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...

46 Commits

Author SHA1 Message Date
Jeffrey Morgan 1066246ab5 fix validation of format field to allow empty strings as it did previously (#369) 2024-12-07 19:37:55 -08:00
Parth Sareen 4b10dee2b2 Structured outputs support with examples (#354) 2024-12-05 15:40:49 -08:00
dependabot[bot] e956a331e8 Merge pull request #358 from ollama/dependabot/pip/pytest-8.3.4 2024-12-03 21:15:57 +00:00
dependabot[bot] 12f7302d5f Bump pytest from 8.3.3 to 8.3.4
Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.3 to 8.3.4.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.3...8.3.4)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-12-02 23:33:31 +00:00
Parth Sareen 366180aa8f Improve tool example to showcase chatting (#352) 2024-11-29 20:34:19 -08:00
Parth Sareen d6528cf731 Fix image serialization for long image string (#348) 2024-11-28 14:12:55 -08:00
Julia Scheaffer b50a65b27d Add Callable type annotation for Tools (#344) 2024-11-27 09:53:26 -08:00
Jeffrey Morgan 758a1d2933 make subscription methods more consistent 2024-11-26 10:52:45 -08:00
Jeffrey Morgan d4c38978d1 Update ollama/_types.py
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
2024-11-26 10:41:53 -08:00
Jeffrey Morgan d8d98e17b2 Update ollama/_types.py
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
2024-11-26 10:41:50 -08:00
Jeffrey Morgan ec2c8fdd8d Update ollama/_types.py
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
2024-11-26 10:41:45 -08:00
Jeffrey Morgan ea0e0dc692 Update ollama/_types.py
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
2024-11-26 10:35:57 -08:00
Shahil Yadav 6c44bb2729 Fix chat-with-history.py example (#337)
Fix chat-with-history.py example

---------

Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
2024-11-24 10:49:46 -08:00
jmorganca 2095fc9107 make subscription methods more consistent with maps 2024-11-23 19:02:28 -08:00
Jeffrey Morgan 64e3723e6b Merge pull request #334 from ollama/mxyng/hasattr-none 2024-11-23 18:27:22 -08:00
jmorganca 1e22f2e118 add test case for explicit None 2024-11-23 18:22:38 -08:00
jmorganca 00c64332cc check defaults that aren't None too 2024-11-23 18:15:05 -08:00
Michael Yang 986fb4c7b3 fix: skip tests on examples, readme 2024-11-23 16:44:09 -08:00
Michael Yang c6ade633b8 fix: hasattr checks if attr is None 2024-11-23 16:44:09 -08:00
Michael Yang 2e05cde1a5 Merge pull request #330 from ollama/parth/qol-disable-tests-for-readmes-and-examples
Disable tests for READMEs and examples
2024-11-21 15:19:22 -08:00
ParthSareen bfea240300 Disable tests for readmes and examples 2024-11-21 15:16:40 -08:00
Parth Sareen 64c1eb78ff Examples refactor (#329)
* Examples and README updates

---------

Co-authored-by: fujitatomoya <tomoya.fujita825@gmail.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
2024-11-21 15:14:59 -08:00
Parth Sareen 139c89e833 Passing Functions as Tools (#321)
* Functions can now be passed as tools
2024-11-20 15:49:50 -08:00
dependabot[bot] da2893b099 Merge pull request #318 from ollama/dependabot/pip/pydantic-2.9.2 2024-11-19 19:05:41 +00:00
Michael Yang da5582ff14 Merge pull request #324 from ollama/mxyng/python3.13
chore: add python3.13 to test matrix
2024-11-19 10:40:18 -08:00
Michael Yang 5ec4d23893 chore: update requirements.txt 2024-11-19 10:39:22 -08:00
dependabot[bot] d0d7894f88 Bump pydantic from 2.9.0 to 2.9.2
Bumps [pydantic](https://github.com/pydantic/pydantic) from 2.9.0 to 2.9.2.
- [Release notes](https://github.com/pydantic/pydantic/releases)
- [Changelog](https://github.com/pydantic/pydantic/blob/main/HISTORY.md)
- [Commits](https://github.com/pydantic/pydantic/compare/v2.9.0...v2.9.2)

---
updated-dependencies:
- dependency-name: pydantic
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-11-19 18:37:53 +00:00
dependabot[bot] 1fea8a8867 Merge pull request #322 from ollama/dependabot/pip/ruff-0.7.4 2024-11-19 18:34:32 +00:00
Michael Yang 52510cca53 chore: add python3.13 to test matrix 2024-11-19 10:24:54 -08:00
dependabot[bot] 425bce2329 Bump ruff from 0.6.9 to 0.7.4
Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.9 to 0.7.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.9...0.7.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-11-15 22:30:48 +00:00
Michael Yang dc38fe4675 Merge pull request #277 from ollama/mxyng/no-head
no head request for create blob
2024-11-08 11:20:43 -08:00
Michael Yang a92f111fc8 Merge pull request #276 from ollama/mxyng/pydantic
add pydantic for validation and serialization
2024-11-08 11:20:28 -08:00
Parth Sareen 72052188c3 Fixing empty header + ensuring security (#313)
* Fixing empty header + ensuring security
2024-11-08 10:03:14 -08:00
Parth Sareen f25834217b Pydantic Fixes and Tests (#311)
* Added SubscriptableBaseModel to the Model classes and added Image codec test

---------

Co-authored-by: Parth Sareen <parth@Parths-MacBook-Pro.local>
2024-11-08 10:02:55 -08:00
Michael Yang b0da4ff2d8 no head
this request is unnecessary since the POST will short circuit the
request if the blob already exists
2024-11-08 09:59:04 -08:00
Michael Yang 0bbc246007 pydantic types 2024-11-08 09:59:03 -08:00
Parth Sareen a7571423d3 Update requirements.txt and poetry.lock (#315)
* Update requirements.txt and poetry.lock
2024-11-07 14:46:41 -08:00
dependabot[bot] ebe332b29d Merge pull request #281 from ollama/dependabot/pip/ruff-0.6.5 2024-09-19 16:37:37 +00:00
dependabot[bot] 9bcd0d6b33 Merge pull request #278 from ollama/dependabot/pip/pytest-8.3.3 2024-09-19 16:37:28 +00:00
Michael Yang f3e72b6c4f update pyproject.toml (#284) 2024-09-18 16:34:54 -07:00
Michael Yang 61c8d0d440 add basic delete/copy tests (#275) 2024-09-18 12:35:27 -07:00
dependabot[bot] 7fda5c9dae Bump ruff from 0.6.3 to 0.6.5
Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.3 to 0.6.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.3...0.6.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-09-13 22:26:57 +00:00
Patrick Devine 1ec88ed994 Merge pull request #216 from ollama/royh/embed-docs
Deprecate embeddings from docs in favor of embed
2024-09-12 17:14:07 -07:00
royjhan a26537c188 update docs 2024-09-12 17:10:29 -07:00
Patrick Devine eff2afd827 update docs 2024-09-12 16:49:38 -07:00
dependabot[bot] ccf8af3aaa Bump pytest from 8.3.2 to 8.3.3
Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.2 to 8.3.3.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.2...8.3.3)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:development
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
2024-09-10 22:55:03 +00:00
41 changed files with 2887 additions and 1121 deletions
+4 -1
View File
@@ -2,12 +2,15 @@ name: test
on:
pull_request:
paths-ignore:
- 'examples/**'
- '**/README.md'
jobs:
test:
strategy:
matrix:
python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
python-version: ['3.8', '3.9', '3.10', '3.11', '3.12', '3.13']
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
+111 -89
View File
@@ -2,6 +2,12 @@
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 <model>` e.g. `ollama pull llama3.2`
- See [Ollama.com](https://ollama.com/search) for more information on the models available.
