NOTE: This is a fork of the official Ollama Python library with loosened dependencies in order to make it compatible with Home Assistant.

Ollama Python Library

The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.

Install

pip install ollama

Usage

import ollama
response = ollama.chat(model='llama2', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response['message']['content'])

Streaming responses

Response streaming can be enabled by setting stream=True, modifying function calls to return a Python generator where each part is an object in the stream.

import ollama

stream = ollama.chat(
    model='llama2',
    messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

API

The Ollama Python library's API is designed around the Ollama REST API

Chat

ollama.chat(model='llama2', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])

Generate

ollama.generate(model='llama2', prompt='Why is the sky blue?')

List

ollama.list()

Show

ollama.show('llama2')

Create

modelfile='''
FROM llama2
SYSTEM You are mario from super mario bros.
'''

ollama.create(model='example', modelfile=modelfile)

Copy

ollama.copy('llama2', 'user/llama2')

Delete

ollama.delete('llama2')

Pull

ollama.pull('llama2')

Push

ollama.push('user/llama2')

Embeddings

ollama.embeddings(model='llama2', prompt='They sky is blue because of rayleigh scattering')

Custom client

A custom client can be created with the following fields:

• host: The Ollama host to connect to
• timeout: The timeout for requests

from ollama import Client
client = Client(host='http://localhost:11434')
response = client.chat(model='llama2', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])

Async client

import asyncio
from ollama import AsyncClient

async def chat():
  message = {'role': 'user', 'content': 'Why is the sky blue?'}
  response = await AsyncClient().chat(model='llama2', messages=[message])

asyncio.run(chat())

Setting stream=True modifies functions to return a Python asynchronous generator:

import asyncio
from ollama import AsyncClient

async def chat():
  message = {'role': 'user', 'content': 'Why is the sky blue?'}
  async for part in await AsyncClient().chat(model='llama2', messages=[message], stream=True):
    print(part['message']['content'], end='', flush=True)

asyncio.run(chat())

Errors

Errors are raised if requests return an error status or if an error is detected while streaming.

model = 'does-not-yet-exist'

try:
  ollama.chat(model)
except ollama.ResponseError as e:
  print('Error:', e.error)
  if e.status_code == 404:
    ollama.pull(model)