分类：AI大模型

This is a forum post requesting a PDF version of the book 'LangChain实战：从原型到生产，动手打造 LLM 应用' (LangChain in Action: From Prototype to Production, Hands-on Building LLM Applications). The user offers forum currency for the book, and another user provides a pre-print version for personal use only. 原文翻译： 这是一个论坛帖子，用户请求获取《LangChain实战：从原型到生产，动手打造 LLM 应用》一书的PDF版本。用户提供论坛币作为交换，另一位用户提供了仅供个人使用的印前版本。

This content is a forum post requesting a comprehensive tutorial or resource on building an AI knowledge base from scratch using LangChain and RAG (Retrieval-Augmented Generation) technologies. The user seeks a step-by-step guide covering the entire development pipeline, likely for educational or project implementation purposes. 原文翻译： 该内容是一个论坛帖子，请求获取关于使用LangChain和RAG（检索增强生成）技术从零开始构建AI知识库的全面教程或资源。用户寻求一个涵盖整个开发流程的逐步指南，可能用于教育或项目实现目的。

This guide provides a comprehensive tutorial on building a local knowledge base Q&A system using LangChain, covering installation, configuration, RAG implementation, and practical deployment scenarios for technical professionals. 原文翻译： 本指南提供了使用LangChain构建本地知识库问答系统的完整教程，涵盖安装、配置、RAG实现以及面向技术专业人员的实际部署场景。

OpenViking is ByteDance's open-source AI agent context database designed to solve complex context management challenges in AI agent systems. It employs a file system paradigm and a three-layer loading strategy to significantly improve performance and reduce costs compared to traditional RAG solutions. This guide provides a comprehensive walkthrough of OpenViking's deployment, configuration, and practical applications, including integration with LangChain and AutoGen, and real-world use cases like intelligent customer service and code generation platforms. 原文翻译： OpenViking是字节跳动开源的AI代理上下文数据库，旨在解决AI代理系统中复杂的上下文管理难题。它采用文件系统范式和三层加载策略，相比传统RAG方案，显著提升性能并降低成本。本指南全面讲解了OpenViking的部署、配置和实战应用，包括与LangChain和AutoGen的集成，以及智能客服系统、代码生成平台等真实案例。

Context engineering is the holistic practice of designing and optimizing all elements within an AI model's context window—including system prompts, instructions, user inputs, structured data, tools, and memory—to achieve superior performance and desired outcomes. It represents the evolution beyond simple prompt engineering, emphasizing interconnected components, iterative refinement, and user-centric design for applications like customer support, content creation, and software development. 原文翻译： 上下文工程是一种整体性实践，旨在设计和优化AI模型上下文窗口内的所有元素——包括系统提示、指令、用户输入、结构化数据、工具和记忆——以实现卓越性能和预期结果。它代表了超越简单提示工程的演进，强调互联组件、迭代优化和以用户为中心的设计，适用于客户支持、内容创作和软件开发等应用场景。

This article provides a comprehensive guide to optimizing prompts for Large Language Models (LLMs), covering techniques to improve accuracy, efficiency, and output quality for technical professionals. 原文翻译： 本文为技术专业人士提供了一份关于优化大语言模型（LLM）提示词的全面指南，涵盖了提高准确性、效率和输出质量的技术。

LangChain has officially released DeepAgents, a multi-agent collaboration framework built on LangChain and LangGraph. It features built-in planning tools, filesystem backend, and subagent spawning capabilities, designed to handle complex tasks through hierarchical collaboration. 原文翻译： LangChain 正式发布了基于 LangChain 和 LangGraph 构建的多智能体协作框架 DeepAgents。该框架具备内置规划工具、文件系统后端和子智能体派生能力，旨在通过层级协作模式处理复杂任务。

LangChain enables natural language SQL query generation and execution through LLMs like GPT-3.5/4, automating database interactions with code examples. 原文翻译： LangChain通过GPT-3.5/4等大语言模型实现自然语言SQL查询生成与执行，提供完整代码示例自动化数据库交互。

MCP (Model Context Protocol) is an open-source standard that enables AI applications to connect to external data sources, tools, and workflows, functioning like a USB-C port for AI systems. 原文翻译： MCP（模型上下文协议）是一个开源标准，使AI应用程序能够连接到外部数据源、工具和工作流程，功能类似于AI系统的USB-C接口。

This article provides a comprehensive technical analysis of the full LLM development pipeline, covering 5 key steps and 15 major open-source frameworks from data governance to RLHF. It offers in-depth technical deconstruction of core frameworks, analyzing their underlying mechanisms, performance metrics, and industry application scenarios. 原文翻译： 本文对LLM开发全链路进行了全面的技术分析，涵盖从数据治理到RLHF的5个关键步骤和15个主要开源框架。文章对核心框架进行了深度的技术解构，分析其底层机制、性能指标及行业应用场景。