Playground for comparing embedding models on Wikipedia+book retrieval
Introducing embeds.ai: an embedding playground to compare how embedding models work on a real world use case (retrieval augmented generation for Wikipedia articles + Elad Gil's High growth handbook) A few weeks ago, Shreyan and I were looking for an embedding model to use for RAG.
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When your embedding provider is good, but could be better for your use-case.
Model2vec
github.comLightning-fast Static Embeddings for RAG/Semantic Search
Rag Framework
github.com🚀 Scale your RAG pipeline using Ragswift: A scalable centralized embeddings management platform
How to Evaluate Different AI Search Models
opensourceconnections.comHow we helped measure AI-powered search and retrieval augmented generation (RAG) platform Vectara using Quepid
Boomerang
vectara.coma new embedding model for RAG and semantic search