Andre Zayarni

Andrey Vasnetsov

Qdrant GmbH

FC Gruppe is a German engineering and digitization firm that provides innovative solutions across various sectors including construction, IT, energy, healthcare, logistics, water, security, and mobility.

FC Gruppe

Qdrant | 27,885 followers on LinkedIn. Massive-Scale Vector Database | Powering the next generation of AI applications
with advanced and high-performant vector similarity search technology.

Qdrant engine is an open-source vector search database. It deploys as an API service providing a search for the nearest high-dimensional vectors.

Staff Platform Engineer at Qdrant

Predrag Knežević

General Partner at Unusual Ventures | Investing in AI, data, security, dev tools, OSS

Wei Lien Dang

VP of Engineering & Product @ Qdrant

Fabrizio Schmidt

Co-founder at Qdrant, The Vector Database.

Qdrant

Build robust GenAI pipelines with LlamaIndex, Qdrant , and MLflow for advanced RAG! Learn how to: 🔍 Streamline RAG workflows for better scalability 📊 Ensure performance consistency across model versions 🚀 Optimize indexing systems for efficiency This step-by-step guide covers: • Setting up MLflow for experiment tracking • Configuring Qdrant for vector storage • Integrating Ollama for LLM and embedding models • Creating a playground app to test the full pipeline Check out the full post here: https://lnkd.in/gCnm9JgE

LlamaIndex

One of the most frequent questions we receive via the Qdrant community and support channels is about 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞, memory 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 and 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧. However, it is precisely like with 𝘎𝘰𝘰𝘥, 𝘍𝘢𝘴𝘵, and 𝘊𝘩𝘦𝘢𝘱. You can pick only two of these, and there will always be a trade-off on the third. ➡ High-speed search and low memory usage: Yes, but you will need to give up high precision. ➡ High precision & low memory usage: Yes, you can utilize other on-disk storage, but your responses will not be the fastest. ➡ High precision & high-speed search: Yes, you will need to put everything into memory, and it will not be cheap. More on this in the 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐆𝐮𝐢𝐝𝐞: https://lnkd.in/dzA3SAiY Also recommended. This article by Benito Martin about Balancing Accuracy and Speed with Qdrant Hyperparameters, Hybrid Search, and Semantic Caching https://lnkd.in/dZdnd5Xy

📢 Join our upcoming Webinar: ColPali + Qdrant in Action! Struggling to retrieve complex, visually rich PDFs? ColPali has you covered with efficient document retrieval using Vision Language Models. What’s on the agenda: ✅ How ColPali Enhances Document Retrieval ✅ Boosting ColPali with Qdrant's Binary Quantization ✅ Using ColPali as a Reranker ✅ ColPali in RAG/Vision RAG Applications Led by Sabrina A. , Evgeniya Sukhodolskaya , and Atita Arora . 📅 Date: Thursday, November 21, 2024 🕗 Time: 8:00 AM PT / 11:00 AM ET / 5:00 PM CET 👉 RSVP: https://lnkd.in/dW9jRHSk

🧠 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐐𝐝𝐫𝐚𝐧𝐭 𝐟𝐨𝐫 𝐁𝐨𝐭𝐡 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐒𝐩𝐞𝐞𝐝 In this two-part series, Benito Martin breaks down hyperparameter fine-tuning in Qdrant, showing how to balance retrieval accuracy and speed. Part 1 covers core configurations like HNSW indexing, different quantization types, and the benefits of hybrid search. You’ll see how parameters such as 𝘮, 𝘦𝘧_𝘤𝘰𝘯𝘴𝘵𝘳𝘶𝘤𝘵, 𝘰𝘯_𝘥𝘪𝘴𝘬, and 𝘮𝘢𝘹_𝘪𝘯𝘥𝘦𝘹𝘪𝘯𝘨_𝘵𝘩𝘳𝘦𝘢𝘥𝘴 influence precision and performance. Part 2 shows a practical setup to speed up responses and cut costs with 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐜𝐚𝐜𝐡𝐢𝐧𝐠. Plus, with a unique SPLADE-based approach to sparse vector embedding and Qdrant’s quantized indexing, this example is designed to maximize efficiency for high-demand tasks. Read both parts here 👇 Part 1: https://lnkd.in/dZRijDZb Part 2: https://lnkd.in/dwG5rTH7

