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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
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.
RAG with User-Defined Functions Based Reranking
Using UDF-based reranking for fine-grained control over your search results with Vectara
Building a RAG Pipeline is Difficult
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,…
Introducing Vectara’s Chain Rerankers
Vectara adds new powerful capabilities to allow rerankers to be “chained” together to give you a balance of business rules and neural reranking
Building AI Assistants with Vectara-agentic and Arize
How to add Observability to your Vectara AI Assistants and Agents with Arize Phoenix
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