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AI Large Model Application Practice
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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.

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Recent Articles

Latest from AI Large Model Application Practice

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AI Large Model Application Practice
AI Large Model Application Practice
Aug 4, 2025 · Backend Development

Mastering Lifespan in FastAPI and MCP Server: Context Managers for Robust Resource Management

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.

FastAPIasynccontextmanagercontext manager
0 likes · 14 min read
Mastering Lifespan in FastAPI and MCP Server: Context Managers for Robust Resource Management
AI Large Model Application Practice
AI Large Model Application Practice
Jul 29, 2025 · Artificial Intelligence

8 Memory Strategies for AI Agents: From Full Recall to Vector Stores

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.

AI memoryLLM context managementSemantic Retrieval
0 likes · 23 min read
8 Memory Strategies for AI Agents: From Full Recall to Vector Stores
AI Large Model Application Practice
AI Large Model Application Practice
Jul 16, 2025 · Artificial Intelligence

Unlocking LLM Integration: A Deep Dive into MCP, A2A, and AG‑UI Protocols

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.

A2AAG-UIAI protocols
0 likes · 14 min read
Unlocking LLM Integration: A Deep Dive into MCP, A2A, and AG‑UI Protocols
AI Large Model Application Practice
AI Large Model Application Practice
Jul 2, 2025 · Artificial Intelligence

Build a PPT‑Powered RAG Engine with Visual Models and MCP Server

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.

LlamaIndexMCP ServerPPT
0 likes · 13 min read
Build a PPT‑Powered RAG Engine with Visual Models and MCP Server
AI Large Model Application Practice
AI Large Model Application Practice
Jun 23, 2025 · Databases

How Google’s MCP Toolbox Simplifies Enterprise Database Access for LLM Agents

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.

DatabasesLLM AgentsMCP Toolbox
0 likes · 11 min read
How Google’s MCP Toolbox Simplifies Enterprise Database Access for LLM Agents
AI Large Model Application Practice
AI Large Model Application Practice
Jun 3, 2025 · Backend Development

Scaling Human‑in‑the‑Loop Agents to Distributed Environments with Robust Fault Recovery

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.

LangGraphdistributed systemsfault-recovery
0 likes · 16 min read
Scaling Human‑in‑the‑Loop Agents to Distributed Environments with Robust Fault Recovery