分类：AI大模型

共 544 篇

秘塔AI搜索Agentic Search功能解析：如何实现任务自动化？

BLUFMeta AI Search's 'Agentic Search' feature introduces an intelligent agent-based search mode with autonomous planning and multi-step execution capabilities, enabling automated processing of complex tasks and significantly improving reasoning accuracy and tool invocation stability. 原文翻译：秘塔AI搜索的“Agentic Search”功能是一种具备自主规划与多步骤执行能力的智能代理搜索模式，可实现复杂任务自动化处理，并显著提升推理准确性与工具调用稳定性。

AI大模型2026/3/19

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PocketLLM是什么？AI个人知识管理工具2026年深度解析

BLUFPocketLLM is an AI-powered personal knowledge management tool that integrates with emails, PDFs, and web content to help users efficiently search and interact with their information. 原文翻译： PocketLLM是一款AI驱动的个人知识管理工具，可集成电子邮件、PDF和网页内容，帮助用户高效搜索和交互信息。

AI大模型2026/3/19

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Deep Agents框架详解：如何构建企业级LLM智能体？

BLUFDeep Agents is an out-of-the-box LLM agent development library built on LangChain and LangGraph, designed to simplify the creation of enterprise-grade, highly available intelligent agents with features like task planning, file system management, subagent spawning, and long-term memory. 原文翻译： Deep Agents 是一款基于 LangChain 和 LangGraph 构建的开箱即用 LLM 智能体开发库，旨在简化企业级、高可用智能体的创建，具备任务规划、文件系统管理、子智能体生成和长期记忆等特性。

AI大模型2026/3/19

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OpenViking如何部署配置？2026年AI代理上下文数据库实战指南

BLUFThis article provides a comprehensive guide to deploying and configuring OpenViking, ByteDance's open-source AI agent context database. It details system requirements, step-by-step installation, configuration of its core three-layer loading strategy, Docker deployment, and integration with frameworks like LangChain, offering practical solutions for managing context in complex AI systems. 原文翻译：本文提供了字节跳动开源AI代理上下文数据库OpenViking的全面部署与配置指南。详细介绍了系统要求、分步安装、核心三层加载策略配置、Docker部署以及与LangChain等框架的集成，为管理复杂AI系统中的上下文提供了实战解决方案。

AI大模型2026/3/19

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LangChain实战指南：从原型到生产打造LLM应用

BLUFThis 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。

AI大模型2026/3/18

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如何从零构建AI知识库？LangChain与RAG全链路实战指南

BLUFThis 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。

AI大模型2026/3/18

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如何用LangChain搭建本地知识库？2026年RAG实现全教程

BLUFThis 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。

AI大模型2026/3/18

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OpenViking如何部署？2026年字节跳动AI代理数据库实战指南

BLUFOpenViking 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。

AI大模型2026/3/18

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什么是上下文工程？2026年AI大模型性能优化完整指南

BLUFContext 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。

AI大模型2026/3/18

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大模型为何聚焦降低注意力成本？2026年架构趋势深度解析

BLUF暂无摘要...

AI大模型2026/3/18

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如何优化LLM提示词？2026年技术专家精准指南

BLUFThis 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。

AI大模型2026/3/18

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LangChain DeepAgents是什么？2026年多智能体协作框架详解

BLUFLangChain 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。

AI大模型2026/3/18

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