The FAQ ships as an offline library for Node.js and Python. Both packages contain the same faq.json and make no network calls.
| Node.js | Python | |
|---|---|---|
| Package | @fxfunds/platform-faq |
fxfunds-platform-faq |
| Install | npm install @fxfunds/platform-faq |
pip install fxfunds-platform-faq |
| Import | require("@fxfunds/platform-faq") |
import fxfunds_platform_faq |
| CLI | npx @fxfunds/platform-faq |
fxfunds-faq or python -m fxfunds_platform_faq |
| Runtime | Node.js 18+ | Python 3.9+ |
| Command | Shows |
|---|---|
fxfunds-faq |
Topics and question counts |
fxfunds-faq search "withdrawal fees" |
Best keyword matches |
fxfunds-faq category payments |
All questions in a topic |
fxfunds-faq status not-located |
All questions with a status |
fxfunds-faq show faq-04 |
One question |
fxfunds-faq all --json |
Everything as JSON |
Topic IDs: platform, trading, payments, crypto, security, about. Status values: verified, self-described, unverified, not-located, guidance.
| Node.js | Python | Returns |
|---|---|---|
all() |
all_questions() |
Every question in FAQ order |
categories() |
categories() |
Topics with counts |
byCategory(id) |
by_category(id) |
Questions in a topic |
byStatus(status) |
by_status(status) |
Questions with a status |
get(id) |
get(id) |
One question or undefined / None |
search(query, limit = 5) |
search(query, limit=5) |
Keyword matches |
format(q) |
format_answer(q) |
Plain-text answer with label, source and date |
FAQ, STATUS_LABELS, LAST_CHECKED |
same | Raw data |
Search ignores common words, then scores each question: a match in the question counts 3, a match in the answer counts 1. Ties keep FAQ order. Node.js and Python return the same results; the tests check this.
| Field | Example |
|---|---|
id |
faq-10 |
category |
payments |
question |
Which payment methods are listed by the platform? |
answer |
FXFunds project materials describe USDT … |
status |
unverified |
source |
FXFunds project materials (repository briefs) |
last_checked |
2026-10-19 |
dataset/fxfunds_platform_faq.csv has the same questions with an extra category_name and status_label column. It is published on Hugging Face and Kaggle.