rag-code-mcp

⚙️ RagCode Configuration Guide

Complete configuration reference for RagCode MCP server.

📁 Installation Directory

RagCode installs to ~/.local/share/ragcode/ with the following structure:

~/.local/share/ragcode/
├── bin/
│   ├── rag-code-mcp      # Main MCP server binary
│   ├── index-all         # CLI indexing tool
│   └── mcp.log           # Server logs
└── config.yaml           # Main configuration file

📄 Configuration File

Edit ~/.local/share/ragcode/config.yaml to customize RagCode:

llm:
  provider: "ollama"
  base_url: "http://localhost:11434"
  model: "phi3:medium"        # LLM for code analysis
  embed_model: "mxbai-embed-large"  # Embedding model

storage:
  vector_db:
    url: "http://localhost:6333"
    collection_prefix: "ragcode"

workspace:
  auto_index: true
  exclude_patterns:
    - "vendor"
    - "node_modules"
    - ".git"
    - "dist"
    - "build"

logging:
  level: "info"           # debug, info, warn, error
  path: "~/.local/share/ragcode/bin/mcp.log"

🤖 Recommended Models

LLM Models (for code analysis)

Embedding Models

| Model | Size | Dimensions | Use Case | |——-|——|————|———-| | mxbai-embed-large | 670 MB | 1024 | Recommended default | | all-minilm | 45 MB | 384 | Faster, lower quality |

🌍 Environment Variables

Environment variables override config.yaml settings. Set these in your IDE’s MCP configuration:

Variable	Default	Description
`OLLAMA_BASE_URL`	`http://localhost:11434`	Ollama server URL
`OLLAMA_MODEL`	`phi3:medium`	LLM model for code analysis
`OLLAMA_EMBED`	`mxbai-embed-large`	Embedding model
`QDRANT_URL`	`http://localhost:6333`	Qdrant vector database URL
`MCP_LOG_LEVEL`	`info`	Log level (debug, info, warn, error)

Example IDE Configuration

{
  "mcpServers": {
    "ragcode": {
      "command": "/home/YOUR_USERNAME/.local/share/ragcode/bin/rag-code-mcp",
      "args": [],
      "env": {
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_MODEL": "phi3:medium",
        "OLLAMA_EMBED": "mxbai-embed-large",
        "QDRANT_URL": "http://localhost:6333"
      }
    }
  }
}

📊 Logs and Monitoring

Log File Location

Main log: ~/.local/share/ragcode/bin/mcp.log

Watch Logs in Real-Time

tail -f ~/.local/share/ragcode/bin/mcp.log

Docker Container Logs

docker logs ragcode-ollama
docker logs ragcode-qdrant

Log Levels

debug - Verbose output, useful for development
info - Normal operation (default)
warn - Warnings only
error - Errors only

🔧 Installer Options

The ragcode-installer supports various configurations:

Flag	Values	Description
`-ollama`	`docker`, `local`	Where to run Ollama
`-qdrant`	`docker`, `remote`	Where to run Qdrant
`-gpu`	(flag)	Enable GPU acceleration for Ollama
`-models-dir`	path	Mount local Ollama models directory
`-skip-build`	(flag)	Skip binary compilation

Common Scenarios

# Everything in Docker (default, recommended)
./ragcode-installer -ollama=docker -qdrant=docker

# Local Ollama + Docker Qdrant
./ragcode-installer -ollama=local -qdrant=docker

# Docker with GPU acceleration
./ragcode-installer -ollama=docker -qdrant=docker -gpu

# Reuse existing Ollama models
./ragcode-installer -ollama=docker -models-dir=$HOME/.ollama

# Re-configure IDEs only (no rebuild)
./ragcode-installer -skip-build

📦 System Requirements

Minimum Requirements

Component	Requirement	Notes
CPU	4 cores	For running Ollama models
RAM	16 GB	8 GB for `phi3:medium`, 1 GB for `mxbai-embed-large`, 7 GB system
Disk	10 GB free	~8 GB for models + 2 GB for data
OS	Linux, macOS, Windows	Docker required for Qdrant

Recommended Requirements

Component	Requirement	Notes
CPU	8+ cores	Better performance for concurrent operations
RAM	32 GB	Allows comfortable multi‑workspace indexing
GPU	NVIDIA GPU with 8 GB+ VRAM	Significantly speeds up Ollama inference (optional)
Disk	20 GB free SSD	Faster indexing and search

Model Sizes

mxbai-embed-large: ~670 MB
phi3:medium: ~7.9 GB
Total: ~8.6 GB for models

🔄 Incremental Indexing

RagCode features smart incremental indexing that only processes changed files.

How It Works

Tracks file modification times and sizes in .ragcode/state.json
Compares current state with saved state on each run
Only indexes new or modified files
Automatically removes outdated chunks from deleted/modified files

Performance

First run: Indexes all files (e.g., 77 files in ~20 seconds)
No changes: Completes instantly with “No code changes detected”
Single file change: Re-indexes only that file (e.g., 1 file in ~1 second)

For technical details, see incremental_indexing.md.

Quick Start - Get started in 5 minutes
IDE Setup - Manual IDE configuration
Troubleshooting - Common problems and solutions
Architecture - Technical deep dive

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