lancedb/docs/web/integrations/embedding/superlinked.mdx

---
title: Superlinked
sidebarTitle: Superlinked
---

[Superlinked](https://superlinked.com) is a self-hosted inference engine (SIE) for embedding, reranking, and extraction. The `sie-lancedb` package registers SIE as a first-class embedding function in LanceDB's embeddings registry, so embeddings are computed automatically on insert and search. You need a running SIE instance - see the [Superlinked quickstart](https://superlinked.com/docs) for deployment options.

## Installation {#installation}

<CodeGroup>
```bash Python icon=Python
pip install sie-lancedb
```

```bash TypeScript icon=js
npm install @superlinked/sie-lancedb @lancedb/lancedb
```
</CodeGroup>

## Registered functions {#registered-functions}

Importing `sie_lancedb` registers two embedding functions in LanceDB's registry:

| Name | Purpose |
|---|---|
| `"sie"` | Dense text embeddings |
| `"sie-multivector"` | ColBERT-style late interaction with MaxSim scoring |

Supported parameters on `.create()`:

| Parameter | Type | Description |
|---|---|---|
| `model` | `str` | Any of 85+ SIE-supported models (e.g. `BAAI/bge-m3`, `NovaSearch/stella_en_400M_v5`, `jinaai/jina-colbert-v2`) |
| `base_url` | `str` | URL of the SIE endpoint (e.g. `http://localhost:8080`) |

## Usage {#usage}

```py Python icon=Python
import tempfile
from pathlib import Path

import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
import sie_lancedb  # registers "sie" and "sie-multivector"

sie = get_registry().get("sie").create(
    model="BAAI/bge-m3",
    base_url="http://localhost:8080",
)

class Documents(LanceModel):
    id: int
    text: str = sie.SourceField()
    vector: Vector(sie.ndims()) = sie.VectorField()

db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "superlinked-demo"))
table = db.create_table("docs", schema=Documents)

table.add([
    {"id": 1, "text": "Machine learning is a subset of AI."},
    {"id": 2, "text": "Neural networks use multiple layers."},
    {"id": 3, "text": "Python is popular for ML development."},
])

results = table.search("What is deep learning?").limit(3).to_list()
```

LanceDB handles embedding generation for both inserts and queries automatically, based on the `SourceField` / `VectorField` declarations on the schema. The `id` column gives each document a stable key, which [entity extraction](#entity-extraction) uses to merge its results back.

## Hybrid search with reranker {#hybrid-search-with-reranker}

`SIEReranker` plugs into LanceDB's hybrid search pipeline. It uses SIE's cross-encoder `score()` to rerank combined vector + full-text search results. You need a full-text search index on the column first:

```py Python icon=Python
from sie_lancedb import SIEReranker

# Create FTS index for hybrid search
table.create_fts_index("text", replace=True)

results = (
    table.search("What is deep learning?", query_type="hybrid")
    .rerank(SIEReranker(model="jinaai/jina-reranker-v2-base-multilingual"))
    .limit(5)
    .to_list()
)

for r in results:
    print(f"{r['_relevance_score']:.3f}  {r['text']}")
```

The reranker also works with pure vector or pure FTS search via `.rerank()`.

## ColBERT and multivector {#colbert-and-multivector}
`SIEMultiVectorEmbeddingFunction` (registered as `"sie-multivector"`) works with LanceDB's native `MultiVector` type and MaxSim scoring for ColBERT and ColPali models. In LanceDB Python 0.40.0, a multivector that LanceDB generates on insert is stored as null unless the document's token count happens to equal the per-token dimension, so this example computes the document multivectors with `compute_source_embeddings()` and passes them in the `vector` column. LanceDB still generates the query multivector automatically:

```py Python icon=Python
from lancedb.pydantic import MultiVector

sie_colbert = get_registry().get("sie-multivector").create(
    model="jinaai/jina-colbert-v2",
    base_url="http://localhost:8080",
)

class ColBERTDocs(LanceModel):
    text: str = sie_colbert.SourceField()
    vector: MultiVector(sie_colbert.ndims()) = sie_colbert.VectorField()

table = db.create_table("colbert_docs", schema=ColBERTDocs)

# Compute the document multivectors and store them with their text
texts = ["Machine learning is a subset of AI."]
vectors = sie_colbert.compute_source_embeddings(texts)
table.add([{"text": text, "vector": vector} for text, vector in zip(texts, vectors)])

# MaxSim search - query and document multivectors compared token-by-token
results = table.search("What is ML?").limit(5).to_list()
```

## Entity extraction {#entity-extraction}

`SIEExtractor` adds entity extraction to LanceDB's data-enrichment workflows. Extract entities from a text column and merge the results back as a structured Arrow column - enabling filtered search on extracted entities. This example enriches the `docs` table from [Usage](#usage), matching rows on its `id` column:

```py Python icon=Python
from sie_lancedb import SIEExtractor

extractor = SIEExtractor(
    base_url="http://localhost:8080",
    model="urchade/gliner_multi-v2.1",
)

docs = db.open_table("docs")
extractor.enrich_table(
    docs,
    source_column="text",
    target_column="entities",
    labels=["person", "technology", "organization"],
    id_column="id",
)
```

The `entities` column stores structured Arrow data (`list<struct<text, label, score, start, end, bbox>>`), so you can filter on extracted entities in queries.

## Links {#links}

- [`sie-lancedb` on PyPI](https://pypi.org/project/sie-lancedb/)
- [`@superlinked/sie-lancedb` on npm](https://www.npmjs.com/package/@superlinked/sie-lancedb)
- [Superlinked on GitHub](https://github.com/superlinked/sie)
- [Superlinked docs](https://superlinked.com/docs)