> For the complete documentation index, see [llms.txt](https://tailwindsdocs.innovativesol.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://tailwindsdocs.innovativesol.com/readme/chatflows/llamaindex/embeddings.md). # Embeddings *** An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness. Embeddings can be used to create a numerical representation of textual data. This numerical representation is useful because it can be used to find similar documents. They are commonly used for: * Search (where results are ranked by relevance to a query string) * Clustering (where text strings are grouped by similarity) * Recommendations (where items with related text strings are recommended) * Anomaly detection (where outliers with little relatedness are identified) * Diversity measurement (where similarity distributions are analyzed) * Classification (where text strings are classified by their most similar label) ### Embedding Nodes: * [Azure OpenAI Embeddings](/readme/chatflows/llamaindex/embeddings/azure-openai-embeddings.md) * [OpenAI Embedding](/readme/chatflows/llamaindex/embeddings/openai-embedding.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://tailwindsdocs.innovativesol.com/readme/chatflows/llamaindex/embeddings.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.