Qdrant

@qdrant.bsky.social

Vector Database & Search Engine

Chunking is a primary control knob for RAG quality. Compare chunking strategies using a reproducible pipeline: • @qdrant.bsky.social for per-experiment indexing and retrieval diffs • Tigris (Forked Buckets) for dataset versioning • CrewAI to orchestrate ingest → chunk → embed → evaluate Code 👇

Voice AI on live calls, powered by real-time retrieval. Community member Henryk Brzozowski built a demo where a dealership’s inventory is indexed in Qdrant - enabling sub-second answers like “Any Fords under $30k?” on live phone calls. RAG + voice. Real inventory. In production.

𝐊𝐲𝐥𝐢𝐞 - 𝐚 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 Spotted this cool project by Jonathan Mukhobe Sr. Kylie handles text, images, voice, tasks, search, and more - built with LangGraph + Groq. 🧠 Powered by Qdrant + MiniLM for long-term memory. Check it out: github.com/jonathanmuk/...

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Vector search is booming - and it’s powering accurate RAG and smarter AI apps. SE Radio’s new episode with Kacper Łukawski breaks down: ⚡ When to use embeddings ⚡ Real-world architectures ⚡ Lessons from scaling search ⚡ Why teams choose Qdrant 🎧 Listen: se-radio.net/2025/10/se-r...

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Qdrant 1.16 is out! 🎉 It brings tiered multitenancy, smarter filtered search with ACORN, faster disk-based HNSW via inline storage, better full-text search, safer conditional updates, and a refreshed Web UI. Read the full release overview to dive deeper: github.com/qdrant/qdran...

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Building multimodal search with Qdrant 👇 Rithvik B shows how CLIP + Whisper + Qdrant enable unified retrieval across text, image & audio in a single system. A powerful example of AI-native search: one query, all modalities. Read more: medium.com/@rithvikbng/...

Building a Multimodal Fusion Search Engine with Qdrant, CLIP & Whisper: Text, Image, and Audio in…

Search beyond words. Imagine describing an image, humming a tune, or typing a phrase and instantly retrieving relevant visuals, sounds, or…

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Quantization isn’t a trade-off anymore, it’s a must for scalable AI. Niranjan Akella breaks down how Qdrant’s 1.5-bit Quantization kills the Float32 tax with 24× compression, no recall loss, and sub-40 ms latency, all powered by Rust for real-time performance. Read more: qdrant.tech/documentatio...

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Powering next-gen e-commerce recommendations with hybrid sparse + dense vectors in Qdrant Sayanteka Chakraborty shares how combining keyword + semantic search boosted relevance and speed ⚡ 3× faster recs 📈 30–40% higher engagement Hybrid search = smarter personalization Read more: t.co/dlVzDhdc1o

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🧩 Community highlight: Matthieu Nicolescu built a fully self-hosted, cloud-native RAG stack using Qdrant, KServe, and Envoy AI Gateway - all on Kubernetes, no external APIs. A great blueprint for scalable, data-sovereign AI pipelines. Check here: medium.com/h7w/cloud-na...

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Dify now integrates Qdrant into its Knowledge Pipeline - bringing: • Fast, low-latency retrieval • Hybrid keyword + semantic search • Easy setup from prototype to production A great step forward for open-source, production-ready RAG pipelines. 🔗 Read the full post here: dify.ai/blog/dify-x-...

Exciting news from our partner @confluent.io! Confluent’s new Streaming Agents and Real-Time Context Engine bring live context to AI agents and enterprise apps. Together, Qdrant × Confluent enable developers to build real-time, AI powered by streaming data and vector search. 👉 t.co/DurLLwYMXO

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