ClassifAIer 0.1.0

A library for text classification using LangChain embeddings and scikit-learn classifiers.

ClassifAIer

ClassifAIer is a Python library that combines scikit-learn classifiers with LangChain embedding libraries, enabling seamless text classification using embeddings from large language models. This library offers a user-friendly interface, allowing you to classify text data in a human-like manner.

Features

• Embedding Support: Ability to work with embeddings from large language models like OpenAI and compatible with embeddings supported by LangChain.
• Parametric Classifiers: Compatibility with a wide range of classifiers from scikit-learn (e.g., RandomForestClassifier, KNeighborsClassifier, etc.).
• Easy to Use: Simplifies text classification tasks with a user-friendly API.
• Save and Load: Allows you to save and reload trained models for reuse.

Requirements

To use this library, the following packages must be installed:

• Python 3.7+
• scikit-learn
• langchain
• numpy

Installation

You can install the required libraries using the following command:

pip install scikit-learn langchain numpy

Usage

from classifaier import ClassifAIer
from langchain.embeddings import OpenAIEmbeddings

# Initialize the embedding provider
embedding_provider = OpenAIEmbeddings(api_key='YOUR_API_KEY')

random_forest_classifier_params = {
    "n_estimators": 100,
    "max_depth": None,
    "min_samples_split": 2,
    "min_samples_leaf": 1,
    "random_state": 42
}

random_forest_classifier = RandomForestClassifier(**random_forest_classifier_params)

# Create a classifier instance
classifier = ClassifAIer(embedding_provider=embedding_provider, classifier=random_forest_classifier)

# Prepare your data
texts = ["This is a positive review.", "This is a negative review."]
labels = ["positive", "negative"]

# Train the model
classifier.fit(texts, labels)

# Make predictions
predictions = classifier.predict(["I love this product!", "I hate this service."])
print(predictions)  # Output: ['positive', 'negative']

# Save the model
classifier.save("my_classifier.pkl")

# Load the model
loaded_classifier = ClassifAIer.load("my_classifier.pkl", embedding_provider)

Contributing

Contributions are welcome! If you have suggestions or improvements, please create a pull request or open an issue.

License

This project is licensed under the MIT License. See the LICENSE file for details.