donkit-ragops-ce 0.2.22

Community Edition CLI agent for building RAG pipelines

RagOps Agent CE (Community Edition)

An LLM-powered CLI agent that automates the creation and maintenance of Retrieval-Augmented Generation (RAG) pipelines. The agent orchestrates built-in tools and Model Context Protocol (MCP) servers to plan, chunk, and load documents into vector stores.

Built by Donkit AI - Open Source RAG Infrastructure.

Key Features

• Interactive REPL — Start an interactive session with readline history and autocompletion
• Checklist-driven workflow — The agent creates project checklists, asks for approval before each step, and tracks progress
• Multi-language support — Automatically detects and responds in the user's language
• Session-scoped checklists — Only current session checklists appear in the UI
• Integrated MCP servers — Built-in support for planning, chunking, and vector loading
• Docker Compose orchestration — Automated deployment of RAG infrastructure (Qdrant, RAG service)
• Multiple LLM providers — Supports Vertex AI (Recommended), OpenAI, Azure OpenAI, Ollama, OpenRouter. Coming soon: Anthropic Claude

Installation

Option A: Using pip

pip install donkit-ragops-ce

Option B: Using Poetry (Recommended for Python 3.12+)

# Create a new project directory
mkdir ~/ragops-workspace
cd ~/ragops-workspace

# Initialize Poetry project
poetry init --no-interaction --python="^3.12"

# Add donkit-ragops-ce
poetry add donkit-ragops-ce

# Activate the virtual environment
poetry shell

After activation, you can run the agent with:

donkit-ragops-ce

Or run directly without activating the shell:

poetry run donkit-ragops-ce

Quick Start

Prerequisites

• Python 3.12+ installed
• Docker Desktop installed and running (required for vector database)
  • Windows users: Docker Desktop with WSL2 backend is fully supported
• API key for your chosen LLM provider (Vertex AI, OpenAI, or Anthropic)

Step 1: Install the package

pip install donkit-ragops-ce

Step 2: Run the agent (first time)

donkit-ragops-ce

On first run, an interactive setup wizard will guide you through configuration:

1. Choose your LLM provider (Vertex AI, OpenAI, Anthropic, or Ollama)
2. Enter API key or credentials path
3. Optional: Configure log level
4. Configuration is saved to .env file automatically

That's it! No manual .env creation needed - the wizard handles everything.

Alternative: Manual configuration

If you prefer to configure manually or reconfigure later:

# Run setup wizard again
donkit-ragops-ce --setup

Or create a .env file manually in your working directory:

# Vertex AI (Google Cloud)
RAGOPS_LLM_PROVIDER=vertexai
RAGOPS_VERTEX_CREDENTIALS=/path/to/service-account-key.json

# OpenAI
RAGOPS_LLM_PROVIDER=openai
RAGOPS_OPENAI_API_KEY=sk-...
RAGOPS_LLM_MODEL=gpt-4o-mini # Specify the OpenAI model to use
# RAGOPS_OPENAI_BASE_URL=https://api.openai.com/v1

# Anthropic Claude
RAGOPS_LLM_PROVIDER=anthropic
RAGOPS_ANTHROPIC_API_KEY=sk-ant-...

# Ollama (local)
RAGOPS_LLM_PROVIDER=ollama
RAGOPS_OLLAMA_BASE_URL=http://localhost:11434

Step 3: Start using the agent

Tell the agent what you want to build:

you> Create a RAG pipeline for my documents in /Users/myname/Documents/work_docs

The agent will automatically:

• ✅ Create a projects/<project_id>/ directory
• ✅ Plan RAG configuration
• ✅ Process and chunk your documents
• ✅ Start Qdrant vector database (via Docker)
• ✅ Load data into the vector store
• ✅ Deploy RAG query service

What gets created

./
├── .env                          # Your configuration (auto-created by wizard)
└── projects/
    └── my-project-abc123/        # Auto-created by agent
        ├── compose/              # Docker Compose files
        │   ├── docker-compose.yml
        │   └── .env
        ├── chunks/               # Processed document chunks
        └── rag_config.json       # RAG configuration

Usage

Note: The command ragops-agent is also available as an alias for backward compatibility.

