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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Natural language processing can ease common frustrations while delivering relevant answers and unexpected insights.
This blog post describes different patterns for using metadata fields within Vectara, gives a real world example, and provides several code samples so you can learn how to improve the discovery of your data and apply Vectara in a variety of use cases beyond the search box.
Conversica delivers Conversation Automation solutions powered by neural networks to help people engage in authentic two-way conversations, get meaningful answers, and enjoy a delightful customer experience.
We’re in a transformative moment in computer science. AI breakthroughs like GPT-3, Copilot, DALL-E and Stable Diffusion are changing how computers understand, interpret, and interact with human language
Not too long ago, chatbots and assistants didn’t work well. The ones that did, like Amazon Alexa, were developed by companies with enormous R&D budgets and specialized ML and NLP…
Semantic search, based on deep neural networks, is changing information retrieval.
Neural rerankers are powerful models for fine-tuning search results and making the top results even better, no matter what the underlying ranking algorithm is.
When a customer’s use case required 1 million discrete corpora for search, we stress-tested our infrastructure and addressed bottlenecks.
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