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目录平台模板

First fully reproducible open-source text embedding model with 8,192 context length. v2 introduces Mixture-of-Experts architecture for multilingual embeddings. Outperforms OpenAI models on benchmarks. This is an OSS model under Apache 2.0 license.

Puck is an open-source vector search engine designed for fast similarity search and retrieval of embedding vectors.

General-purpose embedding model from Voyage AI that outperforms OpenAI by 9.74% average across domains. Features 1024 dimensions and a 32,000 token context window, delivering 3-4x smaller dimension size than competing models while maintaining superior quality.

Multimodal embedding model from Alibaba's Qwen family that processes text, images, and visual documents in a unified embedding space for cross-modal retrieval tasks.

Meta's groundbreaking multimodal embedding model that learns a joint embedding space across six modalities (images, text, audio, depth, thermal, IMU) using only image-paired data, enabling cross-modal retrieval and zero-shot capabilities.

Frontier multilingual text embedding model with 570M parameters and 8192 token-length, featuring task-specific LoRA adapters and outperforming OpenAI and Cohere embeddings on MTEB benchmark.

Microsoft's state-of-the-art multilingual text embedding model supporting 100 languages with 1024-dimensional embeddings, trained on 1 billion multilingual text pairs for robust cross-lingual retrieval.

State-of-the-art Python framework for sentence, text, and image embeddings using siamese BERT networks, providing access to 15,000+ pre-trained models for semantic search, similarity comparison, and clustering.

Groundbreaking multilingual information retrieval model supporting 89 languages with token-level embeddings and late interaction. Features Matryoshka embeddings for flexible efficiency-precision tradeoffs and 8192 token input context.

Commercial text embedding model from Cohere with multilingual support and 1,024-dimensional vectors. Optimized for semantic search and retrieval tasks. This is a commercial API service with pay-per-use pricing.

Module of Google’s Machine Learning Crash Course that explains word and text embeddings, how they are obtained, and the difference between static and contextual embeddings, giving essential background for using vector representations in vector databases and similarity search systems.