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refactor and add tests, v0.2.0

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Austin Godber
2025-08-13 20:07:40 -07:00
parent 76be59254c
commit 809dbeb783
32 changed files with 1401 additions and 32 deletions
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11
.claude/settings.local.json Normal file
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@@ -0,0 +1,11 @@
{
"permissions": {
"allow": [
"Bash(mkdir:*)",
"Bash(uv run:*)",
"Bash(uv add:*)"
],
"deny": [],
"ask": []
}
}

154
CLAUDE.md
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@@ -5,10 +5,11 @@ code in this repository.
## Project Overview
EmbeddingBuddy is a Python Dash web application for interactive exploration and
EmbeddingBuddy is a modular 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.
NDJSON files containing embeddings and visualizes them in 2D/3D plots. The codebase
follows a clean, modular architecture that prioritizes testability and maintainability.
## Development Commands
@@ -21,32 +22,79 @@ uv sync
**Run the application:**
```bash
uv run python app.py
uv run python main.py
```
The app will be available at http://127.0.0.1:8050
**Run tests:**
```bash
uv run pytest tests/ -v
```
**Test with sample data:**
Use the included `sample_data.ndjson` file for testing the application functionality.
Use the included `sample_data.ndjson` and `sample_prompts.ndjson` files for testing the application functionality.
## Architecture
### Core Files
### Project Structure
- `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
The application follows a modular architecture with clear separation of concerns:
```
src/embeddingbuddy/
├── app.py # Main application entry point and factory
├── main.py # Application runner
├── config/
│ └── settings.py # Centralized configuration management
├── data/
│ ├── parser.py # NDJSON parsing logic
│ └── processor.py # Data transformation and processing
├── models/
│ ├── schemas.py # Data models and validation schemas
│ └── reducers.py # Dimensionality reduction algorithms
├── visualization/
│ ├── plots.py # Plot creation and factory classes
│ └── colors.py # Color mapping and management
├── ui/
│ ├── layout.py # Main application layout
│ ├── components/ # Reusable UI components
│ │ ├── sidebar.py # Sidebar component
│ │ └── upload.py # Upload components
│ └── callbacks/ # Organized callback functions
│ ├── data_processing.py # Data upload/processing callbacks
│ ├── visualization.py # Plot update callbacks
│ └── interactions.py # User interaction callbacks
└── utils/ # Utility functions and helpers
```
### 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 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
### Data Format
@@ -56,17 +104,77 @@ The application expects NDJSON files where each line contains:
{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, ...], "text": "Sample text", "category": "news", "subcategory": "politics", "tags": ["election"]}
```
Required fields: `embedding` (array), `text` (string)
Optional fields: `id`, `category`, `subcategory`, `tags`
### 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
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
### Testing Architecture
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
- High code coverage achievable
## 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
- **Core Framework:** Dash + Plotly for web interface and visualization
- **Algorithms:** scikit-learn (PCA), openTSNE, umap-learn for dimensionality reduction
- **Data:** pandas/numpy for data manipulation
- **UI:** dash-bootstrap-components for styling
- **Testing:** pytest for test framework
- **Dev Tools:** uv for package management
## Development Guidelines
**When adding new features:**
1. **Data Models** - Add/update schemas in `models/schemas.py`
2. **Algorithms** - Extend `models/reducers.py` using the abstract base class
3. **UI Components** - Create reusable components in `ui/components/`
4. **Configuration** - Add settings to `config/settings.py`
5. **Tests** - Write tests for all new functionality
**Code Organization Principles:**
- Single responsibility principle
- Clear module boundaries
- Testable, isolated components
- Configuration over hardcoding
- 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

65
README.md
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# EmbeddingBuddy
A web application for interactive exploration and visualization of embedding
A modular Python Dash web application for interactive exploration and visualization of embedding
vectors through dimensionality reduction techniques. Compare documents and prompts
in the same embedding space to understand semantic relationships.
@@ -10,9 +10,10 @@ in the same embedding space to understand semantic relationships.
