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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Open RAG Eval is an open-source framework that lets teams evaluate RAG systems without needing predefined answers, making it faster and easier to compare solutions or configurations. With automated, research-backed metrics like UMBRELA and Hallucination, it brings transparency and rigor to RAG performance testing at any scale.
Vectara's new open-source framework for comprehensive RAG evaluation, open-rag-eval, represents a significant leap forward in ensuring that AI systems deliver accurate, relevant responses.
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We’re excited to announce custom text encoders, empowering users to integrate their preferred text embedding models directly into Vectara. This enhancement offers greater flexibility and control over the text processing pipeline, accommodating various project requirements.
History is repeating itself in AI. Just as enterprises once built siloed databases before embracing centralized data platforms, today’s rapid adoption of Retrieval-Augmented Generation (RAG) is leading to a fragmented mess of custom-built AI systems—what we call RAG Sprawl.
RAG Sprawl—the unchecked proliferation of Retrieval-Augmented Generation implementations across your organization—is silently eroding your AI investments. This growing problem is causing headaches for CIOs and IT departments while draining resources, compromising security, and creating inconsistent user experiences.
As enterprises rush to integrate Retrieval-Augmented Generation (RAG), many are encountering RAG Sprawl—a fragmented, resource-intensive approach that leads to security risks, inefficiencies, and mounting technical debt.
How to develop your generative AI application with seamless integration of Postman with Vectara
Now you can integrate the language model of your choice into Vectara while keeping existing security approvals, fine-tuning efforts, and AI investments intact. Use Vectara’s query and chat APIs while maintaining control over your model choices.
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