The agent starts in interactive REPL mode by default. Use subcommands like ping for specific actions.

Interactive Mode (REPL)

# Start interactive session
donkit-ragops-ce

# With specific provider
donkit-ragops-ce -p vertexai

# With custom model
donkit-ragops-ce -p openai -m gpt-4

Command-line Options

• -p, --provider — Override LLM provider from settings
• -m, --model — Specify model name
• -s, --system — Custom system prompt
• --show-checklist/--no-checklist — Toggle checklist panel (default: shown)
• --mcp-command — Add custom MCP server (can be used multiple times)

Subcommands

# Health check
donkit-ragops-ce ping

Environment Variables

LLM Provider Configuration

• RAGOPS_LLM_PROVIDER — LLM provider name (e.g., openai, vertex, azure_openai, ollama, openrouter)
• RAGOPS_LLM_MODEL — Specify model name (e.g., gpt-4o-mini for OpenAI, gemini-2.5-flash for Vertex)

OpenAI / OpenRouter / Ollama

• RAGOPS_OPENAI_API_KEY — OpenAI API key (also used for OpenRouter and Ollama)
• RAGOPS_OPENAI_BASE_URL — OpenAI base URL (default: https://api.openai.com/v1)
  • OpenRouter: https://openrouter.ai/api/v1
  • Ollama: http://localhost:11434/v1
• RAGOPS_OPENAI_EMBEDDINGS_MODEL — Embedding model name (default: text-embedding-3-small)

Azure OpenAI

• RAGOPS_AZURE_OPENAI_API_KEY — Azure OpenAI API key
• RAGOPS_AZURE_OPENAI_ENDPOINT — Azure OpenAI endpoint URL
• RAGOPS_AZURE_OPENAI_API_VERSION — Azure API version (default: 2025-03-01-preview)
• RAGOPS_AZURE_OPENAI_DEPLOYMENT — Azure deployment name for chat model
• RAGOPS_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT — Azure deployment name for embeddings model

Vertex AI (Google Cloud)

• RAGOPS_VERTEX_CREDENTIALS — Path to Vertex AI service account JSON
• RAGOPS_VERTEX_PROJECT — Google Cloud project ID (optional, extracted from credentials if not set)
• RAGOPS_VERTEX_LOCATION — Vertex AI location (default: us-central1)

Anthropic

• RAGOPS_ANTHROPIC_API_KEY — Anthropic API key

Logging

• RAGOPS_LOG_LEVEL — Logging level (default: INFO)
• RAGOPS_MCP_COMMANDS — Comma-separated list of MCP commands

Agent Workflow

The agent follows a structured workflow:

1. Language Detection — Detects user's language from first message
2. Project Creation — Creates project directory structure
3. Checklist Creation — Generates task checklist in user's language
4. Step-by-Step Execution:
   • Asks for permission before each step
   • Marks item as in_progress
   • Executes the task using appropriate MCP tool
   • Reports results
   • Marks item as completed
5. Deployment — Sets up Docker Compose infrastructure
6. Data Loading — Loads documents into vector store

MCP Servers

RagOps Agent CE includes built-in MCP servers:

ragops-rag-planner

Plans RAG pipeline configuration based on requirements.

# Example usage
donkit-ragops-ce --mcp-command "ragops-rag-planner"

Tools:

• plan_rag_config — Generate RAG configuration from requirements

ragops-chunker

Chunks documents for vector storage.

# Example usage
donkit-ragops-ce --mcp-command "ragops-chunker"

Tools:

• chunk_documents — Split documents into chunks with configurable strategies
• list_chunked_files — List processed chunk files

ragops-vectorstore-loader

Loads chunks into vector databases.

# Example usage
donkit-ragops-ce --mcp-command "ragops-vectorstore-loader"

Tools:

• vectorstore_load — Load documents into Qdrant, Chroma, or Milvus
• delete_from_vectorstore — Remove documents from vector store

ragops-compose-manager

Manages Docker Compose infrastructure.

