面对工具生态系统扩张，大型语言模型的工具选择能力因提示词膨胀而受限。RAG-MCP通过检索增强生成技术实现动态工具选择，显著提升处理效率与准确率。本文深入剖析其技术原理与应用价值。 大型语言模型（LLMs）如GPT-4、Claude和Llama的发展标志着人工智能领域 ...

检索增强生成系统（RAG）正从早期“检索 + 生成”的简单拼接，走向融合自适应知识组织、多轮推理、动态检索的复杂知识系统（典型代表如 DeepResearch、Search-o1）。但这种复杂度的提升，使开发者在方法复现、快速迭代新想法时，面临着高昂的工程实现成本。

What if your AI coding assistant could deliver exactly the information you need—no irrelevant clutter, no privacy concerns, and no compromises? For developers and organizations relying on tools like ...

随着生成式AI从“对话式交互”向“自主式执行”升级，越来越多的AI系统开始具备“理解需求、拆解任务、调用工具、完成闭环”的能力。而支撑这一能力的核心，正是Agent、MCP、RAG、Skill四大组件的协同配合。 要理解四者的关系，首先要明确每个组件的“分工 ...

最近爆火的OpenClaw，具象化体现了什么叫程序员最头疼的事情就是命名。毕竟我做视频期间，它就已经改了两次名。 它的本质是什么？跟大模型和前段时间很火的skills, RAG, mcp, memory 又有什么关系？ 接下来我们就一次性将这些概念串起来带大家看清楚，来一波 ...

But as these models evolve, their capabilities are entering a new phase with the introduction of Model Context Protocol (MCP) – a development that will also reshape how we think about search ...

MCP (Model Context Protocol) provides a universal standard for connecting LLMs to external data sources and tools, eliminating the need to manually copy-paste context into a chat session and enabling ...

The Model Context Protocol (MCP) is an open source framework that aims to provide a standard way for AI systems, like large language models (LLMs), to interact with other tools, computing services, ...