## Install
```sh
@@ -11,25 +17,31 @@ pip install ollama
## Usage
```python
import ollama
response = ollama.chat(model='llama3.1', messages=[
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`, modifying function calls to return a Python generator where each part is an object in the stream.
Response streaming can be enabled by setting `stream=True`.
```python
import ollama
from ollama import chat
stream = ollama.chat(
model='llama3.1',
stream = chat(
model='llama3.2',
messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],
stream=True,
)
@@ -38,92 +50,18 @@ 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](https://github.com/ollama/ollama/blob/main/docs/api.md)
### Chat
```python
ollama.chat(model='llama3.1', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])
```
### Generate
```python
ollama.generate(model='llama3.1', prompt='Why is the sky blue?')
```
### List
```python
ollama.list()
```
### Show
```python
ollama.show('llama3.1')
```
### Create
```python
modelfile='''
FROM llama3.1
SYSTEM You are mario from super mario bros.
'''
ollama.create(model='example', modelfile=modelfile)
```
### Copy
```python
ollama.copy('llama3.1', 'user/llama3.1')
```
### Delete
```python
ollama.delete('llama3.1')
```
### Pull
```python
ollama.pull('llama3.1')
```
### Push
```python
ollama.push('user/llama3.1')
```
### Embeddings
```python
ollama.embeddings(model='llama3.1', prompt='The sky is blue because of rayleigh scattering')
```
### Ps
```python
ollama.ps()
```
## Custom client
A custom client can be created by instantiating `Client` or `AsyncClient` from `ollama`.
A custom client can be created with the following fields:
- `host`: The Ollama host to connect to
- `timeout`: The timeout for requests
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')
response = client.chat(model='llama3.1', messages=[
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?',
@@ -133,13 +71,15 @@ response = client.chat(model='llama3.1', messages=[
## 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='llama3.1', messages=[message])
response = await AsyncClient().chat(model='llama3.2', messages=[message])
asyncio.run(chat())
```
@@ -152,12 +92,94 @@ from ollama import AsyncClient
async def chat():
message = {'role': 'user', 'content': 'Why is the sky blue?'}
async for part in await AsyncClient().chat(model='llama3.1', messages=[message], stream=True):
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.
+63
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@@ -0,0 +1,63 @@
# Running Examples
Run the examples in this directory with:
```sh
# Run example
python3 examples/<example>.py
```
### Chat - Chat with a model
- [chat.py](chat.py)
- [async-chat.py](async-chat.py)
- [chat-stream.py](chat-stream.py) - Streamed outputs
- [chat-with-history.py](chat-with-history.py) - Chat with model and maintain history of the conversation
### Generate - Generate text with a model
- [generate.py](generate.py)
- [async-generate.py](async-generate.py)
- [generate-stream.py](generate-stream.py) - Streamed outputs
- [fill-in-middle.py](fill-in-middle.py) - Given a prefix and suffix, fill in the middle
### Tools/Function Calling - Call a function with a model
- [tools.py](tools.py) - Simple example of Tools/Function Calling
- [async-tools.py](async-tools.py)
### Multimodal with Images - Chat with a multimodal (image chat) model
- [multimodal_chat.py](multimodal_chat.py)
- [multimodal_generate.py](multimodal_generate.py)
### Structured Outputs - Generate structured outputs with a model
- [structured-outputs.py](structured-outputs.py)
- [async-structured-outputs.py](async-structured-outputs.py)
- [structured-outputs-image.py](structured-outputs-image.py)
### Ollama List - List all downloaded models and their properties
- [list.py](list.py)
### Ollama ps - Show model status with CPU/GPU usage
- [ps.py](ps.py)
### Ollama Pull - Pull a model from Ollama
Requirement: `pip install tqdm`
- [pull.py](pull.py)
### Ollama Create - Create a model from a Modelfile
```python
python create.py <model> <modelfile>
```
- [create.py](create.py)
See [ollama/docs/modelfile.md](https://github.com/ollama/ollama/blob/main/docs/modelfile.md) for more information on the Modelfile format.
### Ollama Embed - Generate embeddings with a model
- [embed.py](embed.py)
-3
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@@ -1,3 +0,0 @@
# async-chat-stream
This example demonstrates how to create a conversation history using an asynchronous Ollama client and the chat endpoint. The streaming response is outputted to `stdout` as well as a TTS if enabled with `--speak` and available. Supported TTS are `say` on macOS and `espeak` on Linux.
-59
View File
@@ -1,59 +0,0 @@
import shutil
import asyncio
import argparse
import ollama
async def speak(speaker, content):
if speaker:
p = await asyncio.create_subprocess_exec(speaker, content)
await p.communicate()
async def main():
parser = argparse.ArgumentParser()
parser.add_argument('--speak', default=False, action='store_true')
args = parser.parse_args()
speaker = None
if not args.speak:
...
elif say := shutil.which('say'):
speaker = say
elif (espeak := shutil.which('espeak')) or (espeak := shutil.which('espeak-ng')):
speaker = espeak
client = ollama.AsyncClient()
messages = []
while True:
if content_in := input('>>> '):
messages.append({'role': 'user', 'content': content_in})
content_out = ''
message = {'role': 'assistant', 'content': ''}
async for response in await client.chat(model='mistral', messages=messages, stream=True):
if response['done']:
messages.append(message)
content = response['message']['content']
print(content, end='', flush=True)
content_out += content
if content in ['.', '!', '?', '\n']:
await speak(speaker, content_out)
content_out = ''
message['content'] += content
if content_out:
await speak(speaker, content_out)
print()
try:
asyncio.run(main())
except (KeyboardInterrupt, EOFError):
...
+19
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@@ -0,0 +1,19 @@
import asyncio
from ollama import AsyncClient
async def main():
messages = [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
client = AsyncClient()
response = await client.chat('llama3.2', messages=messages)
print(response['message']['content'])
if __name__ == '__main__':
asyncio.run(main())
+15
View File
@@ -0,0 +1,15 @@
import asyncio
import ollama
async def main():
client = ollama.AsyncClient()
response = await client.generate('llama3.2', 'Why is the sky blue?')
print(response['response'])
if __name__ == '__main__':
try:
asyncio.run(main())
except KeyboardInterrupt:
print('\nGoodbye!')
