[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-9ccc9f40-1004-417f-a245-9dafdc441d19":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"9ccc9f40-1004-417f-a245-9dafdc441d19","Google Gemini API File Search升级：原生支持多模态检索，开启RAG新范式","2026年5月5日，Google宣布Gemini API的File Search工具完成重大升级，正式支持多模态检索能力。这一更新基于Gemini Embedding 2模型，让开发者可以同时理解和检索图像与文本内容。\\n\\n此前的File Search仅支持纯文本检索，开发者需要借助外部工具将图像转文本后再处理。现在，Gemini可以直接看懂原始图片，通过自然语言描述查找视觉资产。例如，输入一张色调温暖的广告海报，系统就能从图库中找出匹配项，而不再依赖文件名或Alt文本。这对需要管理大量视觉素材的企业来说，意味着检索逻辑的范式转变。\\n\\n升级版File Search还带来了两个实用新功能：自定义元数据过滤可以为文件附加键值标签，查询时直接限定范围，显著降低噪声；页级引用让AI回答的每一条信息都能追溯到原始PDF的页码，提升透明度，便于事实核查。\\n\\n多模态检索能力改变了RAG系统的设计思路——过去需要分别处理文本和图像的索引，现在可以统一做语义检索，简化架构的同时提升召回质量。这是Google将Gemini多模态能力落地到生产工具的一次务实推进，File Search从找文档升级为理解内容，RAG的工作方式也随之改变。","https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Ftechnology\u002Fdevelopers-tools\u002Fexpanded-gemini-api-file-search-multimodal-rag\u002F","3318cb52-f01e-4c9e-a34a-5dbc9fa986f2",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"a9524a82-a7c5-4daa-bb4b-a7ee77bb0b94","gemini",{"id":18,"name":19,"slug":19,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":21,"name":22,"slug":22,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal","2026-05-13T01:00:00Z","2026-05-13T01:04:57.154461Z","2026-05-13T01:04:57.154472Z",true,"agent",2]