add docker image build in github on tag

Reviewed-on: godber/embedding-buddy#7

.gitea/workflows/release.yml

@@ -71,22 +71,15 @@ jobs:
           echo '```' >> release-notes.md
 
       - name: Create Release
-        uses: actions/create-release@v1
+        uses: akkuman/gitea-release-action@v1
         env:
-          GITHUB_TOKEN: ${{ secrets.GITEA_TOKEN }}
+          NODE_OPTIONS: '--experimental-fetch'
         with:
+          token: ${{ secrets.GITEA_TOKEN }}
           tag_name: ${{ github.ref_name || github.event.inputs.version }}
           release_name: Release ${{ github.ref_name || github.event.inputs.version }}
           body_path: release-notes.md
           draft: false
           prerelease: false
-
+          files: |-
-      - name: Upload Release Assets
+            dist/*
-        uses: actions/upload-release-asset@v1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITEA_TOKEN }}
-        with:
-          upload_url: ${{ steps.create_release.outputs.upload_url }}
-          asset_path: dist/
-          asset_name: embeddingbuddy-dist
-          asset_content_type: application/zip

.github/workflows/docker-release.yml

@@ -0,0 +1,53 @@
+name: Docker Release
+
+on:
+  push:
+    tags:
+      - 'v[0-9]+.[0-9]+.[0-9]+'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=tag
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+            type=semver,pattern={{major}}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Austin Godber - EmbeddingBuddy
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "embeddingbuddy"
-version = "0.5.1"
+version = "0.6.0"
 description = "A Python Dash application for interactive exploration and visualization of embedding vectors through dimensionality reduction techniques."
 readme = "README.md"
 requires-python = ">=3.11"

run_dev.py

@@ -12,9 +12,15 @@ def main():
     os.environ["EMBEDDINGBUDDY_ENV"] = "development"
     os.environ["EMBEDDINGBUDDY_DEBUG"] = "true"
 
+    # Check for OpenSearch disable flag (optional for testing)
+    # Set EMBEDDINGBUDDY_OPENSEARCH_ENABLED=false to test without OpenSearch
+    opensearch_status = os.getenv("EMBEDDINGBUDDY_OPENSEARCH_ENABLED", "true")
+    opensearch_enabled = opensearch_status.lower() == "true"
+    
     print("🚀 Starting EmbeddingBuddy in development mode...")
     print("📁 Auto-reload enabled - changes will trigger restart")
     print("🌐 Server will be available at http://127.0.0.1:8050")
+    print(f"🔍 OpenSearch: {'Enabled' if opensearch_enabled else 'Disabled'}")
     print("⏹️  Press Ctrl+C to stop")
     
     app = create_app()

run_prod.py

@@ -13,6 +13,9 @@ def main():
     # Force production settings
     os.environ["EMBEDDINGBUDDY_ENV"] = "production"
     os.environ["EMBEDDINGBUDDY_DEBUG"] = "false"
+    # Disable OpenSearch by default in production (can be overridden by setting env var)
+    if "EMBEDDINGBUDDY_OPENSEARCH_ENABLED" not in os.environ:
+        os.environ["EMBEDDINGBUDDY_OPENSEARCH_ENABLED"] = "false"
     
     print("🚀 Starting EmbeddingBuddy in production mode...")
     print(f"⚙️  Workers: {AppSettings.GUNICORN_WORKERS}")

src/embeddingbuddy/app.py

@@ -25,32 +25,23 @@ def create_app():
         meta_tags=[
             {
                 "name": "description",
-                "content": "Interactive embedding visualization tool for exploring high-dimensional vectors through dimensionality reduction techniques like PCA, t-SNE, and UMAP."
+                "content": "Interactive embedding visualization tool for exploring high-dimensional vectors through dimensionality reduction techniques like PCA, t-SNE, and UMAP.",
-            },
-            {
-                "name": "author",
-                "content": "EmbeddingBuddy"
             },
+            {"name": "author", "content": "EmbeddingBuddy"},
             {
                 "name": "keywords",
-                "content": "embeddings, visualization, dimensionality reduction, PCA, t-SNE, UMAP, machine learning, data science"
+                "content": "embeddings, visualization, dimensionality reduction, PCA, t-SNE, UMAP, machine learning, data science",
-            },
-            {
-                "name": "viewport",
-                "content": "width=device-width, initial-scale=1.0"
             },
+            {"name": "viewport", "content": "width=device-width, initial-scale=1.0"},
             {
                 "property": "og:title",
-                "content": "EmbeddingBuddy - Interactive Embedding Visualization"
+                "content": "EmbeddingBuddy - Interactive Embedding Visualization",
             },
             {
                 "property": "og:description",
-                "content": "Explore and visualize embedding vectors through interactive 2D/3D plots with multiple dimensionality reduction techniques."
+                "content": "Explore and visualize embedding vectors through interactive 2D/3D plots with multiple dimensionality reduction techniques.",
             },
-            {
+            {"property": "og:type", "content": "website"},
-                "property": "og:type",
-                "content": "website"
-            }
         ],
     )
 

