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.
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,…
Vectara adds new powerful capabilities to allow rerankers to be “chained” together to give you a balance of business rules and neural reranking
How to add Observability to your Vectara AI Assistants and Agents with Arize Phoenix
A no-code environment for chat with your documents, powered by Vectara
Today, we’re incredibly excited to announce user-defined sorting functions for Vectara!
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