High Quality RAG Apps with Databricks | Databricks Blog
Discover how to build high-quality Retrieval-Augmented Generation (RAG) applications using Databricks.| Databricks
Discover how to build high-quality Retrieval-Augmented Generation (RAG) applications using Databricks.| Databricks
This blog post discusses best practices for evaluating retrieval-augmented generation (RAG) applications using large language models (LLMs).| Databricks
Vector embeddings have proven to be an effective tool in a variety of fields, including natural language processing and computer vision. Comparing vector embeddings and determining their similarity is an essential part of semantic search, recommendation systems, anomaly detection, and much more.| www.pinecone.io
Today, we are excited to introduce DBRX, an open, general-purpose LLM created by Databricks. Across a range of standard benchmarks, DBRX sets a new state-of-the-art for established open LLMs. Moreover, it provides the open community and enterprises building their own LLMs with capabilities that were previously limited to closed model APIs; according to our measurements, it surpasses GPT-3.5, and it is competitive with Gemini 1.0 Pro. It is an especially capable code model, surpassing speciali...| Databricks
Select and customize benchmarks to compare text and image embedding models across various languages and tasks. Choose from a wide range of options including multilingual, domain-specific, and langu...| huggingface.co
Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search[1]. HNSW is a hugely popular technology that time and time again produces state-of-the-art performance with super fast search speeds and fantastic recall.| www.pinecone.io
