Best of Vector Search — September 2025

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·36w

   The Open-source RAG Stack

   A comprehensive guide to building production-ready RAG systems using open-source tools. Covers the complete technology stack from frontend frameworks to data ingestion, including LLM orchestration tools like LangChain and CrewAI, vector databases like Milvus and Chroma, embedding models, and retrieval systems. Also showcases 9 practical MCP (Model Context Protocol) projects for AI engineers, ranging from local MCP clients to voice agents and financial analysts.

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2. 2
   Article

   Hacker News·37w

   Will Amazon S3 Vectors Kill Vector Databases—or Save Them?

   AWS S3 Vectors offers 90% cost savings for vector storage but won't replace dedicated vector databases like Milvus. Instead, it fills the cold storage tier in a three-tier architecture (hot/warm/cold) that balances latency, cost, and scale. S3 Vectors excels at low-QPS scenarios and archival storage but struggles with high-performance search, frequent updates, and complex queries. The future lies in tiered vector storage where different solutions serve different performance and cost requirements.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·37w

   Building a Context Engineering Workflow

   Context engineering involves creating dynamic systems that provide LLMs with the right information, tools, and format to complete tasks effectively. This tutorial demonstrates building a multi-agent research assistant that gathers context from four sources: documents, memory, web search, and ArXiv. The workflow uses Tensorlake for document processing, Milvus for vector storage, Zep for memory management, and Firecrawl for web scraping, orchestrated through CrewAI agents that filter and synthesize responses.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·38w

   [Hands-on] MCP-powered Agentic RAG

   A step-by-step implementation guide for building an Agentic RAG system using the Model Context Protocol (MCP). The system combines vector database search with web search fallback, using Firecrawl for web scraping, Qdrant as the vector database, and Cursor as the MCP client. The tutorial covers setting up an MCP server, creating tools for both vector DB queries and web search, and integrating everything with Cursor for intelligent query routing.

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   Article

   Meilisearch·37w

   What is agentic RAG? How it works, benefits, challenges & more

   Agentic RAG enhances traditional retrieval-augmented generation by adding agent-like decision-making capabilities. Instead of static retrieval, it uses planning, validation, and iterative evaluation to determine when and how to retrieve context. The system works through query understanding, retrieval planning, context evaluation, and feedback loops. Key benefits include improved accuracy, better reasoning, and explainability, while drawbacks involve higher computational costs and implementation complexity. Common tools include LangChain, LlamaIndex, and Meilisearch for building these intelligent pipelines.

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   Article

   Product Hunt·37w

   Vectorize: Build RAG pipelines that are optimized for your data.

   Vectorize 2.0 is a data platform that optimizes RAG pipelines by evaluating the best vectorization methods for your data and providing cloud-scale pipeline management. The new version introduces hosted chat agents, no-code chatbots, one-line website integration, real-time data syncing, and hybrid search with knowledge graphs. The platform handles vector database population and keeps data fresh automatically.

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