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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A Python-native interface for Vectara, built for developers.
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 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.
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.
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.
Vectara takes center stage at GITEX Dubai, showcasing the future of Generative AI and RAG technology in one of the world's premier innovation hubs.
Using UDF-based reranking for fine-grained control over your search results with Vectara
Overview The best RAG systems utilize many different types of models (embedding model, generative LLM) to achieve the best, and highest quality results. When you build a small RAG POC,…
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