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name: Create and publish a Docker image | ||
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# Configures this workflow to run every time a change is pushed to the branch called `release`. | ||
on: | ||
push: | ||
branches: | ||
- main | ||
tags: | ||
- "v*.*.*" | ||
paths-ignore: | ||
- 'README.md' | ||
pull_request: | ||
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# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds. | ||
env: | ||
REGISTRY: ghcr.io | ||
IMAGE_NAME: substratusai/sentence-transformers-api | ||
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# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu. | ||
jobs: | ||
build-and-push-image: | ||
runs-on: ubuntu-latest | ||
# Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job. | ||
permissions: | ||
contents: read | ||
packages: write | ||
# | ||
steps: | ||
- name: Checkout repository | ||
uses: actions/checkout@v4 | ||
# Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here. | ||
- name: Log in to the Container registry | ||
uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 | ||
with: | ||
registry: ${{ env.REGISTRY }} | ||
username: ${{ github.actor }} | ||
password: ${{ secrets.GITHUB_TOKEN }} | ||
# This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels. | ||
- name: Extract metadata (tags, labels) for Docker | ||
id: meta | ||
uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 | ||
with: | ||
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} | ||
# This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages. | ||
# It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository. | ||
# It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step. | ||
- name: Build and push Docker image | ||
uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 | ||
with: | ||
context: . | ||
push: true | ||
tags: ${{ steps.meta.outputs.tags }} | ||
labels: ${{ steps.meta.outputs.labels }} |
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# Sentence Transformers API | ||
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OpenAI compatible endpont for Sentence Transformer embedding models | ||
OpenAI compatible embedding endpoint that uses Sentence Transformer for embedding models | ||
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## Usage (Docker) | ||
Run the API locally using Docker: | ||
``` | ||
docker run -p 8080:8080 -d ghcr.io/substratusai/sentence-transformers-api | ||
``` | ||
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Now you can visit the API docs on [http://localhost:8080/docs][http://localhost:8080/docs] | ||
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You can also use CURL to get embeddings: | ||
```bash | ||
curl http://localhost:8080/v1/embeddings \ | ||
-H "Content-Type: application/json" \ | ||
-d '{ | ||
"input": "Your text string goes here", | ||
"model": "all-MiniLM-L6-v2" | ||
}' | ||
``` | ||
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Even the OpenAI Python client can be used to get embeddings: | ||
```python | ||
import openai | ||
openai.api_base = "http://localhost:8080/v1" | ||
openai.api_key = "this isn't used but openai client requires it" | ||
model = "all-MiniLM-L6-v2" | ||
embedding = openai.Embedding.create(input="Some text", model=model)["data"][0]["embedding"] | ||
print(embedding) | ||
``` | ||
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## Integrations | ||
It's easy to utilize the embedding server with various other tools because | ||
the API is compatible with the OpenAI Embedding API. | ||
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## Development | ||
### Weaviate | ||
TODO Write weaviate guide here | ||
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## Local Development | ||
``` | ||
python -m venv .venv | ||
pip install -r requirements.txt | ||
uvicorn main:app --reload | ||
``` | ||
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Go to http://localhost:8000/docs and try out | ||
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