Any suggestion what should one read/watch to understand the difference between this and relational DB?
https://qdrant.tech/articles/dedicated-service/ - we have some arguments on this
Qdrant 1.7.0
21–30 of 32 posts
Re: Qdrant 1.7.0
#22Qdrant is the vectordb that ChatGPT and Grok use (e.g. when you add docs to a custom GPT or tweets in Grok) https://twitter.com/simonw/status/1722011967886688696
Interesting they both do. Does Qdrant look like a winning horse then? Was about to use Weaviate for a project today and this gives me pause. Anyone have some strong opinions? pg_vector also been on my radar recently. Qdrant vs Weaviate I know is partially a rust vs go topic.
I've been using pgvector, it has worked as expected, which is all I want. Personally my choice was based entirely on the fact that I already use postgres, and that it will still probably exist in it's current form after the dust settles on the vector db market.
Re: Qdrant 1.7.0
#23Qdrant is great vector DB ...with the strangest hero image on their release announcement. A robot crab with a galleon in the background??
Re: Qdrant 1.7.0
#24> Traditional keyword-based search mechanisms often rely on algorithms like TF-IDF, BM25, or comparable methods. While these techniques internally utilize vectors, they typically involve sparse vector representations. In these methods, the vectors are predominantly filled with zeros, containing a relatively small number of non-zero values. Those sparse vectors are theoretically high dimensional, definitely way higher…
> e.g. as in Croft, Metzler & Strohman 7.2.1
Re: Qdrant 1.7.0
#25Earlier quoted context omitted.
Interesting they both do. Does Qdrant look like a winning horse then? Was about to use Weaviate for a project today and this gives me pause. Anyone have some strong opinions? pg_vector also been on my radar recently. Qdrant vs Weaviate I know is partially a rust vs go topic.
As another signal, check out this report by Streamlit, which shows the popularity of different vector databases among Streamlit apps: https://state-of-llm.streamlit.app/#third Faiss and Pinecone are at the top (disclosure: I'm from Pinecone). But Faiss isn't really a full-fledged vector DB. Pinecone is a managed option which is out of the question for a company like Twitter and maybe for you (although you should cons…
We have been working this year to increase our US presence, and we're hiring now: https://join.com/companies/qdrant/9929148-cloud-platform-dev...
Source: I work at Qdrant from the US :)
Re: Qdrant 1.7.0
#26Earlier quoted context omitted.
Interesting they both do. Does Qdrant look like a winning horse then? Was about to use Weaviate for a project today and this gives me pause. Anyone have some strong opinions? pg_vector also been on my radar recently. Qdrant vs Weaviate I know is partially a rust vs go topic.
As another signal, check out this report by Streamlit, which shows the popularity of different vector databases among Streamlit apps: https://state-of-llm.streamlit.app/#third Faiss and Pinecone are at the top (disclosure: I'm from Pinecone). But Faiss isn't really a full-fledged vector DB. Pinecone is a managed option which is out of the question for a company like Twitter and maybe for you (although you should cons…
Interestingly the effect of location is also visible when you compare LMQL and Guidance for constrained generation. LMQL seems to be a great option but has received much less attention compared to Guidance, maybe partly due to the fact that it comes from Europe not the US.
Re: Qdrant 1.7.0
#27> Traditional keyword-based search mechanisms often rely on algorithms like TF-IDF, BM25, or comparable methods. While these techniques internally utilize vectors, they typically involve sparse vector representations. In these methods, the vectors are predominantly filled with zeros, containing a relatively small number of non-zero values. Those sparse vectors are theoretically high dimensional, definitely way higher…
> e.g. as in Croft, Metzler & Strohman 7.2.1 https://ciir.cs.umass.edu/downloads/SEIRiP.pdf
Actually I think I transposed two digits in that reference, it's 7.1.2.
Re: Qdrant 1.7.0
#28I’m a big fan of Qdrant, I also have heard rumors than OpenAI uses Qdrant as their vector database of choice.
I’ve been building a Hasura Data Connector for Qdrant and it’s been too much fun. Glad to see them getting talked about here.
Re: Qdrant 1.7.0
#29Earlier quoted context omitted.
As another signal, check out this report by Streamlit, which shows the popularity of different vector databases among Streamlit apps: https://state-of-llm.streamlit.app/#third Faiss and Pinecone are at the top (disclosure: I'm from Pinecone). But Faiss isn't really a full-fledged vector DB. Pinecone is a managed option which is out of the question for a company like Twitter and maybe for you (although you should cons…
We have been working this year to increase our US presence, and we're hiring now: https://join.com/companies/qdrant/9929148-cloud-platform-dev... Source: I work at Qdrant from the US :)
The local API mode is very nice feature over others. Having an almost sqlite3 style local db that then works the same in server mode is a very good feature.
I will be trying it instead of Weaviate this week. I’m also slightly confused about embedding vector generation vs other db who have that built in. Just need to read more I guess
Re: Qdrant 1.7.0
#30Qdrant is great vector DB ...with the strangest hero image on their release announcement. A robot crab with a galleon in the background??
In rust we trust. I think the whole thing is built with rust hence the crab references all over the place