嵌入模型供应商锁定与高成本重新嵌入问题
问题概述
用户在使用专有嵌入模型时面临供应商锁定风险，切换模型需支付高昂成本重新嵌入已存储的数百万向量；同时缺乏体型适中、支持多模态的嵌入模型，阻碍了本地快速嵌入工具的开发。
痛点标签
- 使用专有嵌入模型会导致供应商锁定，切换模型时需支付高昂成本重新嵌入已存储的数百万向量。
- 缺乏体型适中、支持多模态的嵌入模型，导致本地快速嵌入工具的开发受阻。
待验证问题
- 你是否在使用需要长期存储向量的嵌入模型？切换模型时是否面临重新计算的成本？
- 你是否需要一个体型适中、支持多模态的嵌入模型用于本地部署？
证据
原话 · @Anonymous
I really appreciate that EmbeddingGemma 2 is under the Apache 2.0 license. For embedding models in particular, I don't think it makes sense to use a closed, proprietary, hosted-only model. Most applications of embedding models involve calculating thousands or even millions of embedding vectors and storing them for later comparison. If your model is proprietary, the vendor is likely someday going to decide to stop offering that model. They'll have a better model to replace it, but you still need to pay to re-calculate those millions of stored existing vectors.
原话 · @Anonymous
Finally. I was getting annoyed that there's been an inflection point in how LLMs/agents work but there hasn't been a good moderate-size embeddings model, and this one is multimodal too! 270M for text only is great compared to older embedding models, and a total 440M for text + vision is also fair. I also may or may not have a tool for much faster local embedding creation that I calibrated for EmbeddingGemma but didn't want to release until a better embedding model came along.
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