Dify in Depth: Build Enterprise AI Apps from Zero to Production

Ch01

What Is Dify: From LLM to Production-Grade AI Applications

Ch02

Core Concepts: App Types, Workflows, Knowledge Base and Agent Relationships

Ch03

Quick Start: Your First AI App from Zero to Live

Ch04

Model Integration Guide: OpenAI, Claude, Local Models and Cost Comparison

Ch05

RAG Deep Dive: Vector Search vs Full-Text vs Hybrid Retrieval

Ch06

Building Knowledge Bases: Document Processing, Chunking and Index Optimization

Ch07

Advanced RAG Tuning: Recall Rate, Relevance Scoring and Reranking

Ch08

Multi-Knowledge-Base Queries and Enterprise Document Permission Management

Ch09

Workflow Basics: Node Types, Variable System and Conditional Branching

Ch10

Advanced Workflows: Loops, Parallel Branches and Error Recovery

Ch11

Code Nodes and Custom Functions: Extending Workflows with Python and JS

Ch12

Workflow Debugging, Version Control and Performance Profiling

Ch13

Agent Architecture: ReAct vs Function Calling vs Plan-and-Execute

Ch14

Tool Ecosystem: Built-in Tools and Custom Tool Development

Ch15

Multi-Agent Collaboration: Orchestration Patterns and State Synchronization

Ch16

Production Agents: Security Sandbox, Cost Throttling and Idempotent Retry

Ch17

API Integration Guide: REST, Streaming and WebSocket Implementation

Ch18

Deep Integration with Feishu, WeCom and DingTalk

Ch19

Self-Hosted Deployment: Docker Compose, Kubernetes and High Availability

Ch20

Observability: Logging, Tracing, Cost Control and Alerting

Ch21

Case Study: Enterprise Knowledge Assistant — Full Delivery Lifecycle

Ch22

Case Study: Smart Customer Service — Intent, Multi-Turn and Human Handoff

Ch23

Case Study: Content Generation Pipeline — Batch Processing and QA

Ch24

Case Study: Data Analysis Assistant — NL2SQL, Chart Interpretation and Auto-Reports