EmbeddingBuddy provides an intuitive web interface for analyzing high-dimensional
embedding vectors by applying various dimensionality reduction algorithms and
visualizing the results in interactive 2D and 3D plots. The application supports
dual dataset visualization, allowing you to compare documents and prompts to
understand how queries relate to your content.
visualizing the results in interactive 2D and 3D plots. The application features
a clean, modular architecture that makes it easy to test, maintain, and extend
with new features. It supports dual dataset visualization, allowing you to compare
documents and prompts to understand how queries relate to your content.
## Features
@@ -73,7 +74,7 @@ uv sync
2. **Run the application:**
```bash
uv run python app.py
uv run python main.py
```
3. **Open your browser** to http://127.0.0.1:8050
@@ -83,6 +84,59 @@ uv run python app.py
- Upload `sample_prompts.ndjson` (prompts) to see dual visualization
- Use the "Show prompts" toggle to compare how prompts relate to documents
## Development
### Project Structure
The application follows a modular architecture for improved maintainability and testability:
```
src/embeddingbuddy/
├── config/ # Configuration management
│ └── settings.py # Centralized app settings
├── data/ # Data parsing and processing
│ ├── parser.py # NDJSON parsing logic
│ └── processor.py # Data transformation utilities
├── models/ # Data schemas and algorithms
│ ├── schemas.py # Pydantic data models
│ └── reducers.py # Dimensionality reduction algorithms
├── visualization/ # Plot creation and styling
│ ├── plots.py # Plot factory and creation logic
│ └── colors.py # Color mapping utilities
├── ui/ # User interface components
│ ├── layout.py # Main application layout
│ ├── components/ # Reusable UI components
│ └── callbacks/ # Organized callback functions
└── utils/ # Utility functions
```
### Testing
Run the test suite to verify functionality:
```bash
# Install pytest
uv add pytest
# Run all tests
uv run pytest tests/ -v
# Run specific test file
uv run pytest tests/test_data_processing.py -v
# Run with coverage
uv run pytest tests/ --cov=src/embeddingbuddy
```
### Adding New Features
The modular architecture makes it easy to extend functionality:
- **New reduction algorithms**: Add to `models/reducers.py`
- **New plot types**: Extend `visualization/plots.py`
- **UI components**: Add to `ui/components/`
- **Configuration options**: Update `config/settings.py`
## Tech Stack
- **Python Dash**: Web application framework
@@ -91,4 +145,5 @@ uv run python app.py
- **UMAP-learn**: UMAP dimensionality reduction
- **openTSNE**: Fast t-SNE implementation
- **NumPy/Pandas**: Data manipulation and analysis
- **pytest**: Testing framework
- **uv**: Modern Python package and project manager

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main.py
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from src.embeddingbuddy.app import create_app, run_app
def main():
print("Hello from embeddingbuddy!")
app = create_app()
run_app(app)
if __name__ == "__main__":

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pyproject.toml
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[project]
name = "embeddingbuddy"
version = "0.1.0"
version = "0.2.0"
description = "A Python Dash application for interactive exploration and visualization of embedding vectors through dimensionality reduction techniques."
readme = "README.md"
requires-python = ">=3.11"
@@ -13,5 +13,16 @@ dependencies = [
"dash-bootstrap-components>=1.5.0",
"umap-learn>=0.5.8",
"numba>=0.56.4",
"openTSNE>=1.0.0"
"openTSNE>=1.0.0",
"pytest>=8.4.1",
]
[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.setuptools.packages.find]
where = ["src"]
[tool.setuptools.package-dir]
"" = "src"

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src/embeddingbuddy/__init__.py Normal file
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"""EmbeddingBuddy - Interactive exploration and visualization of embedding vectors."""
