QDrant Loader

by martin-papy

45 573 downloads Not rated yet GPL-3.0

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

License: GPL-3.0

Explore

- 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 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.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "qdrant loader": {
            "qdrant-loader": {
                "command": "uv",
                "args": [
                    "sync",
                    "--all-packages",
                    "--all-extras"
                ]
            }
        }
    }
}

From the repository

pip install qdrant-loader qdrant-loader-mcp-server

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

1. Create a workspace

   mkdir my-workspace && cd my-workspace

2. Initialize workspace with templates

   qdrant-loader init --workspace .

3. Configure your environment (edit .env)


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

4. 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"]

5. Load your data

   qdrant-loader ingest --workspace .

6. Start the MCP server

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

QDrant Loader works with any IDE/tool that supports MCP, including Cursor, Windsurf, and Claude Desktop.

Minimal MCP server entry (adapt path/format to your tool):

{
  "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"
      ]
    }
  }
}

For tool-specific setup and exact config format:

- MCP Setup and Integration - Full guide
- Cursor Setup
- Windsurf Setup
- Claude Desktop Setup

Example queries in AI tools:

- _"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"_

```bash

git clone https://github.com/martin-papy/qdrant-loader.git
cd qdrant-loader

uv sync --all-packages --all-extras

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"
        ]
    }
}

📝 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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About

Details

Explore

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

🎯 What is QDrant Loader?

📦 Packages

🔄 QDrant Loader

⚙️ QDrant Loader Core

🔌 QDrant Loader MCP Server

🚀 Quick Start

Installation

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