fix formatting and bump version to v0.3.0

it doesn't have prompts as well

.claude/settings.local.json

@@ -6,6 +6,7 @@
       "Bash(uv add:*)"
     ],
     "deny": [],
-    "ask": []
+    "ask": [],
+    "defaultMode": "acceptEdits"
   }
 }

.gitea/workflows/release.yml

@@ -0,0 +1,92 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - 'v*'
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Release version (e.g., v1.0.0)'
+        required: true
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --extra test
+
+      - name: Run full test suite
+        run: uv run pytest tests/ -v --cov=src/embeddingbuddy --cov-report=term-missing
+
+  build-and-release:
+    runs-on: ubuntu-latest
+    needs: test
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync
+
+      - name: Build package
+        run: uv build
+
+      - name: Create release notes
+        run: |
+          echo "# Release Notes" > release-notes.md
+          echo "" >> release-notes.md
+          echo "## What's New" >> release-notes.md
+          echo "" >> release-notes.md
+          echo "- Modular architecture with improved testability" >> release-notes.md
+          echo "- Comprehensive test suite" >> release-notes.md
+          echo "- Enhanced documentation" >> release-notes.md
+          echo "- Security scanning and dependency management" >> release-notes.md
+          echo "" >> release-notes.md
+          echo "## Installation" >> release-notes.md
+          echo "" >> release-notes.md
+          echo '```bash' >> release-notes.md
+          echo 'uv sync' >> release-notes.md
+          echo 'uv run python main.py' >> release-notes.md
+          echo '```' >> release-notes.md
+
+      - name: Create Release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITEA_TOKEN }}
+        with:
+          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
+
+      - name: Upload Release Assets
+        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

.gitea/workflows/security.yml

@@ -0,0 +1,70 @@
+name: Security Scan
+
+on:
+  push:
+    branches: ["main", "master", "develop"]
+  pull_request:
+    branches: ["main", "master"]
+  schedule:
+    # Run security scan weekly on Sundays at 2 AM UTC
+    - cron: '0 2 * * 0'
+
+jobs:
+  security:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --extra security
+
+      - name: Run bandit security linter
+        run: uv run bandit -r src/ -f json -o bandit-report.json
+        continue-on-error: true
+
+      - name: Run safety vulnerability check
+        run: uv run safety check --json --save-json safety-report.json
+        continue-on-error: true
+
+      - name: Upload security reports
+        uses: actions/upload-artifact@v3
+        with:
+          name: security-reports
+          path: |
+            bandit-report.json
+            safety-report.json
+
+  dependency-check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Check for dependency vulnerabilities
+        run: |
+          uv sync --extra security
+          uv run pip-audit --format=json --output=pip-audit-report.json
+        continue-on-error: true
+
+      - name: Upload dependency audit report
+        uses: actions/upload-artifact@v3
+        with:
+          name: dependency-audit
+          path: pip-audit-report.json

.gitea/workflows/test.yml

@@ -0,0 +1,104 @@
+name: Test Suite
+
+on:
+  push:
+    branches: 
+      - "main"
+      - "develop"
+  pull_request:
+    branches: 
+      - "main"
+  workflow_dispatch:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.11"]
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --extra test
+
+      - name: Run tests with pytest
+        run: uv run pytest tests/ -v --tb=short
+
+      - name: Run tests with coverage
+        run: uv run pytest tests/ --cov=src/embeddingbuddy --cov-report=term-missing --cov-report=xml
+
+      - name: Upload coverage reports
+        uses: codecov/codecov-action@v4
+        if: matrix.python-version == '3.11'
+        with:
+          file: ./coverage.xml
+          fail_ci_if_error: false
+
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --extra lint
+
+      - name: Run ruff linter
+        run: uv run ruff check src/ tests/
+
+      - name: Run ruff formatter check
+        run: uv run ruff format --check src/ tests/
+
+      # TODO fix this it throws errors
+      # - name: Run mypy type checker
+      #   run: uv run mypy src/embeddingbuddy/ --ignore-missing-imports
+
+  build:
+    runs-on: ubuntu-latest
+    needs: [test, lint]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync
+
+      - name: Build package
+        run: uv build
+
+      - name: Test installation
+        run: |
+          uv run python -c "from src.embeddingbuddy.app import create_app; app = create_app(); print('✅ Package builds and imports successfully')"
+
+      - name: Upload build artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: dist-files
+          path: dist/

.gitignore

@@ -1,12 +1,84 @@
 # Python-generated files
 __pycache__/
 *.py[oc]
+*.py[cod]
+*$py.class
+*.so
+.Python
 build/
+develop-eggs/
 dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
 wheels/
-*.egg-info
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
 
 # Virtual environments
+.env
 .venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Project specific
+*.log
+.mypy_cache/
+.dmypy.json
+dmypy.json
 temp/
-todo/
+todo/
+
+# Security reports
+bandit-report.json
+safety-report.json
+pip-audit-report.json
+
+# Temporary files
+*.tmp

CLAUDE.md

@@ -30,9 +30,28 @@ The app will be available at http://127.0.0.1:8050
 **Run tests:**
 
 ```bash
+uv sync --extra test
 uv run pytest tests/ -v
 ```
 
+**Development tools:**
+
+```bash
+# Install all dev dependencies
+uv sync --extra dev
+
+# Linting and formatting
+uv run ruff check src/ tests/
+uv run ruff format src/ tests/
+
+# Type checking
+uv run mypy src/embeddingbuddy/
+
+# Security scanning
+uv run bandit -r src/
+uv run safety check
+```
+
 **Test with sample data:**
 Use the included `sample_data.ndjson` and `sample_prompts.ndjson` files for testing the application functionality.
 
@@ -42,7 +61,7 @@ Use the included `sample_data.ndjson` and `sample_prompts.ndjson` files for test
 
 The application follows a modular architecture with clear separation of concerns:
 
-```
+```text
 src/embeddingbuddy/
 ├── app.py              # Main application entry point and factory
 ├── main.py             # Application runner
@@ -72,27 +91,32 @@ src/embeddingbuddy/
 ### Key Components
 
 **Data Layer:**
+
 - `data/parser.py` - NDJSON parsing with error handling
 - `data/processor.py` - Data transformation and combination logic
 - `models/schemas.py` - Dataclasses for type safety and validation
 
 **Algorithm Layer:**
+
 - `models/reducers.py` - Modular dimensionality reduction with factory pattern
 - Supports PCA, t-SNE (openTSNE), and UMAP algorithms
 - Abstract base class for easy extension
 
 **Visualization Layer:**
+
 - `visualization/plots.py` - Plot factory with single and dual plot support
 - `visualization/colors.py` - Color mapping and grayscale conversion utilities
 - Plotly-based 2D/3D scatter plots with interactive features
 
 **UI Layer:**
+
 - `ui/layout.py` - Main application layout composition
 - `ui/components/` - Reusable, testable UI components
 - `ui/callbacks/` - Organized callbacks grouped by functionality
 - Bootstrap-styled sidebar with controls and large visualization area
 
 **Configuration:**
+
 - `config/settings.py` - Centralized settings with environment variable support
 - Plot styling, marker configurations, and app-wide constants
 
@@ -112,16 +136,19 @@ Optional fields: `id`, `category`, `subcategory`, `tags`
 The refactored callback system is organized by functionality:
 
 **Data Processing (`ui/callbacks/data_processing.py`):**
+
 - File upload handling
 - NDJSON parsing and validation
 - Data storage in dcc.Store components
 
 **Visualization (`ui/callbacks/visualization.py`):**
+
 - Dimensionality reduction pipeline
 - Plot generation and updates
 - Method/parameter change handling
 
 **Interactions (`ui/callbacks/interactions.py`):**
+
 - Point click handling and detail display
 - Reset functionality
 - User interaction management
@@ -131,15 +158,18 @@ The refactored callback system is organized by functionality:
 The modular design enables comprehensive testing:
 
 **Unit Tests:**
+
 - `tests/test_data_processing.py` - Parser and processor logic
 - `tests/test_reducers.py` - Dimensionality reduction algorithms
 - `tests/test_visualization.py` - Plot creation and color mapping
 
 **Integration Tests:**
+
 - End-to-end data pipeline testing
 - Component integration verification
 
 **Key Testing Benefits:**
+
 - Fast test execution (milliseconds vs seconds)
 - Isolated component testing
 - Easy mocking and fixture creation
@@ -167,6 +197,7 @@ Uses modern Python stack with uv for dependency management:
 5. **Tests** - Write tests for all new functionality
 
 **Code Organization Principles:**
+
 - Single responsibility principle
 - Clear module boundaries  
 - Testable, isolated components
@@ -174,7 +205,8 @@ Uses modern Python stack with uv for dependency management:
 - Error handling at appropriate layers
 
 **Testing Requirements:**
+
 - Unit tests for all core logic
 - Integration tests for data flow
 - Component tests for UI elements
-- Maintain high test coverage
+- Maintain high test coverage

README.md

@@ -90,7 +90,7 @@ uv run python main.py
 
 The application follows a modular architecture for improved maintainability and testability:
 
-```
+```text
 src/embeddingbuddy/
 ├── config/          # Configuration management
 │   └── settings.py  # Centralized app settings
@@ -115,8 +115,8 @@ src/embeddingbuddy/
 Run the test suite to verify functionality:
 
 ```bash
-# Install pytest
+# Install test dependencies
-uv add pytest
+uv sync --extra test
 
 # Run all tests
 uv run pytest tests/ -v
@@ -128,6 +128,31 @@ uv run pytest tests/test_data_processing.py -v
 uv run pytest tests/ --cov=src/embeddingbuddy
 ```
 
+### Development Tools
+
+Install development dependencies for linting, type checking, and security:
+
+```bash
+# Install all dev dependencies
+uv sync --extra dev
+
+# Or install specific groups
+uv sync --extra test        # Testing tools
+uv sync --extra lint        # Linting and formatting
+uv sync --extra security    # Security scanning tools
+
+# Run linting
+uv run ruff check src/ tests/
+uv run ruff format src/ tests/
+
+# Run type checking
+uv run mypy src/embeddingbuddy/
+
+# Run security scans
+uv run bandit -r src/
+uv run safety check
+```
+
 ### Adding New Features
 
 The modular architecture makes it easy to extend functionality:

app.py

@@ -1,515 +0,0 @@
-import json
-import uuid
-from io import StringIO
-import base64
-
-import dash
-from dash import dcc, html, Input, Output, State, callback
-import dash_bootstrap_components as dbc
-import plotly.express as px
-import plotly.graph_objects as go
-import pandas as pd
-import numpy as np
-from sklearn.decomposition import PCA
-import umap
-from openTSNE import TSNE
-
-
-app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
-
-def parse_ndjson(contents):
-    """Parse NDJSON content and return list of documents."""
-    content_type, content_string = contents.split(',')
-    decoded = base64.b64decode(content_string)
-    text_content = decoded.decode('utf-8')
-    
-    documents = []
-    for line in text_content.strip().split('\n'):
-        if line.strip():
-            doc = json.loads(line)
-            if 'id' not in doc:
-                doc['id'] = str(uuid.uuid4())
-            documents.append(doc)
-    return documents
-
-def apply_dimensionality_reduction(embeddings, method='pca', n_components=3):
-    """Apply dimensionality reduction to embeddings."""
-    if method == 'pca':
-        reducer = PCA(n_components=n_components)
-        reduced = reducer.fit_transform(embeddings)
-        variance_explained = reducer.explained_variance_ratio_
-        return reduced, variance_explained
-    elif method == 'tsne':
-        reducer = TSNE(n_components=n_components, random_state=42)
-        reduced = reducer.fit(embeddings)
-        return reduced, None
-    elif method == 'umap':
-        reducer = umap.UMAP(n_components=n_components, random_state=42)
-        reduced = reducer.fit_transform(embeddings)
-        return reduced, None
-    else:
-        raise ValueError(f"Unknown method: {method}")
-
-def create_color_mapping(documents, color_by):
-    """Create color mapping for documents based on specified field."""
-    if color_by == 'category':
-        values = [doc.get('category', 'Unknown') for doc in documents]
-    elif color_by == 'subcategory':
-        values = [doc.get('subcategory', 'Unknown') for doc in documents]
-    elif color_by == 'tags':
-        values = [', '.join(doc.get('tags', [])) if doc.get('tags') else 'No tags' for doc in documents]
-    else:
-        values = ['All'] * len(documents)
-    
-    return values
-
-def create_plot(df, dimensions='3d', color_by='category', method='PCA'):
-    """Create plotly scatter plot."""
-    color_values = create_color_mapping(df.to_dict('records'), color_by)
-    
-    # Truncate text for hover display
-    df_display = df.copy()
-    df_display['text_preview'] = df_display['text'].apply(lambda x: x[:100] + "..." if len(x) > 100 else x)
-    
-    # Include all metadata fields in hover
-    hover_fields = ['id', 'text_preview', 'category', 'subcategory']
-    # Add tags as a string for hover
-    df_display['tags_str'] = df_display['tags'].apply(lambda x: ', '.join(x) if x else 'None')
-    hover_fields.append('tags_str')
-    
-    if dimensions == '3d':
-        fig = px.scatter_3d(
-            df_display, x='dim_1', y='dim_2', z='dim_3',
-            color=color_values,
-            hover_data=hover_fields,
-            title=f'3D Embedding Visualization - {method} (colored by {color_by})'
-        )
-        fig.update_traces(marker=dict(size=5))
-    else:
-        fig = px.scatter(
-            df_display, x='dim_1', y='dim_2',
-            color=color_values,
-            hover_data=hover_fields,
-            title=f'2D Embedding Visualization - {method} (colored by {color_by})'
-        )
-        fig.update_traces(marker=dict(size=8))
-    
-    fig.update_layout(
-        height=None,  # Let CSS height control this
-        autosize=True,
-        margin=dict(l=0, r=0, t=50, b=0)
-    )
-    return fig
-
-def create_dual_plot(doc_df, prompt_df, dimensions='3d', color_by='category', method='PCA', show_prompts=None):
-    """Create plotly scatter plot with separate traces for documents and prompts."""
-    
-    # Create the base figure
-    fig = go.Figure()
-    
-    # Helper function to convert colors to grayscale
-    def to_grayscale_hex(color_str):
-        """Convert a color to grayscale while maintaining some distinction."""
-        import plotly.colors as pc
-        # Try to get RGB values from the color
-        try:
-            if color_str.startswith('#'):
-                # Hex color
-                rgb = tuple(int(color_str[i:i+2], 16) for i in (1, 3, 5))
-            else:
-                # Named color or other format - convert through plotly
-                rgb = pc.hex_to_rgb(pc.convert_colors_to_same_type([color_str], colortype='hex')[0][0])
-            
-            # Convert to grayscale using luminance formula, but keep some color
-            gray_value = int(0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2])
-            # Make it a bit more gray but not completely
-            gray_rgb = (gray_value * 0.7 + rgb[0] * 0.3, 
-                       gray_value * 0.7 + rgb[1] * 0.3, 
-                       gray_value * 0.7 + rgb[2] * 0.3)
-            return f'rgb({int(gray_rgb[0])},{int(gray_rgb[1])},{int(gray_rgb[2])})'
-        except:
-            return 'rgb(128,128,128)'  # fallback gray
-    
-    # Create document plot using plotly express for consistent colors
-    doc_color_values = create_color_mapping(doc_df.to_dict('records'), color_by)
-    doc_df_display = doc_df.copy()
-    doc_df_display['text_preview'] = doc_df_display['text'].apply(lambda x: x[:100] + "..." if len(x) > 100 else x)
-    doc_df_display['tags_str'] = doc_df_display['tags'].apply(lambda x: ', '.join(x) if x else 'None')
-    
-    hover_fields = ['id', 'text_preview', 'category', 'subcategory', 'tags_str']
-    
-    # Create documents plot to get the color mapping
-    if dimensions == '3d':
-        doc_fig = px.scatter_3d(
-            doc_df_display, x='dim_1', y='dim_2', z='dim_3',
-            color=doc_color_values,
-            hover_data=hover_fields
-        )
-    else:
-        doc_fig = px.scatter(
-            doc_df_display, x='dim_1', y='dim_2',
-            color=doc_color_values,
-            hover_data=hover_fields
-        )
-    
-    # Add document traces to main figure
-    for trace in doc_fig.data:
-        trace.name = f'Documents - {trace.name}'
-        if dimensions == '3d':
-            trace.marker.size = 5
-            trace.marker.symbol = 'circle'
-        else:
-            trace.marker.size = 8
-            trace.marker.symbol = 'circle'
-        trace.marker.opacity = 1.0
-        fig.add_trace(trace)
-    
-    # Add prompt traces if they exist
-    if prompt_df is not None and show_prompts and 'show' in show_prompts:
-        prompt_color_values = create_color_mapping(prompt_df.to_dict('records'), color_by)
-        prompt_df_display = prompt_df.copy()
-        prompt_df_display['text_preview'] = prompt_df_display['text'].apply(lambda x: x[:100] + "..." if len(x) > 100 else x)
-        prompt_df_display['tags_str'] = prompt_df_display['tags'].apply(lambda x: ', '.join(x) if x else 'None')
-        
-        # Create prompts plot to get consistent color grouping
-        if dimensions == '3d':
-            prompt_fig = px.scatter_3d(
-                prompt_df_display, x='dim_1', y='dim_2', z='dim_3',
-                color=prompt_color_values,
-                hover_data=hover_fields
-            )
-        else:
-            prompt_fig = px.scatter(
-                prompt_df_display, x='dim_1', y='dim_2',
-                color=prompt_color_values,
-                hover_data=hover_fields
-            )
-        
-        # Add prompt traces with grayed colors
-        for trace in prompt_fig.data:
-            # Convert the color to grayscale
-            original_color = trace.marker.color
-            if hasattr(trace.marker, 'color') and isinstance(trace.marker.color, str):
-                trace.marker.color = to_grayscale_hex(trace.marker.color)
-            
-            trace.name = f'Prompts - {trace.name}'
-            if dimensions == '3d':
-                trace.marker.size = 6
-                trace.marker.symbol = 'diamond'
-            else:
-                trace.marker.size = 10
-                trace.marker.symbol = 'diamond'
-            trace.marker.opacity = 0.8
-            fig.add_trace(trace)
-    
-    title = f'{dimensions.upper()} Embedding Visualization - {method} (colored by {color_by})'
-    fig.update_layout(
-        title=title,
-        height=None,
-        autosize=True,
-        margin=dict(l=0, r=0, t=50, b=0)
-    )
-    
-    return fig
-
-# Layout
-app.layout = dbc.Container([
-    dbc.Row([
-        dbc.Col([
-            html.H1("EmbeddingBuddy", className="text-center mb-4"),
-        ], width=12)
-    ]),
-    
-    dbc.Row([
-        # Left sidebar with controls
-        dbc.Col([
-            html.H5("Upload Data", className="mb-3"),
-            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
-            ),
-            
-            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
-            ),
-            
-            dbc.Button(
-                "Reset All Data",
-                id='reset-button',
-                color='danger',
-                outline=True,
-                size='sm',
-                className='mb-3',
-                style={'width': '100%'}
-            ),
-            
-            html.H5("Visualization Controls", className="mb-3"),
-            
-            dbc.Label("Method:"),
-            dcc.Dropdown(
-                id='method-dropdown',
-                options=[
-                    {'label': 'PCA', 'value': 'pca'},
-                    {'label': 't-SNE', 'value': 'tsne'},
-                    {'label': 'UMAP', 'value': 'umap'}
-                ],
-                value='pca',
-                style={'margin-bottom': '15px'}
-            ),
-            
-            dbc.Label("Color by:"),
-            dcc.Dropdown(
-                id='color-dropdown',
-                options=[
-                    {'label': 'Category', 'value': 'category'},
-                    {'label': 'Subcategory', 'value': 'subcategory'},
-                    {'label': 'Tags', 'value': 'tags'}
-                ],
-                value='category',
-                style={'margin-bottom': '15px'}
-            ),
-            
-            dbc.Label("Dimensions:"),
-            dcc.RadioItems(
-                id='dimension-toggle',
-                options=[
-                    {'label': '2D', 'value': '2d'},
-                    {'label': '3D', 'value': '3d'}
-                ],
-                value='3d',
-                style={'margin-bottom': '20px'}
-            ),
-            
-            dbc.Label("Show Prompts:"),
-            dcc.Checklist(
-                id='show-prompts-toggle',
-                options=[{'label': 'Show prompts on plot', 'value': 'show'}],
-                value=['show'],
-                style={'margin-bottom': '20px'}
-            ),
-            
-            html.H5("Point Details", className="mb-3"),
-            html.Div(id='point-details', children="Click on a point to see details")
-            
-        ], width=3, style={'padding-right': '20px'}),
-        
-        # Main visualization area
-        dbc.Col([
-            dcc.Graph(
-                id='embedding-plot',
-                style={'height': '85vh', 'width': '100%'},
-                config={'responsive': True, 'displayModeBar': True}
-            )
-        ], width=9)
-    ]),
-    
-    dcc.Store(id='processed-data'),
-    dcc.Store(id='processed-prompts')
-], fluid=True)
-
-@callback(
-    Output('processed-data', 'data'),
-    Input('upload-data', 'contents'),
-    State('upload-data', 'filename')
-)
-def process_uploaded_file(contents, filename):
-    if contents is None:
-        return None
-    
-    try:
-        documents = parse_ndjson(contents)
-        embeddings = np.array([doc['embedding'] for doc in documents])
-        
-        # Store original embeddings and documents
-        return {
-            'documents': documents,
-            'embeddings': embeddings.tolist()
-        }
-    except Exception as e:
-        return {'error': str(e)}
-
-@callback(
-    Output('processed-prompts', 'data'),
-    Input('upload-prompts', 'contents'),
-    State('upload-prompts', 'filename')
-)
-def process_uploaded_prompts(contents, filename):
-    if contents is None:
-        return None
-    
-    try:
-        prompts = parse_ndjson(contents)
-        embeddings = np.array([prompt['embedding'] for prompt in prompts])
-        
-        # Store original embeddings and prompts
-        return {
-            'prompts': prompts,
-            'embeddings': embeddings.tolist()
-        }
-    except Exception as e:
-        return {'error': str(e)}
-
-@callback(
-    Output('embedding-plot', 'figure'),
-    [Input('processed-data', 'data'),
-     Input('processed-prompts', 'data'),
-     Input('method-dropdown', 'value'),
-     Input('color-dropdown', 'value'),
-     Input('dimension-toggle', 'value'),
-     Input('show-prompts-toggle', 'value')]
-)
-def update_plot(data, prompts_data, method, color_by, dimensions, show_prompts):
-    if not data or 'error' in data:
-        return go.Figure().add_annotation(
-            text="Upload a valid NDJSON file to see visualization",
-            xref="paper", yref="paper",
-            x=0.5, y=0.5, xanchor='center', yanchor='middle',
-            showarrow=False, font=dict(size=16)
-        )
-    
-    # Prepare embeddings for dimensionality reduction
-    doc_embeddings = np.array(data['embeddings'])
-    all_embeddings = doc_embeddings
-    has_prompts = prompts_data and 'error' not in prompts_data and prompts_data.get('prompts')
-    
-    if has_prompts:
-        prompt_embeddings = np.array(prompts_data['embeddings'])
-        all_embeddings = np.vstack([doc_embeddings, prompt_embeddings])
-    
-    n_components = 3 if dimensions == '3d' else 2
-    
-    # Apply dimensionality reduction to combined data
-    reduced, variance_explained = apply_dimensionality_reduction(
-        all_embeddings, method=method, n_components=n_components
-    )
-    
-    # Split reduced embeddings back
-    doc_reduced = reduced[:len(doc_embeddings)]
-    prompt_reduced = reduced[len(doc_embeddings):] if has_prompts else None
-    
-    # Create dataframes
-    doc_df_data = []
-    for i, doc in enumerate(data['documents']):
-        row = {
-            'id': doc['id'],
-            'text': doc['text'],
-            'category': doc.get('category', 'Unknown'),
-            'subcategory': doc.get('subcategory', 'Unknown'),
-            'tags': doc.get('tags', []),
-            'dim_1': doc_reduced[i, 0],
-            'dim_2': doc_reduced[i, 1],
-            'type': 'document'
-        }
-        if dimensions == '3d':
-            row['dim_3'] = doc_reduced[i, 2]
-        doc_df_data.append(row)
-    
-    doc_df = pd.DataFrame(doc_df_data)
-    
-    prompt_df = None
-    if has_prompts and prompt_reduced is not None:
-        prompt_df_data = []
-        for i, prompt in enumerate(prompts_data['prompts']):
-            row = {
-                'id': prompt['id'],
-                'text': prompt['text'],
-                'category': prompt.get('category', 'Unknown'),
-                'subcategory': prompt.get('subcategory', 'Unknown'),
-                'tags': prompt.get('tags', []),
-                'dim_1': prompt_reduced[i, 0],
-                'dim_2': prompt_reduced[i, 1],
-                'type': 'prompt'
-            }
-            if dimensions == '3d':
-                row['dim_3'] = prompt_reduced[i, 2]
-            prompt_df_data.append(row)
-        
-        prompt_df = pd.DataFrame(prompt_df_data)
-    
-    return create_dual_plot(doc_df, prompt_df, dimensions, color_by, method.upper(), show_prompts)
-
-@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"
-    
-    # Get point info from click
-    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"
-    
-    # Determine which dataset this point belongs to
-    if trace_name == '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 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")
-        ])
-    ])
-
-@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 None, None, "Click on a point to see details"
-
-if __name__ == '__main__':
-    app.run(debug=True)

