Vector Distance Metrics: Cosine, Dot Product, Euclidean
The most counterintuitive thing about vector distance metrics is that for many common use cases, you don't want the "closest" vectors in the Euclidean s.
Vector Databases Articles
50 articles
Vector Database Cost Optimization: Reduce Storage and Query Cost
Vector databases are surprisingly expensive because you're not just storing data; you're storing relationships between data points, and those relationsh.
Vector Database Deletion and TTL: Manage Data Lifecycle
Vector databases don't just store vectors; they're active participants in managing your data's lifecycle, especially when it comes to automatic cleanup.
Vector Dimension Reduction: PCA, UMAP for Embeddings
PCA and UMAP can take high-dimensional embedding vectors and squish them down into a lower, more manageable number of dimensions, making them easier to .
Elasticsearch kNN Search: Vector Search in Elasticsearch
Elasticsearch's kNN search isn't just about finding similar items; it's fundamentally about transforming discrete data points into continuous geometric .
Match Embedding Models to Vector Database Indexes
The most surprising truth about matching embedding models to vector database indexes is that the "best" index isn't determined by the model's dimensiona.
Vector Database Indexing: Embed, Index, and Query
A vector database doesn't store your data as text or numbers; it stores it as points in a high-dimensional space, where proximity implies semantic simil.
Vector Database Enterprise Selection: Criteria and Checklist
Choosing a vector database for enterprise use isn't about finding the "best" one; it's about finding the one that disappears into your existing infrastr.
FAISS Vector Indexing: CPU and GPU Guide
FAISS Vector Indexing: CPU and GPU Guide — practical guide covering vector-databases setup, configuration, and troubleshooting with real-world examples.
Graph and Vector Hybrid Search: Combine for Better Recall
The most surprising thing about hybrid search is that it can often achieve lower recall than its individual components, despite combining their strength.
HNSW vs IVF Vector Indexes: Choose for Your Workload
HNSW and IVF are the two main families of vector indexes, and picking between them isn't just about speed; it's about how you want your approximate near.
Hybrid Vector Search: Combine Sparse and Dense Vectors
Hybrid search is what happens when you realize that both keyword matching and semantic understanding are critical for finding information.
Vector Database Incremental Updates: Add and Replace Vectors
The most surprising thing about vector database incremental updates is that "adding" and "replacing" often boil down to the same underlying operation: a.
Vector Index Type Selection: Flat, HNSW, IVF, PQ
Choosing the right vector index type is less about picking a "faster" one and more about understanding the fundamental trade-offs between search accurac.
Deploy Vector Databases on Kubernetes: Qdrant and Weaviate
Vector databases are surprisingly good at not storing vectors. Let's spin up Qdrant and Weaviate on Kubernetes and see how they handle similarity search
LangChain Vector Database Integration: Setup and Queries
The surprising truth about vector databases is that they don't actually store vectors; they store metadata and pointers to vectors, and their core job i.
ColBERT Late Interaction: Multi-Vector Retrieval
ColBERT's "Late Interaction" is a fascinating departure from traditional retrieval, focusing on fine-grained, token-level comparisons between query and .
Vector Database Latency Optimization: P99 Targets
The P99 latency target for a vector database isn't just about making queries fast; it's about guaranteeing that almost all users have a consistently sna.
LlamaIndex Vector Database Integration: Connect and Query
LlamaIndex doesn't store your data; it orchestrates how you access and query it, and vector databases are a primary way it does that.
Matryoshka Embeddings: Flexible Dimension Reduction
Matryoshka Embeddings let you trade off accuracy for retrieval speed by using a single embedding vector that can be truncated at different lengths.
Vector Database Metadata Filtering: Performance Impact
Vector Database Metadata Filtering: Performance Impact — practical guide covering vector-databases setup, configuration, and troubleshooting with real-w...
Milvus Distributed Setup: Scale Vector Search
Milvus can scale vector search performance by distributing its components across multiple machines, allowing it to handle massive datasets and high quer.
Monitor Vector Databases: Metrics, Latency, and Recall
Vector databases aren't just about speed; their real magic is how they trade off precision for speed, and understanding that trade-off is key to monitor.
Vector Database Multi-Tenancy: Namespaces and Isolation
Namespaces in vector databases are not just logical groupings; they're the fundamental mechanism for achieving true multi-tenancy and robust isolation b.
Multi-Vector Document Retrieval: Chunk-Level Embeddings
The magic of multi-vector document retrieval isn't that it can find documents based on meaning, but that it can find specific sentences or paragraphs wi.
