Superlinked

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### Setup port forwarding and install SDK

Source: https://superlinked.com/docs/deployment/offline.md

Exposes the gateway service locally and installs the required Python SDK.

```bash
kubectl -n sie port-forward svc/sie-sie-cluster-gateway 8080:8080 &

# Install the Python SDK. Requires Python 3.12; see the SDK README for newer or older Python notes.
pip install sie-sdk
```

--------------------------------

### Run the frontend service

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Installs dependencies and starts the development server for the frontend.

```bash
cd frontend
npm install
npm run dev
```

--------------------------------

### Run the project locally

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Commands to install dependencies and start the backend and frontend services for the demo mode.

```bash
cd backend
pip install -r requirements.txt
python cli_seed_demo.py
python -m uvicorn app.main:app --reload --port 8000

cd ../frontend
npm install
npm run dev
```

--------------------------------

### Install and authenticate W&B

Source: https://superlinked.com/docs/evals/custom.md

Setup commands required before using W&B integration.

```bash
pip install wandb
wandb login
```

--------------------------------

### Inference server usage examples

Source: https://superlinked.com/docs/reference/cli.md

Common patterns for starting the inference server, including default settings, specific hardware targeting, and development modes.

```bash
# Start with defaults (auto-detect GPU, port 8080)
sie-server serve

# Specific port and device
sie-server serve --port 8081 --device cuda

# Load specific bundle
sie-server serve --bundle default

# Load specific models only
sie-server serve --models BAAI/bge-m3,BAAI/bge-reranker-v2-m3

# Use cloud model configs
sie-server serve --models-dir s3://my-bucket/sie-models/

# Development mode with auto-reload
sie-server serve --reload --verbose
```

--------------------------------

### Install the SDK

Source: https://superlinked.com/docs/quickstart.md

Install the required package for your environment using your preferred package manager.

```bash
pip install sie-sdk
```

```bash
npm install @superlinked/sie-sdk   # pnpm and yarn work too
```

--------------------------------

### Install SIE SDK

Source: https://superlinked.com/docs/reference/sdk.md

Install the package via pip.

```bash
pip install sie-sdk
```

--------------------------------

### Install SIE LangChain Package

Source: https://superlinked.com/docs/integrations/langchain.md

Install the required package for your environment.

```bash
pip install sie-langchain
```

```bash
pnpm add @superlinked/sie-langchain
```

--------------------------------

### Start SIE server

Source: https://superlinked.com/docs/reference/troubleshooting.md

Commands to start the SIE server using Docker or the CLI.

```bash
docker run -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cpu-default
```

```bash
sie-server serve
```

--------------------------------

### Install NVIDIA Container Toolkit

Source: https://superlinked.com/docs/deployment/docker.md

Required setup for Ubuntu/Debian systems to enable the --gpus flag in Docker.

```bash
# Ubuntu/Debian
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
```

--------------------------------

### Run Document OCR Locally

Source: https://superlinked.com/docs/examples/document-ocr.md

Commands to clone the repository and start the local Docker environment for the document OCR example.

```bash
git clone https://github.com/superlinked/sie
cd sie/examples/document-ocr
npm install
npm start
```

--------------------------------

### Install sie-qdrant package

Source: https://superlinked.com/docs/integrations/qdrant.md

Install the necessary dependencies for the Qdrant integration.

```bash
pip install sie-qdrant
```

--------------------------------

### Run SIE server and ingest data locally

Source: https://superlinked.com/docs/examples/ecommerce-product-search.md

Commands to start the Docker container, install dependencies, fetch the dataset, and launch the ingestion and search server.

```bash
# 1. Start a local SIE server on CPU.
docker run -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cpu-default
#   With an NVIDIA GPU:
#   docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

# 2. In another terminal: clone, install, fetch data.
git clone https://github.com/superlinked/sie
cd sie/examples/ecommerce-product-search

pip install -r python/requirements.txt
python data/fetch_dataset.py

# 3. Build the index and start the search server.
python python/ingest.py
uvicorn --app-dir python server:app --port 8888
```