example/README_elasticsearch.md

@@ -0,0 +1,157 @@
+# Elasticsearch/OpenSearch Sample Data
+
+This directory contains sample data files in Elasticsearch bulk index format for testing the OpenSearch integration in EmbeddingBuddy.
+
+## Files
+
+### Original NDJSON Files
+
+- `sample_data.ndjson` - Original sample documents in EmbeddingBuddy format
+- `sample_prompts.ndjson` - Original sample prompts in EmbeddingBuddy format
+
+### Elasticsearch Bulk Files
+
+- `sample_data_es_bulk.ndjson` - Documents in ES bulk format (index: "embeddings")
+- `sample_prompts_es_bulk.ndjson` - Prompts in ES bulk format (index: "prompts")
+
+## Usage
+
+### 1. Index the data using curl
+
+```bash
+# Index main documents
+curl -X POST "localhost:9200/_bulk" \
+  -H "Content-Type: application/x-ndjson" \
+  --data-binary @sample_data_es_bulk.ndjson
+
+# Index prompts
+curl -X POST "localhost:9200/_bulk" \
+  -H "Content-Type: application/x-ndjson" \
+  --data-binary @sample_prompts_es_bulk.ndjson
+```
+
+### 2. Create proper mappings (recommended)
+
+First create the index with proper dense_vector mapping:
+
+```bash
+# Create embeddings index with dense_vector mapping
+curl -X PUT "localhost:9200/embeddings" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "settings": {
+      "index.knn": true
+    },
+    "mappings": {
+      "properties": {
+        "id": {"type": "keyword"},
+        "embedding": {
+          "type": "knn_vector",
+          "dimension": 8,
+          "method": {
+            "engine": "lucene",
+            "space_type": "cosinesimil",
+            "name": "hnsw",
+            "parameters": {}
+          }
+        },
+        "text": {"type": "text"},
+        "category": {"type": "keyword"},
+        "subcategory": {"type": "keyword"},
+        "tags": {"type": "keyword"}
+      }
+    }
+  }'
+
+# Create dense vector index with alternative field names
+curl -X PUT "localhost:9200/prompts" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "settings": {
+      "index.knn": true
+    },
+    "mappings": {
+      "properties": {
+        "id": {"type": "keyword"},
+        "embedding": {
+          "type": "knn_vector",
+          "dimension": 8,
+          "method": {
+            "engine": "lucene",
+            "space_type": "cosinesimil",
+            "name": "hnsw",
+            "parameters": {}
+          }
+        },
+        "text": {"type": "text"},
+        "category": {"type": "keyword"},
+        "subcategory": {"type": "keyword"},
+        "tags": {"type": "keyword"}
+      }
+    }
+  }'
+```
+
+Then index the data using the bulk files above.
+
+### 3. Test in EmbeddingBuddy
+
+#### For "embeddings" index
+
+- **OpenSearch URL**: `http://localhost:9200`
+- **Index Name**: `embeddings`
+- **Field Mapping**:
+  - Embedding Field: `embedding`
+  - Text Field: `text`
+  - ID Field: `id`
+  - Category Field: `category`
+  - Subcategory Field: `subcategory`
+  - Tags Field: `tags`
+
+#### For "embeddings-dense" index (alternative field names)
+
+- **OpenSearch URL**: `http://localhost:9200`
+- **Index Name**: `embeddings-dense`
+- **Field Mapping**:
+  - Embedding Field: `vector`
+  - Text Field: `content`
+  - ID Field: `doc_id`
+  - Category Field: `type`
+  - Subcategory Field: `subtopic`
+  - Tags Field: `keywords`
+
+## Data Structure
+
+### Original Format (from NDJSON files)
+
+```json
+{
+  "id": "doc_001",
+  "embedding": [0.2, -0.1, 0.8, 0.3, -0.5, 0.7, 0.1, -0.3],
+  "text": "Machine learning algorithms are transforming healthcare...",
+  "category": "technology",
+  "subcategory": "healthcare",
+  "tags": ["ai", "medicine", "prediction"]
+}
+```
+
+### ES Bulk Format
+
+```json
+{"index": {"_index": "embeddings", "_id": "doc_001"}}
+{"id": "doc_001", "embedding": [...], "text": "...", "category": "...", ...}
+```
+
+### Alternative Field Names (dense vector format)
+
+```json
+{"index": {"_index": "embeddings-dense", "_id": "doc_001"}}
+{"doc_id": "doc_001", "vector": [...], "content": "...", "type": "...", ...}
+```
+
+## Notes
+
+- All embedding vectors are 8-dimensional for these sample files
+- The alternative format demonstrates how EmbeddingBuddy's field mapping handles different field names
+- For production use, you may want larger embedding dimensions (e.g., 384, 768, 1536)
+- The `dense_vector` field type in Elasticsearch/OpenSearch enables vector similarity search

example/bad/bad_binary_content.ndjson

@@ -0,0 +1,2 @@
+<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, 0.2], "text": "Binary junk at start"}
+{"id": "doc_002", "embedding": [0.5, 0.1, -0.2, 0.8], "text": "Normal line"}<7D><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>

example/bad/bad_empty_lines.ndjson

@@ -0,0 +1,6 @@
+{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, 0.2], "text": "First line"}
+
+{"id": "doc_002", "embedding": [0.5, 0.1, -0.2, 0.8], "text": "After empty line"}
+
+
+{"id": "doc_003", "embedding": [0.3, 0.4, 0.1, -0.1], "text": "After multiple empty lines"}

example/bad/bad_inconsistent_dimensions.ndjson

@@ -0,0 +1,4 @@
+{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, 0.2], "text": "4D embedding"}
+{"id": "doc_002", "embedding": [0.5, 0.1, -0.2], "text": "3D embedding"}
+{"id": "doc_003", "embedding": [0.3, 0.4, 0.1, -0.1, 0.8], "text": "5D embedding"}
+{"id": "doc_004", "embedding": [0.2, 0.1], "text": "2D embedding"}

example/bad/bad_invalid_embeddings.ndjson

@@ -0,0 +1,8 @@
+{"id": "doc_001", "embedding": "not_an_array", "text": "Embedding as string"}
+{"id": "doc_002", "embedding": [0.1, "text", 0.7, 0.2], "text": "Mixed types in embedding"}
+{"id": "doc_003", "embedding": [], "text": "Empty embedding array"}
+{"id": "doc_004", "embedding": [0.1], "text": "Single dimension embedding"}
+{"id": "doc_005", "embedding": null, "text": "Null embedding"}
+{"id": "doc_006", "embedding": [0.1, 0.2, null, 0.4], "text": "Null value in embedding"}
+{"id": "doc_007", "embedding": [0.1, 0.2, "NaN", 0.4], "text": "String NaN in embedding"}
+{"id": "doc_008", "embedding": [0.1, 0.2, Infinity, 0.4], "text": "Infinity in embedding"}

example/bad/bad_malformed_json.ndjson

@@ -0,0 +1,5 @@
+{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, "text": "Valid line"}
+{"id": "doc_002", "embedding": [0.5, 0.1, -0.2, 0.8], "text": "Missing closing brace"
+{"id": "doc_003" "embedding": [0.3, 0.4, 0.1, -0.1], "text": "Missing colon after id"}
+{id: "doc_004", "embedding": [0.2, 0.1, 0.3, 0.4], "text": "Unquoted key"}
+{"id": "doc_005", "embedding": [0.1, 0.2, 0.3, 0.4], "text": "Valid line again"}

example/bad/bad_missing_embedding.ndjson

@@ -0,0 +1,3 @@
+{"id": "doc_001", "text": "Sample text without embedding field", "category": "test"}
+{"id": "doc_002", "text": "Another text without embedding", "category": "test"}
+{"id": "doc_003", "text": "Third text missing embedding", "category": "test"}

example/bad/bad_missing_text.ndjson

@@ -0,0 +1,3 @@
+{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, 0.2], "category": "test"}
+{"id": "doc_002", "embedding": [0.5, 0.1, -0.2, 0.8], "category": "test"}
+{"id": "doc_003", "embedding": [0.3, 0.4, 0.1, -0.1], "category": "test"}

example/bad/bad_not_ndjson.json

@@ -0,0 +1,4 @@
+[
+  {"id": "doc_001", "embedding": [0.1, -0.3, 0.7, 0.2], "text": "Regular JSON array"},
+  {"id": "doc_002", "embedding": [0.5, 0.1, -0.2, 0.8], "text": "Instead of NDJSON"}
+]

example/sample_data_es_bulk.ndjson

@@ -0,0 +1,40 @@
+{"index": {"_index": "embeddings", "_id": "doc_001"}}
+{"id": "doc_001", "embedding": [0.2, -0.1, 0.8, 0.3, -0.5, 0.7, 0.1, -0.3], "text": "Machine learning algorithms are transforming healthcare by enabling predictive analytics and personalized medicine.", "category": "technology", "subcategory": "healthcare", "tags": ["ai", "medicine", "prediction"]}
+{"index": {"_index": "embeddings", "_id": "doc_002"}}
+{"id": "doc_002", "embedding": [0.1, 0.4, -0.2, 0.6, 0.3, -0.4, 0.8, 0.2], "text": "Climate change poses significant challenges to global food security and agricultural sustainability.", "category": "environment", "subcategory": "agriculture", "tags": ["climate", "food", "sustainability"]}
+{"index": {"_index": "embeddings", "_id": "doc_003"}}
+{"id": "doc_003", "embedding": [-0.3, 0.7, 0.1, -0.2, 0.9, 0.4, -0.1, 0.5], "text": "The rise of electric vehicles is reshaping the automotive industry and urban transportation systems.", "category": "technology", "subcategory": "automotive", "tags": ["electric", "transport", "urban"]}
+{"index": {"_index": "embeddings", "_id": "doc_004"}}
+{"id": "doc_004", "embedding": [0.5, -0.6, 0.3, 0.8, -0.2, 0.1, 0.7, -0.4], "text": "Renewable energy sources like solar and wind are becoming increasingly cost-competitive with fossil fuels.", "category": "environment", "subcategory": "energy", "tags": ["renewable", "solar", "wind"]}
+{"index": {"_index": "embeddings", "_id": "doc_005"}}
+{"id": "doc_005", "embedding": [0.8, 0.2, -0.5, 0.1, 0.6, -0.3, 0.4, 0.9], "text": "Financial markets are experiencing volatility due to geopolitical tensions and inflation concerns.", "category": "finance", "subcategory": "markets", "tags": ["volatility", "inflation", "geopolitics"]}
+{"index": {"_index": "embeddings", "_id": "doc_006"}}
+{"id": "doc_006", "embedding": [-0.1, 0.5, 0.7, -0.4, 0.2, 0.8, -0.6, 0.3], "text": "Quantum computing research is advancing rapidly with potential applications in cryptography and drug discovery.", "category": "technology", "subcategory": "research", "tags": ["quantum", "cryptography", "research"]}
+{"index": {"_index": "embeddings", "_id": "doc_007"}}
+{"id": "doc_007", "embedding": [0.4, -0.3, 0.6, 0.7, -0.8, 0.2, 0.5, -0.1], "text": "Ocean pollution from plastic waste is threatening marine ecosystems and biodiversity worldwide.", "category": "environment", "subcategory": "marine", "tags": ["pollution", "plastic", "marine"]}
+{"index": {"_index": "embeddings", "_id": "doc_008"}}
+{"id": "doc_008", "embedding": [0.3, 0.8, -0.2, 0.5, 0.1, -0.7, 0.6, 0.4], "text": "Artificial intelligence is revolutionizing customer service through chatbots and automated support systems.", "category": "technology", "subcategory": "customer_service", "tags": ["ai", "chatbots", "automation"]}
+{"index": {"_index": "embeddings", "_id": "doc_009"}}
+{"id": "doc_009", "embedding": [-0.5, 0.3, 0.9, -0.1, 0.7, 0.4, -0.2, 0.8], "text": "Global supply chains are being redesigned for resilience after pandemic-related disruptions.", "category": "business", "subcategory": "logistics", "tags": ["supply_chain", "pandemic", "resilience"]}
+{"index": {"_index": "embeddings", "_id": "doc_010"}}
+{"id": "doc_010", "embedding": [0.7, -0.4, 0.2, 0.9, -0.3, 0.6, 0.1, -0.8], "text": "Space exploration missions are expanding our understanding of the solar system and potential for life.", "category": "science", "subcategory": "space", "tags": ["space", "exploration", "life"]}
+{"index": {"_index": "embeddings", "_id": "doc_011"}}
+{"id": "doc_011", "embedding": [-0.2, 0.6, 0.4, -0.7, 0.8, 0.3, -0.5, 0.1], "text": "Cryptocurrency adoption is growing among institutional investors despite regulatory uncertainties.", "category": "finance", "subcategory": "crypto", "tags": ["cryptocurrency", "institutional", "regulation"]}
+{"index": {"_index": "embeddings", "_id": "doc_012"}}
+{"id": "doc_012", "embedding": [0.6, 0.1, -0.8, 0.4, 0.5, -0.2, 0.9, -0.3], "text": "Remote work technologies are transforming traditional office environments and work-life balance.", "category": "technology", "subcategory": "workplace", "tags": ["remote", "work", "balance"]}
+{"index": {"_index": "embeddings", "_id": "doc_013"}}
+{"id": "doc_013", "embedding": [0.1, -0.7, 0.5, 0.8, -0.4, 0.3, 0.2, 0.6], "text": "Gene therapy breakthroughs are offering new hope for treating previously incurable genetic diseases.", "category": "science", "subcategory": "medicine", "tags": ["gene_therapy", "genetics", "medicine"]}
+{"index": {"_index": "embeddings", "_id": "doc_014"}}
+{"id": "doc_014", "embedding": [-0.4, 0.2, 0.7, -0.1, 0.9, -0.6, 0.3, 0.5], "text": "Urban planning is evolving to create more sustainable and livable cities for growing populations.", "category": "environment", "subcategory": "urban", "tags": ["urban_planning", "sustainability", "cities"]}
+{"index": {"_index": "embeddings", "_id": "doc_015"}}
+{"id": "doc_015", "embedding": [0.9, -0.1, 0.3, 0.6, -0.5, 0.8, -0.2, 0.4], "text": "Social media platforms are implementing new policies to combat misinformation and protect user privacy.", "category": "technology", "subcategory": "social_media", "tags": ["social_media", "misinformation", "privacy"]}
+{"index": {"_index": "embeddings", "_id": "doc_016"}}
+{"id": "doc_016", "embedding": [-0.3, 0.8, -0.1, 0.4, 0.7, -0.5, 0.6, -0.9], "text": "Educational technology is personalizing learning experiences and improving student outcomes.", "category": "education", "subcategory": "technology", "tags": ["education", "personalization", "technology"]}
+{"index": {"_index": "embeddings", "_id": "doc_017"}}
+{"id": "doc_017", "embedding": [0.5, 0.3, -0.6, 0.2, 0.8, 0.1, -0.4, 0.7], "text": "Biodiversity conservation efforts are critical for maintaining ecosystem balance and preventing species extinction.", "category": "environment", "subcategory": "conservation", "tags": ["biodiversity", "conservation", "extinction"]}
+{"index": {"_index": "embeddings", "_id": "doc_018"}}
+{"id": "doc_018", "embedding": [0.2, -0.8, 0.4, 0.7, -0.1, 0.5, 0.9, -0.3], "text": "Healthcare systems are adopting telemedicine to improve access and reduce costs for patients.", "category": "technology", "subcategory": "healthcare", "tags": ["telemedicine", "healthcare", "access"]}
+{"index": {"_index": "embeddings", "_id": "doc_019"}}
+{"id": "doc_019", "embedding": [-0.7, 0.4, 0.8, -0.2, 0.3, 0.6, -0.1, 0.9], "text": "Autonomous vehicles are being tested extensively with promises of safer and more efficient transportation.", "category": "technology", "subcategory": "automotive", "tags": ["autonomous", "safety", "efficiency"]}
+{"index": {"_index": "embeddings", "_id": "doc_020"}}
+{"id": "doc_020", "embedding": [0.4, 0.7, -0.3, 0.9, -0.6, 0.2, 0.5, -0.1], "text": "Mental health awareness is increasing with new approaches to therapy and workplace wellness programs.", "category": "health", "subcategory": "mental", "tags": ["mental_health", "therapy", "wellness"]}

