add prompts

CLAUDE.md

@@ -0,0 +1,72 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with
+code in this repository.
+
+## Project Overview
+
+EmbeddingBuddy is a Python Dash web application for interactive exploration and
+visualization of embedding vectors through dimensionality reduction techniques
+(PCA, t-SNE, UMAP). The app provides a drag-and-drop interface for uploading
+NDJSON files containing embeddings and visualizes them in 2D/3D plots.
+
+## Development Commands
+
+**Install dependencies:**
+
+```bash
+uv sync
+```
+
+**Run the application:**
+
+```bash
+uv run python app.py
+```
+
+The app will be available at http://127.0.0.1:8050
+
+**Test with sample data:**
+Use the included `sample_data.ndjson` file for testing the application functionality.
+
+## Architecture
+
+### Core Files
+
+- `app.py` - Main Dash application with complete web interface, data processing,
+   and visualization logic
+- `main.py` - Simple entry point (currently minimal)
+- `pyproject.toml` - Project configuration and dependencies using uv package manager
+
+### Key Components
+
+- **Data Processing**: NDJSON parser that handles embedding documents with
+  required fields (`embedding`, `text`) and optional metadata (`id`, `category`, `subcategory`, `tags`)
+- **Dimensionality Reduction**: Supports PCA, t-SNE (openTSNE), and UMAP algorithms
+- **Visualization**: Plotly-based 2D/3D scatter plots with interactive features
+- **UI Layout**: Bootstrap-styled sidebar with controls and large visualization area
+- **State Management**: Dash callbacks for reactive updates between upload,
+  method selection, and plot rendering
+
+### Data Format
+
+The application expects NDJSON files where each line contains:
+
+```json
+{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, ...], "text": "Sample text", "category": "news", "subcategory": "politics", "tags": ["election"]}
+```
+
+### Callback Architecture
+
+- File upload → Data processing and storage in dcc.Store
+- Method/parameter changes → Dimensionality reduction and plot update
+- Point clicks → Detail display in sidebar
+
+## Dependencies
+
+Uses modern Python stack with uv for dependency management:
+
+- Dash + Plotly for web interface and visualization
+- scikit-learn (PCA), openTSNE, umap-learn for dimensionality reduction
+- pandas/numpy for data manipulation
+- dash-bootstrap-components for styling

app.py

@@ -101,6 +101,117 @@ def create_plot(df, dimensions='3d', color_by='category', method='PCA'):
     )
     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([
@@ -132,6 +243,36 @@ app.layout = dbc.Container([
                 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:"),
@@ -169,6 +310,14 @@ app.layout = dbc.Container([
                 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")
             
@@ -184,7 +333,8 @@ app.layout = dbc.Container([
         ], width=9)
     ]),
     
-    dcc.Store(id='processed-data')
+    dcc.Store(id='processed-data'),
+    dcc.Store(id='processed-prompts')
 ], fluid=True)
 
 @callback(
@@ -208,14 +358,37 @@ def process_uploaded_file(contents, filename):
     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('dimension-toggle', 'value'),
+     Input('show-prompts-toggle', 'value')]
 )
-def update_plot(data, method, color_by, dimensions):
+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",
@@ -224,16 +397,28 @@ def update_plot(data, method, color_by, dimensions):
             showarrow=False, font=dict(size=16)
         )
     
-    # Get embeddings and apply selected method
+    # Prepare embeddings for dimensionality reduction
-    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')
+    
+    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(
-        embeddings, method=method, n_components=n_components
+        all_embeddings, method=method, n_components=n_components
     )
     
-    # Create dataframe with reduced dimensions
+    # Split reduced embeddings back
-    df_data = []
+    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'],
@@ -241,28 +426,52 @@ def update_plot(data, method, color_by, dimensions):
             'category': doc.get('category', 'Unknown'),
             'subcategory': doc.get('subcategory', 'Unknown'),
             'tags': doc.get('tags', []),
-            'dim_1': reduced[i, 0],
+            'dim_1': doc_reduced[i, 0],
-            'dim_2': reduced[i, 1]
+            'dim_2': doc_reduced[i, 1],
+            'type': 'document'
         }
         if dimensions == '3d':
-            row['dim_3'] = reduced[i, 2]
+            row['dim_3'] = doc_reduced[i, 2]
-        df_data.append(row)
+        doc_df_data.append(row)
     
-    df = pd.DataFrame(df_data)
+    doc_df = pd.DataFrame(doc_df_data)
     
-    return create_plot(df, dimensions, color_by, method.upper())
+    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-data', 'data'),
+     State('processed-prompts', 'data')]
 )
-def display_click_data(clickData, 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 index - try different possible keys
+    # 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:
@@ -270,17 +479,37 @@ def display_click_data(clickData, data):
     else:
         return "Could not identify clicked point"
     
-    doc = data['documents'][point_index]
+    # 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"Document: {doc['id']}", className="card-title"),
+            html.H5(f"{item_type}: {item['id']}", className="card-title"),
-            html.P(f"Text: {doc['text']}", className="card-text"),
+            html.P(f"Text: {item['text']}", className="card-text"),
-            html.P(f"Category: {doc.get('category', 'Unknown')}", className="card-text"),
+            html.P(f"Category: {item.get('category', 'Unknown')}", className="card-text"),
-            html.P(f"Subcategory: {doc.get('subcategory', 'Unknown')}", className="card-text"),
+            html.P(f"Subcategory: {item.get('subcategory', 'Unknown')}", className="card-text"),
-            html.P(f"Tags: {', '.join(doc.get('tags', [])) if doc.get('tags') else 'None'}", 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)

sample_prompts.ndjson

@@ -0,0 +1,10 @@
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}
+{"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"]}