Understanding Vector Databases

WTH is a vector database and how does it work? If you’re stepping into the world of AI engineering, this is one of the first systems you need to deeply understand 👇 🧩 Why traditional databases fall short for GenAI Traditional databases (like PostgreSQL or MySQL) were built for structured, scalar data: → Numbers, strings, timestamps → Organized in rows and columns → Optimized for transactions and exact lookups using SQL They work great for business logic and operational systems. But when it comes to unstructured data, like natural language, code, images, or audio- they struggle. These databases can’t search for meaning or handle high-dimensional semantic queries. 🔢 What are vector databases? Vector databases are designed for storing and querying embeddings: high-dimensional numerical representations generated by models. Instead of asking, “Is this field equal to X?”- you’re asking, “What’s semantically similar to this example?” They’re essential for powering: → Semantic search → Retrieval-Augmented Generation (RAG) → Recommendation engines → Agent memory and long-term context → Multi-modal reasoning (text, image, audio, video) ♟️How vector databases actually work → Embedding: Raw input (text/image/code) is passed through a model to get a vector (e.g., 1536-dimensional float array) → Indexing: Vectors are organized using Approximate Nearest Neighbor (ANN) algorithms like HNSW, IVF, or PQ → Querying: A new input is embedded, and the system finds the closest vectors based on similarity metrics (cosine, dot product, L2) This allows fast and scalable semantic retrieval across millions or billions of entries. 🛠️ Where to get started Purpose-built tools: → Pinecone, Weaviate, Milvus, Qdrant, Chroma Embedded options: → pgvector for PostgreSQL → MongoDB Atlas Vector Search → OpenSearch, Elasticsearch (vector-native support) Most modern stacks combine vector search with keyword filtering and metadata, a hybrid retrieval approach that balances speed, accuracy, and relevance. 🤔Do you really need one? It depends on your use case: → For small-scale projects, pgvector inside your Postgres DB is often enough → For high-scale, real-time systems or multi-modal data, dedicated vector DBs offer better indexing, throughput, and scaling → Your real goal should be building smart retrieval pipelines, not just storing vectors 📈📉 Rise & Fall of Vector DBs Back in 2023–2024, vector databases were everywhere. But in 2025, they’ve matured into quiet infrastructure, no longer the star of the show, but still powering many GenAI applications behind the scenes. The real focus now is: → Building smarter retrieval systems → Combining vector + keyword + filter search → Using re-ranking and hybrid logic for precision 〰️〰️〰️〰️ ♻️ Share this with your network 🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

🔊 Here's a list of the most popular vector databases in the market! How do you choose the best one for your use-case? 🚀 In the last year, there has been a huge surge in the variety of vector database options. I've compiled the most popular ones in the image below, although it may not encompass the entire list. 😵 With such a large number of options, how do you navigate and discover the ideal one for your needs? 💡 Keep in mind that there isn't a one-size-fits-all "best" vector database—selecting the right one depends on your unique requirements Here are some factors to consider: 📈 Scalability Scalability is crucial for determining a vector database's ability to effectively handle rapidly expanding data volumes. Evaluating scalability involves considering factors such as load balancing, multiple replications, and the database's ability to handle high-dimensional data and growing query loads over time. 🏆 Performance Performance is crucial in assessing vector databases, using metrics like QPS, recall and latency. Benchmark tools like ANN-Benchmark and VectorDBBench offer comprehensive evaluations. 💰 Cost Factor in the total cost of ownership, encompassing licensing fees, cloud hosting charges, and associated infrastructure costs. A cost-effective system should deliver satisfactory speed and accuracy at a reasonable price. ✍ Developer Experience Evaluate the ease of setup, documentation clarity, and availability of SDKs for smooth development. Ensure compatibility with preferred cloud providers, LLMs, and seamless integration with existing infrastructure. 📲 Support and Ops Ensure your provider meets security and compliance standards while offering expertise tailored to your needs. Confirm their availability and technical support, and assess their monitoring capabilities for efficient database management. 💫 Additional Features Various vector databases differ in their feature offerings, influencing your decision-making process depending on your application's long-term objectives. For example, while most vector databases support features like multi-tenant and disk index, only a few support ephemeral indexing. However, you might require only specific features from this subset for your application. Even after factoring in these considerations, it may still be necessary to conduct individual research on each option. 📖 For example, some commonly known information: ⛳ Pinecone is well known for efficiently handling extensive collections of vectors, particularly in NLP and computer vision applications, but is a bit on the pricier side. ⛳ Qdrant is an pretty lightweight and works best for geospatial data. ⛳ Milvus is an is optimized for large-scale ML applications and excels in building search systems ⛳ pgvector is the most straightforward choice if you have a Postgres database and so on! 🚨 I post #genai content daily, follow along for the latest updates! #genai #llms #vectordb

