What are embeddings?
Text embeddings are a natural language processing (NLP) technique that converts text into numerical vectors. Embeddings capture semantic meaning and context which results in text with similar meanings having closer embeddings. For example, the sentence "I took my dog to the vet" and "I took my cat to the vet" would have embeddings that are close to each other in the vector space since they both describe similar context.
This is important because it unlocks many algorithms that can operate on vectors but not directly on text.
You can use these embeddings/vectors to compare different texts and understand how they relate. For example, if the embeddings of the text "cat" and "dog" are close together you can infer that these words are similar in meaning and/or context. This ability allows a variety of uses cases described in the next section.
Use cases
Text embeddings power a variety of NLP use cases. For example:
- Information Retrieval: The goal is to retrieve semantically similar text given a piece of input text. A variety of applications can be supported by an information retrieval system such as semantic search, answering questions, or summarization. See the document search notebook for an example.
- Classification: You can use embeddings to train a model to classify documents into categories. For example, if you want to classify user comments as negative or positive, you can use the embeddings service to get the vector representation of each comment to train the classifier.
- Clustering: Comparing vectors of text can show how similar or different they are. This feature can be used to train a clustering model that groups similar text or documents together.
- Vector DB: You can store your generated embeddings in a vector DB to improve the accuracy and efficiency of your NLP application. For example, you can use a vector DB to improve the capabilities of a document search.
the above explaination is from : https://developers.generativeai.google/guide/palm_api_overview
标签：what,embeddings,AI,text,use,vector,similar,example From： https://www.cnblogs.com/saaspeter/p/17578552.html