OpenAI Reviews

Vectara Neural Search Reviews

OpenAI mentions (309)

• How even the simplest RAG can empower your team

  If you donʼt know what any of this means, this blog post is for you. You will see how to benefit from the technology without uploading/donating your codebase to external providers like OpenAI. - Source: dev.to / 4 days ago

• Leverage AI with Twilio for Hotelier

  This template ships with Google Gemini models/gemini-1.0-pro-001 as the default. However, thanks to the Vercel AI SDK, you can switch LLM providers to OpenAI, Anthropic, Cohere, Hugging Face, or using LangChain with just a few lines of code. - Source: dev.to / 6 days ago

• Building a Web Page Summarization App with Next.js, OpenAI, LangChain, and Supabase

  In this article, we'll show you how to create a handy web app that can summarize the content of any web page. Using Next.js for a smooth and fast web experience, LangChain for processing language, OpenAI for generating summaries, and Supabase for managing and storing vector data, we'll build a powerful tool together. - Source: dev.to / 7 days ago

• From Zero to Chatbot: How Large Language Models (LLMs) Work and How to Harness Them Easily

  Now, let’s dive into the fun part: building a chatbot using Node.js, LangChain, and OpenAI. We’ll focus on how prompt engineering can enhance the chatbot’s responses. - Source: dev.to / 7 days ago

• OpenAI api RAG system with Qdrant

  I wanted to get a project for running my own pipeline with somewhat interchangeable parts. Models can be swapped around so that you can make the most of the latest models either available on Hugginface, OpenAI or wherever. - Source: dev.to / about 2 months ago

Vectara Neural Search mentions (13)

• 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 / 4 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: 7 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 / 9 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 / 10 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 / 10 months ago