Embeds an in-process vector database into applications for dense, sparse, full-text, and hybrid search with durable local storage, via Python, Node.js, Go, Rust, and Dart SDKs.
- Access
- API
- Auth
- None
- Skills
- 4
Skills
Dense And Sparse Vectors
Stores dense and sparse embeddings with multi-vector queries and a choice of index types that scale from in-memory to on-disk.
Full-Text Search
Queries string fields with natural-language or structured keyword expressions, including an N-gram tokenizer for phrase and code search.
Hybrid Search
Combines vector similarity, full-text search, and structured filters in a single query to narrow results to the precise matches.
Durable Local Storage
Persists collections with write-ahead logging so data survives crashes, and lets multiple processes read one collection at once.
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