Quickstart

Start the Server

# Docker (recommended)
docker run -p 8080:8080 ghcr.io/superlinked/sie:latest

# Or with GPU
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie:latest

The server starts on port 8080 with all models available. Models load on first request.

Install the SDK

Python

pip install sie-sdk

TypeScript

pnpm add @sie/sdk

Generate Embeddings

Python

from sie_sdk import SIEClient
from sie_sdk.types import Item

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

# Single item
result = client.encode("BAAI/bge-m3", Item(text="Hello world"))
print(result["dense"].shape)  # (1024,)

# Batch
results = client.encode("BAAI/bge-m3", [
    Item(text="First document"),
    Item(text="Second document"),
])
print(len(results))  # 2

TypeScript

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

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

// Single item
const result = await client.encode("BAAI/bge-m3", { text: "Hello world" });
console.log(result.dense?.length);  // 1024

// Batch
const results = await client.encode("BAAI/bge-m3", [
  { text: "First document" },
  { text: "Second document" },
]);
console.log(results.length);  // 2

Rerank Search Results

Python

query = Item(text="What is machine learning?")
items = [
    Item(text="Machine learning uses algorithms to learn from data."),
    Item(text="The weather is sunny today."),
]

result = client.score("BAAI/bge-reranker-v2-m3", query, items)

for entry in result["scores"]:
    print(f"Rank {entry['rank']}: score={entry['score']:.3f}")
# Rank 0: score=0.998
# Rank 1: score=0.012

TypeScript

const query = { text: "What is machine learning?" };
const items = [
  { text: "Machine learning uses algorithms to learn from data." },
  { text: "The weather is sunny today." },
];

const result = await client.score("BAAI/bge-reranker-v2-m3", query, items);

for (const entry of result.scores) {
  console.log(`Rank ${entry.rank}: score=${entry.score.toFixed(3)}`);
}
// Rank 0: score=0.998
// Rank 1: score=0.012

Extract Entities

Python

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

for entity in result["entities"]:
    print(f"{entity['label']}: {entity['text']}")
# person: Tim Cook
# organization: Apple

TypeScript

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

for (const entity of result.entities ?? []) {
  console.log(`${entity.label}: ${entity.text}`);
}
// person: Tim Cook
// organization: Apple

What’s Next

• Dense Embeddings - output types, query vs document encoding
• Model Catalog - all 85+ supported models