Merge pull request 'v0.5.0 - rework the sidebar' (#6) from restructure-sidebar into main

Reviewed-on: #6

.claude/settings.local.json

@@ -4,7 +4,9 @@
       "Bash(mkdir:*)",
       "Bash(uv run:*)",
       "Bash(uv add:*)",
-      "Bash(uv sync:*)"
+      "Bash(uv sync:*)",
+      "Bash(tree:*)",
+      "WebFetch(domain:www.dash-bootstrap-components.com)"
     ],
     "deny": [],
     "ask": [],

.dockerignore

@@ -0,0 +1,76 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.venv/
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+.tox/
+coverage.xml
+*.cover
+
+# Development tools
+.mypy_cache/
+.ruff_cache/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Git
+.git/
+.gitignore
+
+# Documentation
+*.md
+!README.md
+
+# Docker
+Dockerfile*
+docker-compose*.yml
+.dockerignore
+
+# Data files (may contain sensitive information)
+*.ndjson
+*.ldjson
+*.json
+
+# Reports
+*-report.json
+bandit-report.json
+safety-report.json
+
+# Screenshots
+*.png
+*.jpg
+*.jpeg
+*.gif
+
+# Logs
+*.log
+
+# Temporary files
+*.tmp
+*.temp

CLAUDE.md

@@ -21,11 +21,29 @@ uv sync
 
 **Run the application:**
 
+Development mode (with auto-reload):
+
 ```bash
-uv run python main.py
+uv run run_dev.py
 ```
 
-The app will be available at http://127.0.0.1:8050
+Production mode (with Gunicorn WSGI server):
+
+```bash
+# First install production dependencies
+uv sync --extra prod
+
+# Then run in production mode
+uv run run_prod.py
+```
+
+Legacy mode (basic Dash server):
+
+```bash
+uv run main.py
+```
+
+The app will be available at <http://127.0.0.1:8050>
 
 **Run tests:**
 

Dockerfile

@@ -0,0 +1,78 @@
+# Two-stage Dockerfile for EmbeddingBuddy
+# Stage 1: Builder
+FROM python:3.11-slim as builder
+
+# Install system dependencies for building Python packages
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv for dependency management
+RUN pip install uv
+
+# Set working directory
+WORKDIR /app
+
+# Copy dependency files
+COPY pyproject.toml uv.lock ./
+
+# Copy source code (needed for editable install)
+COPY src/ src/
+COPY main.py .
+COPY wsgi.py .
+COPY run_prod.py .
+COPY assets/ assets/
+
+# Create virtual environment and install dependencies (including production extras)
+RUN uv venv .venv
+RUN uv sync --frozen --extra prod 
+
+# Stage 2: Runtime
+FROM python:3.11-slim as runtime
+
+# Install runtime dependencies for compiled packages
+RUN apt-get update && apt-get install -y \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy virtual environment from builder stage
+COPY --from=builder /app/.venv /app/.venv
+
+# Copy application files from builder stage
+COPY --from=builder /app/src /app/src
+COPY --from=builder /app/main.py /app/main.py
+COPY --from=builder /app/assets /app/assets
+COPY --from=builder /app/wsgi.py /app/wsgi.py
+COPY --from=builder /app/run_prod.py /app/run_prod.py
+
+# Make sure the virtual environment is in PATH
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Set Python path
+ENV PYTHONPATH="/app/src:$PYTHONPATH"
+
+# Environment variables for production
+ENV EMBEDDINGBUDDY_HOST=0.0.0.0
+ENV EMBEDDINGBUDDY_PORT=8050
+ENV EMBEDDINGBUDDY_DEBUG=false
+ENV EMBEDDINGBUDDY_ENV=production
+
+# Expose port
+EXPOSE 8050
+
+# Create non-root user
+RUN groupadd -r appuser && useradd -r -g appuser appuser
+RUN chown -R appuser:appuser /app
+USER appuser
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=3 \
+    CMD python -c "import requests; requests.get('http://localhost:8050/', timeout=5)" || exit 1
+
+# Run application with Gunicorn in production
+CMD ["python", "run_prod.py"]

README.md

@@ -12,7 +12,7 @@ EmbeddingBuddy provides an intuitive web interface for analyzing high-dimensiona
 embedding vectors by applying various dimensionality reduction algorithms and
 visualizing the results in interactive 2D and 3D plots. The application features
 a clean, modular architecture that makes it easy to test, maintain, and extend
-with new features. It supports dual dataset visualization, allowing you to compare 
+with new features. It supports dual dataset visualization, allowing you to compare
 documents and prompts to understand how queries relate to your content.
 
 ## Features
@@ -73,17 +73,77 @@ uv sync
 
 2. **Run the application:**
 
+**Development mode** (with auto-reload):
+
 ```bash
-uv run python main.py
+uv run run_dev.py
 ```
 
-3. **Open your browser** to http://127.0.0.1:8050
+**Production mode** (with Gunicorn WSGI server):
+
+```bash
+# Install production dependencies
+uv sync --extra prod
+
+# Run in production mode
+uv run run_prod.py
+```
+
+**Legacy mode** (basic Dash server):
+
+```bash
+uv run main.py
+```
+
+3. **Open your browser** to <http://127.0.0.1:8050>
 
 4. **Test with sample data**:
    - Upload `sample_data.ndjson` (documents)
    - Upload `sample_prompts.ndjson` (prompts) to see dual visualization
    - Use the "Show prompts" toggle to compare how prompts relate to documents
 
+## Docker
+
+You can also run EmbeddingBuddy using Docker:
+
+### Basic Usage
+
+```bash
+# Run in the background
+docker compose up -d
+```
+
+The application will be available at <http://127.0.0.1:8050>
+
+### With OpenSearch
+
+To run with OpenSearch for enhanced search capabilities:
+
+```bash
+# Run in the background with OpenSearch
+docker compose --profile opensearch up -d
+```
+
+This will start both the EmbeddingBuddy application and an OpenSearch instance.
+OpenSearch will be available at <http://127.0.0.1:9200>
+
+### Docker Commands
+
+```bash
+# Stop all services
+docker compose down
+
+# Stop and remove volumes
+docker compose down -v
+
+# View logs
+docker compose logs embeddingbuddy
+docker compose logs opensearch
+
+# Rebuild containers
+docker compose build
+```
+
 ## Development
 
