A small DSPy clone built on Instructor
- Pydantic Models: Robust data validation and serialization using Pydantic.
- Optimizers: Includes an Optuna-based few-shot optimizer for hyperparameter tuning.
- Vision Models: Easy to tune few-shot prompts, even with image examples.
- Chat Model Templates: Uses prompt prefilling to and cus 76B8 tom templates to make the most of modern LLM APIs.
- Asynchronous Processing: Utilizes
asynciofor efficient concurrent task handling.
git clone git@github.com:thomasnormal/fewshot.git
cd fewshot
pip install -e .
python examples/simple.pyThe framework supports various AI tasks. Here's a basic example for question answering:
import instructor
import openai
from datasets import load_dataset
from pydantic import Field, BaseModel
from tqdm.asyncio import tqdm
from fewshot import Predictor
from fewshot.optimizers import OptunaFewShot
# DSPy inspired Pydantic classes for inputs.
class Question(BaseModel):
"""Answer questions with short factoid answers."""
question: str
class Answer(BaseModel):
reasoning: str = Field(description="reasoning for the answer")
answer: str = Field(description="often between 1 and 5 words")
async def main():
dataset = load_dataset("hotpot_qa", "fullwiki")
trainset = [(Question(question=x["question"]), x["answer"]) for x in dataset["train"]]
client = instructor.from_openai(openai.AsyncOpenAI()) # Use any Instructor supported LLM
pred = Predictor(client, "gpt-4o-mini", output_type=Answer, optimizer=OptunaFewShot(3))
async for t, (input, expected), answer in pred.as_completed(trainset):
score = int(answer.answer == expected)
t.backwards(score=score) # Update the model, just like PyTorch
pred.inspect_history() # Inspect the messages sent to the LLMCode: examples/circles.py