https://guijiagi.com/tags/rag/Recent content in RAG on 硅基 AGI · 智能体学习与测评Hugozh-cn本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权Thu, 16 Jul 2026 11:02:00 +0800https://guijiagi.com/posts/b1-f95dfde9/Thu, 16 Jul 2026 11:02:00 +0800https://guijiagi.com/posts/b1-f95dfde9/从基础RAG到高级RAG的完整技术路线，涵盖混合检索、重排序、查询改写、自反思等关键技术https://guijiagi.com/posts/b2-d60bc32e/Thu, 16 Jul 2026 10:36:00 +0800https://guijiagi.com/posts/b2-d60bc32e/分析大模型处理超长上下文面临的技术挑战，涵盖注意力衰减、Needle-in-Haystack测试与长上下文优化策略https://guijiagi.com/posts/b2-97b38611/Thu, 16 Jul 2026 10:18:00 +0800https://guijiagi.com/posts/b2-97b38611/探讨知识图谱与大语言模型融合的技术方案，涵盖知识注入、推理增强与可解释性提升https://guijiagi.com/posts/b2-aa65552f/Thu, 16 Jul 2026 10:17:00 +0800https://guijiagi.com/posts/b2-aa65552f/分析AI搜索技术的架构演进，对比Perplexity、SearchGPT与传统搜索引擎的技术差异与用户体验https://guijiagi.com/posts/b2-922f32c3/Thu, 16 Jul 2026 10:02:00 +0800https://guijiagi.com/posts/b2-922f32c3/从BM25到ColBERT，深入探讨RAG系统中混合检索策略、交叉编码器重排序及检索质量评估方法https://guijiagi.com/posts/article-77/Mon, 13 Jul 2026 05:40:00 +0800https://guijiagi.com/posts/article-77/系统对比主流Embedding模型，从维度、语言支持、性能基准到场景适配，帮你做出最佳选择https://guijiagi.com/posts/article-28/Sun, 12 Jul 2026 21:30:00 +0800https://guijiagi.com/posts/article-28/将知识图谱引入RAG系统，解决传统向量检索的语义鸿沟与多跳推理难题https://guijiagi.com/posts/b2-55210222/Sun, 12 Jul 2026 21:30:00 +0800https://guijiagi.com/posts/b2-55210222/将知识图谱引入RAG系统，解决传统向量检索的语义鸿沟与多跳推理难题https://guijiagi.com/posts/article-24/Sun, 12 Jul 2026 20:50:00 +0800https://guijiagi.com/posts/article-24/在统一测试环境下全面对比三大向量数据库的性能、功能和易用性，为技术选型提供参考https://guijiagi.com/posts/b2-caa1d9fe/Sun, 12 Jul 2026 20:50:00 +0800https://guijiagi.com/posts/b2-caa1d9fe/在统一测试环境下全面对比三大向量数据库的性能、功能和易用性，为技术选型提供参考https://guijiagi.com/posts/article-12/Sun, 12 Jul 2026 18:50:00 +0800https://guijiagi.com/posts/article-12/手把手指导企业级RAG系统的架构设计、技术选型和部署方案，覆盖从数据接入到线上监控的全流程https://guijiagi.com/posts/b2-f0acee82/Sun, 12 Jul 2026 18:50:00 +0800https://guijiagi.com/posts/b2-f0acee82/手把手指导企业级RAG系统的架构设计、技术选型和部署方案，覆盖从数据接入到线上监控的全流程https://guijiagi.com/posts/article-02/Sun, 12 Jul 2026 17:10:00 +0800https://guijiagi.com/posts/article-02/探讨GraphRAG如何通过知识图谱增强检索增强生成，解决传统向量检索的多跳推理瓶颈https://guijiagi.com/posts/b2-7138b571/Sun, 12 Jul 2026 17:10:00 +0800https://guijiagi.com/posts/b2-7138b571/探讨GraphRAG如何通过知识图谱增强检索增强生成，解决传统向量检索的多跳推理瓶颈https://guijiagi.com/posts/rag-framework-comparison-2026/Thu, 02 Jul 2026 11:36:00 +0800https://guijiagi.com/posts/rag-framework-comparison-2026/2026年主流RAG框架全面对比，从检索质量到生成效果的深度评测https://guijiagi.com/posts/llamaindex-2026-guide/Thu, 02 Jul 2026 11:31:00 +0800https://guijiagi.com/posts/llamaindex-2026-guide/2026年LlamaIndex框架使用指南，构建数据驱动的LLM应用https://guijiagi.com/posts/rag-vs-fine-tuning-decision/Thu, 02 Jul 2026 11:31:00 +0800https://guijiagi.com/posts/rag-vs-fine-tuning-decision/何时用RAG、何时用微调？