pgvector vs Qdrant vs LanceDB: On-Prem RAG Vector Search (2026)
pgvector, Qdrant, and LanceDB compared for on-prem RAG in 2026: indexing, filtering, latency at scale, and operational overhead.
Agentic RAG Architecture Patterns: When Plain RAG Is Not Enough
Agentic RAG architecture patterns for 2026: planner-executor retrieval loops, tool-augmented search, and when plain RAG breaks down.
LLM Function Calling and Tool Use: A Production Architecture (2026)
LLM function calling explained: JSON-schema tool definitions, parallel tool calls, the agent loop, routing, validation and failure handling in a production tool-use architecture.
Matryoshka Embeddings: Adaptive-Dimension Retrieval Architecture (2026)
Matryoshka embeddings explained: how Matryoshka Representation Learning nests multiple dimensions in one vector for adaptive retrieval - coarse-to-fine search, storage cuts, MRL training and failure modes in 2026.
Hybrid Search Architecture: Dense + Sparse Fusion with RRF (2026)
Hybrid search architecture explained: fusing BM25 sparse retrieval with dense vector search using Reciprocal Rank Fusion - indexing, scoring, rerankers, latency and failure modes for production RAG in 2026.
Agentic RAG Architecture: Retrieval Inside the Agent Loop (2026)
Agentic RAG architecture explained: moving retrieval inside the agent loop with planners, query rewriting, hybrid search, rerankers and reflection - patterns, evals, cost and failure modes in 2026.
Vector Database Benchmarks 2026: 4 Ranked by QPS, Recall & Cost
A 2026 vector database benchmark: Pinecone, Weaviate, Qdrant, and Milvus on recall, latency, throughput, and cost - with what changed in the second half of 2026.
Embedding Models Benchmark: OpenAI, Cohere, Voyage, BGE
A 2026 embedding models benchmark: OpenAI, Cohere, Voyage, and BGE on retrieval quality, dimensions, cost, and MTEB - with what changed for 2026.
pgvector vs Dedicated Vector Database: The 2026 ADR
pgvector vs a dedicated vector database in 2026: recall, latency, filtering, scale, operations, and cost - a decision record for choosing your vector store.
Fine-Tuning vs RAG vs Long-Context: A 2026 Cost/Quality Decision
A 2026 cost and quality decision record for fine-tuning vs RAG vs long-context LLMs: token economics, latency, accuracy trade-offs, and a decision matrix.