Austin Godber
2f458884a2
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- Add MIT license with Austin Godber copyright - Implement optional OpenSearch feature toggle via EMBEDDINGBUDDY_OPENSEARCH_ENABLED - Disable OpenSearch by default in production for security - Add development environment flag to test OpenSearch disable state - Update about modal to open by default with improved content - Reorganize text input component: move model selection below text input - Conditionally show/hide OpenSearch tab and callbacks based on configuration - Update tooltips to reflect OpenSearch availability status
91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
from dash import html, dcc
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import dash_bootstrap_components as dbc
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class AboutComponent:
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def _get_about_content(self):
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return """
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# 🔍 Interactive Embedding Vector Visualization
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EmbeddingBuddy is a web application for interactive exploration and
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visualization of embedding vectors through dimensionality reduction techniques
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(PCA, t-SNE, UMAP).
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You have two ways to get started:
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1. Generate embeddings directly in the browser if it supports WebGPU.
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2. Upload your NDJSON file containing embedding vectors and metadata.
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## Generating Embeddings in Browser
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1. Expand the "Generate Embeddings" section.
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2. Input your text data (one entry per line).
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1. Optionally you can use the built in sample data by clicking "Load Sample Data" button.
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3. Click "Generate Embeddings" to create vectors using a pre-trained model.
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## NDJSON File Format
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```json
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{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, ...], "text": "Sample text content", "category": "news", "subcategory": "politics", "tags": ["election", "politics"]}
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{"id": "doc_002", "embedding": [0.2, -0.1, 0.9, ...], "text": "Another example", "category": "review", "subcategory": "product", "tags": ["tech", "gadget"]}
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```
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## ✨ Features
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- Drag-and-drop NDJSON file upload
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- Multiple dimensionality reduction algorithms
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- 2D/3D interactive plots with Plotly
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- Color coding by categories, subcategories, or tags
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- In-browser embedding generation
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- OpenSearch integration for data loading
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## 🔧 Supported Algorithms
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- **PCA** (Principal Component Analysis)
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- **t-SNE** (t-Distributed Stochastic Neighbor Embedding)
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- **UMAP** (Uniform Manifold Approximation and Projection)
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---
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📂 [View on GitHub](https://github.com/godber/EmbeddingBuddy)
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*Built with: Python, Dash, Plotly, scikit-learn, OpenTSNE, UMAP*
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""".strip()
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def create_about_modal(self):
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return dbc.Modal(
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[
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dbc.ModalHeader(
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dbc.ModalTitle("Welcome to EmbeddingBuddy"),
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close_button=True,
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),
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dbc.ModalBody(
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[dcc.Markdown(self._get_about_content(), className="mb-0")]
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),
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dbc.ModalFooter(
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[
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dbc.Button(
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"Close",
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id="about-modal-close",
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color="secondary",
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n_clicks=0,
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)
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]
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),
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],
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id="about-modal",
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is_open=True,
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size="lg",
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)
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def create_about_button(self):
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return dbc.Button(
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[html.I(className="fas fa-info-circle me-2"), "About"],
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id="about-button",
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color="outline-info",
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size="sm",
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n_clicks=0,
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className="ms-2",
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)
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