Overview

The extract primitive pulls structured data from unstructured content. It handles named entity recognition, relation extraction, text classification, and vision tasks like captioning and OCR.

Quick Example

Python

from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")

text = Item(text="Apple CEO Tim Cook announced the iPhone 16 in Cupertino.")

result = client.extract(
    "urchade/gliner_multi-v2.1",
    text,
    labels=["person", "organization", "product", "location"]
)

for entity in result["entities"]:
    print(f"{entity['label']}: {entity['text']} (score: {entity['score']:.2f})")
# organization: Apple (score: 0.95)
# person: Tim Cook (score: 0.93)
# product: iPhone 16 (score: 0.89)
# location: Cupertino (score: 0.87)

TypeScript

import { SIEClient } from "@sie/sdk";

const client = new SIEClient("http://localhost:8080");

const text = { text: "Apple CEO Tim Cook announced the iPhone 16 in Cupertino." };

const result = await client.extract(
  "urchade/gliner_multi-v2.1",
  text,
  { labels: ["person", "organization", "product", "location"] }
);

for (const entity of result.entities) {
  console.log(`${entity.label}: ${entity.text} (score: ${entity.score.toFixed(2)})`);
}
// organization: Apple (score: 0.95)
// person: Tim Cook (score: 0.93)
// product: iPhone 16 (score: 0.89)
// location: Cupertino (score: 0.87)

await client.close();

Named Entity Recognition (NER)

GLiNER models extract entities with zero-shot label support. Define your own entity types at query time.

Custom Entity Types

No predefined schema. Specify any labels you need:

Python

# Domain-specific entities
result = client.extract(
    "urchade/gliner_multi-v2.1",
    Item(text="The merger between Acme Corp and Beta Inc requires FTC approval."),
    labels=["company", "regulatory_body", "legal_action"]
)

for entity in result["entities"]:
    print(f"{entity['label']}: {entity['text']}")
# company: Acme Corp
# company: Beta Inc
# regulatory_body: FTC

TypeScript

// Domain-specific entities
const result = await client.extract(
  "urchade/gliner_multi-v2.1",
  { text: "The merger between Acme Corp and Beta Inc requires FTC approval." },
  { labels: ["company", "regulatory_body", "legal_action"] }
);

for (const entity of result.entities) {
  console.log(`${entity.label}: ${entity.text}`);
}
// company: Acme Corp
// company: Beta Inc
// regulatory_body: FTC

Entity Positions

Entities include character positions for highlighting or further processing:

Python

result = client.extract(
    "urchade/gliner_multi-v2.1",
    Item(text="Tim Cook works at Apple."),
    labels=["person", "organization"]
)

for entity in result["entities"]:
    print(f"{entity['label']}: '{entity['text']}' at positions [{entity['start']}:{entity['end']}]")
# person: 'Tim Cook' at positions [0:8]
# organization: 'Apple' at positions [18:23]

TypeScript

const result = await client.extract(
  "urchade/gliner_multi-v2.1",
  { text: "Tim Cook works at Apple." },
  { labels: ["person", "organization"] }
);

for (const entity of result.entities) {
  console.log(`${entity.label}: '${entity.text}' at positions [${entity.start}:${entity.end}]`);
}
// person: 'Tim Cook' at positions [0:8]
// organization: 'Apple' at positions [18:23]

Batch Extraction

Process multiple documents efficiently:

Python

documents = [
    Item(id="doc-1", text="Microsoft acquired Activision for $69 billion."),
    Item(id="doc-2", text="Sundar Pichai leads Google's AI initiatives."),
]

results = client.extract(
    "urchade/gliner_multi-v2.1",
    documents,
    labels=["person", "organization", "money"]
)

for result in results:
    print(f"\n{result['id']}:")
    for entity in result["entities"]:
        print(f"  {entity['label']}: {entity['text']}")

TypeScript

const documents = [
  { id: "doc-1", text: "Microsoft acquired Activision for $69 billion." },
  { id: "doc-2", text: "Sundar Pichai leads Google's AI initiatives." },
];

const results = await client.extract(
  "urchade/gliner_multi-v2.1",
  documents,
  { labels: ["person", "organization", "money"] }
);

for (const result of results) {
  console.log(`\n${result.id}:`);
  for (const entity of result.entities) {
    console.log(`  ${entity.label}: ${entity.text}`);
  }
}

Response Format

The ExtractResult contains different fields based on the extraction type:

Field	Type	When Present
id	str | None	Always (if provided)
entities	list[Entity]	NER models (GLiNER)
relations	list[Relation]	Relation extraction (GLiREL)
classifications	list[Classification]	Classification models (GLiClass)
objects	list[DetectedObject]	Object detection (GroundingDINO, OWLv2)
data	dict	Document models (Donut)

Entity Fields

Field	Type	Description
text	str	Extracted text span
label	str	Entity type
score	float	Confidence score (0-1)
start	int	Start character position
end	int	End character position

HTTP API

The server defaults to msgpack. For JSON responses:

curl -X POST http://localhost:8080/v1/extract/urchade/gliner_multi-v2.1 \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -d '{
    "items": [{"text": "Tim Cook is the CEO of Apple."}],
    "params": {"labels": ["person", "organization"]}
  }'

Response:

{
  "model": "urchade/gliner_multi-v2.1",
  "items": [
    {
      "entities": [
        {"text": "Tim Cook", "label": "person", "score": 0.93, "start": 0, "end": 8},
        {"text": "Apple", "label": "organization", "score": 0.95, "start": 24, "end": 29}
      ]
    }
  ]
}

What’s Next

• Vision tasks - image captioning, OCR, and document understanding
• Relations & classification - relation extraction and text classification
• Full model catalog - all supported models