tei

TEI

Hugging Face Text Embeddings Inference server — embed text, rerank candidates, tokenize. Talk to it directly over its HTTP API.

How to call it

Read the base URL from ~/.lab/.env, then curl the TEI API:

TEI_URL=$(grep -E '^TEI_URL=' ~/.lab/.env | cut -d= -f2-)

TEI runs unauthenticated by default. If your deployment is behind auth, add the appropriate header.

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

TEI_URL lives in ~/.lab/.env. Verify connectivity:

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.