 ### Project Structure
@@ -92,22 +152,38 @@ The application follows a modular architecture for improved maintainability and
 
 ```text
 src/embeddingbuddy/
-├── config/          # Configuration management
-│   └── settings.py  # Centralized app settings
-├── data/           # Data parsing and processing
-│   ├── parser.py   # NDJSON parsing logic
-│   └── processor.py # Data transformation utilities
-├── models/         # Data schemas and algorithms
-│   ├── schemas.py  # Pydantic data models
-│   └── reducers.py # Dimensionality reduction algorithms
-├── visualization/ # Plot creation and styling
-│   ├── plots.py   # Plot factory and creation logic
-│   └── colors.py  # Color mapping utilities
-├── ui/            # User interface components
-│   ├── layout.py  # Main application layout
-│   ├── components/ # Reusable UI components
-│   └── callbacks/ # Organized callback functions
-└── utils/         # Utility functions
+├── app.py                     # Main application entry point and factory
+├── config/                    # Configuration management
+│   └── settings.py            # Centralized app settings
+├── data/                      # Data parsing and processing
+│   ├── parser.py              # NDJSON parsing logic
+│   ├── processor.py           # Data transformation utilities
+│   └── sources/               # Data source integrations
+│       └── opensearch.py      # OpenSearch data source
+├── models/                    # Data schemas and algorithms
+│   ├── schemas.py             # Pydantic data models
+│   ├── reducers.py            # Dimensionality reduction algorithms
+│   └── field_mapper.py        # Field mapping utilities
+├── visualization/             # Plot creation and styling
+│   ├── plots.py               # Plot factory and creation logic
+│   └── colors.py              # Color mapping utilities
+├── ui/                        # User interface components
+│   ├── layout.py              # Main application layout
+│   ├── components/            # Reusable UI components
+│   │   ├── sidebar.py         # Sidebar component
+│   │   ├── upload.py          # Upload components
+│   │   ├── textinput.py       # Text input components
+│   │   └── datasource.py      # Data source components
+│   └── callbacks/             # Organized callback functions
+│       ├── data_processing.py # Data upload/processing callbacks
+│       ├── visualization.py   # Plot update callbacks
+│       └── interactions.py    # User interaction callbacks
+└── utils/                     # Utility functions
+
+main.py            # Application runner (at project root)
+main.py            # Application runner (at project root)
+run_dev.py         # Development server runner
+run_prod.py        # Production server runner
 ```
 
 ### Testing

assets/custom.css

@@ -0,0 +1,17 @@
+/* CSS override for transparent hover boxes in Plotly plots */
+
+/* Make hover boxes transparent while preserving text readability */
+.hovertext {
+    fill-opacity: 0.8 !important;
+    stroke-opacity: 1 !important;
+}
+
+/* Alternative selector for different Plotly versions */
+g.hovertext > path {
+    opacity: 0.8 !important;
+}
+
+/* Ensure text remains fully visible */
+.hovertext text {
+    opacity: 1 !important;
+}

assets/embeddings.js

@@ -45,28 +45,12 @@ class TransformersEmbedder {
                 console.log('✅ Using globally loaded Transformers.js pipeline');
             }
             
-            // Show loading progress to user
-            if (window.updateModelLoadingProgress) {
-                window.updateModelLoadingProgress(0, `Loading ${modelName}...`);
-            }
-            
-            this.extractor = await window.transformers.pipeline('feature-extraction', modelName, {
-                progress_callback: (data) => {
-                    if (window.updateModelLoadingProgress && data.progress !== undefined) {
-                        const progress = Math.round(data.progress);
-                        window.updateModelLoadingProgress(progress, data.status || 'Loading...');
-                    }
-                }
-            });
-            
+            this.extractor = await window.transformers.pipeline('feature-extraction', modelName);
+
             this.modelCache.set(modelName, this.extractor);
             this.currentModel = modelName;
             this.isLoading = false;
-            
-            if (window.updateModelLoadingProgress) {
-                window.updateModelLoadingProgress(100, 'Model loaded successfully');
-            }
-            
+
             return { success: true, model: modelName };
         } catch (error) {
             this.isLoading = false;
@@ -116,17 +100,8 @@ class TransformersEmbedder {
                     }
                 });
                 
-                // Update progress
-                const progress = Math.min(100, ((i + batch.length) / texts.length) * 100);
-                if (window.updateEmbeddingProgress) {
-                    window.updateEmbeddingProgress(progress, `Processing ${i + batch.length}/${texts.length} texts`);
-                }
             }
-            
-            if (window.updateEmbeddingProgress) {
-                window.updateEmbeddingProgress(100, `Generated ${embeddings.length} embeddings successfully`);
-            }
-            
+
             return embeddings;
         } catch (error) {
             console.error('Embedding generation error:', error);
@@ -139,30 +114,6 @@ class TransformersEmbedder {
 window.transformersEmbedder = new TransformersEmbedder();
 console.log('📦 TransformersEmbedder instance created');
 
-// Global progress update functions
-window.updateModelLoadingProgress = function(progress, status) {
-    const progressBar = document.getElementById('model-loading-progress');
-    const statusText = document.getElementById('model-loading-status');
-    if (progressBar) {
-        progressBar.style.width = progress + '%';
-        progressBar.setAttribute('aria-valuenow', progress);
-    }
-    if (statusText) {
-        statusText.textContent = status;
-    }
-};
-
-window.updateEmbeddingProgress = function(progress, status) {
-    const progressBar = document.getElementById('embedding-progress');
-    const statusText = document.getElementById('embedding-status');
-    if (progressBar) {
-        progressBar.style.width = progress + '%';
-        progressBar.setAttribute('aria-valuenow', progress);
-    }
-    if (statusText) {
-        statusText.textContent = status;
-    }
-};
 
