
grepai-ollama-setup
Install and configure Ollama for local embeddings with GrepAI. Use this skill when setting up private, local embedding generation.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-ollama-setup- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads grepai-ollama-setup automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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?
| Benefit | Description |
|---|---|
| 🔒 Privacy | Code never leaves your machine |
| 💰 Free | No API costs |
| ⚡ Fast | Local processing, no network latency |
| 🔌 Offline | Works without internet |
Installation
macOS (Homebrew)
# Install Ollama
brew install ollama
# Start the Ollama service
ollama serve
macOS (Direct Download)
- Download from ollama.com
- Open the
.dmgand drag to Applications - Launch Ollama from Applications
Linux
# One-line installer
curl -fsSL https://ollama.com/install.sh | sh
# Start the service
ollama serve
Windows
- Download installer from ollama.com
- Run the installer
- 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
| Model | Dimensions | Size | Best For |
|---|---|---|---|
nomic-embed-text | 768 | 274 MB | General code search |
nomic-embed-text-v2-moe | 768 | 500 MB | Multilingual codebases |
bge-m3 | 1024 | 1.2 GB | Large codebases |
mxbai-embed-large | 1024 | 670 MB | Maximum accuracy |
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 RAMbge-m3: ~1.5 GB RAMmxbai-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
- Start Ollama before GrepAI: Ensure
ollama serveis running - Use recommended model:
nomic-embed-textoffers best balance - Keep Ollama running: Leave it as a background service
- Update periodically:
ollama pull nomic-embed-textfor 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.
FAQ
What does the grepai-ollama-setup skill do?
Install and configure Ollama for local embeddings with GrepAI. Use this skill when setting up private, local embedding generation.
How do I install grepai-ollama-setup?
Run npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-ollama-setup in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/grepai-ollama-setup/ (personal) or .claude/skills/grepai-ollama-setup/ (project).
Which AI agents can use grepai-ollama-setup?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is grepai-ollama-setup safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains grepai-ollama-setup?
It is published by yoanbernabeu in yoanbernabeu/grepai-skills. It has 799 recorded installs.
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