# Example usage
donkit-ragops-ce --mcp-command "ragops-compose-manager"

Tools:

• init_project_compose — Initialize Docker Compose for project
• compose_up — Start services
• compose_down — Stop services
• compose_status — Check service status
• compose_logs — View service logs

ragops-checklist

Manages project checklists and progress tracking.

Tools:

• create_checklist — Create new checklist
• get_checklist — Get current checklist
• update_checklist_item — Update item status

Examples

Basic RAG Pipeline

donkit-ragops-ce

you> Create a RAG pipeline for customer support docs in ./docs folder

The agent will:

1. Create project structure
2. Plan RAG configuration
3. Chunk documents from ./docs
4. Set up Qdrant + RAG service
5. Load data into vector store

Custom Configuration

donkit-ragops-ce -p vertexai -m gemini-1.5-pro

you> Build RAG for legal documents with 1000 token chunks and reranking

Multiple Projects

Each project gets its own:

• Project directory (projects/<project_id>)
• Docker Compose setup
• Vector store collection
• Configuration

Development

Project Structure

donkit-ragops-ce/
├── src/ragops_agent_ce/
│   ├── agent/          # LLM agent core
│   ├── llm/            # LLM provider integrations
│   ├── mcp/            # MCP servers and client
│   │   └── servers/    # Built-in MCP servers
│   ├── cli.py          # CLI commands
│   └── config.py       # Configuration
├── tests/
└── pyproject.toml

Running Tests

poetry run pytest

Code Quality

# Format code
poetry run ruff format .

# Lint code
poetry run ruff check .

Docker Compose Services

The agent can deploy these services:

Qdrant (Vector Database)

services:
  qdrant:
    image: qdrant/qdrant:latest
    ports:
      - "6333:6333"
      - "6334:6334"

RAG Service

services:
  rag-service:
    image: donkit/rag-service:latest
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URI=http://qdrant:6333
      - CONFIG=<base64-encoded-config>

Architecture

┌─────────────────┐
│  RagOps Agent   │
│     (CLI)       │
└────────┬────────┘
         │
         ├── MCP Servers ───────────────┐
         │   ├── ragops-rag-planner     │
         │   ├── ragops-chunker         │
         │   ├── ragops-vectorstore     │
         │   └── ragops-compose         │
         │                              │
         └── LLM Providers ─────────────┤
             ├── Vertex AI              │
             ├── OpenAI                 │
             ├── Anthropic              │
             └── Ollama                 │
                                        │
                                        ▼
                            ┌──────────────────┐
                            │ Docker Compose   │
                            ├──────────────────┤
                            │ • Qdrant         │
                            │ • RAG Service    │
                            └──────────────────┘

Troubleshooting

Windows + Docker Desktop with WSL2

The agent fully supports Windows with Docker Desktop running in WSL2 mode. Path conversion and Docker communication are handled automatically.

Requirements:

• Docker Desktop for Windows with WSL2 backend enabled
• Python 3.12+ installed on Windows (not inside WSL2)
• Run the agent from Windows PowerShell or Command Prompt

How it works:

• The agent detects WSL2 Docker automatically
• Windows paths like C:\Users\... are converted to /mnt/c/Users/... for Docker
• No manual configuration needed

Troubleshooting:

# 1. Verify Docker is accessible from Windows
docker info

# 2. Check Docker reports Linux (indicates WSL2)
docker info --format "{{.OperatingSystem}}"
# Should output: Docker Desktop (or similar with "linux")

# 3. If Docker commands fail, ensure Docker Desktop is running

MCP Server Connection Issues

If MCP servers fail to start:

# Check MCP server logs
RAGOPS_LOG_LEVEL=DEBUG donkit-ragops-ce

Vector Store Connection

Ensure Docker services are running:

cd projects/<project_id>
docker-compose ps
docker-compose logs qdrant

Credentials Issues

Verify your credentials:

# Vertex AI
gcloud auth application-default print-access-token

# OpenAI
echo $RAGOPS_OPENAI_API_KEY

License

This project is licensed under the MIT License - see the LICENSE file for details.

Related Projects

• donkit-chunker — Document chunking library
• donkit-vectorstore-loader — Vector store loading utilities
• donkit-read-engine — Document parsing engine

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Built with ❤️ by Donkit AI