 // Dash clientside callback functions
 window.dash_clientside = window.dash_clientside || {};
@@ -170,31 +121,28 @@ console.log('🔧 Setting up window.dash_clientside.transformers');
 window.dash_clientside.transformers = {
     generateEmbeddings: async function(nClicks, textContent, modelName, tokenizationMethod, category, subcategory) {
         console.log('🚀 generateEmbeddings called with:', { nClicks, modelName, tokenizationMethod, textLength: textContent?.length });
-        
+
         if (!nClicks || !textContent || textContent.trim().length === 0) {
             console.log('⚠️ Early return - missing required parameters');
             return window.dash_clientside.no_update;
         }
-        
+
         try {
             // Initialize model if needed
             const initResult = await window.transformersEmbedder.initializeModel(modelName);
             if (!initResult.success) {
                 return [
-                    { error: initResult.error },
-                    `❌ Model loading error: ${initResult.error}`,
-                    "danger",
+                    { error: `Model loading error: ${initResult.error}` },
                     false
                 ];
             }
-            
+
             // Tokenize text based on method
             let textChunks;
             const trimmedText = textContent.trim();
-            
+
             switch (tokenizationMethod) {
                 case 'sentence':
-                    // Simple sentence splitting - can be enhanced with proper NLP
                     textChunks = trimmedText
                         .split(/[.!?]+/)
                         .map(s => s.trim())
@@ -215,28 +163,24 @@ window.dash_clientside.transformers = {
                 default:
                     textChunks = [trimmedText];
             }
-            
+
             if (textChunks.length === 0) {
                 return [
                     { error: 'No valid text chunks found after tokenization' },
-                    '❌ Error: No valid text chunks found after tokenization',
-                    "danger",
                     false
                 ];
             }
-            
+
             // Generate embeddings
             const embeddings = await window.transformersEmbedder.generateEmbeddings(textChunks);
-            
+
             if (!embeddings || embeddings.length !== textChunks.length) {
                 return [
-                    { error: 'Embedding generation failed - mismatch in text chunks and embeddings' },
-                    '❌ Error: Embedding generation failed',
-                    "danger",
+                    { error: 'Embedding generation failed' },
                     false
                 ];
             }
-            
+
             // Create documents structure
             const documents = textChunks.map((text, i) => ({
                 id: `text_input_${Date.now()}_${i}`,
@@ -246,33 +190,36 @@ window.dash_clientside.transformers = {
                 subcategory: subcategory || "Generated",
                 tags: []
             }));
-            
+
+            // Return the successful embeddings data
+            const embeddingsData = {
+                documents: documents,
+                embeddings: embeddings
+            };
+
+            console.log('✅ Embeddings generated successfully:', embeddingsData);
+
             return [
-                {
-                    documents: documents,
-                    embeddings: embeddings
-                },
-                `✅ Generated embeddings for ${documents.length} text chunks using ${modelName}`,
-                "success",
+                embeddingsData,
                 false
             ];
-            
+
         } catch (error) {
             console.error('Client-side embedding error:', error);
             return [
                 { error: error.message },
-                `❌ Error: ${error.message}`,
-                "danger", 
                 false
             ];
         }
     }
 };
 
+
 console.log('✅ Transformers.js client-side setup complete');
 console.log('Available:', {
     transformersEmbedder: !!window.transformersEmbedder,
     dashClientside: !!window.dash_clientside,
     transformersModule: !!window.dash_clientside?.transformers,
-    generateFunction: typeof window.dash_clientside?.transformers?.generateEmbeddings
+    generateFunction: typeof window.dash_clientside?.transformers?.generateEmbeddings,
+    processAsync: typeof window.processEmbeddingsAsync
 });

assets/transformers-loader.js

@@ -104,17 +104,28 @@ window.dash_clientside = window.dash_clientside || {};
 window.dash_clientside.transformers = {
     generateEmbeddings: async function(nClicks, textContent, modelName, tokenizationMethod, category, subcategory) {
         console.log('🚀 Client-side generateEmbeddings called');
-        
+
         if (!nClicks || !textContent || textContent.trim().length === 0) {
             console.log('⚠️ Missing required parameters');
             return window.dash_clientside.no_update;
         }
-        
+
         try {
+            // Ensure Transformers.js is loaded
+            if (!window.transformersLibraryLoaded) {
+                const loaded = await initializeTransformers();
+                if (!loaded) {
+                    return [
+                        { error: 'Failed to load Transformers.js' },
+                        false
+                    ];
+                }
+            }
+
             // Tokenize text
             let textChunks;
             const trimmedText = textContent.trim();
-            
+
             switch (tokenizationMethod) {
                 case 'sentence':
                     textChunks = trimmedText.split(/[.!?]+/).map(s => s.trim()).filter(s => s.length > 0);
@@ -128,45 +139,50 @@ window.dash_clientside.transformers = {
                 default:
                     textChunks = [trimmedText];
             }
-            
+
             if (textChunks.length === 0) {
-                throw new Error('No valid text chunks after tokenization');
+                return [
+                    { error: 'No valid text chunks after tokenization' },
+                    false
+                ];
             }
-            
+
             // Generate embeddings
             const embeddings = await window.simpleEmbedder.generateEmbeddings(textChunks, modelName);
-            
+
             // Create documents
             const documents = textChunks.map((text, i) => ({
                 id: `text_input_${Date.now()}_${i}`,
                 text: text,
                 embedding: embeddings[i],
                 category: category || "Text Input",
-                subcategory: subcategory || "Generated", 
+                subcategory: subcategory || "Generated",
                 tags: []
             }));
-            
+
+            // Return the successful embeddings data
+            const embeddingsData = {
+                documents: documents,
+                embeddings: embeddings
+            };
+
+            console.log('✅ Embeddings generated successfully:', embeddingsData);
+
             return [
-                {
-                    documents: documents,
-                    embeddings: embeddings
-                },
-                `✅ Generated embeddings for ${documents.length} text chunks using ${modelName}`,
-                "success",
+                embeddingsData,
                 false
             ];
-            
+
         } catch (error) {
             console.error('❌ Error generating embeddings:', error);
             return [
                 { error: error.message },
-                `❌ Error: ${error.message}`,
-                "danger",
                 false
             ];
         }
     }
 };
 
