This is intended to be a langchain native implementation of the innovative principles pioneered by the wonderful DSPy
I believe that the principles they have pioneered should be built on top of more mature primitives with a larger ecosystem & community to support it.
This is intended to be usable in production enterprise environments.
Designed to be interface compatible with both langchain and scikit-learn.
Automatically tunning n-shot prompts based on provided samples. See examples/amazon/generate_slugs.py
if __name__ == "__main__":
output_path = sys.argv[1]
dataset_file= "data/amazon_products_split.json"
with open(dataset_file, 'r') as file:
dataset = json.load(file)
X_train = dataset['train']['X']
y_train = dataset['train']['y']
X_test = dataset['test']['X']
y_test = dataset['test']['y']
model = ProductSlugGenerator(n_jobs=4)
before_test_accuracy = None
if os.path.exists(output_path):
model.load(output_path)
else:
input("Hit enter to evaluate the untrained model...")
before_test_accuracy = evaluate_model(model, X_test, y_test)
print(f"Before Training Accuracy: {before_test_accuracy}")
input("Hit enter to train the model...")
model.fit(X_train, y_train, score_func=slug_similarity, llm=llm, n_examples=2, n_iter=100)
input("Hit enter to evaluate the trained model...")
# Evaluate the model on the test set
test_accuracy = evaluate_model(model, X_test, y_test)
print(f"Before Training Accuracy: {before_test_accuracy}")
print(f"After Training Accuracy: {test_accuracy}")
model.save(output_path)git clone https://github.com/aelaguiz/langdspy
cd langdspy
pip install poetry
poetry install
poetry run test- Unit tests
- Auto-Tuning with RAG steps
- New prmompt strategies (e.g. Chain Of Thought)
- Tighter integration with Langchain templates (can we use their few-shot prompting templates for example?)
Amir Elaguizy is the original author but sincerely hopes to be the smallest piece of this going forward.
Use it as you wish.