EVA DB
Milvus
Qdrant
txtai
Vespa.ai
Zilliz
Meilisearch
Annoy
Weaviate
Qdrant
Milvus
pgvecto.rs
txtai
Zilliz
Vespa.ai
Annoy
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.
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Based on our record, Weaviate seems to be a lot more popular than EVA DB. While we know about 28 links to Weaviate, 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
Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 3 months ago
Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 3 months ago
Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 4 months ago
A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 5 months ago
To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 6 months ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
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/
txtai - AI-powered search engine
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
Vespa.ai - Store, search, rank and organize big data
Zilliz - Data Infrastructure for AI Made Easy
