diff --git a/.github/workflows/docker-push.yml b/.github/workflows/docker-push.yml
new file mode 100644
index 0000000..de86d24
--- /dev/null
+++ b/.github/workflows/docker-push.yml
@@ -0,0 +1,53 @@
+name: Create and publish a Docker image
+
+# 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:
+
+# 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
+
+# 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 }}
diff --git a/README.md b/README.md
index f787f6b..848f247 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,43 @@
 # Sentence Transformers API
 
-OpenAI compatible endpont for Sentence Transformer embedding models
+OpenAI compatible embedding endpoint that uses Sentence Transformer for embedding models
 
 ## Usage (Docker)
+Run the API locally using Docker:
+```
+docker run -p 8080:8080 -d ghcr.io/substratusai/sentence-transformers-api
+```
+
+Now you can visit the API docs on [http://localhost:8080/docs][http://localhost:8080/docs]
+
+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"
+  }'
+```
+
+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)
+```
 
+## Integrations
+It's easy to utilize the embedding server with various other tools because
+the API is compatible with the OpenAI Embedding API.
 
-## Development
+### Weaviate
+TODO Write weaviate guide here
 
+## Local Development
 ```
 python -m venv .venv
 pip install -r requirements.txt
@@ -14,4 +45,3 @@ uvicorn main:app --reload
 ```
 
 Go to http://localhost:8000/docs and try out
-