+32
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@@ -0,0 +1,32 @@
from pydantic import BaseModel
from ollama import AsyncClient
import asyncio
# Define the schema for the response
class FriendInfo(BaseModel):
name: str
age: int
is_available: bool
class FriendList(BaseModel):
friends: list[FriendInfo]
async def main():
client = AsyncClient()
response = await client.chat(
model='llama3.1:8b',
messages=[{'role': 'user', 'content': 'I have two friends. The first is Ollama 22 years old busy saving the world, and the second is Alonso 23 years old and wants to hang out. Return a list of friends in JSON format'}],
format=FriendList.model_json_schema(), # Use Pydantic to generate the schema
options={'temperature': 0}, # Make responses more deterministic
)
# Use Pydantic to validate the response
friends_response = FriendList.model_validate_json(response.message.content)
print(friends_response)
if __name__ == '__main__':
asyncio.run(main())
+92
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@@ -0,0 +1,92 @@
import asyncio
from ollama import ChatResponse
import ollama
def add_two_numbers(a: int, b: int) -> int:
"""
Add two numbers
Args:
a (int): The first number
b (int): The second number
Returns:
int: The sum of the two numbers
"""
return a + b
def subtract_two_numbers(a: int, b: int) -> int:
"""
Subtract two numbers
"""
return a - b
# Tools can still be manually defined and passed into chat
subtract_two_numbers_tool = {
'type': 'function',
'function': {
'name': 'subtract_two_numbers',
'description': 'Subtract two numbers',
'parameters': {
'type': 'object',
'required': ['a', 'b'],
'properties': {
'a': {'type': 'integer', 'description': 'The first number'},
'b': {'type': 'integer', 'description': 'The second number'},
},
},
},
}
messages = [{'role': 'user', 'content': 'What is three plus one?'}]
print('Prompt:', messages[0]['content'])
available_functions = {
'add_two_numbers': add_two_numbers,
'subtract_two_numbers': subtract_two_numbers,
}
async def main():
client = ollama.AsyncClient()
response: ChatResponse = await client.chat(
'llama3.1',
messages=messages,
tools=[add_two_numbers, subtract_two_numbers_tool],
)
if response.message.tool_calls:
# There may be multiple tool calls in the response
for tool in response.message.tool_calls:
# Ensure the function is available, and then call it
if function_to_call := available_functions.get(tool.function.name):
print('Calling function:', tool.function.name)
print('Arguments:', tool.function.arguments)
output = function_to_call(**tool.function.arguments)
print('Function output:', output)
else:
print('Function', tool.function.name, 'not found')
# Only needed to chat with the model using the tool call results
if response.message.tool_calls:
# Add the function response to messages for the model to use
messages.append(response.message)
messages.append({'role': 'tool', 'content': str(output), 'name': tool.function.name})
# Get final response from model with function outputs
final_response = await client.chat('llama3.1', messages=messages)
print('Final response:', final_response.message.content)
else:
print('No tool calls returned from model')
if __name__ == '__main__':
try:
asyncio.run(main())
except KeyboardInterrupt:
print('\nGoodbye!')
@@ -8,8 +8,7 @@ messages = [
},
]
for part in chat('mistral', messages=messages, stream=True):
for part in chat('llama3.2', messages=messages, stream=True):
print(part['message']['content'], end='', flush=True)
# end with a newline
print()
+38
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@@ -0,0 +1,38 @@
from ollama import chat
messages = [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
{
'role': 'assistant',
'content': "The sky is blue because of the way the Earth's atmosphere scatters sunlight.",
},
{
'role': 'user',
'content': 'What is the weather in Tokyo?',
},
{
'role': 'assistant',
'content': 'The weather in Tokyo is typically warm and humid during the summer months, with temperatures often exceeding 30°C (86°F). The city experiences a rainy season from June to September, with heavy rainfall and occasional typhoons. Winter is mild, with temperatures rarely dropping below freezing. The city is known for its high-tech and vibrant culture, with many popular tourist attractions such as the Tokyo Tower, Senso-ji Temple, and the bustling Shibuya district.',
},
]
while True:
user_input = input('Chat with history: ')
response = chat(
'llama3.2',
messages=messages
+ [
{'role': 'user', 'content': user_input},
],
)
# Add the response to the messages to maintain the history
messages += [
{'role': 'user', 'content': user_input},
{'role': 'assistant', 'content': response.message.content},
]
print(response.message.content + '\n')
+1 -2
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@@ -1,6 +1,5 @@
from ollama import chat
messages = [
{
'role': 'user',
@@ -8,5 +7,5 @@ messages = [
},
]
response = chat('mistral', messages=messages)
response = chat('llama3.2', messages=messages)
print(response['message']['content'])
+30
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@@ -0,0 +1,30 @@
import sys
from ollama import create
args = sys.argv[1:]
if len(args) == 2:
# create from local file
path = args[1]
else:
print('usage: python create.py <name> <filepath>')
sys.exit(1)
# TODO: update to real Modelfile values
modelfile = f"""
FROM {path}
"""
example_modelfile = """
FROM llama3.2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 4096
# sets a custom system message to specify the behavior of the chat assistant
SYSTEM You are Mario from super mario bros, acting as an assistant.
"""
for response in create(model=args[0], modelfile=modelfile, stream=True):
print(response['status'])
-20
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@@ -1,20 +0,0 @@
import sys
from ollama import create
args = sys.argv[1:]
if len(args) == 2:
# create from local file
path = args[1]
else:
print('usage: python main.py <name> <filepath>')
sys.exit(1)
# TODO: update to real Modelfile values
modelfile = f"""
FROM {path}
"""
for response in create(model=args[0], modelfile=modelfile, stream=True):
print(response['status'])
+4
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@@ -0,0 +1,4 @@
from ollama import embed
response = embed(model='llama3.2', input='Hello, world!')
print(response['embeddings'])
@@ -1,5 +1,5 @@
from ollama import generate
for part in generate('mistral', 'Why is the sky blue?', stream=True):
for part in generate('llama3.2', 'Why is the sky blue?', stream=True):
print(part['response'], end='', flush=True)
@@ -1,5 +1,5 @@
from ollama import generate
response = generate('mistral', 'Why is the sky blue?')
response = generate('llama3.2', 'Why is the sky blue?')
print(response['response'])
+14
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@@ -0,0 +1,14 @@
from ollama import list
from ollama import ListResponse
response: ListResponse = list()
for model in response.models:
print('Name:', model.model)
print(' Size (MB):', f'{(model.size.real / 1024 / 1024):.2f}')
if model.details:
print(' Format:', model.details.format)
print(' Family:', model.details.family)
print(' Parameter Size:', model.details.parameter_size)
print(' Quantization Level:', model.details.quantization_level)
print('\n')
+23
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@@ -0,0 +1,23 @@
from ollama import chat
# from pathlib import Path
# Pass in the path to the image
path = input('Please enter the path to the image: ')
# You can also pass in base64 encoded image data
# img = base64.b64encode(Path(path).read_bytes()).decode()
# or the raw bytes
# img = Path(path).read_bytes()
response = chat(
model='llama3.2-vision',
messages=[
{
'role': 'user',
'content': 'What is in this image? Be concise.',
'images': [path],
}
],
)
print(response.message.content)
+27
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@@ -0,0 +1,27 @@
from ollama import ps, pull, chat
from ollama import ProcessResponse
# Ensure at least one model is loaded
response = pull('llama3.2', stream=True)
progress_states = set()
for progress in response:
if progress.get('status') in progress_states:
continue
progress_states.add(progress.get('status'))
print(progress.get('status'))
print('\n')
print('Waiting for model to load... \n')
chat(model='llama3.2', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])
response: ProcessResponse = ps()
for model in response.models:
print('Model: ', model.model)
print(' Digest: ', model.digest)
print(' Expires at: ', model.expires_at)
print(' Size: ', model.size)
print(' Size vram: ', model.size_vram)
print(' Details: ', model.details)
print('\n')
-31
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@@ -1,31 +0,0 @@
from ollama import ps, pull, chat
response = pull('mistral', stream=True)
progress_states = set()
for progress in response:
if progress.get('status') in progress_states:
continue
progress_states.add(progress.get('status'))
print(progress.get('status'))
print('\n')
response = chat('mistral', messages=[{'role': 'user', 'content': 'Hello!'}])
print(response['message']['content'])
print('\n')
response = ps()
name = response['models'][0]['name']
size = response['models'][0]['size']
size_vram = response['models'][0]['size_vram']
if size == size_vram:
print(f'{name}: 100% GPU')
elif not size_vram:
print(f'{name}: 100% CPU')
else:
size_cpu = size - size_vram
cpu_percent = round(size_cpu / size * 100)
print(f'{name}: {cpu_percent}% CPU/{100 - cpu_percent}% GPU')
-9
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@@ -1,9 +0,0 @@
# pull-progress
This example emulates `ollama pull` using the Python library and [`tqdm`](https://tqdm.github.io/).
