Ollama Python Library

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

Prerequisites

• Ollama should be installed and running
• Pull a model to use with the library: ollama pull <model> e.g. ollama pull llama3.2
  • See Ollama.com for more information on the models available.

Install

pip install ollama

Usage

fromollamaimportchatfromollamaimportChatResponseresponse: ChatResponse=chat(model='llama3.2', messages=[
{
'role': 'user',
'content': 'Why is the sky blue?',
},
])
print(response['message']['content'])
# or access fields directly from the response objectprint(response.message.content)

See _types.py for more information on the response types.

Streaming responses

Response streaming can be enabled by setting stream=True.

fromollamaimportchatstream=chat(
model='llama3.2',
messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],
stream=True,
)
forchunkinstream:
print(chunk['message']['content'], end='', flush=True)

Custom client

A custom client can be created by instantiating Client or AsyncClient from ollama.

All extra keyword arguments are passed into the httpx.Client.

fromollamaimportClientclient=Client(
host='http://localhost:11434',
headers={'x-some-header': 'some-value'}
)
response=client.chat(model='llama3.2', messages=[
{
'role': 'user',
'content': 'Why is the sky blue?',
},
])

Async client

The AsyncClient class is used to make asynchronous requests. It can be configured with the same fields as the Client class.

importasynciofromollamaimportAsyncClientasyncdefchat():
message= {'role': 'user', 'content': 'Why is the sky blue?'}
response=awaitAsyncClient().chat(model='llama3.2', messages=[message])
asyncio.run(chat())

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

importasynciofromollamaimportAsyncClientasyncdefchat():
message= {'role': 'user', 'content': 'Why is the sky blue?'}
asyncforpartinawaitAsyncClient().chat(model='llama3.2', messages=[message], stream=True):
print(part['message']['content'], end='', flush=True)
asyncio.run(chat())

API

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

Chat

ollama.chat(model='llama3.2', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])

Generate

ollama.generate(model='llama3.2', prompt='Why is the sky blue?')

List

ollama.list()

Show

ollama.show('llama3.2')

Create

ollama.create(model='example', from_='llama3.2', system="You are Mario from Super Mario Bros.")

Copy

ollama.copy('llama3.2', 'user/llama3.2')

Delete

ollama.delete('llama3.2')

Pull

ollama.pull('llama3.2')

Push

ollama.push('user/llama3.2')

Embed

ollama.embed(model='llama3.2', input='The sky is blue because of rayleigh scattering')

Embed (batch)

ollama.embed(model='llama3.2', input=['The sky is blue because of rayleigh scattering', 'Grass is green because of chlorophyll'])

Ps

ollama.ps()

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)
exceptollama.ResponseErrorase:
print('Error:', e.error)
ife.status_code==404:
ollama.pull(model)