kacperlukawski

8 ms·

31.
by kacperlukawski 4y ago
I'm still wondering why OpenSearch and ES have those limits for the dimensionality of the embeddings while the vector databases such as Qdrant do not.
32.
by kacperlukawski 4y ago
It's a different paradigm. I wouldn't expect the relational databases to start competing with the proper vector databases, as vectors do not fit SQL tables. Each problem should be solved with a specialized tool to achieve the best
33.
by kacperlukawski 4y ago
That's a great news! Especially since we implemented the integration between Haystack and Qdrant: https://github.com/qdrant/qdrant-haystack/
34.
by kacperlukawski 4y ago
I'd say doing some sort of training might be the most beneficial thing you can bring to the NGOs. There are also organizations such as Omdena ( https://omdena.com/ ). They solve different challenges and would probably ap
35.
Ask HN: What pieces of code do you still feel ashamed of?
3 points by kacperlukawski 4y ago|0 comments
36.
by kacperlukawski 4y ago
What are your plans for providing some additional metadata except for embeddings? Semantic search often requires additional filtering, as vectors are not all we need. At Qdrant we have a unique mechanism for incorporating metadata filters i
37.
by kacperlukawski 4y ago
Yeah, that's correct. We also believe that's a good direction for improving search results quality and combining different methods of search: https://qdrant.tech/articles/hybrid-search/
38.
Qdrant Scalar Quantization: up to 2x faster, 4x less memory(qdrant.tech)
12 points by kacperlukawski 4y ago|1 comments
39.
by kacperlukawski 4y ago
Qdrant 1.1.0 introduced the scalar quantization mechanism. It means up to 4x lower memory footprint and up to 2x performance increase with little to no impact on the search precision. Disclaimer: I work for Qdrant
40.
by kacperlukawski 4y ago
I'm affiliated with Qdrant and was quite surprised to see us listed in your comparisons with some false statements. If you claim to be the only database with billion-scale vector support, it would be great to make your benchmarks publi
41.
by kacperlukawski 4y ago
Yeah, you need to enable the plugins you want. I'm just saying you can enable all the ones that make sense for you, and you don't have to switch between them.
42.
by kacperlukawski 4y ago
When you develop a plugin, you provide a description that ChatGPT uses to know when to call that particular service. So you don't need to tell people what they need to use - the model will decide independently based on the plugins you
43.
by kacperlukawski 4y ago
The fact that the model does not have to rely on its internal knowledge anymore but can communicate literally with any external system makes me feel it may significantly reduce the hallucination.
44.
by kacperlukawski 4y ago
That's a game-changer! It seems like factuality issues with ChatGPT might be fixed. We wrote a blog post on how to get started with a custom plugin: https://qdrant.tech/articles/chatgpt-plugin/
45.
by kacperlukawski 4y ago
Looks interesting! Have you considered a proper vector database like Qdrant ( https://qdrant.tech )? FAISS runs on a single machine, but if you want to scale things up, then a real database makes it a lot easier. And with a free 1
46.
by kacperlukawski 4y ago
Have you considered using a proper vector database like Qdrant ( https://qdrant.tech )? In the demo, it might be just fine to calculate the distance to all the articles in the knowledge base, but in a real case scenario, you'
47.
by kacperlukawski 4y ago
The cross-encoder will only rerank the results, that's right. And you're also correct that if the initial search returns 100 garbage results, it won't be able to help. But that's true for any reranking method. Even the r
48.
by kacperlukawski 4y ago
It is not only about visual similarity, but the semantics of the images as well (for example, all the dog photos should be close to each other, no matter the colours or the scene). The simplest way to do that is probably to use one of the p
49.
by kacperlukawski 4y ago
It would be interesting to include it in the benchmarks with the vector databases: https://qdrant.tech/benchmarks/ Are there any limits except for the max 2,000 dimension that might be indexed?
50.
by kacperlukawski 4y ago
If you want to perform vector search over your data, then Qdrant ( https://qdrant.tech ) is worth checking out.
51.
by kacperlukawski 4y ago
For that scale FAISS might be not enough, as it keeps the data in memory and is simply hard to scale. It's worth considering to use a proper vector database in a distributed mode to spread the load, or just allow storing some data on d
52.
by kacperlukawski 4y ago
What is a memory usage with 40M vectors?
53.
by kacperlukawski 4y ago
Have you considered using a proper vector database, so you can scale it up and run in a cluster? I mean something like Qdrant.
54.
by kacperlukawski 4y ago
Let me share an article I wrote: https://medium.com/@lukawskikacper/how-to-implement-a-visual... It uses Qdrant and ResNet embeddings to do that. Sources are included if you want to go straight to the implementation. Q
55.
Using LLM to bring out true answers(qdrant.tech)
11 points by kacperlukawski 4y ago|1 comments
56.
by kacperlukawski 4y ago
Since LLMs struggle with providing actual answers, we present how to fix that with a vector database acting as a knowledge base.
57.
by kacperlukawski 4y ago
Vector databases are supposed to require lots of memory, but this is not true. Vectors might be stored on disks, if they are fast enough, and still keep great performance.
58.
Search a million vectors with just 135MB of RAM(qdrant.tech)
8 points by kacperlukawski 4y ago|1 comments
59.
by kacperlukawski 4y ago
Sure thing! If you decide to use a library that other processes or machines will communicate with, you need to build a whole service around it. The library also doesn't scale well if you need to go beyond a single-machine setup. With a
60.
Show HN: Semantic Question Answering as a Service(qdrant.tech)
9 points by kacperlukawski 4y ago|0 comments

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