--------------------------------

### Matrix evaluation example

Source: https://superlinked.com/docs/reference/cli.md

Example of running a matrix evaluation using a configuration file and a cluster gateway.

```bash
sie-bench matrix configs/eval-matrix.yaml --cluster http://gateway:8080 --workers 2
```

--------------------------------

### Install Backend Dependencies

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Commands to navigate to the backend directory and install required Python packages.

```bash
cd backend
pip install -r requirements.txt
```

--------------------------------

### Install Frontend Dependencies

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Commands to navigate to the frontend directory and install required Node.js packages.

```bash
cd frontend
npm install
```

--------------------------------

### Install Superlinked Native SDK

Source: https://superlinked.com/docs/integrations.md

Installation commands for the Python and TypeScript SDKs.

```bash
pip install sie-sdk
# or
pnpm add @superlinked/sie-sdk
```

--------------------------------

### Start sie-gateway server

Source: https://superlinked.com/docs/reference/cli.md

Commands to start the SIE Gateway server with various configuration options.

```bash
sie-gateway serve [OPTIONS]
```

```bash
# Production health source, normally rendered by Helm
sie-gateway serve --health-mode nats

# Local WebSocket health diagnostic
sie-gateway serve -w http://localhost:8080 --health-mode ws
```

--------------------------------

### Clone and Install Dependencies

Source: https://superlinked.com/docs/reference/contributing.md

Commands to clone the repository and install necessary dependencies using mise.

```bash
git clone https://github.com/superlinked/sie.git
cd sie

# Install mise (if not already installed)
curl https://mise.run | sh

# Install dependencies
mise install
```

--------------------------------

### Install sie-top dependencies

Source: https://superlinked.com/docs/reference/cli.md

Install the optional dependencies required for the TUI functionality.

```bash
pip install 'sie-admin[top]'
```

--------------------------------

### Install SIE SDK

Source: https://superlinked.com/docs/reference/typescript-sdk.md

Use a package manager to add the SDK to your project.

```bash
pnpm add @superlinked/sie-sdk
```

```bash
npm install @superlinked/sie-sdk
```

--------------------------------

### Install sie-weaviate package

Source: https://superlinked.com/docs/integrations/weaviate.md

Install the necessary package to enable SIE integration with Weaviate.

```bash
pip install sie-weaviate
```

--------------------------------

### Run Infinity Container

Source: https://superlinked.com/docs/migrate/infinity.md

Start an Infinity server instance for a specific model.

```bash
# Pin a tag in production; :latest shown for brevity.
# See https://hub.docker.com/r/michaelfeil/infinity/tags
docker run --rm -p 7997:7997 michaelfeil/infinity:latest \
  v2 --model-id BAAI/bge-small-en-v1.5
```

--------------------------------

### GET /readyz

Source: https://superlinked.com/docs/deployment/upgrades.md

Checks the readiness of the specified component.

```APIDOC
## GET /readyz

### Description
Returns the readiness status of the component. May return 503 if the component is not yet ready.

### Method
GET

### Endpoint
/readyz

### Response
#### Success Response (200)
- **body** (string/json) - Returns 'ok' or '{"status": "ready"}' depending on the component.

#### Error Response (503)
- **status** (integer) - Returned when the component is not ready.
```

--------------------------------

### Run SIE Server

Source: https://superlinked.com/docs/migrate/infinity.md

Start the SIE server using the mise tool.

```bash
mise run serve -- -m BAAI/bge-small-en-v1.5
```

--------------------------------

### Install sie-top TUI

Source: https://superlinked.com/docs/deployment/monitoring.md

Install the sie-admin package from the official repository using the specified release tag.

```bash
git clone --branch v0.6.21 --depth 1 https://github.com/superlinked/sie.git
cd sie
pip install './packages/sie_admin[top]'
```

--------------------------------

### Install SIE Chroma packages

Source: https://superlinked.com/docs/integrations/chroma.md

Install the necessary dependencies for Python or TypeScript environments.

```bash
pip install sie-chroma
```

```bash
pnpm add @superlinked/sie-chroma
```

--------------------------------

### Run the backend service

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Starts the Uvicorn server for the backend API.

```bash
cd backend
pip install -r requirements.txt
python -m uvicorn app.main:app --reload --port 8000
```

