Author | Lightnews

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Reposted by Qdrant

LlamaIndex @llamaindex.bsky.social · 3d

In this tutorial, @cle-does-things.bsky.social walks you through how to design, build, and deploy an anomaly detection workflow using @qdrant.bsky.social, @supabase.com, and LlamaAgents, the fastest way to take your ideas from concept to cloud.

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Qdrant @qdrant.bsky.social · 10d

🫶 Big thanks to everyone who joined Qdrant’s Discord Office Hours!

Loved the energy, ideas, and stories - and special thanks to Brian, Joshua, Clelia, and Tarun for the great insights.

See you next month for more tech, chats, and demos! 💬

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Qdrant @qdrant.bsky.social · 10d

🤩

miniCOIL: on the Road to Usable Sparse Neural Retrieval - Qdrant

Reposted by Qdrant

Mohamed Arbi @goodnight7.bsky.social · 10d

Had a great time at the Qdrant Hangout Office Hours 💚
Brian O’Grady shared scaling tips, Joshua Mo walked through RIG framework (#in-rust-we-trust), @cle-does-things.bsky.social demoed #LlamaAgents, and Tarun R Jain talked #PyCon.

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Zicklag @zicklag.dev · Jul 26

This is really neat. I really like the way that Qdrant focuses on simplicity and performance while still trying to combine it with the insights from heavier machine learning innovations for meaning extraction and semantic search.

Introducing miniCOIL, a lightweight sparse neural retriever capable of generalization.

Qdrant + DataTalks.Club: Free 10-Week Course on LLM Applications - Qdrant

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Qdrant @qdrant.bsky.social · Jul 10

An open source engineer from @llamaindex.bsky.social shares trial-and-error-learned lessons.

For anyone implementing textual RAG, check the blog by @cle-does-things.bsky.social!
✅ useful tips, from chunking to evals-related;
✅ projects applying each tip in the wild.

👉 qdrant.tech/blog/hitchhi...

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Qdrant @qdrant.bsky.social · Jun 10

Free 10-week course! Learn RAG, vector search, hybrid search, evaluation, and more. Hands-on lessons from Qdrant’s experts.

Learn more 👉 qdrant.tech/blog/datatal...

Gain hands-on experience with RAG, vector search, evaluation, monitoring, and more.

Qdrant @qdrant.bsky.social · Jun 10

@mrscoopers.bsky.social and Kacper Łukawski will be there June 16-17 in Berlin, so if you're into vector search and want to chat over coffee, come find us! Plus Jenny's giving a talk about miniCOIL - our new sparse retrieval model that's pretty cool 🚀

Coffee + vector databases = perfect combo ☕

Bavaria, Advancements in SEarch Development (BASED) Meetup · Luma

Qdrant @qdrant.bsky.social · Jun 10

☕ Guess what? We're sponsoring the coffee breaks at @berlinbuzzwords.de 2025!

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Evgeniya (Jenny) Sukhodolskaya @mrscoopers.bsky.social · Jun 9

⏰ 𝐎𝐧𝐞 𝐰𝐞𝐞𝐤 𝐭𝐢𝐥𝐥 𝐁𝐞𝐫𝐥𝐢𝐧 𝐁𝐮𝐳𝐳𝐰𝐨𝐫𝐝𝐬
I'll really try to make my talk (on the 17th at 11:10 am in Kesselhaus) fit this "𝘏𝘮𝘮𝘮𝘮, 𝘐 𝘴𝘩𝘰𝘶𝘭𝘥 𝘵𝘳𝘺 𝘵𝘩𝘪𝘴 𝘳𝘯" vibe of @berlinbuzzwords.de!
To TLDR, it's about making 𝐬𝐩𝐚𝐫𝐬𝐞 𝐩𝐚𝐫𝐭 𝐢𝐧 𝐡𝐲𝐛𝐫𝐢𝐝 𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐬 as 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 as BM25, but 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐚𝐰𝐚𝐫𝐞 (so 𝐛𝐞𝐭𝐭𝐞𝐫 than BM25)

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Qdrant @qdrant.bsky.social · Jun 2

Join for:

✅ practical advice on vector search in daily #AI developer’s life from our & @llamaindex.bsky.social beloved star Clelia Astra Bertelli
✅ research insights on the role of multimodality in industrial #RAG from Monica Riedler, amazing PhD at @tum.de‬

Register here
👉 lu.ma/cyelt1n6

Meetup Description: Search is evolving fast. From new algorithms and tools to AI-driven solutions, the field is constantly shifting. BASED Meetup is where…

lu.ma

🚀 Building a RAG API with .NET, Semantic Kernel, Phi-3, and Qdrant: Enrich Your E-commerce Experience

Qdrant @qdrant.bsky.social · Jun 2

🥨 Come to the Bavarian Search Meetup!

