grepai-embeddings-ollama

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

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