--------------------------------

### GET /v1/configs/bundles

Source: https://superlinked.com/docs/engine/config-api.md

Lists all available bundles.

```APIDOC
## GET /v1/configs/bundles

### Description
Lists all available bundles and their metadata.

### Method
GET

### Endpoint
/v1/configs/bundles

### Response
#### Success Response (200)
- **bundles** (array) - List of bundle objects.
```

--------------------------------

### Install sie-cluster on AKS

Source: https://superlinked.com/docs/deployment/cloud-azure.md

Configures kubectl for Azure, installs the Helm chart with the AKS overlay, and monitors pod rollout.

```bash
# Configure kubectl from the terraform output, then convert the kubeconfig
# to Azure CLI token auth (the cluster authenticates kubectl through AAD)
$(terraform output -raw kubectl_config_command)
kubelogin convert-kubeconfig -l azurecli

# Install SIE. The -f flag pulls the AKS overlay from the chart's source repo
# at the matching release tag; model_cache_helm_args wires in the blob cache.
helm upgrade --install sie-cluster oci://ghcr.io/superlinked/charts/sie-cluster \
  --version 0.6.18 \
  -f https://raw.githubusercontent.com/superlinked/sie/v0.6.18/deploy/helm/sie-cluster/values-aks.yaml \
  --namespace sie --create-namespace \
  --set "serviceAccount.annotations.azure\.workload\.identity/client-id=$(terraform output -raw sie_workload_identity_client_id)" \
  $(terraform output -raw model_cache_helm_args)

# Wait for rollout
kubectl -n sie get pods -w
```

--------------------------------

### GET /readyz

Source: https://superlinked.com/docs/reference/api.md

Readiness probe to check if the server is ready to accept traffic.

```APIDOC
## GET /readyz

### Description
Readiness probe. On standalone sie-server, returns 200 when the Python process is ready to accept traffic. On the gateway, returns 200 when the process can accept requests.

### Method
GET

### Endpoint
/readyz
```

--------------------------------

### Run the demo locally

Source: https://superlinked.com/docs/examples/stripe-link-fraud.md

Commands to clone the repository, set up the environment, and start the local SIE server and UI.

```bash
git clone https://github.com/superlinked/sie
cd sie/examples/stripe-link-fraud
cp .env.example .env
npm install
npm start
```

--------------------------------

### GET /readyz readiness probe

Source: https://superlinked.com/docs/reference/api.md

Check if the server is ready to accept traffic.

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

--------------------------------

### Common Item usage patterns

Source: https://superlinked.com/docs/reference/sdk.md

Examples of creating Items for different input types.

```python
# Simple text
Item(text="Hello world")

# With ID for tracking
Item(id="doc-1", text="Document text")

# Multimodal (for CLIP, ColPali, etc.)
Item(text="Description", images=["photo.jpg"])

# Document parsing with Docling (PDF/DOCX/HTML)
Item(document={"data": pdf_bytes, "format": "pdf"})
```

--------------------------------

### Initialize Terraform Project

Source: https://superlinked.com/docs/deployment/cloud-gcp.md

Clone the repository and initialize the Terraform environment for the L4 spot GPU example.

```bash
git clone https://github.com/superlinked/terraform-google-sie.git
cd terraform-google-sie/examples/dev-l4-spot

# Set project ID
export TF_VAR_project_id="your-project-id"

# Initialize Terraform
terraform init
```

--------------------------------

### Configure SIE environment variables

Source: https://superlinked.com/docs/examples/taxonomy-classification.md

Set up the connection to the SIE server by copying the example environment file.

```bash
cp .env.example .env
# then edit SIE_BASE_URL (default: http://localhost:8080)
```

--------------------------------

### Configure environment and dependencies

Source: https://superlinked.com/docs/migrate/cohere.md

Sets up the local server, exports the Cohere API key, and installs the cohere package.

```bash
mise run serve -- -m NovaSearch/stella_en_400M_v5,BAAI/bge-reranker-v2-m3
export COHERE_API_KEY=...
uv add cohere
```

