Materials on AI, Python, Git, SQL, Web/API, SAP/ERP, statistics and time series: explanations, examples, practical tips, sources and applicability limits.
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Step-by-step guide to creating an index, generating embeddings, and executing hybrid queries that combine full-text and vector search using Azure AI Search.
A guide to setting up multimodal search in Azure AI Search using vectorization of text and images. Describes steps for creating an index, selecting models (e.g., CLIP), methods for generating embeddings, and executing hybrid queries to improve relevance.
Explore the principles of semantic search, its reliance on embeddings, and the reasons why text that appears similar might not provide the desired answer. Learn about vector search, hybrid search, and their applications in information retrieval.
Learn how to effectively evaluate the precision and recall of AI models, especially when working with small question datasets. This guide covers the concepts, calculation methods, and practical considerations using scikit-learn.
Compare three similarity calculations on small vectors and see when normalization changes the ranking.
How retrieval connects a language model to documents, and why a citation is not a guarantee.