SpringTutorials
    Spring AI Tutorials
    Tutorial 04

    Memory Persistence and Conversational State

    Build stateful AI conversations with chat memory, JDBC persistence, and token management strategies in Spring AI

    What You'll Learn

    6 sections covering stateful AI conversations

    Understanding AI Memory Fundamentals

    Introducing Chat Memory in Spring AI

    Memory Advisors in Spring AI

    From Stateless to Stateful Conversations

    Persistent Chat Memory

    Token Management and Context Control

    1
    Understanding AI Memory Fundamentals

    1.1

    Why AI Models Are Stateless by Design

    Understand the architectural reasons behind LLMs being inherently stateless and processing each request independently.

    1.2

    What "Memory" Really Means in LLM-Based Applications

    Learn how memory is simulated by including conversation history in prompts rather than true model memory.

    Coming Soon

    Detailed content with code examples and best practices is being prepared for this section.

    2
    Introducing Chat Memory in Spring AI

    2.1

    Making Conversations Stateful with ChatMemory

    Discover Spring AI's ChatMemory abstraction and how it maintains conversation context across requests.

    2.2

    How Spring AI Manages Conversation History

    Explore the internal workings of conversation history storage and retrieval in Spring AI.

    Coming Soon

    Detailed content with code examples and best practices is being prepared for this section.

    3
    Memory Advisors in Spring AI

    3.1

    Role of Memory Advisors in AI Workflows

    Understand how Memory Advisors intercept and enhance AI requests with conversation context.

    3.2

    How Spring AI Injects Context Automatically

    Learn the mechanism by which Spring AI automatically includes relevant history in each prompt.

    Coming Soon

    Detailed content with code examples and best practices is being prepared for this section.

    4
    From Stateless to Stateful Conversations

    4.1

    Designing Stateful Chat Experiences

    Best practices for designing conversational AI experiences that feel natural and context-aware.

    4.2

    Conversation Flow with and without Chat Memory

    Compare conversation behaviors with and without memory to understand the impact on user experience.

    Coming Soon

    Detailed content with code examples and best practices is being prepared for this section.

    5
    Persistent Chat Memory

    5.1

    Building Long-Lived Chat Memory with JDBC Persistence

    Implement durable conversation storage using JDBC-backed ChatMemory for production applications.

    5.2

    Storing and Restoring Conversations Across Sessions

    Enable users to continue conversations across browser sessions and application restarts.

    Coming Soon

    Detailed content with code examples and best practices is being prepared for this section.

    6
    Token Management and Context Control

    6.1

    Preventing Token Overflow in Long Conversations

    Strategies to handle long conversations without exceeding model context window limits.

    6.2

    Using maxMessages with Context Window Limits in Mind

    Configure maxMessages and other parameters to optimize for your model's token constraints.

    6.3

    Balancing Memory Depth, Cost, and Performance

    Find the sweet spot between rich conversation history, API costs, and response latency.

    Coming Soon

    Detailed content with code examples and best practices is being prepared for this section.

    What You'll Master

    AI Memory Concepts

    Why LLMs are stateless and how memory is simulated

    ChatMemory Implementation

    Spring AI's approach to conversation history

    Memory Advisors

    Automatic context injection in AI workflows

    JDBC Persistence

    Durable storage for long-lived conversations

    Token Management

    Prevent overflow and optimize costs

    Production Patterns

    Balance memory depth, cost, and performance

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