GABRIEL

GABRIEL (Generalized Attribute Based Ratings Information Extraction Library) turns messy qualitative corpora into analysis-ready datasets with GPT. It handles prompting, batching, retries, checkpointing, and audit trails so you can treat “ask the model” workflows like any other measurement instrument. From rating rhetoric across a million speeches to matching product catalogs, you focus on the research question while GABRIEL handles the operations.

📓 Tutorial notebook (start here!): GABRIEL Colab notebook — also available as gabriel_tutorial_notebook.ipynb in this repo if you’d like to download and run it locally.

You can install the GABRIEL Python library from PyPI with pip install openai-gabriel and then import gabriel.

Read our blog post here, our paper with validation experiments and applied examples here, and submit feedback / bugs / feature requests here.

Table of contents

• Why GABRIEL?
• What can you do with GABRIEL?
• Installation
• Quick start
• Task highlights
• Detailed usage
• Multimodal data and web search
• Custom prompts and model routing
• Saving, logging, and resuming
• Development and testing
• Citation

Why GABRIEL?

Most of the evidence social scientists and analysts care about lives in unstructured formats: interviews, speeches, transcripts, product photos, archival scans. Modern GPT models can judge attributes, extract facts, and reason about this material with high fidelity, but building robust pipelines is still tedious. GABRIEL provides:

• 🧠 Human-level comprehension on demand – express the attribute the way you would brief a human coder; GABRIEL packages the prompt, context, and retries for you.
• 📊 Quantitative outputs – ratings (0–100), grounded comparisons, classifications, and structured extractions return as tidy DataFrames with reproducible settings.
• ⚙️ Operational tooling – automatic parallelism (hundreds of concurrent calls), resumable runs, raw response logs, and helper UIs make it safe to scale to very large corpora.
• 🧰 Extensibility – swap instructions with additional_instructions, bring your own templates, or drop down to gabriel.whatever + custom response_fn for bespoke prompts while still reusing the infrastructure.

The tutorial notebook walks through these ideas step-by-step—from setting up an API key to running multimodal analyses—so skim this README, then dive into the notebook for the full guided tour.

What can you do with GABRIEL?

A) Measure attributes on qualitative data

Function	Purpose & Output Scale	Example Use
gabriel.rate	Asks GPT to score each text / image / audio / item on natural language attributes. Output = 0-100 rating.	Measure “populist rhetoric” in a speech; “toxicity” of tweets; “luxury” in ad images.
gabriel.rank	Pairwise comparisons between texts yields ELO-like attribute ratings. Output = grounded, relative z scores for each text.	Rank technologies by “bulkiness” or artworks by “fine brushwork”.
gabriel.classify	Classifies texts / images / audio / items on whether provided labels apply. Output = one or more classes per item.	Tag news articles, product photos, or interview clips into topical categories.
gabriel.extract	Structured fact extraction on each item. Output = string / numeric values.	For each product, provide the “company”, “CEO”, and “year of invention”.
gabriel.discover	Discovers natural language features which discriminate two classes of data.	Identify what distinguishes 5 star vs. 1 star reviews or successful vs. failed startups.

B) Clean data

Function	Purpose & Output Scale	Example Use
gabriel.merge	Creates crosswalks. Output = merged table with GPT-matched identifiers.	Match two distinct job title directories; link patent titles to product names.
gabriel.deduplicate	Detects conceptual duplicates. Maps all duplicates to one representative term.	Collapse “F-18”, “Super Hornet Fighter Jet”, “f-18 hornet” into “F-18”.
gabriel.filter	High-throughput boolean screening. Outputs items which meet natural language condition.	Subset 18M Wikipedia titles to only technologies.
gabriel.deidentify	Replaces PII with realistic, consistent fake PII. Outputs anonymized text + mapping.	Replace names, employers, addresses before sharing interview corpora.

C) Helper tools

Function	Purpose & Output Scale	Example Use
gabriel.codify	Passage coding: highlights snippets in text that match qualitative codes.	Flag sentences about “economic insecurity” in speeches; “stressors” mentioned in interview.
gabriel.compare	Identifies similarities / differences between paired items. Output = list of differences.	Contrast op-eds from different districts; compare two ad campaigns.
gabriel.bucket	Builds taxonomies from many terms. Output = bucket/cluster labels.	Group technologies, artworks, or HR complaints into emergent categories.
gabriel.seed	Enforces a representative distribution / diversity of seeds.	Initialize unique personas that match US population distribution.
gabriel.ideate	Generates many novel scientific theories and filters the cream of the crop.	Procure novel theories on inflation for potential research.
gabriel.debias	Post-process measurements to remove inference bias.	Ensure GPT isn't guessing climate opinions in speeches based on general political lean.
gabriel.load	Prepares a folder of text / image / audio files into a spreadsheet for use in GABRIEL.	Image directory converted into spreadsheet of file paths.
gabriel.view	UI to view sample texts with ratings / passage coding.	Spot-check classify / rating outputs; view coded passages.
gabriel.paraphrase	Rewrites texts consistently per instructions.	Summarize earnings call transcripts to remove company specifics.
gabriel.whatever	Run any GPT prompts, but leverage GABRIEL's parallelization / checkpointing.	Any set of prompts; slots into any pipeline.