## Setup
```shell
pip install -r requirements.txt
```
-1
View File
@@ -1 +0,0 @@
tqdm==4.66.1
@@ -3,7 +3,7 @@ from ollama import pull
current_digest, bars = '', {}
for progress in pull('mistral', stream=True):
for progress in pull('llama3.2', stream=True):
digest = progress.get('digest', '')
if digest != current_digest and current_digest in bars:
bars[current_digest].close()
+50
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@@ -0,0 +1,50 @@
from pathlib import Path
from pydantic import BaseModel
from typing import List, Optional, Literal
from ollama import chat
from rich import print
# Define the schema for image objects
class Object(BaseModel):
name: str
confidence: float
attributes: Optional[dict] = None
class ImageDescription(BaseModel):
summary: str
objects: List[Object]
scene: str
colors: List[str]
time_of_day: Literal['Morning', 'Afternoon', 'Evening', 'Night']
setting: Literal['Indoor', 'Outdoor', 'Unknown']
text_content: Optional[str] = None
# Get path from user input
path = input('Enter the path to your image: ')
path = Path(path)
# Verify the file exists
if not path.exists():
raise FileNotFoundError(f'Image not found at: {path}')
# Set up chat as usual
response = chat(
model='llama3.2-vision',
format=ImageDescription.model_json_schema(), # Pass in the schema for the response
messages=[
{
'role': 'user',
'content': 'Analyze this image and return a detailed JSON description including objects, scene, colors and any text detected. If you cannot determine certain details, leave those fields empty.',
'images': [path],
},
],
options={'temperature': 0}, # Set temperature to 0 for more deterministic output
)
# Convert received content to the schema
image_analysis = ImageDescription.model_validate_json(response.message.content)
print(image_analysis)
+26
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@@ -0,0 +1,26 @@
from ollama import chat
from pydantic import BaseModel
# Define the schema for the response
class FriendInfo(BaseModel):
name: str
age: int
is_available: bool
class FriendList(BaseModel):
friends: list[FriendInfo]
# schema = {'type': 'object', 'properties': {'friends': {'type': 'array', 'items': {'type': 'object', 'properties': {'name': {'type': 'string'}, 'age': {'type': 'integer'}, 'is_available': {'type': 'boolean'}}, 'required': ['name', 'age', 'is_available']}}}, 'required': ['friends']}
response = chat(
model='llama3.1:8b',
messages=[{'role': 'user', 'content': 'I have two friends. The first is Ollama 22 years old busy saving the world, and the second is Alonso 23 years old and wants to hang out. Return a list of friends in JSON format'}],
format=FriendList.model_json_schema(), # Use Pydantic to generate the schema or format=schema
options={'temperature': 0}, # Make responses more deterministic
)
# Use Pydantic to validate the response
friends_response = FriendList.model_validate_json(response.message.content)
print(friends_response)
+80
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@@ -0,0 +1,80 @@
from ollama import chat
from ollama import ChatResponse
def add_two_numbers(a: int, b: int) -> int:
"""
Add two numbers
Args:
a (int): The first number
b (int): The second number
Returns:
int: The sum of the two numbers
"""
return a + b
def subtract_two_numbers(a: int, b: int) -> int:
"""
Subtract two numbers
"""
return a - b
# Tools can still be manually defined and passed into chat
subtract_two_numbers_tool = {
'type': 'function',
'function': {
'name': 'subtract_two_numbers',
'description': 'Subtract two numbers',
'parameters': {
'type': 'object',
'required': ['a', 'b'],
'properties': {
'a': {'type': 'integer', 'description': 'The first number'},
'b': {'type': 'integer', 'description': 'The second number'},
},
},
},
}
messages = [{'role': 'user', 'content': 'What is three plus one?'}]
print('Prompt:', messages[0]['content'])
available_functions = {
'add_two_numbers': add_two_numbers,
'subtract_two_numbers': subtract_two_numbers,
}
response: ChatResponse = chat(
'llama3.1',
messages=messages,
tools=[add_two_numbers, subtract_two_numbers_tool],
)
if response.message.tool_calls:
# There may be multiple tool calls in the response
for tool in response.message.tool_calls:
# Ensure the function is available, and then call it
if function_to_call := available_functions.get(tool.function.name):
print('Calling function:', tool.function.name)
print('Arguments:', tool.function.arguments)
output = function_to_call(**tool.function.arguments)
print('Function output:', output)
else:
print('Function', tool.function.name, 'not found')
# Only needed to chat with the model using the tool call results
if response.message.tool_calls:
# Add the function response to messages for the model to use
messages.append(response.message)
messages.append({'role': 'tool', 'content': str(output), 'name': tool.function.name})
# Get final response from model with function outputs
final_response = chat('llama3.1', messages=messages)
print('Final response:', final_response.message.content)
else:
print('No tool calls returned from model')
-3
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@@ -1,3 +0,0 @@
# tools
This example demonstrates how to utilize tool calls with an asynchronous Ollama client and the chat endpoint.
