Structured outputs support with examples (#354)

This commit is contained in:
Parth Sareen
2024-12-05 15:40:49 -08:00
committed by GitHub
parent e956a331e8
commit 4b10dee2b2
8 changed files with 355 additions and 18 deletions
+6
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@@ -30,6 +30,12 @@ python3 examples/<example>.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)
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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())
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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)
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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)