+
 console.log('✅ Simple Transformers.js setup complete');
 console.log('Available functions:', Object.keys(window.dash_clientside.transformers));

bump_version.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+"""
+Version bump script for EmbeddingBuddy.
+Automatically updates version in pyproject.toml following semantic versioning.
+"""
+import argparse
+import re
+import sys
+from pathlib import Path
+
+
+def get_current_version(pyproject_path: Path) -> str:
+    """Extract current version from pyproject.toml."""
+    content = pyproject_path.read_text()
+    match = re.search(r'version\s*=\s*"([^"]+)"', content)
+    if not match:
+        raise ValueError("Could not find version in pyproject.toml")
+    return match.group(1)
+
+
+def parse_version(version_str: str) -> tuple[int, int, int]:
+    """Parse semantic version string into major, minor, patch tuple."""
+    match = re.match(r'(\d+)\.(\d+)\.(\d+)', version_str)
+    if not match:
+        raise ValueError(f"Invalid version format: {version_str}")
+    return int(match.group(1)), int(match.group(2)), int(match.group(3))
+
+
+def bump_version(current: str, bump_type: str) -> str:
+    """Bump version based on type (major, minor, patch)."""
+    major, minor, patch = parse_version(current)
+    
+    if bump_type == "major":
+        return f"{major + 1}.0.0"
+    elif bump_type == "minor":
+        return f"{major}.{minor + 1}.0"
+    elif bump_type == "patch":
+        return f"{major}.{minor}.{patch + 1}"
+    else:
+        raise ValueError(f"Invalid bump type: {bump_type}")
+
+
+def update_version_in_file(pyproject_path: Path, new_version: str) -> None:
+    """Update version in pyproject.toml file."""
+    content = pyproject_path.read_text()
+    updated_content = re.sub(
+        r'version\s*=\s*"[^"]+"',
+        f'version = "{new_version}"',
+        content
+    )
+    pyproject_path.write_text(updated_content)
+
+
+def main():
+    """Main version bump function."""
+    parser = argparse.ArgumentParser(
+        description="Bump version in pyproject.toml",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python bump_version.py patch    # 0.3.0 -> 0.3.1
+  python bump_version.py minor    # 0.3.0 -> 0.4.0
+  python bump_version.py major    # 0.3.0 -> 1.0.0
+  python bump_version.py --set 1.2.3  # Set specific version
+
+Semantic versioning guide:
+  - patch: Bug fixes, no API changes
+  - minor: New features, backward compatible
+  - major: Breaking changes, not backward compatible
+        """
+    )
+    
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument(
+        "bump_type",
+        nargs="?",
+        choices=["major", "minor", "patch"],
+        help="Type of version bump"
+    )
+    group.add_argument(
+        "--set",
+        dest="set_version",
+        help="Set specific version (e.g., 1.2.3)"
+    )
+    
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Show what would be changed without making changes"
+    )
+    
+    args = parser.parse_args()
+    
+    # Find pyproject.toml
+    pyproject_path = Path("pyproject.toml")
+    if not pyproject_path.exists():
+        print("❌ pyproject.toml not found in current directory")
+        sys.exit(1)
+    
+    try:
+        current_version = get_current_version(pyproject_path)
+        print(f"📦 Current version: {current_version}")
+        
+        if args.set_version:
+            # Validate the set version format
+            parse_version(args.set_version)
+            new_version = args.set_version
+        else:
+            new_version = bump_version(current_version, args.bump_type)
+        
+        print(f"🚀 New version: {new_version}")
+        
+        if args.dry_run:
+            print("🔍 Dry run - no changes made")
+        else:
+            update_version_in_file(pyproject_path, new_version)
+            print("✅ Version updated in pyproject.toml")
+            print()
+            print("💡 Next steps:")
+            print("   1. Review changes: git diff")
+            print("   2. Commit changes: git add . && git commit -m 'bump version to {}'".format(new_version))
+            print("   3. Tag release: git tag v{}".format(new_version))
+            
+    except ValueError as e:
+        print(f"❌ Error: {e}")
+        sys.exit(1)
+    except Exception as e:
+        print(f"❌ Unexpected error: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

docker-compose.yml

@@ -0,0 +1,69 @@
+services:
+  opensearch:
+    image: opensearchproject/opensearch:2
+    container_name: embeddingbuddy-opensearch
+    profiles:
+      - opensearch
+    environment:
+      - cluster.name=embeddingbuddy-cluster
+      - node.name=embeddingbuddy-node
+      - discovery.type=single-node
+      - bootstrap.memory_lock=true
+      - "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m"
+      - "DISABLE_INSTALL_DEMO_CONFIG=true"
+      - "DISABLE_SECURITY_PLUGIN=true"
+    ulimits:
+      memlock:
+        soft: -1
+        hard: -1
+      nofile:
+        soft: 65536
+        hard: 65536
+    volumes:
+      - opensearch-data:/usr/share/opensearch/data
+    ports:
+      - "9200:9200"
+      - "9600:9600"
+    networks:
+      - embeddingbuddy
+    healthcheck:
+      test: ["CMD-SHELL", "curl -f http://localhost:9200/_cluster/health || exit 1"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 60s
+
+  embeddingbuddy:
+    build: .
+    container_name: embeddingbuddy-app
+    environment:
+      - EMBEDDINGBUDDY_HOST=0.0.0.0
+      - EMBEDDINGBUDDY_PORT=8050
+      - EMBEDDINGBUDDY_DEBUG=false
+      - OPENSEARCH_HOST=opensearch
+      - OPENSEARCH_PORT=9200
+      - OPENSEARCH_SCHEME=http
+      - OPENSEARCH_VERIFY_CERTS=false
+    ports:
+      - "8050:8050"
+    networks:
+      - embeddingbuddy
+    depends_on:
+      opensearch:
+        condition: service_healthy
+        required: false
+    healthcheck:
+      test: ["CMD-SHELL", "python -c 'import requests; requests.get(\"http://localhost:8050/\", timeout=5)'"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 30s
+    restart: unless-stopped
+
+volumes:
+  opensearch-data:
+    driver: local
+
+networks:
+  embeddingbuddy:
+    driver: bridge

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "embeddingbuddy"
-version = "0.3.0"
+version = "0.5.0"
 description = "A Python Dash application for interactive exploration and visualization of embedding vectors through dimensionality reduction techniques."
 readme = "README.md"
 requires-python = ">=3.11"
@@ -12,7 +12,6 @@ dependencies = [
     "scikit-learn>=1.3.2",
     "dash-bootstrap-components>=1.5.0",
     "umap-learn>=0.5.8",
-    "numba>=0.56.4",
     "openTSNE>=1.0.0",
     "mypy>=1.17.1",
     "opensearch-py>=3.0.0",
@@ -32,11 +31,14 @@ security = [
     "safety>=2.3.0",
     "pip-audit>=2.6.0",
 ]
+prod = [
+    "gunicorn>=21.2.0",
+]
 dev = [
     "embeddingbuddy[test,lint,security]",
 ]
 all = [
-    "embeddingbuddy[test,lint,security]",
+    "embeddingbuddy[test,lint,security,prod]",
 ]
 