src/embeddingbuddy/config/settings.py

@@ -85,6 +85,9 @@ class AppSettings:
     GUNICORN_KEEPALIVE = int(os.getenv("GUNICORN_KEEPALIVE", "5"))
 
     # OpenSearch Configuration
+    OPENSEARCH_ENABLED = (
+        os.getenv("EMBEDDINGBUDDY_OPENSEARCH_ENABLED", "True").lower() == "true"
+    )
     OPENSEARCH_DEFAULT_SIZE = 100
     OPENSEARCH_SAMPLE_SIZE = 5
     OPENSEARCH_CONNECTION_TIMEOUT = 30

src/embeddingbuddy/ui/callbacks/data_processing.py

@@ -82,19 +82,23 @@ class DataProcessingCallbacks:
         )
         def render_tab_content(active_tab):
             from ...ui.components.datasource import DataSourceComponent
+            from ...config.settings import AppSettings
 
             datasource = DataSourceComponent()
 
-            if active_tab == "opensearch-tab":
+            if active_tab == "opensearch-tab" and AppSettings.OPENSEARCH_ENABLED:
                 return [datasource.create_opensearch_tab()]
             elif active_tab == "text-input-tab":
                 return [datasource.create_text_input_tab()]
             else:
                 return [datasource.create_file_upload_tab()]
 
-        # Register callbacks for both data and prompts sections
+        # Register callbacks for both data and prompts sections (only if OpenSearch is enabled)
-        self._register_opensearch_callbacks("data", self.opensearch_client_data)
+        if AppSettings.OPENSEARCH_ENABLED:
-        self._register_opensearch_callbacks("prompts", self.opensearch_client_prompts)
+            self._register_opensearch_callbacks("data", self.opensearch_client_data)
+            self._register_opensearch_callbacks(
+                "prompts", self.opensearch_client_prompts
+            )
 
         # Register collapsible section callbacks
         self._register_collapse_callbacks()

src/embeddingbuddy/ui/components/about.py

@@ -5,9 +5,31 @@ import dash_bootstrap_components as dbc
 class AboutComponent:
     def _get_about_content(self):
         return """
-# 🔍 Interactive Embedding Visualization
+# 🔍 Interactive Embedding Vector Visualization
+
+EmbeddingBuddy is a web application for interactive exploration and
+visualization of embedding vectors through dimensionality reduction techniques
+(PCA, t-SNE, UMAP).
+
+You have two ways to get started:
+
+1. Generate embeddings directly in the browser if it supports WebGPU.
+2. Upload your NDJSON file containing embedding vectors and metadata.
+
+## Generating Embeddings in Browser
+
+1. Expand the "Generate Embeddings" section.
+2. Input your text data (one entry per line).
+    1. Optionally you can use the built in sample data by clicking "Load Sample Data" button.
+3. Click "Generate Embeddings" to create vectors using a pre-trained model.
+
+## NDJSON File Format
+
+```json
+{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, ...], "text": "Sample text content", "category": "news", "subcategory": "politics", "tags": ["election", "politics"]}
+{"id": "doc_002", "embedding": [0.2, -0.1, 0.9, ...], "text": "Another example", "category": "review", "subcategory": "product", "tags": ["tech", "gadget"]}
+```
 
-EmbeddingBuddy is a modular Python Dash web application for interactive exploration and visualization of embedding vectors through dimensionality reduction techniques (PCA, t-SNE, UMAP).
 
