality problems emerge in memories when they are projected into vector space, especially in discerning contextually similar but semantically conflicting sentences and highly similar images. This is particularly detrimental to embodied AI as it potentially distorts the robot’s actions. To address this challenge, we propose Conflict Detection Rules (CDRs) to identify and manage data quality issues in vector knowledge bases, which assist in correcting the index structure and further improving the answer quality. Experimental results show that planners with CDRs exceed the basic LLM planner by 15.25% and 14.25% in grammatical accuracy (GA) and interpretation accuracy (IA) on average, respectively. Moreover, the entire workflow has been successfully integrated into various scenarios, demonstrating its practical applicability and robustness in the real world.