> For the complete documentation index, see [llms.txt](https://docs.hyper-space.io/hyperspace-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hyper-space.io/hyperspace-docs/getting-started/overview/hyperspace-advantages.md). # Hyperspace Advantages Hyperspace search enable the [filtering](https://docs.hyper-space.io/hyperspace-docs/projects/score-function-commands/candidate-filtering) and [scoring](https://docs.hyper-space.io/hyperspace-docs/projects/score-function-commands/candidate-score) of candidates using lexical and vector search. Only candidates that passed the filter stage will be assigned a score. In addition, Hyperspace allows [aggregations ](https://docs.hyper-space.io/hyperspace-docs/projects/score-function-commands/aggregations)of candidate fields. Aggregations can be performed on all candidates, and not just those that passed filtering. ### Native Python Syntax and DSL Syntax Hyperspace allows to build lexical, vector and hybrid search queries in either domain specific syntax (.json structure) or native python syntax. ### Multiple Score Methods Hyperspace supports a variety of score mechanisms. These include TF-IDF and BM25 based score, weights and boosts for lexical search, and similarity metrics such as Euclidean and Hamming distance for vector search. ### No Limitation on Number of Metadata Fields Hyperspace efficient memory management allows to include an extremely large number of keyword and value fields in each query, allowing practically unlimited number of fields. ### Full Support of Multi Model Search Hyperspace fully supports multi model vector search, allowing to use multiple vectors in each search query, and to use the results of each vector search in order to filter other vector searches. ### Extremely Large Vectors Hyperspace allows to perform vector search with extremely large vectors, with thousands of elements per vector. ### Filtering and scoring based on both vector and lexical search Hyperspace allows to create sophisticated filtering and scoring logic based on both vector and lexical search results. ### Intuitive Hybrid Search Hyperspace hybrid search that combines lexical and vector search in a simple manner, in a native Python syntax. --- # 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.hyper-space.io/hyperspace-docs/getting-started/overview/hyperspace-advantages.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.