> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trychroma.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-chroma` package exposes SIE as a Chroma `EmbeddingFunction`, giving you access to 85+ dense and sparse text embedding models from a single endpoint. You need a running SIE instance; see the [Superlinked quickstart](https://superlinked.com/docs) for deployment options.

<Tabs>
  <Tab title="Python" icon="python">
    Install the `sie-chroma` package:

    ```bash theme={null}
    pip install sie-chroma
    ```

    Use `SIEEmbeddingFunction` for dense embeddings:

    ```python theme={null}
    import chromadb
    from sie_chroma import SIEEmbeddingFunction

    embedding_function = SIEEmbeddingFunction(
        base_url="http://localhost:8080",
        model="BAAI/bge-m3",
    )

    client = chromadb.Client()
    collection = client.create_collection(
        name="documents",
        embedding_function=embedding_function,
    )

    collection.add(
        documents=[
            "Machine learning is a subset of artificial intelligence.",
            "Neural networks are inspired by biological neurons.",
            "Deep learning uses multiple layers of neural networks.",
        ],
        ids=["doc1", "doc2", "doc3"],
    )

    results = collection.query(query_texts=["What is deep learning?"], n_results=2)
    ```

    For hybrid search on Chroma Cloud, `SIESparseEmbeddingFunction` returns learned sparse vectors (SPLADE / BGE-M3) as `dict[int, float]`:

    ```python theme={null}
    from sie_chroma import SIESparseEmbeddingFunction

    sparse_ef = SIESparseEmbeddingFunction(
        base_url="http://localhost:8080",
        model="naver/splade-v3",
    )
    ```
  </Tab>

  <Tab title="TypeScript" icon="js">
    ```bash theme={null}
    npm install @superlinked/sie-chroma
    ```

    ```typescript theme={null}
    import { ChromaClient } from "chromadb";
    import { SIEEmbeddingFunction } from "@superlinked/sie-chroma";

    const embedder = new SIEEmbeddingFunction({
      baseUrl: "http://localhost:8080",
      model: "BAAI/bge-m3",
    });

    const client = new ChromaClient();
    const collection = await client.createCollection({
      name: "documents",
      embeddingFunction: embedder,
    });

    await collection.add({
      ids: ["doc1", "doc2", "doc3"],
      documents: [
        "Machine learning is a subset of artificial intelligence.",
        "Neural networks are inspired by biological neurons.",
        "Deep learning uses multiple layers of neural networks.",
      ],
    });

    const results = await collection.query({
      queryTexts: ["What is deep learning?"],
      nResults: 2,
    });
    ```
  </Tab>
</Tabs>

## Multimodal

Chroma's `EmbeddingFunction` protocol accepts text input only. For image embedding with SIE-supported multimodal models (CLIP, SigLIP, ColPali), use the SIE SDK directly to pre-compute embeddings and pass them to Chroma via `collection.add(embeddings=...)`:

```python theme={null}
from sie_sdk import SIEClient
from sie_sdk.types import Item
import chromadb

sie = SIEClient("http://localhost:8080")
chroma = chromadb.Client()
collection = chroma.create_collection("images")

results = sie.encode(
    "openai/clip-vit-large-patch14",
    [Item(images=["img1.jpg"]), Item(images=["img2.jpg"])],
    output_types=["dense"],
)

collection.add(
    ids=["img1", "img2"],
    embeddings=[r["dense"].tolist() for r in results],
    metadatas=[{"path": "img1.jpg"}, {"path": "img2.jpg"}],
)
```

## Links

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