Claude
527f4ba7ff
Tests pin down chain ordering (including ties), BaseAdvisor stream behaviour, redaction order versus memory and logging, the tool loop, and how a refusal surfaces on calls, streams and over HTTP. Co-Authored-By: Claude Sonnet 5 <[email protected]> Claude-Session: https://claude.ai/code/session_01Ja4jkzrbQ4LQZBNrb5mkZE
19 lines
4.9 KiB
Markdown
19 lines
4.9 KiB
Markdown
# spring-ai
|
|
|
|
Runnable companion code for the Spring AI articles on [ankurm.com](https://ankurm.com). One directory per module; each module is one commit and carries its own README, tests and captured output.
|
|
|
|
| Module | What it is | Article |
|
|
|---|---|---|
|
|
| [`getting-started/`](getting-started) | One `ChatClient` bean, three endpoints (plain call, templated system prompt, streaming), and a test proving `spring.ai.model.chat` switches providers with zero code change. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Spring AI 2.0 in 10 Minutes: ChatClient on Spring Boot 4.1](https://ankurm.com/spring-ai-2-0-chatclient-boot-4-1/) |
|
|
| [`rag/`](rag) | Ingest PDFs, chunk, retrieve from pgvector, rerank, answer, check the answer. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Production-grade RAG with Spring AI](https://ankurm.com/production-rag-spring-ai-java/) and [the complete example](https://ankurm.com/spring-ai-rag-complete-example/) |
|
|
| [`mcp-server/`](mcp-server) | An order-lookup service exposed as MCP tools, a resource, and a prompt with `@McpTool`/`@McpResource`/`@McpPrompt`, served over Streamable HTTP (Spring AI 2.0's default MCP server transport). Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Build an MCP Server with Spring AI 2.0](https://ankurm.com/spring-ai-2-0-mcp-server-streamable-http/) |
|
|
| [`mcp-client/`](mcp-client) | `ChatClient` calling tools from two real external MCP servers (filesystem, git) over stdio via `defaultToolCallbacks(ToolCallbackProvider...)`, contrasted with a local `@Tool` method, with every call logged through one Micrometer `ObservationHandler`. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Spring AI MCP Client: Calling External MCP Servers from ChatClient](https://ankurm.com/spring-ai-2-0-mcp-client/) |
|
|
| [`mcp-secure/`](mcp-secure) | The mcp-server article's order-lookup tools behind a real OAuth2 resource server: JWT validation, one scope per tool via `@PreAuthorize`, unauthenticated tool discovery rejected outright, and every call audit-logged through MDC -- denials included. Spring Boot 4.1.1, Spring AI 2.0.1, Spring Security 7.1.1, Java 25. | [Securing an MCP Server with Spring Security 7](https://ankurm.com/spring-ai-2-0-mcp-server-security/) |
|
|
| [`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/) |
|
|
| [`advisors/`](advisors) | Three custom advisors -- a logger, a PII redactor (with a stream-safe restore) and a per-request / per-user token budget -- and tests for how the chain is ordered, what `BaseAdvisor` does on a stream, where an advisor sits relative to memory and the tool loop, and what a refusal looks like on a call, a stream and over HTTP (429). A recording stub model, no live model. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Writing Custom Advisors in Spring AI 2.0: Logging, PII Redaction and Token Budgets](https://ankurm.com/spring-ai-2-0-custom-advisors-logging-pii-redaction-token-budgets/) |
|
|
|
|
Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).
|