https://github.com/tiny-systems/embedding-module
In-cluster text embeddings for Tiny Systems flows. Ships TEI as a curated bundle.
https://github.com/tiny-systems/embedding-module
Last synced: 12 days ago
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In-cluster text embeddings for Tiny Systems flows. Ships TEI as a curated bundle.
- Host: GitHub
- URL: https://github.com/tiny-systems/embedding-module
- Owner: tiny-systems
- License: other
- Created: 2026-05-19T09:00:35.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-06-03T20:25:51.000Z (about 1 month ago)
- Last Synced: 2026-06-03T22:11:27.746Z (about 1 month ago)
- Language: Go
- Size: 72.3 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tiny Systems Embedding Module
In-cluster text embeddings for Tiny Systems flows. Wraps HuggingFace text-embeddings-inference (TEI) and ships it as a curated bundle, so installing this module also provisions the TEI service in the same namespace — no external API, no separate helm install.
## Components
### embed_text
Takes a string, returns its dense vector embedding.
- **Input:** `{ context: any, text: string }`
- **Output:** `{ context: any, embedding: float32[], dims: int }`
- **Error:** `{ context: any, error: string }` when enabled
The component reads `TEI_URL` from env. The platform's install flow wires this automatically when the TEI bundle is enabled. To point at an external embedding endpoint, set `baseURL` in the node settings instead.
## Install
The module declares the `tei` bundle with `DefaultEnabled: true`, so a plain `helm upgrade --install` of the operator chart pulls TEI in as a subchart and starts a Deployment + Service alongside the module.
Default model: `BAAI/bge-small-en-v1.5` — 384-dim, CPU-friendly, decent quality for English RAG. Override via `--set bundles.tei.image.tag=cpu-1.5 --set bundles.tei.modelId=intfloat/multilingual-e5-large` if you need a different model.
The TEI Service lands at `-tei:80` inside the namespace; the install flow sets `TEI_URL` on the module pod's env to match.
## Pairs with
- `database-module` vector_search / vector_upsert components (also via a bundle: `pgvector`) — embed + store + retrieve as a three-component RAG slice.