Embeddings
Dense vectors for retrieval, clustering, and classification. Batch up to 512 inputs per call.
Package availability
Wordcab SDKs, CLI tools, Helm charts, model weights, and deployment packages are delivered directly to each customer for self-hosted installation. They are not publicly published package-manager artifacts, so install commands in these docs are placeholders until your Wordcab team provides your private package source or offline bundle.
Create embeddings
POST/v1/embeddings
OpenAI-compatible. Returns a dense vector per input. Batch up to 512 inputs per call.
Body
modelstringRequired
Model id. Defaults vary by deployment; bge-m3 and e5-mistral-7b are common.
inputstring | string[]Required
Single string or array of strings. Per-input cap: 8,192 tokens.
encoding_formatstringOptional
float (default) or base64.
dimensionsintegerOptional
Matryoshka dimension reduction for models that support it.
Response
json
{
"object": "list",
"model": "bge-m3",
"data": [
{"object": "embedding", "index": 0, "embedding": [0.012, -0.044, ...]}
],
"usage": {"prompt_tokens": 8, "total_tokens": 8}
}Models
| Model | Dim | Context | Notes |
|---|---|---|---|
bge-m3 | 1024 | 8,192 | Multilingual, default for RAG. |
e5-mistral-7b | 4096 | 32,768 | Strong on long-document retrieval. |
gte-small-en | 384 | 512 | Cheap, fast, English-only. |