Integrating Vision into RAG Applications
Retrieval Augmented Generation (RAG) now supports multimodal capabilities on Azure, enabling large language models to process text and images, enhancing query responses and improving utility in visual data fields.
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Retrieval Augmented Generation (RAG) is evolving to incorporate multimodal capabilities, allowing large language models (LLMs) to process and respond to queries based on both text and image data. The Azure platform has introduced support for RAG applications that utilize images, enhancing the ability to interpret visual information such as graphs and photos. Key components of this integration include multimodal LLMs like gpt-4o and phi3-vision, which can accept both text and image inputs, and a multimodal embedding API that computes embeddings for text and images. The updated RAG solution involves modifications to the Azure AI Search index to accommodate image embeddings, as well as a refined data ingestion process that converts document pages into images while retaining their filenames for citation purposes. Users can now ask questions that require understanding both text and images, significantly improving the utility of RAG in fields that rely heavily on visual data. Future enhancements may include support for additional file types and more selective embedding strategies to optimize search results. The community is encouraged to contribute to the development of multimodal RAG applications.
- RAG applications can now process both text and images for enhanced query responses.
- Azure offers multimodal LLMs and embedding APIs to support this integration.
- The updated RAG solution includes modifications to the search index and data ingestion processes.
- Users can ask questions that require interpreting both text and images.
- Future improvements may expand file type support and refine embedding strategies.
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