-87
View File
@@ -1,87 +0,0 @@
import json
import ollama
import asyncio
# Simulates an API call to get flight times
# In a real application, this would fetch data from a live database or API
def get_flight_times(departure: str, arrival: str) -> str:
flights = {
'NYC-LAX': {'departure': '08:00 AM', 'arrival': '11:30 AM', 'duration': '5h 30m'},
'LAX-NYC': {'departure': '02:00 PM', 'arrival': '10:30 PM', 'duration': '5h 30m'},
'LHR-JFK': {'departure': '10:00 AM', 'arrival': '01:00 PM', 'duration': '8h 00m'},
'JFK-LHR': {'departure': '09:00 PM', 'arrival': '09:00 AM', 'duration': '7h 00m'},
'CDG-DXB': {'departure': '11:00 AM', 'arrival': '08:00 PM', 'duration': '6h 00m'},
'DXB-CDG': {'departure': '03:00 AM', 'arrival': '07:30 AM', 'duration': '7h 30m'},
}
key = f'{departure}-{arrival}'.upper()
return json.dumps(flights.get(key, {'error': 'Flight not found'}))
async def run(model: str):
client = ollama.AsyncClient()
# Initialize conversation with a user query
messages = [{'role': 'user', 'content': 'What is the flight time from New York (NYC) to Los Angeles (LAX)?'}]
# First API call: Send the query and function description to the model
response = await client.chat(
model=model,
messages=messages,
tools=[
{
'type': 'function',
'function': {
'name': 'get_flight_times',
'description': 'Get the flight times between two cities',
'parameters': {
'type': 'object',
'properties': {
'departure': {
'type': 'string',
'description': 'The departure city (airport code)',
},
'arrival': {
'type': 'string',
'description': 'The arrival city (airport code)',
},
},
'required': ['departure', 'arrival'],
},
},
},
],
)
# Add the model's response to the conversation history
messages.append(response['message'])
# Check if the model decided to use the provided function
if not response['message'].get('tool_calls'):
print("The model didn't use the function. Its response was:")
print(response['message']['content'])
return
# Process function calls made by the model
if response['message'].get('tool_calls'):
available_functions = {
'get_flight_times': get_flight_times,
}
for tool in response['message']['tool_calls']:
function_to_call = available_functions[tool['function']['name']]
function_response = function_to_call(tool['function']['arguments']['departure'], tool['function']['arguments']['arrival'])
# Add function response to the conversation
messages.append(
{
'role': 'tool',
'content': function_response,
}
)
# Second API call: Get final response from the model
final_response = await client.chat(model=model, messages=messages)
print(final_response['message']['content'])
# Run the async function
asyncio.run(run('mistral'))
+18 -16
View File
@@ -1,10 +1,17 @@
from ollama._client import Client, AsyncClient
from ollama._types import (
Options,
Message,
Tool,
GenerateResponse,
ChatResponse,
EmbedResponse,
EmbeddingsResponse,
StatusResponse,
ProgressResponse,
Message,
Options,
ListResponse,
ShowResponse,
ProcessResponse,
RequestError,
ResponseError,
)
@@ -12,25 +19,20 @@ from ollama._types import (
__all__ = [
'Client',
'AsyncClient',
'Options',
'Message',
'Tool',
'GenerateResponse',
'ChatResponse',
'EmbedResponse',
'EmbeddingsResponse',
'StatusResponse',
'ProgressResponse',
'Message',
'Options',
'ListResponse',
'ShowResponse',
'ProcessResponse',
'RequestError',
'ResponseError',
'generate',
'chat',
'embed',
'embeddings',
'pull',
'push',
'create',
'delete',
'list',
'copy',
'show',
'ps',
]
_client = Client()
+585 -398
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+416 -100
View File
@@ -1,43 +1,242 @@
import json
from typing import Any, TypedDict, Sequence, Literal, Mapping
from base64 import b64decode, b64encode
from pathlib import Path
from datetime import datetime
from typing import Any, Mapping, Optional, Union, Sequence
import sys
from pydantic.json_schema import JsonSchemaValue
from typing_extensions import Annotated, Literal
if sys.version_info < (3, 11):
from typing_extensions import NotRequired
else:
from typing import NotRequired
from pydantic import (
BaseModel,
ByteSize,
ConfigDict,
Field,
model_serializer,
)
class BaseGenerateResponse(TypedDict):
model: str
class SubscriptableBaseModel(BaseModel):
def __getitem__(self, key: str) -> Any:
"""
>>> msg = Message(role='user')
>>> msg['role']
'user'
>>> msg = Message(role='user')
>>> msg['nonexistent']
Traceback (most recent call last):
KeyError: 'nonexistent'
"""
if key in self:
return getattr(self, key)
raise KeyError(key)
def __setitem__(self, key: str, value: Any) -> None:
"""
>>> msg = Message(role='user')
>>> msg['role'] = 'assistant'
>>> msg['role']
'assistant'
>>> tool_call = Message.ToolCall(function=Message.ToolCall.Function(name='foo', arguments={}))
>>> msg = Message(role='user', content='hello')
>>> msg['tool_calls'] = [tool_call]
>>> msg['tool_calls'][0]['function']['name']
'foo'
"""
setattr(self, key, value)
def __contains__(self, key: str) -> bool:
"""
>>> msg = Message(role='user')
>>> 'nonexistent' in msg
False
>>> 'role' in msg
True
>>> 'content' in msg
False
>>> msg.content = 'hello!'
>>> 'content' in msg
True
>>> msg = Message(role='user', content='hello!')
>>> 'content' in msg
True
>>> 'tool_calls' in msg
False
>>> msg['tool_calls'] = []
>>> 'tool_calls' in msg
True
>>> msg['tool_calls'] = [Message.ToolCall(function=Message.ToolCall.Function(name='foo', arguments={}))]
>>> 'tool_calls' in msg
True
>>> msg['tool_calls'] = None
>>> 'tool_calls' in msg
True
>>> tool = Tool()
>>> 'type' in tool
True
"""
if key in self.model_fields_set:
return True
if key in self.model_fields:
return self.model_fields[key].default is not None
return False
def get(self, key: str, default: Any = None) -> Any:
"""
>>> msg = Message(role='user')
>>> msg.get('role')
'user'
>>> msg = Message(role='user')
>>> msg.get('nonexistent')
>>> msg = Message(role='user')
>>> msg.get('nonexistent', 'default')
'default'
>>> msg = Message(role='user', tool_calls=[ Message.ToolCall(function=Message.ToolCall.Function(name='foo', arguments={}))])
>>> msg.get('tool_calls')[0]['function']['name']
'foo'
"""
return self[key] if key in self else default
class Options(SubscriptableBaseModel):
# load time options
numa: Optional[bool] = None
num_ctx: Optional[int] = None
num_batch: Optional[int] = None
num_gpu: Optional[int] = None
main_gpu: Optional[int] = None
low_vram: Optional[bool] = None
f16_kv: Optional[bool] = None
logits_all: Optional[bool] = None
vocab_only: Optional[bool] = None
use_mmap: Optional[bool] = None
use_mlock: Optional[bool] = None
embedding_only: Optional[bool] = None
num_thread: Optional[int] = None
# runtime options
num_keep: Optional[int] = None
seed: Optional[int] = None
num_predict: Optional[int] = None
top_k: Optional[int] = None
top_p: Optional[float] = None
tfs_z: Optional[float] = None
typical_p: Optional[float] = None
repeat_last_n: Optional[int] = None
temperature: Optional[float] = None
repeat_penalty: Optional[float] = None
presence_penalty: Optional[float] = None
frequency_penalty: Optional[float] = None
mirostat: Optional[int] = None
mirostat_tau: Optional[float] = None
mirostat_eta: Optional[float] = None
penalize_newline: Optional[bool] = None
stop: Optional[Sequence[str]] = None
class BaseRequest(SubscriptableBaseModel):
model: Annotated[str, Field(min_length=1)]
'Model to use for the request.'
class BaseStreamableRequest(BaseRequest):
stream: Optional[bool] = None
'Stream response.'
class BaseGenerateRequest(BaseStreamableRequest):
options: Optional[Union[Mapping[str, Any], Options]] = None
'Options to use for the request.'
format: Optional[Union[Literal['', 'json'], JsonSchemaValue]] = None
'Format of the response.'
keep_alive: Optional[Union[float, str]] = None
'Keep model alive for the specified duration.'
