Vespa.ai
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Based on our record, Vespa.ai seems to be a lot more popular than OrientDB. While we know about 19 links to Vespa.ai, we've tracked only 1 mention of OrientDB. 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.
If you're serious about scaling up, definitely consider Vespa (https://vespa.ai). At serious scale, Vespa will likely knock all the other options out of the park. - Source: Hacker News / 2 months ago
Yahoo released their geographic data catalogue under open license and it still lives on as https://whosonfirst.org/ Afaik https://en.wikipedia.org/wiki/Apache_ZooKeeper started at Yahoo https://vespa.ai/ was Yahoo's search engine for news and other content product, now spinned off (https://techcrunch.com/2023/10/04/yahoo-spins-out-vespa-its-search-tech-into-an-independent-company/). - Source: Hacker News / 4 months ago
I think https://vespa.ai/ has the right approach in this space by focusing on being hybrid - vectors alone aren't great for production use cases, it's the combining of vectors+text that lets you use ranking to get meaningful result. (I'm an investor so I'm biased; but it's also the reason why I invested). - Source: Hacker News / 5 months ago
So what’s the catch? Why is this not everywhere? Because IR is not quite NLP — it hasn’t gone fully mainstream, and a lot of the IR frameworks are, quite frankly, a bit of a pain to work with in-production. Some solid efforts to bridge the gap like Vespa [1] are gathering steam, but it’s not quite there. [1] https://vespa.ai. - Source: Hacker News / 5 months ago
When it comes to search I cannot disagree more. https://vespa.ai is a purpose built search engine. If you start bolting search onto your database, your relevance will be terrible, you'll be rewriting a lot of table stakes tools/features from scratch, and your technical debt will skyrocket. - Source: Hacker News / 11 months ago
First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language. - Source: dev.to / about 1 year ago
Meilisearch - Ultra relevant, instant, and typo-tolerant full-text search API
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Typesense - Typo tolerant, delightfully simple, open source search 🔍
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
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/
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.