grepai-embeddings-ollama
Configure Ollama as embedding provider for GrepAI. Use this skill for local, private 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-embeddings-ollama- 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-embeddings-ollama 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.
GrepAI Embeddings with Ollama
This skill covers using Ollama as the embedding provider for GrepAI, enabling 100% private, local code search.
When to Use This Skill
- Setting up private, local embeddings
- Choosing the right Ollama model
- Optimizing Ollama performance
- Troubleshooting Ollama connection issues
Why Ollama?
| Advantage | Description |
|---|---|
| 🔒 Privacy | Code never leaves your machine |
| 💰 Free | No API costs or usage limits |
| ⚡ Speed | No network latency |
| 🔌 Offline | Works without internet |
| 🔧 Control | Choose your model |
Prerequisites
- Ollama installed and running
- An embedding model downloaded
# Install Ollama
brew install ollama # macOS
# or
curl -fsSL https://ollama.com/install.sh | sh # Linux
# Start Ollama
ollama serve
# Download model
ollama pull nomic-embed-text
Configuration
Basic Configuration
# .grepai/config.yaml
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://localhost:11434
With Custom Endpoint
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://192.168.1.100:11434 # Remote Ollama server
With Explicit Dimensions
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://localhost:11434
dimensions: 768 # Usually auto-detected
Available Models
Recommended: nomic-embed-text
ollama pull nomic-embed-text
| Property | Value |
|---|---|
| Dimensions | 768 |
| Size | ~274 MB |
| Speed | Fast |
| Quality | Excellent for code |
| Language | English-optimized |
Configuration:
embedder:
provider: ollama
model: nomic-embed-text
Multilingual: nomic-embed-text-v2-moe
ollama pull nomic-embed-text-v2-moe
| Property | Value |
|---|---|
| Dimensions | 768 |
| Size | ~500 MB |
| Speed | Medium |
| Quality | Excellent |
| Language | Multilingual |
Best for codebases with non-English comments/documentation.
Configuration:
embedder:
provider: ollama
model: nomic-embed-text-v2-moe
High Quality: bge-m3
ollama pull bge-m3
| Property | Value |
|---|---|
| Dimensions | 1024 |
| Size | ~1.2 GB |
| Speed | Slower |
| Quality | Very high |
| Language | Multilingual |
Best for large, complex codebases where accuracy is critical.
Configuration:
embedder:
provider: ollama
model: bge-m3
dimensions: 1024
Maximum Quality: mxbai-embed-large
ollama pull mxbai-embed-large
| Property | Value |
|---|---|
| Dimensions | 1024 |
| Size | ~670 MB |
| Speed | Medium |
| Quality | Highest |
| Language | English |
Configuration:
embedder:
provider: ollama
model: mxbai-embed-large
dimensions: 1024
Model Comparison
| Model | Dims | Size | Speed | Quality | Use Case |
|---|---|---|---|---|---|
nomic-embed-text | 768 | 274MB | ⚡⚡⚡ | ⭐⭐⭐ | General use |
nomic-embed-text-v2-moe | 768 | 500MB | ⚡⚡ | ⭐⭐⭐⭐ | Multilingual |
bge-m3 | 1024 | 1.2GB | ⚡ | ⭐⭐⭐⭐⭐ | Large codebases |
mxbai-embed-large | 1024 | 670MB | ⚡⚡ | ⭐⭐⭐⭐⭐ | Maximum accuracy |
Performance Optimization
Memory Management
Models load into RAM. Ensure sufficient memory:
| Model | RAM Required |
|---|---|
nomic-embed-text | ~500 MB |
nomic-embed-text-v2-moe | ~800 MB |
bge-m3 | ~1.5 GB |
mxbai-embed-large | ~1 GB |
GPU Acceleration
Ollama automatically uses:
- macOS: Metal (Apple Silicon)
- Linux/Windows: CUDA (NVIDIA GPUs)
Check GPU usage:
ollama ps
Keeping Model Loaded
By default, Ollama unloads models after 5 minutes of inactivity. Keep loaded:
# Keep model loaded indefinitely
curl http://localhost:11434/api/generate -d '{
"model": "nomic-embed-text",
"keep_alive": -1
}'
Verifying Connection
Check Ollama is Running
curl http://localhost:11434/api/tags
List Available Models
ollama list
Test Embedding
curl http://localhost:11434/api/embeddings -d '{
"model": "nomic-embed-text",
"prompt": "function authenticate(user, password)"
}'
Running Ollama as a Service
macOS (launchd)
Ollama app runs automatically on login.
Linux (systemd)
# Enable service
sudo systemctl enable ollama
# Start service
sudo systemctl start ollama
# Check status
sudo systemctl status ollama
Manual Background
nohup ollama serve > /dev/null 2>&1 &
Remote Ollama Server
Run Ollama on a powerful server and connect remotely:
On the Server
# Allow remote connections
OLLAMA_HOST=0.0.0.0 ollama serve
On the Client
# .grepai/config.yaml
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://server-ip:11434
Common Issues
❌ Problem: Connection refused ✅ Solution:
# Start Ollama
ollama serve
❌ Problem: Model not found ✅ Solution:
# Pull the model
ollama pull nomic-embed-text
❌ Problem: Slow embedding generation ✅ Solutions:
- Use a smaller model (
nomic-embed-text) - Ensure GPU is being used (
ollama ps) - Close memory-intensive applications
- Consider a remote server with better hardware
❌ Problem: Out of memory ✅ Solutions:
- Use a smaller model
- Close other applications
- Upgrade RAM
- Use remote Ollama server
❌ Problem: Embeddings differ after model update ✅ Solution: Re-index after model updates:
rm .grepai/index.gob
grepai watch
Best Practices
- Start with
nomic-embed-text: Best balance of speed/quality - Keep Ollama running: Background service recommended
- Match dimensions: Don't mix models with different dimensions
- Re-index on model change: Delete index and re-run watch
- Monitor memory: Embedding models use significant RAM
Output Format
Successful Ollama configuration:
✅ Ollama Embedding Provider Configured
Provider: Ollama
Model: nomic-embed-text
Endpoint: http://localhost:11434
Dimensions: 768 (auto-detected)
Status: Connected
Model Info:
- Size: 274 MB
- Loaded: Yes
- GPU: Apple Metal
FAQ
What does the grepai-embeddings-ollama skill do?
Configure Ollama as embedding provider for GrepAI. Use this skill for local, private embedding generation.
How do I install grepai-embeddings-ollama?
Run npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-ollama 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-embeddings-ollama/ (personal) or .claude/skills/grepai-embeddings-ollama/ (project).
Which AI agents can use grepai-embeddings-ollama?
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-embeddings-ollama 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-embeddings-ollama?
It is published by yoanbernabeu in yoanbernabeu/grepai-skills. It has 744 recorded installs.
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