 [build-system]

run_dev.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+"""
+Development runner with auto-reload enabled.
+This runs the Dash development server with hot reloading.
+"""
+import os
+from src.embeddingbuddy.app import create_app, run_app
+
+def main():
+    """Run the application in development mode with auto-reload."""
+    # Force development settings
+    os.environ["EMBEDDINGBUDDY_ENV"] = "development"
+    os.environ["EMBEDDINGBUDDY_DEBUG"] = "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("⏹️  Press Ctrl+C to stop")
+    
+    app = create_app()
+    
+    # Run with development server (includes auto-reload when debug=True)
+    run_app(app, debug=True)
+
+if __name__ == "__main__":
+    main()

run_prod.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+"""
+Production runner using Gunicorn WSGI server.
+This provides better performance and stability for production deployments.
+"""
+import os
+import subprocess
+import sys
+from src.embeddingbuddy.config.settings import AppSettings
+
+def main():
+    """Run the application in production mode with Gunicorn."""
+    # Force production settings
+    os.environ["EMBEDDINGBUDDY_ENV"] = "production"
+    os.environ["EMBEDDINGBUDDY_DEBUG"] = "false"
+    
+    print("🚀 Starting EmbeddingBuddy in production mode...")
+    print(f"⚙️  Workers: {AppSettings.GUNICORN_WORKERS}")
+    print(f"🌐 Server will be available at http://{AppSettings.GUNICORN_BIND}")
+    print("⏹️  Press Ctrl+C to stop")
+    
+    # Gunicorn command
+    cmd = [
+        "gunicorn",
+        "--workers", str(AppSettings.GUNICORN_WORKERS),
+        "--bind", AppSettings.GUNICORN_BIND,
+        "--timeout", str(AppSettings.GUNICORN_TIMEOUT),
+        "--keep-alive", str(AppSettings.GUNICORN_KEEPALIVE),
+        "--access-logfile", "-",
+        "--error-logfile", "-",
+        "--log-level", "info",
+        "wsgi:application"
+    ]
+    
+    try:
+        subprocess.run(cmd, check=True)
+    except KeyboardInterrupt:
+        print("\n🛑 Shutting down...")
+        sys.exit(0)
+    except subprocess.CalledProcessError as e:
+        print(f"❌ Error running Gunicorn: {e}")
+        sys.exit(1)
+    except FileNotFoundError:
+        print("❌ Gunicorn not found. Install it with: uv add gunicorn")
+        print("💡 Or run in development mode with: python run_dev.py")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

src/embeddingbuddy/app.py

@@ -15,7 +15,12 @@ def create_app():
     assets_path = os.path.join(project_root, "assets")
 
     app = dash.Dash(
-        __name__, external_stylesheets=[dbc.themes.BOOTSTRAP], assets_folder=assets_path
+        __name__,
+        external_stylesheets=[
+            dbc.themes.BOOTSTRAP,
+            "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css",
+        ],
+        assets_folder=assets_path,
     )
 
     # Allow callbacks to components that are dynamically created in tabs
@@ -45,22 +50,22 @@ def _register_client_side_callbacks(app):
             if (!nClicks || !textContent || !textContent.trim()) {
                 return window.dash_clientside.no_update;
             }
-            
+
             console.log('🔍 Checking for Transformers.js...');
             console.log('window.dash_clientside:', typeof window.dash_clientside);
             console.log('window.dash_clientside.transformers:', typeof window.dash_clientside?.transformers);
             console.log('generateEmbeddings function:', typeof window.dash_clientside?.transformers?.generateEmbeddings);
-            
-            if (typeof window.dash_clientside !== 'undefined' && 
+
+            if (typeof window.dash_clientside !== 'undefined' &&
                 typeof window.dash_clientside.transformers !== 'undefined' &&
                 typeof window.dash_clientside.transformers.generateEmbeddings === 'function') {
-                
+
                 console.log('✅ Calling Transformers.js generateEmbeddings...');
                 return window.dash_clientside.transformers.generateEmbeddings(
                     nClicks, textContent, modelName, tokenizationMethod, category, subcategory
                 );
             }
-            
+
             // More detailed error information
             let errorMsg = '❌ Transformers.js not available. ';
             if (typeof window.dash_clientside === 'undefined') {
@@ -70,21 +75,17 @@ def _register_client_side_callbacks(app):
             } else if (typeof window.dash_clientside.transformers.generateEmbeddings !== 'function') {
                 errorMsg += 'generateEmbeddings function not found.';
             }
-            
+
             console.error(errorMsg);
-            
+
             return [
                 { error: 'Transformers.js not loaded. Please refresh the page and try again.' },
-                errorMsg + ' Please refresh the page.',
-                'danger',
                 false
             ];
         }
         """,
         [
             Output("embeddings-generated-trigger", "data"),
-            Output("text-input-status-immediate", "children"),
-            Output("text-input-status-immediate", "color"),
             Output("generate-embeddings-btn", "disabled", allow_duplicate=True),
         ],
         [Input("generate-embeddings-btn", "n_clicks")],

src/embeddingbuddy/config/settings.py

@@ -73,6 +73,17 @@ class AppSettings:
     HOST = os.getenv("EMBEDDINGBUDDY_HOST", "127.0.0.1")
     PORT = int(os.getenv("EMBEDDINGBUDDY_PORT", "8050"))
 
+    # Environment Configuration
+    ENVIRONMENT = os.getenv(
+        "EMBEDDINGBUDDY_ENV", "development"
+    )  # development, production
+
+    # WSGI Server Configuration (for production)
+    GUNICORN_WORKERS = int(os.getenv("GUNICORN_WORKERS", "4"))
+    GUNICORN_BIND = os.getenv("GUNICORN_BIND", f"{HOST}:{PORT}")
+    GUNICORN_TIMEOUT = int(os.getenv("GUNICORN_TIMEOUT", "120"))
+    GUNICORN_KEEPALIVE = int(os.getenv("GUNICORN_KEEPALIVE", "5"))
+
     # OpenSearch Configuration
     OPENSEARCH_DEFAULT_SIZE = 100
     OPENSEARCH_SAMPLE_SIZE = 5

src/embeddingbuddy/ui/callbacks/data_processing.py

@@ -621,6 +621,12 @@ class DataProcessingCallbacks:
             if not embeddings_data:
                 return no_update, no_update, no_update, no_update, no_update
 
