Qdrant

A very comprehensive guide, pushed away from the spotlight by the winter holidays:) Covers: + Dense vector search (how else?) + Keywords-based search + Obviously, BM25; + Sparse neural retrieval (SPLADE, miniCOIL, etc.). It's when you search based on keywords, but the retriever accounts for their meaning in the context of the query/documents. + Full-text indexing & filtering, which doesn’t break vector search (cause filterable HNSW/ACORN) + How to combine approaches 🌲 https://lnkd.in/dsWUqFwp

Is your Lucene-based vector search hitting a wall? If you are running vector search on Lucene (Elastic/OpenSearch) and struggling with latency or indexing costs, you aren't alone. We’ve seen teams make the switch and see immediate, massive gains. Just look at Sprinklr. After moving to Qdrant, they achieved: 90% faster write times, 80% faster latency, 2.5x higher RPS...all compared to their previous Lucene-based stack. !!We are currently offering a free technical POC comparison for teams looking to benchmark their current setup against Qdrant!! 📩 DM me if you want to apply. Let’s see if we can replicate these numbers for you. more info: https://lnkd.in/dbkaqyNA #VectorDatabase #Qdrant #Lucene #Elasticsearch #AI #MachineLearning #TechInfrastructure

I hope you still like my advanced design skills. Yes, we are hiring again! The Qdrant team opened several positions, and we will add more soon.  If you’re excited about vector search, AI infrastructure, and building developer-first products, this might be the perfect time to join us. 😎 We hire: - Developer Relations Engineers (EU/US) - Benchmark Engineer (Worldvide) - Solution Architects (EU/US/India) - Support Engineers (EU/US/India) - Account Executives (EU/US) - Cloud Engineers (EMEA) - DevOps/SRE (EMEA) - And more here ⤵️ QDRANT.JOIN.COM If you are interested, and you should be. 😉 Please do not write me directly! 𝐈'𝐦 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐡𝐢𝐫𝐢𝐧𝐠 𝐦𝐚𝐧𝐚𝐠𝐞𝐫. Please apply via qdrant.join.com Also, for recruiters. You are welcome, but... we use HireBuffer as a Proxy service to gather all candidates through a single channel. Signup here for free: hirebuffer.com?ref=qdrant and share your candidates with us. #hiring #recruiting #jobs #careers

𝐁𝐞𝐭𝐭𝐞𝐫 𝐑𝐀𝐆 𝐒𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐁𝐞𝐭𝐭𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬 😉 RAG performance often comes down to one question: which chunking strategy actually works best? The Tigris Data RAG Lab shows how reproducible experimentation can make that decision data-driven - by running parallel chunking variants as Tigris bucket forks, and pairing each dataset with its own Qdrant vector index for clean, apples-to-apples evaluation. With fast similarity search and scalable indexing, Qdrant helps teams compare retrieval quality objectively and iterate on RAG systems with confidence - not guesswork. A solid example of how thoughtful data workflows + vector search lead to better AI outcomes. More details here: https://lnkd.in/gSdzUb4F #RAG #VectorSearch #LLMOps #AIInfrastructure #Qdrant #RetrievalAugmentedGeneration

https://lnkd.in/gi8-veCc Here’s my talk on generating embeddings at scale. Huge thanks to Ray Summit for the opportunity to present on the topic. Description: At Ray Summit 2025, Justin Miller from ZEFR shares how his team built a production-grade, multi-platform NLP pipeline using Ray and GPU acceleration to process millions of social media posts across TikTok, YouTube, and Instagram. He begins by describing the challenges of handling massive, fast-changing content streams across multiple platforms—each with unique data formats, ingestion patterns, and quality constraints. To meet these demands, ZEFR engineered a robust distributed pipeline that uses Ray to orchestrate scalable embedding generation, GPU-heavy processing, and high-throughput vector search ingestion. Justin walks through the architecture step-by-step: Snowflake → Ray ingestion: Retrieve rows for each platform with consistent batch scheduling Cleaning, chunking, and preprocessing: Normalize and prepare multimodal content at scale Distributed embedding generation: Use Ray Actors to shard GPU inference tasks across the cluster High-throughput writes: Send results to Google Cloud Storage (GCS), Qdrant for vector search, and back to Snowflake for analytics and pipeline tracking Shard lifecycle management: Delete stale shards, manage multi-platform ingestion, and maintain healthy storage footprints He also shares practical, real-world guidance for operating Ray in production—covering deployment patterns, debugging tips, failure recovery, throughput tuning, and cost management. Whether you’re processing large multi-source datasets, running GPU-heavy inference pipelines, or building modern vector-search–backed systems, this talk provides both code-level insights and actionable advice for running Ray at scale. #ray #anyscale #embeddings

Flipkart’s Trust & Safety team has made significant advancements by replacing slow batch-based similarity checks, which took up to 9 hours, with a real-time vector search pipeline utilizing Qdrant. This transformation involved indexing high-dimensional embeddings and executing online similarity searches, which reduced detection latency to less than 1 minute. This shift enables proactive fraud prevention rather than relying on post-hoc cleanup. This case exemplifies how vector search solutions are evolving from "AI experiments" to essential infrastructure for real-time systems. Check it out: https://lnkd.in/dsi38HKn Thanks to Sourabh Sarkar for contributing to this content piece!

I'm #hiring TWO roles for the Qdrant Developer Relations Team! Are you passionate about AI infrastructure, open source, and empowering developers? The Developer Relations team at 𝗤𝗱𝗿𝗮𝗻𝘁 is looking for a 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 to help us build the world’s most advanced vector search engine! As part of our team, you won’t just talk about the tech, you’ll be building the bridges between our high-performance vector engine and the global AI community. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗱𝗼:  • 𝗕𝘂𝗶𝗹𝗱 & 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁: Create cutting-edge demos using Qdrant with the latest LLM frameworks.  • 𝗔𝗱𝘃𝗼𝗰𝗮𝘁𝗲: Represent Qdrant at global conferences and within the AI ecosystem.  • 𝗘𝗱𝘂𝗰𝗮𝘁𝗲: Produce technical content that solves real-world RAG and semantic search challenges.  • 𝗖𝗼𝗻𝗻𝗲𝗰𝘁: Engage with our growing open-source community to gather feedback and drive product innovation. If you love Rust, Python, and the fast-paced world of AI and Vector Search, we want to meet you! 𝗔𝗣𝗣𝗟𝗬 𝗛𝗘𝗥𝗘:  • 𝗦𝗮𝗻 𝗙𝗿𝗮𝗻𝗰𝗶𝘀𝗰𝗼: https://lnkd.in/e3RAA_fK  • 𝗘𝘂𝗿𝗼𝗽𝗲 (𝗠𝗮𝗷𝗼𝗿 𝗖𝗶𝘁𝗶𝗲𝘀): https://lnkd.in/erhXVKDt #DevRel #AI #VectorDatabase #VectorSearch #OpenSource #Hiring #Qdrant

Read here to see how Flipkart’s Trust & Safety team built real-time multimodal similarity search with Qdrant Link: https://lnkd.in/gqs9Pm-P