Retrieval augmented generation (RAG) for technical documents creates
challenges as embeddings do not often capture domain information. We review
prior art for important factors affecting RAG and perform experiments to
highlight best practices and potential challenges to build RAG systems for
technical documents.

技术文件的检索增强生成（RAG）面临的挑战在于嵌入不经常捕捉到域信息。我们回顾了影响 RAG 的重要因素的先前研究，并进行实验以突出最佳实践和建立技术文件 RAG 系统的潜在挑战。

构建技术文档的 RAG 系统的观察

Observations on Building RAG Systems for Technical Documents

Much computer vision research has focused on natural images, but technical
documents typically consist of abstract images, such as charts, drawings,
diagrams, and schematics. How well do general web search engines discover
abstract images? Recent advancements in computer vision and machine learning
have led to the rise of reverse image search engines. Where conventional search
engines accept a text query and return a set of document results, including
images, a reverse image search accepts an image as a query and returns a set of
images as results. This paper evaluates how well common reverse image search
engines discover abstract images. We conducted an experiment leveraging images
from Wikimedia Commons, a website known to be well indexed by Baidu, Bing,
Google, and Yandex. We measure how difficult an image is to find again
(retrievability), what percentage of images returned are relevant (precision),
and the average number of results a visitor must review before finding the
submitted image (mean reciprocal rank). When trying to discover the same image
again among similar images, Yandex performs best. When searching for pages
containing a specific image, Google and Yandex outperform the others when
discovering photographs with precision scores ranging from 0.8191 to 0.8297,
respectively. In both of these cases, Google and Yandex perform better with
natural images than with abstract ones achieving a difference in retrievability
as high as 54\% between images in these categories. These results affect anyone
applying common web search engines to search for technical documents that use
abstract images.

本文评估常见反向图像搜索引擎在发现技术文档中抽象图像方面的表现，发现自然图像上，Yandex 表现更好，在搜索包含特定图像的页面时，Google 和 Yandex 的精度得分约为 0.8191 至 0.8297，且在这两种情况下，Google 和 Yandex 在自然图像上的表现优于抽象图像，这些结果影响着使用常见网络搜索引擎搜索使用抽象图像的技术文档的人们