Superlinked – Vector Embeddings for Structured and Unstructured Data
Hi HN, I'm Daniel from Superlinked! We have built an open-source framework that improves vector search relevance and usefulness by combining structured metadata with unstructured data in your embeddings. We included self-hostable API server that sits between your data sources and vector database. Docs: https://docs.superlinked.com/ We're launching our cloud offering soon where you can use Superlinked to orchestrate high-performance retrieval for RAG, Search & Recommendation apps in your own cloud. Looking for feedback and happy to answer questions!
Superlinked – Vector Embeddings for Structured and Unstructured Data launched on December 2, 2024 with 7 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Contribute to superlinked/superlinked development by creating an account on GitHub.
Superlinked helped developers build better vector search by encoding metadata alongside unstructured data into rich, multi-modal embeddings, powering recommendations, RAG, and analytics beyond naive semantic search. But the biggest pain point in production was always inference , running embedding models efficiently at scale. That was the real blocker between a prototype and a production-grade system. So we went all-in on solving it. Build high-performance AI search applications for both humans andfrom github.com
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