# LangChain4j Glossary

## Table of Contents

- [Core Concepts](#core-concepts)
- [LangChain4j Components](#langchain4j-components)
- [AI/ML Concepts](#aiml-concepts)
- [Guardrails](#guardrails)
- [Prompt Engineering](#prompt-engineering---module-02)
- [RAG (Retrieval-Augmented Generation)](#rag-retrieval-augmented-generation---module-03)
- [Agents and Tools](#agents-and-tools---module-04)
- [Agentic Module](#agentic-module---module-05)
- [Model Context Protocol (MCP)](#model-context-protocol-mcp---module-05)
- [Azure Services](#azure-services---module-01)
- [Testing and Development](#testing-and-development---testing-guide)

Quick reference for terms and concepts used throughout the course.

## Core Concepts

**AI Agent** - System that uses AI to reason and act autonomously. [Module 04](../04-tools/README.md)

**Chain** - Sequence of operations where output feeds into the next step.

**Chunking** - Breaking documents into smaller pieces. Typical: 300-500 tokens with overlap. [Module 03](../03-rag/README.md)

**Context Window** - Maximum tokens a model can process. GPT-5.2: 400K tokens (up to 272K input, 128K output).

**Embeddings** - Numerical vectors representing text meaning. [Module 03](../03-rag/README.md)

**Function Calling** - Model generates structured requests to call external functions. [Module 04](../04-tools/README.md)

**Hallucination** - When models generate incorrect but plausible information.

**Prompt** - Text input to a language model. [Module 02](../02-prompt-engineering/README.md)

**Semantic Search** - Search by meaning using embeddings, not keywords. [Module 03](../03-rag/README.md)

**Stateful vs Stateless** - Stateless: no memory. Stateful: maintains conversation history. [Module 01](../01-introduction/README.md)

**Tokens** - Basic text units models process. Affects costs and limits. [Module 01](../01-introduction/README.md)

**Tool Chaining** - Sequential tool execution where output informs next call. [Module 04](../04-tools/README.md)

## LangChain4j Components

**AiServices** - Creates type-safe AI service interfaces.

**OpenAiOfficialChatModel** - Unified client for OpenAI and Azure OpenAI models.

**OpenAiOfficialEmbeddingModel** - Creates embeddings using OpenAI Official client (supports both OpenAI and Azure OpenAI).

**ChatModel** - Core interface for language models.

**ChatMemory** - Maintains conversation history.

**ContentRetriever** - Finds relevant document chunks for RAG.

**DocumentSplitter** - Breaks documents into chunks.

**EmbeddingModel** - Converts text into numerical vectors.

**EmbeddingStore** - Stores and retrieves embeddings.

**MessageWindowChatMemory** - Maintains sliding window of recent messages.

**PromptTemplate** - Creates reusable prompts with `{{variable}}` placeholders.

**TextSegment** - Text chunk with metadata. Used in RAG.

**ToolExecutionRequest** - Represents tool execution request.

**UserMessage / AiMessage / SystemMessage** - Conversation message types.

## AI/ML Concepts

**Few-Shot Learning** - Providing examples in prompts. [Module 02](../02-prompt-engineering/README.md)

**Large Language Model (LLM)** - AI models trained on vast text data.

**Reasoning Effort** - GPT-5.2 parameter controlling thinking depth. [Module 02](../02-prompt-engineering/README.md)

**Temperature** - Controls output randomness. Low=deterministic, high=creative.

**Vector Database** - Specialized database for embeddings. [Module 03](../03-rag/README.md)

**Zero-Shot Learning** - Performing tasks without examples. [Module 02](../02-prompt-engineering/README.md)

## Guardrails

**Defense in Depth** - Multi-layer security approach combining application-level guardrails with provider safety filters.

**Hard Block** - Provider throws HTTP 400 error for severe content violations.

**InputGuardrail** - LangChain4j interface for validating user input before it reaches the LLM. Saves cost and latency by blocking harmful prompts early.

**InputGuardrailResult** - Return type for guardrail validation: `success()` or `fatal("reason")`.

**OutputGuardrail** - Interface for validating AI responses before returning to users.

**Provider Safety Filters** - Built-in content filters from AI providers (e.g., Azure OpenAI) that catch violations at the API level.

