Author

AI Large Model Application Practice

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.

131

Articles

Likes

111

Views

Comments

Latest from AI Large Model Application Practice

100 recent articles max

AI Large Model Application Practice

May 6, 2025 · Artificial Intelligence

How to Build an Agentic RAG System from Scratch Using MCP Architecture

This article walks through the design and full implementation of an Agentic Retrieval‑Augmented Generation (RAG) system built on the MCP standard, covering the conceptual fusion of MCP and RAG, server‑side tool creation with LlamaIndex, client‑side agent construction with LangGraph, configuration files, caching strategies, code examples, and an end‑to‑end demonstration.

Agentic RAGLLMLangGraph

0 likes · 15 min read

How to Build an Agentic RAG System from Scratch Using MCP Architecture

AI Large Model Application Practice

Apr 28, 2025 · Backend Development

What’s New in MCP 2025: Streamable HTTP, OAuth2.1, JSON‑RPC Batch & Annotations

The MCP 2025‑03‑26 specification introduces a Streamable HTTP transport mode, an OAuth 2.1 authorization framework, JSON‑RPC batch processing, and enhanced tool annotations, detailing background, motivations, implementation changes, client‑server interaction patterns, and migration impacts for developers.

MCPOAuth2.1

0 likes · 12 min read

What’s New in MCP 2025: Streamable HTTP, OAuth2.1, JSON‑RPC Batch & Annotations

AI Large Model Application Practice

Apr 21, 2025 · Artificial Intelligence

How to Scale Distributed AI Agent Systems: Architectures, Challenges, and Solutions

The article explains why modern AI agent systems need horizontal and vertical scaling, outlines the engineering challenges such as state consistency, scheduling, protocol design, and message efficiency, and compares three collaboration approaches—AutoGen's distributed runtime, classic RPC/MCP, and Google's A2A—while providing concrete code examples and deployment steps.

A2AAI agentsAutoGen

0 likes · 14 min read

How to Scale Distributed AI Agent Systems: Architectures, Challenges, and Solutions

AI Large Model Application Practice

Apr 14, 2025 · Artificial Intelligence

What Is Google’s New Agent2Agent (A2A) Protocol and How Does It Enable AI Agent Interoperability?

This article explains the motivation behind Google’s Agent2Agent (A2A) protocol, describes its architecture and key components, compares it with the MCP protocol, and provides a step‑by‑step demo with code showing how to build, run, and test an A2A‑enabled AI agent system.

A2AAI InteroperabilityAgent2Agent

0 likes · 14 min read

What Is Google’s New Agent2Agent (A2A) Protocol and How Does It Enable AI Agent Interoperability?

AI Large Model Application Practice

Apr 7, 2025 · Artificial Intelligence

8 Leading LLM Agent Frameworks and How to Plug In MCP Server

This article surveys eight popular large‑language‑model (LLM) agent development frameworks—OpenAI Agents SDK, LangGraph, LlamaIndex, AutoGen, Pydantic AI, SmolAgents, Camel, and CrewAI—explaining each’s key features and providing concrete Python code to integrate the MCP Server for tool access.

LLMMCPPython

0 likes · 15 min read

8 Leading LLM Agent Frameworks and How to Plug In MCP Server

AI Large Model Application Practice

Mar 27, 2025 · Artificial Intelligence

Mastering AutoGen 0.4: Build Multi‑Agent Tools with Python and MCP

This article walks through the major changes in Microsoft AutoGen 0.4, explains its layered modular architecture and event‑driven multi‑agent design, details the built‑in Tools types, and provides step‑by‑step Python code for creating a Tools Agent and integrating it with an MCP server.

AutoGenLLMMCP

0 likes · 9 min read

Mastering AutoGen 0.4: Build Multi‑Agent Tools with Python and MCP

AI Large Model Application Practice

Mar 24, 2025 · Artificial Intelligence

How to Build a Multimodal RAG Pipeline for PPT Documents with Vision LLMs

This article explains a step‑by‑step implementation of a multimodal Retrieval‑Augmented Generation system that parses PPT/PDF files, extracts rich text and images with vision models, indexes them in a vector store, and generates answers that combine markdown and relevant slide screenshots.

LLMMultimodalPython

0 likes · 9 min read

How to Build a Multimodal RAG Pipeline for PPT Documents with Vision LLMs

AI Large Model Application Practice

Mar 20, 2025 · Artificial Intelligence

Mastering Model Context Protocol (MCP): Build AI Agents with LlamaIndex & LangGraph

This guide explains the Model Context Protocol (MCP), its architecture, and how to create and debug MCP servers and clients in Python, then shows how to integrate third‑party MCP servers with LlamaIndex or LangGraph to quickly build powerful LLM agents.

LLMLangGraphLlamaIndex

0 likes · 12 min read

Mastering Model Context Protocol (MCP): Build AI Agents with LlamaIndex & LangGraph

AI Large Model Application Practice

Mar 18, 2025 · Artificial Intelligence

Master OpenAI’s New Agents SDK: 10 Core Concepts with a Complete Example

This guide walks you through OpenAI's open‑source Agents SDK, explaining ten essential concepts—from model configuration and agent creation to runners, tools, context handling, guardrails, handoffs, structured output, tracing, and orchestration—while providing runnable Python code and visual demos.

LLMOpenAI AgentsPython

0 likes · 17 min read

Master OpenAI’s New Agents SDK: 10 Core Concepts with a Complete Example

AI Large Model Application Practice

Mar 14, 2025 · Artificial Intelligence

Why Softmax Is the Secret Behind LLM Probabilities and Creative Generation

This article explains how the Softmax function converts raw neural‑network scores into a proper probability distribution, why this conversion is essential for training and inference in large language models, and how the temperature parameter shapes the model's creativity and diversity.

LLMLanguage ModelsSoftmax

0 likes · 9 min read

Why Softmax Is the Secret Behind LLM Probabilities and Creative Generation