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Retrieval, RAG & Search

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Illustration for the Kimbodo News & Research briefing “Build Faster, Cheaper and More Accurate RAG Systems by Choosing the Right Vector Store and Search Pattern” (Retrieval, RAG & Search).

Build Faster, Cheaper and More Accurate RAG Systems by Choosing the Right Vector Store and Search Pattern

Retrieval, RAG & Search1 day ago15Views0Likes0Comments
What Happened Recent advances in vector database storage and memory management change the cost/latency/accuracy trade-offs for retrieval-augmented generation (RAG). Qdrant 1.19 introduces three features that are immediately relevant to production RAG deployments: a 4-bit TurboQuant compressed vector datatype that discards full-precision vectors for large storage reduction, unified memory tiers (pinned, cached, cold) for per-component placement,…

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Illustration for the Kimbodo News & Research briefing “Replace Fragile Keyword Search with RAG and Vector Databases to Find Lost Assets Faster” (Retrieval, RAG & Search).

Replace Fragile Keyword Search with RAG and Vector Databases to Find Lost Assets Faster

Retrieval, RAG & SearchJuly 30, 202633Views0Likes0Comments
What Happened Creative and knowledge teams accumulate large, heterogeneous content over years—images, sketches, audio, notes and many iterative revisions. Traditional keyword and folder-based search breaks when naming conventions change, metadata is incomplete, or people leave, making it often faster to recreate than to locate existing work. Semantic search, powered by vector embeddings for text, images…

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Illustration for the Kimbodo News & Research briefing “Retrieval, RAG & Search — July 28, 2026” (Retrieval, RAG & Search).

Retrieval, RAG & Search — July 28, 2026

Retrieval, RAG & SearchJuly 28, 202639Views0Likes0Comments
What Happened Several recent engineering advances and findings change practical choices for retrieval‑augmented generation (RAG) and production vector search: Elastic Agent Builder now emits full OpenTelemetry traces for every LLM call and tool execution; teams can convert those traces into token‑cost and performance dashboards in Kibana to operationalize cost and latency visibility [1].…

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Illustration for the Kimbodo News & Research briefing “Cut RAG Latency and Improve Retrieval Accuracy by Adding Listwise Rerankers and Modern Vector‑store Patterns” (Retrieval, RAG & Search).

Cut RAG Latency and Improve Retrieval Accuracy by Adding Listwise Rerankers and Modern Vector‑store Patterns

Retrieval, RAG & SearchJuly 27, 202639Views0Likes0Comments
What Happened Jina released a new text-only listwise reranker, jina-reranker-v3.5, a ~597–600M parameter model with a 131,072-token context window that uses a “last-but-not-late” one-pass scoring approach and a modified self-attention to reduce quadratic cost and speed inference. The model was trained with three-stage self-distillation on a multilingual (52 languages) and domain-expanded corpus (legal, medical, financial,…

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Deliver Accurate, Low-Latency RAG Search: Practical Architecture and Best Practices for Vector Databases

Retrieval, RAG & SearchJuly 25, 202637Views0Likes0Comments
What Happened Vector databases and retrieval-augmented generation (RAG) tooling have converged into repeatable patterns for production search: dense-vector candidate generation, scalar-filtered recall, and a separate ranking/merchandising layer that composes multiple signals. Tooling improvements in Qdrant, Pinecone, Milvus, Weaviate and orchestration libraries (LlamaIndex, LangChain, Haystack) make sub-100ms query paths and large-scale catalogs practical. Qdrant’s recent engineering…

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Retrieval, RAG & Search — July 24, 2026

Retrieval, RAG & SearchJuly 24, 202645Views0Likes0Comments
What Happened Over the last few years RAG systems have moved from demos to production services. Vector databases (Pinecone, Qdrant, Milvus, Weaviate) and search engines (Elasticsearch, Vespa, Elasticsearch KNN/ANN plugins, Haystack orchestration) now offer mature features for hybrid lexical + semantic retrieval, filtering, metadata/scoring, and operational controls. Orchestration libraries (LlamaIndex, LangChain, Haystack) have standardized pipelines…

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Retrieval, RAG & Search — July 23, 2026

Retrieval, RAG & SearchJuly 23, 202643Views0Likes0Comments
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…

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Build Reliable Retrieval-Augmented Generation: Choosing Vector Stores, Hybrid Search, and Production Observability

Retrieval, RAG & SearchJuly 22, 202641Views0Likes0Comments
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,…

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How to Build Reliable Retrieval-Augmented Generation: auto-tuned vectors, over-retrieve+rerank, and live query profiling

Retrieval, RAG & SearchJuly 21, 202651Views0Likes0Comments
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…

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Cut RAG Costs and Hallucinations by Precomputing Structured Context and Using Hybrid Search

Retrieval, RAG & SearchJuly 20, 202652Views0Likes0Comments
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…

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Retrieval, RAG & Search — July 17, 2026

Retrieval, RAG & SearchJuly 17, 202647Views0Likes0Comments
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…

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