class Image(BaseModel):
value: Union[str, bytes, Path]
@model_serializer
def serialize_model(self):
if isinstance(self.value, (Path, bytes)):
return b64encode(self.value.read_bytes() if isinstance(self.value, Path) else self.value).decode()
if isinstance(self.value, str):
try:
if Path(self.value).exists():
return b64encode(Path(self.value).read_bytes()).decode()
except Exception:
# Long base64 string can't be wrapped in Path, so try to treat as base64 string
pass
# String might be a file path, but might not exist
if self.value.split('.')[-1] in ('png', 'jpg', 'jpeg', 'webp'):
raise ValueError(f'File {self.value} does not exist')
try:
# Try to decode to check if it's already base64
b64decode(self.value)
return self.value
except Exception:
raise ValueError('Invalid image data, expected base64 string or path to image file') from Exception
class GenerateRequest(BaseGenerateRequest):
prompt: Optional[str] = None
'Prompt to generate response from.'
suffix: Optional[str] = None
'Suffix to append to the response.'
system: Optional[str] = None
'System prompt to prepend to the prompt.'
template: Optional[str] = None
'Template to use for the response.'
context: Optional[Sequence[int]] = None
'Tokenized history to use for the response.'
raw: Optional[bool] = None
images: Optional[Sequence[Image]] = None
'Image data for multimodal models.'
class BaseGenerateResponse(SubscriptableBaseModel):
model: Optional[str] = None
'Model used to generate response.'
created_at: str
created_at: Optional[str] = None
'Time when the request was created.'
done: bool
done: Optional[bool] = None
'True if response is complete, otherwise False. Useful for streaming to detect the final response.'
done_reason: str
done_reason: Optional[str] = None
'Reason for completion. Only present when done is True.'
total_duration: int
total_duration: Optional[int] = None
'Total duration in nanoseconds.'
load_duration: int
load_duration: Optional[int] = None
'Load duration in nanoseconds.'
prompt_eval_count: int
prompt_eval_count: Optional[int] = None
'Number of tokens evaluated in the prompt.'
prompt_eval_duration: int
prompt_eval_duration: Optional[int] = None
'Duration of evaluating the prompt in nanoseconds.'
eval_count: int
eval_count: Optional[int] = None
'Number of tokens evaluated in inference.'
eval_duration: int
eval_duration: Optional[int] = None
'Duration of evaluating inference in nanoseconds.'
@@ -49,43 +248,22 @@ class GenerateResponse(BaseGenerateResponse):
response: str
'Response content. When streaming, this contains a fragment of the response.'
context: Sequence[int]
context: Optional[Sequence[int]] = None
'Tokenized history up to the point of the response.'
class ToolCallFunction(TypedDict):
"""
Tool call function.
"""
name: str
'Name of the function.'
arguments: NotRequired[Mapping[str, Any]]
'Arguments of the function.'
class ToolCall(TypedDict):
"""
Model tool calls.
"""
function: ToolCallFunction
'Function to be called.'
class Message(TypedDict):
class Message(SubscriptableBaseModel):
"""
Chat message.
"""
role: Literal['user', 'assistant', 'system', 'tool']
"Assumed role of the message. Response messages always has role 'assistant' or 'tool'."
"Assumed role of the message. Response messages has role 'assistant' or 'tool'."
content: NotRequired[str]
content: Optional[str] = None
'Content of the message. Response messages contains message fragments when streaming.'
images: NotRequired[Sequence[Any]]
images: Optional[Sequence[Image]] = None
"""
Optional list of image data for multimodal models.
@@ -97,33 +275,61 @@ class Message(TypedDict):
Valid image formats depend on the model. See the model card for more information.
"""
tool_calls: NotRequired[Sequence[ToolCall]]
class ToolCall(SubscriptableBaseModel):
"""
Model tool calls.
"""
class Function(SubscriptableBaseModel):
"""
Tool call function.
"""
name: str
'Name of the function.'
arguments: Mapping[str, Any]
'Arguments of the function.'
function: Function
'Function to be called.'
tool_calls: Optional[Sequence[ToolCall]] = None
"""
Tools calls to be made by the model.
"""
class Property(TypedDict):
type: str
description: str
enum: NotRequired[Sequence[str]] # `enum` is optional and can be a list of strings
class Tool(SubscriptableBaseModel):
type: Optional[Literal['function']] = 'function'
class Function(SubscriptableBaseModel):
name: Optional[str] = None
description: Optional[str] = None
class Parameters(SubscriptableBaseModel):
type: Optional[Literal['object']] = 'object'
required: Optional[Sequence[str]] = None
class Property(SubscriptableBaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
type: Optional[str] = None
description: Optional[str] = None
properties: Optional[Mapping[str, Property]] = None
parameters: Optional[Parameters] = None
function: Optional[Function] = None
class Parameters(TypedDict):
type: str
required: Sequence[str]
properties: Mapping[str, Property]
class ChatRequest(BaseGenerateRequest):
messages: Optional[Sequence[Union[Mapping[str, Any], Message]]] = None
'Messages to chat with.'
class ToolFunction(TypedDict):
name: str
description: str
parameters: Parameters
class Tool(TypedDict):
type: str
function: ToolFunction
tools: Optional[Sequence[Tool]] = None
'Tools to use for the chat.'
class ChatResponse(BaseGenerateResponse):
@@ -135,47 +341,157 @@ class ChatResponse(BaseGenerateResponse):
'Response message.'
class ProgressResponse(TypedDict):
status: str
completed: int
total: int
digest: str
class EmbedRequest(BaseRequest):
input: Union[str, Sequence[str]]
'Input text to embed.'
truncate: Optional[bool] = None
'Truncate the input to the maximum token length.'
options: Optional[Union[Mapping[str, Any], Options]] = None
'Options to use for the request.'
keep_alive: Optional[Union[float, str]] = None
class Options(TypedDict, total=False):
# load time options
numa: bool
num_ctx: int
num_batch: int
num_gpu: int
main_gpu: int
low_vram: bool
f16_kv: bool
logits_all: bool
vocab_only: bool
use_mmap: bool
use_mlock: bool
embedding_only: bool
num_thread: int
class EmbedResponse(BaseGenerateResponse):
"""
Response returned by embed requests.
"""
# runtime options
num_keep: int
seed: int
num_predict: int
top_k: int
top_p: float
tfs_z: float
typical_p: float
repeat_last_n: int
temperature: float
repeat_penalty: float
presence_penalty: float
frequency_penalty: float
mirostat: int
mirostat_tau: float
mirostat_eta: float
penalize_newline: bool
stop: Sequence[str]
embeddings: Sequence[Sequence[float]]
'Embeddings of the inputs.'
class EmbeddingsRequest(BaseRequest):
prompt: Optional[str] = None
'Prompt to generate embeddings from.'
options: Optional[Union[Mapping[str, Any], Options]] = None
'Options to use for the request.'
keep_alive: Optional[Union[float, str]] = None
class EmbeddingsResponse(SubscriptableBaseModel):
"""
Response returned by embeddings requests.
"""
embedding: Sequence[float]
'Embedding of the prompt.'
class PullRequest(BaseStreamableRequest):
"""
Request to pull the model.
"""
insecure: Optional[bool] = None
'Allow insecure (HTTP) connections.'
class PushRequest(BaseStreamableRequest):
"""
Request to pull the model.
"""
insecure: Optional[bool] = None
'Allow insecure (HTTP) connections.'
class CreateRequest(BaseStreamableRequest):
"""
Request to create a new model.
