QDrant Loader
About
Enterprise-ready vector database toolkit for building searchable knowledge bases from multiple data sources. Supports multi-project management, automatic ingestion from Confluence/JIRA/Git, intelligent file conversion (PDF/Office/images), and semantic search. Includes MCP server
Details
- Author
- martin-papy
- GitHub stars
- 45
- Downloads
- 573
- Categories
- Database, Other, Knowledge Base, AI, Developer Tools
Jump to
- Multi-source connectors: Git, Confluence, JIRA, Public Docs, Local Files.
- File conversion: PDF, Office docs, images, audio, EPUB, and more.
- Smart chunking with hierarchical context and incremental updates.
- Provider-agnostic LLM support: OpenAI, Azure OpenAI, Ollama, custom endpoints.
- MCP protocol 2025-06-18 with dual transport (stdio + HTTP).
- Advanced search tools: semantic, hierarchy-aware, similarity, clustering, knowledge graphs.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
QDrant LoaderCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install via pip install qdrant-loader qdrant-loader-mcp-server, initialize a workspace with qdrant-loader init, configure data sources in config.yaml and environment variables in .env, ingest data with qdrant-loader ingest, then start the MCP server using the command mcp-qdrant-loader --env /path/to/.env.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"qdrant loader": {
"qdrant-loader": {
"command": "uv",
"args": [
"sync",
"--all-packages",
"--all-extras"
]
}
}
}
}
McpServers
{
"qdrant-loader": {
"command": "uv",
"args": [
"sync",
"--all-packages",
"--all-extras"
]
}
}
QDrant Loader
📝 Changelog v1.0.3 - Latest improvements and bug fixes
<div align="left">
A comprehensive toolkit for loading data into Qdrant vector database with advanced MCP server support for AI-powered development workflows.
</div>
🎯 What is QDrant Loader?
QDrant Loader is a data ingestion and retrieval system that collects content from multiple sources, processes and vectorizes it, then provides intelligent search capabilities through a Model Context Protocol (MCP) server for AI development tools.
Perfect for:
- 🤖 AI-powered development with Cursor, Windsurf, and other MCP-compatible tools
- 📚 Knowledge base creation from technical documentation
- 🔍 Intelligent code assistance with contextual information
- 🏢 Enterprise content integration from multiple data sources
📦 Packages
This monorepo contains three 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
- File conversion: PDF, Office docs (Word, Excel, PowerPoint), images, audio, EPUB, ZIP, and more using MarkItDown
- Smart chunking: Modular chunking strategies with intelligent document processing and hierarchical context
- Incremental updates: Change detection and efficient synchronization
- Multi-project support: Organize sources into projects with shared collections
- Provider-agnostic LLM: OpenAI, Azure OpenAI, Ollama, and custom endpoints with unified configuration
⚙️ QDrant Loader Core
Core library and LLM abstraction layer
Provides the foundational components and provider-agnostic LLM interface used by other packages.
Key Features:
- LLM Provider Abstraction: Unified interface for OpenAI, Azure OpenAI, Ollama, and custom endpoints
- Configuration Management: Centralized settings and validation for LLM providers
- Rate Limiting: Built-in rate limiting and request management
- Error Handling: Robust error handling and retry mechanisms
- Logging: Structured logging with configurable levels
🔌 QDrant Loader MCP Server
AI development integration layer
Model Context Protocol server providing search capabilities to AI development tools.
Key Features:
- MCP Protocol 2025-06-18: Latest protocol compliance with dual transport support (stdio + HTTP)
- Advanced search tools: Semantic search, hierarchy-aware search, attachment discovery, and conflict detection
- Cross-document intelligence: Document similarity, clustering, relationship analysis, and knowledge graphs
- Streaming capabilities: Server-Sent Events (SSE) for real-time search results
- Production-ready: HTTP transport with security, session management, and health checks
🚀 Quick Start
Installation
```bash
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