example/sample_prompts_es_bulk.ndjson

@@ -0,0 +1,20 @@
+{"index": {"_index": "prompts", "_id": "prompt_001"}}
+{"id": "prompt_001", "embedding": [0.15, -0.28, 0.65, 0.42, -0.11, 0.33, 0.78, -0.52], "text": "Find articles about machine learning applications", "category": "search", "subcategory": "technology", "tags": ["AI", "research"]}
+{"index": {"_index": "prompts", "_id": "prompt_002"}}
+{"id": "prompt_002", "embedding": [0.72, 0.18, -0.35, 0.51, 0.09, -0.44, 0.27, 0.63], "text": "Show me product reviews for smartphones", "category": "search", "subcategory": "product", "tags": ["mobile", "reviews"]}
+{"index": {"_index": "prompts", "_id": "prompt_003"}}
+{"id": "prompt_003", "embedding": [-0.21, 0.59, 0.34, -0.67, 0.45, 0.12, -0.38, 0.76], "text": "What are the latest political developments?", "category": "search", "subcategory": "news", "tags": ["politics", "current events"]}
+{"index": {"_index": "prompts", "_id": "prompt_004"}}
+{"id": "prompt_004", "embedding": [0.48, -0.15, 0.72, 0.31, -0.58, 0.24, 0.67, -0.39], "text": "Summarize recent tech industry trends", "category": "analysis", "subcategory": "technology", "tags": ["tech", "trends", "summary"]}
+{"index": {"_index": "prompts", "_id": "prompt_005"}}
+{"id": "prompt_005", "embedding": [-0.33, 0.47, -0.62, 0.28, 0.71, -0.18, 0.54, 0.35], "text": "Compare different smartphone models", "category": "analysis", "subcategory": "product", "tags": ["comparison", "mobile", "evaluation"]}
+{"index": {"_index": "prompts", "_id": "prompt_006"}}
+{"id": "prompt_006", "embedding": [0.64, 0.21, 0.39, -0.45, 0.13, 0.58, -0.27, 0.74], "text": "Analyze voter sentiment on recent policies", "category": "analysis", "subcategory": "politics", "tags": ["sentiment", "politics", "analysis"]}
+{"index": {"_index": "prompts", "_id": "prompt_007"}}
+{"id": "prompt_007", "embedding": [0.29, -0.43, 0.56, 0.68, -0.22, 0.37, 0.14, -0.61], "text": "Generate a summary of machine learning research", "category": "generation", "subcategory": "technology", "tags": ["AI", "research", "summary"]}
+{"index": {"_index": "prompts", "_id": "prompt_008"}}
+{"id": "prompt_008", "embedding": [-0.17, 0.52, -0.48, 0.36, 0.74, -0.29, 0.61, 0.18], "text": "Create a product recommendation report", "category": "generation", "subcategory": "product", "tags": ["recommendation", "report", "analysis"]}
+{"index": {"_index": "prompts", "_id": "prompt_009"}}
+{"id": "prompt_009", "embedding": [0.55, 0.08, 0.41, -0.37, 0.26, 0.69, -0.14, 0.58], "text": "Write a news brief on election updates", "category": "generation", "subcategory": "news", "tags": ["election", "news", "brief"]}
+{"index": {"_index": "prompts", "_id": "prompt_010"}}
+{"id": "prompt_010", "embedding": [0.23, -0.59, 0.47, 0.61, -0.35, 0.18, 0.72, -0.26], "text": "Explain how neural networks work", "category": "explanation", "subcategory": "technology", "tags": ["AI", "education", "neural networks"]}

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "embeddingbuddy"
-version = "0.2.0"
+version = "0.3.0"
 description = "A Python Dash application for interactive exploration and visualization of embedding vectors through dimensionality reduction techniques."
 readme = "README.md"
 requires-python = ">=3.11"
@@ -14,7 +14,29 @@ dependencies = [
     "umap-learn>=0.5.8",
     "numba>=0.56.4",
     "openTSNE>=1.0.0",
+    "mypy>=1.17.1",
+    "opensearch-py>=3.0.0",
+]
+
+[project.optional-dependencies]
+test = [
     "pytest>=8.4.1",
+    "pytest-cov>=4.1.0",
+]
+lint = [
+    "ruff>=0.1.0",
+    "mypy>=1.5.0",
+]
+security = [
+    "bandit[toml]>=1.7.5",
+    "safety>=2.3.0",
+    "pip-audit>=2.6.0",
+]
+dev = [
+    "embeddingbuddy[test,lint,security]",
+]
+all = [
+    "embeddingbuddy[test,lint,security]",
 ]
 
 [build-system]

src/embeddingbuddy/__init__.py

@@ -1,3 +1,3 @@
 """EmbeddingBuddy - Interactive exploration and visualization of embedding vectors."""
 
-__version__ = "0.1.0"
+__version__ = "0.1.0"

src/embeddingbuddy/app.py

@@ -8,32 +8,32 @@ from .ui.callbacks.interactions import InteractionCallbacks
 
 
 def create_app():
-    app = dash.Dash(
+    app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
-        __name__, 
+
-        external_stylesheets=[dbc.themes.BOOTSTRAP]
+    # Allow callbacks to components that are dynamically created in tabs
-    )
+    app.config.suppress_callback_exceptions = True
-    
+
     layout_manager = AppLayout()
     app.layout = layout_manager.create_layout()
-    
+
     DataProcessingCallbacks()
     VisualizationCallbacks()
     InteractionCallbacks()
-    
+
     return app
 
 
 def run_app(app=None, debug=None, host=None, port=None):
     if app is None:
         app = create_app()
-    
+
     app.run(
         debug=debug if debug is not None else AppSettings.DEBUG,
         host=host if host is not None else AppSettings.HOST,
-        port=port if port is not None else AppSettings.PORT
+        port=port if port is not None else AppSettings.PORT,
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     app = create_app()
-    run_app(app)
+    run_app(app)

src/embeddingbuddy/config/settings.py

@@ -3,105 +3,106 @@ import os
 
 
 class AppSettings:
-    
     # UI Configuration
     UPLOAD_STYLE = {
-        'width': '100%',
+        "width": "100%",
-        'height': '60px',
+        "height": "60px",
-        'lineHeight': '60px',
+        "lineHeight": "60px",
-        'borderWidth': '1px',
+        "borderWidth": "1px",
-        'borderStyle': 'dashed',
+        "borderStyle": "dashed",
-        'borderRadius': '5px',
+        "borderRadius": "5px",
-        'textAlign': 'center',
+        "textAlign": "center",
-        'margin-bottom': '20px'
+        "margin-bottom": "20px",
     }
-    
+
-    PROMPTS_UPLOAD_STYLE = {
+    PROMPTS_UPLOAD_STYLE = {**UPLOAD_STYLE, "borderColor": "#28a745"}
-        **UPLOAD_STYLE,
+
-        'borderColor': '#28a745'
+    PLOT_CONFIG = {"responsive": True, "displayModeBar": True}
-    }
+
-    
+    PLOT_STYLE = {"height": "85vh", "width": "100%"}
-    PLOT_CONFIG = {
+
-        'responsive': True,
-        'displayModeBar': True
-    }
-    
-    PLOT_STYLE = {
-        'height': '85vh',
-        'width': '100%'
-    }
-    
     PLOT_LAYOUT_CONFIG = {
-        'height': None,
+        "height": None,
-        'autosize': True,
+        "autosize": True,
-        'margin': dict(l=0, r=0, t=50, b=0)
+        "margin": dict(l=0, r=0, t=50, b=0),
     }
-    
+
     # Dimensionality Reduction Settings
     DEFAULT_N_COMPONENTS_3D = 3
     DEFAULT_N_COMPONENTS_2D = 2
     DEFAULT_RANDOM_STATE = 42
-    
+
     # Available Methods
     REDUCTION_METHODS = [
-        {'label': 'PCA', 'value': 'pca'},
+        {"label": "PCA", "value": "pca"},
-        {'label': 't-SNE', 'value': 'tsne'},
+        {"label": "t-SNE", "value": "tsne"},
-        {'label': 'UMAP', 'value': 'umap'}
+        {"label": "UMAP", "value": "umap"},
     ]
-    
+
     COLOR_OPTIONS = [
-        {'label': 'Category', 'value': 'category'},
+        {"label": "Category", "value": "category"},
-        {'label': 'Subcategory', 'value': 'subcategory'},
+        {"label": "Subcategory", "value": "subcategory"},
-        {'label': 'Tags', 'value': 'tags'}
+        {"label": "Tags", "value": "tags"},
     ]
-    
+
-    DIMENSION_OPTIONS = [
+    DIMENSION_OPTIONS = [{"label": "2D", "value": "2d"}, {"label": "3D", "value": "3d"}]
-        {'label': '2D', 'value': '2d'},
+
-        {'label': '3D', 'value': '3d'}
-    ]
-    
     # Default Values
-    DEFAULT_METHOD = 'pca'
+    DEFAULT_METHOD = "pca"
-    DEFAULT_COLOR_BY = 'category'
+    DEFAULT_COLOR_BY = "category"
-    DEFAULT_DIMENSIONS = '3d'
+    DEFAULT_DIMENSIONS = "3d"
-    DEFAULT_SHOW_PROMPTS = ['show']
+    DEFAULT_SHOW_PROMPTS = ["show"]
-    
+
     # Plot Marker Settings
     DOCUMENT_MARKER_SIZE_2D = 8
     DOCUMENT_MARKER_SIZE_3D = 5
     PROMPT_MARKER_SIZE_2D = 10
     PROMPT_MARKER_SIZE_3D = 6
-    
+
-    DOCUMENT_MARKER_SYMBOL = 'circle'
+    DOCUMENT_MARKER_SYMBOL = "circle"
-    PROMPT_MARKER_SYMBOL = 'diamond'
+    PROMPT_MARKER_SYMBOL = "diamond"
-    
+
     DOCUMENT_OPACITY = 1.0
     PROMPT_OPACITY = 0.8
-    
+
     # Text Processing
     TEXT_PREVIEW_LENGTH = 100
-    
+
     # App Configuration
-    DEBUG = os.getenv('EMBEDDINGBUDDY_DEBUG', 'True').lower() == 'true'
+    DEBUG = os.getenv("EMBEDDINGBUDDY_DEBUG", "True").lower() == "true"
-    HOST = os.getenv('EMBEDDINGBUDDY_HOST', '127.0.0.1')
+    HOST = os.getenv("EMBEDDINGBUDDY_HOST", "127.0.0.1")
-    PORT = int(os.getenv('EMBEDDINGBUDDY_PORT', '8050'))
+    PORT = int(os.getenv("EMBEDDINGBUDDY_PORT", "8050"))
-    
+
+    # OpenSearch Configuration
+    OPENSEARCH_DEFAULT_SIZE = 100
+    OPENSEARCH_SAMPLE_SIZE = 5
+    OPENSEARCH_CONNECTION_TIMEOUT = 30
+    OPENSEARCH_VERIFY_CERTS = True
+
     # Bootstrap Theme
-    EXTERNAL_STYLESHEETS = ['https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css']
+    EXTERNAL_STYLESHEETS = [
-    
+        "https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css"
+    ]
+
     @classmethod
-    def get_plot_marker_config(cls, dimensions: str, is_prompt: bool = False) -> Dict[str, Any]:
+    def get_plot_marker_config(
+        cls, dimensions: str, is_prompt: bool = False
+    ) -> Dict[str, Any]:
         if is_prompt:
-            size = cls.PROMPT_MARKER_SIZE_3D if dimensions == '3d' else cls.PROMPT_MARKER_SIZE_2D
+            size = (
+                cls.PROMPT_MARKER_SIZE_3D
+                if dimensions == "3d"
+                else cls.PROMPT_MARKER_SIZE_2D
+            )
             symbol = cls.PROMPT_MARKER_SYMBOL
             opacity = cls.PROMPT_OPACITY
         else:
-            size = cls.DOCUMENT_MARKER_SIZE_3D if dimensions == '3d' else cls.DOCUMENT_MARKER_SIZE_2D
+            size = (
+                cls.DOCUMENT_MARKER_SIZE_3D
+                if dimensions == "3d"
+                else cls.DOCUMENT_MARKER_SIZE_2D
+            )
             symbol = cls.DOCUMENT_MARKER_SYMBOL
             opacity = cls.DOCUMENT_OPACITY
-        
+
-        return {
+        return {"size": size, "symbol": symbol, "opacity": opacity}
-            'size': size,
-            'symbol': symbol,
-            'opacity': opacity
-        }

src/embeddingbuddy/data/parser.py

@@ -1,39 +1,72 @@
 import json
 import uuid
 import base64
-from typing import List, Union
+from typing import List
-from ..models.schemas import Document, ProcessedData
+from ..models.schemas import Document
 
 
 class NDJSONParser:
-    
     @staticmethod
     def parse_upload_contents(contents: str) -> List[Document]:
-        content_type, content_string = contents.split(',')
+        content_type, content_string = contents.split(",")
         decoded = base64.b64decode(content_string)
-        text_content = decoded.decode('utf-8')
+        text_content = decoded.decode("utf-8")
         return NDJSONParser.parse_text(text_content)
-    
+
     @staticmethod
     def parse_text(text_content: str) -> List[Document]:
         documents = []
-        for line in text_content.strip().split('\n'):
+        for line_num, line in enumerate(text_content.strip().split("\n"), 1):
             if line.strip():
-                doc_dict = json.loads(line)
+                try:
-                doc = NDJSONParser._dict_to_document(doc_dict)
+                    doc_dict = json.loads(line)
-                documents.append(doc)
+                    doc = NDJSONParser._dict_to_document(doc_dict)
+                    documents.append(doc)
+                except json.JSONDecodeError as e:
+                    raise json.JSONDecodeError(
+                        f"Invalid JSON on line {line_num}: {e.msg}", e.doc, e.pos
+                    )
+                except KeyError as e:
+                    raise KeyError(f"Missing required field {e} on line {line_num}")
+                except (TypeError, ValueError) as e:
+                    raise ValueError(
+                        f"Invalid data format on line {line_num}: {str(e)}"
+                    )
         return documents
-    
+
     @staticmethod
     def _dict_to_document(doc_dict: dict) -> Document:
-        if 'id' not in doc_dict:
+        if "id" not in doc_dict:
-            doc_dict['id'] = str(uuid.uuid4())
+            doc_dict["id"] = str(uuid.uuid4())
-        
+
+        # Validate required fields
+        if "text" not in doc_dict:
+            raise KeyError("'text'")
+        if "embedding" not in doc_dict:
+            raise KeyError("'embedding'")
+
+        # Validate embedding format
+        embedding = doc_dict["embedding"]
+        if not isinstance(embedding, list):
+            raise ValueError(
+                f"Embedding must be a list, got {type(embedding).__name__}"
+            )
+
+        if not embedding:
+            raise ValueError("Embedding cannot be empty")
+
+        # Check that all embedding values are numbers
+        for i, val in enumerate(embedding):
+            if not isinstance(val, (int, float)) or val != val:  # NaN check
+                raise ValueError(
+                    f"Embedding contains invalid value at index {i}: {val}"
+                )
+
         return Document(
-            id=doc_dict['id'],
+            id=doc_dict["id"],
-            text=doc_dict['text'],
+            text=doc_dict["text"],
-            embedding=doc_dict['embedding'],
+            embedding=embedding,
-            category=doc_dict.get('category'),
+            category=doc_dict.get("category"),
-            subcategory=doc_dict.get('subcategory'),
+            subcategory=doc_dict.get("subcategory"),
-            tags=doc_dict.get('tags')
+            tags=doc_dict.get("tags"),
-        )
+        )

src/embeddingbuddy/data/processor.py

@@ -1,22 +1,24 @@
 import numpy as np
 from typing import List, Optional, Tuple
 from ..models.schemas import Document, ProcessedData
+from ..models.field_mapper import FieldMapper
 from .parser import NDJSONParser
 
 
 class DataProcessor:
-    
     def __init__(self):
         self.parser = NDJSONParser()
-    
+
-    def process_upload(self, contents: str, filename: Optional[str] = None) -> ProcessedData:
+    def process_upload(
+        self, contents: str, filename: Optional[str] = None
+    ) -> ProcessedData:
         try:
             documents = self.parser.parse_upload_contents(contents)
             embeddings = self._extract_embeddings(documents)
             return ProcessedData(documents=documents, embeddings=embeddings)
         except Exception as e:
             return ProcessedData(documents=[], embeddings=np.array([]), error=str(e))
-    
+
     def process_text(self, text_content: str) -> ProcessedData:
         try:
             documents = self.parser.parse_text(text_content)
@@ -24,31 +26,71 @@ class DataProcessor:
             return ProcessedData(documents=documents, embeddings=embeddings)
         except Exception as e:
             return ProcessedData(documents=[], embeddings=np.array([]), error=str(e))
-    
+
+    def process_opensearch_data(
+        self, raw_documents: List[dict], field_mapping
+    ) -> ProcessedData:
+        """Process raw OpenSearch documents using field mapping."""
+        try:
+            # Transform documents using field mapping
+            transformed_docs = FieldMapper.transform_documents(
+                raw_documents, field_mapping
+            )
+
+            # Parse transformed documents
+            documents = []
+            for doc_dict in transformed_docs:
+                try:
+                    # Ensure required fields are present with defaults if needed
+                    if "id" not in doc_dict or not doc_dict["id"]:
+                        doc_dict["id"] = f"doc_{len(documents)}"
+
+                    doc = Document(**doc_dict)
+                    documents.append(doc)
+                except Exception:
+                    continue  # Skip invalid documents
+
+            if not documents:
+                return ProcessedData(
+                    documents=[],
+                    embeddings=np.array([]),
+                    error="No valid documents after transformation",
+                )
+
+            embeddings = self._extract_embeddings(documents)
+            return ProcessedData(documents=documents, embeddings=embeddings)
+
+        except Exception as e:
+            return ProcessedData(documents=[], embeddings=np.array([]), error=str(e))
+
     def _extract_embeddings(self, documents: List[Document]) -> np.ndarray:
         if not documents:
             return np.array([])
         return np.array([doc.embedding for doc in documents])
-    
+
-    def combine_data(self, doc_data: ProcessedData, prompt_data: Optional[ProcessedData] = None) -> Tuple[np.ndarray, List[Document], Optional[List[Document]]]:
+    def combine_data(
+        self, doc_data: ProcessedData, prompt_data: Optional[ProcessedData] = None
+    ) -> Tuple[np.ndarray, List[Document], Optional[List[Document]]]:
         if not doc_data or doc_data.error:
             raise ValueError("Invalid document data")
-        
+
         all_embeddings = doc_data.embeddings
         documents = doc_data.documents
         prompts = None
-        
+
         if prompt_data and not prompt_data.error and prompt_data.documents:
             all_embeddings = np.vstack([doc_data.embeddings, prompt_data.embeddings])
             prompts = prompt_data.documents
-        
+
         return all_embeddings, documents, prompts
-    
+
-    def split_reduced_data(self, reduced_embeddings: np.ndarray, n_documents: int, n_prompts: int = 0) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+    def split_reduced_data(
+        self, reduced_embeddings: np.ndarray, n_documents: int, n_prompts: int = 0
+    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
         doc_reduced = reduced_embeddings[:n_documents]
         prompt_reduced = None
-        
+
         if n_prompts > 0:
-            prompt_reduced = reduced_embeddings[n_documents:n_documents + n_prompts]
+            prompt_reduced = reduced_embeddings[n_documents : n_documents + n_prompts]
-        
+
-        return doc_reduced, prompt_reduced
+        return doc_reduced, prompt_reduced

