1 min read
Vector Database Selection: Comparing Azure AI Search, Cosmos DB, and PostgreSQL
Azure AI Search, Cosmos DB with vector indexing, and Azure Database for PostgreSQL with pgvector each provide vector search capabilities with different…
Azure AI Search
20 articles
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…
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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.
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Vector Database Selection: Choosing the Right Solution for RAG
The best vector database depends on your specific requirements. Azure AI Search excels for hybrid search with integrated semantic ranking. Pinecone offers…
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
Vector Database Selection: Comparing Azure AI Search, Pinecone, and Qdrant
Best for organizations already invested in Azure with hybrid search requirements. Pros: Hybrid search, semantic ranking, enterprise security, managed…
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
Building RAG Applications with Azure AI Search and GPT-4o
RAG works by first retrieving relevant documents from a search index, then passing those documents as context to an LLM for generation. This approach…
1 min read
Building AI-Powered Search with Azure AI Search and Semantic Ranker
Monitor reranker scores and caption extraction quality. A/B test semantic ranking against pure vector or keyword search to quantify improvements for your…
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Multimodal RAG: Combining Text, Images, and Tables for Enhanced Search
Multimodal RAG ensures users find relevant information regardless of how it's represented in the source documents.
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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…
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Azure AI Search Index Projections: Chunking Documents at Index Time
Index projections let you chunk documents at index time — I used them to turn long manuals into searchable, LLM-friendly chunks. Here's a practical approach…
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Azure AI Search Index Management: Operations and Maintenance
Production search indexes require operational discipline. From index versioning to blue-green deploys, these are the ops patterns I use to avoid downtime…
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Hybrid Search Optimization: Combining Vector, Keyword, and Semantic Search
Combining vector, keyword, and semantic search solved many relevance problems for us. This post distils the hybrid strategies I used to get the best of each…
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Vector Compression in Azure AI Search: Reducing Costs Without Sacrificing Quality
Vector compression reduced storage costs dramatically in a production index I worked on. Here are the practical trade-offs and configuration tips I used to…
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Azure AI Search Semantic Ranker: Deep Dive and Optimization
Semantic ranking improved my search relevance considerably; the main cost was configuration complexity. This deep dive explains how I tuned the ranker for…
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Integrated Vectorization in Azure AI Search: Simplifying RAG Pipelines
Integrated vectorization removed an entire pipeline step in a recent project. I'll show how it simplifies RAG pipelines and where to be cautious when…
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Azure AI Search Updates: January 2024 Features and Capabilities
I've been integrating Azure AI Search into RAG systems; the January 2024 updates simplify common workflows. Below are the changes I judged most impactful…
1 min read
Advanced Chunking Strategies for RAG: Beyond Fixed-Size Splits
In projects I've worked on, chunking decisions alone changed retrieval quality more than model choice ever did. This deep dive pulls together advanced…
2 min read
RAG Architecture Maturity Model: From Basic to Production-Grade
I've seen hundreds of RAG prototypes. The gap between a demo and a production-grade system usually comes down to retrieval quality, freshness, and…