Cloud SQL: managed relational database service for MySQL, PostgreSQL, and SQL Server. - Source: dev.to / 6 months ago

All cloud platforms are going to have a Postgres database support. Google has something called AlloyDB that looks fantastic for reliability and scalability. Cloud SQL is a bit more standard though. Source: about 1 year ago

Google SQL - not familiar with, does it solve the cons above? Source: about 1 year ago

Cloud SQL with MySQL as I can run it locally and know its queries will be expressive enough (where I am uncertain about Firestore, for instance). Source: over 1 year ago

For example, a Cloud Run Container is a provider for Services, whereas a Cloud SQL Server is a provider for databases. The providers are wrapped in parent containers that encapsulate the capabilities of their children. - Source: dev.to / over 1 year 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 / 4 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 / 4 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 / 5 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 / 6 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 / 7 months ago