grepai-ollama-setup

Imported from yoanbernabeu/grepai-skills (skills/getting-started/grepai-ollama-setup/SKILL.md) via skills.sh. Install upstream with npx skills add yoanbernabeu/grepai-skills --skill grepai-ollama-setup. Copyright stays with the author.

Ollama Setup for GrepAI

This skill covers installing and configuring Ollama as the local embedding provider for GrepAI. Ollama enables 100% private code search where your code never leaves your machine.

When to Use This Skill

• Setting up GrepAI with local, private embeddings
• Installing Ollama for the first time
• Choosing and downloading embedding models
• Troubleshooting Ollama connection issues

Why Ollama?

Installation

macOS (Homebrew)

# Install Ollama
brew install ollama

# Start the Ollama service
ollama serve

macOS (Direct Download)

1. Download from ollama.com
2. Open the .dmg and drag to Applications
3. Launch Ollama from Applications

Linux

# One-line installer
curl -fsSL https://ollama.com/install.sh | sh

# Start the service
ollama serve

Windows

1. Download installer from ollama.com
2. Run the installer
3. Ollama starts automatically as a service

Downloading Embedding Models

GrepAI requires an embedding model to convert code into vectors.

Recommended Model: nomic-embed-text

# Download the recommended model (768 dimensions)
ollama pull nomic-embed-text

Specifications:

• Dimensions: 768
• Size: ~274 MB
• Performance: Excellent for code search
• Language: English-optimized

Alternative Models

# Multilingual support (better for non-English code/comments)
ollama pull nomic-embed-text-v2-moe

# Larger, more accurate
ollama pull bge-m3

# Maximum quality
ollama pull mxbai-embed-large

Verifying Installation

Check Ollama is Running

# Check if Ollama server is responding
curl http://localhost:11434/api/tags

# Expected output: JSON with available models

List Downloaded Models

ollama list

# Output:
# NAME                     ID           SIZE    MODIFIED
# nomic-embed-text:latest  abc123...    274 MB  2 hours ago

Test Embedding Generation

# Quick test (should return embedding vector)
curl http://localhost:11434/api/embeddings -d '{
  "model": "nomic-embed-text",
  "prompt": "function hello() { return world; }"
}'

Configuring GrepAI for Ollama

After installing Ollama, configure GrepAI to use it:

# .grepai/config.yaml
embedder:
  provider: ollama
  model: nomic-embed-text
  endpoint: http://localhost:11434

This is the default configuration when you run grepai init, so no changes are needed if using nomic-embed-text.

Running Ollama

Foreground (Development)

# Run in current terminal (see logs)
ollama serve

Background (macOS/Linux)

# Using nohup
nohup ollama serve &

# Or as a systemd service (Linux)
sudo systemctl enable ollama
sudo systemctl start ollama

Check Status

# Check if running
pgrep -f ollama

# Or test the API
curl -s http://localhost:11434/api/tags | head -1

Resource Considerations

Memory Usage

Embedding models load into RAM:

• nomic-embed-text: ~500 MB RAM
• bge-m3: ~1.5 GB RAM
• mxbai-embed-large: ~1 GB RAM

CPU vs GPU

Ollama uses CPU by default. For faster embeddings:

• macOS: Uses Metal (Apple Silicon) automatically
• Linux/Windows: Install CUDA for NVIDIA GPU support

Common Issues

❌ Problem: connection refused to localhost:11434 ✅ Solution: Start Ollama:

ollama serve

❌ Problem: Model not found ✅ Solution: Pull the model first:

ollama pull nomic-embed-text

❌ Problem: Slow embedding generation ✅ Solution:

• Use a smaller model
• Ensure Ollama is using GPU (check ollama ps)
• Close other memory-intensive applications

❌ Problem: Out of memory ✅ Solution: Use a smaller model or increase system RAM

Best Practices

1. Start Ollama before GrepAI: Ensure ollama serve is running
2. Use recommended model: nomic-embed-text offers best balance
3. Keep Ollama running: Leave it as a background service
4. Update periodically: ollama pull nomic-embed-text for updates

Output Format

After successful setup:

✅ Ollama Setup Complete

   Ollama Version: 0.1.x
   Endpoint: http://localhost:11434
   Model: nomic-embed-text (768 dimensions)
   Status: Running

   GrepAI is ready to use with local embeddings.
   Your code will never leave your machine.