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https://github.com/ollama/ollama-python

Ollama Python library
https://github.com/ollama/ollama-python

ollama python

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Ollama Python library

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README

        

# Ollama Python Library

The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with [Ollama](https://github.com/ollama/ollama).

## Prerequisites

- [Ollama](https://ollama.com/download) should be installed and running
- Pull a model to use with the library: `ollama pull ` e.g. `ollama pull llama3.2`
- See [Ollama.com](https://ollama.com/search) for more information on the models available.

## Install

```sh
pip install ollama
```

## Usage

```python
from ollama import chat
from ollama import ChatResponse

response: 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 object
print(response.message.content)
```

See [_types.py](ollama/_types.py) for more information on the response types.

## Streaming responses

Response streaming can be enabled by setting `stream=True`.

```python
from ollama import chat

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

for chunk in stream:
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`](https://www.python-httpx.org/api/#client).

```python
from ollama import Client
client = 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.

```python
import asyncio
from ollama import AsyncClient

async def chat():
message = {'role': 'user', 'content': 'Why is the sky blue?'}
response = await AsyncClient().chat(model='llama3.2', messages=[message])

asyncio.run(chat())
```

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

```python
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='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](https://github.com/ollama/ollama/blob/main/docs/api.md)

### Chat

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

### Generate

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

### List

```python
ollama.list()
```

### Show

```python
ollama.show('llama3.2')
```

### Create

```python
modelfile='''
FROM llama3.2
SYSTEM You are mario from super mario bros.
'''

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

### Copy

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

### Delete

```python
ollama.delete('llama3.2')
```

### Pull

```python
ollama.pull('llama3.2')
```

### Push

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

### Embed

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

### Embed (batch)

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

### Ps

```python
ollama.ps()
```

## Errors

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

```python
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)
```