API Reference

Extract named entities from text using GLiNER PII model

post
https://integrate.api.nvidia.com/v1/chat/completions

Extract named entities and PII (Personally Identifiable Information) from input text.
The model supports custom entity labels and returns entities with positions, scores,
and tagged text output. The API is OpenAI-compatible, wrapping entity extraction
results in a chat completion format.

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Body Params
string
Defaults to nvidia/gliner-pii

The GLiNER model to use for entity extraction

messages
array of objects
required
length ≥ 1

A list of messages with the text to analyze. Only the last user message
content will be used for entity extraction.

Messages*
string
enum
required

The role of the message author

Allowed:
string
required

For user messages: the text to analyze for entity extraction.
For assistant messages: JSON string containing entity extraction results
with fields: total_entities, entities (array of {text, label,
start, end, score}), and tagged_text.

labels
array of strings

Entity types to detect. None uses the default PII labels
(55 categories including email, phone_number,
ssn, first_name, last_name, address, etc.).

Labels
number
0 to 1
Defaults to 0.5

Confidence threshold for entity detection (0.0 to 1.0).
Lower values detect more entities but may include false positives.

integer
1 to 2048
Defaults to 384

Context window size for processing. Longer texts are
automatically split into chunks with overlap for complete coverage.
Must be greater than overlap.

integer
0 to 512
Defaults to 128

Token overlap between chunks to prevent entity clipping. Must be less than chunk_length.

boolean
Defaults to false

When True, prevents overlapping entity spans. When False, may return
nested entities (e.g., both "John Doe" as a name and "John" as first_name).

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Updated 2 months ago

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