superlinked-server 1.56.5

Superlinked server enables fast and scalable vector search and storage

SIE: Superlinked Inference Engine is an open-source inference server for small AI models: embeddings, reranking, and extraction.
It runs on your own infrastructure, from a laptop to a production Kubernetes cluster.
The legacy superlinked-server package is deprecated for new projects. Run SIE Server and use sie-sdk instead.

Superlinked Server (Deprecated)

Deprecated for new projects

The superlinked-server package is deprecated for new projects.

This package was part of the legacy Superlinked Python framework stack. It is not the current Superlinked server product.

Superlinked now focuses on SIE: Superlinked Inference Engine, an open-source inference server for small AI models. SIE runs encoders, rerankers, and entity extractors on your own infrastructure, from a laptop to a production Kubernetes cluster.

New projects should run SIE Server and use the SIE SDK instead:

docker run -p 8080:8080 \
  -v sie-hf-cache:/app/.cache/huggingface \
  ghcr.io/superlinked/sie-server:latest-cpu-default

pip install sie-sdk

SIE GitHub: https://github.com/superlinked/sie
SIE docs: https://superlinked.com/docs
Superlinked website: https://superlinked.com

Use SIE Server for new projects

SIE Server is the current Superlinked inference server for semantic search, retrieval, RAG, reranking, and information extraction workloads. It exposes one HTTP API around three primitives:

• encode converts text or images to vectors for semantic search and RAG.
• score reranks query-document pairs for higher-precision retrieval.
• extract pulls entities and structured data from unstructured text.

Use SIE Server when you need to:

• Serve 85+ supported models from one API.
• Serve dense, sparse, multi-vector, and vision embedding models.
• Serve reranking models for higher-quality relevance ranking.
• Serve extraction models for entity extraction and structured information extraction.
• Run inference locally with Docker.
• Deploy a production inference service on Kubernetes.
• Connect applications through the SIE Python SDK, TypeScript SDK, or HTTP API.

The legacy superlinked-server package is not a drop-in replacement for SIE Server. For new applications and new infrastructure, use SIE.

Quickstart with SIE

SIE runs as a Docker container. Your application calls it over HTTP. Start the server, install the SDK, then call encode, score, or extract.

SIE's primary target is x86 Linux nodes with NVIDIA GPUs. The CPU image is useful for trying SIE locally.

1. Run the engine

# macOS (Apple Silicon)
docker run --platform linux/amd64 -p 8080:8080 \
  -v sie-hf-cache:/app/.cache/huggingface \
  ghcr.io/superlinked/sie-server:latest-cpu-default

# Linux (CPU)
docker run -p 8080:8080 \
  -v sie-hf-cache:/app/.cache/huggingface \
  ghcr.io/superlinked/sie-server:latest-cpu-default

# Linux (NVIDIA GPU)
docker run --gpus all -p 8080:8080 \
  -v sie-hf-cache:/app/.cache/huggingface \
  ghcr.io/superlinked/sie-server:latest-cuda12-default

The server starts on port 8080. Models are available through the API and load on first request.

Optional readiness check:

curl http://localhost:8080/readyz
# "ok"

2. Install the SDK

pip install sie-sdk           # Python
pnpm add @superlinked/sie-sdk # TypeScript

3. Generate embeddings

from sie_sdk import SIEClient
from sie_sdk.types import Item

client = SIEClient("http://localhost:8080")

result = client.encode("BAAI/bge-m3", Item(text="Hello world"))
print(result["dense"].shape)  # (1024,)

4. Rerank search results

query = Item(text="What is machine learning?")
items = [
    Item(text="Machine learning uses algorithms to learn from data."),
    Item(text="The weather is sunny today."),
]

result = client.score("BAAI/bge-reranker-v2-m3", query, items)

for entry in result["scores"]:
    print(f"Rank {entry['rank']}: score={entry['score']:.3f}")

5. Extract entities

result = client.extract(
    "urchade/gliner_multi-v2.1",
    Item(text="Tim Cook is the CEO of Apple."),
    labels=["person", "organization"],
)

for entity in result["entities"]:
    print(f"{entity['label']}: {entity['text']}")

What changed?

The superlinked-server package was designed to serve applications built with the deprecated superlinked Python framework.

That legacy framework and server stack are now deprecated for new projects.

Superlinked now focuses on SIE: Superlinked Inference Engine. SIE is the current Superlinked server product for unified model inference across embeddings, reranking, and information extraction.

Should I still use this package?

Use superlinked-server only if you are maintaining an existing deployment that already depends on the legacy Superlinked framework stack.

Do not start new projects with this package. For new development, run SIE Server and use the SIE SDK.

Links

• SIE GitHub: https://github.com/superlinked/sie
• SIE docs: https://superlinked.com/docs
• SIE quickstart: https://superlinked.com/docs/quickstart
• SIE HTTP API reference: https://superlinked.com/docs/reference/api
• SIE models: https://superlinked.com/models
• SIE examples: https://superlinked.com/docs/examples
• Python SDK on PyPI: https://pypi.org/project/sie-sdk/
• Superlinked website: https://superlinked.com