--------------------------------

### Run server with custom environment variables

Source: https://superlinked.com/docs/deployment/docker.md

Example of configuring server performance and logging settings at runtime using the -e flag.

```bash
docker run --gpus all -p 8080:8080 \
  -e SIE_DEVICE=cuda \
  -e SIE_MAX_BATCH_REQUESTS=128 \
  -e SIE_MEMORY_PRESSURE_THRESHOLD_PERCENT=85 \
  -e SIE_LOG_JSON=true \
  ghcr.io/superlinked/sie-server:latest-cuda12-default
```

--------------------------------

### Deploy Wine Recommender with Docker Compose

Source: https://superlinked.com/docs/examples/wine-recommender.md

Commands to navigate to the example directory, configure environment variables, and launch the backend and frontend services.

```bash
cd examples/wine-recommender
cp .env.example .env
# If SIE is not running on your host at port 8080, edit CLUSTER_URL in .env.
docker compose up --build
```

--------------------------------

### Start SIE and Qdrant servers

Source: https://superlinked.com/docs/integrations/qdrant.md

Run the SIE embedding server and Qdrant vector database using Docker.

```bash
# SIE server
docker run -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cpu-default

# Or with GPU
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

# Qdrant
docker run -d -p 6333:6333 -p 6334:6334 qdrant/qdrant:v1.13.2
```

--------------------------------

### Install MLflow

Source: https://superlinked.com/docs/evals/custom.md

Command to install the MLflow package required for tracking.

```bash
pip install mlflow
```

--------------------------------

### Complete SDK Workflow

Source: https://superlinked.com/docs/reference/typescript-sdk.md

A comprehensive example demonstrating client initialization, embedding generation, reranking, and entity extraction.

```typescript

// Initialize client
const client = new SIEClient("http://localhost:8080", { timeout: 60000 });

// Dense embeddings
const documents = [
  "Machine learning is a subset of artificial intelligence.",
  "Python is a popular programming language.",
  "Neural networks are inspired by the human brain.",
];

const embeddings = await client.encode(
  "BAAI/bge-m3",
  documents.map((text, i) => ({ id: `doc-${i}`, text }))
);

// Store in vector database
for (const result of embeddings) {
  if (result.dense) {
    // vectorDb.insert(result.id, result.dense);
    console.log(`Stored ${result.id}: ${result.dense.length} dimensions`);
  }
}

// Query with reranking
const query = { text: "What is machine learning?" };

// Stage 1: Vector search
const queryEmb = await client.encode("BAAI/bge-m3", query, { isQuery: true });
// const candidates = await vectorDb.search(queryEmb.dense, { topK: 100 });

// Stage 2: Rerank (using documents directly for this example)
const rerankResult = await client.score(
  "BAAI/bge-reranker-v2-m3",
  query,
  documents.map((text, i) => ({ id: `doc-${i}`, text }))
);

// Top results
console.log("\nTop results:");
for (const entry of rerankResult.scores.slice(0, 3)) {
  console.log(`  ${entry.rank + 1}. ${entry.itemId} (score: ${entry.score.toFixed(3)})`);
}

// Entity extraction
const extractResult = await client.extract(
  "urchade/gliner_multi-v2.1",
  { text: "Elon Musk founded SpaceX and leads Tesla." },
  { labels: ["person", "organization"] }
);

console.log("\nExtracted entities:");
for (const entity of extractResult.entities) {
  console.log(`  ${entity.label}: ${entity.text}`);
}

// Clean up
await client.close();
```

--------------------------------

### Install sie-dspy package

Source: https://superlinked.com/docs/integrations/dspy.md

Install the necessary package and its dependencies via pip.

```bash
pip install sie-dspy
```

--------------------------------

### Install sie-lancedb dependencies

Source: https://superlinked.com/docs/integrations/lancedb.md

Install the necessary packages for Python or TypeScript environments.

```bash
pip install sie-lancedb
```

```bash
pnpm add @superlinked/sie-lancedb @lancedb/lancedb
```

