📋 Release Notes v0.7.4 - Latest improvements and bug fixes

A comprehensive toolkit for loading data into Qdrant vector database with advanced MCP server support for AI-powered development workflows.

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

This monorepo contains three complementary packages:

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

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

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

# 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

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)

   # 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

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

Add to your Cursor settings (.cursor/mcp.json):

{
  "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"
      ]
    }
  }
}

Example queries in Cursor:

• "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"

• Installation Guide - Complete setup instructions
• Quick Start - Step-by-step tutorial
• Core Concepts - Covered inline in Getting Started

• Configuration - Complete configuration reference
• Data Sources - Git, Confluence, JIRA setup
• File Conversion - File processing capabilities
• MCP Server - AI tool integration

• New unified configuration: global.llm.* replaces legacy global.embedding.* and file_conversion.markitdown.*
• Provider-agnostic: Now supports OpenAI, Azure OpenAI, Ollama, and custom endpoints
• Legacy support: Old configuration still works but shows deprecation warnings
• Action required: Update your config.yaml to use the new syntax (see examples above)

• Configuration File Reference - Complete new schema
• Environment Variables - Updated variable names

• Architecture - System design overview
• Testing - Testing guide and best practices
• Contributing - Development setup and guidelines

We welcome contributions! See our Contributing Guide for:

• Development environment setup
• Code style and standards
• Pull request process

# Clone and setup
git clone https://github.com/martin-papy/qdrant-loader.git
cd qdrant-loader
python -m venv venv
source venv/bin/activate

# Install packages in development mode
pip install -e ".[dev]"
pip install -e "packages/qdrant-loader-core[dev,openai,ollama]"
pip install -e "packages/qdrant-loader[dev]"
pip install -e "packages/qdrant-loader-mcp-server[dev]"

This project is licensed under the GNU GPLv3 - see the LICENSE file for details.

Ready to get started? Check out our Quick Start Guide or browse the complete documentation.