Meilisearch Reviews

Vectara Neural Search Reviews

Meilisearch mentions (3)

• Show HN: Hyvor Blogs – Multi-language blogging platform

  Meilisearch [https://meilisearch.com] for the search index. - Source: Hacker News / about 1 year ago

• Meilisearch, the Rust search engine, just raised $5M

  Meilisearch is an open-source, lightning-fast, and hyper-relevant search engine that fits effortlessly into your apps, websites, and workflow. You can find more info on our website https://meilisearch.com. Source: over 2 years ago

• Search engines for website

  Algolia.com - new plans are very affordable Meilisearch.com - open source. Source: about 3 years 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 / 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: 6 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 / 8 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 / 9 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 / 9 months ago