Text Clean API

Cleans a text string with optional lowercasing, whitespace collapsing, punctuation stripping, and accent folding. Deterministic and composable with the text-intelligence operations.

What it does

Normalize whitespace, case, and punctuation in text.

When to use it

Reach for Text Clean when you need to answer: “Can you tidy up this free text?” It sits in the Prepare stage of the catalog — clean and de-duplicate raw data.

Endpoint & cost

POST /api/v1/text-clean

Cost: Per credit. Zaps draw down your shared monthly allowance; requests that fail validation are not charged.

Authentication

Send your key as a bearer token: Authorization: Bearer zap_live_…. See Authentication for key management and the alternate header.

Example request

curl https://zapinner.com/api/v1/text-clean \
  -H "Authorization: Bearer zap_live_••••••" \
  -H "Content-Type: application/json" \
  -d '{"text":"  Hello,   WORLD!!  ","options":{"lowercase":true,"collapse_whitespace":true,"strip_punctuation":true}}'

Every response is structured JSON and includes a request_id. Full field-level request and response schemas for this endpoint live in the OpenAPI spec and the API reference.

Errors & limits

Errors use one consistent shape ( error.code, error.message, request_id). See Errors and Rate limits.

SDK

Text Clean has typed methods in both the JavaScript and Python SDKs. Both are implemented and package-ready but not yet published to npm / PyPI — until they ship, vendor the package source or install from a git ref, or call the endpoint over HTTPS with the example above.

Try it

Select Text Clean in the playground to see the request, or run it live with your API key.

Repair — Send messy data. Get production-ready data back.
Map — Turn incoming data into the exact schema your application needs.
Guard — Validate, repair, or reject data before it reaches your application.
Normalize — Turn messy, inconsistent fields into clean, structured, usable data.
Dedupe — Find duplicate records within a dataset.
Transform — Apply declarative field operations to reshape records.
Validate — Check records against a declared field ruleset.
Standardize — Coerce every value into the canonical form for its column's type.
Parse — Parse declared string fields into typed values and name components.
Type Inference — Infer each column's data type by majority vote over its values.
Schema Map — Map source records onto a target schema with per-field confidence.
Redact — Remove detected PII from string fields, replaced with placeholders.
Mask — Mask detected PII while preserving recognizable shape.
Impute — Fill missing values with a chosen strategy.
Normalize Phone — Normalize phone numbers to E.164 format.
Normalize Address — Canonicalize address strings to a consistent form.
Normalize URL — Canonicalize URLs (scheme, host, port, path, query).
Cluster — Group records into clusters by similarity.
Rank — Weighted-score and rank records by chosen fields.
Segment — Assign records to segments with first-match rules.
JSON Repair — Repair common JSON syntax errors into valid JSON.
Flatten — Flatten nested records into dot-path keys.
Unflatten — Rebuild nested records from dot-path keys.
Convert — Convert records between JSON and CSV/TSV.