Reasons for the Rising Popularity of Vector Databases

"𝘞𝘩𝘺 𝘤𝘢𝘯'𝘵 𝘸𝘦 𝘫𝘶𝘴𝘵 𝘴𝘵𝘰𝘳𝘦 𝘷𝘦𝘤𝘵𝘰𝘳 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘢𝘴 𝘑𝘚𝘖𝘕𝘴 𝘢𝘯𝘥 𝘲𝘶𝘦𝘳𝘺 𝘵𝘩𝘦𝘮 𝘪𝘯 𝘢 𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦?" This is a common question I hear. While transactional databases (OLTP) are versatile and excellent for structured data, they are not optimized for the unique challenges of vector-based workloads, especially at the scale demanded by modern AI applications. Vector databases implement specialized capabilities for indexing, querying, and storage. Let’s break it down: 𝟭. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 Traditional indexing methods (e.g., B-trees, hash indexes) struggle with high-dimensional vector similarity. Vector databases use advanced techniques: • HNSW (Hierarchical Navigable Small World): A graph-based approach for efficient nearest neighbor searches, even in massive vector spaces. • Product Quantization (PQ): Compresses vectors into subspaces using clustering techniques to optimize storage and retrieval. • Locality-Sensitive Hashing (LSH): Maps similar vectors into the same buckets for faster lookups. Most transactional databases do not natively support these advanced indexing mechanisms. 𝟮. 𝗤𝘂𝗲𝗿𝘆 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 For AI workloads, queries often involve finding "similar" data points rather than exact matches. Vector databases specialize in: • Approximate Nearest Neighbor (ANN): Delivers fast and accurate results for similarity queries. • Advanced Distance Metrics: Metrics like cosine similarity, Euclidean distance, and dot product are deeply optimized. • Hybrid Queries: Combine vector similarity with structured data filtering (e.g., "Find products like this image, but only in category 'Electronics'"). These capabilities are critical for enabling seamless integration with AI applications. 𝟯. 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 Vectors aren’t just simple data points—they’re dense numerical arrays like [0.12, 0.53, -0.85, ...]. Vector databases optimize storage through: • Durability Layers: Leverage systems like RocksDB for persistent storage. • Quantization: Techniques like Binary or Product Quantization (PQ) compress vectors for efficient storage and retrieval. • Memory-Mapped Files: Reduce I/O overhead for frequently accessed vectors, enhancing performance. In building or scaling AI applications, understanding how vector databases can fit into your stack is important. #DataScience #AI #VectorDatabases #MachineLearning #AIInfrastructure

Start-ups keep making the same fatal data engineering mistakes with LLM projects. They think traditional data pipeline workflows will save them. After managing multiple real life projects, I've noticed some new patterns: 1) 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐧𝐞𝐞𝐝 𝐝𝐚𝐭𝐚; 𝐢𝐭 𝐝𝐞𝐦𝐚𝐧𝐝𝐬 𝐜𝐥𝐞𝐚𝐧, 𝐜𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥𝐥𝐲 𝐫𝐢𝐜𝐡 𝐭𝐞𝐱𝐭 𝐭𝐡𝐚𝐭'𝐬 𝐛𝐞𝐞𝐧 𝐜𝐡𝐮𝐧𝐤𝐞𝐝 𝐚𝐧𝐝 𝐞𝐦𝐛𝐞𝐝𝐝𝐞𝐝: 📄 You need to convert various formats like PDFs and docs into clean text. This isn't just about extraction; it's about ensuring the text is usable by LLMs. (pre-processing) ℹ️ Keep the source info, timestamps, and access controls intact. This metadata adds value to the LLM's understanding. (metadata) ⚖️ Think about how you balance context windows with semantic meaning. Too small, and you lose context; too large, and you overwhelm the model. (data chunking) >> Don't confuse data jobs for RAG with what's needed for fine-tuning. 2) 𝐅𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐭𝐨 𝐛𝐞 𝐞𝐱𝐞𝐦𝐩𝐥𝐚𝐫𝐲, 𝐫𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐱𝐚𝐜𝐭 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬 𝐲𝐨𝐮𝐫 𝐦𝐨𝐝𝐞𝐥 𝐰𝐢𝐥𝐥 𝐞𝐧𝐜𝐨𝐮𝐧𝐭𝐞𝐫. 🧪 Mistakes in your training data can propagate through your model, so rigorous checks are non-negotiable. This is very different from preparing data for RAG. >> With fine-tuning, your data, just like your code, needs version control to track changes and improvements over time. 3) 𝐕𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐚𝐫𝐞 𝐚 𝐦𝐮𝐬𝐭.. Vector DBs like Azure AI Search and AWS MemoryDB are now critical because they: > Store and index high-dimensional embeddings efficiently, which traditional databases can't handle well. > Support semantic search operations, allowing for more nuanced data retrieval. > Scale horizontally to manage large document collections, something essential for LLM applications. > Maintain performance even with real-time updates, ensuring your data is always current. 𝐄𝐓𝐋/𝐄𝐋 𝐭𝐨𝐨𝐥𝐬 𝐡𝐚𝐯𝐞 𝐚𝐥𝐬𝐨 𝐞𝐯𝐨𝐥𝐯𝐞𝐝.. > You will still need tools to pull data from various sources into your pipeline. > But now you will need to prepare your text for LLM consumption. > Transformation tools are still needed, but now their focus is 100% on text parsing. > And finally, you will need Vector DB-specific loaders - to efficiently import / load data. 𝐓𝐡𝐞 𝐧𝐞𝐰 𝐄𝐓𝐋/𝐄𝐋 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐰𝐢𝐥𝐥 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐢𝐧𝐜𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞: > 𝐓𝐞𝐱𝐭 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 to ensure that your text is free from noise. > 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 to create vector representations of your text. > 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠 to divide text in a way that retains meaning. > 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐩𝐫𝐞𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧 to keep context for better model performance. Finally, there are considerations around access controls, feedback loops, and cost - none of which are trivial 🤷

