donkit-ragops 0.4.7

CLI agent for building RAG pipelines

RAGOps Agent

Optimal RAG in hours, not months.

An LLM-powered CLI agent that automates the creation, experimentation, and deployment of Retrieval-Augmented Generation (RAG) pipelines. Instead of spending months manually tuning indexes, chunking strategies, embeddings, and evaluators — let the agent explore the design space, run experiments, and converge on what works for your data.

Built by Donkit AI — Automated Context Engineering.

Who is this for?

• AI Engineers building internal assistants and agents
• Teams needing accuracy-sensitive RAG where errors compound across steps
• Organizations running agent-dependent systems requiring trustworthy retrieval

Think of RAG like a library—with indexes, storage, and a librarian. Donkit is the library factory.

Key Features

• Interactive REPL — Start an interactive session with readline history and autocompletion
• Web UI — Browser-based interface at http://localhost:8067 (donkit-ragops-web)
• Checklist-driven workflow — The agent creates project checklists, asks for approval before each step, and tracks progress
• Session-scoped checklists — Only current session checklists appear in the UI
• Integrated MCP servers — Built-in support for full RAG build pipeline (planning, reading, chunking, vector loading, querying, evaluation)
• Docker Compose orchestration — Automated deployment of RAG infrastructure (vector DB, RAG service)
• Enterprise mode — Connect to Donkit cloud for experiments
• Multiple LLM providers — Supports Vertex AI (Recommended), OpenAI, Anthropic Claude, Azure OpenAI, Ollama, OpenRouter

Quick Install

The fastest way to install Donkit RAGOps. The installer automatically handles Python and dependencies.

macOS / Linux:

curl -sSL https://raw.githubusercontent.com/donkit-ai/ragops/main/scripts/install.sh | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/donkit-ai/ragops/main/scripts/install.ps1 | iex

After installation:

donkit-ragops        # Start CLI agent
donkit-ragops-web    # Start Web UI at http://localhost:8067

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Installation (Alternative Methods)

Option A: Using pipx (Recommended)

# Install pipx if you don't have it
pip install pipx
pipx ensurepath

# Install donkit-ragops
pipx install donkit-ragops

Option B: Using pip

pip install donkit-ragops

Option C: Using Poetry (for development)

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

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

# Add donkit-ragops
poetry add donkit-ragops

# Activate the virtual environment
poetry shell

After activation, you can run the agent with:

donkit-ragops

Or run directly without activating the shell:

poetry run donkit-ragops

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

Step 2: Run the agent (first time)

donkit-ragops

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 --setup

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

# Vertex AI (Google Cloud)
RAGOPS_LLM_PROVIDER=vertex
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

# With specific provider
donkit-ragops -p vertexai

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

# Start in enterprise mode (requires login first)
donkit-ragops --enterprise

REPL Commands

Inside the interactive session, use these commands:

• /help, /h, /? — Show available commands
• /exit, /quit, /q — Exit the agent
• /clear — Clear conversation history and screen
• /provider — Switch LLM provider interactively
• /model — Switch LLM model interactively

Command-line Options

• -p, --provider — Override LLM provider from settings
• -m, --model — Specify model name
• -s, --system — Custom system prompt
• --local — Force local mode (default)
• --enterprise — Force enterprise mode (requires login)
• --setup — Run setup wizard to reconfigure
• --show-checklist/--no-checklist — Toggle checklist panel (default: shown)

Subcommands

# Health check
donkit-ragops ping

# Enterprise mode authentication
donkit-ragops login --token YOUR_TOKEN  # Login to Donkit cloud
donkit-ragops logout                    # Remove stored token
donkit-ragops status                    # Show mode and auth status

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: 2024-02-15-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

Web UI

RAGOps includes a browser-based interface for easier interaction:

# Start Web UI server
donkit-ragops-web

Open http://localhost:8067 in your browser. The Web UI provides:

• Visual project management
• File upload and attachment
• Real-time agent responses
• Checklist visualization
• Settings configuration

Enterprise Mode (SaaS)

Enterprise mode is a fully managed SaaS platform. All backend infrastructure — databases, vector stores, RAG services, and experiment runners — is hosted by Donkit. You get the same CLI interface, but with powerful cloud features.

Setup

# 1. Login with your API token
donkit-ragops login --token YOUR_API_TOKEN

# 2. Start in enterprise mode
donkit-ragops --enterprise

# 3. Check status
donkit-ragops status

# 4. Logout when done
donkit-ragops logout

What's Included

• Managed infrastructure — No Docker, no local setup. Everything runs in Donkit cloud
• Automated experiments — Run 100+ RAG architecture iterations to find optimal configuration
• Experiment tracking — Compare chunking strategies, embeddings, retrievers side-by-side
• Evaluation pipelines — Batch evaluation with precision/recall/accuracy metrics
• File attachments — Attach files using @/path/to/file syntax in chat
• Persistent history — Conversation and project history preserved across sessions
• MCP over HTTP — All MCP tools executed server-side

Local vs Enterprise

Feature	Local Mode	Enterprise Mode
Infrastructure	Self-hosted (Docker)	Managed by Donkit
Vector stores	Local Qdrant/Milvus/Chroma	Cloud-hosted
Experiments	Manual	Automated iterations
Evaluation	Basic	Full pipeline with metrics
Data persistence	Local files	Cloud database

MCP Servers

RAGOps Agent CE includes built-in MCP servers:

ragops-rag-planner

Plans RAG pipeline configuration based on requirements.

