QDrant Loader

📋 Release Notes v0.7.2 - Latest improvements and bug fixes

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 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

# 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

mkdir my-workspace && cd my-workspace

Initialize workspace with templates

qdrant-loader init --workspace .

Configure your environment (edit .env)

   # Qdrant connection
   QDRANT_URL=http://localhost:6333
   QDRANT_COLLECTION_NAME=my_docs
# LLM provider (new unified configuration)
   OPENAI_API_KEY=your_openai_key
   LLM_PROVIDER=openai
   LLM_BASE_URL=https://api.openai.com/v1
   LLM_EMBEDDING_MODEL=text-embedding-3-small
   LLM_CHAT_MODEL=gpt-4o-mini

Configure data sources (edit config.yaml)

global: qdrant: url: "http://localhost:6333" collection_name: "my_docs" llm: provider: "openai" base_url: "https://api.openai.com/v1" api_key: "${OPENAI_API_KEY}" models: embeddings: "text-embedding-3-small" chat: "gpt-4o-mini" embeddings: vector_size: 1536

projects: my-project: project_id: "my-project" sources: git: docs-repo: base_url: "https://github.com/your-org/your-repo.git" branch: "main" file_types: [".md", ".rst"]

Load your data

qdrant-loader ingest --workspace .

Start the MCP server

mcp-qdrant-loader --env /path/tp/your/.env

🔧 Integration with Cursor

Add to your Cursor settings (.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"
      }
    }
  }
}

Alternative: Use configuration file (recommended for complex setups):

{
  "mcpServers": {
    "qdrant-loader": {
      "command": "/path/to/venv/bin/mcp-qdrant-loader",
      "args": ["--config", "/path/to/your/config.yaml", "--env", "/path/to/your/.env"]
    }
  }
}

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"

📚 Documentation

🚀 Getting Started

Installation Guide - Complete setup instructions
Quick Start - Step-by-step tutorial
Core Concepts - Covered inline in Getting Started

👥 User Guides

Configuration - Complete configuration reference
Data Sources - Git, Confluence, JIRA setup
File Conversion - File processing capabilities
MCP Server - AI tool integration

⚠️ Migration Guide (v0.7.1+)

LLM Configuration Migration Required

New unified configuration: global.llm.* replaces legacy global.embedding.* and file_conversion.markitdown.*
Provider-agnostic: Now supports OpenAI, Azure OpenAI, Ollama, and custom endpoints
Legacy support: Old configuration still works but shows deprecation warnings
Action required: Update your config.yaml to use the new syntax (see examples above)

Migration Resources

Configuration File Reference - Complete new schema
Environment Variables - Updated variable names

🛠️ Developer Resources

Architecture - System design overview
Testing - Testing guide and best practices
Contributing - Development setup and guidelines

🤝 Contributing

We welcome contributions! See our Contributing Guide for:

Development environment setup
Code style and standards
Pull request process

Quick Development Setup

# Clone and setup
git clone https://github.com/martin-papy/qdrant-loader.git
cd qdrant-loader
python -m venv venv
source venv/bin/activate

# Install packages in development mode
pip install -e ".[dev]"
pip install -e "packages/qdrant-loader-core[dev,openai,ollama]"
pip install -e "packages/qdrant-loader[dev]"
pip install -e "packages/qdrant-loader-mcp-server[dev]"

📄 License

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

Ready to get started? Check out our Quick Start Guide or browse the complete documentation.