src/embeddingbuddy/data/sources/opensearch.py

@@ -0,0 +1,189 @@
+from typing import Dict, List, Optional, Any, Tuple
+import logging
+from opensearchpy import OpenSearch
+from opensearchpy.exceptions import OpenSearchException
+
+
+logger = logging.getLogger(__name__)
+
+
+class OpenSearchClient:
+    def __init__(self):
+        self.client: Optional[OpenSearch] = None
+        self.connection_info: Optional[Dict[str, Any]] = None
+
+    def connect(
+        self,
+        url: str,
+        username: Optional[str] = None,
+        password: Optional[str] = None,
+        api_key: Optional[str] = None,
+        verify_certs: bool = True,
+    ) -> Tuple[bool, str]:
+        """
+        Connect to OpenSearch instance.
+
+        Returns:
+            Tuple of (success: bool, message: str)
+        """
+        try:
+            # Parse URL to extract host and port
+            if url.startswith("http://") or url.startswith("https://"):
+                host = url
+            else:
+                host = f"https://{url}"
+
+            # Build auth configuration
+            auth_config = {}
+            if username and password:
+                auth_config["http_auth"] = (username, password)
+            elif api_key:
+                auth_config["api_key"] = api_key
+
+            # Create client
+            self.client = OpenSearch([host], verify_certs=verify_certs, **auth_config)
+
+            # Test connection
+            info = self.client.info()
+            self.connection_info = {
+                "url": host,
+                "cluster_name": info.get("cluster_name", "Unknown"),
+                "version": info.get("version", {}).get("number", "Unknown"),
+            }
+
+            return (
+                True,
+                f"Connected to {info.get('cluster_name', 'OpenSearch cluster')}",
+            )
+
+        except OpenSearchException as e:
+            logger.error(f"OpenSearch connection error: {e}")
+            return False, f"Connection failed: {str(e)}"
+        except Exception as e:
+            logger.error(f"Unexpected error connecting to OpenSearch: {e}")
+            return False, f"Unexpected error: {str(e)}"
+
+    def get_index_mapping(self, index_name: str) -> Tuple[bool, Optional[Dict], str]:
+        """
+        Get the mapping for a specific index.
+
+        Returns:
+            Tuple of (success: bool, mapping: Dict or None, message: str)
+        """
+        if not self.client:
+            return False, None, "Not connected to OpenSearch"
+
+        try:
+            mapping = self.client.indices.get_mapping(index=index_name)
+            return True, mapping, "Mapping retrieved successfully"
+        except OpenSearchException as e:
+            logger.error(f"Error getting mapping for index {index_name}: {e}")
+            return False, None, f"Failed to get mapping: {str(e)}"
+
+    def analyze_fields(self, index_name: str) -> Tuple[bool, Optional[Dict], str]:
+        """
+        Analyze index fields to detect potential embedding and text fields.
+
+        Returns:
+            Tuple of (success: bool, analysis: Dict or None, message: str)
+        """
+        success, mapping, message = self.get_index_mapping(index_name)
+        if not success:
+            return False, None, message
+
+        try:
+            # Extract field information from mapping
+            index_mapping = mapping[index_name]["mappings"]["properties"]
+
+            analysis = {
+                "vector_fields": [],
+                "text_fields": [],
+                "keyword_fields": [],
+                "numeric_fields": [],
+                "all_fields": [],
+            }
+
+            for field_name, field_info in index_mapping.items():
+                field_type = field_info.get("type", "unknown")
+                analysis["all_fields"].append(field_name)
+
+                if field_type == "dense_vector":
+                    analysis["vector_fields"].append(
+                        {
+                            "name": field_name,
+                            "dimension": field_info.get("dimension", "unknown"),
+                        }
+                    )
+                elif field_type == "text":
+                    analysis["text_fields"].append(field_name)
+                elif field_type == "keyword":
+                    analysis["keyword_fields"].append(field_name)
+                elif field_type in ["integer", "long", "float", "double"]:
+                    analysis["numeric_fields"].append(field_name)
+
+            return True, analysis, "Field analysis completed"
+
+        except Exception as e:
+            logger.error(f"Error analyzing fields: {e}")
+            return False, None, f"Field analysis failed: {str(e)}"
+
+    def fetch_sample_data(
+        self, index_name: str, size: int = 5
+    ) -> Tuple[bool, List[Dict], str]:
+        """
+        Fetch sample documents from the index.
+
+        Returns:
+            Tuple of (success: bool, documents: List[Dict], message: str)
+        """
+        if not self.client:
+            return False, [], "Not connected to OpenSearch"
+
+        try:
+            response = self.client.search(
+                index=index_name, body={"query": {"match_all": {}}, "size": size}
+            )
+
+            documents = [hit["_source"] for hit in response["hits"]["hits"]]
+            return True, documents, f"Retrieved {len(documents)} sample documents"
+
+        except OpenSearchException as e:
+            logger.error(f"Error fetching sample data: {e}")
+            return False, [], f"Failed to fetch sample data: {str(e)}"
+
+    def fetch_data(
+        self, index_name: str, size: int = 100
+    ) -> Tuple[bool, List[Dict], str]:
+        """
+        Fetch documents from the index.
+
+        Returns:
+            Tuple of (success: bool, documents: List[Dict], message: str)
+        """
+        if not self.client:
+            return False, [], "Not connected to OpenSearch"
+
+        try:
+            response = self.client.search(
+                index=index_name, body={"query": {"match_all": {}}, "size": size}
+            )
+
+            documents = [hit["_source"] for hit in response["hits"]["hits"]]
+            total_hits = response["hits"]["total"]["value"]
+
+            message = f"Retrieved {len(documents)} documents from {total_hits} total"
+            return True, documents, message
+
+        except OpenSearchException as e:
+            logger.error(f"Error fetching data: {e}")
+            return False, [], f"Failed to fetch data: {str(e)}"
+
+    def disconnect(self):
+        """Disconnect from OpenSearch."""
+        if self.client:
+            self.client = None
+            self.connection_info = None
+
+    def is_connected(self) -> bool:
+        """Check if connected to OpenSearch."""
+        return self.client is not None

src/embeddingbuddy/models/field_mapper.py

@@ -0,0 +1,254 @@
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Any
+import logging
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class FieldMapping:
+    """Configuration for mapping OpenSearch fields to standard format."""
+
+    embedding_field: str
+    text_field: str
+    id_field: Optional[str] = None
+    category_field: Optional[str] = None
+    subcategory_field: Optional[str] = None
+    tags_field: Optional[str] = None
+
+
+class FieldMapper:
+    """Handles field mapping and data transformation from OpenSearch to standard format."""
+
+    @staticmethod
+    def suggest_mappings(field_analysis: Dict) -> Dict[str, List[str]]:
+        """
+        Suggest field mappings based on field analysis.
+
+        Each dropdown will show ALL available fields, but ordered by relevance
+        with the most likely candidates first.
+
+        Args:
+            field_analysis: Analysis results from OpenSearchClient.analyze_fields
+
+        Returns:
+            Dictionary with suggested fields for each mapping (ordered by relevance)
+        """
+        all_fields = field_analysis.get("all_fields", [])
+        vector_fields = [vf["name"] for vf in field_analysis.get("vector_fields", [])]
+        text_fields = field_analysis.get("text_fields", [])
+        keyword_fields = field_analysis.get("keyword_fields", [])
+
+        # Helper function to create ordered suggestions
+        def create_ordered_suggestions(primary_candidates, all_available_fields):
+            # Start with primary candidates, then add all other fields
+            ordered = []
+            # Add primary candidates first
+            for field in primary_candidates:
+                if field in all_available_fields and field not in ordered:
+                    ordered.append(field)
+            # Add remaining fields
+            for field in all_available_fields:
+                if field not in ordered:
+                    ordered.append(field)
+            return ordered
+
+        suggestions = {}
+
+        # Embedding field suggestions (vector fields first, then name-based candidates, then all fields)
+        embedding_candidates = vector_fields.copy()
+        # Add fields that likely contain embeddings based on name
+        embedding_name_candidates = [
+            f
+            for f in all_fields
+            if any(
+                keyword in f.lower()
+                for keyword in ["embedding", "embeddings", "vector", "vectors", "embed"]
+            )
+        ]
+        # Add name-based candidates that aren't already in vector_fields
+        for candidate in embedding_name_candidates:
+            if candidate not in embedding_candidates:
+                embedding_candidates.append(candidate)
+        suggestions["embedding"] = create_ordered_suggestions(
+            embedding_candidates, all_fields
+        )
+
+        # Text field suggestions (text fields first, then all fields)
+        text_candidates = text_fields.copy()
+        suggestions["text"] = create_ordered_suggestions(text_candidates, all_fields)
+
+        # ID field suggestions (ID-like fields first, then all fields)
+        id_candidates = [
+            f
+            for f in keyword_fields
+            if any(keyword in f.lower() for keyword in ["id", "_id", "doc", "document"])
+        ]
+        id_candidates.append("_id")  # _id is always available
+        suggestions["id"] = create_ordered_suggestions(id_candidates, all_fields)
+
+        # Category field suggestions (category-like fields first, then all fields)
+        category_candidates = [
+            f
+            for f in keyword_fields
+            if any(
+                keyword in f.lower()
+                for keyword in ["category", "class", "type", "label"]
+            )
+        ]
+        suggestions["category"] = create_ordered_suggestions(
+            category_candidates, all_fields
+        )
+
+        # Subcategory field suggestions (subcategory-like fields first, then all fields)
+        subcategory_candidates = [
+            f
+            for f in keyword_fields
+            if any(
+                keyword in f.lower()
+                for keyword in ["subcategory", "subclass", "subtype", "subtopic"]
+            )
+        ]
+        suggestions["subcategory"] = create_ordered_suggestions(
+            subcategory_candidates, all_fields
+        )
+
+        # Tags field suggestions (tag-like fields first, then all fields)
+        tags_candidates = [
+            f
+            for f in keyword_fields
+            if any(
+                keyword in f.lower()
+                for keyword in ["tag", "tags", "keyword", "keywords"]
+            )
+        ]
+        suggestions["tags"] = create_ordered_suggestions(tags_candidates, all_fields)
+
+        return suggestions
+
+    @staticmethod
+    def validate_mapping(
+        mapping: FieldMapping, available_fields: List[str]
+    ) -> List[str]:
+        """
+        Validate that the field mapping is correct.
+
+        Returns:
+            List of validation errors (empty if valid)
+        """
+        errors = []
+
+        # Required fields validation
+        if not mapping.embedding_field:
+            errors.append("Embedding field is required")
+        elif mapping.embedding_field not in available_fields:
+            errors.append(
+                f"Embedding field '{mapping.embedding_field}' not found in index"
+            )
+
+        if not mapping.text_field:
+            errors.append("Text field is required")
+        elif mapping.text_field not in available_fields:
+            errors.append(f"Text field '{mapping.text_field}' not found in index")
+
+        # Optional fields validation
+        optional_fields = {
+            "id_field": mapping.id_field,
+            "category_field": mapping.category_field,
+            "subcategory_field": mapping.subcategory_field,
+            "tags_field": mapping.tags_field,
+        }
+
+        for field_name, field_value in optional_fields.items():
+            if field_value and field_value not in available_fields:
+                errors.append(
+                    f"Field '{field_value}' for {field_name} not found in index"
+                )
+
+        return errors
+
+    @staticmethod
+    def transform_documents(
+        documents: List[Dict[str, Any]], mapping: FieldMapping
+    ) -> List[Dict[str, Any]]:
+        """
+        Transform OpenSearch documents to standard format using field mapping.
+
+        Args:
+            documents: Raw documents from OpenSearch
+            mapping: Field mapping configuration
+
+        Returns:
+            List of transformed documents in standard format
+        """
+        transformed = []
+
+        for doc in documents:
+            try:
+                # Build standard format document
+                standard_doc = {}
+
+                # Required fields
+                if mapping.embedding_field in doc:
+                    standard_doc["embedding"] = doc[mapping.embedding_field]
+                else:
+                    logger.warning(
+                        f"Missing embedding field '{mapping.embedding_field}' in document"
+                    )
+                    continue
+
+                if mapping.text_field in doc:
+                    standard_doc["text"] = str(doc[mapping.text_field])
+                else:
+                    logger.warning(
+                        f"Missing text field '{mapping.text_field}' in document"
+                    )
+                    continue
+
+                # Optional fields
+                if mapping.id_field and mapping.id_field in doc:
+                    standard_doc["id"] = str(doc[mapping.id_field])
+
+                if mapping.category_field and mapping.category_field in doc:
+                    standard_doc["category"] = str(doc[mapping.category_field])
+
+                if mapping.subcategory_field and mapping.subcategory_field in doc:
+                    standard_doc["subcategory"] = str(doc[mapping.subcategory_field])
+
+                if mapping.tags_field and mapping.tags_field in doc:
+                    tags = doc[mapping.tags_field]
+                    # Handle both string and list tags
+                    if isinstance(tags, list):
+                        standard_doc["tags"] = [str(tag) for tag in tags]
+                    else:
+                        standard_doc["tags"] = [str(tags)]
+
+                transformed.append(standard_doc)
+
+            except Exception as e:
+                logger.error(f"Error transforming document: {e}")
+                continue
+
+        logger.info(f"Transformed {len(transformed)} documents out of {len(documents)}")
+        return transformed
+
+    @staticmethod
+    def create_mapping_from_dict(mapping_dict: Dict[str, str]) -> FieldMapping:
+        """
+        Create a FieldMapping from a dictionary.
+
+        Args:
+            mapping_dict: Dictionary with field mappings
+
+        Returns:
+            FieldMapping instance
+        """
+        return FieldMapping(
+            embedding_field=mapping_dict.get("embedding", ""),
+            text_field=mapping_dict.get("text", ""),
+            id_field=mapping_dict.get("id") or None,
+            category_field=mapping_dict.get("category") or None,
+            subcategory_field=mapping_dict.get("subcategory") or None,
+            tags_field=mapping_dict.get("tags") or None,
+        )

src/embeddingbuddy/models/reducers.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
 import numpy as np
-from typing import Optional, Tuple
 from sklearn.decomposition import PCA
 import umap
 from openTSNE import TSNE
@@ -8,88 +7,89 @@ from .schemas import ReducedData
 
 
 class DimensionalityReducer(ABC):
-    
     def __init__(self, n_components: int = 3, random_state: int = 42):
         self.n_components = n_components
         self.random_state = random_state
         self._reducer = None
-    
+
     @abstractmethod
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
         pass
-    
+
     @abstractmethod
     def get_method_name(self) -> str:
         pass
 
 
 class PCAReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
         self._reducer = PCA(n_components=self.n_components)
         reduced = self._reducer.fit_transform(embeddings)
         variance_explained = self._reducer.explained_variance_ratio_
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=variance_explained,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "PCA"
 
 
 class TSNEReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
-        self._reducer = TSNE(n_components=self.n_components, random_state=self.random_state)
+        self._reducer = TSNE(
+            n_components=self.n_components, random_state=self.random_state
+        )
         reduced = self._reducer.fit(embeddings)
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=None,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "t-SNE"
 
 
 class UMAPReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
-        self._reducer = umap.UMAP(n_components=self.n_components, random_state=self.random_state)
+        self._reducer = umap.UMAP(
+            n_components=self.n_components, random_state=self.random_state
+        )
         reduced = self._reducer.fit_transform(embeddings)
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=None,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "UMAP"
 
 
 class ReducerFactory:
-    
     @staticmethod
-    def create_reducer(method: str, n_components: int = 3, random_state: int = 42) -> DimensionalityReducer:
+    def create_reducer(
+        method: str, n_components: int = 3, random_state: int = 42
+    ) -> DimensionalityReducer:
         method_lower = method.lower()
-        
+
-        if method_lower == 'pca':
+        if method_lower == "pca":
             return PCAReducer(n_components=n_components, random_state=random_state)
-        elif method_lower == 'tsne':
+        elif method_lower == "tsne":
             return TSNEReducer(n_components=n_components, random_state=random_state)
-        elif method_lower == 'umap':
+        elif method_lower == "umap":
             return UMAPReducer(n_components=n_components, random_state=random_state)
         else:
             raise ValueError(f"Unknown reduction method: {method}")
-    
+
     @staticmethod
     def get_available_methods() -> list:
-        return ['pca', 'tsne', 'umap']
+        return ["pca", "tsne", "umap"]

src/embeddingbuddy/models/schemas.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Any, Dict
+from typing import List, Optional
 from dataclasses import dataclass
 import numpy as np
 
@@ -50,9 +50,11 @@ class PlotData:
     coordinates: np.ndarray
     prompts: Optional[List[Document]] = None
     prompt_coordinates: Optional[np.ndarray] = None
-    
+
     def __post_init__(self):
         if not isinstance(self.coordinates, np.ndarray):
             self.coordinates = np.array(self.coordinates)
-        if self.prompt_coordinates is not None and not isinstance(self.prompt_coordinates, np.ndarray):
+        if self.prompt_coordinates is not None and not isinstance(
-            self.prompt_coordinates = np.array(self.prompt_coordinates)
+            self.prompt_coordinates, np.ndarray
+        ):
+            self.prompt_coordinates = np.array(self.prompt_coordinates)

src/embeddingbuddy/ui/callbacks/data_processing.py

@@ -1,61 +1,523 @@
-import numpy as np
+from dash import callback, Input, Output, State, no_update
-from dash import callback, Input, Output, State
 from ...data.processor import DataProcessor
+from ...data.sources.opensearch import OpenSearchClient
+from ...models.field_mapper import FieldMapper
+from ...config.settings import AppSettings
 
