Model Gallery
Need help picking the perfect model to enhance your Retrieval Augmented Generation (RAG) pipeline? Explore our curated gallery, showcasing a selection of the latest, most popular, and highly effective models.
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Latest embedding model from OpenAI. Performance and cost optimization gains.
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Latest embedding model from OpenAI. Performance and cost optimization gains.
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High performance text reranking model. Typically used as the second stage in two-stage retrieval pipelines.
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Medium-sized open source text embedding model, offers great performance comparable to much larger models.
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Good performance open source text embedding model.
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Sentence-transformers model that maps sentences & paragraphs to vector space and can be used for tasks like clustering or semantic search.
multi-modal
Open source multi-modal model from OpenAI trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image.
multi-modal
multi-lingual version of the OpenAI CLIP-ViT-B32 model capable of encoding between text and image modalities.
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Open source text embedding model, ideal for multilingual applications.
multi-modal
CLIP model trained on a massive dataset of text-image pairs to understand relationships between text and images.
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High performance text embedding model accessible via API.
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High performance text embedding model accessible via API.
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Good performance text embedding model that outputs compressed vectors. Closed source model behind API.
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Designed with retrieval capabilities in mind. It achieves a retrieval score of 55.26 on MTEB.
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Highest quality generalist text embedding model from second-generation of Voyage family. Includes a 16K context window.
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Code embedding model from the second generation of the Voyage family. Includes a 16K context window.
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Base text embedding model from second-generation of Voyage family. Mid-sized model optimized for latency and quality.
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Smallest text embedding model from second-generation of Voyage family. Optimized for latency and instruction-tuned for classification, clustering, and semantic similarity.
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Popular, good-performing text embedding model accessible via API.
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Sentence-transformers model that maps sentences & paragraphs to vector space and can be used for tasks like clustering or semantic search.
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Strong text embedding model capable of embedding large text passages.
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Finetuned version of bigscience/bloom-7b1 that excels at multi-lingual text embeddings.
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Good performance open source text embedding model.
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Medium-sized open source text embedding model, offers great performance for the size.
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Small text embedding model that can generate text embeddings tailored to any task or domain via natural language instructions.
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Medium-size text embedding model that can generate text embeddings tailored to any task or domain via natural language instructions.
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Large text embedding model that can generate text embeddings tailored to any task or domain via natural language instructions.
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Highest quality text embedding model from first-generation of Voyage family (for general purpose text embeddings).
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Fastest inference-time performance model for first-generation of Voyage family.
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Instruction-tuned model from first-generation of the Voyage family.