9 min read
Six Vector Stores for RAG and Where Each One Hurts
Azure AI Search, DocumentDB, Cosmos DB for NoSQL, pgvector, Pinecone and Redis each fail differently in RAG. Where each one hurts in January 2026.
Vector Database
15 articles
1 min read
Comparing Vector Databases: Azure AI Search vs Pinecone vs Weaviate
Choosing the right vector database is crucial for RAG applications. After implementing production systems with all three major options in 2025, here's my…
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…
1 min read
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
AI Agent Memory: Implementing Long-Term Context
Long-term memory transforms one-shot interactions into ongoing relationships. Users feel understood when the AI remembers their preferences, past issues…
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
Semantic Kernel Memory: Building Persistent AI Context
Use retrieved memories to enrich the system prompt or provide context for the AI. This enables personalized responses without requiring the user to repeat…
1 min read
Vector Database Evolution: What's Changing in 2025
Vector databases in 2025 are more capable, efficient, and integrated than ever. Choose based on your specific requirements for scale, latency, and…
8 min read
Where Should Your Vectors Live on Azure? Search Index vs Database
Azure AI Search, Cosmos DB for NoSQL and PostgreSQL with pgvector can all hold embeddings now. Here is how I decide between a search index and your database.
1 min read
Long-Term Agent Memory: Persistent Knowledge Storage
1. Categorize memories : Different types need different handling 2. Extract automatically : Don't rely on explicit save commands 3. Maintain regularly :…
6 min read
Qdrant 0.11 with Azure OpenAI: Point IDs, Payload Indexes and Filters
Run Qdrant 0.11 with Azure OpenAI embeddings: valid point IDs, payload indexes, filtered HNSW search, memory settings, and when Qdrant isn't the right choice.
7 min read
Milvus 2.2 with Azure OpenAI Embeddings: A Self-Hosted Start
Run Milvus 2.2 standalone, load Azure OpenAI embeddings with pymilvus, and learn the schema, index, consistency and delete rules before you self-host it.
7 min read
Weaviate 1.17 with Azure OpenAI: Bring Your Own Vectors
Run Weaviate 1.17 with embeddings from Azure OpenAI: schema choices, batch imports, filtered vector search, BM25 and the hybrid search caveats to know first.
8 min read
Pinecone With Azure OpenAI Embeddings: Design the Index First
Set up a Pinecone index for Azure OpenAI embeddings: dimensions, pod types, namespaces, metadata indexing and idempotent upserts, plus when to skip it.
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.