#qdrant

Semantic search over Nordic economic data — Market announcements, quarterly reports (162 companies), macro data (NO/SE/DK/FI), commodity prices, and press releases. 180,000+ vectors. Deep context.

MCP persistent memory service that gives AI assistants long-term memory across sessions and projects. Powered by Qdrant vector search + Ollama/Gemini embedding with hybrid retrieval (vector + BM25). Features include: semantic memory save/search/forget, content deduplication via SHA-256, automatic sensitive data redaction, dual-shell architecture (MCP stdio + HTTP REST), Web Admin Panel with analytics, multi-user auth & API key management, and audit logging.

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.

CodeCompass: AI-powered Vibe Coding with MCP. Connects Git repositories to AI assistants like Claude, using Ollama for privacy or OpenAI for cloud. Integrates with VSCode, Cursor, and more.

A powerful Model Context Protocol (MCP) server implementation with integrations for GitLab, Jira, Confluence, YouTube, and more. This server provides AI-powered search capabilities and various utility tools for development workflows.

This is an LLM application with chat functionality, featuring chat using RAG, a database, and MCP server capabilities. The UI is designed for Japanese users.

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

ローカル環境でQdrantとMCP-server-qdrantを簡単に立ち上げるためのDocker Compose設定

A simple MCP server to access Qdrant

qdrant + mcp-qdrant-server

Mirror of

A Model Context Protocol (MCP) server implementation for RAG

MCP server for semantic search with Qdrant vector database