 
 class DataProcessingCallbacks:
-    
     def __init__(self):
         self.processor = DataProcessor()
+        self.opensearch_client_data = OpenSearchClient()  # For data/documents
+        self.opensearch_client_prompts = OpenSearchClient()  # For prompts
         self._register_callbacks()
-    
+
     def _register_callbacks(self):
-        
         @callback(
-            Output('processed-data', 'data'),
+            [
-            Input('upload-data', 'contents'),
+                Output("processed-data", "data", allow_duplicate=True),
-            State('upload-data', 'filename')
+                Output("upload-error-alert", "children", allow_duplicate=True),
+                Output("upload-error-alert", "is_open", allow_duplicate=True),
+            ],
+            Input("upload-data", "contents"),
+            State("upload-data", "filename"),
+            prevent_initial_call=True,
         )
         def process_uploaded_file(contents, filename):
             if contents is None:
-                return None
+                return None, "", False
-            
+
             processed_data = self.processor.process_upload(contents, filename)
-            
+
             if processed_data.error:
-                return {'error': processed_data.error}
+                error_message = self._format_error_message(
-            
+                    processed_data.error, filename
-            return {
+                )
-                'documents': [self._document_to_dict(doc) for doc in processed_data.documents],
+                return (
-                'embeddings': processed_data.embeddings.tolist()
+                    {"error": processed_data.error},
-            }
+                    error_message,
-        
+                    True,  # Show error alert
+                )
+
+            return (
+                {
+                    "documents": [
+                        self._document_to_dict(doc) for doc in processed_data.documents
+                    ],
+                    "embeddings": processed_data.embeddings.tolist(),
+                },
+                "",
+                False,  # Hide error alert
+            )
+
         @callback(
-            Output('processed-prompts', 'data'),
+            Output("processed-prompts", "data", allow_duplicate=True),
-            Input('upload-prompts', 'contents'),
+            Input("upload-prompts", "contents"),
-            State('upload-prompts', 'filename')
+            State("upload-prompts", "filename"),
+            prevent_initial_call=True,
         )
         def process_uploaded_prompts(contents, filename):
             if contents is None:
                 return None
-            
+
             processed_data = self.processor.process_upload(contents, filename)
-            
+
             if processed_data.error:
-                return {'error': processed_data.error}
+                return {"error": processed_data.error}
-            
+
             return {
-                'prompts': [self._document_to_dict(doc) for doc in processed_data.documents],
+                "prompts": [
-                'embeddings': processed_data.embeddings.tolist()
+                    self._document_to_dict(doc) for doc in processed_data.documents
+                ],
+                "embeddings": processed_data.embeddings.tolist(),
             }
-    
+
+        # OpenSearch callbacks
+        @callback(
+            [
+                Output("tab-content", "children"),
+            ],
+            [Input("data-source-tabs", "active_tab")],
+            prevent_initial_call=False,
+        )
+        def render_tab_content(active_tab):
+            from ...ui.components.datasource import DataSourceComponent
+
+            datasource = DataSourceComponent()
+
+            if active_tab == "opensearch-tab":
+                return [datasource.create_opensearch_tab()]
+            else:
+                return [datasource.create_file_upload_tab()]
+
+        # Register callbacks for both data and prompts sections
+        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()
+
+    def _register_opensearch_callbacks(self, section_type, opensearch_client):
+        """Register callbacks for a specific section (data or prompts)."""
+
+        @callback(
+            Output(f"{section_type}-auth-collapse", "is_open"),
+            [Input(f"{section_type}-auth-toggle", "n_clicks")],
+            [State(f"{section_type}-auth-collapse", "is_open")],
+            prevent_initial_call=True,
+        )
+        def toggle_auth(n_clicks, is_open):
+            if n_clicks:
+                return not is_open
+            return is_open
+
+        @callback(
+            Output(f"{section_type}-auth-toggle", "children"),
+            [Input(f"{section_type}-auth-collapse", "is_open")],
+            prevent_initial_call=False,
+        )
+        def update_auth_button_text(is_open):
+            return "Hide Authentication" if is_open else "Show Authentication"
+
+        @callback(
+            [
+                Output(f"{section_type}-connection-status", "children"),
+                Output(f"{section_type}-field-mapping-section", "children"),
+                Output(f"{section_type}-field-mapping-section", "style"),
+                Output(f"{section_type}-load-data-section", "style"),
+                Output(f"{section_type}-load-opensearch-data-btn", "disabled"),
+                Output(f"{section_type}-embedding-field-dropdown", "options"),
+                Output(f"{section_type}-text-field-dropdown", "options"),
+                Output(f"{section_type}-id-field-dropdown", "options"),
+                Output(f"{section_type}-category-field-dropdown", "options"),
+                Output(f"{section_type}-subcategory-field-dropdown", "options"),
+                Output(f"{section_type}-tags-field-dropdown", "options"),
+            ],
+            [Input(f"{section_type}-test-connection-btn", "n_clicks")],
+            [
+                State(f"{section_type}-opensearch-url", "value"),
+                State(f"{section_type}-opensearch-index", "value"),
+                State(f"{section_type}-opensearch-username", "value"),
+                State(f"{section_type}-opensearch-password", "value"),
+                State(f"{section_type}-opensearch-api-key", "value"),
+            ],
+            prevent_initial_call=True,
+        )
+        def test_opensearch_connection(
+            n_clicks, url, index_name, username, password, api_key
+        ):
+            if not n_clicks or not url or not index_name:
+                return (
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                    no_update,
+                )
+
+            # Test connection
+            success, message = opensearch_client.connect(
+                url=url,
+                username=username,
+                password=password,
+                api_key=api_key,
+                verify_certs=AppSettings.OPENSEARCH_VERIFY_CERTS,
+            )
+
+            if not success:
+                return (
+                    self._create_status_alert(f"❌ {message}", "danger"),
+                    [],
+                    {"display": "none"},
+                    {"display": "none"},
+                    True,
+                    [],  # empty options for hidden dropdowns
+                    [],
+                    [],
+                    [],
+                    [],
+                    [],
+                )
+
+            # Analyze fields
+            success, field_analysis, analysis_message = (
+                opensearch_client.analyze_fields(index_name)
+            )
+
+            if not success:
+                return (
+                    self._create_status_alert(f"❌ {analysis_message}", "danger"),
+                    [],
+                    {"display": "none"},
+                    {"display": "none"},
+                    True,
+                    [],  # empty options for hidden dropdowns
+                    [],
+                    [],
+                    [],
+                    [],
+                    [],
+                )
+
+            # Generate field suggestions
+            field_suggestions = FieldMapper.suggest_mappings(field_analysis)
+
+            from ...ui.components.datasource import DataSourceComponent
+
+            datasource = DataSourceComponent()
+            field_mapping_ui = datasource.create_field_mapping_interface(
+                field_suggestions, section_type
+            )
+
+            return (
+                self._create_status_alert(f"✅ {message}", "success"),
+                field_mapping_ui,
+                {"display": "block"},
+                {"display": "block"},
+                False,
+                [
+                    {"label": field, "value": field}
+                    for field in field_suggestions.get("embedding", [])
+                ],
+                [
+                    {"label": field, "value": field}
+                    for field in field_suggestions.get("text", [])
+                ],
+                [
+                    {"label": field, "value": field}
+                    for field in field_suggestions.get("id", [])
+                ],
+                [
+                    {"label": field, "value": field}
+                    for field in field_suggestions.get("category", [])
+                ],
+                [
+                    {"label": field, "value": field}
+                    for field in field_suggestions.get("subcategory", [])
+                ],
+                [
+                    {"label": field, "value": field}
+                    for field in field_suggestions.get("tags", [])
+                ],
+            )
+
+        # Determine output target based on section type
+        output_target = (
+            "processed-data" if section_type == "data" else "processed-prompts"
+        )
+
+        @callback(
+            [
+                Output(output_target, "data", allow_duplicate=True),
+                Output("opensearch-success-alert", "children", allow_duplicate=True),
+                Output("opensearch-success-alert", "is_open", allow_duplicate=True),
+                Output("opensearch-error-alert", "children", allow_duplicate=True),
+                Output("opensearch-error-alert", "is_open", allow_duplicate=True),
+            ],
+            [Input(f"{section_type}-load-opensearch-data-btn", "n_clicks")],
+            [
+                State(f"{section_type}-opensearch-index", "value"),
+                State(f"{section_type}-opensearch-query-size", "value"),
+                State(f"{section_type}-embedding-field-dropdown-ui", "value"),
+                State(f"{section_type}-text-field-dropdown-ui", "value"),
+                State(f"{section_type}-id-field-dropdown-ui", "value"),
+                State(f"{section_type}-category-field-dropdown-ui", "value"),
+                State(f"{section_type}-subcategory-field-dropdown-ui", "value"),
+                State(f"{section_type}-tags-field-dropdown-ui", "value"),
+            ],
+            prevent_initial_call=True,
+        )
+        def load_opensearch_data(
+            n_clicks,
+            index_name,
+            query_size,
+            embedding_field,
+            text_field,
+            id_field,
+            category_field,
+            subcategory_field,
+            tags_field,
+        ):
+            if not n_clicks or not index_name or not embedding_field or not text_field:
+                return no_update, no_update, no_update, no_update, no_update
+
+            try:
+                # Validate and set query size
+                if not query_size or query_size < 1:
+                    query_size = AppSettings.OPENSEARCH_DEFAULT_SIZE
+                elif query_size > 1000:
+                    query_size = 1000  # Cap at reasonable maximum
+
+                # Create field mapping
+                field_mapping = FieldMapper.create_mapping_from_dict(
+                    {
+                        "embedding": embedding_field,
+                        "text": text_field,
+                        "id": id_field,
+                        "category": category_field,
+                        "subcategory": subcategory_field,
+                        "tags": tags_field,
+                    }
+                )
+
+                # Fetch data from OpenSearch
+                success, raw_documents, message = opensearch_client.fetch_data(
+                    index_name, size=query_size
+                )
+
+                if not success:
+                    return (
+                        no_update,
+                        "",
+                        False,
+                        f"❌ Failed to fetch {section_type}: {message}",
+                        True,
+                    )
+
+                # Process the data
+                processed_data = self.processor.process_opensearch_data(
+                    raw_documents, field_mapping
+                )
+
+                if processed_data.error:
+                    return (
+                        {"error": processed_data.error},
+                        "",
+                        False,
+                        f"❌ {section_type.title()} processing error: {processed_data.error}",
+                        True,
+                    )
+
+                success_message = f"✅ Successfully loaded {len(processed_data.documents)} {section_type} from OpenSearch"
+
+                # Format for appropriate target (data vs prompts)
+                if section_type == "data":
+                    return (
+                        {
+                            "documents": [
+                                self._document_to_dict(doc)
+                                for doc in processed_data.documents
+                            ],
+                            "embeddings": processed_data.embeddings.tolist(),
+                        },
+                        success_message,
+                        True,
+                        "",
+                        False,
+                    )
+                else:  # prompts
+                    return (
+                        {
+                            "prompts": [
+                                self._document_to_dict(doc)
+                                for doc in processed_data.documents
+                            ],
+                            "embeddings": processed_data.embeddings.tolist(),
+                        },
+                        success_message,
+                        True,
+                        "",
+                        False,
+                    )
+
+            except Exception as e:
+                return (no_update, "", False, f"❌ Unexpected error: {str(e)}", True)
+
+        # Sync callbacks to update hidden dropdowns from UI dropdowns
+        @callback(
+            Output(f"{section_type}-embedding-field-dropdown", "value"),
+            Input(f"{section_type}-embedding-field-dropdown-ui", "value"),
+            prevent_initial_call=True,
+        )
+        def sync_embedding_dropdown(value):
+            return value
+
+        @callback(
+            Output(f"{section_type}-text-field-dropdown", "value"),
+            Input(f"{section_type}-text-field-dropdown-ui", "value"),
+            prevent_initial_call=True,
+        )
+        def sync_text_dropdown(value):
+            return value
+
+        @callback(
+            Output(f"{section_type}-id-field-dropdown", "value"),
+            Input(f"{section_type}-id-field-dropdown-ui", "value"),
+            prevent_initial_call=True,
+        )
+        def sync_id_dropdown(value):
+            return value
+
+        @callback(
+            Output(f"{section_type}-category-field-dropdown", "value"),
+            Input(f"{section_type}-category-field-dropdown-ui", "value"),
+            prevent_initial_call=True,
+        )
+        def sync_category_dropdown(value):
+            return value
+
+        @callback(
+            Output(f"{section_type}-subcategory-field-dropdown", "value"),
+            Input(f"{section_type}-subcategory-field-dropdown-ui", "value"),
+            prevent_initial_call=True,
+        )
+        def sync_subcategory_dropdown(value):
+            return value
+
+        @callback(
+            Output(f"{section_type}-tags-field-dropdown", "value"),
+            Input(f"{section_type}-tags-field-dropdown-ui", "value"),
+            prevent_initial_call=True,
+        )
+        def sync_tags_dropdown(value):
+            return value
+
+    def _register_collapse_callbacks(self):
+        """Register callbacks for collapsible sections."""
+
+        # Data section collapse callback
+        @callback(
+            [
+                Output("data-collapse", "is_open"),
+                Output("data-collapse-icon", "className"),
+            ],
+            [Input("data-collapse-toggle", "n_clicks")],
+            [State("data-collapse", "is_open")],
+            prevent_initial_call=True,
+        )
+        def toggle_data_collapse(n_clicks, is_open):
+            if n_clicks:
+                new_state = not is_open
+                icon_class = (
+                    "fas fa-chevron-down me-2"
+                    if new_state
+                    else "fas fa-chevron-right me-2"
+                )
+                return new_state, icon_class
+            return is_open, "fas fa-chevron-down me-2"
+
+        # Prompts section collapse callback
+        @callback(
+            [
+                Output("prompts-collapse", "is_open"),
+                Output("prompts-collapse-icon", "className"),
+            ],
+            [Input("prompts-collapse-toggle", "n_clicks")],
+            [State("prompts-collapse", "is_open")],
+            prevent_initial_call=True,
+        )
+        def toggle_prompts_collapse(n_clicks, is_open):
+            if n_clicks:
+                new_state = not is_open
+                icon_class = (
+                    "fas fa-chevron-down me-2"
+                    if new_state
+                    else "fas fa-chevron-right me-2"
+                )
+                return new_state, icon_class
+            return is_open, "fas fa-chevron-down me-2"
+
     @staticmethod
     def _document_to_dict(doc):
         return {
-            'id': doc.id,
+            "id": doc.id,
-            'text': doc.text,
+            "text": doc.text,
-            'embedding': doc.embedding,
+            "embedding": doc.embedding,
-            'category': doc.category,
+            "category": doc.category,
-            'subcategory': doc.subcategory,
+            "subcategory": doc.subcategory,
-            'tags': doc.tags
+            "tags": doc.tags,
-        }
+        }
+
+    @staticmethod
+    def _format_error_message(error: str, filename: str | None = None) -> str:
+        """Format error message with helpful guidance for users."""
+        file_part = f" in file '{filename}'" if filename else ""
+
+        # Check for common error patterns and provide helpful messages
+        if "embedding" in error.lower() and (
+            "key" in error.lower() or "required field" in error.lower()
+        ):
+            return (
+                f"❌ Missing 'embedding' field{file_part}. "
+                "Each line must contain an 'embedding' field with a list of numbers."
+            )
+        elif "text" in error.lower() and (
+            "key" in error.lower() or "required field" in error.lower()
+        ):
+            return (
+                f"❌ Missing 'text' field{file_part}. "
+                "Each line must contain a 'text' field with the document content."
+            )
+        elif "json" in error.lower() and "decode" in error.lower():
+            return (
+                f"❌ Invalid JSON format{file_part}. "
+                "Please check that each line is valid JSON with proper syntax (quotes, braces, etc.)."
+            )
+        elif "unicode" in error.lower() or "decode" in error.lower():
+            return (
+                f"❌ File encoding issue{file_part}. "
+                "Please ensure the file is saved in UTF-8 format and contains no binary data."
+            )
+        elif "array" in error.lower() or "list" in error.lower():
+            return (
+                f"❌ Invalid embedding format{file_part}. "
+                "Embeddings must be arrays/lists of numbers, not strings or other types."
+            )
+        else:
+            return (
+                f"❌ Error processing file{file_part}: {error}. "
+                "Please check that your file is valid NDJSON with required 'text' and 'embedding' fields."
+            )
+
+    @staticmethod
+    def _create_status_alert(message: str, color: str):
+        """Create a status alert component."""
+        import dash_bootstrap_components as dbc
+
+        return dbc.Alert(message, color=color, className="mb-2")

src/embeddingbuddy/ui/callbacks/interactions.py

@@ -4,63 +4,79 @@ import dash_bootstrap_components as dbc
 
 
 class InteractionCallbacks:
-    
     def __init__(self):
         self._register_callbacks()
-    
+
     def _register_callbacks(self):
-        
         @callback(
-            Output('point-details', 'children'),
+            Output("point-details", "children"),
-            Input('embedding-plot', 'clickData'),
+            Input("embedding-plot", "clickData"),
-            [State('processed-data', 'data'),
+            [State("processed-data", "data"), State("processed-prompts", "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]
+            point_data = clickData["points"][0]
-            trace_name = point_data.get('fullData', {}).get('name', 'Documents')
+            trace_name = point_data.get("fullData", {}).get("name", "Documents")
-            
+
-            if 'pointIndex' in point_data:
+            if "pointIndex" in point_data:
-                point_index = point_data['pointIndex']
+                point_index = point_data["pointIndex"]
-            elif 'pointNumber' in point_data:
+            elif "pointNumber" in point_data:
-                point_index = point_data['pointNumber']
+                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:
+            if (
-                item = prompts_data['prompts'][point_index]
+                trace_name.startswith("Prompts")
-                item_type = 'Prompt'
+                and prompts_data
+                and "prompts" in prompts_data
+            ):
+                item = prompts_data["prompts"][point_index]
+                item_type = "Prompt"
             else:
-                item = data['documents'][point_index]
+                item = data["documents"][point_index]
-                item_type = 'Document'
+                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("processed-data", "data", allow_duplicate=True),
-             Output('point-details', 'children', allow_duplicate=True)],
+                Output("processed-prompts", "data", allow_duplicate=True),
-            Input('reset-button', 'n_clicks'),
+                Output("point-details", "children", allow_duplicate=True),
-            prevent_initial_call=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 None, None, "Click on a point to see details"
-    
+
     @staticmethod
     def _create_detail_card(item, item_type):
-        return dbc.Card([
+        return dbc.Card(
-            dbc.CardBody([
+            [
-                html.H5(f"{item_type}: {item['id']}", className="card-title"),
+                dbc.CardBody(
-                html.P(f"Text: {item['text']}", className="card-text"),
+                    [
-                html.P(f"Category: {item.get('category', 'Unknown')}", className="card-text"),
+                        html.H5(f"{item_type}: {item['id']}", className="card-title"),
-                html.P(f"Subcategory: {item.get('subcategory', 'Unknown')}", className="card-text"),
+                        html.P(f"Text: {item['text']}", className="card-text"),
-                html.P(f"Tags: {', '.join(item.get('tags', [])) if item.get('tags') else 'None'}", className="card-text"),
+                        html.P(
-                html.P(f"Type: {item_type}", className="card-text text-muted")
+                            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"),
+                    ]
+                )
+            ]
+        )

src/embeddingbuddy/ui/callbacks/visualization.py

@@ -7,81 +7,102 @@ from ...visualization.plots import PlotFactory
 
 
 class VisualizationCallbacks:
-    
     def __init__(self):
         self.plot_factory = PlotFactory()
         self._register_callbacks()
-    
+
     def _register_callbacks(self):
-        
         @callback(
-            Output('embedding-plot', 'figure'),
+            Output("embedding-plot", "figure"),
-            [Input('processed-data', 'data'),
+            [
-             Input('processed-prompts', 'data'),
+                Input("processed-data", "data"),
-             Input('method-dropdown', 'value'),
+                Input("processed-prompts", "data"),
-             Input('color-dropdown', 'value'),
+                Input("method-dropdown", "value"),
-             Input('dimension-toggle', 'value'),
+                Input("color-dropdown", "value"),
-             Input('show-prompts-toggle', 'value')]
+                Input("dimension-toggle", "value"),
+                Input("show-prompts-toggle", "value"),
+            ],
         )
         def update_plot(data, prompts_data, method, color_by, dimensions, show_prompts):
-            if not data or 'error' in data:
+            if not data or "error" in data:
                 return go.Figure().add_annotation(
                     text="Upload a valid NDJSON file to see visualization",
-                    xref="paper", yref="paper",
+                    xref="paper",
-                    x=0.5, y=0.5, xanchor='center', yanchor='middle',
+                    yref="paper",
-                    showarrow=False, font=dict(size=16)
+                    x=0.5,
+                    y=0.5,
+                    xanchor="center",
+                    yanchor="middle",
+                    showarrow=False,
+                    font=dict(size=16),
                 )
-            
+
             try:
-                doc_embeddings = np.array(data['embeddings'])
+                doc_embeddings = np.array(data["embeddings"])
                 all_embeddings = doc_embeddings
-                has_prompts = prompts_data and 'error' not in prompts_data and prompts_data.get('prompts')
+                has_prompts = (
-                
+                    prompts_data
+                    and "error" not in prompts_data
+                    and prompts_data.get("prompts")
+                )
+
                 if has_prompts:
-                    prompt_embeddings = np.array(prompts_data['embeddings'])
+                    prompt_embeddings = np.array(prompts_data["embeddings"])
                     all_embeddings = np.vstack([doc_embeddings, prompt_embeddings])
-                
+
-                n_components = 3 if dimensions == '3d' else 2
+                n_components = 3 if dimensions == "3d" else 2
-                
+
-                reducer = ReducerFactory.create_reducer(method, n_components=n_components)
+                reducer = ReducerFactory.create_reducer(
+                    method, n_components=n_components
+                )
                 reduced_data = reducer.fit_transform(all_embeddings)
-                
+
-                doc_reduced = reduced_data.reduced_embeddings[:len(doc_embeddings)]
+                doc_reduced = reduced_data.reduced_embeddings[: len(doc_embeddings)]
                 prompt_reduced = None
                 if has_prompts:
-                    prompt_reduced = reduced_data.reduced_embeddings[len(doc_embeddings):]
+                    prompt_reduced = reduced_data.reduced_embeddings[
-                
+                        len(doc_embeddings) :
-                documents = [self._dict_to_document(doc) for doc in data['documents']]
+                    ]
+
+                documents = [self._dict_to_document(doc) for doc in data["documents"]]
                 prompts = None
                 if has_prompts:
-                    prompts = [self._dict_to_document(prompt) for prompt in prompts_data['prompts']]
+                    prompts = [
-                
+                        self._dict_to_document(prompt)
+                        for prompt in prompts_data["prompts"]
+                    ]
+
                 plot_data = PlotData(
                     documents=documents,
                     coordinates=doc_reduced,
                     prompts=prompts,
-                    prompt_coordinates=prompt_reduced
+                    prompt_coordinates=prompt_reduced,
                 )
-                
+
                 return self.plot_factory.create_plot(
                     plot_data, dimensions, color_by, reduced_data.method, show_prompts
                 )
-                
+
             except Exception as e:
                 return go.Figure().add_annotation(
                     text=f"Error creating visualization: {str(e)}",
-                    xref="paper", yref="paper",
+                    xref="paper",
-                    x=0.5, y=0.5, xanchor='center', yanchor='middle',
+                    yref="paper",
-                    showarrow=False, font=dict(size=16)
+                    x=0.5,
+                    y=0.5,
+                    xanchor="center",
+                    yanchor="middle",
+                    showarrow=False,
+                    font=dict(size=16),
                 )
-    
+
     @staticmethod
     def _dict_to_document(doc_dict):
         return Document(
-            id=doc_dict['id'],
+            id=doc_dict["id"],
-            text=doc_dict['text'],
+            text=doc_dict["text"],
-            embedding=doc_dict['embedding'],
+            embedding=doc_dict["embedding"],
-            category=doc_dict.get('category'),
+            category=doc_dict.get("category"),
-            subcategory=doc_dict.get('subcategory'),
+            subcategory=doc_dict.get("subcategory"),
-            tags=doc_dict.get('tags', [])
+            tags=doc_dict.get("tags", []),
-        )
+        )

