ViDoRAG utilizes multi-agent iterative reasoning and hybrid retrieval strategies to enhance performance in visual document retrieval-augmented generation tasks.
更新时间:2025-03-05 13:53:54
ViDoRAG is a cutting-edge framework developed to handle complex tasks involving visual documents. It combines visual retrieval with text-based reasoning through a dynamic iterative approach, creating a more robust AI system. This framework is particularly useful for tasks where documents contain both textual and visual information, enabling the system to reason over both modalities to generate more accurate and relevant responses.
To use ViDoRAG, first set up the environment by creating a Conda environment and installing dependencies. Then, download the dataset and set up an index database. Use the multi-modal retriever for data retrieval and the multi-agent generation module for generating answers from the retrieved content. You can also perform evaluations with the provided scripts to assess performance on your dataset.
ViDoRAG is an open-source framework, and its usage and evaluation code are freely available on GitHub.
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