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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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.
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.
Vectara's Intelligent Query Rewriting (Tech Preview) improves RAG search accuracy by separating queries into their natural language and filter expression components.
Vectara’s Hughes Hallucination Evaluation Model (HHEM) now supports 8 languages, expanding from English, German, and French to include Portuguese, Spanish, Arabic, Chinese - Simplified, and Korean.
Vectara's new query observability feature provides detailed insights into query history, configurations, and system behavior, empowering users to optimize search performance and build trust in AI systems.
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