We’ve seen the power of vector embeddings in semantic search, RAG and recommender systems. However, optimizing the retrieval is a painful ranking, filtering and re-ranking process. We solve this problem by allowing you to include more information in your vectors. With better representations of your entity in the initial vector, search results will require less or no post-processing or re-ranking, and overall quality will improve – up to 10x from our research. However, encoding and combining different types of information into a single vector is tricky. Superlinked is a platform that allows you to encode text, tabular data, and more complex scenarios into a vector, evaluating and refining it in an interactive environment. Once finalised, you deploy the same description from the experimentation phase into production. Choose your VectorDB, plug in your data, and start querying.
TLDR? Here’s what you’ll learn:
– How you can make going from a notebook experiment to production deployment simple
– Avoid the pain of post-processing & reranking by creating a single multi-modal vector
– 5 building blocks to validate your prototype & start adding value without complex pipelines to manage
Technical level: Technical practitioner
Session Length: 40 minutes
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