Integrate Semantic Kernel Memory with Vector Stores

This skill provides five working Python examples that demonstrate how to integrate memory and vector store capabilities into Semantic Kernel applications. It covers both simple memory operations and complex memory patterns, including data model definitions, Pandas dataframe integration, and utility functions for memory management.

Use this skill when you need to add persistent memory or vector search capabilities to your Semantic Kernel-based AI assistant, when you want to store and retrieve contextual information across conversations, or when you need to integrate structured data from Pandas dataframes into your AI workflows. Use it when you're building applications that require semantic search over stored information or when you need concrete implementation patterns for memory operations.

Do not use this if you need production-ready memory solutions without modification, as these are sample implementations meant for learning and adaptation. Do not use this if you're working with languages other than Python, as all examples are Python-specific.

You provide your Semantic Kernel Python environment and specific memory requirements. The skill delivers five example files: simple_memory.py for basic memory operations, complex_memory.py for advanced patterns, data_models.py for memory data structure definitions, memory_with_pandas.py for dataframe integration, and utils.py for shared utility functions. Each file contains executable code demonstrating different aspects of memory integration.

This skill is designed for Python developers building AI applications with Semantic Kernel who need to implement memory and vector store functionality. It serves developers who learn best from working code examples rather than abstract documentation, and those who need to understand the progression from simple to complex memory patterns. It's particularly valuable for engineers integrating structured data sources like Pandas dataframes into their AI assistants, and for teams evaluating different memory architecture approaches before committing to a production implementation.