Power your agent
การติดตั้งจากผู้เผยแพร่ ไม่ใช่เอนด์พอยต์ของ PlugMCP
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน 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.
เปิดจากผู้เผยแพร่
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
ภาพรวม
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.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Qdrant Loader ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 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.