EVA DB
Milvus
Qdrant
txtai
Vespa.ai
Weaviate
Zilliz
Meilisearch
Vectara Neural Search
txtai
Dify.AI
Haystack NLP Framework
Annoy
LangChain
Milvus
Prompts
EVA is an open-source AI-relational database with first-class support for deep learning models. It aims to support AI-powered database applications that operate on both structured (tables) and unstructured data (videos, text, podcasts, PDFs, etc.) with deep learning models.
EVA DB Vectara Neural SearchBased on our record, Vectara Neural Search seems to be a lot more popular than EVA DB. While we know about 13 links to Vectara Neural Search, we've tracked only 1 mention of EVA DB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
EvaDB plugs AI into traditional SQL databases, so as a first step, we’ll need to install a database. For this article, we’ll use SQLite because it's fast enough for our tests and does not require a proper database server running somewhere. You may choose a different database, if you prefer. - Source: dev.to / 4 months ago
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
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
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
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
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
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
txtai - AI-powered search engine
Qdrant - Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Dify.AI - Open-source platform for LLMOps,Define your AI-native Apps
Haystack NLP Framework - Haystack is an open source NLP framework to build applications with Transformer models and LLMs.
Vespa.ai - Store, search, rank and organize big data