Documentation Index
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Superlinked 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 for deployment options.
Install the sie-chroma package:Use SIEEmbeddingFunction for dense embeddings: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)
SIESparseEmbeddingFunction returns learned sparse vectors (SPLADE / BGE-M3) as dict[int, float]:from sie_chroma import SIESparseEmbeddingFunction
sparse_ef = SIESparseEmbeddingFunction(
base_url="http://localhost:8080",
model="naver/splade-v3",
)
npm install @superlinked/sie-chroma
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,
});
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=...):
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
