Based on our record, Weaviate should be more popular than Apache Solr. It has been mentiond 28 times since March 2021. 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.
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
Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: dev.to / 9 months ago
Apache Solr can be used to index and search text-based documents. It supports a wide range of file formats including PDFs, Microsoft Office documents, and plain text files. https://solr.apache.org/. Source: about 1 year ago
If so, then https://solr.apache.org/ can be a solution, though there's a bit of setup involved. Oh yea, you get to write your own "search interface" too which would end up calling solr's api to find stuff. Source: over 1 year ago
Developers will use their SQL database when searching for specific things like client names, product names, or address search. Now when you want to level up from there and search all tables you better off using a separated server with a specific program like https://solr.apache.org/. Source: almost 2 years ago
We’re using a self-managed OpenSearch node here, but you can use Lucene, SOLR, ElasticSearch or Atlas Search. Source: almost 2 years ago
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/
ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
Typesense - Typo tolerant, delightfully simple, open source search 🔍