基于多维度评估的系统化决策框架https://guijiagi.com/posts/haystack-2026-rag/Thu, 02 Jul 2026 11:30:00 +0800https://guijiagi.com/posts/haystack-2026-rag/2026年Haystack框架在企业RAG系统中的实践与优化https://guijiagi.com/posts/rag-pipeline-optimization-2026/Thu, 02 Jul 2026 11:18:00 +0800https://guijiagi.com/posts/rag-pipeline-optimization-2026/从文档处理到检索增强，系统优化RAG管线的每个环节https://guijiagi.com/posts/embedding-model-selection-2026/Thu, 02 Jul 2026 10:56:00 +0800https://guijiagi.com/posts/embedding-model-selection-2026/2026年文本嵌入模型全面选型指南，覆盖多语言、长文本、代码等场景https://guijiagi.com/posts/ai-long-tail-knowledge/Thu, 02 Jul 2026 10:46:00 +0800https://guijiagi.com/posts/ai-long-tail-knowledge/AI在常见任务上强大，但在长尾知识领域为何表现不佳https://guijiagi.com/posts/advanced-rag-2026-patterns/Thu, 02 Jul 2026 10:35:00 +0800https://guijiagi.com/posts/advanced-rag-2026-patterns/系统解析2026年高级RAG架构的设计模式，包括多跳检索、自适应检索与推理增强检索https://guijiagi.com/posts/agentic-rag-evolution-2026/Tue, 30 Jun 2026 16:50:00 +0800https://guijiagi.com/posts/agentic-rag-evolution-2026/RAG 技术全景：从基础检索增强生成到 Agentic RAG 的架构演进，对比主流方案并给出选型指南https://guijiagi.com/posts/hallucination-root-cause-2026/Tue, 30 Jun 2026 16:30:00 +0800https://guijiagi.com/posts/hallucination-root-cause-2026/深入分析大模型幻觉产生的根本原因，对比2026年主流幻觉检测与缓解技术的原理、效果与适用场景https://guijiagi.com/posts/agent-memory-architecture-2026/Tue, 30 Jun 2026 16:20:00 +0800https://guijiagi.com/posts/agent-memory-architecture-2026/2026年Agent记忆系统技术全景：向量数据库、知识图谱、长期记忆架构的工程实践与前沿研究https://guijiagi.com/posts/agentic-rag-architecture-revolution/Tue, 30 Jun 2026 09:40:00 +0800https://guijiagi.com/posts/agentic-rag-architecture-revolution/Agentic RAG将检索增强生成从被动管道升级为主动决策系统，本文深入解析其架构设计、核心模式与工程实践https://guijiagi.com/posts/rag-vs-long-context-comparison/Tue, 30 Jun 2026 09:40:00 +0800https://guijiagi.com/posts/rag-vs-long-context-comparison/深度对比RAG与长上下文模型的优劣势，通过实测数据给出清晰的选型决策框架https://guijiagi.com/posts/rag-chunking-strategies-comparison/Tue, 30 Jun 2026 09:40:00 +0800https://guijiagi.com/posts/rag-chunking-strategies-comparison/分块是RAG系统中影响检索质量最关键的环节，本文深度对比六种分块策略的原理、效果与适用场景https://guijiagi.com/posts/rag-production-pitfalls-guide/Tue, 30 Jun 2026 09:40:00 +0800https://guijiagi.com/posts/rag-production-pitfalls-guide/RAG系统上线后最常见的三大难题的实战解决方案，来自生产环境的真实排坑经验https://guijiagi.com/posts/llamaindex-2026-rag-to-agent-platform/Tue, 30 Jun 2026 09:30:00 +0800https://guijiagi.com/posts/llamaindex-2026-rag-to-agent-platform/解析LlamaIndex 2026如何从RAG框架演进为Agent平台，涵盖AgentWorkflow、数据代理、多模态RAG等核心特性https://guijiagi.com/posts/llm-hallucination-mitigation/Tue, 30 Jun 2026 09:20:00 +0800https://guijiagi.com/posts/llm-hallucination-mitigation/深入分析大模型幻觉问题的根因，系统介绍从训练到推理全链路的缓解技术https://guijiagi.com/posts/rag-evolution-2026/Tue, 30 Jun 2026 09:10:00 +0800https://guijiagi.com/posts/rag-evolution-2026/2026年RAG技术全景：从Naive RAG到Agentic