bertopic-easy 0.2.0

No project description provided

BERTopic Easy

Polishing BERTopic output using OpenAI's o3-mini.

Motivations

• OpenAI's o3-mini names clusters well.
• OpenAI's o3-mini reduces outliers better than BERTopic's default method.

Example usage

import os

from dotenv import load_dotenv
from openai import AsyncOpenAI, OpenAI
from rich import print

from bertopic_easy.main import bertopic_easy

load_dotenv()

texts = [
    "16/8 fasting",
    "16:8 fasting",
    "24-hour fasting",
    "24-hour one meal a day (OMAD) eating pattern",
    "2:1 ketogenic diet, low-glycemic-index diet",
    "30-day nutrition plan",
    "36-hour fast",
    "4-day fast",
    "40 hour fast, low carb meals",
    "4:3 fasting",
    "5-day fasting-mimicking diet (FMD) program",
    "7 day fast",
    "84-hour fast",
    "90/10 diet",
    "Adjusting macro and micro nutrient intake",
    "Adjusting target macros",
    "Macro and micro nutrient intake",
    "AllerPro formula",
    "Alternate Day Fasting (ADF), One Meal A Day (OMAD)",
    "American cheese",
    "Atkin's diet",
    "Atkins diet",
    "Avoid seed oils",
    "Avoiding seed oils",
    "Limiting seed oils",
    "Limited seed oils and processed foods",
    "Avoiding seed oils and processed foods",
]

clusters = bertopic_easy(
    texts=texts,
    openai=OpenAI(api_key=os.environ.get("OPENAI_API_KEY")),
    async_openai=AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY")),
    reasoning_effort="low",
    subject="personal diet intervention outcomes",
)
print(clusters)

Example output

What's happening under the hood? The three steps...

This is a opinionated hybrid approach to topic modeling using a combination of embeddings and LLM completions. The embeddings are for clustering and the LLM completions are for naming and outlier classification.

graph TD;
    A[Start] -->|sentences| B{1.Run Bertopic};
    B -->|clusters| C[2.Name clusters];
    C -->|target classifications| D;;
    B -->|outliers| D[3.Classify and merge outliers];

Step 1 - Cluster sentences

Bertopic library clusters using embeddings from a text-embedding-3-large LLM model.

Step 2 - Name clusters

Names are generated by a o3-mini LLM model for the resulting clusters from Step 1.

Step 3 - Re-group outliers (not implemented yet)

Outlier sentences, those that did not fit into any of the Bertopic clusters from Step 1, are classified by the o3-mini LLM using the resulting cluster names from Step 2.

Install

Pre-requisites

• python = ">=3.11,<3.13"

pip install bertopic-easy

Run smoke test

poetry run pytest tests/test_main.py::test_bertopic_easy