__version__ = "0.1.0"

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src/embeddingbuddy/app.py Normal file
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import dash
import dash_bootstrap_components as dbc
from .config.settings import AppSettings
from .ui.layout import AppLayout
from .ui.callbacks.data_processing import DataProcessingCallbacks
from .ui.callbacks.visualization import VisualizationCallbacks
from .ui.callbacks.interactions import InteractionCallbacks
def create_app():
app = dash.Dash(
__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP]
)
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
)
if __name__ == '__main__':
app = create_app()
run_app(app)

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src/embeddingbuddy/config/__init__.py Normal file
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src/embeddingbuddy/config/settings.py Normal file
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from typing import Dict, Any
import os
class AppSettings:
# UI Configuration
UPLOAD_STYLE = {
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin-bottom': '20px'
}
PROMPTS_UPLOAD_STYLE = {
**UPLOAD_STYLE,
'borderColor': '#28a745'
}
PLOT_CONFIG = {
'responsive': True,
'displayModeBar': True
}
PLOT_STYLE = {
'height': '85vh',
'width': '100%'
}
PLOT_LAYOUT_CONFIG = {
'height': None,
'autosize': True,
'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': 't-SNE', 'value': 'tsne'},
{'label': 'UMAP', 'value': 'umap'}
]
COLOR_OPTIONS = [
{'label': 'Category', 'value': 'category'},
{'label': 'Subcategory', 'value': 'subcategory'},
{'label': 'Tags', 'value': 'tags'}
]
DIMENSION_OPTIONS = [
{'label': '2D', 'value': '2d'},
{'label': '3D', 'value': '3d'}
]
# Default Values
DEFAULT_METHOD = 'pca'
DEFAULT_COLOR_BY = 'category'
DEFAULT_DIMENSIONS = '3d'
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'
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'
HOST = os.getenv('EMBEDDINGBUDDY_HOST', '127.0.0.1')
PORT = int(os.getenv('EMBEDDINGBUDDY_PORT', '8050'))
# Bootstrap Theme
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]:
if is_prompt:
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
symbol = cls.DOCUMENT_MARKER_SYMBOL
opacity = cls.DOCUMENT_OPACITY
return {
'size': size,
'symbol': symbol,
'opacity': opacity
}

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src/embeddingbuddy/data/__init__.py Normal file
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src/embeddingbuddy/data/parser.py Normal file
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import json
import uuid
import base64
from typing import List, Union
from ..models.schemas import Document, ProcessedData
class NDJSONParser:
@staticmethod
def parse_upload_contents(contents: str) -> List[Document]:
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
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'):
if line.strip():
doc_dict = json.loads(line)
doc = NDJSONParser._dict_to_document(doc_dict)
documents.append(doc)
return documents
@staticmethod
def _dict_to_document(doc_dict: dict) -> Document:
if 'id' not in doc_dict:
doc_dict['id'] = str(uuid.uuid4())
return Document(
id=doc_dict['id'],
text=doc_dict['text'],
embedding=doc_dict['embedding'],
category=doc_dict.get('category'),
subcategory=doc_dict.get('subcategory'),
tags=doc_dict.get('tags')
)

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src/embeddingbuddy/data/processor.py Normal file
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import numpy as np
from typing import List, Optional, Tuple
from ..models.schemas import Document, ProcessedData
from .parser import NDJSONParser
class DataProcessor:
def __init__(self):
self.parser = NDJSONParser()
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)
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]]]:
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]]:
doc_reduced = reduced_embeddings[:n_documents]
prompt_reduced = None
if n_prompts > 0:
prompt_reduced = reduced_embeddings[n_documents:n_documents + n_prompts]
return doc_reduced, prompt_reduced

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src/embeddingbuddy/models/__init__.py Normal file
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src/embeddingbuddy/models/reducers.py Normal file
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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
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
)
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)
reduced = self._reducer.fit(embeddings)
return ReducedData(
reduced_embeddings=reduced,
variance_explained=None,
method=self.get_method_name(),
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)
reduced = self._reducer.fit_transform(embeddings)
return ReducedData(
