nomyo-router/doc/usage.md

Usage Guide

Quick Start

1. Install the Router

git clone https://github.com/nomyo-ai/nomyo-router.git
cd nomyo-router
python3 -m venv .venv/router
source .venv/router/bin/activate
pip3 install -r requirements.txt

2. Configure Endpoints

Edit config.yaml:

endpoints:
  - http://localhost:11434

max_concurrent_connections: 2

3. Run the Router

uvicorn router:app --host 0.0.0.0 --port 12434

4. Use the Router

Configure your frontend to point to http://localhost:12434 instead of your Ollama instance.

API Endpoints

Ollama-Compatible Endpoints

The router provides all standard Ollama API endpoints:

Endpoint	Method	Description
/api/generate	POST	Generate text
/api/chat	POST	Chat completions
/api/embed	POST	Embeddings
/api/tags	GET	List available models
/api/ps	GET	List loaded models
/api/show	POST	Show model details
/api/pull	POST	Pull a model
/api/push	POST	Push a model
/api/create	POST	Create a model
/api/copy	POST	Copy a model
/api/delete	DELETE	Delete a model

OpenAI-Compatible Endpoints

For OpenAI API compatibility:

Endpoint	Method	Description
/v1/chat/completions	POST	Chat completions
/v1/completions	POST	Text completions
/v1/embeddings	POST	Embeddings
/v1/models	GET	List models

Monitoring Endpoints

Endpoint	Method	Description
/api/usage	GET	Current connection counts
/api/token_counts	GET	Token usage statistics
/api/stats	POST	Detailed model statistics
/api/aggregate_time_series_days	POST	Aggregate time series data into daily
/api/version	GET	Ollama version info
/api/config	GET	Endpoint configuration
/api/usage-stream	GET	Real-time usage updates (SSE)
/health	GET	Health check

Making Requests

Basic Chat Request

curl http://localhost:12434/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3:latest",
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ],
    "stream": false
  }'

Streaming Response

curl http://localhost:12434/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3:latest",
    "messages": [
      {"role": "user", "content": "Tell me a story"}
    ],
    "stream": true
  }'

OpenAI API Format

curl http://localhost:12434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-nano",
    "messages": [
      {"role": "user", "content": "Hello"}
    ]
  }'

Advanced Features

Multiple Opinions Ensemble (MOE)

Prefix your model name with moe- to enable the MOE system:

curl http://localhost:12434/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moe-llama3:latest",
    "messages": [
      {"role": "user", "content": "Explain quantum computing"}
    ]
  }'

The MOE system:

1. Generates 3 responses from different endpoints
2. Generates 3 critiques of those responses
3. Selects the best response
4. Generates a final refined response

Token Tracking

The router automatically tracks token usage:

curl http://localhost:12434/api/token_counts

Response:

{
  "total_tokens": 1542,
  "breakdown": [
    {
      "endpoint": "http://localhost:11434",
      "model": "llama3",
      "input_tokens": 120,
      "output_tokens": 1422,
      "total_tokens": 1542
    }
  ]
}

Real-time Monitoring

Use Server-Sent Events to monitor usage in real-time:

curl http://localhost:12434/api/usage-stream

Integration Examples

Python Client

import requests

url = "http://localhost:12434/api/chat"
data = {
    "model": "llama3",
    "messages": [{"role": "user", "content": "What is AI?"}],
    "stream": False
}

response = requests.post(url, json=data)
print(response.json())

JavaScript Client

const response = await fetch('http://localhost:12434/api/chat', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'llama3',
    messages: [{ role: 'user', content: 'Hello!' }],
    stream: false
  })
});

const data = await response.json();
console.log(data);

Streaming with JavaScript

const eventSource = new EventSource('http://localhost:12434/api/usage-stream');

eventSource.onmessage = (event) => {
  const usage = JSON.parse(event.data);
  console.log('Current usage:', usage);
};

Python Ollama Client

from ollama import Client

# Configure the client to use the router
client = Client(host='http://localhost:12434')

# Chat with a model
response = client.chat(
    model='llama3:latest',
    messages=[
        {'role': 'user', 'content': 'Explain quantum computing'}
    ]
)
print(response['message']['content'])

# Generate text
response = client.generate(
    model='llama3:latest',
    prompt='Write a short poem about AI'
)
print(response['response'])

# List available models
models = client.list()['models']
print(f"Available models: {[m['name'] for m in models]}")

Python OpenAI Client

from openai import OpenAI

# Configure the client to use the router
client = OpenAI(
    base_url='http://localhost:12434/v1',
    api_key='not-needed'  # API key is not required for local usage
)

# Chat completions
response = client.chat.completions.create(
    model='gpt-4o-nano',
    messages=[
        {'role': 'user', 'content': 'What is the meaning of life?'}
    ]
)
print(response.choices[0].message.content)

# Text completions
response = client.completions.create(
    model='llama3:latest',
    prompt='Once upon a time'
)
print(response.choices[0].text)

# Embeddings
response = client.embeddings.create(
    model='llama3:latest',
    input='The quick brown fox jumps over the lazy dog'
)
print(f"Embedding length: {len(response.data[0].embedding)}")

# List models
response = client.models.list()
print(f"Available models: {[m.id for m in response.data]}")

Best Practices

1. Model Selection

• Use the same model name across all endpoints
• For Ollama, append :latest or a specific version tag
• For OpenAI endpoints, use the model name without version

2. Connection Management

• Set max_concurrent_connections appropriately for your hardware
• Monitor /api/usage to ensure endpoints aren't overloaded
• Consider using the MOE system for critical queries

3. Error Handling

• Check the /health endpoint regularly
• Implement retry logic with exponential backoff
• Monitor error rates and connection failures

4. Performance

• Keep frequently used models loaded on multiple endpoints
• Use streaming for large responses
• Monitor token usage to optimize costs

Troubleshooting

Common Issues

Problem: Model not found

• Solution: Ensure the model is pulled on at least one endpoint
• Check: curl http://localhost:12434/api/tags

Problem: Connection refused

• Solution: Verify Ollama endpoints are running and accessible
• Check: curl http://localhost:12434/health

Problem: High latency

• Solution: Check endpoint health and connection counts
• Check: curl http://localhost:12434/api/usage

Problem: Token tracking not working

• Solution: Ensure database path is writable
• Check: ls -la token_counts.db

Examples

See the examples directory for complete integration examples.