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Superlinked 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 for deployment options.

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Installation

pip install sie-lancedb

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Registered functions

Importing sie_lancedb registers two embedding functions in LanceDB’s registry:
NamePurpose
"sie"Dense text embeddings
"sie-multivector"ColBERT-style late interaction with MaxSim scoring
Supported parameters on .create():
ParameterTypeDescription
modelstrAny of 85+ SIE-supported models (e.g. BAAI/bge-m3, NovaSearch/stella_en_400M_v5, jinaai/jina-colbert-v2)
base_urlstrURL of the SIE endpoint (e.g. http://localhost:8080)

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Usage

Python
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.

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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:
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().

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ColBERT / multivector

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

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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:
Python
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.