Dify 1.9.0 (2025-09-22)
公式リリースノート: https://github.com/langgenius/dify/releases/tag/1.9.0
1.9.0 – Orchestrating Knowledge, Powering Workflows
このバージョンの記事
| # | テーマ | 状態 |
|---|---|---|
| 01-core-capabilities | コア機能 (1.9.0) | draft |
| 02-core-capabilities | コア機能 (1.9.0) | draft |
| 03-quickstart | クイックスタート (1.9.0) | draft |
| 04-faq | FAQ (1.9.0) | draft |
| 05-future-plans | 今後の計画 (1.9.0) | draft |
リリースノート抜粋
🚀 Introduction
In Dify 1.9.0, we are introducing two major new capabilities: the Knowledge Pipeline and the Queue-based Graph Engine.
The Knowledge Pipeline provides a modularized and extensible workflow for knowledge ingestion and processing, while the Queue-based Graph Engine makes workflow execution more robust and controllable. We believe these will help you build and debug AI applications more smoothly, and we look forward to your experiences to help us continuously improve.
📚 Knowledge Pipeline
✨ Introduction
With the brand-new orchestration interface for knowledge pipelines, we introduce a fundamental architectural upgrade that reshapes how document processing are designed and executed, providing a more modular and flexible workflow that enables users to orchestrate every stage of the pipeline. Enhanced with a wide range of powerful plugins available in the marketplace, it empowers users to flexibly integrate diverse data sources and processing tools. Ultimately, this architecture enables building highly customized, domain-specific RAG solutions that meet enterprises’ growing demands for scalability, adaptability, and precision.
❓ Why Do We Need It?
Previously, Dify's RAG users still encounter persistent challenges in real-world adoption — from inaccurate knowledge retrieval and information loss to limited data integration and extensibility. Common pain points include:
- 🔗 restricted integration of data sources
- 🖼️ missing critical elements such as tables and images
- ✂️ suboptimal chunking results
All of them lead to poor answer quality and hinder the model's overall performance.
In response, we reimagined RAG in Dify as an open and modular architecture, enabling developers, integrators, and domain experts to build document processing pipelines tailored to their specific requirements—from data ingestion to chunk storage and retrieval.
🛠️ Core Capabilities
🧩 Knowledge Pipeline Architecture
The Knowledge Pipeline is a visual, node-based orchestration system dedicated to document ingestion. It provides a customizable way to automate complex document processing, enabling fine-grained transformations and bridging raw content with structured, retrievable knowledge. Developers can build workflows step by step, like assembling puzzle pieces, making docum
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