Zilliz

Our 𝗠𝗶𝗹𝘃𝘂𝘀 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗠𝗲𝗲𝘁𝘂𝗽 is almost here — and it’s part of #OpenSourceAIWeek, happening Oct 18–26 in the SF Bay Area! Come join us 𝗧𝘂𝗲𝘀𝗱𝗮𝘆, 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟮𝟭𝘀𝘁 to see what our amazing Milvus users have been building! 🙌 Don’t miss the latest 𝗠𝗶𝗹𝘃𝘂𝘀 𝟮.𝟲 updates — enhanced full-text search, faster JSON filtering, a streamlined data-in/data-out embedding pipeline, cost savings with intelligent tiered storage, RaBitQ 1-bit quantization, and more. 🚀

𝗪𝗵𝗲𝗻 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 𝘃𝘀 𝗩𝗲𝗰𝘁𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 — 𝗼𝗿 𝗯𝗼𝘁𝗵? 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 provide structure — explicit relationships between entities stored in graph databases like Neo4j. Think: [Albert Einstein] --[developed]--> [Theory of Relativity] 𝗩𝗲𝗰𝘁𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 capture semantic meaning — numerical representations in high-dimensional space and are stored in a vector database like Milvus, created by Zilliz. In a vector space, “king” and “queen” sit close together because they share semantic similarity. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗴𝗿𝗮𝗽𝗵𝘀 𝗲𝘅𝗰𝗲𝗹 𝗮𝘁: ✓ Clear, interpretable relationships ✓ Logical reasoning over explicit connections 𝗩𝗲𝗰𝘁𝗼𝗿 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗲𝘅𝗰𝗲𝗹 𝗮𝘁: ✓ Handling unstructured data (text, images) ✓ Capturing implicit semantic relationships The future of RAG? Combining both approaches for systems that leverage structured reasoning and semantic understanding. Learn the tutorial: https://lnkd.in/g_62QxCZ —— Follow Zilliz to stay ahead in vector search and AI infra.

In our data-driven world, we're drowning in information but starving for connections. Knowledge graph is the technology powering Google Search, Netflix recommendations, and modern AI systems. 𝐓𝐡𝐢𝐧𝐤 𝐨𝐟 𝐢𝐭 𝐚𝐬 𝐚 𝐛𝐫𝐚𝐢𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚:  → 𝐍𝐨𝐝𝐞𝐬 represent entities (people, companies, concepts) → 𝐄𝐝𝐠𝐞𝐬 show relationships between them → 𝐋𝐚𝐛𝐞𝐥𝐬 add context and meaning. 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐞𝐦 𝐬𝐩𝐞𝐜𝐢𝐚𝐥? Unlike traditional databases with rigid tables, knowledge graphs are: 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞—add new connections without breaking existing data 𝐂𝐨𝐧𝐭𝐞𝐱𝐭-𝐚𝐰𝐚𝐫𝐞—understand relationships, not just keywords 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞—grow seamlessly as your data expands Real impact: search transformed from "strings to things". Search "Albert Einstein" today, and you get a connected web of his theories, collaborators, and timeline, not just a list of links. What complex data relationships are you trying to make sense of? Learn more: https://lnkd.in/gYZ_drWZ

