grepai-embeddings-ollama

SKILL.md

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