Manage and Summarize Chat History

This skill provides chat history management capabilities for Semantic Kernel applications in Python. It enables developers to handle conversation context through summarization, truncation, and reducer functions, with practical implementations for serializing chat history and storing it in Azure Cosmos DB.

Use this skill when you need to manage growing conversation histories in chatbot or AI assistant applications, prevent token limits from being exceeded in LLM calls, or persist chat sessions across user interactions. It is particularly valuable when building production chat applications that require durable storage or when conversation context needs to be condensed without losing critical information.

Do not use this skill if your application involves only single-turn interactions with no conversation state, or if you are working with a different SDK or language runtime outside the Python Semantic Kernel ecosystem.

Users provide chat history objects containing conversation turns between users and assistants. The skill accepts these history objects and applies management operations such as truncation (removing older messages), summarization (condensing conversation context), or reduction (applying custom logic to manage history size).

The outputs include managed chat history objects optimized for token usage, serialized representations of chat history suitable for storage or transmission, and stored chat sessions in Azure Cosmos DB when using the database integration example.

The skill integrates with Azure Cosmos DB for persistent chat history storage, as demonstrated in the store_chat_history_in_cosmosdb.py example. It works within the Semantic Kernel Python framework and supports serialization workflows through the serialize_chat_history.py example.

This skill is designed for Python developers building conversational AI applications with Semantic Kernel who need production-grade chat history management. It serves backend engineers implementing chatbots that require session persistence, AI application developers optimizing token usage in multi-turn conversations, and teams building enterprise chat solutions that need durable storage in Azure Cosmos DB. The skill is particularly relevant for developers already invested in the Microsoft Semantic Kernel ecosystem who need ready-to-use patterns for common chat history challenges.