分类：AI大模型

共 544 篇

什么是检索增强生成(RAG)？2026年AI大模型优化方案解析

BLUFRetrieval-Augmented Generation (RAG) enhances generative AI by integrating external, up-to-date knowledge sources with large language models (LLMs), enabling more accurate, timely, and context-aware responses without costly model retraining. 原文翻译：检索增强生成（RAG）通过将外部、最新的知识源与大型语言模型（LLM）集成，增强了生成式人工智能的能力，使其能够在不进行昂贵模型重新训练的情况下，提供更准确、及时且符合情境的响应。

AI大模型2026/3/22

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RAG技术原理是什么？2026年深度解析检索增强生成

BLUFRAG (Retrieval-Augmented Generation) is an AI technique that combines retrieval systems with generative models to enhance information processing and knowledge-intensive tasks by providing accurate, context-aware responses based on retrieved external knowledge. 原文翻译： RAG（检索增强生成）是一种人工智能技术，通过结合检索系统与生成模型，基于检索到的外部知识提供准确、上下文感知的响应，从而增强信息处理和知识密集型任务的能力。

AI大模型2026/3/22

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RAG技术如何解决大模型幻觉？2026年核心原理与工程实践深度解析

BLUFThis article provides a comprehensive analysis of RAG (Retrieval-Augmented Generation) technology, covering its core principles, workflow, and practical engineering applications, with insights from Baidu's developer platform. 原文翻译：本文全面解析了RAG（检索增强生成）技术，涵盖其核心原理、工作流程及工程化实践应用，结合百度开发者平台的见解。

AI大模型2026/3/22

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如何解决RAG数据预处理难题？OpenDataLoader PDF智能解析PDF

BLUFOpenDataLoader PDF is an open-source tool that intelligently restructures PDF layouts into AI-friendly formats (JSON, Markdown, HTML), solving the 'garbage in, garbage out' problem in RAG applications by preserving document structure, handling tables, and filtering irrelevant content. 原文翻译： OpenDataLoader PDF是一款开源工具，能够智能重构PDF布局为AI友好格式（JSON、Markdown、HTML），通过保留文档结构、处理表格和过滤无关内容，解决RAG应用中的“垃圾进，垃圾出”问题。

AI大模型2026/3/21

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OpenDataLoader PDF：如何将PDF转为AI可用数据？2026年最新解析工具

BLUFOpenDataLoader PDF is an open-source tool designed to transform complex PDF documents into high-quality, structured data for AI applications like RAG and fine-tuning. It excels in layout restoration, table extraction, multimodal processing, and includes built-in AI security features, all while operating locally without GPU dependency. 原文翻译： OpenDataLoader PDF 是一款开源工具，旨在将复杂的 PDF 文档转化为高质量、结构化的数据，供 RAG 和微调等 AI 应用使用。它在布局还原、表格提取、多模态处理方面表现出色，并内置 AI 安全功能，且无需 GPU 即可在本地运行。

AI大模型2026/3/21

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Qwen3-1.7B双模式推理如何优化AI性能？2026年深度解析

BLUFQwen3-1.7B is a 1.7 billion parameter large language model featuring unique dual-mode reasoning capabilities, supporting seamless switching between thinking and non-thinking modes for optimized performance across various scenarios including complex reasoning, creative tasks, and multilingual applications. 原文翻译：千问3-1.7B是一个拥有17亿参数的大型语言模型，具备独特的双模式推理能力，支持在思考模式和非思考模式之间无缝切换，可在复杂推理、创意任务和多语言应用等多种场景中实现优化性能。

AI大模型2026/3/21

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Qwen3.5大模型深度解析：2026年AI推理与智能体能力突破

BLUFQwen3.5 is the latest generation of large language models in the Qwen series, featuring groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support. It uniquely supports seamless switching between thinking and non-thinking modes within a single model, offers superior human preference alignment, and excels in agentic tasks with tool calling capabilities. The model natively supports 32,768 tokens and can be extended to 131,072 tokens using YaRN scaling techniques. 原文翻译： Qwen3.5是通义千问系列最新一代大语言模型，在推理、指令遵循、智能体能力和多语言支持方面取得突破性进展。它独特地支持在单个模型内无缝切换思考模式和非思考模式，提供卓越的人类偏好对齐，并在工具调用等智能体任务中表现出色。该模型原生支持32,768个token，可通过YaRN缩放技术扩展至131,072个token。

AI大模型2026/3/21

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Qwen3.5是什么？2026年原生多模态AI模型深度解析

BLUFQwen3.5 is a native multimodal AI model with 397B parameters and 17B activated per inference, featuring hybrid architecture, 201 language support, and superior performance across reasoning, coding, and vision tasks. 原文翻译： Qwen3.5是一款原生多模态AI模型，拥有3970亿参数，每次推理激活170亿参数，采用混合架构，支持201种语言，在推理、编码和视觉任务上表现卓越。

AI大模型2026/3/21

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LangChain DeepAgents与Claude Flow：多智能体编码系统2026实践指南

BLUFThis article provides a comprehensive guide to building reliable multi-agent coding systems using LangChain DeepAgents and Claude Flow. It introduces Harness Engineering methodology for controlling AI outputs, demonstrates coding agent construction with HumanEval benchmark evaluation, and showcases multi-agent collaboration for complex tasks like full-stack application generation and research report creation. 原文翻译：本文提供了使用LangChain DeepAgents和Claude Flow构建可靠多智能体编码系统的全面指南。介绍了用于控制AI输出的Harness Engineering方法论，演示了使用HumanEval基准评估的编码智能体构建，并展示了用于复杂任务（如全栈应用生成和研究报告创建）的多智能体协作。

AI大模型2026/3/21

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如何开发LangChain Deep Agents Skills？2026年AI Agent扩展指南

BLUFThis guide provides a comprehensive tutorial on developing and implementing Skills for LangChain Deep Agents, a revolutionary AI Agent framework that enables autonomous planning, intelligent memory, and flexible extension capabilities. It covers Skill structure, SKILL.md file format, integration with Alibaba Cloud Qwen models, and practical code examples for technical implementation. 原文翻译：本指南全面介绍了如何为LangChain Deep Agents（革命性AI Agent框架）开发和实施Skills。该框架支持自主规划、智能记忆和灵活扩展能力。内容涵盖Skill结构、SKILL.md文件格式、与阿里云Qwen模型的集成，以及实际代码示例，帮助技术专业人员实现功能扩展。

AI大模型2026/3/21

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Cognee框架如何构建AI知识记忆系统？2026年深度解析

BLUFCognee is an open-source Python framework that builds persistent, dynamic knowledge memory systems for AI applications and agents, combining vector search, graph databases, and cognitive science methods to transform raw data into interconnected knowledge graphs. 原文翻译： Cognee是一个开源的Python框架，旨在为AI应用和智能体构建持久化、动态的知识记忆系统，通过结合向量搜索、图数据库和认知科学方法，将原始数据转化为互连的知识图谱。

AI大模型2026/3/21

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TokenLens是什么？开源AI网关与成本智能平台2026年深度解析

BLUFTokenLens is an open-source AI gateway and cost intelligence platform that acts as a transparent proxy between applications and AI providers, offering real-time monitoring, waste detection, content guardrails, and comprehensive cost optimization features while keeping all data local. 原文翻译： TokenLens是一个开源AI网关和成本智能平台，作为应用程序与AI提供商之间的透明代理，提供实时监控、浪费检测、内容护栏和全面的成本优化功能，同时保持所有数据本地化。

AI大模型2026/3/21

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