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Introducing layered ranking for RAG applications
Introducing layered ranking: The missing piece for context engineering at scale.
Perplexity builds AI Search at scale on Vespa.ai
Perplexity chose to build on Vespa.ai to provide the world's most used RAG application.
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Vespa Newsletter, June 2025
Advances in Vespa features and performance include layered ranking for RAG applications, chunking, and facet filtering.
Introducing layered ranking for RAG applications
Introducing layered ranking: The missing piece for context engineering at scale.
Image generated using OpenAI.
Search me if You Can: Building the Next Generation Advanced RAG Solution in Lifesciences
It is now the age of RAG, semantic + hybrid search, domain reasoning, powering copilots and AI agents. Lets see how - lets build your next Scientific Search engine!
Beyond Vectors: AI for Life Sciences Needs More Than Vectors—Here’s Why
Learn how how Vespa’s native tensor capabilities are redefining AI-powered search and retrieval in life sciences, enabling faster, more accurate insights across complex, multimodal scientific data.
Beyond Simple RAG: Crafting a Sophisticated Retail AI Assistant with Vespa and LangGraph
Example of an end-to-end implementation of an agentic retail chatbot assistant that provides an advanced conversational search experience through an agentic workflow encapsulating tool usage.
Vespa Guide for Solr Users
Vespa functionality from a Solr user's perspective. Where it overlaps and where it differs. Why would you migrate and what challenges to expect.
Why a Search Platform (Not a Vector Database) is the Smarter Choice for AI Search
AI search requires more than a vector database. A search platform bridges the gaps.
Perplexity builds AI Search at scale on Vespa.ai
Perplexity chose to build on Vespa.ai to provide the world's most used RAG application.
Transforming E‑Commerce with AI: Join Our Webinar to See How Vespa Delivers Smarter Search and Recommendations
Live Webinar: Unlock the Future of eCommerce – May 8, 2 PM CET.
Quick Start with Logstash: from data to Vespa schema
Fastest way to get your data into Vespa. Logstash generates the schema. Then deploys the application package to Vespa. Next Logstash run does the actual writes.
Introducing Document Enrichment with Large Language Models in Vespa
Document enrichment with LLMs can be used to transform raw text into structured form and expand it with additional contextual information. This helps to improve search relevance and create a...
Vespa Newsletter, April 2025
Advances in Vespa features and performance include Lexical Search Query Performance, Pyvespa Relevance Evaluator, Global-phase rank-score-drop-limit, and Compact tensor representation.
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