> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 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

<CodeGroup>
  ```bash Python icon=Python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
  pip install sie-lancedb
  ```

  ```bash TypeScript icon=js theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
  npm install @superlinked/sie-lancedb @lancedb/lancedb
  ```
</CodeGroup>

## 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

```py Python icon=Python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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):
    text: str = sie.SourceField()
    vector: Vector(sie.ndims()) = sie.VectorField()

db = lancedb.connect("~/.lancedb")
table = db.create_table("docs", schema=Documents, mode="overwrite")

table.add([
    {"text": "Machine learning is a subset of AI."},
    {"text": "Neural networks use multiple layers."},
    {"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.

## 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 theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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 / multivector

`SIEMultiVectorEmbeddingFunction` (registered as `"sie-multivector"`) works with LanceDB's native `MultiVector` type and MaxSim scoring for ColBERT and ColPali models:

```py Python icon=Python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
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, mode="overwrite")
table.add([{"text": "Machine learning is a subset of AI."}])

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

## 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:

```py Python icon=Python theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from sie_lancedb import SIEExtractor

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

extractor.enrich_table(
    table,
    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

* [`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)