+            # Check if this is a request trigger (contains textContent) vs actual embeddings data
+            if isinstance(embeddings_data, dict) and "textContent" in embeddings_data:
+                # This is a processing request trigger, not the actual results
+                # The JavaScript will handle the async processing and update the UI directly
+                return no_update, no_update, no_update, no_update, no_update
+
             processed_data = self.processor.process_client_embeddings(embeddings_data)
 
             if processed_data.error:

src/embeddingbuddy/ui/callbacks/interactions.py

@@ -1,6 +1,5 @@
 import dash
-from dash import callback, Input, Output, State, html
-import dash_bootstrap_components as dbc
+from dash import callback, Input, Output
 
 
 class InteractionCallbacks:
@@ -8,75 +7,16 @@ class InteractionCallbacks:
         self._register_callbacks()
 
     def _register_callbacks(self):
-        @callback(
-            Output("point-details", "children"),
-            Input("embedding-plot", "clickData"),
-            [State("processed-data", "data"), State("processed-prompts", "data")],
-        )
-        def display_click_data(clickData, data, prompts_data):
-            if not clickData or not data:
-                return "Click on a point to see details"
-
-            point_data = clickData["points"][0]
-            trace_name = point_data.get("fullData", {}).get("name", "Documents")
-
-            if "pointIndex" in point_data:
-                point_index = point_data["pointIndex"]
-            elif "pointNumber" in point_data:
-                point_index = point_data["pointNumber"]
-            else:
-                return "Could not identify clicked point"
-
-            if (
-                trace_name.startswith("Prompts")
-                and prompts_data
-                and "prompts" in prompts_data
-            ):
-                item = prompts_data["prompts"][point_index]
-                item_type = "Prompt"
-            else:
-                item = data["documents"][point_index]
-                item_type = "Document"
-
-            return self._create_detail_card(item, item_type)
-
         @callback(
             [
                 Output("processed-data", "data", allow_duplicate=True),
                 Output("processed-prompts", "data", allow_duplicate=True),
-                Output("point-details", "children", allow_duplicate=True),
             ],
             Input("reset-button", "n_clicks"),
             prevent_initial_call=True,
         )
         def reset_data(n_clicks):
             if n_clicks is None or n_clicks == 0:
-                return dash.no_update, dash.no_update, dash.no_update
+                return dash.no_update, dash.no_update
 
-            return None, None, "Click on a point to see details"
-
-    @staticmethod
-    def _create_detail_card(item, item_type):
-        return dbc.Card(
-            [
-                dbc.CardBody(
-                    [
-                        html.H5(f"{item_type}: {item['id']}", className="card-title"),
-                        html.P(f"Text: {item['text']}", className="card-text"),
-                        html.P(
-                            f"Category: {item.get('category', 'Unknown')}",
-                            className="card-text",
-                        ),
-                        html.P(
-                            f"Subcategory: {item.get('subcategory', 'Unknown')}",
-                            className="card-text",
-                        ),
-                        html.P(
-                            f"Tags: {', '.join(item.get('tags', [])) if item.get('tags') else 'None'}",
-                            className="card-text",
-                        ),
-                        html.P(f"Type: {item_type}", className="card-text text-muted"),
-                    ]
-                )
-            ]
-        )
+            return None, None

src/embeddingbuddy/ui/components/datasource.py

@@ -1,13 +1,11 @@
 from dash import dcc, html
 import dash_bootstrap_components as dbc
 from .upload import UploadComponent
-from .textinput import TextInputComponent
 
 
 class DataSourceComponent:
     def __init__(self):
         self.upload_component = UploadComponent()
-        self.text_input_component = TextInputComponent()
 
     def create_tabbed_interface(self):
         """Create tabbed interface for different data sources."""
@@ -19,7 +17,6 @@ class DataSourceComponent:
                             [
                                 dbc.Tab(label="File Upload", tab_id="file-tab"),
                                 dbc.Tab(label="OpenSearch", tab_id="opensearch-tab"),
-                                dbc.Tab(label="Text Input", tab_id="text-input-tab"),
                             ],
                             id="data-source-tabs",
                             active_tab="file-tab",
@@ -211,10 +208,6 @@ class DataSourceComponent:
             ]
         )
 
-    def create_text_input_tab(self):
-        """Create text input tab content for browser-based embedding generation."""
-        return html.Div([self.text_input_component.create_text_input_interface()])
-
     def _create_opensearch_section(self, section_type):
         """Create a complete OpenSearch section for either 'data' or 'prompts'."""
         section_id = section_type  # 'data' or 'prompts'

src/embeddingbuddy/ui/components/sidebar.py

@@ -2,31 +2,26 @@ from dash import dcc, html
 import dash_bootstrap_components as dbc
 from .upload import UploadComponent
 from .datasource import DataSourceComponent
+from .textinput import TextInputComponent
 
 
 class SidebarComponent:
     def __init__(self):
         self.upload_component = UploadComponent()
         self.datasource_component = DataSourceComponent()
+        self.textinput_component = TextInputComponent()
 
     def create_layout(self):
         return dbc.Col(
             [
-                html.H5("Data Sources", className="mb-3"),
-                self.datasource_component.create_error_alert(),
-                self.datasource_component.create_success_alert(),
-                self.datasource_component.create_tabbed_interface(),
-                html.H5("Visualization Controls", className="mb-3 mt-4"),
-            ]
-            + self._create_method_dropdown()
-            + self._create_color_dropdown()
-            + self._create_dimension_toggle()
-            + self._create_prompts_toggle()
-            + [
-                html.H5("Point Details", className="mb-3"),
-                html.Div(
-                    id="point-details", children="Click on a point to see details"
-                ),
+                dbc.Accordion(
+                    [
+                        self._create_data_sources_item(),
+                        self._create_generate_embeddings_item(),
+                        self._create_visualization_controls_item(),
+                    ],
+                    always_open=True,
+                )
             ],
             width=3,
             style={"padding-right": "20px"},
@@ -86,3 +81,63 @@ class SidebarComponent:
                 style={"margin-bottom": "20px"},
             ),
         ]
+
+    def _create_generate_embeddings_item(self):
+        return dbc.AccordionItem(
+            [
+                self.textinput_component.create_text_input_interface(),
+            ],
+            title=html.Span(
+                [
+                    "Generate Embeddings ",
+                    html.I(
+                        className="fas fa-info-circle text-muted",
+                        style={"cursor": "pointer"},
+                        id="generate-embeddings-info-icon",
+                        title="Create new embeddings from text input using various in-browser models",
+                    ),
+                ]
+            ),
+            item_id="generate-embeddings-accordion",
+        )
+
+    def _create_data_sources_item(self):
+        return dbc.AccordionItem(
+            [
+                self.datasource_component.create_error_alert(),
+                self.datasource_component.create_success_alert(),
+                self.datasource_component.create_tabbed_interface(),
+            ],
+            title=html.Span(
+                [
+                    "Load Embeddings ",
+                    html.I(
+                        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",
+                    ),
+                ]
+            ),
+            item_id="data-sources-accordion",
+        )
+
+    def _create_visualization_controls_item(self):
+        return dbc.AccordionItem(
+            self._create_method_dropdown()
+            + self._create_color_dropdown()
+            + self._create_dimension_toggle()
+            + self._create_prompts_toggle(),
+            title=html.Span(
+                [
+                    "Visualization Controls ",
+                    html.I(
+                        className="fas fa-info-circle text-muted",
+                        style={"cursor": "pointer"},
+                        id="visualization-controls-info-icon",
+                        title="Configure plot settings: select dimensionality reduction method, colors, and display options",
+                    ),
+                ]
+            ),
+            item_id="visualization-controls-accordion",
+        )

