Qdrant

@qdrant_engine

High-performance Rust-based vector search engine. discord.com/invite/qdrant

qdrant.tech

Joined December 2020

113 Following

13.6K Followers

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Qdrant
@qdrant_engine
Oct 14
21× smaller vectors. 99.9% of baseline retrieval quality retained with rescoring. We tested Perplexity’s pplx-embed-v2-late with Qdrant’s TurboQuant across 4 ViDoRe v3 datasets. 856 KB → 40 KB per page, with a trade-off: rescoring requires the original vectors. ⁠Full
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Qdrant
@qdrant_engine
Oct 13
780 ms during background optimization. 4.3 ms after it finished. Same Qdrant collection, very different search latency. We tested 13 optimizer configurations on 1.76M vectors to understand the trade-offs between indexing, recovery time, and query performance. Here’s what we
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Qdrant
@qdrant_engine
Oct 9
Your AI agents need memory that lasts beyond a single conversation. LangGraph supports long-term memory, allowing agents to store information and recall it across conversations. Now, you can use Qdrant as the storage layer with our new memory store integration. Qdrant brings
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Qdrant
@qdrant_engine
Oct 9
Take the free course on @DeepLearningAI today, and learn how to give your robots real memory with #QdrantEdge
DeepLearning.AI
@DeepLearningAI
Oct 8
This assistant remembers where you left your keys. No network connection. No remote server. Build one yourself in a course built in partnership with Qdrant and taught by Dylan Couzon. 👉 Enroll for free: hubs.la/Q04zGYrF0 #AI #VectorSearch #AIEngineering
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Qdrant
@qdrant_engine
Oct 7
77× less vector RAM. 94.5% of baseline retrieval quality. We got early access to Google EmbeddingGemma 2 and tested how far its Matryoshka-compatible embeddings could be compressed in Qdrant. With shorter vectors + quantization + rescoring, we reduced the vector memory
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