Tag: #semantic-search11 results
Build video semantic search on Amazon Bedrock with Nova Multimodal Embeddings. It understands user intent and retrieves accurate results across all video signal types simultaneously. Includes a deployable reference implementation for your content.
This post explains audio embeddings and Amazon Nova Multimodal Embeddings for semantic audio search. It provides code for indexing, querying audio libraries, enabling production-ready systems.
Build a scalable multimodal video search system using Amazon Nova models and Amazon OpenSearch Service. Enables natural language queries across large video datasets. Shifts from manual tagging to semantic search capturing full video richness.
This study explores how AI and retrieval-based strategies can improve the discovery and reuse of simulation models. It evaluates the impact of data representation, embedding models, and reranking techniques on retrieval performance.
RAGless is a semantic retrieval system that replaces standard RAG with direct Question-to-Question matching. By eliminating the generative step, it provides faster, more precise answers for predefined FAQ datasets.
Vector databases in RAG suffer from context fragmentation and semantic dilution, leading to errors in critical domains like medical and legal. Knowledge graphs structure data via entities, relations, and attributes for precise relational retrieval. Ontologies provide the semantic soul for advanced reasoning.
Weaviate has released a managed .NET client for C# developers. It enables semantic search and RAG using code-as-schema approach. Key features include attribute-driven schema, type-safe queries, and safe migrations in idiomatic .NET.
The article examines three blind spots of relying exclusively on vector retrieval in RAG systems. It highlights the need to look beyond semantic similarity when designing reliable knowledge retrieval pipelines.
Blog post introduces experimental differentiable clustering method blending mutual info, semantic proximity, and constraints like forced co-clustering. Enables catalog search via clusters. Developed from work project, with code and details shared.
Nothing updated its AI app Essential Space with semantic search for meaning-based queries and event recognition from screenshots/photos. Events appear as cards with date, time, location, featured on a new For You page. Available now on 2025-2026 Nothing/CMF phones.