In the AI era, your database isn’t just a backend choice — it’s a strategic enabler. AI systems today are not just consuming data. They're reasoning over it, retrieving it, embedding it, and traversing relationships across it. And that changes everything about how we choose databases. Here’s a side-by-side comparison I created to show how different databases align with modern AI workloads: • 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗕𝘀 — Still critical for structured systems (ERP, Finance), but struggle with unstructured and high-dimensional data.    • 𝗡𝗼𝗦𝗤𝗟 𝗗𝗕𝘀 — Great for flexible, high-throughput ingestion (IoT, real-time analytics), but limited for complex joins and semantic context.    • 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 — The core of GenAI. They make semantic search, embeddings, and RAG architectures possible.    • 𝗚𝗿𝗮𝗽𝗵 𝗗𝗕𝘀 — Ideal for modeling relationships, reasoning, and powering agent memory and decision graphs. In the AI-native stack, Vector and Graph databases are foundational: • LLMs retrieve semantically matched chunks via vector search    • Agents reason through graph traversals and decision paths    • Hybrid models use all four — ingesting via NoSQL, storing core logic in relational, retrieving via vector, and reasoning via graph.   It’s not just about data storage — it’s about enabling intelligence.

Third feature for #elasticsearch / Elastic Stack 8️⃣.1️⃣5️⃣: More efficient vector search with every release — int4 quantization and bit vectors + Hamming distance. It took me some time to wrap my head around dense_vector — hope this helps others 🙃 dense_vector is the representation your inference is providing and it can come as an array of float (default, 4 byte), byte, or bit (🆕, the inference needs to provide this precision) values in up to 4K dimensions. By default, dense_vector is stored as part of the _source but it is large / expensive to load and often not necessary to retrieve (you need it for searching, not displaying). So you can disable it (recommended) but then you cannot reindex your data without redoing the inference. Or you can use synthetic source, which restores it from the indexed data (more in a moment) if needed. That has some overhead at query-time, which is often a great tradeoff for observability or security but search is commonly too latency sensitive for it. Also, synthetic source is not GA for search yet. By default, the dense_vector is also indexed as doc_value, which is used for scoring and exact kNN search. Out of the box as flat (same data type as provided by the inference), or you can quantize a float to int8_flat or int4_flat to save some disk space. Additionally, dense_vector can also be indexed in HNSW for approximate kNN search (uses doc_value for scoring). HNSW should always fit into memory using the same data type as provided by the inference; or quantized to int8_hnsw (default for float values) or int4_hnsw — reducing memory and storage 4x or 8x. If you have a dense_vector of bits, you can also use the hamming distance 🆕, giving you a highly performant comparison algorithm. tl;dr: Your dense_vector is stored in up to 3 different ways for storage (_source), scoring + exact kNN (doc_value), and approximate kNN (HNSW). the most costly one, since it needs to fit into memory for good performance, is HNSW but it also scales best. https://lnkd.in/djNnxkrW for the full docs.

