# Embeddings ## Explanation * Embeddings are numerical representations of data, typically used to represent words, sentences, or other objects in a vector space. * In natural language processing (NLP), word embeddings are widely used to convert words into dense vectors. Each word is represented by a unique vector in such a way that semantically similar words have similar vectors. * Popular word embedding methods include Word2Vec, GloVe, and FastText. * Word embeddings are essential in various NLP tasks such as sentiment analysis, machine translation, and named entity recognition. * They capture semantic relationships between words, allowing models to understand context and meaning. * In addition to words, entire sentences or paragraphs can be embedded into fixed-length vectors, preserving the semantic information of the text. * Sentence embeddings are useful for tasks like text classification, document clustering, and information retrieval ### Supported Embeddings: Currently we support one Embedding platforms , they are: * Langchain By default you can get a embedding function which is HuggingFace --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://genaistack.aiplanet.com/components/embedding.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.