Qdrant

📈 Indexing Best Practices in Vector Databases After working extensively with vector databases, I wanted to share three key insights about indexing that can significantly impact your application's performance: ▪️ HNSW Configuration Properly configured HNSW parameters are the backbone of efficient vector search. It's not about maxing out every setting—it's about finding the right balance between search speed and accuracy for your specific use case. ▪️ Smart Payload Indexing Index what you filter, not everything you store. Strategic payload indexing can dramatically improve query performance, especially when dealing with complex filtering operations. ▪️ Segment Management Your read/write patterns should guide your segment configuration. More segments aren't always better - it's about matching your configuration to your actual usage patterns. What's been your experience with vector database indexing? Have you found other configurations that work particularly well? #VectorDatabases #SearchOptimization #DataEngineering #Performance

🚀 Building AI Search & RAG Pipelines – A 3-Part Guide by FutureSmart AI The FutureSmart AI team has published an in-depth series on building AI-powered search RAG systems using Qdrant, FastAPI, and LangChain. 🔍 Part 1: Qdrant Setup & Optimization Learn how to install, configure, and optimize Qdrant for maximum performance: https://lnkd.in/dnmE4_sf ⚡ Part 2: Async Similarity Search with FastAPI & Qdrant A step-by-step tutorial on building a fast, scalable similarity search system using FastAPI: https://lnkd.in/dDAAkGZi 🤖 Part 3: RAG System with Qdrant, LangChain & FastAPI Deploy a fully asynchronous RAG pipeline using Qdrant, LangChain, and OpenAI models: https://lnkd.in/dEtDfYRu 📖 Full series: https://lnkd.in/d5wJVPhu 👏 Shoutout to Pradip Nichite and the FutureSmart AI team for putting this together!

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🚀 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗛𝗲𝗿𝗲 Managing multiple social platforms is a full-time job… but what if you had an AI-powered agent to do it for you? Kameshwara Pavan Kumar’s latest blog explores how Agno + Qdrant enables intelligent systems that automate content creation, engagement, and analytics across YouTube, Discord, X (Twitter), Slack, and LinkedIn. 🔹 PR Manager Agent → Routes tasks to platform-specific sub-agents 🔹 Qdrant → Vector store for fast similarity search on social media insights 🔹 Agno AI → LLM-driven processing with o3-mini & Claude models 💡 The modular, event-driven architecture ensures scalability, low-latency responses, and seamless AI-driven automation across multiple social platforms. Read more: https://lnkd.in/d-iiRibt

Lyzr AI Agent Studio moves to Qdrant as the default vector database. At Lyzr, we've been working with Fortune 500 customers and enterprise developers to automate several job functions with multi-agent systems. The use cases vary from simpler ones like document search to more complex ones like equipment troubleshooting for an offshore oil rig. And most of these use cases involve RAG on a large knowledge base. With thousands of collections and continuous updates to the knowledge base, we wanted to choose a vector database that can scale with data and still excel in performance aspects like latency, accuracy, and ease-of-use. After testing almost all market-leading vector databases, we can now safely say that Qdrant is the best out there in the market. Andre Zayarni As an enterprise developer, you will continue to have a lot of vector database options in Lyzr Agent Studio. But my personal recommendation would be to choose Qdrant. Start building now (no credit card required) - https://studio.lyzr.ai/ #Lyzr #Agents #VectorDatabase #Qdrant #RAG #Performance #EnterpriseAI #SafeAI #ResponsibleAI 

🎟️🚀Qdrant is heading to AI Dev 25… and we’ve got a ticket for YOU! 🚀🎟️ The AI Developer Conference hosted by @DeepLearning.AI & Andrew Ng is SOLD OUT—but we’re giving away a free ticket to one lucky commenter! 💡 Want to join 400+ AI devs in San Francisco on March 14? Comment your favorite thing about Qdrant on this post by February 28th, and you could snag a ticket to this exclusive event! 🔗Conference details: https://lnkd.in/e5-GXM5s 👩⚖️See the comments for full Terms & Conditions

In today’s data-driven world, video content is a rich source of information that combines multiple modalities, including visuals, audio, and text. However, due to their complexity, extracting meaningful insights from videos and enabling semantic search across them can be challenging. This is where the integration of TwelveLabs Embed API and Qdrant comes into play. The TwelveLabs Embed API empowers developers to create multimodal embeddings that capture the essence of video content, including visual expressions, body language, spoken words, and contextual cues. These embeddings are optimized for a unified vector space, enabling seamless cross-modal understanding. On the other hand, Qdrant is a powerful vector similarity search engine that allows you to store and query these embeddings efficiently. Our new integration demonstrates how to build a semantic video search workflow by combining TwelveLabs’ multimodal embedding capabilities with Qdrant’s vector search engine: ✅ Generate multimodal embeddings for videos using the TwelveLabs Embed API. ✅ Store and manage these embeddings in Qdrant. ✅ Perform semantic searches across video content using text or other modalities. Relevant links: ☑️ Complete tutorial: https://lnkd.in/gCzfPnfk ☑️ Colab notebook: https://lnkd.in/ghzFU6ne ☑️ TwelveLabs on Qdrant docs: https://lnkd.in/gWCHjJ-w

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Scaling Vector Search with GPU Acceleration 🚀 Ivan Pleshkov from Qdrant and Dmitri Laptev from Amazon Web Services (AWS) will be presenting at the GenAI Loft in Berlin on February 27th. Join us for a deep dive into how GPU-accelerated AWS instances can supercharge vector search index construction, delivering 5-10x speedups. Expect real-world benchmarks, in-depth performance insights, and advanced optimization techniques that push the boundaries of scalable retrieval systems. 📈 Save your spot: https://lnkd.in/d_CysftW

𝗧𝗵𝗿𝗲𝗲 𝗞𝗲𝘆 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 As vector search becomes increasingly crucial for AI applications, optimizing your vector database can make a significant difference in performance and cost-efficiency. Here are three practical tips we've found valuable: ✅ Compress Data With Quantization Reduce memory usage without sacrificing search quality by using 𝘀𝗰𝗮𝗹𝗮𝗿 𝗼𝗿 𝗯𝗶𝗻𝗮𝗿𝘆 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Qdrant’s quantization methods can shrink storage by up to 32x, making large-scale search feasible on lower-cost infrastructure. ✅ Optimize Your Indexing Fine-tune 𝗛𝗡𝗦𝗪 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀 𝗹𝗶𝗸𝗲 𝗺 𝗮𝗻𝗱 𝗲𝗳 to balance speed, accuracy, and memory consumption. A well-configured index can drastically cut down search latency while maintaining high recall. ✅ Choose the Right Storage Strategy Your choice between 𝗥𝗔𝗠-𝗯𝗮𝘀𝗲𝗱 (𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱) 𝗮𝗻𝗱 𝗱𝗶𝘀𝗸-𝗯𝗮𝘀𝗲𝗱 (𝘀𝘁𝗼𝗿𝗮𝗴𝗲-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱) configurations should align with your specific needs. For high-performance scenarios, prioritize RAM. For large-scale deployments, fast SSDs can offer a cost-effective alternative. What optimization strategies have worked well in your vector search implementations? Full Article: https://lnkd.in/dcZ8WmdZ