{"info":{"author":null,"author_email":"hello@ollama.com","bugtrack_url":null,"classifiers":[],"description":"# Ollama Python Library\n\nThe Ollama Python library provides the easiest way to integrate Python 3.8+ projects with [Ollama](https://github.com/ollama/ollama).\n\n## Prerequisites\n\n- [Ollama](https://ollama.com/download) should be installed and running\n- Pull a model to use with the library: `ollama pull <model>` e.g. `ollama pull gemma3`\n  - See [Ollama.com](https://ollama.com/search) for more information on the models available.\n\n## Install\n\n```sh\npip install ollama\n```\n\n## Usage\n\n```python\nfrom ollama import chat\nfrom ollama import ChatResponse\n\nresponse: ChatResponse = chat(model='gemma3', messages=[\n  {\n    'role': 'user',\n    'content': 'Why is the sky blue?',\n  },\n])\nprint(response['message']['content'])\n# or access fields directly from the response object\nprint(response.message.content)\n```\n\nSee [_types.py](ollama/_types.py) for more information on the response types.\n\n## Streaming responses\n\nResponse streaming can be enabled by setting `stream=True`.\n\n```python\nfrom ollama import chat\n\nstream = chat(\n    model='gemma3',\n    messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],\n    stream=True,\n)\n\nfor chunk in stream:\n  print(chunk['message']['content'], end='', flush=True)\n```\n\n## Cloud Models\n\nRun larger models by offloading to Ollama’s cloud while keeping your local workflow.\n\n- Supported models: `deepseek-v3.1:671b-cloud`, `gpt-oss:20b-cloud`, `gpt-oss:120b-cloud`, `kimi-k2:1t-cloud`, `qwen3-coder:480b-cloud`, `kimi-k2-thinking` See [Ollama Models - Cloud](https://ollama.com/search?c=cloud) for more information\n\n### Run via local Ollama\n\n1) Sign in (one-time):\n\n```\nollama signin\n```\n\n2) Pull a cloud model:\n\n```\nollama pull gpt-oss:120b-cloud\n```\n\n3) Make a request:\n\n```python\nfrom ollama import Client\n\nclient = Client()\n\nmessages = [\n  {\n    'role': 'user',\n    'content': 'Why is the sky blue?',\n  },\n]\n\nfor part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True):\n  print(part.message.content, end='', flush=True)\n```\n\n### Cloud API (ollama.com)\n\nAccess cloud models directly by pointing the client at `https://ollama.com`.\n\n1) Create an API key from [ollama.com](https://ollama.com/settings/keys) , then set:\n\n```\nexport OLLAMA_API_KEY=your_api_key\n```\n\n2) (Optional) List models available via the API:\n\n```\ncurl https://ollama.com/api/tags\n```\n\n3) Generate a response via the cloud API:\n\n```python\nimport os\nfrom ollama import Client\n\nclient = Client(\n    host='https://ollama.com',\n    headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')}\n)\n\nmessages = [\n  {\n    'role': 'user',\n    'content': 'Why is the sky blue?',\n  },\n]\n\nfor part in client.chat('gpt-oss:120b', messages=messages, stream=True):\n  print(part.message.content, end='', flush=True)\n```\n\n## Custom client\nA custom client can be created by instantiating `Client` or `AsyncClient` from `ollama`.\n\nAll extra keyword arguments are passed into the [`httpx.Client`](https://www.python-httpx.org/api/#client).\n\n```python\nfrom ollama import Client\nclient = Client(\n  host='http://localhost:11434',\n  headers={'x-some-header': 'some-value'}\n)\nresponse = client.chat(model='gemma3', messages=[\n  {\n    'role': 'user',\n    'content': 'Why is the sky blue?',\n  },\n])\n```\n\n## Async client\n\nThe `AsyncClient` class is used to make asynchronous requests. It can be configured with the same fields as the `Client` class.\n\n```python\nimport asyncio\nfrom ollama import AsyncClient\n\nasync def chat():\n  message = {'role': 'user', 'content': 'Why is the sky blue?'}\n  response = await AsyncClient().chat(model='gemma3', messages=[message])\n\nasyncio.run(chat())\n```\n\nSetting `stream=True` modifies functions to return a Python asynchronous generator:\n\n```python\nimport asyncio\nfrom ollama import AsyncClient\n\nasync def chat():\n  message = {'role': 'user', 'content': 'Why is the sky blue?'}\n  async for part in await AsyncClient().chat(model='gemma3', messages=[message], stream=True):\n    print(part['message']['content'], end='', flush=True)\n\nasyncio.run(chat())\n```\n\n## API\n\nThe Ollama Python library's API is designed around the [Ollama REST API](https://github.com/ollama/ollama/blob/main/docs/api.md)\n\n### Chat\n\n```python\nollama.chat(model='gemma3', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])\n```\n\n### Generate\n\n```python\nollama.generate(model='gemma3', prompt='Why is the sky blue?')\n```\n\n### List\n\n```python\nollama.list()\n```\n\n### Show\n\n```python\nollama.show('gemma3')\n```\n\n### Create\n\n```python\nollama.create(model='example', from_='gemma3', system=\"You are Mario from Super Mario Bros.\")\n```\n\n### Copy\n\n```python\nollama.copy('gemma3', 'user/gemma3')\n```\n\n### Delete\n\n```python\nollama.delete('gemma3')\n```\n\n### Pull\n\n```python\nollama.pull('gemma3')\n```\n\n### Push\n\n```python\nollama.push('user/gemma3')\n```\n\n### Embed\n\n```python\nollama.embed(model='gemma3', input='The sky is blue because of rayleigh scattering')\n```\n\n### Embed (batch)\n\n```python\nollama.embed(model='gemma3', input=['The sky is blue because of rayleigh scattering', 'Grass is green because of chlorophyll'])\n```\n\n### Ps\n\n```python\nollama.ps()\n```\n\n## Errors\n\nErrors are raised if requests return an error status or if an error is detected 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