Spring AI / LangChain4J Skill

Detect the framework in use, then apply the correct patterns.

Step 1 — Detect framework and version

Check pom.xml or build.gradle:

• spring-ai-* dependency → Spring AI (note version: 1.0.x GA or 0.8.x milestone)
• langchain4j-* dependency → LangChain4J (note version: 0.x or 1.x)
• Neither present → offer to add one (recommend Spring AI for Spring Boot 3.x, LangChain4J for Boot 2.x)

Check Spring Boot version:

• Boot 3.x → Spring AI 1.x preferred, LangChain4J 0.35+
• Boot 2.x → LangChain4J 0.30.x (Spring AI requires Boot 3.x)

Mode: review

User asks to review existing AI code. Check for:

Spring AI:

• [ ] ChatClient built via ChatClient.Builder (not raw ChatModel) for fluent API
• [ ] Prompt templates use PromptTemplate with variables — no string concatenation
• [ ] Streaming uses stream().content() or Flux<String> — not blocking .call() for real-time responses
• [ ] @Retryable or Spring AI retry config on ChatClient calls — LLMs are flaky
• [ ] Secrets (spring.ai.openai.api-key) come from env vars or Vault, never hardcoded
• [ ] VectorStore queries use SearchRequest.query(text).withTopK(n) — not raw SQL
• [ ] RAG advisor (QuestionAnswerAdvisor) attached to ChatClient — not manual context injection
• [ ] Token usage logged at DEBUG, not INFO (avoid log noise)

LangChain4J:

• [ ] AI services use @AiService interface — not ChatLanguageModel.generate() directly
• [ ] System prompts in @SystemMessage annotation — not hardcoded strings
• [ ] Memory uses MessageWindowChatMemory or TokenWindowChatMemory — not unlimited history
• [ ] Streaming via StreamingChatLanguageModel with TokenStream — not blocking
• [ ] Embeddings via EmbeddingModel + EmbeddingStore for RAG — not in-memory list search
• [ ] Tools annotated with @Tool on service methods — not manual function dispatch
• [ ] API key from @Value("${langchain4j.openai.api-key}") — never literal

Mode: chat

User asks to add a basic chatbot or chat endpoint.

Spring AI

1. Add dependency (see references/patterns.md → Spring AI Setup)
2. Inject ChatClient.Builder, build a ChatClient bean
3. Create ChatController with @PostMapping("/chat")
4. Use chatClient.prompt().user(message).call().content() for simple response
5. For streaming: return Flux<String> with chatClient.prompt().user(message).stream().content()
6. Add ANTHROPIC_API_KEY / OPENAI_API_KEY to application.yml via ${env-var}

LangChain4J

1. Add langchain4j-spring-boot-starter + provider dependency
2. Define @AiService interface with @SystemMessage
3. Register as Spring bean via AiServices.builder(MyAssistant.class).chatLanguageModel(model).build()
4. Expose via @RestController

Mode: rag

User asks to implement RAG (chat over documents, knowledge base, semantic search).

Spring AI RAG

1. Choose vector store: PgVector (PostgreSQL), Chroma, Redis, Weaviate, Qdrant (see references/patterns.md)
2. Add spring-ai-{store}-store-spring-boot-starter
3. Ingest pipeline:

• DocumentReader (PDF, text, web) → TokenTextSplitter → VectorStore.add()
• Run at startup via ApplicationRunner or dedicated @PostMapping("/ingest")

1. Query pipeline:

• Attach QuestionAnswerAdvisor(vectorStore) to ChatClient
• Spring AI auto-retrieves context and injects into prompt

1. Tune: SearchRequest.withTopK(5).withSimilarityThreshold(0.7)

LangChain4J RAG

1. Add EmbeddingStore (Chroma, Qdrant, in-memory for dev)
2. EmbeddingStoreIngestor with DocumentSplitter and EmbeddingModel
3. EmbeddingStoreContentRetriever → RetrievalAugmentor → AiServices builder

Mode: tools

User asks to give the AI the ability to call Java methods (function/tool calling).

Spring AI

1. Define a @Bean of type Function<Input, Output> — Spring AI auto-registers it
2. Or use @Description on a record parameter for rich schema
3. Pass function names to ChatClient: .options(OpenAiChatOptions.builder().withFunction("myFunction").build())
4. Spring AI handles the tool call loop automatically

LangChain4J

1. Annotate service methods with @Tool("description of what this tool does")
2. Register the service as a tool: AiServices.builder(...).tools(myToolService).build()
3. The model decides when to call — no manual dispatch needed

Mode: memory

User asks to add conversation memory / chat history.

Spring AI

• MessageChatMemoryAdvisor with InMemoryChatMemory for single-instance apps
• JdbcChatMemory for persistent / multi-instance memory (requires spring-ai-jdbc store)
• Key: pass conversationId (e.g., session ID or user ID) to scope memory per user

LangChain4J

• MessageWindowChatMemory.withMaxMessages(20) — keeps last N messages
• TokenWindowChatMemory — keeps messages within token budget
• For persistence: implement ChatMemoryStore backed by Redis or JDBC

Output format

For review mode: list findings as [CRITICAL] / [HIGH] / [MEDIUM] / [LOW] with file:line references.

For implementation modes (chat, rag, tools, memory):

1. Show exact Maven/Gradle dependencies with versions
2. Show full working code snippets (not pseudocode)
3. Show application.yml configuration
4. Note: state the minimum Spring Boot and Java version required

Always note version-specific differences:

• Spring AI 1.0.x (GA) vs 0.8.x (milestone) — API changes between these
• LangChain4J 1.x vs 0.x — AiServices API changed in 1.x
• Spring Boot 3.x required for Spring AI; Boot 2.x → use LangChain4J