Best of Vector Search — October 2025

  1. 1
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·34w

    A 100% Open-source Alternative to n8n!

    Sim is an open-source drag-and-drop platform for building agentic workflows that runs locally with any LLM. The article demonstrates building a finance assistant connected to Telegram using agents, MCP servers, and APIs. It also covers four RAG indexing strategies: chunk indexing (splitting documents into embedded chunks), sub-chunk indexing (breaking chunks into finer pieces while retrieving larger context), query indexing (generating hypothetical questions for better semantic matching), and summary indexing (using LLM-generated summaries for dense data).

  2. 2
    Article
    Avatar of opensuseopenSUSE·33w

    GSoC 2025, Building a Semantic Search Engine for Any Video

    A GSoC 2025 project that built an end-to-end semantic video search engine capable of finding specific moments within videos using natural language queries. The system uses a two-part architecture: an ingestion pipeline that processes videos with AI models (TransNetV2, WhisperX, BLIP, VideoMAE) to extract shots, transcripts, captions, and actions, then segments them intelligently and enriches them with LLM-generated summaries; and a search application with FastAPI backend that performs hybrid text-visual searches using ChromaDB vector database and Reciprocal Rank Fusion for result ranking, paired with a Streamlit frontend for user interaction.

  3. 3
    Article
    Avatar of singlestoreSingleStore·32w

    Context Engineering: A Definitive Guide

    Context engineering is a systematic approach to building AI systems that goes beyond prompt engineering by designing the complete environment in which AI operates. It involves structuring data sources, integrating tools, maintaining memory across interactions, and ensuring AI agents have access to relevant information when needed. The article explains how context engineering differs from prompt engineering and RAG systems, introduces the Model Context Protocol (MCP) as a standardized interface for managing data sources, and demonstrates building context-aware workflows using SingleStore as a long-term memory layer with vector search capabilities.

  4. 4
    Article
    Avatar of faunFaun·33w

    Deploying a Complete RAG Ecosystem with a Single Command: My Ultimate Docker Stack

    A comprehensive Docker Compose stack that deploys a complete RAG (Retrieval-Augmented Generation) infrastructure with a single command. The setup includes Ollama for local LLM execution, Qdrant for vector search, MongoDB for document storage, Redis for caching, Neo4j for knowledge graphs, Keycloak for authentication, and n8n for workflow automation. The stack can be configured for CPU-only, GPU-accelerated, or external API usage, with automated setup scripts that handle dependencies and provide instant access to all services. Neo4j integration enables advanced relationship mapping between documents and entities, enriching context beyond traditional vector search.

See all Vector Search archives