What is TopK?
TopK offers a comprehensive solution combining embeddings with reranking and a robust query engine through a single API, designed to significantly streamline the search stack. It facilitates semantic search integrated with keyword-based filtering and reranking within one API call, eliminating the need for multiple provider integrations or complex data pipeline constructions. The platform supports storing documents and managing collections efficiently.
Engineered with developer experience as a core principle, TopK aims for effortless implementation, allowing developers to concentrate on building applications. It supports vector search on dense or sparse vectors, text document indexing using BM25 scoring, and hybrid multi-vector plus text retrieval for enhanced result relevance. Additionally, it provides efficient and flexible filtering capabilities to refine search outcomes.
Engineered with developer experience as a core principle, TopK aims for effortless implementation, allowing developers to concentrate on building applications. It supports vector search on dense or sparse vectors, text document indexing using BM25 scoring, and hybrid multi-vector plus text retrieval for enhanced result relevance. Additionally, it provides efficient and flexible filtering capabilities to refine search outcomes.
Features
- Unified Search API: Combines embeddings, database, ranking, semantic search, keyword filtering, and reranking in one call.
- Document Storage & Management: Provides capabilities for storing documents and managing collections.
- Vector Search: Supports search on dense or sparse vectors with native quantization support.
- Text Search: Indexes text documents and retrieves relevant results using BM25 scoring.
- Hybrid Retrieval: Merges multi-vector and text retrieval for optimal relevance.
- Flexible Filtering: Offers efficient filtering options to narrow down search results.
- Scalability: Designed to scale to billions of documents.
- High Availability: Offers 99.9+% availability across multiple regions.
- Deployment Options: Available as a managed service or deployable to a private cloud.
Use Cases
- Enhancing RAG (Retrieval-Augmented Generation) models with real-time, relevant data.
- Powering semantic search solutions for precise results from unstructured datasets.
- Enabling multi-modal search across text, images, audio, and video.
- Building dynamic recommendation engines tailored to user data and behavior.
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