🤖 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐜𝐮𝐬𝐭𝐨𝐦 𝐬𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐢𝐧 Langtail ’s 𝐔𝐈 𝐰𝐢𝐭𝐡 Qdrant We live in a world where from “𝘖𝘯𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘐 𝘩𝘢𝘷𝘦 𝘪𝘴 𝘵𝘩𝘢𝘵 𝘸𝘩𝘦𝘯 𝘐 𝘢𝘮 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘖𝘱𝘦𝘯𝘈𝘐 𝘈𝘗𝘐 𝘸𝘪𝘵𝘩 𝘓𝘓𝘔, 𝘪𝘵 𝘬𝘦𝘦𝘱𝘴 𝘶𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘰𝘭𝘥 𝘈𝘗𝘐, 𝘸𝘩𝘪𝘤𝘩 𝘪𝘴 𝘷𝘦𝘳𝘺 𝘢𝘯𝘯𝘰𝘺𝘪𝘯𝘨.” to an AI assistant that solves this, there are a couple of simple steps. Daniel Melo provides detailed instructions, code and configurations on how he built his #RAG assistant with Langtail and Qdrant . 👉 Check it out https://lnkd.in/dUk8cmFD

👋 Atita Arora at Big Data Conference Europe 2024 in Vilnius! Our Solution Architect, Atita Arora , has been invited to speak at: ✅ 𝐏𝐚𝐧𝐞𝐥 𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧 | 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐇𝐲𝐩𝐞: 𝐑𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜 𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐀𝐈 ✅ 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡: 𝐔𝐧𝐫𝐚𝐯𝐞𝐥𝐢𝐧𝐠 𝐏𝐮𝐫𝐩𝐨𝐬𝐞-𝐛𝐮𝐢𝐥𝐭 𝐯𝐬. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐟𝐨𝐫 𝐆𝐞𝐧 𝐀𝐈 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 She’ll highlight the impact of vector search on #GenAI applications, comparing purpose-built databases with traditional databases incorporating vector capabilities. 😊 Come meet her—she’s Qdrant ’s go-to expert and is excited to share her insights with you! 👉 Conference https://lnkd.in/dhET9Gr

#GenerativeAI in Action. Listen to this podcast generated with NotebookLM based on our new blog post about 𝐂𝐨𝐥𝐏𝐚𝐥𝐢 ( https://lnkd.in/dk4-qWjU ). The two 𝐀𝐈 𝐚𝐯𝐚𝐭𝐚𝐫𝐬 in the podcast have a vital conversation about the new approach and information retrieval in general. "𝘐𝘵'𝘴 𝘧𝘢𝘴𝘤𝘪𝘯𝘢𝘵𝘪𝘯𝘨 𝘵𝘰 𝘵𝘩𝘪𝘯𝘬 𝘢𝘣𝘰𝘶𝘵 𝘩𝘰𝘸 𝘵𝘩𝘪𝘴 𝘤𝘰𝘶𝘭𝘥 𝘪𝘮𝘱𝘢𝘤𝘵 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘰𝘧 𝘴𝘦𝘢𝘳𝘤𝘩 𝘢𝘯𝘥 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 𝘢𝘴 𝘢 𝘸𝘩𝘰𝘭𝘦. 𝘛𝘩𝘪𝘴 𝘧𝘦𝘦𝘭𝘴 𝘭𝘪𝘬𝘦 𝘢 𝘴𝘵𝘦𝘱𝘱𝘪𝘯𝘨 𝘴𝘵𝘰𝘯𝘦 𝘵𝘰 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘦𝘷𝘦𝘯 𝘣𝘪𝘨𝘨𝘦𝘳, 𝘢 𝘸𝘩𝘰𝘭𝘦 𝘯𝘦𝘸 𝘸𝘢𝘺 𝘰𝘧 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯. 𝘈𝘯𝘥 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳, 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘫𝘶𝘴𝘵 𝘵𝘩𝘦 𝘣𝘦𝘨𝘪𝘯𝘯𝘪𝘯𝘨. 𝘈𝘴 𝘚𝘦𝘮𝘢𝘯𝘵𝘪𝘤 𝘚𝘦𝘢𝘳𝘤𝘩 𝘤𝘰𝘯𝘵𝘪𝘯𝘶𝘦𝘴 𝘵𝘰 𝘦𝘷𝘰𝘭𝘷𝘦, 𝘸𝘦 𝘤𝘢𝘯 𝘦𝘹𝘱𝘦𝘤𝘵 𝘦𝘷𝘦𝘯 𝘮𝘰𝘳𝘦 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘷𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘪𝘯 𝘩𝘰𝘸 𝘸𝘦 𝘢𝘤𝘤𝘦𝘴𝘴 𝘢𝘯𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯. 𝘚𝘰 𝘩𝘦𝘳𝘦'𝘴 𝘢 𝘧𝘪𝘯𝘢𝘭 𝘵𝘩𝘰𝘶𝘨𝘩𝘵 𝘵𝘰 𝘭𝘦𝘢𝘷𝘦 𝘺𝘰𝘶 𝘸𝘪𝘵𝘩 𝘢𝘴 𝘴𝘦𝘢𝘳𝘤𝘩 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘮𝘰𝘳𝘦 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘢𝘯𝘥 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘪𝘻𝘦𝘥, 𝘩𝘰𝘸 𝘮𝘪𝘨𝘩𝘵 𝘵𝘩𝘢𝘵 𝘤𝘩𝘢𝘯𝘨𝘦 𝘵𝘩𝘦 𝘸𝘢𝘺 𝘸𝘦 𝘭𝘦𝘢𝘳𝘯, 𝘸𝘰𝘳𝘬, 𝘢𝘯𝘥 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘸𝘰𝘳𝘭𝘥 𝘢𝘳𝘰𝘶𝘯𝘥 𝘶𝘴?" 🤖 Even though there are still some small mistakes, I'm pretty impressed. It took just around 5 minutes to generate it. Listen in. ⬇

