struct-gpt
Get structured output from LLM's.
from struct_gpt import OpenAiBase
from pydantic import Field
class SentimentSchema(OpenAiBase):
"""
Determine the sentiment of the given text:
{content}
"""
# 👆this becomes the prompt
sentiment: str = Field(description="Either -1, 0, or 1.")
result = SentimentSchema.from_openai(content="I love pizza!").json()
# 👆this ends up in the prompt
print(result)
outputs:
{
"sentiment": "1"
}
Features
- Easy creation of custom models using the OpenAI API
- Integration with Pydantic for model validation and serialization
- Flexible configuration with retries and temperature settings
Installation
pip install struct-gpt
Usage
- Template variables in the class' docstring are replaced with the keyword arguments passed to
from_openai - Classes can reference one another
- You can use the
OpenAiMixinto add functionality to existing Pydantic classes - Requires
openai>=1.0. By defaultfrom_openaibuilds anopenai.OpenAI()client, which readsOPENAI_API_KEYfrom the environment. Passclient=to use your own (e.g.AzureOpenAIor a custombase_url) - JSON mode (
response_format={"type": "json_object"}) is on by default. Passjson_mode=Falsefor models that don't support it - The default model is
gpt-4o-mini; passmodel=to change it
from struct_gpt import OpenAiBase, OpenAiMixin
from pydantic import Field, BaseModel
from typing import Mapping
class SentimentSchema(OpenAiBase):
sentiment: str = Field(description="Either -1, 0, or 1.")
class SentimentAnalysis(BaseModel, OpenAiMixin):
"""
Determine the sentiment of each word in the following: {text}
Also determine the overall sentiment of the text and who the narrator is.
"""
words_to_sentiment: Mapping[str, SentimentSchema]
overall_sentiment: SentimentSchema
narrator: str
print(
SentimentAnalysis.from_openai(
text="As president, I loved the beautiful scenery, but the long hike was exhausting."
).json(indent=2)
)
See Output
{
"words_to_sentiment": {
"As": {
"sentiment": "0"
},
"president,": {
"sentiment": "1"
},
"I": {
"sentiment": "0"
},
"loved": {
"sentiment": "1"
},
"the": {
"sentiment": "0"
},
"beautiful": {
"sentiment": "1"
},
"scenery,": {
"sentiment": "1"
},
"but": {
"sentiment": "-1"
},
"long": {
"sentiment": "-1"
},
"hike": {
"sentiment": "-1"
},
"was": {
"sentiment": "0"
},
"exhausting.": {
"sentiment": "-1"
}
},
"overall_sentiment": {
"sentiment": "0"
},
"narrator": "president"
}
Improving reliability with examples
from_openai can accept a list of examples to guide the model and improve its accuracy.
Each example is a dictionary containing an input and output key.
output should be an instance of the model, a dictionary, or its json string representation,.
from struct_gpt import OpenAiBase
from pydantic import Field
class SentimentSchema(OpenAiBase):
"""
Determine the sentiment of the given text:
{content}
"""
sentiment: str = Field(description="Either -1, 0, or 1.")
examples = [
{
"input": "I love the beach!",
"output": {"sentiment": "1"},
},
{
"input": "don't touch that",
"output": '{"sentiment": "-1"}',
},
{
"input": "this library is neat!",
"output": SentimentSchema(sentiment="1"),
},
]
print(SentimentSchema.from_openai(content="I love pizza!", examples=examples).json())
outputs:
{
"sentiment": "1"
}
1 maintainer
Metadata
1 maintainer
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