Best of Vector Search — January 2026

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·19w

   6 Components of Context Engineering

   Context engineering is the practice of optimizing how information flows to AI models, comprising six core components: prompting techniques (few-shot, chain-of-thought), query augmentation (rewriting, expansion, decomposition), long-term memory (vector/graph databases for episodic, semantic, and procedural memory), short-term memory (conversation history management), knowledge base retrieval (RAG pipelines with pre-retrieval, retrieval, and augmentation layers), and tools/agents (single and multi-agent architectures, MCPs). While model selection and prompts contribute only 25% to output quality, the remaining 75% comes from properly engineering these context components to deliver the right information at the right time in the right format.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·18w

   Your RAG System Has a Hidden UX Problem

   RAG systems often use semantic retrieval but fall back to keyword-based highlighting, creating a UX disconnect where users can't see why documents are relevant. Zilliz released an open-source semantic highlighting model that identifies semantically relevant text spans instead of just keyword matches. The bilingual model (English/Chinese) handles 8K context windows, runs fast enough for production use, and outperforms existing solutions on both in-domain and out-of-domain benchmarks. It's being integrated into Milvus as a native API and is available on Hugging Face under MIT license.

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3. 3
   Article

   databricks·20w

   How 7‑Eleven Transformed Maintenance Technician Knowledge Access with Databricks Agent Bricks

   7-Eleven built an AI-powered Technician's Maintenance Assistant using Databricks Agent Bricks to help field technicians quickly access equipment documentation. The system uses vector search with embeddings, routing agents for document and image queries, and integrates with Microsoft Teams. By migrating from AWS services to a unified Databricks platform, they reduced response times from minutes to seconds, eliminated manual reindexing, and improved first-time-fix rates while lowering infrastructure overhead.

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   Article

   Weaviate·19w

   Announcing the Weaviate C# Client

   Weaviate has released an official C# client library (v1.0.0) for .NET developers. The client features a collection-centric API design, strong typing with generic support, fluent chainable filtering, integrated vector search and RAG capabilities, dependency injection support, and comprehensive error handling. Key features include automatic connection management, type-safe schema generation from C# classes, backup/restore functionality, and seamless integration with modern .NET applications. The library is available via NuGet and aims to provide a native, intuitive experience for building AI-powered applications in the .NET ecosystem.

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