FeaturedRetrieval Augmented Generation
Think building your own Retrieval-Augmented Generation (RAG) system will give you a competitive edge? Think again. Companies are sinking months and hundreds of thousands of dollars into in-house AI projects, only to end up with sluggish, unsecure, and overpriced systems that can't hold a candle to existing solutions.
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The Hacker News startup community website deserves better search
Discover the transformative impact of Conversational AI, including its evolution, current trends, and role in revolutionizing customer support. If you’re thinking about why and how to adopt this technology for customer support, we offer some insights
Regardless of whether you are considering building or buying a RAG solution, you need to understand the players, their offerings, where they accel, and where they are still trying to catch up
Building a RAG pipeline with Vectara and Elasticsearch
Ingesting data from your database into Vectara: a step by step guide.
Today, we’re incredibly excited to announce that we’re releasing a new “reranking” capability built into Vectara: one that’s focused on increasing the amount of diversity of the results near the top of the result list.
In RAG, retrieving the right facts from your data is crucial, and choosing the right embedding model to power your retrieval matters!
AI-powered search is everywhere – new projects, products and companies are appearing to solve age-old challenges. But how ‘good’ is an AI-powered search engine – and how can we measure this?
Hybrid search has the power to vastly improve in-app experience allowing users to find what they are looking for quickly, and allows tuning between semantic and lexical search
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