11–20 of 32 posts
I’m interested in building a locally ran app. Is qdrant appropriate for that? Is it like SQLite where there is little overhead for doing a server less implementation?
I was looking for the fine print on their "Try For Free"/"Free Tier Available" and was pleasantly surprised by
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I’m interested in building a locally ran app. Is qdrant appropriate for that? Is it like SQLite where there is little overhead for doing a server less implementation?
Local mode: https://github.com/qdrant/qdrant-client#local-mode
I’m interested in building a locally ran app. Is qdrant appropriate for that? Is it like SQLite where there is little overhead for doing a server less implementation?
Tried a few DBs that didn't work well (e.g. I think it was ChromaDB that didn't support Python 3.12) and ended up picking LanceDB.
Very simple onboarding (just built on top of parquet) but there are a few rough edges.
Curious how it compares with qdrant for non-crazy problems
I’m interested in building a locally ran app. Is qdrant appropriate for that? Is it like SQLite where there is little overhead for doing a server less implementation?
I was in that spot a few weeks ago. My requirements were not huge but a) I was on Windows and b), didn't want to waste too much time setting it up. Tried a few DBs that didn't work well (e.g. I think it was ChromaDB that didn't support Python 3.12) and ended up picking LanceDB. Very simple onboarding (just built on top of parquet) but there are a few rough edges. Curious how it compares with qdrant for non-crazy prob…
Qdrant 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.
Earlier 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…
If Twitter chose to use Qdrant for Grok, it doesn’t matter that Qdrant is out of Berlin.
What matters is that Qdrant is the most performant, and it’s an open-source vectordb, not a closed-source vectordb like Pinecone.
Just the other day I played with qdrant, using its Python client. Pretty smooth onboarding experience. I came across two questions. Perhaps some kind folks with more experience can shed some light on these qdrant use cases. 1. for embeddings for use cases such as LLM chat bots, I split internal data into chunks. Those chunks are then vectorized and stored. Alongside the entry itself, I stored the original chunk in me…
For my applications, I use pgvector since I can also use fulltext indexes and JOINs with the rest of my business logic which is stored in a postgres database. This also makes it easier to implement hybrid search, where the fulltext results and semantic search results are combined and reranked.
I think the main selling-point for standalone vector databases is scale, i.e., when you have a single "corpus" of over 10^7 chunks and embedding vectors that needs to serve hundreds of req/s. In my opinion, the overhead of maintaining a separate database that requires syncing with your primary database did not make sense for my application.
Just the other day I played with qdrant, using its Python client. Pretty smooth onboarding experience. I came across two questions. Perhaps some kind folks with more experience can shed some light on these qdrant use cases. 1. for embeddings for use cases such as LLM chat bots, I split internal data into chunks. Those chunks are then vectorized and stored. Alongside the entry itself, I stored the original chunk in me…
2. You often can perform the embedding in the DB, but there are a lot of use cases where you want to manage your embedding models outside the DB. This way you aren't dependent on which models the DB supports and you don't duplicate them throughout your system