src/embeddingbuddy/ui/components/datasource.py

@@ -0,0 +1,519 @@
+from dash import dcc, html
+import dash_bootstrap_components as dbc
+from .upload import UploadComponent
+
+
+class DataSourceComponent:
+    def __init__(self):
+        self.upload_component = UploadComponent()
+
+    def create_tabbed_interface(self):
+        """Create tabbed interface for different data sources."""
+        return dbc.Card(
+            [
+                dbc.CardHeader(
+                    [
+                        dbc.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",
+                        )
+                    ]
+                ),
+                dbc.CardBody([html.Div(id="tab-content")]),
+            ]
+        )
+
+    def create_file_upload_tab(self):
+        """Create file upload tab content."""
+        return html.Div(
+            [
+                self.upload_component.create_error_alert(),
+                self.upload_component.create_data_upload(),
+                self.upload_component.create_prompts_upload(),
+                self.upload_component.create_reset_button(),
+            ]
+        )
+
+    def create_opensearch_tab(self):
+        """Create OpenSearch tab content with separate Data and Prompts sections."""
+        return html.Div(
+            [
+                # Data Section
+                dbc.Card(
+                    [
+                        dbc.CardHeader(
+                            [
+                                dbc.Button(
+                                    [
+                                        html.I(
+                                            className="fas fa-chevron-down me-2",
+                                            id="data-collapse-icon",
+                                        ),
+                                        "📄 Documents/Data",
+                                    ],
+                                    id="data-collapse-toggle",
+                                    color="link",
+                                    className="text-start p-0 w-100 text-decoration-none",
+                                    style={
+                                        "border": "none",
+                                        "font-size": "1.25rem",
+                                        "font-weight": "500",
+                                    },
+                                ),
+                            ]
+                        ),
+                        dbc.Collapse(
+                            [dbc.CardBody([self._create_opensearch_section("data")])],
+                            id="data-collapse",
+                            is_open=True,
+                        ),
+                    ],
+                    className="mb-4",
+                ),
+                # Prompts Section
+                dbc.Card(
+                    [
+                        dbc.CardHeader(
+                            [
+                                dbc.Button(
+                                    [
+                                        html.I(
+                                            className="fas fa-chevron-down me-2",
+                                            id="prompts-collapse-icon",
+                                        ),
+                                        "💬 Prompts",
+                                    ],
+                                    id="prompts-collapse-toggle",
+                                    color="link",
+                                    className="text-start p-0 w-100 text-decoration-none",
+                                    style={
+                                        "border": "none",
+                                        "font-size": "1.25rem",
+                                        "font-weight": "500",
+                                    },
+                                ),
+                            ]
+                        ),
+                        dbc.Collapse(
+                            [
+                                dbc.CardBody(
+                                    [self._create_opensearch_section("prompts")]
+                                )
+                            ],
+                            id="prompts-collapse",
+                            is_open=True,
+                        ),
+                    ],
+                    className="mb-4",
+                ),
+                # Hidden dropdowns to prevent callback errors (for both sections)
+                html.Div(
+                    [
+                        # Data dropdowns (hidden sync targets)
+                        dcc.Dropdown(
+                            id="data-embedding-field-dropdown",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="data-text-field-dropdown", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="data-id-field-dropdown", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="data-category-field-dropdown", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="data-subcategory-field-dropdown",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="data-tags-field-dropdown", style={"display": "none"}
+                        ),
+                        # Data UI dropdowns (hidden placeholders)
+                        dcc.Dropdown(
+                            id="data-embedding-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="data-text-field-dropdown-ui", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="data-id-field-dropdown-ui", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="data-category-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="data-subcategory-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="data-tags-field-dropdown-ui", style={"display": "none"}
+                        ),
+                        # Prompts dropdowns (hidden sync targets)
+                        dcc.Dropdown(
+                            id="prompts-embedding-field-dropdown",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-text-field-dropdown", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-id-field-dropdown", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-category-field-dropdown",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-subcategory-field-dropdown",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-tags-field-dropdown", style={"display": "none"}
+                        ),
+                        # Prompts UI dropdowns (hidden placeholders)
+                        dcc.Dropdown(
+                            id="prompts-embedding-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-text-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-id-field-dropdown-ui", style={"display": "none"}
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-category-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-subcategory-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                        dcc.Dropdown(
+                            id="prompts-tags-field-dropdown-ui",
+                            style={"display": "none"},
+                        ),
+                    ],
+                    style={"display": "none"},
+                ),
+            ]
+        )
+
+    def _create_opensearch_section(self, section_type):
+        """Create a complete OpenSearch section for either 'data' or 'prompts'."""
+        section_id = section_type  # 'data' or 'prompts'
+
+        return html.Div(
+            [
+                # Connection section
+                html.H6("Connection", className="mb-2"),
+                dbc.Row(
+                    [
+                        dbc.Col(
+                            [
+                                dbc.Label("OpenSearch URL:"),
+                                dbc.Input(
+                                    id=f"{section_id}-opensearch-url",
+                                    type="text",
+                                    placeholder="https://opensearch.example.com:9200",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=12,
+                        ),
+                    ]
+                ),
+                dbc.Row(
+                    [
+                        dbc.Col(
+                            [
+                                dbc.Label("Index Name:"),
+                                dbc.Input(
+                                    id=f"{section_id}-opensearch-index",
+                                    type="text",
+                                    placeholder="my-embeddings-index",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                        dbc.Col(
+                            [
+                                dbc.Label("Query Size:"),
+                                dbc.Input(
+                                    id=f"{section_id}-opensearch-query-size",
+                                    type="number",
+                                    value=100,
+                                    min=1,
+                                    max=1000,
+                                    placeholder="100",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                    ]
+                ),
+                dbc.Row(
+                    [
+                        dbc.Col(
+                            [
+                                dbc.Button(
+                                    "Test Connection",
+                                    id=f"{section_id}-test-connection-btn",
+                                    color="primary",
+                                    className="mb-3",
+                                ),
+                            ],
+                            width=12,
+                        ),
+                    ]
+                ),
+                # Authentication section (collapsible)
+                dbc.Collapse(
+                    [
+                        html.Hr(),
+                        html.H6("Authentication (Optional)", className="mb-2"),
+                        dbc.Row(
+                            [
+                                dbc.Col(
+                                    [
+                                        dbc.Label("Username:"),
+                                        dbc.Input(
+                                            id=f"{section_id}-opensearch-username",
+                                            type="text",
+                                            className="mb-2",
+                                        ),
+                                    ],
+                                    width=6,
+                                ),
+                                dbc.Col(
+                                    [
+                                        dbc.Label("Password:"),
+                                        dbc.Input(
+                                            id=f"{section_id}-opensearch-password",
+                                            type="password",
+                                            className="mb-2",
+                                        ),
+                                    ],
+                                    width=6,
+                                ),
+                            ]
+                        ),
+                        dbc.Label("OR"),
+                        dbc.Input(
+                            id=f"{section_id}-opensearch-api-key",
+                            type="text",
+                            placeholder="API Key",
+                            className="mb-2",
+                        ),
+                    ],
+                    id=f"{section_id}-auth-collapse",
+                    is_open=False,
+                ),
+                dbc.Button(
+                    "Show Authentication",
+                    id=f"{section_id}-auth-toggle",
+                    color="link",
+                    size="sm",
+                    className="p-0 mb-3",
+                ),
+                # Connection status
+                html.Div(id=f"{section_id}-connection-status", className="mb-3"),
+                # Field mapping section (hidden initially)
+                html.Div(
+                    id=f"{section_id}-field-mapping-section", style={"display": "none"}
+                ),
+                # Load data button (hidden initially)
+                html.Div(
+                    [
+                        dbc.Button(
+                            f"Load {section_type.title()}",
+                            id=f"{section_id}-load-opensearch-data-btn",
+                            color="success",
+                            className="mb-2",
+                            disabled=True,
+                        ),
+                    ],
+                    id=f"{section_id}-load-data-section",
+                    style={"display": "none"},
+                ),
+                # OpenSearch status/results
+                html.Div(id=f"{section_id}-opensearch-status", className="mb-3"),
+            ]
+        )
+
+    def create_field_mapping_interface(self, field_suggestions, section_type="data"):
+        """Create field mapping interface based on detected fields."""
+        return html.Div(
+            [
+                html.Hr(),
+                html.H6("Field Mapping", className="mb-2"),
+                html.P(
+                    "Map your OpenSearch fields to the required format:",
+                    className="text-muted small",
+                ),
+                # Required fields
+                dbc.Row(
+                    [
+                        dbc.Col(
+                            [
+                                dbc.Label(
+                                    "Embedding Field (required):", className="fw-bold"
+                                ),
+                                dcc.Dropdown(
+                                    id=f"{section_type}-embedding-field-dropdown-ui",
+                                    options=[
+                                        {"label": field, "value": field}
+                                        for field in field_suggestions.get(
+                                            "embedding", []
+                                        )
+                                    ],
+                                    value=field_suggestions.get("embedding", [None])[
+                                        0
+                                    ],  # Default to first suggestion
+                                    placeholder="Select embedding field...",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                        dbc.Col(
+                            [
+                                dbc.Label(
+                                    "Text Field (required):", className="fw-bold"
+                                ),
+                                dcc.Dropdown(
+                                    id=f"{section_type}-text-field-dropdown-ui",
+                                    options=[
+                                        {"label": field, "value": field}
+                                        for field in field_suggestions.get("text", [])
+                                    ],
+                                    value=field_suggestions.get("text", [None])[
+                                        0
+                                    ],  # Default to first suggestion
+                                    placeholder="Select text field...",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                    ]
+                ),
+                # Optional fields
+                html.H6("Optional Fields", className="mb-2 mt-3"),
+                dbc.Row(
+                    [
+                        dbc.Col(
+                            [
+                                dbc.Label("ID Field:"),
+                                dcc.Dropdown(
+                                    id=f"{section_type}-id-field-dropdown-ui",
+                                    options=[
+                                        {"label": field, "value": field}
+                                        for field in field_suggestions.get("id", [])
+                                    ],
+                                    value=field_suggestions.get("id", [None])[
+                                        0
+                                    ],  # Default to first suggestion
+                                    placeholder="Select ID field...",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                        dbc.Col(
+                            [
+                                dbc.Label("Category Field:"),
+                                dcc.Dropdown(
+                                    id=f"{section_type}-category-field-dropdown-ui",
+                                    options=[
+                                        {"label": field, "value": field}
+                                        for field in field_suggestions.get(
+                                            "category", []
+                                        )
+                                    ],
+                                    value=field_suggestions.get("category", [None])[
+                                        0
+                                    ],  # Default to first suggestion
+                                    placeholder="Select category field...",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                    ]
+                ),
+                dbc.Row(
+                    [
+                        dbc.Col(
+                            [
+                                dbc.Label("Subcategory Field:"),
+                                dcc.Dropdown(
+                                    id=f"{section_type}-subcategory-field-dropdown-ui",
+                                    options=[
+                                        {"label": field, "value": field}
+                                        for field in field_suggestions.get(
+                                            "subcategory", []
+                                        )
+                                    ],
+                                    value=field_suggestions.get("subcategory", [None])[
+                                        0
+                                    ],  # Default to first suggestion
+                                    placeholder="Select subcategory field...",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                        dbc.Col(
+                            [
+                                dbc.Label("Tags Field:"),
+                                dcc.Dropdown(
+                                    id=f"{section_type}-tags-field-dropdown-ui",
+                                    options=[
+                                        {"label": field, "value": field}
+                                        for field in field_suggestions.get("tags", [])
+                                    ],
+                                    value=field_suggestions.get("tags", [None])[
+                                        0
+                                    ],  # Default to first suggestion
+                                    placeholder="Select tags field...",
+                                    className="mb-2",
+                                ),
+                            ],
+                            width=6,
+                        ),
+                    ]
+                ),
+            ]
+        )
+
+    def create_error_alert(self):
+        """Create error alert component for OpenSearch issues."""
+        return dbc.Alert(
+            id="opensearch-error-alert",
+            dismissable=True,
+            is_open=False,
+            color="danger",
+            className="mb-3",
+        )
+
+    def create_success_alert(self):
+        """Create success alert component for OpenSearch operations."""
+        return dbc.Alert(
+            id="opensearch-success-alert",
+            dismissable=True,
+            is_open=False,
+            color="success",
+            className="mb-3",
+        )

src/embeddingbuddy/ui/components/sidebar.py

@@ -1,82 +1,88 @@
 from dash import dcc, html
 import dash_bootstrap_components as dbc
 from .upload import UploadComponent
+from .datasource import DataSourceComponent
 
 
 class SidebarComponent:
-    
     def __init__(self):
         self.upload_component = UploadComponent()
-    
+        self.datasource_component = DataSourceComponent()
+
     def create_layout(self):
-        return dbc.Col([
+        return dbc.Col(
-            html.H5("Upload Data", className="mb-3"),
+            [
-            self.upload_component.create_data_upload(),
+                html.H5("Data Sources", className="mb-3"),
-            self.upload_component.create_prompts_upload(),
+                self.datasource_component.create_error_alert(),
-            self.upload_component.create_reset_button(),
+                self.datasource_component.create_success_alert(),
-            
+                self.datasource_component.create_tabbed_interface(),
-            html.H5("Visualization Controls", className="mb-3"),
+                html.H5("Visualization Controls", className="mb-3 mt-4"),
-            self._create_method_dropdown(),
+            ]
-            self._create_color_dropdown(),
+            + self._create_method_dropdown()
-            self._create_dimension_toggle(),
+            + self._create_color_dropdown()
-            self._create_prompts_toggle(),
+            + 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")
+                html.H5("Point Details", className="mb-3"),
-            
+                html.Div(
-        ], width=3, style={'padding-right': '20px'})
+                    id="point-details", children="Click on a point to see details"
-    
+                ),
+            ],
+            width=3,
+            style={"padding-right": "20px"},
+        )
+
     def _create_method_dropdown(self):
         return [
             dbc.Label("Method:"),
             dcc.Dropdown(
-                id='method-dropdown',
+                id="method-dropdown",
                 options=[
-                    {'label': 'PCA', 'value': 'pca'},
+                    {"label": "PCA", "value": "pca"},
-                    {'label': 't-SNE', 'value': 'tsne'},
+                    {"label": "t-SNE", "value": "tsne"},
-                    {'label': 'UMAP', 'value': 'umap'}
+                    {"label": "UMAP", "value": "umap"},
                 ],
-                value='pca',
+                value="pca",
-                style={'margin-bottom': '15px'}
+                style={"margin-bottom": "15px"},
-            )
+            ),
         ]
-    
+
     def _create_color_dropdown(self):
         return [
             dbc.Label("Color by:"),
             dcc.Dropdown(
-                id='color-dropdown',
+                id="color-dropdown",
                 options=[
-                    {'label': 'Category', 'value': 'category'},
+                    {"label": "Category", "value": "category"},
-                    {'label': 'Subcategory', 'value': 'subcategory'},
+                    {"label": "Subcategory", "value": "subcategory"},
-                    {'label': 'Tags', 'value': 'tags'}
+                    {"label": "Tags", "value": "tags"},
                 ],
-                value='category',
+                value="category",
-                style={'margin-bottom': '15px'}
+                style={"margin-bottom": "15px"},
-            )
+            ),
         ]
-    
+
     def _create_dimension_toggle(self):
         return [
             dbc.Label("Dimensions:"),
             dcc.RadioItems(
-                id='dimension-toggle',
+                id="dimension-toggle",
                 options=[
-                    {'label': '2D', 'value': '2d'},
+                    {"label": "2D", "value": "2d"},
-                    {'label': '3D', 'value': '3d'}
+                    {"label": "3D", "value": "3d"},
                 ],
-                value='3d',
+                value="3d",
-                style={'margin-bottom': '20px'}
+                style={"margin-bottom": "20px"},
-            )
+            ),
         ]
-    
+
     def _create_prompts_toggle(self):
         return [
             dbc.Label("Show Prompts:"),
             dcc.Checklist(
-                id='show-prompts-toggle',
+                id="show-prompts-toggle",
-                options=[{'label': 'Show prompts on plot', 'value': 'show'}],
+                options=[{"label": "Show prompts on plot", "value": "show"}],
-                value=['show'],
+                value=["show"],
-                style={'margin-bottom': '20px'}
+                style={"margin-bottom": "20px"},
-            )
+            ),
-        ]
+        ]

src/embeddingbuddy/ui/components/upload.py

@@ -3,58 +3,62 @@ import dash_bootstrap_components as dbc
 
 
 class UploadComponent:
-    
     @staticmethod
     def create_data_upload():
         return dcc.Upload(
-            id='upload-data',
+            id="upload-data",
-            children=html.Div([
+            children=html.Div(["Drag and Drop or ", html.A("Select Files")]),
-                'Drag and Drop or ',
-                html.A('Select Files')
-            ]),
             style={
-                'width': '100%',
+                "width": "100%",
-                'height': '60px',
+                "height": "60px",
-                'lineHeight': '60px',
+                "lineHeight": "60px",
-                'borderWidth': '1px',
+                "borderWidth": "1px",
-                'borderStyle': 'dashed',
+                "borderStyle": "dashed",
-                'borderRadius': '5px',
+                "borderRadius": "5px",
-                'textAlign': 'center',
+                "textAlign": "center",
-                'margin-bottom': '20px'
+                "margin-bottom": "20px",
             },
-            multiple=False
+            multiple=False,
         )
-    
+
     @staticmethod
     def create_prompts_upload():
         return dcc.Upload(
-            id='upload-prompts',
+            id="upload-prompts",
-            children=html.Div([
+            children=html.Div(["Drag and Drop Prompts or ", html.A("Select Files")]),
-                'Drag and Drop Prompts or ',
-                html.A('Select Files')
-            ]),
             style={
-                'width': '100%',
+                "width": "100%",
-                'height': '60px',
+                "height": "60px",
-                'lineHeight': '60px',
+                "lineHeight": "60px",
-                'borderWidth': '1px',
+                "borderWidth": "1px",
-                'borderStyle': 'dashed',
+                "borderStyle": "dashed",
-                'borderRadius': '5px',
+                "borderRadius": "5px",
-                'textAlign': 'center',
+                "textAlign": "center",
-                'margin-bottom': '20px',
+                "margin-bottom": "20px",
-                'borderColor': '#28a745'
+                "borderColor": "#28a745",
             },
-            multiple=False
+            multiple=False,
         )
-    
+
     @staticmethod
     def create_reset_button():
         return dbc.Button(
             "Reset All Data",
-            id='reset-button',
+            id="reset-button",
-            color='danger',
+            color="danger",
             outline=True,
-            size='sm',
+            size="sm",
-            className='mb-3',
+            className="mb-3",
-            style={'width': '100%'}
+            style={"width": "100%"},
-        )
+        )
+
+    @staticmethod
+    def create_error_alert():
+        """Create error alert component for data upload issues."""
+        return dbc.Alert(
+            id="upload-error-alert",
+            dismissable=True,
+            is_open=False,
+            color="danger",
+            className="mb-3",
+        )

