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What are common LangChain4j anti-patterns to avoid in production applications?

As LangChain4j adoption has grown, several recurring mistakes in production deployments have emerged. Knowing these saves debugging time and prevents costly incidents.

1. Creating ChatLanguageModel or AI Services as request-scoped beans. These are expensive to initialize (TCP connections, key validation, token counting setup). They must be singletons — one instance per application lifecycle, not one per request.

2. Using InMemoryEmbeddingStore in production. Linear scan becomes unacceptably slow above ~50,000 chunks, there is no filtering support, and multiple pods cannot share it. Switch to PgVector or a managed vector DB before going live.

3. Not configuring timeouts. LLM API calls can stall for 60+ seconds. Without a .timeout(Duration.ofSeconds(30)) on the model builder, a hung upstream provider will exhaust your thread pool in a synchronous Spring MVC application.

4. Logging full prompts in production. System messages often contain proprietary business logic. User messages may contain PII. Log only token counts and model names by default; log full prompts only at DEBUG with PII scrubbing.

5. Ignoring ModerationException in safety-critical applications. If you enable @Moderate, surround every AI Services call with a try-catch for ModerationException and return a safe fallback. Uncaught exceptions surface as 500 errors.

6. Embedding the same corpus on every application startup. The ingestion pipeline (load → split → embed → store) should run once and persist results. Re-embedding on startup wastes API budget and delays readiness for large corpora.

7. Hardcoding model names as String literals. Use the constants provided by each provider module (e.g., OpenAiChatModelName.GPT_4_O) so model upgrades are refactor-friendly and typos are caught at compile time.

What is the consequence of creating ChatLanguageModel as a Spring request-scoped bean?
What is the recommended way to reference model names in LangChain4j to avoid typos and ease future upgrades?

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What is LangChain4j and what problem does it solve for Java developers? What are the core modules of LangChain4j? What is the AI Services feature in LangChain4j and how do you define one? How does ChatMemory work in LangChain4j and what types are available? What is Retrieval-Augmented Generation (RAG) in LangChain4j and how do you build a pipeline? What are Tools in LangChain4j and how does tool calling work? How do you integrate LangChain4j with Spring Boot? What is the EmbeddingModel in LangChain4j and which providers are supported? What EmbeddingStores does LangChain4j support and how do you choose one? What is document splitting in LangChain4j and why is it necessary? What is the @SystemMessage and @UserMessage annotation in LangChain4j AI Services? How does streaming work in LangChain4j and when should you use it? What is the ContentRetriever and RetrievalAugmentor in LangChain4j advanced RAG? How does LangChain4j handle structured output from LLMs? What is the PromptTemplate in LangChain4j and how does it differ from @UserMessage? What LLM providers does LangChain4j support and how do you switch between them? What is an Agent in LangChain4j and how does it differ from a simple AI Services call? How do you implement multi-turn conversation with memory per user in a Spring REST API using LangChain4j? What is the ImageModel in LangChain4j and which providers support image generation? How do you handle errors and retries in LangChain4j? How do you test LangChain4j AI Services without making real LLM API calls? What is the DocumentLoader API in LangChain4j and what sources does it support? What is the @Moderate annotation in LangChain4j and how does content moderation work? How does LangChain4j support vision (multi-modal) LLMs that accept images as input? What is the difference between synchronous and asynchronous execution in LangChain4j? What is LangChain4j's support for Quarkus and how does it differ from Spring Boot integration? How does LangChain4j implement the ReAct agent pattern and what are its limitations? What is the ModerationModel interface in LangChain4j and how can you implement a custom one? What is the Tokenizer interface in LangChain4j and why does it matter for memory management? How do you persist ChatMemory across application restarts in LangChain4j? What are the best practices for prompt engineering within LangChain4j AI Services? How does LangChain4j integrate with observability tools like OpenTelemetry? What is the InMemoryEmbeddingStore and when should you migrate to a real vector database? What are common LangChain4j anti-patterns to avoid in production applications? How does LangChain4j support multi-modal input processing for audio or documents beyond text and images? How do you implement a custom Tool with complex parameter types in LangChain4j? What is the HypotheticalDocumentEmbedder (HyDE) technique and how does LangChain4j support it? How do you handle LLM output parsing failures gracefully in LangChain4j? What is LangChain4j's support for graph-based RAG or knowledge graph integration? What is the LangChain4j EvaluationResult API and how do you measure RAG pipeline quality?
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