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OpenAI presents a contrastive pre-training approach for generating high-quality text and code embeddings at scale without supervision, achieving state-of-the-art results on linear-probe classification, semantic search, and code search benchmarks.
This article provides an in-depth comparison of the advantages and disadvantages of BM25 lexical search and embedding semantic search in RAG retrieval, offers a practical framework for selecting retrieval methods based on query type and system trade-offs, and emphasizes the importance of treating RAG as a system rather than a simple component.
Supermemory is an open-source memory and context engine for AI that automatically learns from conversations, extracts facts, builds user profiles, and delivers personalized context. It ranks #1 on several AI memory benchmarks and provides a single API for adding memory, RAG, and connectors to AI agents.
A CLIP-based embedding model hosted on Replicate that generates 768-dimensional embeddings for both images and text using the clip-vit-large-patch14 architecture, costing ~$0.00022 per run.
A model on Replicate that outputs CLIP ViT-L/14 features for text and images, allowing similarity computation between inputs.