Retrieval, RAG & Search

What Happened Two industry trends are changing how businesses design retrieval-augmented generation (RAG) systems. First, Jina released fully offline, self-contained Docker deployment options for all 28 of its embedding and reranking models, enabling local inference with no external calls and standard API compatibility — targeted at air-gapped, regulated, and latency‑sensitive environments [1]. Second, Elasticsearch improved…

What Happened Retrieval-augmented generation (RAG) architectures continue to converge on a few practical patterns: (1) dense vector search for semantic recall, (2) hybrid combinations with lexical search (BM25) for precise matches, (3) lightweight orchestration layers (LlamaIndex, LangChain, Haystack) that connect embedding models, retrievers and LLMs, and (4) an expanding set of vector databases (Pinecone, Qdrant,…

What Happened Recent advances move practical RAG and vector search from manual tuning to more operational, measurable workflows across two fronts: (1) index-time auto‑tuning of vector quantization to meet a recall budget and (2) tighter integration of model inference and live query profiling for troubleshooting. Elasticsearch demonstrated an auto-tuning approach that predicts recall under quantization…

What Happened Production teams running retrieval-augmented generation (RAG) and LLM agents found that starting agents without domain-specific context drives exploratory tool calls, higher latency, token cost and hallucinations. The practical fix is to assemble a structured, precomputed context layer from catalog, policy and session signals and provide it to the model before the agent’s first…

Executive summary Short: A 2026 benchmark on a 5,000-item apparel & footwear catalog found that a simple average of image and text embeddings (L2-normalize each, mean, then L2-normalize) produced substantially better product search results than image-only or text-only retrieval—up to ~1.5× improvement on top-of-list metrics (Recall@1/5/10, MRR, nDCG@10). The study also showed multimodal averaging improved…