Gemini Embedding 2
Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
- Vision
AI model category
The search layer behind knowledge assistants, semantic search and recommendations.
Embedding models turn text into lists of numbers, called vectors, so that similar meanings end up close together. They power the search step in retrieval-augmented generation, where an assistant first finds the right passages and then answers from them. Rerank models take the top results and sort them more precisely.
Embeddings are cheap per request, but switching models later means re-embedding every document, so the choice deserves a proper test.
Embeddings
Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
OpenAI
OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
OpenAI
OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
Cohere
Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
Cohere
Cohere's multilingual rerank models, which sort search results by relevance, in a best-quality Pro version and a low-latency Fast version.
Voyage AI by MongoDB
Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
How to choose
Build a small test set of real questions with the passages that should answer them, and measure how often each model finds them. Check language support if your documents are not all in English, and the maximum input length per chunk. Adding a rerank step is often the cheapest way to improve results.
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