> For the complete documentation index, see [llms.txt](https://genaistack.aiplanet.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://genaistack.aiplanet.com/components/embedding.md).

# 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


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