Gemini API 文件搜索工具近日扩展三项功能更新，旨在帮助开发者更轻松地构建高精度多模态检索增强生成系统。更新包括：多模态支持，通过Gemini Embedding 2模型实现对图像和文本的同步推理；自定义元数据过滤，允许为文件添加键值标签以结构化非结构化数据，从而提升搜索速度；精确引用功能，能够捕获并返回每条索引信息的精确来源，如页码。开发者可通过Google AI Studio的示例应用体验这些功能，与图像和文档库交互，提问并追溯答案来源。
如果你在用 Gemini 搭 RAG 系统，这三项更新能直接改善搜索精度和可解释性，多模态搜索终于把图片和文档打通了，值得马上试试。
We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision:
+ Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously.
+ Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed.
+ Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed.
See multimodal File Search in action with our example app in @GoogleAIStudio. Chat with your entire image and doc library, ask questions, and trace answers back to the source: http://goo.gle/4tKSz1k
来源：Google AI Developers · x.com