reduced_embeddings=reduced,
variance_explained=None,
method=self.get_method_name(),
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:
method_lower = method.lower()
if method_lower == 'pca':
return PCAReducer(n_components=n_components, random_state=random_state)
elif method_lower == 'tsne':
return TSNEReducer(n_components=n_components, random_state=random_state)
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']

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src/embeddingbuddy/models/schemas.py Normal file
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from typing import List, Optional, Any, Dict
from dataclasses import dataclass
import numpy as np
@dataclass
class Document:
id: str
text: str
embedding: List[float]
category: Optional[str] = None
subcategory: Optional[str] = None
tags: Optional[List[str]] = None
def __post_init__(self):
if self.tags is None:
self.tags = []
if self.category is None:
self.category = "Unknown"
if self.subcategory is None:
self.subcategory = "Unknown"
@dataclass
class ProcessedData:
documents: List[Document]
embeddings: np.ndarray
error: Optional[str] = None
def __post_init__(self):
if self.embeddings is not None and not isinstance(self.embeddings, np.ndarray):
self.embeddings = np.array(self.embeddings)
@dataclass
class ReducedData:
reduced_embeddings: np.ndarray
variance_explained: Optional[np.ndarray] = None
method: str = "unknown"
n_components: int = 2
def __post_init__(self):
if not isinstance(self.reduced_embeddings, np.ndarray):
self.reduced_embeddings = np.array(self.reduced_embeddings)
@dataclass
class PlotData:
documents: List[Document]
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):
self.prompt_coordinates = np.array(self.prompt_coordinates)

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src/embeddingbuddy/ui/__init__.py Normal file
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src/embeddingbuddy/ui/callbacks/__init__.py Normal file
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src/embeddingbuddy/ui/callbacks/data_processing.py Normal file
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import numpy as np
from dash import callback, Input, Output, State
from ...data.processor import DataProcessor
class DataProcessingCallbacks:
def __init__(self):
self.processor = DataProcessor()
self._register_callbacks()
def _register_callbacks(self):
@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
processed_data = self.processor.process_upload(contents, filename)
if processed_data.error:
return {'error': processed_data.error}
return {
'documents': [self._document_to_dict(doc) for doc in processed_data.documents],
'embeddings': processed_data.embeddings.tolist()
}
@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
processed_data = self.processor.process_upload(contents, filename)
if processed_data.error:
return {'error': processed_data.error}
return {
'prompts': [self._document_to_dict(doc) for doc in processed_data.documents],
'embeddings': processed_data.embeddings.tolist()
}
@staticmethod
def _document_to_dict(doc):
return {
'id': doc.id,
'text': doc.text,
'embedding': doc.embedding,
'category': doc.category,
'subcategory': doc.subcategory,
'tags': doc.tags
}

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

87
src/embeddingbuddy/ui/callbacks/visualization.py Normal file
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@@ -0,0 +1,87 @@
import numpy as np
from dash import callback, Input, Output
import plotly.graph_objects as go
from ...models.reducers import ReducerFactory
from ...models.schemas import Document, PlotData
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'),
[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)
)
try:
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
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)]
prompt_reduced = None
if has_prompts:
prompt_reduced = reduced_data.reduced_embeddings[len(doc_embeddings):]
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']]
plot_data = PlotData(
documents=documents,
coordinates=doc_reduced,
prompts=prompts,
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",
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'],
text=doc_dict['text'],
embedding=doc_dict['embedding'],
category=doc_dict.get('category'),
subcategory=doc_dict.get('subcategory'),
tags=doc_dict.get('tags', [])
)

0
src/embeddingbuddy/ui/components/__init__.py Normal file
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82
src/embeddingbuddy/ui/components/sidebar.py Normal file
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@@ -0,0 +1,82 @@
from dash import dcc, html
import dash_bootstrap_components as dbc
from .upload import UploadComponent
class SidebarComponent:
def __init__(self):
self.upload_component = UploadComponent()
