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Voyage Multimodal 3
Overview :
voyage-multimodal-3, launched by Voyage AI, is a multimodal embedding model that vectorizes text and images (including screenshots of PDFs, slides, and tables) while capturing key visual features. This advancement significantly enhances document retrieval accuracy for rich visual and textual information within knowledge bases, making it important for RAG and semantic search applications. On multimodal retrieval tasks, voyage-multimodal-3 achieves an average improvement of 19.63% in retrieval accuracy compared to other models.
Target Users :
The target audience includes enterprises and research institutions that need to process and retrieve documents containing rich visual and textual information. voyage-multimodal-3 enhances their ability to manage and utilize information in knowledge bases effectively by providing high-precision multimodal retrieval capabilities, thereby improving work efficiency and the accuracy of information retrieval.
Use Cases
In the legal field, for matching queries with document screenshots containing legal clauses.
In finance, for retrieving documents that include financial statements and charts.
In education, for retrieving academic documents containing teaching materials and diagrams.
Features
Supports text and content-rich images, such as screenshots of text, charts, tables, PDFs, and slides.
Captures key textual and visual features such as font size, text positioning, and whitespace without complex document parsing.
Allows for maximum flexibility in interleaving text and images, processing both modalities through a unified representation.
On multimodal retrieval tasks, achieves average improvements of 41.44% and 43.37% in retrieval accuracy compared to models like OpenAI CLIP large and Cohere multimodal v3.
Effectively captures the semantic content of screenshots through a unified processing approach, performing exceptionally well even with mixed modality data.
Eliminates the need for screen parsing models, layout analysis, or complex text extraction processes, allowing direct vectorization of knowledge bases containing pure text documents and unstructured data.
How to Use
1. Visit the official Voyage AI website or documentation to understand the basic information and usage requirements of voyage-multimodal-3.
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2. Register to obtain API access and start a free trial.
3. Learn how to vectorize text and image data using the provided sample notebook or documentation.
4. Integrate voyage-multimodal-3 into your existing knowledge management system to enhance retrieval efficiency.
5. Use voyage-multimodal-3 to process complex documents containing text and images, such as PDFs and slides.
6. Evaluate the performance of voyage-multimodal-3 in practical applications by comparing retrieval results.
7. Contact Voyage AI for further technical support or customizations to the model as needed.
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