---

name: serving-deployment description: "Serve text2vec embeddings and similarity workflows through FastAPI, Jina, or Gradio with explicit request, response, and lifecycle guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

serving-deployment

Use this sub-skill when you need to expose text2vec inference as a service, integration endpoint, or lightweight UI.

Covers

• FastAPI embedding endpoints with POST /emb.
• Jina server/client patterns and hub/network caveats.
• Gradio demo UIs for manual inspection, not production defaults.
• Service-layer composition around embeddings and similarity-search.

Route elsewhere

• Batch CLI encoding from files -> embeddings.
• Pair scoring, vector search, or BM25 ranking -> similarity-search.
• Training or fine-tuning a serving model -> training-finetuning.
• Benchmarking model quality -> evaluation-benchmarks.

Bundled assets

• serving workflows
• troubleshooting
• FastAPI app template

Quick use

1. Read references/serving-workflows.md for endpoint shapes and deployment choices.
2. Use scripts/fastapi_app_template.py as the safe FastAPI scaffold.
3. Check references/troubleshooting.md for optional deps, lazy model loading, host/port binding, CORS, GPU memory, and forever-server issues.

What this sub-skill does not do

• It does not define model training commands.
• It does not implement batch embedding file pipelines.
• It does not evaluate benchmarks or compare leaderboard scores.