RAG，GraphRAG、Multi-modal RAG、Adaptive RAG等前沿方案的架构解析与实战对比https://guijiagi.com/posts/ai-customer-service-system-2026/Sun, 28 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/ai-customer-service-system-2026/2026年AI客服系统全流程构建指南，涵盖知识库搭建、多轮对话设计、意图识别与人工转接策略https://guijiagi.com/posts/llm-hallucination-2026-analysis/Sun, 28 Jun 2026 11:10:00 +0800https://guijiagi.com/posts/llm-hallucination-2026-analysis/LLM幻觉问题的深度根因分析与2026年最新缓解策略：从RAG到自验证的完整方案https://guijiagi.com/posts/dify-platform-2026-review/Sun, 28 Jun 2026 11:00:00 +0800https://guijiagi.com/posts/dify-platform-2026-review/深度评测 Dify 2026 版本，从可视化编排、Agent 能力、RAG 引擎到企业部署的全面分析https://guijiagi.com/posts/llamaindex-2026-agent-platform/Sun, 28 Jun 2026 11:00:00 +0800https://guijiagi.com/posts/llamaindex-2026-agent-platform/探索 LlamaIndex 2026 如何从一个 RAG 框架进化为完整的 Agent 平台，涵盖 Workflows、Tool Abstraction 与 Data Agentshttps://guijiagi.com/posts/agentic-rag-when-rag-meets-agent/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/agentic-rag-when-rag-meets-agent/探索 Agentic RAG 的设计理念、架构模式和工程实现，理解为什么 Agent 是 RAG 的自然进化方向https://guijiagi.com/posts/embedding-model-2026-chinese-retrieval-benchmark/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/embedding-model-2026-chinese-retrieval-benchmark/2026年主流Embedding模型在中文检索场景的全面排行与实测对比https://guijiagi.com/posts/graphrag-production-deployment-guide/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/graphrag-production-deployment-guide/从零到一搭建 GraphRAG 生产系统，涵盖知识图谱构建、社区检测、查询引擎和运维监控https://guijiagi.com/posts/rag-troubleshooting-hallucination-miss-retrieval-latency/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-troubleshooting-hallucination-miss-retrieval-latency/系统梳理 RAG 系统中的高频故障模式，提供从症状到根因的诊断框架和修复方案https://guijiagi.com/posts/rag-chunking-strategies-semantic-vs-document-aware-vs-hierarchical/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-chunking-strategies-semantic-vs-document-aware-vs-hierarchical/深入对比 2026 年主流 RAG 分块策略，包含实验数据、代码实现和选型建议https://guijiagi.com/posts/rag-architecture-2026-naive-to-graphrag-agent/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-architecture-2026-naive-to-graphrag-agent/系统梳理 RAG 架构演进，从最基础的 Naive RAG 到 2026 年最前沿的 GraphRAG+Agent 范式，附完整架构对比与代码示例https://guijiagi.com/posts/rag-evaluation-framework-2026-ragas-custom-metrics/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-evaluation-framework-2026-ragas-custom-metrics/系统介绍 RAG 系统的评估方法论，涵盖 RAGAS 框架、自定义指标设计和端到端评测流水线https://guijiagi.com/posts/rag-data-pipeline-pdf-html-database-to-knowledge-base/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-data-pipeline-pdf-html-database-to-knowledge-base/系统讲解 RAG 