Vector databasing is a powerful tool for AI. In two minutes I’ll explain the concept and why it matters using spices as an analogy! What Is a Vector Database? A vector database stores each item as a high‑dimensional embedding vector (often 128 to 512 numbers) that captures its essence. Instead of indexing on exact keywords it indexes on geometric proximity so that similar items sit near each other in vector space. How It Works 1. Data to Embeddings Before storing any data you convert it into a numeric fingerprint called an embedding. Text example: “Spicy chicken sandwich recipe” → [0.12, 0.47, …] capturing spicy, savory and recipe aspects Image example: a photo of blue sneakers → [0.05, 0.88, …] encoding color, shape and style 2. Indexing for Speed The database builds a nearest‑neighbor index (for example HNSW or k‑NN) so that when you ask “What is similar?” it finds the closest vectors in milliseconds. Imagine arranging spice jars not alphabetically but by flavor similarity. Warm spices like cinnamon, nutmeg and cardamom form one cluster. Hot spices like chili, cayenne and paprika form another. When you look up cinnamon you instantly see nutmeg and allspice neighbors. A vector database creates these clusters automatically and finds them in a fraction of a second. Why It Matters? 1. Massive Scale Comparing raw embeddings across millions of items would take minutes or hours. Vector indexes cut that to milliseconds. 2. Semantic Power It finds similarity by meaning. Garam masala and cumin cluster together even if you never tagged them as seasoning. This enables smarter recommendations. 3. Real-World Use Cases Netflix uses embeddings for movie suggestions. Pinterest powers visual search with image vectors. 4. Managed Services Providers such as Pinecone, AWS Kendra and Weaviate handle sharding, indexing and real‑time updates so you focus on building your app. Quick Recap (Danny’s Flavor Cheat Sheet) - Embedding vector: a numeric fingerprint of each item - Cluster: the neighborhood where similar fingerprints hang out - Vector database: the spice rack that jumps straight to the right neighborhood for meaning-driven search Hope this helps you see how vector databases power AI features like semantic search, recommendation engines and anomaly detection.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗼𝘄 𝗚𝗲𝗻𝗔𝗜 𝗳𝗶𝗻𝗱𝘀 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 𝗶𝗻 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘁𝗲𝘅𝘁. ⬇️ And yes it all starts with vector databases — not magic. This is the mechanism that powers AI Agent memory, RAG and semantic search. And this diagram below? Nails the entire flow — from raw data to relevant answers. Let's break it down (the explanation shows of how a vector database works — using the simple example prompt: “Who am I): ⬇️ 1. 𝗜𝗻𝗽𝘂𝘁: ➜ There are two inputs: Data = the source text (docs, chat history, product descriptions...) and the query = the question or prompt you’re asking. These are processed in exactly the same way — so they can be compared mathematically later. 2. 𝗪𝗼𝗿𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 ➜ Each word (like “how”, “are”, “you”) is transformed into a list of numbers — a word embedding. These word embeddings capture semantic meaning, so that for example "bank" (money) and "finance" land closer than "bank" (river). This turns raw text into numerical signals. 3. 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 ➜ Both data and query go through this stack: - Encoder: Transforms word embeddings based on their context (e.g. transformers like BERT). - Linear Layer: Projects these high-dimensional embeddings into a more compact space. -ReLU Activation: Introduces non-linearity — helping the model focus on important features. The output? A single text embedding that represents the entire sentence or chunk. 4. 𝗠𝗲𝗮𝗻 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 ➜ Now we take the average of all token embeddings — one clean vector per chunk. This is the "semantic fingerprint" of your text. 5. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ➜ All document vectors are indexed — meaning they’re structured for fast similarity search. This is where vector databases like FAISS or Pinecone come in. 6. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗗𝗼𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗔𝗿𝗴𝗺𝗮𝘅) ➜ When you submit a query.: The query is also embedded and pooled into a vector. The system compares your query to all indexed vectors using dot product — a measure of similarity. Argmax finds the closest match — i.e. the most relevant chunk. This is semantic search at work. - Keyword search finds strings. - Vector search finds meaning. 7. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 ➜ All document vectors live in persistent vector storage — always ready for future retrieval and use by the LLM. This is basically the database layer behind: - RAG - Semantic search - Agent memory - Enterprise GenAI apps - etc. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 — 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗻. --- Need an AI Consultant or help building your career in AI? Message me now

