FindOrg 1.0.1

Extract organization names from text using LLMs (OpenAI or Google Gemini).

FindOrg — Named Entity Recognition for Organizations using LLMs

FindOrg is a Python package that performs Named Entity Recognition (NER) targeting organizations within a given text. It leverages large language models from OpenAI or Google Gemini to extract organization names and return them as a tidy pandas DataFrame.

Installation

pip install FindOrg

To enable saving results as Excel files (.xlsx), install with the optional extra:

pip install FindOrg[excel]

Quick start

from findorg import org

text = (
    "In the heart of Silicon Valley, a collaboration has emerged between "
    "global tech giants such as Google, Apple, and Meta, aiming to "
    "revolutionize the digital landscape."
)

# OpenAI (default provider)
df = org(text, api_key="YOUR_OPENAI_API_KEY")
print(df)

  Organizations
0        Google
1         Apple
2          Meta

Using Google Gemini instead

df = org(text, provider="gemini", api_key="YOUR_GEMINI_API_KEY")

API keys via environment variables (recommended)

If api_key is omitted, FindOrg reads it from the environment: OPENAI_API_KEY for OpenAI or GEMINI_API_KEY for Gemini. This keeps keys out of your source code:

df = org(text)                      # uses OPENAI_API_KEY
df = org(text, provider="gemini")   # uses GEMINI_API_KEY

Analyzing multiple texts at once

Pass a list of strings. The result gains a Text_ID column (1-based index of the input text):

texts = [
    "Petrobras signed an agreement with the World Bank.",
    "The event took place in Brasília, with no companies involved.",
]

df = org(texts, verbose=True)
print(df)

   Text_ID Organizations
0        1     Petrobras
1        1    World Bank

Saving results

df = org(text, save_path="organizations.xlsx")   # Excel (requires openpyxl)
df = org(text, save_path="organizations.csv")    # CSV

Arguments

Argument	Type	Default	Description
text	str or list[str]	required	Text(s) to be analyzed.
api_key	str	None	API key for the chosen provider. If omitted, read from OPENAI_API_KEY or GEMINI_API_KEY.
provider	str	"openai"	"openai" or "gemini".
model	str	provider default	Model name. Defaults to gpt-4o-mini (OpenAI) or gemini-3.5-flash (Gemini).
save_path	str	None	If given, saves the result to this path (.xlsx or .csv).
unique	bool	False	If True, removes duplicated organization names.
verbose	bool	False	If True, prints progress messages.

Returns

pandas.DataFrame with one row per extracted organization:

• Single text input → column Organizations.
• List input → columns Text_ID and Organizations.

Extraction rules

• Geographic locations (countries, states, cities) are not extracted — organization names only.
• When an organization's name is followed by its acronym in brackets, only the full name is extracted.
• Texts with no organizations return an empty DataFrame.

Migrating from version 0.x

Version 1.0.0 introduces breaking changes:

Version 0.x	Version 1.0.0
from FindOrg import org	from findorg import org (lowercase)
org(text, openai_key="...")	org(text, api_key="...")
save=True (fixed filename)	save_path="organizations.xlsx" (any path, .xlsx or .csv)
OpenAI only, gpt-3.5-turbo	OpenAI or Gemini; default model is now gpt-4o-mini

The change of default model was required because OpenAI is shutting down gpt-3.5-turbo in October 2026.

How to cite

Neves, L. F. F. (2026). FindOrg: Named Entity Recognition for Organizations using LLMs (Version 1.0.0) [Python package]. https://pypi.org/project/FindOrg/

Contact

[e-mail]