**Soft Refusal** - Model politely declines to answer without throwing an error.

## Prompt Engineering - [Module 02](../02-prompt-engineering/README.md)

**Chain-of-Thought** - Step-by-step reasoning for better accuracy.

**Constrained Output** - Enforcing specific format or structure.

**High Eagerness** - GPT-5.2 pattern for thorough reasoning.

**Low Eagerness** - GPT-5.2 pattern for quick answers.

**Multi-Turn Conversation** - Maintaining context across exchanges.

**Role-Based Prompting** - Setting model persona via system messages.

**Self-Reflection** - Model evaluates and improves its output.

**Structured Analysis** - Fixed evaluation framework.

**Task Execution Pattern** - Plan → Execute → Summarize.

## RAG (Retrieval-Augmented Generation) - [Module 03](../03-rag/README.md)

**Document Processing Pipeline** - Load → chunk → embed → store.

**In-Memory Embedding Store** - Non-persistent storage for testing.

**RAG** - Combines retrieval with generation to ground responses.

**Similarity Score** - Measure (0-1) of semantic similarity.

**Source Reference** - Metadata about retrieved content.

## Agents and Tools - [Module 04](../04-tools/README.md)

**@Tool Annotation** - Marks Java methods as AI-callable tools.

**ReAct Pattern** - Reason → Act → Observe → Repeat.

**Session Management** - Separate contexts for different users.

**Tool** - Function an AI agent can call.

**Tool Description** - Documentation of tool purpose and parameters.

## Agentic Module - [Module 05](../05-mcp/README.md)

**@Agent Annotation** - Marks interfaces as AI agents with declarative behavior definition.

**Agent Listener** - Hook for monitoring agent execution via `beforeAgentInvocation()` and `afterAgentInvocation()`.

**Agentic Scope** - Shared memory where agents store outputs using `outputKey` for downstream agents to consume.

**AgenticServices** - Factory for creating agents using `agentBuilder()` and `supervisorBuilder()`.

**Conditional Workflow** - Route based on conditions to different specialist agents.

**Human-in-the-Loop** - Workflow pattern adding human checkpoints for approval or content review.

**langchain4j-agentic** - Maven dependency for declarative agent building (experimental).

**Loop Workflow** - Iterate agent execution until a condition is met (e.g., quality score ≥ 0.8).

**outputKey** - Agent annotation parameter specifying where results are stored in Agentic Scope.

**Parallel Workflow** - Run multiple agents simultaneously for independent tasks.

**Response Strategy** - How supervisor formulates final answer: LAST, SUMMARY, or SCORED.

**Sequential Workflow** - Execute agents in order where output flows to the next step.

**Supervisor Agent Pattern** - Advanced agentic pattern where a supervisor LLM dynamically decides which sub-agents to invoke.

## Model Context Protocol (MCP) - [Module 05](../05-mcp/README.md)

**langchain4j-mcp** - Maven dependency for MCP integration in LangChain4j.

**MCP** - Model Context Protocol: standard for connecting AI apps to external tools. Build once, use everywhere.

**MCP Client** - Application that connects to MCP servers to discover and use tools.

**MCP Server** - Service exposing tools via MCP with clear descriptions and parameter schemas.

**McpToolProvider** - LangChain4j component that wraps MCP tools for use in AI services and agents.

**McpTransport** - Interface for MCP communication. Implementations include Stdio and HTTP.

**Stdio Transport** - Local process transport via stdin/stdout. Useful for filesystem access or command-line tools.

**StdioMcpTransport** - LangChain4j implementation spawning MCP server as subprocess.

**Tool Discovery** - Client queries server for available tools with descriptions and schemas.

## Azure Services - [Module 01](../01-introduction/README.md)

**Azure AI Search** - Cloud search with vector capabilities. [Module 03](../03-rag/README.md)

**Azure Developer CLI (azd)** - Deploys Azure resources.

**Azure OpenAI** - Microsoft's enterprise AI service.

**Bicep** - Azure infrastructure-as-code language. [Infrastructure Guide](../01-introduction/infra/README.md)

**Deployment Name** - Name for model deployment in Azure.

**GPT-5.2** - Latest OpenAI model with reasoning control. [Module 02](../02-prompt-engineering/README.md)

## Testing and Development - [Testing Guide](TESTING.md)

**Dev Container** - Containerized development environment. [Configuration](../.devcontainer/devcontainer.json)

**In-Memory Testing** - Testing with in-memory storage.

**Integration Testing** - Testing with real infrastructure.

**Maven** - Java build automation tool.

**Mockito** - Java mocking framework.

**Spring Boot** - Java application framework. [Module 01](../01-introduction/README.md)