Most engineering teams face the same dilemma: BERT delivers excellent NLP results, but its 110M parameters create real bottlenecks in production environments. 𝐃𝐢𝐬𝐭𝐢𝐥𝐁𝐄𝐑𝐓  solves this through knowledge distillation -𝐚 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 𝐰𝐡𝐞𝐫𝐞 𝐁𝐄𝐑𝐓 𝐚𝐜𝐭𝐬 𝐚𝐬 𝐚 "𝐭𝐞𝐚𝐜𝐡𝐞𝐫" 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 "𝐬𝐭𝐮𝐝𝐞𝐧𝐭" 𝐦𝐨𝐝𝐞𝐥.  Here's what you get: 40% smaller (66M vs 110M parameters) 60% faster at inference 97% of BERT's accuracy retained 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: What makes DistilBERT effective is its triple loss function during training: 𝐌𝐚𝐬𝐤𝐞𝐝 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐥𝐨𝐬𝐬 - Standard word prediction task 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 𝐥𝐨𝐬𝐬 - Learning from BERT's soft probabilities using temperature scaling 𝐂𝐨𝐬𝐢𝐧𝐞 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐥𝐨𝐬𝐬 - Aligning internal representations with the teacher model This isn't random compression - it's strategic knowledge transfer that preserves what matters most. By reducing BERT's 12 layers to 6, the result is a compact 207MB model that maintains competitive performance on standard NLP benchmarks while being practical for edge devices and mobile deployments. DistilBERT embeddings integrate seamlessly with vector databases like 𝐌𝐢𝐥𝐯𝐮𝐬, enabling efficient semantic search, RAG systems, and recommendation engines where both speed and accuracy matter. Learn more: https://lnkd.in/gyyfhYni

We spent weeks testing NLP tools, so you don't have to. Here are 10 essential NLP tools that every data scientist should know in 2025. 𝐅𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 & 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:  𝐍𝐋𝐓𝐊 - The go-to library for learning NLP fundamentals with extensive documentation 𝐓𝐞𝐱𝐭𝐁𝐥𝐨𝐛 - Simple API perfect for quick prototyping and sentiment analysis 𝐅𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐒𝐩𝐞𝐞𝐝: 𝐒𝐩𝐚𝐂𝐲 - Lightning-fast processing ideal for real-time applications 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 - Access to pre-trained models (BERT, GPT-2, T5) with minimal code 𝐅𝐨𝐫 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 𝐓𝐞𝐱𝐭 - A Powerful framework for building custom NLP models 𝐀𝐥𝐥𝐞𝐧𝐍𝐋𝐏 - PyTorch-based library designed for cutting-edge research 𝐅𝐨𝐫 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐓𝐚𝐬𝐤𝐬: 𝐆𝐞𝐧𝐬𝐢𝐦 - Topic modeling and document similarity at scale 𝐎𝐩𝐞𝐧𝐍𝐋𝐏 - A Robust toolkit for entity recognition and parsing 𝐂𝐨𝐫𝐞𝐍𝐋𝐏 - Stanford's highly accurate suite for linguistic analysis 𝐅𝐨𝐫 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞: 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐀𝐈 - Cloud-native platform with 100+ language support and AutoML capabilities 🔥 Pro Tip: Combine these tools with vector databases like Milvus to implement Retrieval Augmented Generation (RAG) and reduce LLM hallucinations by grounding responses in domain-specific data. Learn more: https://lnkd.in/gHJ9CXJa Which NLP tool is your favorite? Drop a comment below! 👇

𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝘀 𝗯𝗿𝗼𝗸𝗲𝗻. A customer searches for "running shoes for bad knees." Keyword matching shows "running shoes" and "knee pain." But what do they really need? Cushioned, low-impact footwear recommendations. Keyword matching misses customer intent. It delivers irrelevant results. Engagement drops. Conversion rates suffer. Vector databases, such as 𝗠𝗶𝗹𝘃𝘂𝘀 and 𝗭𝗶𝗹𝗹𝗶𝘇 (fully managed Milvus), change the game. They understand 𝗠𝗘𝗔𝗡𝗜𝗡𝗚, not just words. 𝗧𝗵𝗲𝘆 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗶𝗻𝘁𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀. 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗲𝗲𝗹𝘀 𝗻𝗮𝘁𝘂𝗿𝗮𝗹. 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝘀. Rule-based segmentation is dead. AI-driven understanding is the future. Are you still stuck on keywords, or have you made the leap to semantic search? 👉 Read the full story: https://lnkd.in/gX4wYEDv

