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Show HN: Superlinked – Vector Embeddings for Structured and Unstructured Data (github.com)

7 points by supo ↗ HN
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!

7 comments

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borabora4
Sounds interesting! Do you have any case studies to share?
supo
We are mostly focused on natural language search in e-commerce/marketplace/travel settings right now. There we have a production deployment that drove $15M of incremental revenue through personalization of a shopping feed for a fashion e-commerce business. Happy to share more if you are interested, feel free to email me at daniel@superlinked.com!
paoloap
What are the supported vector databases and data sources? How seamless is the integration with popular vector databases like Pinecone, Faiss, or Weaviate?
tejaskumar_
+1. I'd love to hear an answer to this because I imagine combining Astra DB data with Redis cache data (both of which support vector) might be an interesting use case for some with a large data landscape.
supo
Supported VDBs: Currently MongoDB, Redis Search and Qdrant https://docs.superlinked.com/run-in-production/index-1

As you say, AstraDB also offers vector search (you can see it at https://superlinked.com/vector-db-comparison) but we haven't built the integration with them yet. We focus on fewer but higher quality integrations in general. Do you like AstraDB? Any advantages vs competition that you care about?

In terms of the plan you mentioned Astra+Redis - you don't have to do that, you can just use redis for both key-value store and vector search.

paoloap
How well does it perform outside of text, say images, pdfs, or metadata?
supo
We focus on text, images, numerical, categorical and timestamp-properties for the objects you vectorize with Superlinked. The performance will depend on which models you chose to use with the framework, your queries etc. Happy to elaborate if you could describe your use-case :-)