Pangram Labs Quickstart Guide

Usage

Install pangram

pip install pangram-sdk

Add your API key

Either export it as an environment variable:

export PANGRAM_API_KEY=<your API key>

Or pass it directly to the constructor:

from pangram import Pangram

my_api_key = ''  # Fill this in with your API key.
pangram_client = Pangram(api_key=my_api_key)

Make a request

Main prediction

Returns detailed analysis with AI-assistance detection and segment-level metrics The SDK submits to Pangram’s async inference API and waits for the completed result.

from pangram import Pangram

pangram_client = Pangram()
result = pangram_client.predict(text)
stage = result['stage']  # "STAGE_SUCCESS" after predict() completes.

# Analysis with AI-assistance detection.
fraction_ai = result['fraction_ai']
fraction_ai_assisted = result['fraction_ai_assisted']
fraction_human = result['fraction_human']
num_ai_segments = result['num_ai_segments']
# Access individual window classifications
for window in result['windows']:
    label = window['label']
    ai_assistance_score = window['ai_assistance_score']
    confidence = window['confidence']

Upload files

Use predict_file() or predict_files() when you want Pangram to extract text from .docx, .pdf, or .rtf documents and create AI detection results. Each result includes the extracted text, prediction fields, window-level analysis, and the uploaded filename. Set public_dashboard_link=True to include a dashboard_link.

from pangram import Pangram

pangram_client = Pangram()

result = pangram_client.predict_file(
    "path/to/document.docx",
    public_dashboard_link=True,
)

print(result["dashboard_link"])
print(result["prediction_short"])
print(result["filename"])

To upload multiple files in one request:

results = pangram_client.predict_files(
    ["path/to/first.docx", "path/to/second.pdf"],
    public_dashboard_link=True,
)

for result in results:
    print(result["dashboard_link"])

Submit a Bulk API job

Use the Bulk API for asynchronous AI detection across many inputs. Submit either a text list or an items list. items can include customer-defined id values that are returned with item status and results.

Bulk jobs are processed asynchronously. Completion time depends on the number and length of submitted items and current system load. Use get_bulk_status() or wait_for_bulk() to monitor progress.

from pangram import Pangram

pangram_client = Pangram()

bulk = pangram_client.submit_bulk(items=[
    {"id": "row-001", "text": "First text to analyze"},
    {"id": "row-002", "text": "Second text to analyze"},
])

bulk_id = bulk["bulk_id"]
status = pangram_client.wait_for_bulk(bulk_id, poll_interval=2)
results = pangram_client.get_bulk_results(bulk_id)

for item in results["items"]:
    if item["result"] is not None:
        print(item["id"], item["result"]["prediction_short"])

for failed in results["failed_items"]:
    print(failed["id"], failed["error"])

You can also inspect jobs without waiting:

status = pangram_client.get_bulk_status(bulk_id)
items = pangram_client.get_bulk_items(bulk_id, offset=0, limit=100)
results_page = pangram_client.get_bulk_results_page(bulk_id, offset=0, limit=100)

For large jobs, use get_bulk_results_page() in a loop instead of get_bulk_results() to process one page at a time without holding the full result set in memory:

offset = 0
limit = 1000

while True:
    page = pangram_client.get_bulk_results_page(bulk_id, offset=offset, limit=limit)
    for item in page["items"]:
        process(item)
    for failed in page["failed_items"]:
        handle_failure(failed)

    offset += limit
    if offset >= page["total_items"]:
        break

Check for Plagiarism

The plagiarism detection API helps you identify potential plagiarism by comparing text against a vast database of online content:

from pangram import Pangram

pangram_client = Pangram()

text = "Text to check for plagiarism"
result = pangram_client.check_plagiarism(text)

if result['plagiarism_detected']:
    print(f"Plagiarism detected! {result['percent_plagiarized']}% of the text may be plagiarized.")
    for content in result['plagiarized_content']:
        print(f"Found match at {content['source_url']}")
        print(f"Matched text: {content['matched_text']}")

The plagiarism detection response includes

Whether plagiarism was detected
List of plagiarized content with source URLs
Total number of sentences checked
List of plagiarized sentences
Percentage of text that was plagiarized