--------------------------------

### Install SIE LlamaIndex Package

Source: https://superlinked.com/docs/integrations/llamaindex.md

Install the required package for Python or TypeScript environments.

```bash
pip install sie-llamaindex
```

```bash
pnpm add @superlinked/sie-llamaindex
```

--------------------------------

### Configure Backend Environment Variables

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Example of a minimal .env file configuration for connecting to live services.

```env
OPENROUTER_API_KEY=sk-or-...
SIE_API_ENDPOINT=https://your-sie-host
# Optional: only needed for managed/auth-enabled SIE clusters.
SIE_API_KEY=
HF_TOKEN=hf_...        # optional
```

--------------------------------

### Install sie-crewai package

Source: https://superlinked.com/docs/integrations/crewai.md

Install the necessary package to enable CrewAI tools and embedders.

```bash
pip install sie-crewai
```

--------------------------------

### Build and Run SIE Server Image

Source: https://superlinked.com/docs/examples/regulatory-intelligence-rag.md

Commands to build the custom server image from the plugin directory and start the container with GPU or CPU support.

```bash
# From this directory
docker build -t sie-regulatory --build-arg SIE_TAG=latest-cuda12-default ./server-plugin
docker run --gpus all -p 8080:8080 sie-regulatory
# CPU-only also works for the tiny sample corpus:
# docker build -t sie-regulatory --build-arg SIE_TAG=latest-cpu-default ./server-plugin
# docker run -p 8080:8080 sie-regulatory
```

--------------------------------

### Install sie-haystack package

Source: https://superlinked.com/docs/integrations/haystack.md

Install the package via pip to include sie-sdk and haystack-ai dependencies.

```bash
pip install sie-haystack
```

--------------------------------

### Install Helm chart from local archive

Source: https://superlinked.com/docs/deployment/offline.md

Use this method for fully air-gapped environments after pulling the chart to a local machine.

```bash
helm pull oci://ghcr.io/superlinked/charts/sie-cluster --version 0.6.6
# Move sie-cluster-0.6.6.tgz onto the air-gapped workstation, then:
helm upgrade --install sie ./sie-cluster-0.6.6.tgz \
  -f values-offline.yaml \
  -n sie --create-namespace
```

--------------------------------

### Start the SIE inference server

Source: https://superlinked.com/docs/reference/cli.md

Command to initiate the inference server with optional configuration flags.

```bash
sie-server serve [OPTIONS]
```

--------------------------------

### Start SIE and Weaviate servers

Source: https://superlinked.com/docs/integrations/weaviate.md

Run the SIE embedding server and the Weaviate vector database instance using Docker.

```bash
# SIE server
docker run -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cpu-default

# Or with GPU
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

# Weaviate
docker run -d -p 8090:8080 -p 50051:50051 \
    -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
    -e DEFAULT_VECTORIZER_MODULE=none \
    cr.weaviate.io/semitechnologies/weaviate:1.36.6
```

--------------------------------

### Install SIE Cluster via Helm

Source: https://superlinked.com/docs/deployment/cloud-aws.md

Configures kubectl and installs the SIE cluster chart with necessary IRSA and worker pool settings.

```bash
# Configure kubectl from the terraform output
$(terraform output -raw kubectl_config_command)

# Install SIE (pulls the chart from GHCR, wires up IRSA from the terraform output)
# `workers.pools.l4.enabled=true` is required; the chart's pools default to enabled: false.
IRSA_ARN=$(terraform output -raw sie_irsa_role_arn)

helm upgrade --install sie oci://ghcr.io/superlinked/charts/sie-cluster \
  --version 0.6.6 \
  -n sie --create-namespace \
  --set "serviceAccount.annotations.eks\.amazonaws\.com/role-arn=$IRSA_ARN" \
  --set workers.pools.l4.enabled=true \
  --set workers.pools.l4.minReplicas=1 \
  --set hfToken.create=true \
  --set hfToken.value="$HF_TOKEN"

# Wait for rollout
kubectl -n sie get pods -w
```

--------------------------------

### Initialize and use SIEAsyncClient

Source: https://superlinked.com/docs/reference/sdk.md

Demonstrates how to use the asynchronous client to encode an item.

```python
from sie_sdk import SIEAsyncClient

async with SIEAsyncClient("http://localhost:8080") as client:
    result = await client.encode("BAAI/bge-m3", Item(text="Hello"))
```

