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brinicle
Extremely fast/RAM-friendly search engine
brinicle is a disk-first HNSW retrieval engine for vector search, structured item search, hybrid search, and autocomplete. On 1.2M Amazon products, brinicle achieved sub-ms P99 hybrid search (lexical, semantic) while using substantially less search memory than Weaviate, OpenSearch, Typesense, and Meilisearch. The report: https://brinicle.bicardinal.com/search_benchmark
I wanted to design an extremely fast vector search engine that does not blow your RAM, while performs competitive in terms of accuracy. So, I built brinicle. brinicle supports: ANN Vector Engine. Item Search Engine. And Autocomplete Engine. I compared it against Milvus, Chroma, Qdrant, and Weaviate in different datasets and published results here: brinicle.bicardinal.com/benchmark Then, I created a benchmark for its item search ability on two datasets and compared it against Weaviate, OpenSearch, Typesense, and Meilisearch. brinicle outperforms in terms of search latency, and memory consumption, while keeping the accuracy, or outperforming in some metrics. Results, and the approach are explained in this comprehensive report: brinicle.bicardinal.com/search_benchmark
About brinicle on Product Hunt
“Extremely fast/RAM-friendly search engine”
brinicle was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #60 on the daily leaderboard. brinicle is a disk-first HNSW retrieval engine for vector search, structured item search, hybrid search, and autocomplete. On 1.2M Amazon products, brinicle achieved sub-ms P99 hybrid search (lexical, semantic) while using substantially less search memory than Weaviate, OpenSearch, Typesense, and Meilisearch. The report: https://brinicle.bicardinal.com/search_benchmark
On the analytics side, brinicle competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how brinicle performed against the three products that launched closest to it on the same day.
Who hunted brinicle?
brinicle was hunted by Saeed Dehqan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of brinicle including community comment highlights and product details, visit the product overview.
Saeed Dehqan
I wanted to design an extremely fast vector search engine that does not blow your RAM, while performs competitive in terms of accuracy. So, I built brinicle.
brinicle supports:
ANN Vector Engine.
Item Search Engine.
And Autocomplete Engine.
I compared it against Milvus, Chroma, Qdrant, and Weaviate in different datasets and published results here: brinicle.bicardinal.com/benchmark
Then, I created a benchmark for its item search ability on two datasets and compared it against Weaviate, OpenSearch, Typesense, and Meilisearch. brinicle outperforms in terms of search latency, and memory consumption, while keeping the accuracy, or outperforming in some metrics. Results, and the approach are explained in this comprehensive report: brinicle.bicardinal.com/search_benchmark