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Fix formatting issues #218

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Merged
prrao87 merged 1 commit into from
Apr 21, 2026
Merged

Fix formatting issues #218

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7 changes: 6 additions & 1 deletion docs/integrations/embedding/superlinked.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -8,9 +8,11 @@ sidebarTitle: Superlinked
## Installation

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

```bash TypeScript icon=js
npm install @superlinked/sie-lancedb @lancedb/lancedb
```
</CodeGroup>
Expand All @@ -33,6 +35,7 @@ Supported parameters on `.create()`:

## Usage

```py Python icon=Python
import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
Expand Down Expand Up @@ -65,6 +68,7 @@ LanceDB handles embedding generation for both inserts and queries automatically,

`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
Expand All @@ -87,6 +91,7 @@ The reranker also works with pure vector or pure FTS search via `.rerank()`.

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

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

sie_colbert = get_registry().get("sie-multivector").create(
Expand All @@ -109,7 +114,7 @@ results = table.search("What is ML?").limit(5).to_list()

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

extractor = SIEExtractor(
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2 changes: 0 additions & 2 deletions docs/snippets/quickstart.mdx
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Expand Up @@ -18,8 +18,6 @@ export const PyQuickstartVectorSearch1Async = "# Let's search for vectors simila

export const PyQuickstartVectorSearch2 = "# Let's search for vectors similar to \"wizard\"\nquery_vector = [0.7, 0.3, 0.5]\n\nresults = table.search(query_vector).limit(2).to_polars()\nprint(results)\n";

export const TsQuickstartOutputPandas = "result = await table.search(queryVector).limit(2).toArray();\n";

export const TsQuickstartAddData = "const moreData = [\n { id: \"7\", text: \"mage\", vector: [0.6, 0.3, 0.4] },\n { id: \"8\", text: \"bard\", vector: [0.3, 0.8, 0.4] },\n];\n\n// Add data to table\nawait table.add(moreData);\n";

export const TsQuickstartCreateTable = "const data = [\n { id: \"1\", text: \"knight\", vector: [0.9, 0.4, 0.8] },\n { id: \"2\", text: \"ranger\", vector: [0.8, 0.4, 0.7] },\n { id: \"9\", text: \"priest\", vector: [0.6, 0.2, 0.6] },\n { id: \"4\", text: \"rogue\", vector: [0.7, 0.4, 0.7] },\n];\nlet table = await db.createTable(\"adventurers\", data, { mode: \"overwrite\" });\n";
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