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Tagged: langchain4j
Neo4j and LangChain4j: GraphRAG Ingestors, Filtering and Chat Memory
Neo4j with LangChain4j for GraphRAG: schema discovery, LLM graph building, Cypher DSL, in-index filtering, parent-child retrieval and graph-backed chat memory.
18 min readLangChain4j for Java Teams: Tools, Agents, Skills and A2A in Spring Services
How to bring tool calling, agent pipelines, on-demand skills, pause-and-resume workflows and remote A2A agents into an existing Java service with LangChain4j.
7 min readDo Small-Scale RAG Apps Really Need a Vector Database?
LangChain4j MemFile keeps embeddings in JVM memory and chunk text on disk. When that's enough for small RAG apps and when to move to a real vector database.
5 min readDiagnosing CI Build Failures With Rules, RAG and a Local LLM
A Java system for CI failure analysis that parses logs deterministically, retrieves troubleshooting notes from PGVector with Ollama, then asks an LLM to reason.
7 min readSpring AI vs LangChain4j: Setup, First Endpoint and Design Style
Compare Spring AI and LangChain4j on architecture, Spring Boot starters, configuration and a first REST chat endpoint, and learn which style suits your team.
6 min readModel Serving Architecture: Why the API Is Only the Front Door
A guide to the platform behind an inference endpoint: routing, scaling, versioning, observability, security, and calling served models from Spring AI or LangChain4j.
9 min readJava as an AI Production Language: Spring Boot, RAG and pgvector
A practical map of building RAG services on the JVM with Spring AI or LangChain4j, picking a vector store, and handling tenancy, cost, evaluation and fallbacks.
11 min readSpring AI vs LangChain4j: same RAG, different hidden choices
The same Java RAG app built twice, Spring AI vs LangChain4j: the line-count gap shrinks with Spring wiring, and a wrong answer exposed the chunking defaults.
4 min read
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