Docalysis VS Weaviate

What are you trying to do more specifically? You can use https://docalysis.com/ for most document RAG tasks. - Source: Hacker News / 5 months ago

We have >200 page PDFs at https://docalysis.com/ and there's on-the-fly retrieval. It's not more computationally expensive than something like searching one's inbox (I'd image you have more than 200 pages worth of emails in your inbox). - Source: Hacker News / 9 months ago

Docalysis [https://docalysis.com/] (suggested by u/upsontown). Source: 11 months ago

100% agree, better to go directly to a website like docalysis.com and just use that directly. Source: 12 months ago

How does it compare to something like https://docalysis.com? - Source: Hacker News / 12 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