src/embeddingbuddy/ui/layout.py

@@ -4,41 +4,44 @@ from .components.sidebar import SidebarComponent
 
 
 class AppLayout:
-    
     def __init__(self):
         self.sidebar = SidebarComponent()
-    
+
     def create_layout(self):
-        return dbc.Container([
+        return dbc.Container(
-            self._create_header(),
+            [self._create_header(), self._create_main_content()]
-            self._create_main_content(),
+            + self._create_stores(),
-            self._create_stores()
+            fluid=True,
-        ], fluid=True)
+        )
-    
+
     def _create_header(self):
-        return dbc.Row([
+        return dbc.Row(
-            dbc.Col([
+            [
-                html.H1("EmbeddingBuddy", className="text-center mb-4"),
+                dbc.Col(
-            ], width=12)
+                    [
-        ])
+                        html.H1("EmbeddingBuddy", className="text-center mb-4"),
-    
+                    ],
+                    width=12,
+                )
+            ]
+        )
+
     def _create_main_content(self):
-        return dbc.Row([
+        return dbc.Row(
-            self.sidebar.create_layout(),
+            [self.sidebar.create_layout(), self._create_visualization_area()]
-            self._create_visualization_area()
+        )
-        ])
+
-    
     def _create_visualization_area(self):
-        return dbc.Col([
+        return dbc.Col(
-            dcc.Graph(
+            [
-                id='embedding-plot',
+                dcc.Graph(
-                style={'height': '85vh', 'width': '100%'},
+                    id="embedding-plot",
-                config={'responsive': True, 'displayModeBar': True}
+                    style={"height": "85vh", "width": "100%"},
-            )
+                    config={"responsive": True, "displayModeBar": True},
-        ], width=9)
+                )
-    
+            ],
+            width=9,
+        )
+
     def _create_stores(self):
-        return [
+        return [dcc.Store(id="processed-data"), dcc.Store(id="processed-prompts")]
-            dcc.Store(id='processed-data'),
-            dcc.Store(id='processed-prompts')
-        ]

src/embeddingbuddy/visualization/colors.py

@@ -1,33 +1,36 @@
-from typing import List, Dict, Any
+from typing import List
 import plotly.colors as pc
 from ..models.schemas import Document
 
 
 class ColorMapper:
-    
     @staticmethod
     def create_color_mapping(documents: List[Document], color_by: str) -> List[str]:
-        if color_by == 'category':
+        if color_by == "category":
             return [doc.category for doc in documents]
-        elif color_by == 'subcategory':
+        elif color_by == "subcategory":
             return [doc.subcategory for doc in documents]
-        elif color_by == 'tags':
+        elif color_by == "tags":
-            return [', '.join(doc.tags) if doc.tags else 'No tags' for doc in documents]
+            return [", ".join(doc.tags) if doc.tags else "No tags" for doc in documents]
         else:
-            return ['All'] * len(documents)
+            return ["All"] * len(documents)
-    
+
     @staticmethod
     def to_grayscale_hex(color_str: str) -> str:
         try:
-            if color_str.startswith('#'):
+            if color_str.startswith("#"):
-                rgb = tuple(int(color_str[i:i+2], 16) for i in (1, 3, 5))
+                rgb = tuple(int(color_str[i : i + 2], 16) for i in (1, 3, 5))
             else:
-                rgb = pc.hex_to_rgb(pc.convert_colors_to_same_type([color_str], colortype='hex')[0][0])
+                rgb = pc.hex_to_rgb(
-            
+                    pc.convert_colors_to_same_type([color_str], colortype="hex")[0][0]
+                )
+
             gray_value = int(0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2])
-            gray_rgb = (gray_value * 0.7 + rgb[0] * 0.3, 
+            gray_rgb = (
-                       gray_value * 0.7 + rgb[1] * 0.3, 
+                gray_value * 0.7 + rgb[0] * 0.3,
-                       gray_value * 0.7 + rgb[2] * 0.3)
+                gray_value * 0.7 + rgb[1] * 0.3,
-            return f'rgb({int(gray_rgb[0])},{int(gray_rgb[1])},{int(gray_rgb[2])})'
+                gray_value * 0.7 + rgb[2] * 0.3,
-        except:
+            )
-            return 'rgb(128,128,128)'
+            return f"rgb({int(gray_rgb[0])},{int(gray_rgb[1])},{int(gray_rgb[2])})"
+        except:  # noqa: E722
+            return "rgb(128,128,128)"

src/embeddingbuddy/visualization/plots.py

@@ -7,139 +7,172 @@ from .colors import ColorMapper
 
 
 class PlotFactory:
-    
     def __init__(self):
         self.color_mapper = ColorMapper()
-    
+
-    def create_plot(self, plot_data: PlotData, dimensions: str = '3d', 
+    def create_plot(
-                   color_by: str = 'category', method: str = 'PCA',
+        self,
-                   show_prompts: Optional[List[str]] = None) -> go.Figure:
+        plot_data: PlotData,
-        
+        dimensions: str = "3d",
-        if plot_data.prompts and show_prompts and 'show' in show_prompts:
+        color_by: str = "category",
+        method: str = "PCA",
+        show_prompts: Optional[List[str]] = None,
+    ) -> go.Figure:
+        if plot_data.prompts and show_prompts and "show" in show_prompts:
             return self._create_dual_plot(plot_data, dimensions, color_by, method)
         else:
             return self._create_single_plot(plot_data, dimensions, color_by, method)
-    
+
-    def _create_single_plot(self, plot_data: PlotData, dimensions: str, 
+    def _create_single_plot(
-                           color_by: str, method: str) -> go.Figure:
+        self, plot_data: PlotData, dimensions: str, color_by: str, method: str
-        df = self._prepare_dataframe(plot_data.documents, plot_data.coordinates, dimensions)
+    ) -> go.Figure:
-        color_values = self.color_mapper.create_color_mapping(plot_data.documents, color_by)
+        df = self._prepare_dataframe(
-        
+            plot_data.documents, plot_data.coordinates, dimensions
-        hover_fields = ['id', 'text_preview', 'category', 'subcategory', 'tags_str']
+        )
-        
+        color_values = self.color_mapper.create_color_mapping(
-        if dimensions == '3d':
+            plot_data.documents, color_by
+        )
+
+        hover_fields = ["id", "text_preview", "category", "subcategory", "tags_str"]
+
+        if dimensions == "3d":
             fig = px.scatter_3d(
-                df, x='dim_1', y='dim_2', z='dim_3',
+                df,
+                x="dim_1",
+                y="dim_2",
+                z="dim_3",
                 color=color_values,
                 hover_data=hover_fields,
-                title=f'3D Embedding Visualization - {method} (colored by {color_by})'
+                title=f"3D Embedding Visualization - {method} (colored by {color_by})",
             )
             fig.update_traces(marker=dict(size=5))
         else:
             fig = px.scatter(
-                df, x='dim_1', y='dim_2',
+                df,
+                x="dim_1",
+                y="dim_2",
                 color=color_values,
                 hover_data=hover_fields,
-                title=f'2D Embedding Visualization - {method} (colored by {color_by})'
+                title=f"2D Embedding Visualization - {method} (colored by {color_by})",
             )
             fig.update_traces(marker=dict(size=8))
-        
+
-        fig.update_layout(
+        fig.update_layout(height=None, autosize=True, margin=dict(l=0, r=0, t=50, b=0))
-            height=None,
-            autosize=True,
-            margin=dict(l=0, r=0, t=50, b=0)
-        )
         return fig
-    
+
-    def _create_dual_plot(self, plot_data: PlotData, dimensions: str, 
+    def _create_dual_plot(
-                         color_by: str, method: str) -> go.Figure:
+        self, plot_data: PlotData, dimensions: str, color_by: str, method: str
+    ) -> go.Figure:
         fig = go.Figure()
-        
+
-        doc_df = self._prepare_dataframe(plot_data.documents, plot_data.coordinates, dimensions)
+        doc_df = self._prepare_dataframe(
-        doc_color_values = self.color_mapper.create_color_mapping(plot_data.documents, color_by)
+            plot_data.documents, plot_data.coordinates, dimensions
-        
+        )
-        hover_fields = ['id', 'text_preview', 'category', 'subcategory', 'tags_str']
+        doc_color_values = self.color_mapper.create_color_mapping(
-        
+            plot_data.documents, color_by
-        if dimensions == '3d':
+        )
+
+        hover_fields = ["id", "text_preview", "category", "subcategory", "tags_str"]
+
+        if dimensions == "3d":
             doc_fig = px.scatter_3d(
-                doc_df, x='dim_1', y='dim_2', z='dim_3',
+                doc_df,
+                x="dim_1",
+                y="dim_2",
+                z="dim_3",
                 color=doc_color_values,
-                hover_data=hover_fields
+                hover_data=hover_fields,
             )
         else:
             doc_fig = px.scatter(
-                doc_df, x='dim_1', y='dim_2',
+                doc_df,
+                x="dim_1",
+                y="dim_2",
                 color=doc_color_values,
-                hover_data=hover_fields
+                hover_data=hover_fields,
             )
-        
+
         for trace in doc_fig.data:
-            trace.name = f'Documents - {trace.name}'
+            trace.name = f"Documents - {trace.name}"
-            if dimensions == '3d':
+            if dimensions == "3d":
                 trace.marker.size = 5
-                trace.marker.symbol = 'circle'
+                trace.marker.symbol = "circle"
             else:
                 trace.marker.size = 8
-                trace.marker.symbol = 'circle'
+                trace.marker.symbol = "circle"
             trace.marker.opacity = 1.0
             fig.add_trace(trace)
-        
+
         if plot_data.prompts and plot_data.prompt_coordinates is not None:
-            prompt_df = self._prepare_dataframe(plot_data.prompts, plot_data.prompt_coordinates, dimensions)
+            prompt_df = self._prepare_dataframe(
-            prompt_color_values = self.color_mapper.create_color_mapping(plot_data.prompts, color_by)
+                plot_data.prompts, plot_data.prompt_coordinates, dimensions
-            
+            )
-            if dimensions == '3d':
+            prompt_color_values = self.color_mapper.create_color_mapping(
+                plot_data.prompts, color_by
+            )
+
+            if dimensions == "3d":
                 prompt_fig = px.scatter_3d(
-                    prompt_df, x='dim_1', y='dim_2', z='dim_3',
+                    prompt_df,
+                    x="dim_1",
+                    y="dim_2",
+                    z="dim_3",
                     color=prompt_color_values,
-                    hover_data=hover_fields
+                    hover_data=hover_fields,
                 )
             else:
                 prompt_fig = px.scatter(
-                    prompt_df, x='dim_1', y='dim_2',
+                    prompt_df,
+                    x="dim_1",
+                    y="dim_2",
                     color=prompt_color_values,
-                    hover_data=hover_fields
+                    hover_data=hover_fields,
                 )
-            
+
             for trace in prompt_fig.data:
-                if hasattr(trace.marker, 'color') and isinstance(trace.marker.color, str):
+                if hasattr(trace.marker, "color") and isinstance(
-                    trace.marker.color = self.color_mapper.to_grayscale_hex(trace.marker.color)
+                    trace.marker.color, str
-                
+                ):
-                trace.name = f'Prompts - {trace.name}'
+                    trace.marker.color = self.color_mapper.to_grayscale_hex(
-                if dimensions == '3d':
+                        trace.marker.color
+                    )
+
+                trace.name = f"Prompts - {trace.name}"
+                if dimensions == "3d":
                     trace.marker.size = 6
-                    trace.marker.symbol = 'diamond'
+                    trace.marker.symbol = "diamond"
                 else:
                     trace.marker.size = 10
-                    trace.marker.symbol = 'diamond'
+                    trace.marker.symbol = "diamond"
                 trace.marker.opacity = 0.8
                 fig.add_trace(trace)
-        
+
-        title = f'{dimensions.upper()} Embedding Visualization - {method} (colored by {color_by})'
+        title = f"{dimensions.upper()} Embedding Visualization - {method} (colored by {color_by})"
         fig.update_layout(
-            title=title,
+            title=title, height=None, autosize=True, margin=dict(l=0, r=0, t=50, b=0)
-            height=None,
-            autosize=True,
-            margin=dict(l=0, r=0, t=50, b=0)
         )
-        
+
         return fig
-    
+
-    def _prepare_dataframe(self, documents: List[Document], coordinates, dimensions: str) -> pd.DataFrame:
+    def _prepare_dataframe(
+        self, documents: List[Document], coordinates, dimensions: str
+    ) -> pd.DataFrame:
         df_data = []
         for i, doc in enumerate(documents):
             row = {
-                'id': doc.id,
+                "id": doc.id,
-                'text': doc.text,
+                "text": doc.text,
-                'text_preview': doc.text[:100] + "..." if len(doc.text) > 100 else doc.text,
+                "text_preview": doc.text[:100] + "..."
-                'category': doc.category,
+                if len(doc.text) > 100
-                'subcategory': doc.subcategory,
+                else doc.text,
-                'tags_str': ', '.join(doc.tags) if doc.tags else 'None',
+                "category": doc.category,
-                'dim_1': coordinates[i, 0],
+                "subcategory": doc.subcategory,
-                'dim_2': coordinates[i, 1],
+                "tags_str": ", ".join(doc.tags) if doc.tags else "None",
+                "dim_1": coordinates[i, 0],
+                "dim_2": coordinates[i, 1],
             }
-            if dimensions == '3d':
+            if dimensions == "3d":
-                row['dim_3'] = coordinates[i, 2]
+                row["dim_3"] = coordinates[i, 2]
             df_data.append(row)
-        
+
-        return pd.DataFrame(df_data)
+        return pd.DataFrame(df_data)

tests/test_bad_data.py

@@ -0,0 +1,197 @@
+"""Tests for handling bad/invalid data files."""
+
+import pytest
+import json
+import base64
+from src.embeddingbuddy.data.parser import NDJSONParser
+from src.embeddingbuddy.data.processor import DataProcessor
+
+
+class TestBadDataHandling:
+    """Test suite for various types of invalid input data."""
+
+    def setup_method(self):
+        """Set up test fixtures."""
+        self.parser = NDJSONParser()
+        self.processor = DataProcessor()
+
+    def _create_upload_contents(self, text_content: str) -> str:
+        """Helper to create upload contents format."""
+        encoded = base64.b64encode(text_content.encode("utf-8")).decode("utf-8")
+        return f"data:application/json;base64,{encoded}"
+
+    def test_missing_embedding_field(self):
+        """Test files missing required embedding field."""
+        bad_content = '{"id": "doc_001", "text": "Sample text", "category": "test"}'
+
+        with pytest.raises(KeyError, match="embedding"):
+            self.parser.parse_text(bad_content)
+
+        # Test processor error handling
+        upload_contents = self._create_upload_contents(bad_content)
+        result = self.processor.process_upload(upload_contents)
+        assert result.error is not None
+        assert "embedding" in result.error
+
+    def test_missing_text_field(self):
+        """Test files missing required text field."""
+        bad_content = (
+            '{"id": "doc_001", "embedding": [0.1, 0.2, 0.3], "category": "test"}'
+        )
+
+        with pytest.raises(KeyError, match="text"):
+            self.parser.parse_text(bad_content)
+
+        # Test processor error handling
+        upload_contents = self._create_upload_contents(bad_content)
+        result = self.processor.process_upload(upload_contents)
+        assert result.error is not None
+        assert "text" in result.error
+
+    def test_malformed_json_lines(self):
+        """Test files with malformed JSON syntax."""
+        # Missing closing brace
+        bad_content = '{"id": "doc_001", "embedding": [0.1, 0.2], "text": "test"'
+
+        with pytest.raises(json.JSONDecodeError):
+            self.parser.parse_text(bad_content)
+
+        # Test processor error handling
+        upload_contents = self._create_upload_contents(bad_content)
+        result = self.processor.process_upload(upload_contents)
+        assert result.error is not None
+
+    def test_invalid_embedding_types(self):
+        """Test files with invalid embedding data types."""
+        test_cases = [
+            # String instead of array
+            '{"id": "doc_001", "embedding": "not_an_array", "text": "test"}',
+            # Mixed types in array
+            '{"id": "doc_002", "embedding": [0.1, "text", 0.3], "text": "test"}',
+            # Empty array
+            '{"id": "doc_003", "embedding": [], "text": "test"}',
+            # Null embedding
+            '{"id": "doc_004", "embedding": null, "text": "test"}',
+        ]
+
+        for bad_content in test_cases:
+            upload_contents = self._create_upload_contents(bad_content)
+            result = self.processor.process_upload(upload_contents)
+            assert result.error is not None, f"Should fail for: {bad_content}"
+
+    def test_inconsistent_embedding_dimensions(self):
+        """Test files with embeddings of different dimensions."""
+        bad_content = """{"id": "doc_001", "embedding": [0.1, 0.2, 0.3, 0.4], "text": "4D embedding"}
+{"id": "doc_002", "embedding": [0.1, 0.2, 0.3], "text": "3D embedding"}"""
+
+        upload_contents = self._create_upload_contents(bad_content)
+        result = self.processor.process_upload(upload_contents)
+
+        # This might succeed parsing but fail in processing
+        # The error depends on where dimension validation occurs
+        if result.error is None:
+            # If parsing succeeds, check that embeddings have inconsistent shapes
+            assert len(result.documents) == 2
+            assert len(result.documents[0].embedding) != len(
+                result.documents[1].embedding
+            )
+
+    def test_empty_lines_in_ndjson(self):
+        """Test files with empty lines mixed in."""
+        content_with_empty_lines = """{"id": "doc_001", "embedding": [0.1, 0.2], "text": "First line"}
+
+{"id": "doc_002", "embedding": [0.3, 0.4], "text": "After empty line"}"""
+
+        # This should work - empty lines should be skipped
+        documents = self.parser.parse_text(content_with_empty_lines)
+        assert len(documents) == 2
+        assert documents[0].id == "doc_001"
+        assert documents[1].id == "doc_002"
+
+    def test_not_ndjson_format(self):
+        """Test regular JSON array instead of NDJSON."""
+        json_array = """[
+  {"id": "doc_001", "embedding": [0.1, 0.2], "text": "First"},
+  {"id": "doc_002", "embedding": [0.3, 0.4], "text": "Second"}
+]"""
+
+        with pytest.raises(json.JSONDecodeError):
+            self.parser.parse_text(json_array)
+
+    def test_binary_content_in_file(self):
+        """Test files with binary content mixed in."""
+        # Simulate binary content that can't be decoded
+        binary_content = (
+            b'\x00\x01\x02{"id": "doc_001", "embedding": [0.1], "text": "test"}'
+        )
+
+        # This should result in an error when processing
+        encoded = base64.b64encode(binary_content).decode("utf-8")
+        upload_contents = f"data:application/json;base64,{encoded}"
+        result = self.processor.process_upload(upload_contents)
+
+        # Should either fail with UnicodeDecodeError or JSON parsing error
+        assert result.error is not None
+
+    def test_extremely_large_embeddings(self):
+        """Test embeddings with very large dimensions."""
+        large_embedding = [0.1] * 10000  # 10k dimensions
+        content = json.dumps(
+            {
+                "id": "doc_001",
+                "embedding": large_embedding,
+                "text": "Large embedding test",
+            }
+        )
+
+        # This should work but might be slow
+        upload_contents = self._create_upload_contents(content)
+        result = self.processor.process_upload(upload_contents)
+
+        if result.error is None:
+            assert len(result.documents) == 1
+            assert len(result.documents[0].embedding) == 10000
+
+    def test_special_characters_in_text(self):
+        """Test handling of special characters and unicode."""
+        special_content = json.dumps(
+            {
+                "id": "doc_001",
+                "embedding": [0.1, 0.2],
+                "text": 'Special chars: 🚀 ñoñó 中文 \n\t"',
+            },
+            ensure_ascii=False,
+        )
+
+        upload_contents = self._create_upload_contents(special_content)
+        result = self.processor.process_upload(upload_contents)
+
+        assert result.error is None
+        assert len(result.documents) == 1
+        assert "🚀" in result.documents[0].text
+
+    def test_processor_error_structure(self):
+        """Test that processor returns proper error structure."""
+        bad_content = '{"invalid": "json"'  # Missing closing brace
+        upload_contents = self._create_upload_contents(bad_content)
+
+        result = self.processor.process_upload(upload_contents)
+
+        # Check error structure
+        assert result.error is not None
+        assert isinstance(result.error, str)
+        assert len(result.documents) == 0
+        assert result.embeddings.size == 0
+
+    def test_multiple_errors_in_file(self):
+        """Test file with multiple different types of errors."""
+        multi_error_content = """{"id": "doc_001", "text": "Missing embedding"}
+{"id": "doc_002", "embedding": "wrong_type", "text": "Wrong embedding type"}
+{"id": "doc_003", "embedding": [0.1, 0.2], "text": "Valid line"}
+{"id": "doc_004", "embedding": [0.3, 0.4]"""  # Missing text and closing brace
+
+        upload_contents = self._create_upload_contents(multi_error_content)
+        result = self.processor.process_upload(upload_contents)
+
+        # Should fail on first error encountered
+        assert result.error is not None