The 2nd meeting of 𝐁avaria, 𝐀dvancements in 𝐒𝐞arch 𝐃evelopment meetup, co-organized by our @mrscoopers.bsky.social, is happening in #Munich on the 12th of June!

👇

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Reposted by Qdrant

Visual Studio @visualstudio.com · Apr 25

Create smart chat apps with custom data and enjoy easy setup in Visual Studio, VS Code, or CLI.

Preview 2 of the .NET AI Template is here, and now it's easier than ever to build cloud-native #AI apps with #dotNETAspire and #Qdrant vector DB integration. Learn. https://msft.it/63326S1eg4

spot art.
text reads: Announcing .NET AI Chat Web App Template

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Qdrant @qdrant.bsky.social · Apr 25

Yeah! @qdrant.bsky.social 😍 .NET AI :)

Reposted by Qdrant

juanluelguerre.bsky.social @juanluelguerre.bsky.social · Apr 22

🚀 Build a real RAG API with .NET 8, Semantic Kernel, Phi-3, and Qdrant!

🎯 Enrich LLMs with real product data
⚙️ Local ONNX inference with Phi-3
🔥 Full API endpoints ready!

Dive into the architecture 👉 wp.me/p29SK-Yu

#DotNet #SemanticKernel #RAG #Qdrant #Phi3 #AI #MachineLearning #ONNX

Learn how to build a powerful RAG (Retrieval-Augmented Generation) API using .NET, Microsoft Semantic Kernel, Phi-3, and Qdrant. Combine your private e-commerce data with LLMs to create smarter, gr…

elguerre.com

Qdrant @qdrant.bsky.social · Apr 14

Want to see MCP in action? Learn how to orchestrate OpenAI agents using MCP, AugmentCode, and Qdrant.

📅 April 29 @ 11 am ET

🔗 Save your spot: try.qdrant.tech/mcp-agent-in...

Relevance Feedback in Informational Retrieval - Qdrant

Qdrant @qdrant.bsky.social · Apr 2

The article explores the gap between theory and practice — and why relevance feedback hasn't made it yet into neural search at scale.

If you're building or working with retrieval systems, it’s worth a read.

👉 qdrant.tech/articles/sea...

Relerance feedback: from ancient history to LLMs. Why relevance feedback techniques are good on paper but not popular in neural search, and what we can do about it.

Qdrant @qdrant.bsky.social · Apr 2

Relevance feedback helps search systems iteratively improve results toward relevance. It’s been studied for 60+ years — yet remains rare in modern neural search.

We explored the field to understand why — and gathered this summary of methods proposed over the years.

⬇️

Large Scale Search - Qdrant

Reposted by Qdrant

LlamaIndex @llamaindex.bsky.social · Mar 28

MCP week: Learn to use LlamaIndex to prep docs for Claude with a pre-built MCP server for @qdrant.bsky.social, using @angular.dev docs. Set up vector DB, process AI-friendly docs, configure MCP server, implement RAG pipeline:
https://www.aiboosted.dev/p/building-your-own-rag-system-typescript

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Qdrant @qdrant.bsky.social · Mar 28

🧵 Tutorial: qdrant.tech/documentatio...
📁 Code: github.com/qdrant/laion...

Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.

Large Scale Search - Qdrant

Qdrant @qdrant.bsky.social · Mar 28

We documented everything: memory estimates, observed metrics, config breakdowns, and open-sourced all the scripts.

🧵 Tutorial: qdrant.tech/documentatio...
📁 Code: github.com/qdrant/laion...

Qdrant is an Open-Source Vector Database and Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.

Qdrant @qdrant.bsky.social · Mar 28

Here's what we ran:
✅ Stored vectors as FLOAT16 instead of FLOAT32
✅ Used binary quantization to keep compressed vectors in RAM
✅ Kept full-precision vectors on disk for query-time rescoring
✅ Tuned HNSW for lower memory
✅ Enabled async disk I/O with io_uring to parallelize reads

Qdrant @qdrant.bsky.social · Mar 28

🚧 We ran vector search over 400M CLIP embeddings from LAION-400M — using Qdrant and just 64GB RAM.

We wanted to see how far we could push Qdrant with minimal hardware and how much we could squeeze out of quantization, indexing, and async I/O.

Built for Vector Search - Qdrant

Qdrant @qdrant.bsky.social · Mar 28

Our opinion on this qdrant.tech/articles/ded...

Why add-on vector search looks good — until you actually use it.