QDrant Loader

by martin-papy

45 stars
573 downloads
Not rated
GitHub Website

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

- 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:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name QDrant Loader
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. 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

PyPI - qdrant-loader
PyPI - mcp-server
PyPI - qdrant-loader-core
CodeRabbit Pull Request Reviews
Test Coverage
License: GPL v3

📝 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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