Power your agent
Instal dari penerbit. Bukan endpoint PlugMCP.
URL remote atau perintah di mesin Anda? Perbedaan kedua cara instal
Jalankan di PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
BukaDari penerbit
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 for seamless AI assistant integration.
📝 Changelog v1.0.4 - Latest improvements and bug fixes
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:
This monorepo contains three complementary packages:
Data ingestion and processing engine
Collects and vectorizes content from multiple sources into QDrant vector database.
Key Features:
Core library and LLM abstraction layer
Provides the foundational components and provider-agnostic LLM interface used by other packages.
Key Features:
AI development integration layer
Model Context Protocol server providing search capabilities to AI development tools.
Key Features:
# 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
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
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:
Example queries in AI tools:
We welcome contributions! See our Contributing Guide for:
# Clone and setup
git clone https://github.com/martin-papy/qdrant-loader.git
cd qdrant-loader
# Sync workspace environment (recommended)
uv sync --all-packages --all-extras
# Add a new dependency during development
uv add fastapi
uv sync
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Ready to get started? Check out our Quick Start Guide or browse the complete documentation.
Enterprise-ready vector database toolkit for building searchable knowledge bases from multiple data sources. Supports multi-project management, automatic ingestion from Confluence/JIRA/Git, intelligen
Ringkasan umum
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 for seamless AI assistant integration.
Tool
Belum ada daftar tool. Server remote mendapatkannya setelah pemeriksaan kesehatan pertama.
FAQ
Salin konfigurasi stdio untuk Cursor, Claude Desktop, Claude Code, atau VS Code di halaman ini. Konfigurasi ini memakai instalasi dari penerbit, bukan proxy PlugMCP.
Tidak ada variabel lingkungan wajib yang tercantum di catatan ini.
Qdrant Loader berjalan secara lokal melalui stdio.
Terakhir diperiksa 2026-10-27. 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 for seamless AI assistant integration.