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Blog - page 8
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Announcing Typescript SDK
A Typescript-native interface for Vectara, simplifying Integration for Developers
Introducing Knee Reranking: smart result filtering for better results
With Vectara's new Knee Reranking, you can automatically improve result quality and reduce GenAI latency
HHEM: expanded language support
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.
Announcing Vectara’s Python-SDK
A Python-native interface for Vectara, built for developers.
Metadata updates
Hello Vectara community! Today, we’re very happy to announce another new feature in Vectara: the ability to update the metadata of documents after they’ve been indexed. This has been one of the most requested features in Vectara by our users. In this blog, we’ll walk you through how it works, why it’s been so requested, and what you can do with Vectara now.
Table Data Understanding
Table Data Understanding enables you to query and analyze table data from PDFs. Extract specific cell values, rows, or entire tables for easier data access. Perfect for use cases across finance, research, and more.
Why building your own RAG stack can be a costly mistake
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.
Enhanced query observability for greater insight and control
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.
Is Semantic Chunking worth the computational cost?
Discover why fixed-size chunking often outperforms semantic chunking in real-world RAG systems. This study explores the trade-offs between simplicity, computational efficiency, and retrieval accuracy, challenging the assumption that semantic chunking is always the superior choice.
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