Vector embeddings performance tanks as data grows 📉. Vector indexing solves this, keeping searches fast and accurate. Let's explore the key indexing methods that make this possible 🔍⚡️. Vector indexing organizes embeddings into clusters so you can find what you need faster and with pinpoint accuracy. Without indexing every query would require a brute-force search through all vectors 🐢. But the right indexing technique dramatically speeds up this process: 1️⃣ Flat Indexing ▪️ The simplest form where vectors are stored as they are without any modifications. ▪️ While it ensures precise results, it’s not efficient for large databases due to high computational costs. 2️⃣ Locality-Sensitive Hashing (LSH) ▪️ Uses hashing to group similar vectors into buckets. ▪️ This method reduces the search space and improves efficiency but may sacrifice some accuracy. 3️⃣ Inverted File Indexing (IVF) ▪️ Organizes vectors into clusters using techniques like K-means clustering. ▪️ There are variations like: IVF_FLAT (which uses brute-force within clusters), IVF_PQ (which compresses vectors for faster searches), and IVF_SQ (which further simplifies vectors for memory efficiency). 4️⃣ Disk-Based ANN (DiskANN) ▪️ Designed for large datasets, DiskANN leverages SSDs to store and search vectors efficiently using a graph-based approach. ▪️ It reduces the number of disk reads needed by creating a graph with a smaller search diameter, making it scalable for big data. 5️⃣ SPANN ▪️ A hybrid approach that combines in-memory and disk-based storage. ▪️ SPANN keeps centroid points in memory for quick access and uses dynamic pruning to minimize unnecessary disk operations, allowing it to handle even larger datasets than DiskANN. 6️⃣ Hierarchical Navigable Small World (HNSW) ▪️ A more complex method that uses hierarchical graphs to organize vectors. ▪️ It starts with broad, less accurate searches at higher levels and refines them as it moves to lower levels, ultimately providing highly accurate results. 🤔 Choosing the right Method ▪️ For smaller datasets or when absolute precision is critical, start with Flat Indexing. ▪️ As you scale, transition to IVF for a good balance of speed and accuracy. ▪️ For massive datasets, consider DiskANN or SPANN to leverage SSD storage. ▪️ If you need real-time performance on large in-memory datasets, HNSW is the go-to choice. Always benchmark multiple methods on your specific data and query patterns to find the optimal solution for your use case. The image depicts ANN methods in a really cool and unconventional way!

Binary quantization makes it possible to create much larger vector indexes while keeping vector index size, and thus cost and performance, in good shape. We just announced built-in binary quantization in Azure AI Search, along with support for oversampling and reranking, which can help recover the recall loss that comes from quantizing vectors. The combination of binary quantization with oversampling + reranking enable the creation of cost-effective, very large indexes by taking advantage of the memory hierarchy, keeping a lower-precision compact index in memory and full-precision vectors on SSD-backed local and remote storage. In this blog post Farzad Sunavala goes through the details of binary quantization in Azure AI Search, including results on various evaluations we did regarding quality, size and performance implications. https://lnkd.in/gXU_3KFE

"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

Thrilled to share our latest work, DISTRIBUTEDANN, where we take a single 50 billion-vector DISKANN graph and shard it seamlessly across over a thousand machines—delivering 26 ms median latency at 100 K QPS! 🚀 By blending a distributed key–value store with an in-memory head index and near-data compute, we achieve 6× better efficiency than classic partition-and-route systems. Proud to see this powering Bing’s vector search at web-scale. Dive into the paper and explore how we’re rethinking ANN architecture for the next generation of Retrieval-Augmented Generation. https://lnkd.in/dVAetJjq #VectorSearch #DistributedSystems #MachineLearning #ICML2025 #bing #microsoftai