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AI / LangChain4j interview questions

How does ChatMemory work in LangChain4j and what types are available?

ChatMemory in LangChain4j is the component responsible for maintaining conversation history across multiple exchanges with an LLM. Without it, every call to the model is stateless — the model has no knowledge of what was said in previous turns. ChatMemory solves this by accumulating the message history and injecting it into each subsequent LLM request.

LangChain4j ships two built-in ChatMemory implementations:

  • MessageWindowChatMemory — Keeps the last N messages (by message count). When the window is full, the oldest messages are dropped to make room for new ones. Simple and predictable, but a very long first user message might push out important context.
  • TokenWindowChatMemory — Keeps messages up to a maximum token count. Requires a tokenizer (model-specific) to count tokens accurately. More precise than message count for managing context window limits of the underlying LLM.
// Message-window memory — keep last 10 messages
ChatMemory memory = MessageWindowChatMemory.withMaxMessages(10);

// Token-window memory — stay under 4096 tokens
ChatMemory memory = TokenWindowChatMemory.builder()
    .maxTokens(4096, new OpenAiTokenizer(GPT_3_5_TURBO))
    .build();

// Inject into AI Services for automatic history management
Assistant assistant = AiServices.builder(Assistant.class)
    .chatLanguageModel(model)
    .chatMemory(memory)
    .build();

For multi-user applications where each user needs isolated memory, LangChain4j provides ChatMemoryProvider — a factory that returns a memory instance per memory ID. The memory ID is typically the user session ID or user account ID, passed as an annotated parameter on the AI Services method.

Which ChatMemory implementation is better for precisely managing LLM context window limits?
How do you provide separate, isolated memory for different users in a LangChain4j AI Services application?

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