数据管道的工程设计，涵盖文档解析、清洗、增强、质量控制和自动化运维https://guijiagi.com/posts/rag-reranking-cohere-bge-jina-comparison/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-reranking-cohere-bge-jina-comparison/深入对比三大主流重排序模型，包含性能基准、成本分析和工程选型建议https://guijiagi.com/posts/reranker-model-selection-2026-cohere-bge-jina/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/reranker-model-selection-2026-cohere-bge-jina/2026年主流Reranker模型深度对比：Cohere、BGE、Jina在中文与英文检索重排场景的全面评测https://guijiagi.com/posts/multimodal-rag-image-text-hybrid-retrieval/Sun, 28 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/multimodal-rag-image-text-hybrid-retrieval/深入多模态 RAG 的工程实现，涵盖图文统一编码、混合检索策略、跨模态重排序等核心技术https://guijiagi.com/posts/graphrag-graph-retrieval/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/graphrag-graph-retrieval/GraphRAG图检索增强生成https://guijiagi.com/posts/graphrag-knowledge-graph-rag/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/graphrag-knowledge-graph-rag/GraphRAG图检索增强生成的原理、架构与实践，突破传统向量检索的局限性https://guijiagi.com/posts/rag-agent-fusion-architecture/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-agent-fusion-architecture/RAG+Agent融合架构实践https://guijiagi.com/posts/ragflow-opensource-rag/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/ragflow-opensource-rag/RAGFlow开源RAG方案https://guijiagi.com/posts/rag-chunking-strategy-evaluation/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-chunking-strategy-evaluation/固定分块、语义分块、递归分块等RAG分块策略的深度对比与评估https://guijiagi.com/posts/rag-chunking-strategy/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-chunking-strategy/RAG分块策略对比评估https://guijiagi.com/posts/rag-hybrid-search/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-hybrid-search/RAG混合检索方案设计https://guijiagi.com/posts/rag-system-from-scratch/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-system-from-scratch/RAG系统从零搭建完整教程https://guijiagi.com/posts/rag-eval-metrics/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-eval-metrics/RAG系统评估指标体系https://guijiagi.com/posts/rag-eval-optimization/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-eval-optimization/RAG效果评估与优化闭环https://guijiagi.com/posts/rag-rerank-strategy/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/rag-rerank-strategy/RAG系统中重排序策略的原理、模型选择与工程实践，提升检索精度的关键环节https://guijiagi.com/posts/multimodal-rag-practice/Sat, 27 Jun 2026 15:00:00 +0800https://guijiagi.com/posts/multimodal-rag-practice/多模态RAG架构实践https://guijiagi.com/posts/rag-vs-fine-tuning-2026/Fri, 26 Jun 2026 14:00:00 +0800https://guijiagi.com/posts/rag-vs-fine-tuning-2026/RAG 和微调不是非此即彼的选择，而是互补的知识注入策略。本文从 2026 年的实践视角，给出全场景决策框架。https://guijiagi.com/posts/agent-hallucination-mitigation/Fri, 26 Jun 2026 14:00:00 +0800https://guijiagi.com/posts/agent-hallucination-mitigation/系统梳理 AGI 智能体幻觉问题的成因，提供从检测、缓解到修复的全链路策略，附实战代码与架构建议。https://guijiagi.com/posts/embedding-model-guide/Thu, 25 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/embedding-model-guide/深度对比 2026 年主流 Embedding 模型：BGE-M3、E5-Mistral、OpenAI text-embedding-3、Cohere embed-v4 在 RAG、语义搜索、聚类等场景的表现与选型建议。