本文会带你从零搭建一个完整的概念验证项目（POC），技术栈涵盖 Adaptive RAG、LangGraph、FastAPI 和 Streamlit 四个核心组件。Adaptive RAG 负责根据查询复杂度自动调整检索策略；LangGraph 把多步 LLM 推理组织成有状态的可靠工作流；FastAPI 作为高性能后端暴露整条 AI 管道 ...

Retrieval-Augmented Generation (RAG) is rapidly emerging as a robust framework for organizations seeking to harness the full power of generative AI with their business data. As enterprises seek to ...

Model Context Protocol (MCP) 这个协议简单说就是给大语言模型接入外部数据和工具提供了一套标准化方案。MCP 统一了模型和各种数据源、工具服务之间的交互方式。 FastMCP 是目前用 Python 构建 MCP 服务器最顺手的框架，把底层那些复杂的协议实现全都封装好了，开发者 ...

A new study from Google researchers introduces "sufficient context," a novel perspective for understanding and improving retrieval augmented generation (RAG) systems in large language models (LLMs).

Retrieval Augmented Generation (RAG) is supposed to help improve the accuracy of enterprise AI by providing grounded content. While that is often the case, there is also an unintended side effect.

RAG can make your AI analytics way smarter — but only if your data’s clean, your prompts sharp and your setup solid. The arrival of generative AI-enhanced business intelligence (GenBI) for enterprise ...

Retrieval augmented generation, or 'RAG' for short, creates a more customized and accurate generative AI model that can greatly reduce anomalies such as hallucinations. As more organizations turn to ...