langgenius/dify

5 articles

Prerequisites

›Basic familiarity with Flask and Python web applications
›Docker Compose fundamentals
›General understanding of LLM application concepts

Dify Architecture Overview: Navigating a 6,000-File LLM Platform

A high-level map of the Dify codebase covering the two-process architecture, Flask extensions, blueprint routing, Pydantic configuration, and Docker Compose topology.

From HTTP Request to LLM Response: The App Execution Pipeline

Traces the complete lifecycle of an API request through the service layer to the Generator/Runner/QueueManager/TaskPipeline four-stage pattern.

Inside the Workflow Engine: Graph Execution, Node Factory, and the Layer System

Deep dive into the graph engine covering the Layer pattern, DifyNodeFactory dependency injection, node self-registration, and the pause/resume mechanism.

The RAG Pipeline: Document Indexing, Vector Storage, and Knowledge Retrieval

Explores how Dify processes documents from upload through extraction, cleaning, splitting, embedding, and vector storage, then retrieves them at query time.

Model Providers, Tool Ecosystem, and the Plugin Architecture

Examines how Dify abstracts LLM providers behind a unified interface, the tool type taxonomy, and the plugin daemon's backwards invocation architecture.