Vector Database Collection Design: Schema and Namespaces
A vector database doesn't actually store vectors; it stores metadata that points to vectors, and those pointers are what get queried.
Open Source vs Managed Vector Database: Compare Options
Managed vector databases can often be more expensive than self-hosting open-source solutions, but they abstract away significant operational complexity.
OpenSearch Neural Search: Vector Search Plugin Setup
OpenSearch's Neural Search plugin lets you do vector search, but setting it up can feel like trying to thread a needle in the dark.
Benchmark Vector Databases: QPS, Recall, and Latency
The surprising truth about benchmarking vector databases is that the "best" database isn't a fixed entity; it's a moving target defined by your specific.
pgvector: Vector Search in PostgreSQL
pgvector is a PostgreSQL extension that lets you store and search high-dimensional vectors, which are the core of modern AI applications like recommenda.
Pinecone Production Setup: Indexes and Namespaces
Pinecone indexes are not just storage containers; they are active, queryable entities that continuously rebalance their data to maintain optimal query p.
Migrate Vector Databases to Production: Zero-Downtime
Migrating a vector database to production without downtime isn't just about copying data; it's about orchestrating a seamless transition of real-time se.
Qdrant Deployment Guide: Setup and Configuration
Qdrant can store up to 100x more vectors in RAM than you might expect, given its memory usage. Let's get Qdrant up and running
Vector Quantization: Reduce Memory with Product Quantization
Product Quantization is a clever way to compress high-dimensional vectors, allowing you to store and search massive datasets of embeddings in memory wit.
Evaluate RAG Retrieval: Vector Database Accuracy Metrics
A vector database's "accuracy" is less about hitting an exact match and more about surfacing the most relevant information, even if it's not a perfect l.
Redis Vector Search: Semantic Search with RediSearch
Redis Vector Search lets you find similar items based on their meaning, not just keywords. Here's how it looks in action
Vector Database Replication: HA and Failover Setup
Vector databases don't replicate data in the way traditional relational databases do; instead, they replicate the state of the system to achieve high av.
Vector Database Schema Design for RAG Pipelines
The most surprising thing about designing a vector database schema for Retrieval Augmented Generation RAG is that you're not just storing vectors; you'r.
Vector Database Security: API Keys and Access Control
Vector databases, despite their advanced capabilities for similarity search, often expose their most critical security vulnerability through their API, .
Sparse Embeddings with SPLADE: Keyword-Aware Search
SPLADE is a neural retrieval model that, unlike dense embeddings, uses sparse, interpretable vectors that look like TF-IDF but are learned.
Scale Vector Database Throughput: Sharding and Replicas
A vector database can process millions of queries per second, but it's not by making individual nodes infinitely fast; it's by distributing the load.
Weaviate Schema Configuration: Classes and Properties
The most surprising thing about Weaviate schema configuration is that it's not about defining what data you have, but rather what relationships your dat.
Vector Database Python Client: API Guide and Examples
Vector Database Python Client: API Guide and Examples — practical guide covering vector-databases setup, configuration, and troubleshooting with real-wo...
ANN Search in Vector Databases: HNSW and IVF Explained
The most surprising thing about Approximate Nearest Neighbor ANN search is that it's fundamentally a trade-off between speed and accuracy, and the best .
Vector Database Backup and Restore: Strategies for DR
Restoring a vector database from a backup isn't just about bringing data back; it's about ensuring your AI applications can resume their complex, contex.
Vector Database Batch Upsert: High-Throughput Ingestion
Vector Database Batch Upsert: High-Throughput Ingestion — Vector databases don't actually store vectors for searching. They store metadata and a pointer...
Vector Database Hybrid Search: BM25 and Semantic Fusion
Hybrid search in vector databases blends keyword-based BM25 and semantic vector embedding search to give you the best of both worlds, but the real magic.
Chroma Vector Database: Local Development Setup
Setting up Chroma locally is surprisingly easy, but the real trick is understanding how it manages its data persistence and retrieval, which often trips.
Vector Database Cloud vs Self-Hosted: Trade-offs
A vector database isn't just a fancy index; it's a fundamental shift in how we store and query information, treating meaning and similarity as first-cla.
Pinecone vs Weaviate vs Qdrant: Compare Vector Databases
Pinecone, Weaviate, and Qdrant are all vector databases designed to store and search high-dimensional vectors efficiently, but they approach this proble.