> For the complete documentation index, see [llms.txt](https://docs.dat-hub.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dat-hub.com/overview/core-concepts/generator.md). # Generator High-dimensional vectors are created by converting data points into numerical forms within a high-dimensional space. A generator is a tool or model that produces these vector embeddings. These embeddings capture key features and relationships of the data, making them valuable for various machine learning applications. #### Key Components of an Embedding Generator 1. **Data Input**: The generator takes in raw data, such as text, images, or other types of data. 2. **Feature Extraction**: It identifies and extracts relevant features from the input data. 3. **Model Training**: The generator uses a machine learning model, such as a neural network, to learn the relationships and patterns in the data. 4. **Vector Representation**: After training, the model can transform new data points into vector embeddings. Some examples are Cohere, OpenAI etc. When selecting a generator, consider factors such as data nature, complexity, computational ability, cost, task requirements, and overfitting risk. Choosing dimensionality involves balancing the need for detailed information with computational efficiency. Higher dimensions capture more details but may increase overfitting and computational costs. --- # 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://docs.dat-hub.com/overview/core-concepts/generator.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.