https://guijiagi.com/posts/graph-rag-explained/Thu, 25 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/graph-rag-explained/深入解析 GraphRAG 的完整架构：实体抽取、关系构建、社区检测、层级摘要与多跳检索，对比传统 RAG 的优劣，附 Neo4j + LLM 的完整代码实现。https://guijiagi.com/posts/rag-production-pitfalls/Thu, 25 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/rag-production-pitfalls/从向量检索失效到多跳推理失败，总结 RAG 系统在生产环境中常见的 12 个陷阱及其工程解决方案。https://guijiagi.com/posts/rag-reranking-guide/Thu, 25 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/rag-reranking-guide/深入对比三种主流 RAG 重排序方案：Cohere Rerank API、BAAI bge-reranker、自训练 Cross-Encoder，涵盖原理、部署方式、代码实现、延迟测试与效果对比，给出不同场景的最佳选择。https://guijiagi.com/posts/advanced-rag-patterns/Thu, 25 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/advanced-rag-patterns/深入解析四种高级 RAG 模式：HyDE 假设文档嵌入、CRAG 纠正性检索增强生成、Self-RAG 自反思检索增强生成、FLARE 主动检索增强生成，附完整代码实现与性能对比。https://guijiagi.com/posts/fine-tuning-vs-rag-decision/Thu, 25 Jun 2026 12:00:00 +0800https://guijiagi.com/posts/fine-tuning-vs-rag-decision/从知识更新频率、推理深度、成本预算、数据隐私等维度，系统对比微调与 RAG 的适用场景，给出决策框架和混合方案。https://guijiagi.com/posts/llama-index-agent-review/Thu, 25 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/llama-index-agent-review/LlamaIndex 不只是 RAG 框架。Data Agent、Agent Worker、Agent Runner 如何将检索增强生成进化为智能体？RAG+Agent 融合有哪些实战模式？https://guijiagi.com/posts/rag-vs-finetune-decision/Thu, 25 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-vs-finetune-decision/深入对比 RAG 与微调的技术特征、成本结构和适用场景，提供决策矩阵和混合方案建议https://guijiagi.com/posts/rag-production-debugging/Thu, 25 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-production-debugging/系统性梳理 RAG 在生产环境中的常见问题、调试工具链与全链路排障方法论https://guijiagi.com/posts/rag-production-architecture/Thu, 25 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-production-architecture/生产级 RAG 架构全解：嵌入、检索、重排、生成四层设计，向量数据库选型与混合检索策略https://guijiagi.com/posts/rag-data-pipeline/Thu, 25 Jun 2026 10:00:00 +0800https://guijiagi.com/posts/rag-data-pipeline/RAG 数据管道的完整构建指南，覆盖数据源接入、清洗标准化、分块策略、嵌入生成、增量更新与质量监控https://guijiagi.com/posts/rag-vs-long-context/Wed, 24 Jun 2026 16:00:00 +0800https://guijiagi.com/posts/rag-vs-long-context/系统对比 RAG 与长上下文模型方案，提供决策矩阵与混合架构建议https://guijiagi.com/posts/ai-customer-service-build/Wed, 24 Jun 2026 14:00:00 +0800https://guijiagi.com/posts/ai-customer-service-build/完整指南：构建生产级 AI 客服系统，涵盖需求分析、知识库构建、意图识别、多轮对话管理、人工转接与满意度评估https://guijiagi.com/posts/llama-index-review/Wed, 24 Jun 2026 14:00:00 +0800https://guijiagi.com/posts/llama-index-review/深度评测 LlamaIndex 框架，涵盖核心架构、索引类型、查询引擎、Agent 模式及与 LangChain 的对比https://guijiagi.com/posts/rag-architecture-patterns/Wed, 24 Jun 2026 10:10:00 +0800https://guijiagi.com/posts/rag-architecture-patterns/系统梳理 RAG 架构的演进路径、设计模式和工程选型