Installation

pip install openai-gabriel

# or install directly from GitHub
pip install \
  --force-reinstall \
  git+https://github.com/openai/GABRIEL.git@main

# then run import gabriel

Before running a GABRIEL call, declare your GPT API key:

export OPENAI_API_KEY="sk-..."
# or os.environ['OPENAI_API_KEY'] = "sk-..." inside a Jupyter notebook

Quick start

The tutorial notebook walks through many complete projects; here’s the minimal rating flow the notebook starts with. Paste this into Colab or a notebook cell so you can use await directly:

import os
import pandas as pd

import gabriel

PATH = os.path.expanduser("~/Documents/gabriel_runs")
toy_data = pd.DataFrame(
    {
        "entity": [
            "turkey",
            "pumpkin pie",
            "green bean casserole",
            "cornbread",
        ]
    }
)

attributes = {
    "savory taste": "How savory the dish is",
    "sweet taste": "Dessert-like sweetness",
    "tangy taste": "Notes of tartness or acidity",
}

rate_results = await gabriel.rate(
    toy_data,
    column_name="entity",
    attributes=attributes,
    save_dir=os.path.join(PATH, "toy_rate"),
    model="gpt-5-mini",
    n_runs=1,
    modality="entity",
    reset_files=True,
)
rate_results.head()

The helper returns a pandas.DataFrame with one column per attribute and writes raw model responses + configs to save_dir. Running the same code in a plain Python script just requires wrapping the coroutine with asyncio.run(...).

Task highlights

The tutorial notebook covers full projects end-to-end. The list below matches its main use cases so you can jump to the right helper quickly.

Measurement primitives

• gabriel.rate – assign 0–100 scores per attribute across text, entities, images, audio, or web-sourced context.
• gabriel.rank – pairwise tournaments that surface relative winners with grounded z-scores.
• gabriel.classify – single- or multi-label tagging with label definitions and consensus columns.
• gabriel.extract – turn passages or multimodal product cards into tidy tables with optional schemas.
• gabriel.discover – contrast two labeled corpora to learn discriminating features.

Qualitative coding and review

• gabriel.codify highlights snippets that match qualitative codes and pairs with gabriel.view for UI-based auditing.
• gabriel.compare contrasts paired items (drafts, policies, campaigns) with concise differences/similarities.
• gabriel.bucket groups terms/entities into emergent taxonomies that feed back into rate/classify flows.

Data prep and cleanup

• gabriel.load converts folders of media into spreadsheets with clean IDs and file paths.
• gabriel.merge / gabriel.deduplicate produce fuzzy joins and de-duplicated lists using embeddings plus GPT checks.
• gabriel.filter screens large candidate lists with natural-language conditions.
• gabriel.deidentify replaces PII with realistic stand-ins to protect privacy.

Ideation and custom prompts

• gabriel.ideate and gabriel.seed generate diverse candidates before deeper measurement.
• gabriel.whatever runs bespoke prompts (with optional web search or custom response_fn) while reusing retries, logging, and checkpointing.

Multimodal data and web search

Set modality to text, entity, pdf, image, audio, or web on any measurement helper. Pair gabriel.load with folders of media to build the right DataFrame, and use web_search=True when GPT should gather context before rating or extracting. The tutorial’s county-level example shows how to chain web search → rating → mapping in one flow.

Custom prompts and model routing

• Add clarifications with additional_instructions (e.g., mandate mutually exclusive labels).
• Swap in your own Jinja template_path while keeping retries and checkpoints.
• Drop to gabriel.whatever for fully custom prompts, attachments, or routing logic.

Saving, logging, and resuming

Each run expands save_dir (tilde and environment variables supported), writes structured outputs (file_name CSV/Parquet), and saves raw model payloads under responses/ with metadata for auditability. Leave reset_files=False to resume partially completed runs; delete the folder or pass reset_files=True to start fresh. gabriel.view reads these outputs for quick spot checks, and helpers like gabriel.utils.mapmaker.MapMaker can consume the same files for downstream visualization.

Development and testing

Install development extras and run tests:

pip install -e .[dev]
pytest

Tests rely on the built-in dummy responses, so no API key is necessary. Linting and type checks (ruff, mypy) are included in the dev extras.

Citation

If you use GABRIEL in your research, please cite:

The Generalized Attribute Based Ratings Information Extraction Library (GABRIEL). Hemanth Asirvatham and Elliott Mokski (2026). https://github.com/openai/GABRIEL

License

GABRIEL is released under the Apache 2.0 License. See LICENSE.