Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.
This guide walks you through the fundamentals of OAuth 2.0, explains how the MCP protocol supports it, and provides a complete Python example that integrates Google OAuth 2.0 into an MCP server and client, covering configuration, code implementation, and testing procedures.
This article presents a detailed design and implementation of an LLM‑based intelligent resume matching system that combines semantic vector retrieval, structured rule filtering, multi‑dimensional weighted scoring, and natural‑language interaction to create a fast, quantifiable, and explainable hiring pipeline.
This article explains how to use Python's context manager protocol and FastAPI's lifespan feature to automatically initialize and clean up resources in both standard FastAPI applications and MCP Server, covering implementation methods, code examples, and differences across transport modes.
The article examines eight common AI memory techniques—from simple full‑history retention to sophisticated vector‑store and knowledge‑graph approaches—detailing their principles, Python‑style implementations, advantages, drawbacks, and ideal application scenarios for large‑language‑model agents in production environments.
This article introduces three emerging standards—MCP, A2A, and AG‑UI—that simplify connecting large language models to external tools, other agents, and user interfaces, explaining their origins, architectures, development workflows, key features, and how they complement each other in AI application development.
This article explains what overfitting is, introduces dropout as a regularization technique, describes how dropout randomly deactivates neurons during training and rescales outputs during inference, discusses its limitations, and outlines why large language models may use alternative strategies.
This article explains how to construct a Retrieval‑Augmented Generation (RAG) pipeline for multi‑page PPT documents by converting slides to images, extracting content with a vision model, indexing with LlamaIndex and Chroma, and exposing the functionality through an MCP Server with tools for adding, querying, and managing PPTs.
This guide explains Google’s open‑source MCP Toolbox for Databases, covering its core concepts, installation, configuration, two usage modes (native SDK and MCP), example LangGraph agent integration, security features, observability, and practical code snippets for building reliable LLM‑driven database tools.
This article explains how to extend a single‑process Human‑in‑the‑Loop (HITL) agent to a distributed, multi‑user API service using FastAPI, detailing session management, interrupt handling, client and server fault‑recovery strategies, and providing concrete code snippets and architectural diagrams.
This article explains the mathematical foundation of layer normalization, why it is needed for deep neural networks like Transformers, how scaling (γ) and bias (β) parameters restore important signal variations, and practical placement tips for stable training.