src/embeddingbuddy/ui/components/textinput.py

@@ -30,9 +30,6 @@ class TextInputComponent:
                 # Generation controls
                 self._create_generation_controls(),
                 html.Hr(),
-                # Progress indicators
-                self._create_progress_indicators(),
-                html.Hr(),
                 # Status and results
                 self._create_status_section(),
                 # Hidden components for data flow
@@ -297,65 +294,10 @@ class TextInputComponent:
             ]
         )
 
-    def _create_progress_indicators(self):
-        """Create progress bars for model loading and embedding generation."""
-        return html.Div(
-            [
-                # Model loading progress
-                html.Div(
-                    [
-                        html.H6("Model Loading Progress", className="mb-2"),
-                        dbc.Progress(
-                            id="model-loading-progress",
-                            value=0,
-                            striped=True,
-                            animated=True,
-                            className="mb-2",
-                        ),
-                        html.Small(
-                            id="model-loading-status",
-                            children="No model loading in progress",
-                            className="text-muted",
-                        ),
-                    ],
-                    id="model-loading-section",
-                    style={"display": "none"},
-                ),
-                html.Br(),
-                # Embedding generation progress
-                html.Div(
-                    [
-                        html.H6("Embedding Generation Progress", className="mb-2"),
-                        dbc.Progress(
-                            id="embedding-progress",
-                            value=0,
-                            striped=True,
-                            animated=True,
-                            className="mb-2",
-                        ),
-                        html.Small(
-                            id="embedding-status",
-                            children="No embedding generation in progress",
-                            className="text-muted",
-                        ),
-                    ],
-                    id="embedding-progress-section",
-                    style={"display": "none"},
-                ),
-            ]
-        )
-
     def _create_status_section(self):
         """Create status alerts and results preview."""
         return html.Div(
             [
-                # Immediate status (from client-side)
-                dbc.Alert(
-                    id="text-input-status-immediate",
-                    children="Ready to generate embeddings",
-                    color="light",
-                    className="mb-3",
-                ),
                 # Server-side status
                 dbc.Alert(
                     id="text-input-status",

src/embeddingbuddy/ui/components/upload.py

@@ -5,39 +5,75 @@ import dash_bootstrap_components as dbc
 class UploadComponent:
     @staticmethod
     def create_data_upload():
-        return dcc.Upload(
-            id="upload-data",
-            children=html.Div(["Drag and Drop or ", html.A("Select Files")]),
-            style={
-                "width": "100%",
-                "height": "60px",
-                "lineHeight": "60px",
-                "borderWidth": "1px",
-                "borderStyle": "dashed",
-                "borderRadius": "5px",
-                "textAlign": "center",
-                "margin-bottom": "20px",
-            },
-            multiple=False,
+        return html.Div(
+            [
+                dcc.Upload(
+                    id="upload-data",
+                    children=html.Div(
+                        [
+                            "Upload Data ",
+                            html.I(
+                                className="fas fa-info-circle",
+                                style={"color": "#6c757d", "fontSize": "14px"},
+                                id="data-upload-info",
+                            ),
+                        ]
+                    ),
+                    style={
+                        "width": "100%",
+                        "height": "60px",
+                        "lineHeight": "60px",
+                        "borderWidth": "1px",
+                        "borderStyle": "dashed",
+                        "borderRadius": "5px",
+                        "textAlign": "center",
+                        "margin-bottom": "20px",
+                    },
+                    multiple=False,
+                ),
+                dbc.Tooltip(
+                    "Click here or drag and drop NDJSON files containing document embeddings",
+                    target="data-upload-info",
+                    placement="top",
+                ),
+            ]
         )
 
     @staticmethod
     def create_prompts_upload():
-        return dcc.Upload(
-            id="upload-prompts",
-            children=html.Div(["Drag and Drop Prompts or ", html.A("Select Files")]),
-            style={
-                "width": "100%",
-                "height": "60px",
-                "lineHeight": "60px",
-                "borderWidth": "1px",
-                "borderStyle": "dashed",
-                "borderRadius": "5px",
-                "textAlign": "center",
-                "margin-bottom": "20px",
-                "borderColor": "#28a745",
-            },
-            multiple=False,
+        return html.Div(
+            [
+                dcc.Upload(
+                    id="upload-prompts",
+                    children=html.Div(
+                        [
+                            "Upload Prompts ",
+                            html.I(
+                                className="fas fa-info-circle",
+                                style={"color": "#6c757d", "fontSize": "14px"},
+                                id="prompts-upload-info",
+                            ),
+                        ]
+                    ),
+                    style={
+                        "width": "100%",
+                        "height": "60px",
+                        "lineHeight": "60px",
+                        "borderWidth": "1px",
+                        "borderStyle": "dashed",
+                        "borderRadius": "5px",
+                        "textAlign": "center",
+                        "margin-bottom": "20px",
+                        "borderColor": "#28a745",
+                    },
+                    multiple=False,
+                ),
+                dbc.Tooltip(
+                    "Click here or drag and drop NDJSON files containing prompt embeddings",
+                    target="prompts-upload-info",
+                    placement="top",
+                ),
+            ]
         )
 