def create_layout(self):
return dbc.Col([
html.H5("Upload Data", className="mb-3"),
self.upload_component.create_data_upload(),
self.upload_component.create_prompts_upload(),
self.upload_component.create_reset_button(),
html.H5("Visualization Controls", className="mb-3"),
self._create_method_dropdown(),
self._create_color_dropdown(),
self._create_dimension_toggle(),
self._create_prompts_toggle(),
html.H5("Point Details", className="mb-3"),
html.Div(id='point-details', children="Click on a point to see details")
], width=3, style={'padding-right': '20px'})
def _create_method_dropdown(self):
return [
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'}
)
]
def _create_color_dropdown(self):
return [
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'}
)
]
def _create_dimension_toggle(self):
return [
dbc.Label("Dimensions:"),
dcc.RadioItems(
id='dimension-toggle',
options=[
{'label': '2D', 'value': '2d'},
{'label': '3D', 'value': '3d'}
],
value='3d',
style={'margin-bottom': '20px'}
)
]
def _create_prompts_toggle(self):
return [
dbc.Label("Show Prompts:"),
dcc.Checklist(
id='show-prompts-toggle',
options=[{'label': 'Show prompts on plot', 'value': 'show'}],
value=['show'],
style={'margin-bottom': '20px'}
)
]

60
src/embeddingbuddy/ui/components/upload.py Normal file
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@@ -0,0 +1,60 @@
from dash import dcc, html
import dash_bootstrap_components as dbc
class UploadComponent:
@staticmethod
def create_data_upload():
return dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin-bottom': '20px'
},
multiple=False
)
@staticmethod
def create_prompts_upload():
return dcc.Upload(
id='upload-prompts',
children=html.Div([
'Drag and Drop Prompts or ',
html.A('Select Files')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin-bottom': '20px',
'borderColor': '#28a745'
},
multiple=False
)
@staticmethod
def create_reset_button():
return dbc.Button(
"Reset All Data",
id='reset-button',
color='danger',
outline=True,
size='sm',
className='mb-3',
style={'width': '100%'}
)

44
src/embeddingbuddy/ui/layout.py Normal file
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@@ -0,0 +1,44 @@
from dash import dcc, html
import dash_bootstrap_components as dbc
from .components.sidebar import SidebarComponent
class AppLayout:
def __init__(self):
self.sidebar = SidebarComponent()
def create_layout(self):
return dbc.Container([
self._create_header(),
self._create_main_content(),
self._create_stores()
], fluid=True)
def _create_header(self):
return dbc.Row([
dbc.Col([
html.H1("EmbeddingBuddy", className="text-center mb-4"),
], width=12)
])
def _create_main_content(self):
return dbc.Row([
self.sidebar.create_layout(),
self._create_visualization_area()
])
def _create_visualization_area(self):
return dbc.Col([
dcc.Graph(
id='embedding-plot',
style={'height': '85vh', 'width': '100%'},
config={'responsive': True, 'displayModeBar': True}
)
], width=9)
def _create_stores(self):
return [
dcc.Store(id='processed-data'),
dcc.Store(id='processed-prompts')
]

0
src/embeddingbuddy/utils/__init__.py Normal file
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0
src/embeddingbuddy/visualization/__init__.py Normal file
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33
src/embeddingbuddy/visualization/colors.py Normal file
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@@ -0,0 +1,33 @@
from typing import List, Dict, Any
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':
return [doc.category for doc in documents]
elif color_by == 'subcategory':
return [doc.subcategory for doc in documents]
elif color_by == 'tags':
return [', '.join(doc.tags) if doc.tags else 'No tags' for doc in documents]
else:
return ['All'] * len(documents)
@staticmethod
def to_grayscale_hex(color_str: str) -> str:
try:
if color_str.startswith('#'):
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])
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_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)'

145
src/embeddingbuddy/visualization/plots.py Normal file
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@@ -0,0 +1,145 @@
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from typing import List, Optional
from ..models.schemas import Document, PlotData
from .colors import ColorMapper
class PlotFactory:
def __init__(self):
self.color_mapper = ColorMapper()
def create_plot(self, plot_data: PlotData, dimensions: str = '3d',
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,
color_by: str, method: str) -> go.Figure:
df = self._prepare_dataframe(plot_data.documents, plot_data.coordinates, dimensions)
color_values = self.color_mapper.create_color_mapping(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',
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, 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,
