Contributed to the mem0ai/mem0 repository by enhancing the Azure AI Search vector store to support filtering search results by user-defined fields such as user_id, run_id, and agent_id. Refactored both document generation and search query logic in Python to ensure filters are consistently applied, improving the precision and relevance of user-specific queries. Addressed a critical issue with query filter application, resulting in more accurate and targeted search outcomes. Leveraged skills in API integration, backend development, and vector databases to deliver maintainable code that aligns with business needs and establishes a foundation for future field-based filtering and analytics capabilities.