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Add chat-memory module: MessageWindowChatMemory, JDBC and Redis repositories, per-user conversation IDs

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Real PostgreSQL 16 and Redis Stack 7.4; 24 tests write output/01-24. Covers the 20-message
default window with no property, the 36-character conversation_id, tool messages dropped by
JdbcChatMemoryRepository, concurrent add() on one conversation, a 1.x table under the 2.0
repository, the Redis repository silently backing off for a custom ChatMemory, and the
removal of PromptChatMemoryAdvisor.

Co-Authored-By: Claude Sonnet 5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01Ja4jkzrbQ4LQZBNrb5mkZE
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Claude
2026-09-24 15:56:47 +00:00
parent c1e36c6c09
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@@ -12,5 +12,6 @@ Runnable companion code for the Spring AI articles on [ankurm.com](https://ankur
| [`tool-calling/`](tool-calling) | `@Tool` methods, `ToolCallingAdvisor` (the advisor-layer replacement for Spring AI 1.x's per-model tool loop), `returnDirect`, `ToolContext`, and `ToolSearchToolCallingAdvisor` for progressive disclosure across a 230-tool synthetic library -- every test driven by a hand-written `ScriptedChatModel`, no live model anywhere. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Tool Calling in Spring AI 2.0](https://ankurm.com/spring-ai-2-0-tool-calling/) |
| [`structured-output/`](structured-output) | `ChatClient.entity()` mapping LLM responses to Java records, lists and maps; `StructuredOutputValidationAdvisor` retrying non-conforming JSON with a real enum-constrained schema, including a captured run that exhausts every retry without throwing. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Structured Output in Spring AI 2.0](https://ankurm.com/spring-ai-2-0-structured-output/) |
| [`ollama-local/`](ollama-local) | Chat and embeddings against a real local `qwen2.5:0.5b`/`all-minilm`, no API key, driven by a Testcontainers-managed Ollama container started from a baked image; a confirmed model unload via `keep_alive: 0` and `/api/ps`, not a scripted model anywhere. Spring Boot 4.1.1, Spring AI 2.0.1, Testcontainers 2.0.5, Java 25. | [Run LLMs Locally with Spring AI and Ollama](https://ankurm.com/spring-ai-2-0-ollama-local/) |
| [`chat-memory/`](chat-memory) | `MessageChatMemoryAdvisor`, `MessageWindowChatMemory`, the JDBC and Redis `ChatMemoryRepository`, per-user conversation IDs and a token-budget memory of our own, with the traps reproduced against a real PostgreSQL 16 and Redis Stack: a 36-character `conversation_id`, tool messages dropped on save, concurrent writers, a 1.x table under the 2.0 repository, and a Redis repository that silently steps aside for a custom `ChatMemory`. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Chat Memory in Spring AI 2.0: JDBC, Redis and Windowed Conversations](https://ankurm.com/spring-ai-2-0-chat-memory-jdbc-redis-windowed-conversations/) |
Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).