Intfloat: E5-Base-v2

Intfloat: E5-Base-v2 is an embedding model from Intfloat, listed on api14 under the model id intfloat/e5-base-v2. It sits in the embeddings category, which means it is not a chat or text generation model. Instead of producing replies, it turns text into vectors that you can store, compare and search over. The provider release date recorded for it is 2025-11-18.

The model takes text as its only input and returns embeddings as its only output. It encodes English sentences and paragraphs into a 768-dimensional dense vector space, and the provider describes the result as efficient, high-quality semantic embeddings. The context window is 512 tokens, so each request should contain a single passage or query that fits within that budget. The maximum output is 460, which in practice bounds the size of the text unit you send in one call. There are no additional supported parameters beyond the core request fields, so you cannot tune behaviour with sampling style options the way you would with a generative model.

Those characteristics point to a fairly specific set of uses. Semantic search is the obvious one: embed your documents once, embed the user query at request time, then rank by vector similarity. The same mechanism covers similarity scoring between two pieces of text, deduplication of near-identical records, clustering of sentences or paragraphs, and retrieval steps in a RAG pipeline where you need a compact English embedding to fetch candidate passages before handing them to a generation model. Because the vectors are dense and 768-dimensional, they work with standard vector stores and cosine similarity tooling without special handling.

You call it through api14's OpenAI-compatible API using the model id intfloat/e5-base-v2. Because the interface follows the OpenAI embeddings shape, you send your text in the input field and read the vector back from the response, so existing OpenAI client libraries and embedding pipelines generally need only a base URL and model id change. The model supports no extra parameters, so keep requests to the essentials and avoid passing tuning options that have no effect here.

The main constraint to plan around is the 512-token context. Long documents will need chunking before embedding, and anything beyond English is outside what the provider description covers. If your task is generating text rather than representing it, a chat or completion model is the right choice; E5-Base-v2 is best treated as the retrieval and similarity component of a larger system.