autosize=True,
margin=dict(l=0, r=0, t=50, b=0)
)
return fig
def _create_dual_plot(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_color_values = self.color_mapper.create_color_mapping(plot_data.documents, color_by)
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',
color=doc_color_values,
hover_data=hover_fields
)
else:
doc_fig = px.scatter(
doc_df, x='dim_1', y='dim_2',
color=doc_color_values,
hover_data=hover_fields
)
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)
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_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',
color=prompt_color_values,
hover_data=hover_fields
)
else:
prompt_fig = px.scatter(
prompt_df, x='dim_1', y='dim_2',
color=prompt_color_values,
hover_data=hover_fields
)
for trace in prompt_fig.data:
if hasattr(trace.marker, 'color') and isinstance(trace.marker.color, str):
trace.marker.color = self.color_mapper.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
def _prepare_dataframe(self, documents: List[Document], coordinates, dimensions: str) -> pd.DataFrame:
df_data = []
for i, doc in enumerate(documents):
row = {
'id': doc.id,
'text': doc.text,
'text_preview': doc.text[:100] + "..." if len(doc.text) > 100 else doc.text,
'category': doc.category,
'subcategory': doc.subcategory,
'tags_str': ', '.join(doc.tags) if doc.tags else 'None',
'dim_1': coordinates[i, 0],
'dim_2': coordinates[i, 1],
}
if dimensions == '3d':
row['dim_3'] = coordinates[i, 2]
df_data.append(row)
return pd.DataFrame(df_data)

0
tests/__init__.py Normal file
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73
tests/test_data_processing.py Normal file
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@@ -0,0 +1,73 @@
import pytest
import numpy as np
from src.embeddingbuddy.data.parser import NDJSONParser
from src.embeddingbuddy.data.processor import DataProcessor
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]}'
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])
]
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]])
)
prompt_data = ProcessedData(
documents=[Document(id="p1", text="prompt", embedding=[0.3, 0.4])],
embeddings=np.array([[0.3, 0.4]])
)
processor = DataProcessor()
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
assert documents[0].id == "1"
assert prompts[0].id == "p1"
if __name__ == "__main__":
pytest.main([__file__])

89
tests/test_reducers.py Normal file
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@@ -0,0 +1,89 @@
import pytest
import numpy as np
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)
assert isinstance(reducer, PCAReducer)
assert reducer.n_components == 2
def test_create_tsne_reducer(self):
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)
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')
def test_available_methods(self):
methods = ReducerFactory.get_available_methods()
assert 'pca' in methods
assert 'tsne' 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__])

47
uv.lock generated
View File

@@ -133,7 +133,7 @@ wheels = [
[[package]]
name = "embeddingbuddy"
version = "0.1.0"
source = { virtual = "." }
source = { editable = "." }
dependencies = [
{ name = "dash" },
{ name = "dash-bootstrap-components" },
@@ -142,6 +142,7 @@ dependencies = [
{ name = "opentsne" },
{ name = "pandas" },
{ name = "plotly" },
{ name = "pytest" },
{ name = "scikit-learn" },
{ name = "umap-learn" },
]
@@ -155,6 +156,7 @@ requires-dist = [
{ name = "opentsne", specifier = ">=1.0.0" },
{ name = "pandas", specifier = ">=2.1.4" },
{ name = "plotly", specifier = ">=5.17.0" },
{ name = "pytest", specifier = ">=8.4.1" },
{ name = "scikit-learn", specifier = ">=1.3.2" },
{ name = "umap-learn", specifier = ">=0.5.8" },
]
@@ -197,6 +199,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656, upload-time = "2025-04-27T15:29:00.214Z" },
]
[[package]]
name = "iniconfig"
version = "2.1.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/f2/97/ebf4da567aa6827c909642694d71c9fcf53e5b504f2d96afea02718862f3/iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7", size = 4793, upload-time = "2025-03-19T20:09:59.721Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050, upload-time = "2025-03-19T20:10:01.071Z" },
]
[[package]]
name = "itsdangerous"
version = "2.2.0"
@@ -473,6 +484,24 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/95/a9/12e2dc726ba1ba775a2c6922d5d5b4488ad60bdab0888c337c194c8e6de8/plotly-6.3.0-py3-none-any.whl", hash = "sha256:7ad806edce9d3cdd882eaebaf97c0c9e252043ed1ed3d382c3e3520ec07806d4", size = 9791257, upload-time = "2025-08-12T20:22:09.205Z" },
]
[[package]]
name = "pluggy"
version = "1.6.0"
source = { registry = "https://pypi.org/simple" }
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