"""
modelfile: Optional[str] = None
quantize: Optional[str] = None
class ModelDetails(SubscriptableBaseModel):
parent_model: Optional[str] = None
format: Optional[str] = None
family: Optional[str] = None
families: Optional[Sequence[str]] = None
parameter_size: Optional[str] = None
quantization_level: Optional[str] = None
class ListResponse(SubscriptableBaseModel):
class Model(SubscriptableBaseModel):
model: Optional[str] = None
modified_at: Optional[datetime] = None
digest: Optional[str] = None
size: Optional[ByteSize] = None
details: Optional[ModelDetails] = None
models: Sequence[Model]
'List of models.'
class DeleteRequest(BaseRequest):
"""
Request to delete a model.
"""
class CopyRequest(BaseModel):
"""
Request to copy a model.
"""
source: str
'Source model to copy.'
destination: str
'Destination model to copy to.'
class StatusResponse(SubscriptableBaseModel):
status: Optional[str] = None
class ProgressResponse(StatusResponse):
completed: Optional[int] = None
total: Optional[int] = None
digest: Optional[str] = None
class ShowRequest(BaseRequest):
"""
Request to show model information.
"""
class ShowResponse(SubscriptableBaseModel):
modified_at: Optional[datetime] = None
template: Optional[str] = None
modelfile: Optional[str] = None
license: Optional[str] = None
details: Optional[ModelDetails] = None
modelinfo: Optional[Mapping[str, Any]] = Field(alias='model_info')
parameters: Optional[str] = None
class ProcessResponse(SubscriptableBaseModel):
class Model(SubscriptableBaseModel):
model: Optional[str] = None
name: Optional[str] = None
digest: Optional[str] = None
expires_at: Optional[datetime] = None
size: Optional[ByteSize] = None
size_vram: Optional[ByteSize] = None
details: Optional[ModelDetails] = None
models: Sequence[Model]
class RequestError(Exception):
+87
View File
@@ -0,0 +1,87 @@
from __future__ import annotations
from collections import defaultdict
import inspect
from typing import Callable, Union
import re
import pydantic
from ollama._types import Tool
def _parse_docstring(doc_string: Union[str, None]) -> dict[str, str]:
parsed_docstring = defaultdict(str)
if not doc_string:
return parsed_docstring
key = hash(doc_string)
for line in doc_string.splitlines():
lowered_line = line.lower().strip()
if lowered_line.startswith('args:'):
key = 'args'
elif lowered_line.startswith('returns:') or lowered_line.startswith('yields:') or lowered_line.startswith('raises:'):
key = '_'
else:
# maybe change to a list and join later
parsed_docstring[key] += f'{line.strip()}\n'
last_key = None
for line in parsed_docstring['args'].splitlines():
line = line.strip()
if ':' in line:
# Split the line on either:
# 1. A parenthetical expression like (integer) - captured in group 1
# 2. A colon :
# Followed by optional whitespace. Only split on first occurrence.
parts = re.split(r'(?:\(([^)]*)\)|:)\s*', line, maxsplit=1)
arg_name = parts[0].strip()
last_key = arg_name
# Get the description - will be in parts[1] if parenthetical or parts[-1] if after colon
arg_description = parts[-1].strip()
if len(parts) > 2 and parts[1]: # Has parenthetical content
arg_description = parts[-1].split(':', 1)[-1].strip()
parsed_docstring[last_key] = arg_description
elif last_key and line:
parsed_docstring[last_key] += ' ' + line
return parsed_docstring
def convert_function_to_tool(func: Callable) -> Tool:
doc_string_hash = hash(inspect.getdoc(func))
parsed_docstring = _parse_docstring(inspect.getdoc(func))
schema = type(
func.__name__,
(pydantic.BaseModel,),
{
'__annotations__': {k: v.annotation if v.annotation != inspect._empty else str for k, v in inspect.signature(func).parameters.items()},
'__signature__': inspect.signature(func),
'__doc__': parsed_docstring[doc_string_hash],
},
).model_json_schema()
for k, v in schema.get('properties', {}).items():
# If type is missing, the default is string
types = {t.get('type', 'string') for t in v.get('anyOf')} if 'anyOf' in v else {v.get('type', 'string')}
if 'null' in types:
schema['required'].remove(k)
types.discard('null')
schema['properties'][k] = {
'description': parsed_docstring[k],
'type': ', '.join(types),
}
tool = Tool(
function=Tool.Function(
name=func.__name__,
description=schema.get('description', ''),
parameters=Tool.Function.Parameters(**schema),
)
)
return Tool.model_validate(tool)
Generated
+275 -113
View File
@@ -1,14 +1,28 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
files = [
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
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]
[package.dependencies]
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
[[package]]
name = "anyio"
version = "4.3.0"
version = "4.5.2"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
files = [
{file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
{file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
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]
[package.dependencies]
@@ -18,19 +32,19 @@ sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.23)"]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "certifi"
version = "2024.2.2"
version = "2024.8.30"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
{file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
{file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
{file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
{file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
]
[[package]]
@@ -46,63 +60,83 @@ files = [
[[package]]
name = "coverage"
version = "7.4.4"
version = "7.6.1"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.8"
files = [
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version = "2.0.2"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
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python-versions = "^3.8"
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@@ -5,12 +5,13 @@ description = "The official Python client for Ollama."
authors = ["Ollama <hello@ollama.com>"]
license = "MIT"
readme = "README.md"
homepage = "https://ollama.ai"
repository = "https://github.com/jmorganca/ollama-python"
homepage = "https://ollama.com"
repository = "https://github.com/ollama/ollama-python"
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httpx = "^0.27.0"
pydantic = "^2.9.0"
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pytest = ">=7.4.3,<9.0.0"
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ruff = ">=0.1.8,<0.7.0"
ruff = ">=0.1.8,<0.8.0"
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requires = ["poetry-core"]
+117 -21
View File
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from base64 import b64encode
from pathlib import Path
import pytest
from ollama._types import Image
import tempfile
def test_image_serialization_bytes():
image_bytes = b'test image bytes'
encoded_string = b64encode(image_bytes).decode()
img = Image(value=image_bytes)
assert img.model_dump() == encoded_string
def test_image_serialization_base64_string():
b64_str = 'dGVzdCBiYXNlNjQgc3RyaW5n'
img = Image(value=b64_str)
assert img.model_dump() == b64_str # Should return as-is if valid base64
def test_image_serialization_long_base64_string():
b64_str = 'dGVzdCBiYXNlNjQgc3RyaW5n' * 1000
img = Image(value=b64_str)
assert img.model_dump() == b64_str # Should return as-is if valid base64
def test_image_serialization_plain_string():
img = Image(value='not a path or base64')
assert img.model_dump() == 'not a path or base64' # Should return as-is
def test_image_serialization_path():
with tempfile.NamedTemporaryFile() as temp_file:
temp_file.write(b'test file content')
temp_file.flush()
img = Image(value=Path(temp_file.name))
assert img.model_dump() == b64encode(b'test file content').decode()
def test_image_serialization_string_path():
with tempfile.NamedTemporaryFile() as temp_file:
temp_file.write(b'test file content')
temp_file.flush()
img = Image(value=temp_file.name)
assert img.model_dump() == b64encode(b'test file content').decode()
with pytest.raises(ValueError):
img = Image(value='some_path/that/does/not/exist.png')
img.model_dump()
with pytest.raises(ValueError):
img = Image(value='not an image')
img.model_dump()
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import json
import sys
from typing import Dict, List, Mapping, Sequence, Set, Tuple, Union
from ollama._utils import convert_function_to_tool
def test_function_to_tool_conversion():
def add_numbers(x: int, y: Union[int, None] = None) -> int:
"""Add two numbers together.
args:
x (integer): The first number
y (integer, optional): The second number
Returns:
integer: The sum of x and y
"""
return x + y
tool = convert_function_to_tool(add_numbers).model_dump()
assert tool['type'] == 'function'
assert tool['function']['name'] == 'add_numbers'
assert tool['function']['description'] == 'Add two numbers together.'
assert tool['function']['parameters']['type'] == 'object'
assert tool['function']['parameters']['properties']['x']['type'] == 'integer'
assert tool['function']['parameters']['properties']['x']['description'] == 'The first number'
assert tool['function']['parameters']['required'] == ['x']
def test_function_with_no_args():
def simple_func():
"""
A simple function with no arguments.
