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You are here		michael-lewis.com Vector search and Retrieval Augmented Generation · Michael I Lewis
\|	\|	unstructured.io Choosing the right embedding model for your RAG application: a comprehensive guide - Unstructured	7.9 parsecs away Travel
\|	\|	Navigate the Massive Text Embedding Benchmark (MTEB) leaderboard with confidence! Understand the difference between Bi-Encoders and Cross-Encoders, learn how text embedding models are pre-trained and benchmarked, and how to make the best choice for your specific use case.	7.9 parsecs away Travel
\|	\|	zackproser.com How are embeddings models trained for RAG?	9.3 parsecs away Travel
\|	\|	Embeddings models are the secret sauce that makes RAG work. How are THEY made?	9.3 parsecs away Travel
\|	\|	bdtechtalks.com No-code retrieval augmented generation (RAG) with LlamaIndex and ChatGPT - TechTalks	9.4 parsecs away Travel
\|	\|	Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision.	9.4 parsecs away Travel
\|	\|	www.sentra.io Access Controls that Move with Data \| Sentra	42.5 parsecs away Travel
\|		Discover how Data Security Posture Management (DSPM) allows access control policy to travel with the data throughout the public cloud.	42.5 parsecs away Travel