The world's fastest growing search
Power up AI applications with hyper performance and seamless user experience at a fraction of the cost.
Build GenAI apps with superior architecture
Efficiently handle and retrieve high-dimensional data with Hyperspace’s vector search. Transform unstructured data into vector embeddings to enable context-aware searches that go beyond simple keyword matching.
Semantic Understanding
Capture the context and meaning of words, making it capable of understanding synonyms, typos, and paraphrases
Flexibility
Seamlessly handle complex queries and retrieve documents missed by traditional search.
Scalability
efficiently sclae large datasets using modern hardware like GPUs and distributed computing
Search with ease
Hyperspace support vector embeddings natively. These tools offer built-in features for indexing, querying, and ranking vectorized data, simplifying implementation and enabling efficient, scalable semantic search
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Customize data retrieval
Vector search similarity function can be controlled using a script score, allowing for custom scoring logic. By scripting the similarity calculation, you can fine-tune how vectors are compared, optimizing search relevance based on specific criteria or blending multiple similarity measures to meet unique requirements.
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Leverage multi vector search
Hyperspace multi-vector search integrates multiple vector embeddings simultaneously, allowing for a richer and more nuanced retrieval process. By combining different embeddings, it captures various aspects of the data, enabling a more comprehensive analysis of user queries and delivering highly relevant and context-aware search results.
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Optimize search relevancy by combining classic and vector search, achieving breadth and depth in information retrieval.
Efficiently handle and retrieve high-dimensional data with Hyperspace’s vector search. Transform unstructured data into vector embeddings to enable context-aware searches that go beyond simple keyword matching.
Search Engines
Lexical search is crucial for exact matches, even with modern semantic techniques.
Databases
Quickly find records with specific keywords or phrases. The SQL LIKE operator is a common example.
Text Editors
Features like "Find" and "Replace" in text editors rely on lexical search to locate specific strings within documents.
Code Editors and IDEs
Find occurrences of variables, functions, or other code snippets within large codebases.
Command-Line Tools
Tools like grep in Unix-based systems perform lexical search to filter and find text within files.
