Embeddings

Turn text into vectors for RAG, semantic search, clustering, and recommendations. The embedding model is text-embedding-v4.

Supported models

Model	Dim	Ctx	Price / 1M	Best for
text-embedding-v4	1024	8K	$0.13	General, English-leaning

Example

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://test.sealink.io/v1",
    api_key="<your-sealink-key>",
)

# Single string
res = client.embeddings.create(
    model="text-embedding-v4",
    input="SeaLink helps SEA developers ship AI faster."
)
vec = res.data[0].embedding   # 1024-dimensional vector

# Batch (recommended for performance)
texts = ["Doc 1 content", "Doc 2 content", "Doc 3 content"]
res = client.embeddings.create(model="text-embedding-v4", input=texts)
vectors = [d.embedding for d in res.data]

Which one?

text-embedding-v4: General-purpose text embedding model for RAG, semantic search, and recommendation baselines. It returns 1024-dimensional vectors.

Performance tips

Prefer batch input (array form) to reduce request overhead and improve throughput.
If you use cosine similarity, L2-normalize before storing so retrieval can use dot product.
Chunk size: 500-800 tokens with 50-100 token overlap is a safe default.
Before launch, evaluate retrieval quality, reranking, and cost with a representative query set.