     @staticmethod

src/embeddingbuddy/visualization/plots.py

@@ -38,9 +38,9 @@ class PlotFactory:
         if dimensions == "3d":
             fig = px.scatter_3d(
                 df,
-                x="dim_1",
-                y="dim_2",
-                z="dim_3",
+                x="x",
+                y="y",
+                z="z",
                 color=color_values,
                 hover_data=hover_fields,
                 title=f"3D Embedding Visualization - {method} (colored by {color_by})",
@@ -49,8 +49,8 @@ class PlotFactory:
         else:
             fig = px.scatter(
                 df,
-                x="dim_1",
-                y="dim_2",
+                x="x",
+                y="y",
                 color=color_values,
                 hover_data=hover_fields,
                 title=f"2D Embedding Visualization - {method} (colored by {color_by})",
@@ -77,17 +77,17 @@ class PlotFactory:
         if dimensions == "3d":
             doc_fig = px.scatter_3d(
                 doc_df,
-                x="dim_1",
-                y="dim_2",
-                z="dim_3",
+                x="x",
+                y="y",
+                z="z",
                 color=doc_color_values,
                 hover_data=hover_fields,
             )
         else:
             doc_fig = px.scatter(
                 doc_df,
-                x="dim_1",
-                y="dim_2",
+                x="x",
+                y="y",
                 color=doc_color_values,
                 hover_data=hover_fields,
             )
@@ -114,17 +114,17 @@ class PlotFactory:
             if dimensions == "3d":
                 prompt_fig = px.scatter_3d(
                     prompt_df,
-                    x="dim_1",
-                    y="dim_2",
-                    z="dim_3",
+                    x="x",
+                    y="y",
+                    z="z",
                     color=prompt_color_values,
                     hover_data=hover_fields,
                 )
             else:
                 prompt_fig = px.scatter(
                     prompt_df,
-                    x="dim_1",
-                    y="dim_2",
+                    x="x",
+                    y="y",
                     color=prompt_color_values,
                     hover_data=hover_fields,
                 )
@@ -168,11 +168,11 @@ class PlotFactory:
                 "category": doc.category,
                 "subcategory": doc.subcategory,
                 "tags_str": ", ".join(doc.tags) if doc.tags else "None",
-                "dim_1": coordinates[i, 0],
-                "dim_2": coordinates[i, 1],
+                "x": coordinates[i, 0],
+                "y": coordinates[i, 1],
             }
             if dimensions == "3d":
-                row["dim_3"] = coordinates[i, 2]
+                row["z"] = coordinates[i, 2]
             df_data.append(row)
 
         return pd.DataFrame(df_data)

uv.lock

@@ -412,13 +412,12 @@ wheels = [
 
 [[package]]
 name = "embeddingbuddy"
-version = "0.3.0"
+version = "0.5.0"
 source = { editable = "." }
 dependencies = [
     { name = "dash" },
     { name = "dash-bootstrap-components" },
     { name = "mypy" },
-    { name = "numba" },
     { name = "numpy" },
     { name = "opensearch-py" },
     { name = "opentsne" },
@@ -431,6 +430,7 @@ dependencies = [
 [package.optional-dependencies]
 all = [
     { name = "bandit" },
+    { name = "gunicorn" },
     { name = "mypy" },
     { name = "pip-audit" },
     { name = "pytest" },
@@ -451,6 +451,9 @@ lint = [
     { name = "mypy" },
     { name = "ruff" },
 ]
+prod = [
+    { name = "gunicorn" },
+]
 security = [
     { name = "bandit" },
     { name = "pip-audit" },
@@ -466,11 +469,11 @@ requires-dist = [
     { name = "bandit", extras = ["toml"], marker = "extra == 'security'", specifier = ">=1.7.5" },
     { name = "dash", specifier = ">=2.17.1" },
     { name = "dash-bootstrap-components", specifier = ">=1.5.0" },
-    { name = "embeddingbuddy", extras = ["test", "lint", "security"], marker = "extra == 'all'" },
     { name = "embeddingbuddy", extras = ["test", "lint", "security"], marker = "extra == 'dev'" },
+    { name = "embeddingbuddy", extras = ["test", "lint", "security", "prod"], marker = "extra == 'all'" },
+    { name = "gunicorn", marker = "extra == 'prod'", specifier = ">=21.2.0" },
     { name = "mypy", specifier = ">=1.17.1" },
     { name = "mypy", marker = "extra == 'lint'", specifier = ">=1.5.0" },
-    { name = "numba", specifier = ">=0.56.4" },
     { name = "numpy", specifier = ">=1.24.4" },
     { name = "opensearch-py", specifier = ">=3.0.0" },
     { name = "opentsne", specifier = ">=1.0.0" },
@@ -484,7 +487,7 @@ requires-dist = [
     { name = "scikit-learn", specifier = ">=1.3.2" },
     { name = "umap-learn", specifier = ">=0.5.8" },
 ]
-provides-extras = ["test", "lint", "security", "dev", "all"]
+provides-extras = ["test", "lint", "security", "prod", "dev", "all"]
 
 [[package]]
 name = "events"
@@ -520,6 +523,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3d/68/9d4508e893976286d2ead7f8f571314af6c2037af34853a30fd769c02e9d/flask-3.1.1-py3-none-any.whl", hash = "sha256:07aae2bb5eaf77993ef57e357491839f5fd9f4dc281593a81a9e4d79a24f295c", size = 103305, upload-time = "2025-05-13T15:01:15.591Z" },
 ]
 
+[[package]]
+name = "gunicorn"
+version = "23.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/34/72/9614c465dc206155d93eff0ca20d42e1e35afc533971379482de953521a4/gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec", size = 375031, upload-time = "2024-08-10T20:25:27.378Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/7d/6dac2a6e1eba33ee43f318edbed4ff29151a49b5d37f080aad1e6469bca4/gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d", size = 85029, upload-time = "2024-08-10T20:25:24.996Z" },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"

wsgi.py

@@ -0,0 +1,20 @@
+"""
+WSGI entry point for production deployment.
+Use this with a production WSGI server like Gunicorn.
+"""
+from src.embeddingbuddy.app import create_app
+
+# Create the application instance
+application = create_app()
+
+# For compatibility with different WSGI servers
+app = application
+
+if __name__ == "__main__":
+    # This won't be used in production, but useful for testing
+    from src.embeddingbuddy.config.settings import AppSettings
+    application.run(
+        host=AppSettings.HOST,
+        port=AppSettings.PORT,
+        debug=False
+    )