--------------------------------

### GET /v1/models

Source: https://superlinked.com/docs/reference/api.md

Lists all available models.

```APIDOC
## GET /v1/models

### Description
Lists all available models.

### Method
GET

### Endpoint
/v1/models
```

--------------------------------

### Run SIE with Docker

Source: https://superlinked.com/docs/deployment.md

Start the SIE server container using either CPU or GPU configurations.

```bash
# CPU only
docker run -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cpu-default

# With GPU (recommended)
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default
```

--------------------------------

### Seed the bundled demo catalog

Source: https://superlinked.com/docs/examples/semantic-hf-model-search.md

Initializes the local catalog for a no-credentials demo environment.

```bash
cd backend
python cli_seed_demo.py
```

--------------------------------

### GET /healthz

Source: https://superlinked.com/docs/engine/router.md

Checks the liveness of the gateway.

```APIDOC
## GET /healthz

### Description
Gateway liveness check.

### Method
GET

### Endpoint
/healthz
```

--------------------------------

### GET /v1/configs/models

Source: https://superlinked.com/docs/engine/router.md

List all models known to the gateway.

```APIDOC
## GET /v1/configs/models

### Description
List models known to this gateway.

### Method
GET

### Endpoint
/v1/configs/models
```

--------------------------------

### Prepare Shopify dataset

Source: https://superlinked.com/docs/examples/taxonomy-classification.md

Run the necessary scripts to download, clean, and trim the Shopify product dataset for experimentation.

```bash
uv sync
uv run download-shopify-dataset
uv run download-shopify-taxonomy
uv run clean-shopify-dataset
uv run trim-shopify-dataset-to-l3
```

--------------------------------

### Run server with GPU support

Source: https://superlinked.com/docs/deployment/docker.md

Commands to launch the container with access to all available GPUs or specific device IDs.

```bash
docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default
```

```bash
# Use GPU 0 only
docker run --gpus '"device=0"' -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

# Use GPUs 0 and 1
docker run --gpus '"device=0,1"' -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default
```

--------------------------------

### GET /metrics

Source: https://superlinked.com/docs/deployment/upgrades.md

Retrieves Prometheus metrics for the component.

```APIDOC
## GET /metrics

### Description
Exposes Prometheus-formatted metrics for the Gateway, Config service, Python adapter, or worker sidecar.

### Method
GET

### Endpoint
/metrics
```

--------------------------------

### GET /healthz

Source: https://superlinked.com/docs/deployment/upgrades.md

Checks the liveness of the specified component.

```APIDOC
## GET /healthz

### Description
Returns the liveness status of the component (Gateway, Config service, Python adapter, or worker sidecar).

### Method
GET

### Endpoint
/healthz

### Response
#### Success Response (200)
- **body** (string/json) - Returns 'ok' or '{"status": "ok"}' depending on the component.
```

--------------------------------

### GET /v1/configs/epoch

Source: https://superlinked.com/docs/deployment/config-gitops.md

Retrieves the current configuration epoch.

```APIDOC
## GET /v1/configs/epoch

### Description
Retrieves the current configuration epoch.

### Method
GET

### Endpoint
/v1/configs/epoch

### Response
#### Success Response (200)
- **epoch** (integer) - The current epoch value
```

--------------------------------

### Use extract method

Source: https://superlinked.com/docs/reference/sdk.md

Example of extracting entities from text.

```python
result = client.extract(
    "urchade/gliner_multi-v2.1",
    Item(text="Tim Cook leads Apple."),
    labels=["person", "organization"]
)

for entity in result["entities"]:
    print(f"{entity['label']}: {entity['text']} (score: {entity['score']:.2f})")
# Output:
# person: Tim Cook (score: 0.95)
# organization: Apple (score: 0.92)
```

--------------------------------

### Use encode method

Source: https://superlinked.com/docs/reference/sdk.md

Examples of single and batch encoding.

```python
# Single item
result = client.encode("BAAI/bge-m3", Item(text="Hello"))
print(result["dense"][:5])

# Batch
results = client.encode("BAAI/bge-m3", [
    Item(text="First"),
    Item(text="Second"),
])
```

