8 min read
DiskANN in Azure SQL Is GA: Do You Still Need a Separate Vector Store?
DiskANN vector indexes are now GA in Azure SQL and Fabric SQL. When embeddings belong next to your rows, and when Azure AI Search still wins.
Vector Search
41 articles
1 min read
Building Production RAG Systems: Best Practices for 2026
Production RAG is iterative. Start simple, measure everything, and improve based on real user feedback.
1 min read
Azure AI Search: Implementing Hybrid Search for Better RAG Results
Pure vector search excels at semantic similarity but can miss exact keyword matches. Pure keyword search finds exact terms but misses conceptually similar…
1 min read
Implementing RAG with LangChain and Azure OpenAI
Experiment with chunk sizes, overlap, and retrieval strategies. Hybrid search combining semantic and keyword matching often provides the best results for…
1 min read
Azure Cosmos DB for AI Applications: Vector Search and Change Feed Patterns
Use Change Feed to trigger embedding generation and synchronization with other AI services automatically when documents are created or updated.
1 min read
Azure AI Search: Implementing Hybrid Search with Vectors and Keywords
Hybrid search significantly improves retrieval quality for RAG applications by leveraging both semantic understanding and exact keyword matching.
1 min read
Semantic Caching for LLM Applications: Reducing Costs and Latency
Unlike exact-match caching, semantic caching uses embeddings to find similar queries even when worded differently.
1 min read
Hybrid Search: Combining Vector and Keyword Search in Azure AI Search
Vector search excels at semantic similarity but can miss exact matches. Keyword search finds precise terms but misses synonyms and context. Together, they…
1 min read
Embedding Models: Choosing Between OpenAI, Azure, and Open Source
The text-embedding-ada-002 and newer text-embedding-3 models provide high-quality embeddings with minimal setup.
1 min read
Implementing Hybrid Search in Azure AI Search
Keyword search excels at exact matches and rare terms. Vector search captures semantic similarity. Combining them leverages both strengths while mitigating…
1 min read
RAG Chunking Strategies: Finding the Optimal Approach
The simplest approach splits text at regular intervals with optional overlap. Semantic chunking respects content boundaries like paragraphs and sections.
1 min read
Cosmos DB Vector Search: Implementing Semantic Search at Global Scale
Configure your Cosmos account with write regions near your users and read replicas globally. Vector searches automatically route to the nearest replica…
1 min read
Azure AI Search Vector Optimization: Reducing Costs While Improving Recall
Vector search costs can spiral quickly at scale. After optimizing Azure AI Search deployments processing 50 million vectors, I've identified key patterns…
1 min read
Vector Search Optimization: Scaling to Billions of Vectors
Quantization and HNSW tuning enable vector search at billion-scale with reasonable latency.
7 min read
Hybrid Search in PostgreSQL: pgvector, Full-Text and RRF in One Query
How to build hybrid search inside PostgreSQL with pgvector, full-text search and weighted reciprocal rank fusion, and when a dedicated search service is better.
8 min read
RAG Beyond Top-K: Retrieval Upgrades Worth Making in January 2025
Which RAG retrieval upgrades Azure AI Search gives you as GA or preview in January 2025, which you build yourself, and the order I'd add them in.
1 min read
OneLake AI Workloads: Running AI Directly on Your Data Lake
OneLake AI Workloads bring AI capabilities directly to your data, eliminating data movement and enabling efficient large-scale AI processing.
1 min read
Filtered Vector Search in Azure AI Search: Best Practices
Pre-filtering: Filter before vector search Post-filtering: Filter after vector search Azure AI Search uses pre-filtering with automatic optimization.
1 min read
HNSW Tuning for Azure AI Search: A Practical Guide
HNSW builds a multi layer graph where: Higher layers : Sparse, for fast long range navigation Lower layers : Dense, for precise local search Layer 0 :…
1 min read
Product Quantization for Extreme-Scale Vector Search
PQ divides each vector into subvectors and quantizes each independently: For even faster search, combine with inverted file index: Dataset Size n subvectors…
1 min read
Scalar Quantization for Vector Search: A Practical Guide
Scalar quantization maps floating point values to integers by dividing the value range into buckets: Understanding quantization error helps set…
2 min read
Binary Vectors and Quantization in Azure AI Search
With quantization, this can be reduced to 1-2 GB.