Databases have been the backbone of data storage and retrieval for ages. They're rock-solid, they are scalable, and they have served us well.  Yet as ML models advanced a colossal gap has emerged. State-of-the-art machine learning thrives on high-dimensional, unstructured data that traditional tabular databases cannot handle. Here's where vector embeddings emerged as the missing link between our traditional data pipelines and the emerging ML stack. Vector representation enables two key capabilities: 1️⃣ Conversion of any data format into lightweight, portable, vector representations - this allows ML models to ingest any data type. 2️⃣ Queries on contextual similarity rather than just exact matches like traditional databases. The vector search layer is a fundamental shift in how we approach data storage, retrieval, and utilization.  Without it, integrating traditional databases with ML systems would be challenging, costly, and inefficient.  We'd constantly need to convert between different data representations in both directions - ingesting data into one system and exporting out - a transformation, compatibility, and scalability nightmare. But wait, there's gotta be a catch, right? Well, the only catch is that you'll need to level up your skills to keep up. Get a handle on: 🔢 vector embedding models that transform data into vectors, 🧰 vector databases and other tools to store, index, and query embeddings, 🏗 the infrastructure integrating these components as data and requirements evolve. So, buckle up, hit those tutorials 👉 https://lnkd.in/ebfgNHvG and get ready to ride the vector search wave. The future of AI/ML is vector-shaped, and those who embrace it early will be the ones calling the shots.

Vector databases are increasingly important in AI, especially for applications using Retrieval-Augmented Generation (RAG). These databases are good at managing and finding complex, high-dimensional data, like the kind used in advanced AI systems. In the context of AI, vector databases are key for embedding-based retrieval (EBR), a process essential for working with language models and unstructured data. This function is crucial for RAG systems, which need to find relevant information and then use it to generate language. This helps AI to give more relevant and precise answers. A recent report, "Survey of Vector Database Management Systems," provides an in-depth analysis of current vector database management systems (VDBMSs). Here's a summary the attached report from researchers from Purdue and Tsinghua Universities 🔍 Introduction to VDBMS: The paper discusses over 20 commercial VDBMSs, focusing on embedding-based retrieval (EBR) and similarity search, driven by large language models and unstructured data needs. 📈 Obstacles in Vector Data Management: Identifies five main challenges: semantic similarity vagueness, vector size, similarity comparison cost, lack of natural partitioning for indexing, and hybrid query difficulties. 🖥️ Techniques in Query Processing: Explores various techniques in query processing, storage, indexing, and optimization, emphasizing the need for low latency, high result quality, and throughput. 📊 Query Interfaces and Optimization: Details query interfaces, optimization, and execution strategies, including hybrid operators and hardware-accelerated query execution. 📚 Review of Current Systems: Classifies current VDBMSs into native systems designed for vectors and extended systems incorporating vector capabilities into existing systems. 📋 Benchmarks and Challenges: Discusses benchmarks for evaluating VDBMSs and outlines several research challenges and directions for future work. 🔮 Conclusion: Concludes with a summary of research challenges and open problems in the field of vector database management systems. It's a good albeit geeky read for those that are interested in how to store and use data alongside large language models.