--------------------------------

### GET /v1/models/:model

Source: https://superlinked.com/docs/reference/api.md

Retrieves details for a specific model.

```APIDOC
## GET /v1/models/:model

### Description
Retrieves details for a specific model.

### Method
GET

### Endpoint
/v1/models/:model

### Parameters
#### Path Parameters
- **model** (string) - Required - The identifier of the model.
```

--------------------------------

### Plan and Apply Terraform Configuration

Source: https://superlinked.com/docs/deployment/cloud-gcp.md

Review and deploy the infrastructure changes to GCP.

```bash
# Review changes
terraform plan

# Deploy cluster (15-20 minutes)
terraform apply
```

--------------------------------

### GET /v1/configs/bundles/{id}

Source: https://superlinked.com/docs/engine/config-api.md

Retrieves metadata for a specific bundle.

```APIDOC
## GET /v1/configs/bundles/{id}

### Description
Retrieves bundle metadata as YAML including the adapter list.

### Method
GET

### Endpoint
/v1/configs/bundles/{id}

### Parameters
#### Path Parameters
- **id** (string) - Required - The unique identifier of the bundle.
```

--------------------------------

### Embed and Search with Qdrant and SIE

Source: https://superlinked.com/docs/integrations/qdrant.md

Demonstrates initializing the SIE vectorizer, creating a Qdrant collection, embedding documents, and performing a similarity search.

```python
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from sie_qdrant import SIEVectorizer

# 1. Create vectorizer - this talks to SIE
vectorizer = SIEVectorizer(
    base_url="http://localhost:8080",
    model="NovaSearch/stella_en_400M_v5",
)

# 2. Connect to Qdrant
qdrant = QdrantClient("http://localhost:6333")
try:
    # 3. Create a collection (Stella produces 1024-dim dense vectors)
    qdrant.create_collection(
        collection_name="documents",
        vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
    )

    # 4. Embed texts with SIE, then store in Qdrant
    texts = [
        "Machine learning is a subset of artificial intelligence.",
        "Neural networks are inspired by biological neurons.",
        "Deep learning uses multiple layers of neural networks.",
        "Python is popular for machine learning development.",
    ]
    vectors = vectorizer.embed_documents(texts)
    qdrant.upsert(
        collection_name="documents",
        points=[
            PointStruct(id=i, vector=v, payload={"text": t})
            for i, (t, v) in enumerate(zip(texts, vectors))
        ],
    )

    # 5. Embed query with SIE, then search in Qdrant
    query_vec = vectorizer.embed_query("What is deep learning?")
    results = qdrant.query_points(
        collection_name="documents",
        query=query_vec,
        limit=2,
    )

    for point in results.points:
        print(point.payload["text"])
finally:
    qdrant.close()
```

--------------------------------

### Get Bundle Details

Source: https://superlinked.com/docs/engine/config-api.md

Retrieves metadata for a specific bundle.

```bash
curl http://sie-gateway:8080/v1/configs/bundles/default
```

--------------------------------

### GET /v1/models

Source: https://superlinked.com/docs/engine/router.md

Retrieves the list of models from the gateway registry.

```APIDOC
## GET /v1/models

### Description
Model list from the gateway registry.

### Method
GET

### Endpoint
/v1/models
```

--------------------------------

### Run SIE Server and Configure Environment

Source: https://superlinked.com/docs/migrate/openai.md

Commands to start the Superlinked Inference Engine (SIE) with a preloaded model and set up the local environment for OpenAI API interaction.

```bash
# Start SIE with E5 loaded.
docker run -p 8080:8080 \
  -v sie-hf-cache:/app/.cache/huggingface \
  -e SIE_PRELOAD_MODELS=intfloat/e5-base-v2 \
  ghcr.io/superlinked/sie-server:latest-cpu-default

# Run the OpenAI 'before' script and the SIE 'after' script
# from this page. Compare the printed embeddings.
export OPENAI_API_KEY=sk-...
uv add openai
```
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