handrew / wordcel

Swiss army-knife for composing LLM outputs

😶 Wordcel

wordcel is a library of functions that help users create text-based features from large language models.

Today, it is comprised of a single function apply_io_bound_function, which is conceptually similar to Pandas' .apply. Because LLMs are primarily consumed via API, users are subject to network latency, errors, and inference costs. Accordingly, the aforementioned wordcel function differs from Pandas' .apply in that it handles (a) caching outputs from LLM providers and (b) threading, which increases speed.

However, it is up to the user to handle a variety of odds and ends such as API retrying (though wordcel.llm_providers.openai_call is provided as a convenience, and popular libraries like Langchain and Llama Index also provide support for retrying) and chunking text.

Installation

You can simply pip install wordcel.

Example Usage

First import the relevant functions.

import pandas as pd
from wordcel.featurize import apply_io_bound_function
from wordcel.llm_providers import openai_call

Then, load your data.

data = {
    "id": [1, 2, 3, 4, 5],
    "text": [
        "I love this product! It's amazing.",
        "The service was terrible. I'm very disappointed.",
        "The weather today is just perfect.",
        "This movie is fantastic. I highly recommend it.",
        "I had a bad experience with this company's customer support.",
    ],
}
df = pd.DataFrame(data)

Define your LLM function for extracting features from the text.

def sentiment_classify(text):
    prompt = f"Classify the sentiment of the following text into one of two categories, POS or NEG. Respond in one word only.\n\n{text}"
    return openai_call(prompt, model="gpt-3.5-turbo", max_tokens=32)

Finally, give apply_io_bound_function your df, function, column to process, a unique identifying column, and optionally the number of threads you'd like to use and a cache folder (if none is provided then one will be created for you).

results = apply_io_bound_function(
    df,
    sentiment_classify,
    text_column="text",
    id_column="id",
    num_threads=4,
    cache_folder="cache",
)
print(results)
joined_results = df.join(results.set_index("id"), on="id")
print()
print(joined_results)

This will output:

   id result
0   1    POS
1   2    NEG
2   3    POS
3   4    POS
4   5    NEG

   id                                               text result
0   1                 I love this product! It's amazing.    POS
1   2   The service was terrible. I'm very disappointed.    NEG
2   3                 The weather today is just perfect.    POS
3   4    This movie is fantastic. I highly recommend it.    POS
4   5  I had a bad experience with this company's cus...    NEG