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Installation Guide

This guide walks you through installing QDrant Loader and its MCP server on your system. Choose the installation method that best fits your needs.

Use this page only for platform-specific notes, dependency choices, and install troubleshooting.

Primary onboarding path: Quick Start

📋 Overview

  • qdrant-loader - Main data ingestion and processing tool
  • qdrant-loader-core - Shared core library with LLM abstraction (automatically installed as dependency)
  • qdrant-loader-mcp-server - Model Context Protocol server for AI tool integration

The core library is automatically installed as a dependency. Most users will want both the main package and MCP server for the complete experience.

🚀 Package options

  • Main package only:
pip install qdrant-loader
  • MCP server only:
pip install qdrant-loader-mcp-server
  • Full experience (recommended):
pip install qdrant-loader qdrant-loader-mcp-server

🔧 Prerequisites

System Requirements

Component Minimum Recommended
Python 3.12+ 3.12+
Memory 4GB RAM 8GB+ RAM
Storage 2GB free 10GB+ free
OS Windows 10+, macOS 10.15+, Linux Latest versions

Required Services

QDrant Vector Database

QDrant Loader requires a QDrant instance to store vectors and metadata.

Option 1: Docker
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
Option 2: QDrant Cloud

Use QDrant Cloud and copy your cluster URL + API key into .env.

Option 3: Local Installation

Use the official QDrant installation guide for your platform.

🤖 Optional LLM extras

When installing from source or customizing environments, ensure provider dependencies are available.

  • OpenAI/Azure/OpenAI-compatible:
pip install "qdrant-loader-core[openai]"
  • Ollama:
pip install "qdrant-loader-core[ollama]"

🔄 Development environment (recommended)

Use this short workflow:

  • Dev adds a new library: uv add <package>
  • Pull latest code: uv sync
  • CI/Prod: uv sync --frozen

Setup commands:

# Initial workspace setup
uv sync --all-packages --all-extras

# Verify installation
uv run qdrant-loader --version
uv run mcp-qdrant-loader --version

When you need a new dependency during development:

uv add fastapi
uv sync

🧠 Virtual environment note

With uv, you normally do not need to manually create or activate a virtual environment. uv sync manages the project environment automatically.

Create and activate your own venv only if your team or tooling explicitly requires manual venv control.

📖 Platform-specific notes

  • Windows: Use PowerShell and activate venv with \.venv\Scripts\Activate.ps1.
  • macOS/Linux: Activate venv with source .venv/bin/activate.
  • Permissions: If global pip install fails, prefer uv workflow or a project virtual environment.

For command-level options (--workspace, --config, --env), see CLI Commands.

Method 3: Virtual Environment (Isolated)

For users who want to keep QDrant Loader isolated:

python -m venv .venv
source .venv/bin/activate
pip install qdrant-loader qdrant-loader-mcp-server

📝 Installation Checklist

  • Python 3.12+ installed and accessible
  • QDrant database running (Docker, Cloud, or local)
  • LLM API key obtained and configured (OpenAI, Azure OpenAI, Ollama, or compatible)
  • qdrant-loader package installed
  • qdrant-loader-mcp-server package installed (if using MCP)
  • qdrant-loader --version works
  • mcp-qdrant-loader --version works (if MCP server installed)
  • Basic configuration created
  • QDrant connection tested
  • You can run qdrant-loader init --workspace . without configuration errors
  • Ready for Quick Start guide

📝 Verification checklist

  • qdrant-loader --version works
  • mcp-qdrant-loader --version works
  • QDrant is reachable at configured URL
  • LLM API key is set

Then continue with Quick Start.

🔧 Install troubleshooting

🔗 Next Steps

After successful installation:

  1. Quick Start Guide - Get up and running in 5 minutes
  2. Core Concepts - Key concepts explained
  3. Basic Configuration - Set up your first data sources
  4. User Guides - Explore detailed feature documentation