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Add providers module: one app on the real OpenAI, Anthropic and Gemini Spring AI models against a local server in three wire formats; options, prompt caching, cost from price sheets, failover with retry layers measured

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Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01JXVi2GMQ7bR5EmbUFdDj7N
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Claude
2026-10-09 08:01:02 +00:00
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@@ -18,7 +18,8 @@ Runnable companion code for the Spring AI articles on [ankurm.com](https://ankur
| [`evaluation/`](evaluation) | Testing an LLM app: `RelevancyEvaluator` and `FactCheckingEvaluator` (exactly what they send and which judge replies they accept), a 12-case golden dataset with a pass-rate gate, a deterministic judge for CI, simulated judge noise, a 1-5 graded evaluator and a composite. Stub models only; the one live-judge test is skipped without a key. Spring Boot 4.1.1, Spring AI 2.0.1, JUnit 6, Java 25. | [Testing LLM Apps in Java: Spring AI Evaluators and LLM-as-Judge in JUnit 6](https://ankurm.com/spring-ai-2-0-testing-llm-apps-evaluators-llm-as-judge-junit-6/) |
| [`observability/`](observability) | What Spring AI 2.0.1 records on its own (model, chat client, advisor and tool meters, spans for a tool call), token usage turned into cost per endpoint, a Grafana dashboard checked against live Prometheus and Grafana, and the traps: histograms are opt-in, a response with no usage looks like a free call, prompt text is logged only if switched on. The real `OpenAiChatModel` against a local fake server, so counts are approximate and prices illustrative. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Observability for Spring AI: Tokens, Latency and Cost with Micrometer and OpenTelemetry](https://ankurm.com/spring-ai-2-0-observability-micrometer-opentelemetry-tokens-cost/) |
| [`guardrails/`](guardrails) | Prompt injection against a Spring AI assistant with tools: a poisoned document, a poisoned tool result, a markdown-image leak and a system prompt leak, run against a document filter, a tool allow-list with argument policies, and output validation, alone and together (6 of 6 attacks succeed with no defence, 0 of 6 with all three). A deliberately gullible stub model, so it measures what each defence stops when the model *is* fooled, not how often a real model is. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Prompt Injection Defense in Spring AI](https://ankurm.com/prompt-injection-defense-spring-ai-guardrails-tool-allow-lists-output-validation/) |
Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).
| [`multimodal/`](multimodal) | A receipt image through `Media` and `ChatClient.entity(...)` into a Java record, on the real `OpenAiChatModel`, `AnthropicChatModel` and `OllamaChatModel` against a local server that OCRs the image it receives (so accuracy figures describe OCR, not any vision model). The same image on three wire formats, arithmetic validation and a repair retry, accuracy under tilt, shrinking and noise, and an image-token estimate from a documented formula. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Multimodal Spring AI: Extract Structured Data from Images](https://ankurm.com/multimodal-spring-ai-extract-structured-data-from-images-receipts-java-records/) |
| [`text-to-sql/`](text-to-sql) | A question to SQL to rows, safely, on a real PostgreSQL 16: a schema prompt built through the restricted role, a JSqlParser guard (one statement, SELECT only, listed tables and functions), a read-only role with column grants and a statement timeout, a row cap, and evaluation by comparing results. Sixteen queries against four setups (13 harmful: 13 succeed with neither protection, 0 with both). The model is a script, not a language model. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Text-to-SQL with Spring AI, Done Safely](https://ankurm.com/text-to-sql-spring-ai-read-only-roles-query-validation/) |
| [`providers/`](providers) | One ticket-summarising app on the real `OpenAiChatModel`, `AnthropicChatModel` and `GoogleGenAiChatModel` against a local server in all three wire formats: where each puts the system prompt and what it sends by default, portable versus provider-specific options (and the per-call `ChatOptions` that crashes two providers and silently resets the third), prompt caching, a cost table from the vendors' price sheets, and failover with the retry multiplication measured. No vendor API called; token counts and cache hits are simulated from documented rules; latency not measured. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Anthropic Claude vs OpenAI vs Gemini in Spring AI 2.0](https://ankurm.com/spring-ai-claude-vs-openai-vs-gemini-switch-providers-compare-cost/) |
Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).