# Retrieval

VARAG
VARAG
VARAG is a system that supports various retrieval technologies, optimized for different use cases of text, image, and multimodal document retrieval. It simplifies traditional retrieval workflows by embedding document pages as images and enhances retrieval accuracy and efficiency through advanced visual language models. VARAG's primary advantage lies in its capability to handle complex visual and textual content, providing robust support for document retrieval.
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DenserRetriever
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DenserRetriever is an open-source AI retrieval model designed specifically for Retrieval-Augmented Generation (RAG). Leveraging the collaborative power of the community and XGBoost machine learning technology, it effectively combines diverse retrieval engines to meet the needs of large enterprises. It is easy to deploy and supports rapid startup via Docker. It has achieved state-of-the-art accuracy in the MTEB retrieval benchmark and is also featured on the Hugging Face leaderboard.
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Snowflake Arctic Embed
Snowflake Arctic Embed
Snowflake Arctic Embed is a family of open-source text embedding models, licensed under Apache 2.0, specifically designed for retrieval use cases. These models offer leading retrieval performance on the Massive Text Embedding Benchmark (MTEB) and provide organizations with new advantages when combining proprietary datasets with Large Language Models (LLMs) for Retrieval Augmented Generation (RAG) or semantic search services. They come in various sizes, from extra-small (xs) to large (l), with different context windows and parameter counts to meet the diverse latency, cost, and retrieval performance needs of businesses.
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FastVLM
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