Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
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Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Based on our record, Qdrant should be more popular than Vespa.ai. It has been mentiond 39 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.
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 8 days ago
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 19 days ago
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 26 days ago
I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 2 months ago
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 2 months ago
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 / about 1 month 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 / 3 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 / 4 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 / 10 months ago
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