langchain4j-testing-strategies

langchain4j-tool-function-calling-patterns

giuseppe-trisciuoglio

Tool and function calling patterns with LangChain4j. Define tools, handle function calls, and integrate with LLM agents. Use when building agentic applications that interact with tools.

langchain4j-rag-implementation-patterns

giuseppe-trisciuoglio

Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j. Retrieve documents, embed them, and augment LLM prompts with context. Use when building knowledge-powered AI applications.

langchain4j-vector-stores-configuration

giuseppe-trisciuoglio

Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.

qdrant-java-development

giuseppe-trisciuoglio

Qdrant vector database integration patterns with LangChain4j. Store embeddings, similarity search, and vector management. Use when implementing vector-based retrieval for RAG systems.

langchain4j-spring-boot-integration

giuseppe-trisciuoglio

Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.

context-manager

Toowiredd

Manages permanent memory storage for decisions, blockers, context, preferences, and procedures. Use when user says "remember", "save this decision", "what did we decide", "recall", "search memories", "any blockers", or when making important architectural decisions. Provides SDAM compensation through external memory.

vector-database-management

manutej

Comprehensive guide for managing vector databases including Pinecone, Weaviate, and Chroma for semantic search, RAG systems, and similarity-based applications

langchain-orchestration

manutej

Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration

rag-implementation

camoneart

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

ml-pipeline-workflow

camoneart

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

prompt-engineering-patterns

camoneart

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

langchain-architecture

camoneart

Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.