Tools:

• plan_rag_config — Generate RAG configuration from requirements

ragops-read-engine

Processes and converts documents from various formats.

Tools:

• process_documents — Convert PDF, DOCX, PPTX, XLSX, images to text/json/markdown

ragops-chunker

Chunks documents for vector storage.

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.

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.

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-rag-query

Executes RAG queries against deployed services.

Tools:

• search_documents — Search for relevant documents in vector database
• get_rag_prompt — Get formatted RAG prompt with retrieved context

rag-evaluation

Evaluates RAG pipeline performance with batch processing.

Tools:

• evaluate_batch — Run batch evaluation from CSV/JSON, compute Precision/Recall/Accuracy

donkit-ragops-mcp

Unified MCP server that combines all servers above into a single endpoint.

# Run unified server
donkit-ragops-mcp

Claude Desktop configuration:

{
  "mcpServers": {
    "donkit-ragops-mcp": {
      "command": "donkit-ragops-mcp"
    }
  }
}

All tools are available with prefixes:

• chunker_* — Document chunking
• compose_* — Docker Compose orchestration
• evaluation_* — RAG evaluation
• planner_* — RAG configuration planning
• query_* — RAG query execution
• reader_* — Document reading/parsing
• vectorstore_* — Vector store operations

Note: Checklist management is now handled by built-in agent tools, not MCP.

Examples

Basic RAG Pipeline

donkit-ragops

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 -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

Prerequisites

• Python 3.12+
• Poetry for dependency management
• Docker Desktop (for testing vector stores and RAG services)

Setup

# Clone the repository
git clone https://github.com/donkit-ai/ragops.git
cd ragops/ragops-agent-cli

# Install dependencies
poetry install

# Activate virtual environment
poetry shell

Project Structure

ragops-agent-cli/
├── src/donkit_ragops/
│   ├── agent/              # LLM agent core and local tools
│   │   ├── agent.py        # Main LLMAgent class
│   │   ├── prompts.py      # System prompts for different providers
│   │   └── local_tools/    # Built-in agent tools
│   ├── llm/                # LLM provider integrations
│   │   └── providers/      # OpenAI, Vertex, Anthropic, etc.
│   ├── mcp/                # Model Context Protocol
│   │   ├── client.py       # MCP client implementation
│   │   └── servers/        # Built-in MCP servers
│   ├── repl/               # REPL implementation
│   │   ├── base.py         # Base REPL context
│   │   ├── local_repl.py   # Local mode REPL
│   │   └── enterprise_repl.py  # Enterprise mode REPL
│   ├── web/                # Web UI (FastAPI + WebSocket)
│   │   ├── app.py          # FastAPI application
│   │   └── routes/         # API endpoints
│   ├── enterprise/         # Enterprise mode components
│   ├── cli.py              # CLI entry point (Typer)
│   └── config.py           # Configuration management
├── tests/                  # Test suite (170+ tests)
└── pyproject.toml          # Poetry project configuration

Running the CLI Locally

# Run CLI
poetry run donkit-ragops

# Run with specific provider
poetry run donkit-ragops -p openai -m gpt-4o

# Run Web UI
poetry run donkit-ragops-web

# Run unified MCP server
poetry run donkit-ragops-mcp

Running Tests

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=donkit_ragops

# Run specific test file
poetry run pytest tests/test_agent.py

# Run specific test
poetry run pytest tests/test_agent.py::test_function_name -v

Code Quality

# Format code (REQUIRED before commit)
poetry run ruff format .

# Lint and auto-fix (REQUIRED before commit)
poetry run ruff check . --fix

# Check without fixing
poetry run ruff check .

Version Management

IMPORTANT: Version must be incremented in pyproject.toml for every PR:

# Check current version
grep "^version" pyproject.toml

# Increment version in pyproject.toml before committing
# patch: 0.4.5 → 0.4.6 (bug fixes)
# minor: 0.4.5 → 0.5.0 (new features)
# major: 0.4.5 → 1.0.0 (breaking changes)

Adding a New MCP Server

Step 1. Create server file in src/donkit_ragops/mcp/servers/:

from fastmcp import FastMCP
from pydantic import BaseModel, Field

server = FastMCP("my-server")

class MyToolArgs(BaseModel):
    param: str = Field(description="Parameter description")

@server.tool(name="my_tool", description="What the tool does")
async def my_tool(args: MyToolArgs) -> str:
    # Implementation
    return "result"

def main() -> None:
    server.run(transport="stdio")

Step 2. Add entry point in pyproject.toml:

[tool.poetry.scripts]
ragops-my-server = "donkit_ragops.mcp.servers.my_server:main"

Step 3. Mount in unified server (donkit_ragops_mcp.py):

from .my_server import server as my_server
unified_server.mount(my_server, prefix="my")

Adding a New LLM Provider

1. Create provider in src/donkit_ragops/llm/providers/
2. Register in provider_factory.py
3. Add configuration to config.py
4. Update supported_models.py

Debugging

# Enable debug logging
RAGOPS_LOG_LEVEL=DEBUG poetry run donkit-ragops

# Debug MCP servers
RAGOPS_LOG_LEVEL=DEBUG poetry run donkit-ragops-mcp

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

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