Launching a new home-grown embedding LLM for RAG
Hi HN! Vectara is a "batteries included" retrieval augmented generation platform. You can upload your rich text documents like PDFs, HTML pages, word docs, etc, or semi-structured JSON and Vectara handles the text and metadata extraction, segmentation, vector embedding, and vector storage, and keyword storage. You can ask a question or perform a search in the UI or via our APIs and Vectara will automatically handle the vectorization, structured metadata filtering, vector+keyword retrieval, hybrid blending, and generative summarization of the results. We're focusing on building and…
Launching a new home-grown embedding LLM for RAG launched on September 28, 2023 with 12 votes, #1,472 of 4,228 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
Vectara is a retrieval augmented generation platform that handles document processing, vector embedding, and storage for users who want to build AI applications. Users upload documents like PDFs and Word files, and the platform automatically extracts text, segments content, creates embeddings, and stores them. Through APIs or a web interface, users can search documents and retrieve summarized answers. The platform combines vector and keyword search methods to improve retrieval accuracy.
written from the facts on this page · September 2026
From the sources
We’re happy to announce the release of Vectara’s new embedding model, Boomerang. In this blog post, we’re going to dive into what that means, and why it’s important: both from a traditional search perspective as well as a Grounded Generation (or Retrieval Augmented Generation) perspective. When we talk about search (the “retrieval” in retrieval augmented generation), it’s important that the retrieval engine finds the right things . Traditional keyword-based systems do this by following language rules like stemming , synonym expansion, and other language-dependent rules, and then using an inverted index to retrieve the related text. For example, let’s say we were to index the following…from vectara.com
In the maker’s words, at launch
Hi HN! Vectara is a "batteries included" retrieval augmented generation platform. You can upload your rich text documents like PDFs, HTML pages, word docs, etc, or semi-structured JSON and Vectara handles the text and metadata extraction, segmentation, vector embedding, and vector storage, and keyword storage. You…
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Source: Show HN post ↗ · launch text quoted in part
