oshepherd 0.0.6

The Oshepherd guiding the Ollama(s) inference orchestration.

oshepherd

The Oshepherd guiding the Ollama(s) inference orchestration.

A centralized Flask API service, using Celery (RabbitMQ + Redis) to orchestrate multiple Ollama servers as workers.

Install

pip install oshepherd

Usage

1. Setup RabbitMQ and Redis:

   Celery uses RabbitMQ as message broker, and Redis as backend, you'll need to create one instance for each. You can create small instances for free in cloudamqp.com and redislabs.com respectively.

2. Setup Flask API Server:

   # define configuration env file
   #   use credentials for redis and rabbitmq
   cp .api.env.template .api.env
   
   # start api
   oshepherd start-api --env-file .api.env
   

3. Setup Celery/Ollama Worker(s):

   # install ollama https://ollama.com/download
   ollama run mistral
   
   # define configuration env file
   #   use credentials for redis and rabbitmq
   cp .worker.env.template .worker.env
   
   # start worker
   oshepherd start-worker --env-file .worker.env
   

4. Done, now you're ready to execute Ollama completions remotely. You can point your Ollama client to your oshepherd api server by setting the host, and it will return your requested completions from any of the workers:

   • ollama-python client:

   import ollama
   client = ollama.Client(host="http://127.0.0.1:5001")
   ollama_response = client.generate({"model": "mistral", "prompt": "Why is the sky blue?"})
   

   • ollama-js client:

   import { Ollama } from "ollama/browser";
   const ollama = new Ollama({ host: "http://127.0.0.1:5001" });
   const ollamaResponse = await ollama.generate({
       model: "mistral",
       prompt: "Why is the sky blue?",
   });
   

   • Raw http request:

   curl -X POST -H "Content-Type: application/json" -L http://127.0.0.1:5001/api/generate/ -d '{
       "model": "mistral",
       "prompt":"Why is the sky blue?"
   }'
   

Words of advice 🚨

This package is in alpha, its architecture and api might change in the near future. Currently this is getting tested in a controlled environment by real users, but haven't been audited, nor tested thorugly. Use it at your own risk.

Disclaimer on Support

As this is an alpha version, support and responses might be limited. We'll do our best to address questions and issues as quickly as possible.

Contribution Guidelines

We welcome contributions! If you find a bug or have suggestions for improvements, please open an issue or submit a pull request. Before creating a new issue/pull request, take a moment to search through the existing issues/pull requests to avoid duplicates.

Conda Support

To run and build locally you can use conda:

conda create -n oshepherd python=3.8
conda activate oshepherd
pip install -r requirements.txt

# install oshepherd
pip install -e .

Tests

Follow usage instructions to start api server and celery worker using a local ollama, and then run the tests:

pytest -s tests/

Author

This is a project developed and maintained by mnemonica.ai.

License

MIT