QDrant Loader

📋 Release Notes v0.4.6 - Latest improvements and bug fixes (June 3, 2025)

A comprehensive toolkit for loading data into Qdrant vector database with advanced MCP server support for AI-powered development workflows.

🎯 What is QDrant Loader?

QDrant Loader is a powerful data ingestion and retrieval system that bridges the gap between your technical content and AI development tools. It collects, processes, and vectorizes content from multiple sources, then provides intelligent search capabilities through a Model Context Protocol (MCP) server.

Perfect for:

🤖 AI-powered development with Cursor, Windsurf, and GitHub Copilot
📚 Knowledge base creation from scattered documentation
🔍 Intelligent code assistance with contextual documentation
🏢 Enterprise content integration from Confluence, JIRA, and Git repositories

📦 Packages

This monorepo contains two complementary packages:

🔄 QDrant Loader

Data ingestion and processing engine

Collects and vectorizes content from multiple sources into QDrant vector database.

Key Features:

Multi-source connectors: Git, Confluence (Cloud & Data Center), JIRA (Cloud & Data Center), Public Docs, Local Files
Advanced file conversion: 20+ file types including PDF, Office docs, images with AI-powered processing
Intelligent chunking: Smart document processing with metadata extraction
Incremental updates: Change detection and efficient synchronization
Flexible embeddings: OpenAI, local models, and custom endpoints

🔌 QDrant Loader MCP Server

AI development integration layer

Model Context Protocol server providing RAG capabilities to AI development tools.

Key Features:

MCP protocol compliance: Full integration with Cursor, Windsurf, and Claude Desktop
Advanced search tools: Semantic, hierarchy-aware, and attachment-focused search
Confluence intelligence: Deep understanding of page hierarchies and relationships
File attachment support: Comprehensive attachment discovery with parent document context
Real-time processing: Streaming responses for large result sets

🚀 Quick Start

Installation

# Install both packages
pip install qdrant-loader qdrant-loader-mcp-server

# Or install individually
pip install qdrant-loader          # Data ingestion only
pip install qdrant-loader-mcp-server  # MCP server only

5-Minute Setup

Create a workspace

bash mkdir my-qdrant-workspace && cd my-qdrant-workspace

Download configuration templates

bash curl -o config.yaml https://raw.githubusercontent.com/martin-papy/qdrant-loader/main/packages/qdrant-loader/conf/config.template.yaml curl -o .env https://raw.githubusercontent.com/martin-papy/qdrant-loader/main/packages/qdrant-loader/conf/.env.template

Configure your environment (edit .env)

bash QDRANT_URL=http://localhost:6333 QDRANT_COLLECTION_NAME=my_docs OPENAI_API_KEY=your_openai_key

Configure data sources (edit config.yaml)

yaml sources: git: - url: "https://github.com/your-org/your-repo.git" branch: "main"

Load your data

bash qdrant-loader --workspace . init qdrant-loader --workspace . ingest

Start the MCP server

bash mcp-qdrant-loader

🎉 You're ready! Your content is now searchable through AI development tools.

🔧 Integration Examples

Cursor IDE Integration

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "qdrant-loader": {
      "command": "/path/to/venv/bin/mcp-qdrant-loader",
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT_COLLECTION_NAME": "my_docs",
        "OPENAI_API_KEY": "your_key",
        "MCP_DISABLE_CONSOLE_LOGGING": "true"
      }
    }
  }
}

Example Queries in Cursor

"Find documentation about authentication in our API"
"Show me examples of error handling patterns"
"What are the deployment requirements for this service?"
"Find all attachments related to database schema"

📁 Project Structure

qdrant-loader/
├── packages/
│   ├── qdrant-loader/           # Core data ingestion package
│   └── qdrant-loader-mcp-server/ # MCP server for AI integration
├── docs/                        # Comprehensive documentation
├── website/                     # Documentation website generator
└── README.md                   # This file

📚 Documentation

🚀 Getting Started

What is QDrant Loader? - Project overview and use cases
Installation Guide - Complete installation instructions
Quick Start - 5-minute getting started guide
Core Concepts - Vector databases and embeddings explained

👥 For Users

User Documentation - Comprehensive user guides
Data Sources - Git, Confluence, JIRA, and more
File Conversion - PDF, Office docs, images processing
MCP Server - AI development integration
Configuration - Complete configuration reference

🛠️ For Developers

Developer Documentation - Architecture and contribution guides
Architecture - System design and components
Testing - Testing guide and best practices
Deployment - Deployment guide and configurations
Extending - Custom data sources and processors

📦 Package Documentation

QDrant Loader Package - Core loader documentation
MCP Server Package - MCP server documentation
Website Generator - Documentation website

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details on:

Setting up the development environment
Code style and standards
Pull request process
Issue reporting guidelines

Quick Development Setup

# Clone the repository
git clone https://github.com/martin-papy/qdrant-loader.git
cd qdrant-loader

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e packages/qdrant-loader[dev]
pip install -e packages/qdrant-loader-mcp-server[dev]

# Run tests
pytest

🆘 Support

Issues - Bug reports and feature requests
Discussions - Community discussions and Q&A
Documentation - Comprehensive guides and references

📄 License

This project is licensed under the GNU GPLv3 - see the LICENSE file for details.

Ready to supercharge your AI development workflow? Start with our Quick Start Guide or explore the complete documentation.

Back to Documentation

Generated from README.md