Master's graduate from Beijing Institute of Technology, published four top‑journal papers, previously worked as a developer at ByteDance and Alibaba. Currently researching large models at a major state‑owned enterprise. Committed to sharing concise, practical AI large‑model development experience, believing that AI large models will become as essential as PCs in the future. Let's start experimenting now!
This article provides a comprehensive, hands‑on guide to Coze's knowledge base, covering its core concepts, key features, practical use‑case scenarios, detailed creation steps, configuration options, prompt design, testing methods, and a comparison with variables, memory, and databases.
This tutorial extends a LangChain 1.0 multimodal RAG project by adding end‑to‑end image analysis and audio transcription features using Qwen3‑Omni, detailing data structures, utility classes, API changes, and Postman testing procedures.
This article walks through the design and implementation of a multimodal Retrieval‑Augmented Generation (RAG) system using LangChain 1.0, detailing a front‑end/back‑end separated architecture, FastAPI service setup, multimodal data handling, conversation history management, streaming responses, and Postman testing to verify the intelligent Q&A module.
The article provides an in‑depth analysis of GPT‑5.1, highlighting its enhanced emotional conversation, stronger instruction‑following, superior code generation and physics simulation, and the new adaptive reasoning mechanism with two model variants, while comparing concrete test results against GPT‑5.
This guide dives into the four advanced capabilities of LangChain 1.0's create_agent API—MCP tool integration, structured output, memory management, and middleware—showcasing practical examples such as an Amap MCP planner, Pydantic‑based response formatting, InMemorySaver chat history, and custom middleware for dynamic model selection.
This tutorial walks through setting up a Python environment, explains the three essential components of LangChain 1.0’s create_agent API, details the built‑in message types, and demonstrates four streaming output modes using a weather‑assistant example to help developers quickly adopt the new agent framework.
This guide walks through LangChain 1.0’s three major upgrades— the new create_agent API that replaces legacy agent builders, standardized content_blocks for unified model output, and a streamlined package structure—while showing how middleware hooks, built‑in and custom middleware, and improved structured output simplify production‑grade AI agent development.
This guide walks through Coze’s variable and long‑term memory features, showing how to define and use variables for user data, configure persistent memory, and combine both to build personalized, context‑aware AI agents, with step‑by‑step screenshots, code snippets, and real‑world scenarios.
This article provides a detailed analysis of LangChain's OpenDeepResearch project, comparing its Graph workflow and Multi‑Agent architectures, explaining core node implementations, execution flows, and offering step‑by‑step instructions for configuring and deploying the system locally.
This article traces OCR's evolution from early CNN‑LSTM systems to modern multimodal VLMs, analyzes leading open‑source models such as DeepSeek‑OCR, PaddleOCR, and MonkeyOCR, and offers practical guidance for long‑document, academic, and edge‑computing scenarios.
