Qdrant

🧩 Piecing Together the Ultimate IT Support Bot From initial data preparation to final deployment, learn to build a system that understands and responds to complex IT queries with Vansh Khaneja's latest tutorial! 🤖 Key features: ☑ Self-query retrieval with smart metadata filtering ☑ Efficient data handling and vector embedding ☑ Optimized response generation With Qdrant, LangChain, Groq, and Streamlit! Learn how to build it: https://lnkd.in/exJudkYj

Building Smarter Agents with LlamaIndex and Qdrant's Hybrid Search 🦙🔍 Kameshwara Pavan Kumar explores an advanced RAG architecture combining LlamaIndex agents with Qdrant's hybrid search capabilities! The setup leverages both dense and sparse vector embeddings for precise data retrieval. Key components: 1️⃣ Orchestrator: Coordinates workflow via RabbitMQ 2️⃣ Info_tool_agent: Retrieves data using Qdrant's hybrid search 3️⃣ Summary_tool: Compiles coherent responses 4️⃣ Hybrid search: Combines dense and sparse embeddings The implementation uses Snowflake/snowflake-arctic-embed-s for dense embeddings and prithivida/Splade_PP_en_v1 for sparse, with Mistral AI through Ollama for LLM tasks. Pavan gives us a detailed look at the architecture and implementation, showcasing how to build complex, efficient workflows for AI-driven data solutions. Check out the full article published in GoPenAI: https://lnkd.in/dhZa4caT

Join us on July 18 for an exclusive 45-minute hands-on tutorial: "𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐔𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐇𝐲𝐛𝐫𝐢𝐝 𝐒𝐞𝐚𝐫𝐜𝐡 𝐰𝐢𝐭𝐡 𝐐𝐝𝐫𝐚𝐧𝐭" 🚀 In this tutorial, we'll transform existing dense embedding pipeline into a 𝐡𝐲𝐛𝐫𝐢𝐝 𝐨𝐧𝐞. You'll discover how the recently released Qdrant 1.10 can enrich your semantic search pipeline with new search modes and support multiple vector representation. 💱 👉 RSVP: https://buff.ly/4cRGKxk

We're excited to see #Qdrant powering advanced applications like https://voir.news! Voir is a state-of-the-art forecasting tool, processing millions of news articles in real-time. 📰 It's built on: ✅ AskNews data pipeline ✅ Qdrant for large-scale hybrid indexing and retrieval ✅ Flowdapt.ai for distributed processing Voir offers state-of-the-art forecasting with just one line of code. Impressive work by the Emergent Methods team! We're looking forward to watching Voir compete in the Metaculus 🥇 AI benchmark tournament this month: https://lnkd.in/dR2NZCHj Join our Vector Search Office Hours today to hear Robert Caulk discuss how they built this impressive system! Starting in 1 hour: https://lnkd.in/dGjGK8iX

I just published a new article on the Malt Engineering blog about our latest work on enhancing our freelancer recommendation system. The article details how we used a vector database and a two-step retriever-ranker architecture to improve precision and scalability. In the article, you’ll discover: 🔴 The challenges we faced with our previous system 🛠️ How we designed the new architecture 🔍 Our thorough process for selecting the best vector database 🚀 The significant efficiency boost from using vector databases 📈 The results and exciting future directions for our recommendation system If you’re interested in AI, ML, or data architecture, I invite you to read the full article (link in the first comment). #MLOps #DataScience #VectorDatabase #NLP #RecommendationSystem

Architect and build a real-world LLM system 🌍 Paul Iusztin explains how to build and design high-performance RAG inference pipelines in lesson 9 of the free LLM Twin course. He walks us through: ☑ Microservice vs monolithic LLM architectures ☑ Building a production-grade RAG business module with #Qdrant and Superlinked ☑ Deploying LLM microservices on Qwak ☑ Implementing prompt monitoring with Comet ML 👉 Read the article: https://lnkd.in/gEPv2d2T 👉 See all 11 lessons of the LLM Twin course: https://lnkd.in/gXFE4ezv Thank you to Paul and the whole Decoding ML team, Alex Vesa, Rares Istoc, and Alex Razvant, for their amazing work in this course!

We welcome Toni Reina Pérez aboard! He joins Qdrant as a Staff Engineer to empower our Cloud team. Toni works from Barcelona, Spain. Welcome! 🎉

Check out the updated schedule for #OPEA Virtual Community Day - July 16th. We will be ratifying our roadmap, getting an update on the latest release (0.7), and listening to you the #community on how we should collaborate with other projects, partners, and technology trends. #GenAI #RAG #opensource https://lnkd.in/d3K852ay

We welcome Dominic Page to Qdrant as a Senior Support Engineer. Working from Valencia, Spain, Dominic brings extensive experience with search engines. Besides his work, he enjoys playing the nylon string guitar and sailing. Welcome! 🎉