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Structured outputs support with examples (#354)
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@@ -30,6 +30,12 @@ python3 examples/<example>.py
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- [multimodal_generate.py](multimodal_generate.py)
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### Structured Outputs - Generate structured outputs with a model
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- [structured-outputs.py](structured-outputs.py)
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- [async-structured-outputs.py](async-structured-outputs.py)
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- [structured-outputs-image.py](structured-outputs-image.py)
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### Ollama List - List all downloaded models and their properties
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- [list.py](list.py)
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@@ -0,0 +1,32 @@
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from pydantic import BaseModel
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from ollama import AsyncClient
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import asyncio
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# Define the schema for the response
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class FriendInfo(BaseModel):
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name: str
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age: int
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is_available: bool
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class FriendList(BaseModel):
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friends: list[FriendInfo]
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async def main():
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client = AsyncClient()
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response = await client.chat(
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model='llama3.1:8b',
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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'}],
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format=FriendList.model_json_schema(), # Use Pydantic to generate the schema
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options={'temperature': 0}, # Make responses more deterministic
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)
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# Use Pydantic to validate the response
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friends_response = FriendList.model_validate_json(response.message.content)
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print(friends_response)
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if __name__ == '__main__':
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asyncio.run(main())
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@@ -0,0 +1,50 @@
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from pathlib import Path
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from pydantic import BaseModel
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from typing import List, Optional, Literal
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from ollama import chat
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from rich import print
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# Define the schema for image objects
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class Object(BaseModel):
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name: str
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confidence: float
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attributes: Optional[dict] = None
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class ImageDescription(BaseModel):
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summary: str
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objects: List[Object]
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scene: str
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colors: List[str]
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time_of_day: Literal['Morning', 'Afternoon', 'Evening', 'Night']
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setting: Literal['Indoor', 'Outdoor', 'Unknown']
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text_content: Optional[str] = None
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# Get path from user input
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path = input('Enter the path to your image: ')
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path = Path(path)
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# Verify the file exists
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if not path.exists():
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raise FileNotFoundError(f'Image not found at: {path}')
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# Set up chat as usual
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response = chat(
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model='llama3.2-vision',
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format=ImageDescription.model_json_schema(), # Pass in the schema for the response
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messages=[
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{
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'role': 'user',
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'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.',
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'images': [path],
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},
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],
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options={'temperature': 0}, # Set temperature to 0 for more deterministic output
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)
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# Convert received content to the schema
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image_analysis = ImageDescription.model_validate_json(response.message.content)
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print(image_analysis)
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@@ -0,0 +1,26 @@
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from ollama import chat
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from pydantic import BaseModel
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# Define the schema for the response
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class FriendInfo(BaseModel):
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name: str
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age: int
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is_available: bool
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class FriendList(BaseModel):
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friends: list[FriendInfo]
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# 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']}
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response = chat(
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model='llama3.1:8b',
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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'}],
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format=FriendList.model_json_schema(), # Use Pydantic to generate the schema or format=schema
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options={'temperature': 0}, # Make responses more deterministic
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)
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# Use Pydantic to validate the response
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friends_response = FriendList.model_validate_json(response.message.content)
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print(friends_response)
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