QDrant Loader

Name: QDrant Loader
Author: martin-papy

@martin-papy

关于 QDrant Loader

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

#python #openai #developer-tools #knowledge-base #file-conversion #enterprise-ready #semantic-search #multi-project #cli-tool #document-processing #embbedings #git-integration #rag #jira-integration #cursor-ide #llm-integration #mcp-server #confluence-integration

基本信息

分类

数据库

许可证

GPL-3.0

运行时

python

传输方式

stdio

发布者

martin-papy

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

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概览

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-powered development workflows.

How to use QDrant Loader?

Install via pip install qdrant-loader qdrant-loader-mcp-server, initialize a workspace with qdrant-loader init, configure data sources in config.yaml and environment variables in .env, ingest data with qdrant-loader ingest, then start the MCP server using the command mcp-qdrant-loader --env /path/to/.env.

Key features of QDrant Loader

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.

Use cases of QDrant Loader

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.

FAQ from QDrant Loader

What does QDrant Loader integrate with?

QDrant Loader integrates with Qdrant vector database, MCP-compatible tools (Cursor, Windsurf, Claude Desktop), and multiple LLM providers (OpenAI, Azure OpenAI, Ollama, custom endpoints).

What are the dependencies and runtime requirements?

QDrant Loader requires Python, a running Qdrant instance, and an LLM provider endpoint. Environment variables for Qdrant URL, collection name, and LLM API key must be configured in a .env file.

Where does ingested data live?

Data is stored in a Qdrant vector database collection specified by the QDRANT_COLLECTION_NAME environment variable. Configuration files (config.yaml) define source connections and processing settings.

What transports and authentication does the MCP server support?

The MCP server supports stdio and HTTP transport (with SSE for streaming). Authentication is handled via environment variables (e.g., OPENAI_API_KEY) and configuration files, with no built-in auth for the HTTP transport mentioned.

How do I configure QDrant Loader for my project?

Create a config.yaml file with project sources (Git, Confluence, JIRA, etc.) and Qdrant/LLM settings, then define environment variables in a .env file for sensitive values. Use qdrant-loader init --workspace . to generate templates.