⁉️ 𝐇𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚𝐧𝐝 𝐦𝐚𝐧𝐚𝐠𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐆𝐞𝐧𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰, 𝐏𝐚𝐫𝐭 𝐈 Kameshwara Pavan Kumar Mantha shares how to set up a robust, highly explainable, and reliable pipeline when working on #GenAI applications. In the first article of the series, we get a detailed HowTo on setting up and running MLflow , Ollama and Qdrant , all connected by LlamaIndex . 👉 Check out Pavan’s recommendations https://lnkd.in/dGhQ4kKh

🧬 𝐑𝐞𝐀𝐜𝐭 𝐚𝐠𝐞𝐧𝐭-𝐛𝐚𝐬𝐞𝐝 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Adria Cabello Blanque developed an assistant to provide intuitive guidance for users interacting with the molecular visualization system PyMOL ( Schrödinger ). Using LangGraph ( LangChain ), FastAPI , and Qdrant , he made a real-time search of PyMOL documentation possible, along with executing commands based on users' natural language instructions. 👉 Read here how drastically simplifying the use of complex tools works: https://lnkd.in/dNSEqqfS

🔥 𝐅𝐫𝐞𝐞 𝐁𝐨𝐨𝐭𝐜𝐚𝐦𝐩 𝐨𝐧 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐆𝐞𝐧𝐀𝐈 ❓ 𝐖𝐡𝐞𝐧: November 15th & 16th, 2024 ❓ 𝐖𝐡𝐨: Our distinguished ambassador, Kameshwara Pavan Kumar Mantha , and CEO of Antz , Sashank Pappu ❓ 𝐖𝐡𝐚𝐭: Getting an advanced conceptual understanding of Multimodal RAG & Agentic RAG-based solutions ❓ 𝐖𝐡𝐞𝐫𝐞: https://lnkd.in/g-mvmxgb

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Company Name	Qdrant GmbH	FC Gruppe
Symbol
MSH ID	TECH:QDRANT	DE:FC-GRUPPE
Market	PRIVATE	PRIVATE
Sector		Commercial & Professional Services
Industry	Software Development	Engineering and Digital Solutions
Country	DE	DE
Stage
Employee Count		251
Website	qdrant.tech	fc-gruppe.de
LinkedIn	qdrant
Founders

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Market Cap	58.45M
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Monthly Web Traffic		1.60K
Web Traffic Growth		1.11
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TECH:QDRANT

Qdrant GmbH

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