 ## ✨ Features
 
@@ -35,26 +57,25 @@ EmbeddingBuddy is a modular Python Dash web application for interactive explorat
         return dbc.Modal(
             [
                 dbc.ModalHeader(
-                    dbc.ModalTitle("About EmbeddingBuddy"),
+                    dbc.ModalTitle("Welcome to EmbeddingBuddy"),
                     close_button=True,
                 ),
-                dbc.ModalBody([
+                dbc.ModalBody(
-                    dcc.Markdown(
+                    [dcc.Markdown(self._get_about_content(), className="mb-0")]
-                        self._get_about_content(),
+                ),
-                        className="mb-0"
+                dbc.ModalFooter(
-                    )
+                    [
-                ]),
+                        dbc.Button(
-                dbc.ModalFooter([
+                            "Close",
-                    dbc.Button(
+                            id="about-modal-close",
-                        "Close",
+                            color="secondary",
-                        id="about-modal-close",
+                            n_clicks=0,
-                        color="secondary",
+                        )
-                        n_clicks=0
+                    ]
-                    )
+                ),
-                ]),
             ],
             id="about-modal",
-            is_open=False,
+            is_open=True,
             size="lg",
         )
 
@@ -65,5 +86,5 @@ EmbeddingBuddy is a modular Python Dash web application for interactive explorat
             color="outline-info",
             size="sm",
             n_clicks=0,
-            className="ms-2"
+            className="ms-2",
         )

src/embeddingbuddy/ui/components/datasource.py

@@ -1,6 +1,7 @@
 from dash import dcc, html
 import dash_bootstrap_components as dbc
 from .upload import UploadComponent
+from embeddingbuddy.config.settings import AppSettings
 
 
 class DataSourceComponent:
@@ -9,15 +10,18 @@ class DataSourceComponent:
 
     def create_tabbed_interface(self):
         """Create tabbed interface for different data sources."""
+        tabs = [dbc.Tab(label="File Upload", tab_id="file-tab")]
+
+        # Only add OpenSearch tab if enabled
+        if AppSettings.OPENSEARCH_ENABLED:
+            tabs.append(dbc.Tab(label="OpenSearch", tab_id="opensearch-tab"))
+
         return dbc.Card(
             [
                 dbc.CardHeader(
                     [
                         dbc.Tabs(
-                            [
+                            tabs,
-                                dbc.Tab(label="File Upload", tab_id="file-tab"),
-                                dbc.Tab(label="OpenSearch", tab_id="opensearch-tab"),
-                            ],
                             id="data-source-tabs",
                             active_tab="file-tab",
                         )

src/embeddingbuddy/ui/components/sidebar.py

@@ -3,6 +3,7 @@ import dash_bootstrap_components as dbc
 from .upload import UploadComponent
 from .datasource import DataSourceComponent
 from .textinput import TextInputComponent
+from embeddingbuddy.config.settings import AppSettings
 
 
 class SidebarComponent:
@@ -102,6 +103,10 @@ class SidebarComponent:
         )
 
     def _create_data_sources_item(self):
+        tooltip_text = "Load existing embeddings: upload files"
+        if AppSettings.OPENSEARCH_ENABLED:
+            tooltip_text += " or read from OpenSearch"
+
         return dbc.AccordionItem(
             [
                 self.datasource_component.create_error_alert(),
@@ -115,7 +120,7 @@ class SidebarComponent:
                         className="fas fa-info-circle text-muted",
                         style={"cursor": "pointer"},
                         id="load-embeddings-info-icon",
-                        title="Load existing embeddings: upload files or read from OpenSearch",
+                        title=tooltip_text,
                     ),
                 ]
             ),

src/embeddingbuddy/ui/components/textinput.py

@@ -16,14 +16,14 @@ class TextInputComponent:
         """Create the complete text input interface with model selection and processing options."""
         return html.Div(
             [
-                # Model selection section
-                self._create_model_selection(),
-                html.Hr(),
                 # Text input section
                 self._create_text_input_area(),
                 # Text action buttons
                 self._create_text_action_buttons(),
                 html.Hr(),
+                # Model selection section
+                self._create_model_selection(),
+                html.Hr(),
                 # Processing options
                 self._create_processing_options(),
                 html.Hr(),

src/embeddingbuddy/ui/layout.py

@@ -24,13 +24,16 @@ class AppLayout:
                     [
                         html.Div(
                             [
-                                html.H1("EmbeddingBuddy", className="text-center mb-4 d-inline"),
+                                html.H1(
+                                    "EmbeddingBuddy",
+                                    className="text-center mb-4 d-inline",
+                                ),
                                 html.Div(
                                     [self.about.create_about_button()],
-                                    className="float-end"
+                                    className="float-end",
                                 ),
                             ],
-                            className="d-flex justify-content-between align-items-center"
+                            className="d-flex justify-content-between align-items-center",
                         ),
                         # Load Transformers.js from CDN
                         html.Script(