From 87eae49468c4b17b2d990523d5de80d82b50a78e Mon Sep 17 00:00:00 2001 From: 0007 <0007@qq.com> Date: Wed, 27 Aug 2025 19:57:14 +0800 Subject: [PATCH] Add File --- docs/en/core/embedding.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 docs/en/core/embedding.md diff --git a/docs/en/core/embedding.md b/docs/en/core/embedding.md new file mode 100644 index 0000000..7eb8f09 --- /dev/null +++ b/docs/en/core/embedding.md @@ -0,0 +1,13 @@ +# Embedding + +Embedding can be understood simply as follows: there is an algorithm (or model) that can map high-dimensional data to a low-dimensional vector space. This mapping process is essentially a process of feature extraction. + +> Low-dimensional vector data can reduce the complexity of data, thereby improving the efficiency of model training and inference. + +## Samples + +```java +Llm llm = OpenAILlm.of("sk-rts5NF6n*******"); +VectorData embeddings = llm.embed(Document.of("some document text")); +System.out.println(Arrays.toString(embeddings.getVector())); +```