Nimble | Web Search Agents Radar

2026 three-stage stack; KAG as KG reasoning. New: FalkorDB GraphRAG, pgvector index mismatches, Databricks medallion streaming, BM25+FAISS+RRF+Cohere no-chunking, Qwen3-Embedding/Milvus, Ettin ModernBERT rerankers, cuVS GPU, Milvus hybridSearch, PageIndex vectorless, DCI exact-match, n8n workflows. Latest: Snowflake Cortex Agents, document parsing deep-dive, FalkorDB anti-patterns, GCP Vertex AI Vector Search ingestion pipeline. New: Cypher on Velox (Meta) federated graph+vector search, Go+ripgrep agentic RAG, Knowledge Store 6-stage pipeline, Mastra+Elasticsearch RAG agent, BRANE per-query config (89% cost reduction), Supabase pgvector three-layer architecture, Gemini Embedding 2 multimodal, Summary RAG, turbovec. Fresh: AWS OpenSearch Serverless GA, Pinecone vs Chroma vs Weaviate decision matrix, production RAG on Kubernetes deep-dive, production crash patterns, Superlinked SIE, OmniRetrieval, position bias reduction (57-87%), Yoni Levin GraphRAG mistakes, RAG dev vs prod gap. Today: Azure HorizonDB (DiskANN, 3x throughput), Pinecone Nexus (OneLake, 95% token reduction, 30x speedup), Vector DB scaling paradox on HPC, Spatial Graph RAG, 10 Common RAG Mistakes, Production RAG failure root causes, Amazon OpenSearch Service GA, Zilliz Vector Lakebase, Vector Lakebase architecture, Vector DB benchmark. New signals today: RuVector 14x speedup to 1.5ms with 97% recall, miniReranker achieving 96-99% of dense reranker quality, RVBench benchmark for hybrid relational-vector workloads, multi-field hybrid retrieval with integrated evaluation framework, uncertainty-aware hybrid retrieval for long-document RAG, modern retrieval pipeline patterns, best vector databases for RAG 2026 comparison. New from today's articles: scientific taxonomy pipeline at scale (SPECTER2, Leiden clustering, Qdrant, multi-strategy candidate retrieval, LLM reranker), practical tutorial on wrapping RAG as an agent tool. Also: bias towards short text segments in vector search (mitigation: larger topK + external normalization). Today's additions: MODE-RAG manifold outlier diagnosis and energy-based methods for multimodal RAG; reranking as cheap RAG upgrade (cost-benefit insights); agent-native retrieval in Lakebase Postgres (immediate indexing between turns); Amazon S3 Vectors now supports 10,000 results per query (100x increase); awesome-rag-production curated list of production RAG tools; 'Your AI Is Not Failing, Your Context Is' article on context quality and governance. New: 'Your RAG Stack Is Solving the 2023 Problem' article challenging current RAG assumptions; Efficient Multimodal Reranking through Visual Cache; RSRank reranking method using representational shifts. Latest: RAG vs long context 2026 decision framework with cost comparisons — critical design choice. Retrieval pipeline vulnerabilities and indirect prompt injection in hybrid search — new security concern. RAGFlow tutorial for hybrid retrieval with grounded citations. Today's reading added: entity linking as a pre-filtering step before hybrid search improves context quality and precision — practical enhancement for production RAG. Today's reading: LanceDB hybrid search for codebase RAG (Part 2); Zilliz article on AI agents mastering vector databases; Production RAG Architecture on Amazon Bedrock (Retrieve vs Knowledge Bases); Amazon Bedrock Managed Knowledge Base launch. New from today's articles: Hierarchical multi-modal retrieval using document structure — practical enhancement for production RAG. Solidigm SSD-based AI memory for RAG and KV-cache, with DiskANN vs HNSW benchmarks at 100M vectors showing storage-first can outperform memory-centric at high concurrency, and KV-cache offload improving time-to-first-token. RAG Is Not Just Search article reinforces chunking, ranking, reranking, and hallucination control as core production concerns. Today's reading added: full-stack RAG system with Qdrant (hybrid search, hierarchical chunking, cross-encoder reranking, RBAC) — practical reference. Also: How to Handle Small Context Window Limits in RAG (three-level document processing) — practical technique for hybrid retrieval. Today's new signals: Voyage AI multimodal embeddings; TOON token-efficient structured data representation; Dual-channel OCR RAG system; RAFT fine-tuning; Agent Retrieval demo (Gemini Enterprise); FAQ on context engine. Today's reading added: Lilbee single-executable local AI search engine using hybrid search (BM25+vector+RRF) and reranking — useful for local/edge retrieval patterns. Weaviate announces free forever cloud tier — lowers barrier for vector DB prototyping. Latest from today's reading (12 articles): Shift-left performance engineering for RAG/LLM platforms (six-layer architecture, deviation score gate, CI/CD) — actionable for production evaluation. PixelRAG: vision-based embedding of page tiles to avoid parser loss (1/3 failures due to parser loss) — important for multimodal RAG. Jingra: open-source vector search benchmarking framework across Elasticsearch, OpenSearch, Qdrant — useful for vendor selection. RAG system design at 500M doc scale (DiskANN, RaBitQ, conformal prediction, hierarchical documents) — production scaling reference. Today's additions: pgvector scaling guide (sub-20ms latency, billions of vectors, high write throughput); Zilliz Cloud adds Functions and Model Inference on top of BM25/hybrid rankers; Hands-on workshop on vector DBs and semantic search with evaluation framework (recall@k, MRR). Today's reading (14 articles) added: Practical hybrid search tutorial (BM25+vector+reranker) lifting recall from 62% to 91% — concrete implementation pattern. Cross-encoder reranking feature addition for production RAG pipelines. Enterprise RAG failure analysis highlighting missing permission models as a critical failure mode. VertexRanker reranker from Google Cloud with fallback to RRF — practical tooling signal.