💡 𝗔𝗜 𝗙𝗔𝗤 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 — 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗤𝘂𝗮𝗹𝗶𝘁𝘆, the foundations of effective search and generation. As AI applications grow, embedding and retrieval quality can make the difference between precise answers and frustrating results. Choosing the right strategy is key to unlocking richer context for RAG. In this edition, we’re exploring answers to four most asked quetions: ❓ What is embedding dimensionality? ❓ How do you choose embedding dimensionality? ❓ What are dense and sparse embeddings? ❓ Which models are best for generating video embeddings? —— Follow Zilliz for more regular FAQ roundups with practical insights for your AI projects! #zillizfaq

𝗪𝗲 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗔𝗜 𝘄𝗼𝘂𝗹𝗱 𝗳𝗿𝗲𝗲 𝘂𝗽 𝗼𝘂𝗿 𝘁𝗶𝗺𝗲. 𝗜𝗻𝘀𝘁𝗲𝗮𝗱, 𝗶𝘁'𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝘄𝗼𝗿𝗸 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿. A Harvard/BCG study found that ChatGPT improved marketers' performance by 40%. But here's the twist: this efficiency boost led to MORE content production, not less work. We're flooding the market with content on an unprecedented scale. When everyone can generate content at lightning speed, we face homogenized messaging, content overload, volume over value, and AI training on AI content. Use AI to curate, not just create. Tools like Milvus vector database help surface your BEST existing content through semantic search. Focus on quality augmentation over blind generation. AI's superpower isn't just creation — it's curation. The future belongs to marketers who work smarter, not just faster. Full story: https://lnkd.in/gX4wYEDv

Modern AI agents don’t just answer prompts — they 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗲 in a continuous loop. This is the 𝗥𝗲𝗔𝗰𝘁 𝗟𝗼𝗼𝗽, the backbone of today’s most capable agent workflows. 🧠 𝗥𝗲𝗮𝘀𝗼𝗻 — break problems into steps, plan strategies, correct mistakes ⚡ 𝗔𝗰𝘁 — call tools and APIs based on reasoning 👀 𝗢𝗯𝘀𝗲𝗿𝘃𝗲 — analyze results and feed them back into the next step That’s powerful in theory — but how do you actually implement it? This is where 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 comes in, structuring ReAct into repeatable, reliable workflows. And with the 𝗹𝗮𝗻𝗴𝗴𝗿𝗮𝗽𝗵-𝘂𝗽-𝗿𝗲𝗮𝗰𝘁 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲, you can skip weeks of setup and start building agents today. 👉 Check out our hands-on tutorial to build your own agent: https://lnkd.in/gZkj6DJ6 —— Follow Zilliz to stay ahead in vector search and AI infra.

🔥 When Anthropic and Cognition argued about multi-agent vs. single-agent architectures, the real issue wasn’t about “how many agents” — it was about 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Think of LLMs as CPUs, and context windows as RAM. RAM is always limited, while AI agents generate massive information in multi-step tasks. Without context engineering, agents crash, forget, or cost $100+ per run. That’s why 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 created a 𝗳𝗼𝘂𝗿-𝗽𝗶𝗹𝗹𝗮𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 to tackle context failures: 1️⃣ 𝗪𝗿𝗶𝘁𝗲 — external memory logs to avoid redundant processing 2️⃣ 𝗦𝗲𝗹𝗲𝗰𝘁 — filter only relevant snippets instead of loading everything 3️⃣ 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀 — auto-summarize when nearing context limits 4️⃣ 𝗜𝘀𝗼𝗹𝗮𝘁𝗲 — modularize tasks so agents don’t pollute each other’s reasoning 👉 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗯𝘂𝗶𝗹𝘁 𝗯𝘆 𝗯𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗯𝘂𝘁 𝗯𝘆 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. LangChain’s framework shows how to structure memory, but true scalability requires 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗠𝗶𝗹𝘃𝘂𝘀 to provide low-latency, cost-efficient retrieval from massive external knowledge bases. 🔗 Dive deeper into context strategies from LangChain, Lossfunk, Manus and more: https://lnkd.in/gKNd9zZ8 ——— Follow Zilliz to stay ahead in vector search and AI infra.