vector-database

Shotit is a screenshot-to-video search engine tailored for TV & Film, blazing-fast and compute-efficient.

A high-performance, in-memory vector database written in Rust, designed for semantic search and top-k nearest neighbor queries in AI-driven applications, with binary file persistence for durability.

A structured learning repo for retrieval-augmented generation, from foundations to production patterns.

🧠 AI Second Brain — 100% Local Knowledge Management Private, self-hosted second brain. Store, search, and synthesize documents, images, and ideas with local LLMs (LLaVA, CLIP) and PostgreSQL + pgvector. Features multimodal search, knowledge graphs, gDrive streaming, and real-time analysis — all offline, no API keys, no cloud, no limits.

SQL Server as a vector database, SQL Server Extenstion for RAG

wrapper for multiple linkml storage engines

AI assistant for Dota 2 with real-time game analysis via GSI and advice from LLM

Local-first vector similarity search for Jazz Tools

🧠 Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here.

Fine-tuning black-box OpenAI embedding models

Chatbot web-applications with LLM, OpenAI API Assistants, LangChain, vector databases, and other AI stuff

Production-ready multi-agent platform delivering orchestration, session governance and audit capabilities. Stack: Go/Python/Rust microservices, multi-type data storage, Next.js admin UI, automated CI/CD pipelines, AI code review triggered via PR comment mentions.

End-to-End-Graphrag-implementation

Build your own perplexity for your applications using research4j and integrate them for any domain specific usecases

Serverless, lightweight, and fast vector database on top of DynamoDB

A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.

Edge Vector search engine with Vulkan GPU acceleration, hierarchical indexing (HRM2), and native LangChain integration. Gaussian splat-based architecture for similarity search on resource-constrained devices.

Semantic search knowledge base for MCP-enabled AI assistants. Index local files or GitHub repos, query with natural language. Built on LanceDB vector storage. Works with Claude Desktop, Cursor, and other MCP clients.

Turn your Readwise library into a blazing-fast, self-hosted semantic search engine – complete with nightly syncs, vector search API, Prometheus metrics, and a streaming MCP server for LLM clients.

Qdrant operator creates and manages Qdrant clusters running in Kubernetes