Args:
None
Returns:
None
"""
pass
tool = convert_function_to_tool(simple_func).model_dump()
assert tool['function']['name'] == 'simple_func'
assert tool['function']['description'] == 'A simple function with no arguments.'
assert tool['function']['parameters']['properties'] == {}
def test_function_with_all_types():
if sys.version_info >= (3, 10):
def all_types(
x: int,
y: str,
z: list[int],
w: dict[str, int],
v: int | str | None,
) -> int | dict[str, int] | str | list[int] | None:
"""
A function with all types.
Args:
x (integer): The first number
y (string): The second number
z (array): The third number
w (object): The fourth number
v (integer | string | None): The fifth number
"""
pass
else:
def all_types(
x: int,
y: str,
z: Sequence,
w: Mapping[str, int],
d: Dict[str, int],
s: Set[int],
t: Tuple[int, str],
l: List[int], # noqa: E741
o: Union[int, None],
) -> Union[Mapping[str, int], str, None]:
"""
A function with all types.
Args:
x (integer): The first number
y (string): The second number
z (array): The third number
w (object): The fourth number
d (object): The fifth number
s (array): The sixth number
t (array): The seventh number
l (array): The eighth number
o (integer | None): The ninth number
"""
pass
tool_json = convert_function_to_tool(all_types).model_dump_json()
tool = json.loads(tool_json)
assert tool['function']['parameters']['properties']['x']['type'] == 'integer'
assert tool['function']['parameters']['properties']['y']['type'] == 'string'
if sys.version_info >= (3, 10):
assert tool['function']['parameters']['properties']['z']['type'] == 'array'
assert tool['function']['parameters']['properties']['w']['type'] == 'object'
assert set(x.strip().strip("'") for x in tool['function']['parameters']['properties']['v']['type'].removeprefix('[').removesuffix(']').split(',')) == {'string', 'integer'}
assert tool['function']['parameters']['properties']['v']['type'] != 'null'
assert tool['function']['parameters']['required'] == ['x', 'y', 'z', 'w']
else:
assert tool['function']['parameters']['properties']['z']['type'] == 'array'
assert tool['function']['parameters']['properties']['w']['type'] == 'object'
assert tool['function']['parameters']['properties']['d']['type'] == 'object'
assert tool['function']['parameters']['properties']['s']['type'] == 'array'
assert tool['function']['parameters']['properties']['t']['type'] == 'array'
assert tool['function']['parameters']['properties']['l']['type'] == 'array'
assert tool['function']['parameters']['properties']['o']['type'] == 'integer'
assert tool['function']['parameters']['properties']['o']['type'] != 'null'
assert tool['function']['parameters']['required'] == ['x', 'y', 'z', 'w', 'd', 's', 't', 'l']
def test_function_docstring_parsing():
from typing import List, Dict, Any
def func_with_complex_docs(x: int, y: List[str]) -> Dict[str, Any]:
"""
Test function with complex docstring.
Args:
x (integer): A number
with multiple lines
y (array of string): A list
with multiple lines
Returns:
object: A dictionary
with multiple lines
"""
pass
tool = convert_function_to_tool(func_with_complex_docs).model_dump()
assert tool['function']['description'] == 'Test function with complex docstring.'
assert tool['function']['parameters']['properties']['x']['description'] == 'A number with multiple lines'
assert tool['function']['parameters']['properties']['y']['description'] == 'A list with multiple lines'
def test_skewed_docstring_parsing():
def add_two_numbers(x: int, y: int) -> int:
"""
Add two numbers together.
Args:
x (integer): : The first number
y (integer ): The second number
Returns:
integer: The sum of x and y
"""
pass
tool = convert_function_to_tool(add_two_numbers).model_dump()
assert tool['function']['parameters']['properties']['x']['description'] == ': The first number'
assert tool['function']['parameters']['properties']['y']['description'] == 'The second number'
def test_function_with_no_docstring():
def no_docstring():
pass
def no_docstring_with_args(x: int, y: int):
pass
tool = convert_function_to_tool(no_docstring).model_dump()
assert tool['function']['description'] == ''
tool = convert_function_to_tool(no_docstring_with_args).model_dump()
assert tool['function']['description'] == ''
assert tool['function']['parameters']['properties']['x']['description'] == ''
assert tool['function']['parameters']['properties']['y']['description'] == ''
def test_function_with_only_description():
def only_description():
"""
A function with only a description.
"""
pass
tool = convert_function_to_tool(only_description).model_dump()
assert tool['function']['description'] == 'A function with only a description.'
assert tool['function']['parameters'] == {'type': 'object', 'properties': {}, 'required': None}
def only_description_with_args(x: int, y: int):
"""
A function with only a description.
"""
pass
tool = convert_function_to_tool(only_description_with_args).model_dump()
assert tool['function']['description'] == 'A function with only a description.'
assert tool['function']['parameters'] == {
'type': 'object',
'properties': {
'x': {'type': 'integer', 'description': ''},
'y': {'type': 'integer', 'description': ''},
},
'required': ['x', 'y'],
}
def test_function_with_yields():
def function_with_yields(x: int, y: int):
"""
A function with yields section.
Args:
x: the first number
y: the second number
Yields:
The sum of x and y
"""
pass
tool = convert_function_to_tool(function_with_yields).model_dump()
assert tool['function']['description'] == 'A function with yields section.'
assert tool['function']['parameters']['properties']['x']['description'] == 'the first number'
assert tool['function']['parameters']['properties']['y']['description'] == 'the second number'
def test_function_with_no_types():
def no_types(a, b):
"""
A function with no types.
"""
pass
tool = convert_function_to_tool(no_types).model_dump()
assert tool['function']['parameters']['properties']['a']['type'] == 'string'
assert tool['function']['parameters']['properties']['b']['type'] == 'string'
def test_function_with_parentheses():
def func_with_parentheses(a: int, b: int) -> int:
"""
A function with parentheses.
Args:
a: First (:thing) number to add
b: Second number to add
Returns:
int: The sum of a and b
"""
pass
def func_with_parentheses_and_args(a: int, b: int):
"""
A function with parentheses and args.
Args:
a(integer) : First (:thing) number to add
b(integer) :Second number to add
"""
pass
tool = convert_function_to_tool(func_with_parentheses).model_dump()
assert tool['function']['parameters']['properties']['a']['description'] == 'First (:thing) number to add'
assert tool['function']['parameters']['properties']['b']['description'] == 'Second number to add'
tool = convert_function_to_tool(func_with_parentheses_and_args).model_dump()
assert tool['function']['parameters']['properties']['a']['description'] == 'First (:thing) number to add'
assert tool['function']['parameters']['properties']['b']['description'] == 'Second number to add'