How do you build data extraction with an OpenAI-compatible API?

Key facts

Endpoint	POST https://api.plugsky.com/v1/chat/completions with JSON mode and an explicit schema
Compatibility	Same shape as OpenAI; change base_url and model name
Validation	Schema checks plus business rules; one corrective retry, then human review
Models	30+ models; plugsky-micro and plugsky-lite handle uniform documents efficiently
Pricing model	Flat monthly self-serve plans with unlimited fair-use usage
Free tier	Free plan with 2 free AI models, no card required
Throughput	Bounded concurrency with idempotency keys; the batch endpoint is coming soon
Governance	Scoped keys and audit logs per extraction pipeline

TL;DR

• JSON mode gives structure; your validators give correctness.
• Define nullability explicitly so missing data is visible.
• Retry once with the exact validation error, then queue for review.
• Make writes idempotent with a document hash key.
• Track per-field accuracy and straight-through processing, not aggregate accuracy.

How it works, step by step

1. Change the base URL to Plugsky and confirm an existing structured-output request parses unchanged.
2. Write the extraction schema with types, formats, allowed values and explicit nullability.
3. Send the document with JSON mode and a concise instruction to extract only schema fields.
4. Validate each field; on failure retry once with the exact validation error included.
5. Flag low-confidence or unresolved fields and route them to a human review queue.
6. Persist results with idempotency keys and monitor per-field accuracy on a golden set.

Original data

Try it yourself

Open the OpenAI-compatible API tester →

Architecture of an extraction service

Five parts around one call:

• Ingest: normalize the document, strip markup and keep page or section markers as provenance.
• Schema registry: versioned schemas per document type, including nullability and formats.
• Extractor: JSON-mode call with the document, schema and a small number of examples.
• Validator: schema, format and business-rule checks, with one corrective retry.
• Review queue: flagged fields routed to humans with the source context attached.

Implementation details

Choices that prevent silent data corruption:

• Require explicit nulls for absent fields; an omitted key is indistinguishable from a parsing bug.
• Normalize formats in code — dates, amounts and identifiers — rather than trusting the model to standardize them.
• Keep examples minimal and representative; too many examples bloat prompts and bias extraction toward their formatting.
• Use a document hash as the idempotency key so retries and replays never duplicate records.
• Cap max_tokens so an extraction cannot ramble into an essay about the document.
• For long documents, extract per section and merge, rather than truncating the input.

Evaluation and limitations

Score fields, not documents:

• Per-field precision and recall: a 99% document-level score can hide a failing critical field.
• Formatted-value accuracy: exact match for dates, currencies and identifiers.
• Invalid-output rate: schema failures per thousand documents.
• Review yield: reviewer agreement with flagged items — calibration for your thresholds.
• Straight-through processing: share of documents requiring no human touch.

Limitations: no correctness guarantee without validation and review, scanned inputs need OCR first, schema changes require re-evaluation, long documents exceed context limits if you do not section them, and the batch endpoint is coming soon — bulk runs today need queueing, concurrency limits and retries.

Honest comparison

Capability	Extraction on Plugsky API	Hosted document AI service	Custom NER models
Setup	Schema plus prompt in your stack	Vendor document models	Labeling and training
Flexibility	Any field set you define	Prebuilt document types	Depends on training data
Validation	Your code, one corrective retry	Vendor confidence APIs	Model metrics
Integration	OpenAI-compatible call	Service SDK	MLOps pipeline
Cost shape	Flat monthly self-serve, unlimited fair use	Per-page pricing	Engineering and GPU time

Frequently asked questions

How does JSON mode help extraction?

It constrains the response to a schema so your parser receives structured fields rather than prose, which removes most post-processing and retry logic.

What should happen when a field is missing?

Return an explicit null with a confidence marker and validate downstream. Silent omission hides data problems.

How many retries should I allow?

One corrective retry with the exact validation error, then route to review. More retries add latency without new information.

How do I process documents in bulk?

The batch endpoint is coming soon. Today, run a queue with bounded concurrency, idempotency keys and backoff, and store failures for reprocessing.

Which model is best for extraction?

Uniform documents run well on plugsky-micro or plugsky-lite, while messy or ambiguous documents may benefit from a stronger alias. Benchmark per document type.

Can extraction drive automated decisions?

Only with validation and review gates. Keep irreversible actions behind a human or a rule-based check until field accuracy is proven over time.

Can I try extraction for free?

Yes. JSON mode is live, the free plan includes two free models with no card, and the 14-day full-access trial lets you evaluate stronger aliases on your documents.