tests/test_data_processing.py

@@ -6,62 +6,64 @@ from src.embeddingbuddy.models.schemas import Document
 
 
 class TestNDJSONParser:
-    
     def test_parse_text_basic(self):
-        text_content = '{"id": "test1", "text": "Hello world", "embedding": [0.1, 0.2, 0.3]}'
+        text_content = (
+            '{"id": "test1", "text": "Hello world", "embedding": [0.1, 0.2, 0.3]}'
+        )
         documents = NDJSONParser.parse_text(text_content)
-        
+
         assert len(documents) == 1
         assert documents[0].id == "test1"
         assert documents[0].text == "Hello world"
         assert documents[0].embedding == [0.1, 0.2, 0.3]
-    
+
     def test_parse_text_with_metadata(self):
         text_content = '{"id": "test1", "text": "Hello", "embedding": [0.1, 0.2], "category": "greeting", "tags": ["test"]}'
         documents = NDJSONParser.parse_text(text_content)
-        
+
         assert documents[0].category == "greeting"
         assert documents[0].tags == ["test"]
-    
+
     def test_parse_text_missing_id(self):
         text_content = '{"text": "Hello", "embedding": [0.1, 0.2]}'
         documents = NDJSONParser.parse_text(text_content)
-        
+
         assert len(documents) == 1
         assert documents[0].id is not None  # Should be auto-generated
 
 
 class TestDataProcessor:
-    
     def test_extract_embeddings(self):
         documents = [
             Document(id="1", text="test1", embedding=[0.1, 0.2]),
-            Document(id="2", text="test2", embedding=[0.3, 0.4])
+            Document(id="2", text="test2", embedding=[0.3, 0.4]),
         ]
-        
+
         processor = DataProcessor()
         embeddings = processor._extract_embeddings(documents)
-        
+
         assert embeddings.shape == (2, 2)
         assert np.allclose(embeddings[0], [0.1, 0.2])
         assert np.allclose(embeddings[1], [0.3, 0.4])
-    
+
     def test_combine_data(self):
         from src.embeddingbuddy.models.schemas import ProcessedData
-        
+
         doc_data = ProcessedData(
             documents=[Document(id="1", text="doc", embedding=[0.1, 0.2])],
-            embeddings=np.array([[0.1, 0.2]])
+            embeddings=np.array([[0.1, 0.2]]),
         )
-        
+
         prompt_data = ProcessedData(
             documents=[Document(id="p1", text="prompt", embedding=[0.3, 0.4])],
-            embeddings=np.array([[0.3, 0.4]])
+            embeddings=np.array([[0.3, 0.4]]),
         )
-        
+
         processor = DataProcessor()
-        all_embeddings, documents, prompts = processor.combine_data(doc_data, prompt_data)
+        all_embeddings, documents, prompts = processor.combine_data(
-        
+            doc_data, prompt_data
+        )
+
         assert all_embeddings.shape == (2, 2)
         assert len(documents) == 1
         assert len(prompts) == 1
@@ -70,4 +72,4 @@ class TestDataProcessor:
 
 
 if __name__ == "__main__":
-    pytest.main([__file__])
+    pytest.main([__file__])

tests/test_data_processor_opensearch.py

@@ -0,0 +1,155 @@
+from unittest.mock import patch
+from src.embeddingbuddy.data.processor import DataProcessor
+from src.embeddingbuddy.models.field_mapper import FieldMapping
+
+
+class TestDataProcessorOpenSearch:
+    def test_process_opensearch_data_success(self):
+        processor = DataProcessor()
+
+        # Mock raw OpenSearch documents
+        raw_documents = [
+            {
+                "vector": [0.1, 0.2, 0.3],
+                "content": "Test document 1",
+                "doc_id": "doc1",
+                "type": "news",
+            },
+            {
+                "vector": [0.4, 0.5, 0.6],
+                "content": "Test document 2",
+                "doc_id": "doc2",
+                "type": "blog",
+            },
+        ]
+
+        # Create field mapping
+        field_mapping = FieldMapping(
+            embedding_field="vector",
+            text_field="content",
+            id_field="doc_id",
+            category_field="type",
+        )
+
+        # Process the data
+        processed_data = processor.process_opensearch_data(raw_documents, field_mapping)
+
+        # Assertions
+        assert processed_data.error is None
+        assert len(processed_data.documents) == 2
+        assert processed_data.embeddings.shape == (2, 3)
+
+        # Check first document
+        doc1 = processed_data.documents[0]
+        assert doc1.text == "Test document 1"
+        assert doc1.embedding == [0.1, 0.2, 0.3]
+        assert doc1.id == "doc1"
+        assert doc1.category == "news"
+
+        # Check second document
+        doc2 = processed_data.documents[1]
+        assert doc2.text == "Test document 2"
+        assert doc2.embedding == [0.4, 0.5, 0.6]
+        assert doc2.id == "doc2"
+        assert doc2.category == "blog"
+
+    def test_process_opensearch_data_with_tags(self):
+        processor = DataProcessor()
+
+        # Mock raw OpenSearch documents with tags
+        raw_documents = [
+            {
+                "vector": [0.1, 0.2, 0.3],
+                "content": "Test document with tags",
+                "keywords": ["tag1", "tag2"],
+            }
+        ]
+
+        # Create field mapping
+        field_mapping = FieldMapping(
+            embedding_field="vector", text_field="content", tags_field="keywords"
+        )
+
+        processed_data = processor.process_opensearch_data(raw_documents, field_mapping)
+
+        assert processed_data.error is None
+        assert len(processed_data.documents) == 1
+        doc = processed_data.documents[0]
+        assert doc.tags == ["tag1", "tag2"]
+
+    def test_process_opensearch_data_invalid_documents(self):
+        processor = DataProcessor()
+
+        # Mock raw documents with missing required fields
+        raw_documents = [
+            {
+                "vector": [0.1, 0.2, 0.3],
+                # Missing text field
+            }
+        ]
+
+        field_mapping = FieldMapping(embedding_field="vector", text_field="content")
+
+        processed_data = processor.process_opensearch_data(raw_documents, field_mapping)
+
+        # Should return error since no valid documents
+        assert processed_data.error is not None
+        assert "No valid documents" in processed_data.error
+        assert len(processed_data.documents) == 0
+
+    def test_process_opensearch_data_partial_success(self):
+        processor = DataProcessor()
+
+        # Mix of valid and invalid documents
+        raw_documents = [
+            {
+                "vector": [0.1, 0.2, 0.3],
+                "content": "Valid document",
+            },
+            {
+                "vector": [0.4, 0.5, 0.6],
+                # Missing content field - should be skipped
+            },
+            {
+                "vector": [0.7, 0.8, 0.9],
+                "content": "Another valid document",
+            },
+        ]
+
+        field_mapping = FieldMapping(embedding_field="vector", text_field="content")
+
+        processed_data = processor.process_opensearch_data(raw_documents, field_mapping)
+
+        # Should process valid documents only
+        assert processed_data.error is None
+        assert len(processed_data.documents) == 2
+        assert processed_data.documents[0].text == "Valid document"
+        assert processed_data.documents[1].text == "Another valid document"
+
+    @patch("src.embeddingbuddy.models.field_mapper.FieldMapper.transform_documents")
+    def test_process_opensearch_data_transformation_error(self, mock_transform):
+        processor = DataProcessor()
+
+        # Mock transformation error
+        mock_transform.side_effect = Exception("Transformation failed")
+
+        raw_documents = [{"vector": [0.1], "content": "test"}]
+        field_mapping = FieldMapping(embedding_field="vector", text_field="content")
+
+        processed_data = processor.process_opensearch_data(raw_documents, field_mapping)
+
+        assert processed_data.error is not None
+        assert "Transformation failed" in processed_data.error
+        assert len(processed_data.documents) == 0
+
+    def test_process_opensearch_data_empty_input(self):
+        processor = DataProcessor()
+
+        raw_documents = []
+        field_mapping = FieldMapping(embedding_field="vector", text_field="content")
+
+        processed_data = processor.process_opensearch_data(raw_documents, field_mapping)
+
+        assert processed_data.error is not None
+        assert "No valid documents" in processed_data.error
+        assert len(processed_data.documents) == 0

tests/test_opensearch.py

@@ -0,0 +1,310 @@
+from unittest.mock import Mock, patch
+from src.embeddingbuddy.data.sources.opensearch import OpenSearchClient
+from src.embeddingbuddy.models.field_mapper import FieldMapper, FieldMapping
+
+
+class TestOpenSearchClient:
+    def test_init(self):
+        client = OpenSearchClient()
+        assert client.client is None
+        assert client.connection_info is None
+
+    @patch("src.embeddingbuddy.data.sources.opensearch.OpenSearch")
+    def test_connect_success(self, mock_opensearch):
+        # Mock the OpenSearch client
+        mock_client_instance = Mock()
+        mock_client_instance.info.return_value = {
+            "cluster_name": "test-cluster",
+            "version": {"number": "2.0.0"},
+        }
+        mock_opensearch.return_value = mock_client_instance
+
+        client = OpenSearchClient()
+        success, message = client.connect("https://localhost:9200")
+
+        assert success is True
+        assert "test-cluster" in message
+        assert client.client is not None
+        assert client.connection_info["cluster_name"] == "test-cluster"
+
+    @patch("src.embeddingbuddy.data.sources.opensearch.OpenSearch")
+    def test_connect_failure(self, mock_opensearch):
+        # Mock connection failure
+        mock_opensearch.side_effect = Exception("Connection failed")
+
+        client = OpenSearchClient()
+        success, message = client.connect("https://localhost:9200")
+
+        assert success is False
+        assert "Connection failed" in message
+        assert client.client is None
+
+    def test_analyze_fields(self):
+        client = OpenSearchClient()
+        client.client = Mock()
+
+        # Mock mapping response
+        mock_mapping = {
+            "test-index": {
+                "mappings": {
+                    "properties": {
+                        "embedding": {"type": "dense_vector", "dimension": 768},
+                        "text": {"type": "text"},
+                        "category": {"type": "keyword"},
+                        "id": {"type": "keyword"},
+                        "count": {"type": "integer"},
+                    }
+                }
+            }
+        }
+        client.client.indices.get_mapping.return_value = mock_mapping
+
+        success, analysis, message = client.analyze_fields("test-index")
+
+        assert success is True
+        assert len(analysis["vector_fields"]) == 1
+        assert analysis["vector_fields"][0]["name"] == "embedding"
+        assert analysis["vector_fields"][0]["dimension"] == 768
+        assert "text" in analysis["text_fields"]
+        assert "category" in analysis["keyword_fields"]
+        assert "count" in analysis["numeric_fields"]
+
+    def test_fetch_sample_data(self):
+        client = OpenSearchClient()
+        client.client = Mock()
+
+        # Mock search response
+        mock_response = {
+            "hits": {
+                "hits": [
+                    {"_source": {"text": "doc1", "embedding": [0.1, 0.2]}},
+                    {"_source": {"text": "doc2", "embedding": [0.3, 0.4]}},
+                ]
+            }
+        }
+        client.client.search.return_value = mock_response
+
+        success, documents, message = client.fetch_sample_data("test-index", size=2)
+
+        assert success is True
+        assert len(documents) == 2
+        assert documents[0]["text"] == "doc1"
+        assert documents[1]["text"] == "doc2"
+
+
+class TestFieldMapper:
+    def test_suggest_mappings(self):
+        field_analysis = {
+            "vector_fields": [{"name": "embedding", "dimension": 768}],
+            "text_fields": ["content", "description"],
+            "keyword_fields": ["doc_id", "category", "type", "tags"],
+            "numeric_fields": ["count"],
+            "all_fields": [
+                "embedding",
+                "content",
+                "description",
+                "doc_id",
+                "category",
+                "type",
+                "tags",
+                "count",
+            ],
+        }
+
+        suggestions = FieldMapper.suggest_mappings(field_analysis)
+
+        # Check that all dropdowns contain all fields
+        all_fields = [
+            "embedding",
+            "content",
+            "description",
+            "doc_id",
+            "category",
+            "type",
+            "tags",
+            "count",
+        ]
+        for field_type in [
+            "embedding",
+            "text",
+            "id",
+            "category",
+            "subcategory",
+            "tags",
+        ]:
+            for field in all_fields:
+                assert field in suggestions[field_type], (
+                    f"Field '{field}' missing from {field_type} suggestions"
+                )
+
+        # Check that best candidates are first
+        assert (
+            suggestions["embedding"][0] == "embedding"
+        )  # vector field should be first
+        assert suggestions["text"][0] in [
+            "content",
+            "description",
+        ]  # text fields should be first
+        assert suggestions["id"][0] == "doc_id"  # ID-like field should be first
+        assert suggestions["category"][0] in [
+            "category",
+            "type",
+        ]  # category-like field should be first
+        assert suggestions["tags"][0] == "tags"  # tags field should be first
+
+    def test_suggest_mappings_name_based_embedding(self):
+        """Test that fields named 'embedding' are prioritized even without vector type."""
+        field_analysis = {
+            "vector_fields": [],  # No explicit vector fields detected
+            "text_fields": ["content", "description"],
+            "keyword_fields": ["doc_id", "category", "type", "tags"],
+            "numeric_fields": ["count"],
+            "all_fields": [
+                "content",
+                "description",
+                "doc_id",
+                "category",
+                "embedding",
+                "type",
+                "tags",
+                "count",
+            ],
+        }
+
+        suggestions = FieldMapper.suggest_mappings(field_analysis)
+
+        # Check that 'embedding' field is prioritized despite not being detected as vector type
+        assert suggestions["embedding"][0] == "embedding", (
+            "Field named 'embedding' should be first priority"
+        )
+
+        # Check that all fields are still available
+        all_fields = [
+            "content",
+            "description",
+            "doc_id",
+            "category",
+            "embedding",
+            "type",
+            "tags",
+            "count",
+        ]
+        for field_type in [
+            "embedding",
+            "text",
+            "id",
+            "category",
+            "subcategory",
+            "tags",
+        ]:
+            for field in all_fields:
+                assert field in suggestions[field_type], (
+                    f"Field '{field}' missing from {field_type} suggestions"
+                )
+
+    def test_validate_mapping_success(self):
+        mapping = FieldMapping(
+            embedding_field="embedding", text_field="text", id_field="doc_id"
+        )
+        available_fields = ["embedding", "text", "doc_id", "category"]
+
+        errors = FieldMapper.validate_mapping(mapping, available_fields)
+
+        assert len(errors) == 0
+
+    def test_validate_mapping_missing_required(self):
+        mapping = FieldMapping(embedding_field="missing_field", text_field="text")
+        available_fields = ["text", "category"]
+
+        errors = FieldMapper.validate_mapping(mapping, available_fields)
+
+        assert len(errors) == 1
+        assert "missing_field" in errors[0]
+        assert "not found" in errors[0]
+
+    def test_validate_mapping_missing_optional(self):
+        mapping = FieldMapping(
+            embedding_field="embedding",
+            text_field="text",
+            category_field="missing_category",
+        )
+        available_fields = ["embedding", "text"]
+
+        errors = FieldMapper.validate_mapping(mapping, available_fields)
+
+        assert len(errors) == 1
+        assert "missing_category" in errors[0]
+
+    def test_transform_documents(self):
+        mapping = FieldMapping(
+            embedding_field="vector",
+            text_field="content",
+            id_field="doc_id",
+            category_field="type",
+        )
+
+        raw_documents = [
+            {
+                "vector": [0.1, 0.2, 0.3],
+                "content": "Test document 1",
+                "doc_id": "doc1",
+                "type": "news",
+            },
+            {
+                "vector": [0.4, 0.5, 0.6],
+                "content": "Test document 2",
+                "doc_id": "doc2",
+                "type": "blog",
+            },
+        ]
+
+        transformed = FieldMapper.transform_documents(raw_documents, mapping)
+
+        assert len(transformed) == 2
+        assert transformed[0]["embedding"] == [0.1, 0.2, 0.3]
+        assert transformed[0]["text"] == "Test document 1"
+        assert transformed[0]["id"] == "doc1"
+        assert transformed[0]["category"] == "news"
+
+    def test_transform_documents_missing_required(self):
+        mapping = FieldMapping(embedding_field="vector", text_field="content")
+
+        raw_documents = [
+            {
+                "vector": [0.1, 0.2, 0.3],
+                # Missing content field
+            }
+        ]
+
+        transformed = FieldMapper.transform_documents(raw_documents, mapping)
+
+        assert len(transformed) == 0  # Document should be skipped
+
+    def test_create_mapping_from_dict(self):
+        mapping_dict = {
+            "embedding": "vector_field",
+            "text": "text_field",
+            "id": "doc_id",
+            "category": "cat_field",
+            "subcategory": "subcat_field",
+            "tags": "tags_field",
+        }
+
+        mapping = FieldMapper.create_mapping_from_dict(mapping_dict)
+
+        assert mapping.embedding_field == "vector_field"
+        assert mapping.text_field == "text_field"
+        assert mapping.id_field == "doc_id"
+        assert mapping.category_field == "cat_field"
+        assert mapping.subcategory_field == "subcat_field"
+        assert mapping.tags_field == "tags_field"
+
+    def test_create_mapping_from_dict_minimal(self):
+        mapping_dict = {"embedding": "vector_field", "text": "text_field"}
+
+        mapping = FieldMapper.create_mapping_from_dict(mapping_dict)
+
+        assert mapping.embedding_field == "vector_field"
+        assert mapping.text_field == "text_field"
+        assert mapping.id_field is None
+        assert mapping.category_field is None

tests/test_reducers.py

@@ -1,89 +1,90 @@
 import pytest
 import numpy as np
-from src.embeddingbuddy.models.reducers import ReducerFactory, PCAReducer, TSNEReducer, UMAPReducer
+from src.embeddingbuddy.models.reducers import (
+    ReducerFactory,
+    PCAReducer,
+    TSNEReducer,
+    UMAPReducer,
+)
 
 
 class TestReducerFactory:
-    
     def test_create_pca_reducer(self):
-        reducer = ReducerFactory.create_reducer('pca', n_components=2)
+        reducer = ReducerFactory.create_reducer("pca", n_components=2)
         assert isinstance(reducer, PCAReducer)
         assert reducer.n_components == 2
-    
+
     def test_create_tsne_reducer(self):
-        reducer = ReducerFactory.create_reducer('tsne', n_components=3)
+        reducer = ReducerFactory.create_reducer("tsne", n_components=3)
         assert isinstance(reducer, TSNEReducer)
         assert reducer.n_components == 3
-    
+
     def test_create_umap_reducer(self):
-        reducer = ReducerFactory.create_reducer('umap', n_components=2)
+        reducer = ReducerFactory.create_reducer("umap", n_components=2)
         assert isinstance(reducer, UMAPReducer)
         assert reducer.n_components == 2
-    
+
     def test_invalid_method(self):
         with pytest.raises(ValueError, match="Unknown reduction method"):
-            ReducerFactory.create_reducer('invalid_method')
+            ReducerFactory.create_reducer("invalid_method")
-    
+
     def test_available_methods(self):
         methods = ReducerFactory.get_available_methods()
-        assert 'pca' in methods
+        assert "pca" in methods
-        assert 'tsne' in methods
+        assert "tsne" in methods
-        assert 'umap' in methods
+        assert "umap" in methods
 
 
 class TestPCAReducer:
-    
     def test_fit_transform(self):
         embeddings = np.random.rand(100, 512)
         reducer = PCAReducer(n_components=2)
-        
+
         result = reducer.fit_transform(embeddings)
-        
+
         assert result.reduced_embeddings.shape == (100, 2)
         assert result.variance_explained is not None
         assert result.method == "PCA"
         assert result.n_components == 2
-    
+
     def test_method_name(self):
         reducer = PCAReducer()
         assert reducer.get_method_name() == "PCA"
 
 
 class TestTSNEReducer:
-    
     def test_fit_transform_small_dataset(self):
         embeddings = np.random.rand(30, 10)  # Small dataset for faster testing
         reducer = TSNEReducer(n_components=2)
-        
+
         result = reducer.fit_transform(embeddings)
-        
+
         assert result.reduced_embeddings.shape == (30, 2)
         assert result.variance_explained is None  # t-SNE doesn't provide this
         assert result.method == "t-SNE"
         assert result.n_components == 2
-    
+
     def test_method_name(self):
         reducer = TSNEReducer()
         assert reducer.get_method_name() == "t-SNE"
 
 
 class TestUMAPReducer:
-    
     def test_fit_transform(self):
         embeddings = np.random.rand(50, 10)
         reducer = UMAPReducer(n_components=2)
-        
+
         result = reducer.fit_transform(embeddings)
-        
+
         assert result.reduced_embeddings.shape == (50, 2)
         assert result.variance_explained is None  # UMAP doesn't provide this
         assert result.method == "UMAP"
         assert result.n_components == 2
-    
+
     def test_method_name(self):
         reducer = UMAPReducer()
         assert reducer.get_method_name() == "UMAP"
 
 
 if __name__ == "__main__":
-    pytest.main([__file__])
+    pytest.main([__file__])