#vector-embeddings

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#semantic-search #semantic-caching #retrieval-augmented-generation-rag #model-evaluation #llm-cost-reduction
Artificial intelligence
fromInfoQ
5 days ago

Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: A Banking Case Study

Semantic caching stores query-response vector embeddings to reuse answers, reducing LLM calls while improving response speed, consistency, and cost efficiency.
Artificial intelligence
fromTheregister
4 weeks ago

Researchers tout vector-based automated tuning in PostgreSQL

Automated vector-embedding-based tuning can improve PostgreSQL default performance by 2–10× by optimizing interdependent system, physical design, query, and lifecycle choices.
Artificial intelligence
fromAmazon Web Services
1 month ago

Search++, Going Beyond Keywords with Amazon OpenSearch Service | Amazon Web Services

AI-powered search systems incorporate searcher intelligence using LLMs and vector embeddings to deliver context-aware, intent-based search and reduce user query iteration.
Artificial intelligence
fromMedium
2 months ago

Orchestrating RAG pipelines with Apache Airflow

Apache Airflow provides flexible, reliable orchestration for production GenAI pipelines, enabling tool-agnostic, extensible, retry-capable workflows for embeddings, vector storage, and query pipelines.
fromTechzine Global
2 months ago

Microsoft SQL Server 2025 RC0 comes with Ubuntu 24.04 support

RC0 introduces a number of notable new features, including official support for Ubuntu 24.04 in development and testing scenarios and the default enablement of TLS 1.3. Support for Ubuntu 24.04 is currently limited to the Enterprise Evaluation Edition, which is available for 180 days. For production, Microsoft currently recommends SQL Server 2022 in combination with Ubuntu 22.04 or RHEL 9. This gives organizations the opportunity to experiment with the latest Linux distribution and prepare for a later production-ready release.
Artificial intelligence
fromHackernoon
8 months ago

Turn Your PDF Library into a Searchable Research Database with 100 Lines of Code | HackerNoon

Extracting metadata from research papers facilitates better academic search and retrieval dynamics. By building semantic embeddings for metadata, users gain enhanced search capabilities that match user queries with relevant content.
Online learning
Data science
fromHackernoon
7 months ago

The AI Engineer's Playbook: Mastering Vector Search & Management (Part 2) | HackerNoon

Vector embeddings require effective management systems to realize their potential in AI.
Vector Search & Management (VS&M) is crucial for value-driven data utilization.
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