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This guide walks you through upgrading Dify using Docker Compose or source code deployment, running required migration commands, backing up data, and fixing the "Invalid context structure" error caused by iterator output changes in version 1.9.1, with detailed code snippets and troubleshooting steps.
This guide walks you through configuring a high‑performance AI inference server on Oracle Linux, covering hardware specs, NVIDIA driver and CUDA installation, Conda environment setup, Xinference deployment, service startup, and example model loading commands, all with clear code snippets and images.
This article compares low‑code development platforms with open‑source large‑model frameworks such as LangChain and LlamaIndex, outlining their features, advantages, limitations, and suitability for building retrieval‑augmented generation (RAG) applications in various enterprise scenarios.
This article outlines the fundamental RAG workflow—from data indexing and querying to advanced modular designs—highlights key challenges such as retrieval accuracy, model robustness, context limits, and performance, and traces the evolution from naive to modular RAG systems.
This article explains how Retrieval‑Augmented Generation (RAG) and long‑term memory systems like MenoBase enable large language models to overcome short‑term memory limits, dynamically retrieve up‑to‑date knowledge, and personalize interactions, with practical Dify implementation steps and real‑world use cases across industries.
This article compiles a comprehensive, up‑to‑date inventory of open‑source large language models from Chinese and international organizations, detailing each model’s architecture, parameter count, multilingual capabilities, deployment requirements, and associated tools, offering a valuable reference for AI researchers and developers.
This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.
This guide explains three ways Dify can invoke Java programs—using HTTP requests, defining custom tools with OpenAPI schemas, and configuring MCP communication—providing step‑by‑step setup, code examples, and configuration snippets for each approach.
This guide explains how to organize a knowledge base, select embedding and rerank models, clean user queries, and construct a Dify DSL workflow that iteratively retrieves and merges information before handing it to a large language model for answer generation.
This article compares six leading open‑source PDF‑to‑Markdown converters, detailing their architectures, core features, suitable use cases, and pros and cons to help developers quickly choose the most appropriate tool for their documentation workflows.
