Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essential for tasks like information retrieval, semantic clustering, and text re-ranking, most existing open-source models, especially those built on architectures like BERT, struggle to represent lengthy documents and often resort to truncation. One common approach to mitigate this challenge involves splitting documents into smaller paragraphs for embedding. However, this strategy results in a much larger set of vectors, consequently leading to increased memory consumption and computationally intensive vector searches with elevated latency. To address these challenges, we introduceJina  Embeddings v2, an open-source text embedding model111Base model (0.27G) :https://huggingface.co/jinaai/jina-embeddings-v2-base-enSmall model (0.07G) :https://huggingface.co/jinaai/jina-embeddings-v2-small-enAPI:https://jina.ai/embeddingscapable of accommodating up totokens. This model is designed to transcend the conventional-token limit and adeptly process long documents.Jina  Embeddings v2not only achieves state-of-the-art performance on a range of embedding-related tasks in the MTEB benchmark but also matches the performance of OpenAI’s proprietarytext-embedding-ada-002model. Additionally, our experiments indicate that an extended context can enhance performance in tasks such as NarrativeQA.