Langchain's new member-A SQL+Vector database built on ClickHouse
MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter with a WHERE clause, even JOIN when you want to jointly search vectors with filters on relevant metadata from other tables. MyScale is now open for registration and offers millions of vectors‘ free tier plan for you! (https://myscale.com/) Now you can also use…
Langchain's new member-A SQL+Vector database built on ClickHouse launched on April 25, 2023 with 5 votes, #2,314 of 4,853 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 3 years on.
What it does
Explore MyScale, the next-gen AI database fusing vector search with SQL analytics to deliver a streamlined, fully-managed, and high-performance experience. Unlock insights from massive multimodal vector datasets with unparalleled speed and efficiency.
In the maker’s words, at launch
MyScale is designed for the storage and analysis of massive vector data with structured metadata. If you are eager to find a high-performance vector search using SQL queries, MyScale could be your preferred option. Thanks to the advantages of native structural database support, it provides you with a flexible filter…
Does a similar job
all alternatives →
MyScaleDB open-sourced: a SQL vector database to Build AI APPs with SQL2024 · github.com · ▲12A @ClickHouse fork that supports high-performance vector search and full-text search. - myscale/MyScaleDB
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Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
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Source: Show HN post ↗ · launch text quoted in part
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