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Vectara Neural Search

Neural search as a service API with breakthrough relevance

Vectara Neural Search Reviews and details

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  • Vectara Neural Search Landing page
    Landing page //
    2023-08-02

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Social recommendations and mentions

We have tracked the following product recommendations or mentions on various public social media platforms and blogs. They can help you see what people think about Vectara Neural Search and what they use it for.
  • Launch HN: Danswer (YC W24) – Open-source AI search and chat over private data
    Nice to see yet another open source approach to LLM/RAG. For those who do not want to meddle with the complexity of do-it-youself, Vectara (https://vectara.com) provides a RAG-as-a-service approach - pretty helpful if you want to stay away from having to worry about all the details, scalability, security, etc - and just focus on building your RAG application. - Source: Hacker News / 3 months ago
  • Which LLM framework(s) do you use in production and why?
    You should also check us out (https://vectara.com) - we provide RAG as a service so you don't have to do all the heavy lifting and putting together the pieces yourself. Source: 5 months ago
  • Show HN: Quepid now works with vetor search
    Hi HN! I lead product for Vectara (https://vectara.com) and we recently worked with OpenSource connections to both evaluate our new home-grown embedding model (Boomerang) as well as to help users start more quantitatively evaluating these systems on their own data/with their own queries. OSC maintains a fantastic open source tool, Quepid, and we worked with them to integrate Vectara (and to use it to... - Source: Hacker News / 7 months ago
  • A Comprehensive Guide for Building Rag-Based LLM Applications
    RAG is a very useful flow but I agree the complexity is often overwhelming, esp as you move from a toy example to a real production deployment. It's not just choosing a vector DB (last time I checked there were about 50), managing it, deciding on how to chunk data, etc. You also need to ensure your retrieval pipeline is accurate and fast, ensuring data is secure and private, and manage the whole thing as it... - Source: Hacker News / 8 months ago
  • Do we think about vector dbs wrong?
    I agree. My experience is that hybrid search does provide better results in many cases, and is honestly not as easy to implement as may seem at first. In general, getting search right can be complicated today and the common thinking of "hey I'm going to put up a vector DB and use that" is simplistic. Disclaimer: I'm with Vectara (https://vectara.com), we provide an end-to-end platform for building GenAI products. - Source: Hacker News / 8 months ago
  • What is a GenAI Platform?
    In this article I discuss my long-held belief that it's time we shifted the discussion from "which vector database to use" for GenAI and instead think about "how do we make this whole architecture simpler to use", a focus of GenAI platforms like https://vectara.com. Source: 9 months ago
  • Comparison of Vector Databases
    With Vectara (full disclosure: I work there; https://vectara.com) we provide a simple API to implement applications with Grounded Generation (aka retrieval augmented generation). The embeddings model, the vector store, the retrieval engine and all the other functionality - implemented by the Vectara platform, so you don't have to choose which vector DB to use, which embeddings model to use, and so on. Makes life... - Source: Hacker News / 9 months ago
  • Train a model based on text from pdfs
    You can also use vectara to implement this. Just upload the docs via the indexing API and then run queries via the search API. It tends to be less complicated with Vectara since we take care of many things internally (vectorDB, embeddings, etc). Let me know if I can help further with that. Source: 10 months ago
  • ChatGPT-like interface for product search
    I found vectara.com but all examples seem to be about feeding text. I'm not super technical so I may be missing something. Please let me know if I need to elaborate further. Source: 11 months ago
  • ChatGPT made everyone realize that we don't want to search, we want answers.
    Yes agreed that if ChatGPT becomes monetized the same way as Google, then it the fun will be over. We'll have to wait and see. I think though that this innovation is not just applicable to web search or consumer search, and with products like vectara.com providing this type of user experience in the enterprise there is a significant net gain here overall. Source: 11 months ago
  • Welcome to r/vectara: A Community for Developers and users of Vectara!
    Join us in shaping the future of GenAI applications using [vectara](vectara.com). We're excited to have your participation, ideas, and feedback. If you have any suggestions to make this community better, feel free to share! Source: 11 months ago
  • How to create an AI model that learns from textbooks and can be asked about the information in them
    Rasa is a popular tool used right now to build these applications. If you're looking for a serious turn-key solution I would check out Vectara. Source: about 1 year ago
  • Ask HN: Developer opinions on SaaS in-product walkthroughs
    Hi HN! We recently launched our company/product (https://vectara.com) a few weeks ago. Just as background is a new neural search SaaS product (think a bit like Elasticsearch or Algolia I guess, but with natural language understanding). We know we have a lot of work to make our UI/UX in the administrative console better, but we've received really good feedback from users that do enough onboarding to reach the... - Source: Hacker News / over 1 year ago

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