> For the complete documentation index, see [llms.txt](https://epsilla-inc.gitbook.io/epsilladb/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://epsilla-inc.gitbook.io/epsilladb/knowledge-base/advanced-settings/embedding.md). # Embedding **Embedding** setting allows users to select specific embedding models for efficient text representation, improving search quality. ### Choose Embedding Model Choose an embedding model from the dropdown to use for embedding data chunks:
The default **epsilla/text-embedding-3-small** and **epsilla/text-embedding-3-large** models leverage Epsilla's built-in OpenAI integration and do not require any additional config from user side. However, if you choose other embedding models, you must first add an integration for the respective vendors:
Read more about [embedding](/epsilladb/epsilla-vector-database/advanced-topics/embeddings.md). ### Which Embedding Model Fits My Needs Best When selecting an embedding model for your specific use case, it's important to recognize that there is no one-size-fits-all model. The ideal model depends on factors such as the nature of your use case, cost considerations, and performance requirements. OpenAI offers large and small embedding models: the large embedding model excels in general-purpose scenarios where higher quality is crucial, while the smaller model is a more cost-efficient option for use cases with tighter budget constraints. JinaAI provides embedding models well-suited for multilingual applications, making them an excellent choice for use cases involving diverse languages. For more specialized needs, VoyageAI models are optimized for vertical domains, such as financial or legal contexts, offering domain-specific insights and improved accuracy in these areas. For an overview of different models' performance across various benchmarks, you can refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). However, it's important not to blindly rely on these results, as the datasets used in MTEB might not fully represent your specific use case and could be biased. Always consider evaluating models in the context of your own data and requirements. --- # 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://epsilla-inc.gitbook.io/epsilladb/knowledge-base/advanced-settings/embedding.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.