1 min read
Improved Vector Search in Azure AI Search: Deep Dive
Vector search finds similar items by comparing their mathematical representations (embeddings). The key components: Embeddings : Dense vectors representing…
1 min read
Azure AI Search Updates: What's New in Summer 2024
Expanded Vector Dimensions Semantic Ranking Improvements Customer-Managed Keys for Vectors
1 min read
Databricks Vector Search Deep Dive: Production Patterns
Taking Vector Search to production requires careful consideration of performance, reliability, and maintenance. This guide covers production-ready patterns.
1 min read
Vector Search in Databricks: Semantic Search at Scale
Databricks Vector Search enables semantic similarity search over your lakehouse data. Build RAG applications, recommendation systems, and intelligent search…
1 min read
Semantic Caching: Intelligent Response Reuse for LLMs
1. Start conservative Higher threshold (0.95+) for critical applications 2. Tune with data Evaluate on your actual query patterns 3. Monitor quality Track…
7 min read
Azure AI Search Index Projections: Keys, Deletes and Chunk Order
The edge cases of index projections in Azure AI Search: what the chunk index schema needs, how keys change on re-index, deletes, and keeping chunk order.
8 min read
Azure AI Search Index Operations: Schema Changes, Rebuilds and Aliases
Which Azure AI Search index changes work in place, which force a rebuild, and how to swap versioned indexes, load documents and check health safely.
7 min read
Tuning Hybrid Search in Azure AI Search: What RRF Lets You Change
Which hybrid search knobs Azure AI Search exposes on the 2023-11-01 API, why I avoid client-side score weighting, and how to measure changes before shipping.
7 min read
Keeping Azure AI Search Vector Indexes Small Without Compression
Azure AI Search has no built-in vector compression yet. How to estimate vector index size, read the quota, and cut it with chunking and dimension choices.
7 min read
Integrated Vectorization in Azure AI Search: What It Does and Doesn't Do
What integrated vectorization in Azure AI Search actually removes from a RAG pipeline, what it leaves behind, and when I'd still embed in my own code.
7 min read
Azure AI Search After Ignite 2023: What's GA and What's Still Preview
A January 2024 stock-take of Azure AI Search for RAG: which features are GA, which are preview, and which SDK and REST versions to build on.
7 min read
A RAG Maturity Model: Diagnosing Where Your System Falls Short
A five-level RAG maturity model for Azure AI Search and Azure OpenAI, covering what each level fixes, what it costs, and when to stop climbing.
1 min read
RAG Improvements: Advanced Retrieval Techniques
Advanced techniques for improving Retrieval-Augmented Generation systems for better accuracy and relevance.
1 min read
Hybrid Retrieval Patterns for RAG Applications
Hybrid retrieval improves RAG quality by combining the strengths of different search approaches. Tomorrow, I will cover Azure Dev Box and development…
1 min read
Azure Cognitive Search Vector Search: Building Semantic Search Applications
Vector search enables powerful semantic search capabilities. Tomorrow, I will cover hybrid retrieval patterns in more detail.
7 min read
Azure Cognitive Search and Embeddings Before Native Vector Search
Azure Cognitive Search can't search vectors in January 2023. Here's how to re-rank its keyword results with Azure OpenAI embeddings, and when not to bother.
8 min read
Do You Need a Vector Database Yet? ANN Indexes and Trade-offs
What a vector database adds over brute force: ANN indexes, recall, filtering and updates, and how Pinecone, Weaviate, Milvus, Qdrant and pgvector compare.
1 min read
Hybrid Search Patterns in Azure Cognitive Search
Hybrid search patterns enable building search systems that handle diverse query types effectively.
1 min read
Vector Search Preview in Azure Cognitive Search
Vector search enables powerful similarity-based retrieval that complements traditional keyword search.