Qdrant VS Vespa.ai

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications. - Source: dev.to / 20 days ago

Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals. - Source: dev.to / 29 days ago

Qdrant is a modern, open-source vector search engine specifically designed for handling and retrieving high-dimensional data, such as embeddings. It plays a crucial role in various machine learning and data analytics applications, particularly those involving similarity searches in large datasets. Understanding Qdrant's capabilities and architecture is key to leveraging its full potential. - Source: dev.to / about 1 month ago

This is undocumented (frustrating) but it looks like it's chunking them, running embeddings on the chunks and storing the results in a https://qdrant.tech/ vector database. We know it's Qdrant because an error message leaked that detail: https://twitter.com/altryne/status/1721989500291989585. - Source: Hacker News / 3 months ago

As an open-source and self-hosted solution, developers can deploy their own version of the plugin and register it with ChatGPT. The plugin leverages OpenAI embeddings and allows developers to choose a vector database (Milvus, Pinecone, Qdrant, Redis, Weaviate or Zilliz) for indexing and searching documents. Information sources can be synchronized with the database using webhooks. Source: 8 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 / 20 days 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 / about 1 month 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 / about 2 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 / 8 months ago

Milvus (https://milvus.io) and Vespa (https://vespa.ai) are great choices if you're looking for hardened, scalable, and production-ready vector databases. We (Milvus) also have `milvus-lite` if you'd like something pip installable:. - Source: Hacker News / 10 months ago