tei

name

tei

description

Use when the user wants to call Hugging Face Text Embeddings Inference (TEI) to check health or model info, generate embeddings, create sparse embeddings, rerank candidate texts, tokenize input, or use the OpenAI-compatible embeddings endpoint. Trigger phrases include "embed this text", "convert to a vector", "rerank these results", "what model is my TEI server running", "tokenize this sentence", and "check TEI health".

TEI

Hugging Face Text Embeddings Inference server: embed text, rerank candidates, and tokenize input through the TEI HTTP API.

How to call it

Prefer scripts/tei-api.sh for common TEI requests. It resolves TEI_URL and optional TEI_AUTH_HEADER from the plugin-generated config first, then the environment or legacy ~/.lab/.env, and exposes health, info, embed, embed-batch, sparse, rerank, tokenize, and openai-embed.

Configure tei_url and optional sensitive tei_auth_header in Claude plugin settings or Gemini extension settings. The hook writes ${XDG_CONFIG_HOME:-~/.config}/lab-tei/config.env with mode 600.

Use the TEI base URL from plugin config, the runtime environment, or legacy lab configuration:

source "${XDG_CONFIG_HOME:-$HOME/.config}/lab-tei/config.env" 2>/dev/null || true
TEI_URL=${TEI_URL:-$(grep -E '^TEI_URL=' ~/.lab/.env 2>/dev/null | cut -d= -f2-)}
test -n "$TEI_URL" || echo "TEI_URL is not configured"

TEI often runs unauthenticated on trusted networks. If the deployment is behind auth, set tei_auth_header to the full header, for example Authorization: Bearer <token>. Do not put real tokens in examples or committed files.

Common operations

Intent	Request
Health	curl -sS "$TEI_URL/health" -w '\nHTTP %{http_code}\n'
Loaded model / runtime info	curl -sS "$TEI_URL/info"
Embed text	curl -sS -X POST "$TEI_URL/embed" -H 'Content-Type: application/json' -d '{"inputs":"hello world"}'
Embed (batch)	curl -sS -X POST "$TEI_URL/embed" -H 'Content-Type: application/json' -d '{"inputs":["a","b"]}'
Sparse embeddings (SPLADE)	curl -sS -X POST "$TEI_URL/embed_sparse" -H 'Content-Type: application/json' -d '{"inputs":"hello"}'
Rerank against a query	curl -sS -X POST "$TEI_URL/rerank" -H 'Content-Type: application/json' -d '{"query":"fruit","texts":["apple","car"]}'
Tokenize	curl -sS -X POST "$TEI_URL/tokenize" -H 'Content-Type: application/json' -d '{"inputs":"hello world"}'
OpenAI-compatible embeddings	curl -sS -X POST "$TEI_URL/v1/embeddings" -H 'Content-Type: application/json' -d '{"input":"hello","model":"tei"}'

/embed and /rerank depend on the loaded model: an embedding model serves /embed (and /rerank returns a 424 model is not a re-ranker error), while a reranker model serves /rerank. Check /info to see which is loaded. /rerank accepts at most 100 texts per call - split larger batches across requests.

Full API reference: https://huggingface.github.io/text-embeddings-inference/

Configuration

Verify the resolved URL before making model calls:

curl -sS "$TEI_URL/health" -w '\nHTTP %{http_code}\n'

When NOT to use this skill

• The user wants to store or search vectors - that's the qdrant skill.
• The phrase is a "teach/team" typo, or the Text Encoding Initiative XML standard - not this skill.