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Based on our record, Weaviate seems to be a lot more popular than pgvecto.rs. While we know about 28 links to Weaviate, we've tracked only 1 mention of pgvecto.rs. 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.
Pgvecto.rs adopted a design akin to FreshDiskANN, resembling the Log-Structured Merge (LSM) tree concept. This architecture comprises three components: the writing segment, the growing segment, and the sealed segment. New vectors are initially written to the writing segment. A background process then asynchronously transforms them into the immutable growing segment. Subsequently, the growing segment undergoes a... - Source: dev.to / 2 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
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