Manage and Summarize Chat History
Semantic Kernel chat history management with serialization, CosmosDB storage, summarization, truncation, and reducer capabilities for conversational AI
Why it matters
Efficiently manage and summarize conversational data to maintain context and reduce storage needs. This asset helps in processing chat logs for better analysis and retrieval.
Outcomes
What it gets done
Summarize lengthy chat conversations.
Serialize chat history for storage.
Store chat history in Cosmos DB.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/sk-concept-chathistory | bash Overview
Semantic Kernel - Chat History
This skill manages chat history in Semantic Kernel applications through serialization, database storage, summarization, truncation, and reducer functions. It includes two Python examples: one for serializing chat history data and another for storing conversations in CosmosDB. Use this when building conversational AI that needs to maintain context across sessions, persist chat data to CosmosDB, or manage memory by summarizing or truncating long conversations. It fits applications requiring durable conversation storage and retrieval.
What it does
This skill provides chat history management capabilities for Semantic Kernel applications, enabling developers to serialize, store, summarize, truncate, and reduce conversational data. It includes practical implementations for persisting chat history in CosmosDB and serializing conversation data for long-term storage or transfer.
When to use - and when NOT to
Use this skill when building conversational AI applications that need to maintain context across sessions, persist chat data to external databases, or manage memory constraints by summarizing or truncating long conversations. It is ideal when you need to store chat history in CosmosDB for scalability and retrieval, or when you must serialize conversation data for backup, analysis, or migration purposes.
Do not use this skill if your application requires only in-memory, ephemeral conversations with no persistence needs. Avoid it when you need real-time streaming chat without any history management overhead.
Inputs and outputs
Users provide chat history objects from Semantic Kernel conversations that need to be managed. For serialization, you input chat history data and receive serialized output suitable for storage or transmission. For CosmosDB storage, you provide chat history along with database connection details and receive confirmation of successful persistence with retrieval capabilities.
Integrations
CosmosDB: The skill integrates with Azure CosmosDB for scalable, distributed storage of chat history data, enabling persistent conversation management across sessions and users.
Who it's for
This skill is designed for Python developers building conversational AI applications with Semantic Kernel who need robust chat history management. It serves backend engineers implementing chatbots, virtual assistants, or customer service automation where conversation context must persist beyond single sessions. Data engineers who need to archive, analyze, or migrate conversational data will find the serialization capabilities essential for their pipelines.
Source README
Chat history management: summarization, truncation, reducers
Examples (2 files):
serialize_chat_history.pystore_chat_history_in_cosmosdb.py
Discussion
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