This skill covers using Ollama as the embedding provider for GrepAI, enabling 100% private, local code search.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongrepai-embeddings-ollamaExecute the skills CLI command in your project's root directory to begin installation:
Fetches grepai-embeddings-ollama from yoanbernabeu/grepai-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate grepai-embeddings-ollama. Access via /grepai-embeddings-ollama in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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This skill covers using Ollama as the embedding provider for GrepAI, enabling 100% private, local code search.
| 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 |
# 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
# .grepai/config.yaml
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://localhost:11434
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://192.168.1.100:11434 # Remote Ollama server
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://localhost:11434
dimensions: 768 # Usually auto-detected
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
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
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
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 | 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 |
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 |
Ollama automatically uses:
Check GPU usage:
ollama ps
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
}'
curl http://localhost:11434/api/tags
ollama list
curl http://localhost:11434/api/embeddings -d '{
"model": "nomic-embed-text",
"prompt": "function authenticate(user, password)"
}'
Ollama app runs automatically on login.
# Enable service
sudo systemctl enable ollama
# Start service
sudo systemctl start ollama
# Check status
sudo systemctl status ollama
nohup ollama serve > /dev/null 2>&1 &
Run Ollama on a powerful server and connect remotely:
# Allow remote connections
OLLAMA_HOST=0.0.0.0 ollama serve
# .grepai/config.yaml
embedder:
provider: ollama
model: nomic-embed-text
endpoint: http://server-ip:11434
❌ 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:
nomic-embed-text)ollama ps)❌ Problem: Out of memory ✅ Solutions:
❌ Problem: Embeddings differ after model update ✅ Solution: Re-index after model updates:
rm .grepai/index.gob
grepai watch
nomic-embed-text: Best balance of speed/qualitySuccessful 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
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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grepai-embeddings-ollama fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for grepai-embeddings-ollama matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend grepai-embeddings-ollama for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
grepai-embeddings-ollama fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: grepai-embeddings-ollama is focused, and the summary matches what you get after install.
Useful defaults in grepai-embeddings-ollama — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added grepai-embeddings-ollama from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added grepai-embeddings-ollama from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: grepai-embeddings-ollama is focused, and the summary matches what you get after install.
We added grepai-embeddings-ollama from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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