Conceptual
Core AI/ML concepts, domains, and tasks
Technical
Infrastructure, tools, and technical components
Contextual
Industry, governance, and application domains
Communicative
Media types and communication channels
## Use Cases
### Feed Categorization
Topics are used to categorize and filter RSS/Atom feeds:
```python
from ai_web_feeds.taxonomy import load_taxonomy
taxonomy = load_taxonomy()
# Get all LLM-related topics
llm_topic = taxonomy.get_topic("llm")
llm_children = taxonomy.get_children("llm")
# Filter feeds by topic
conceptual_topics = taxonomy.get_topics_by_facet_group("conceptual")
```
### Recommendation Systems
Use the taxonomy for content recommendations:
```python
# Find related topics
topic = taxonomy.get_topic("llm")
related = topic.relations.get("related_to", [])
# Get topic dependencies
dependencies = topic.relations.get("depends_on", [])
```
### Analytics & Insights
Generate insights about your feed collection:
```python
visualizer = TaxonomyVisualizer(taxonomy)
stats = visualizer.get_statistics()
print(f"Facet distribution: {stats['facets']}")
print(f"Average depth: {stats['avg_depth']:.2f}")
```
## Advanced Features
### Filtering by Depth
Visualize only top-level topics:
```python
mermaid_code = visualizer.to_mermaid(max_depth=2)
```
### Filtering by Facet
Focus on specific topic types:
```python
mermaid_code = visualizer.to_mermaid(
filter_facets=["domain", "task"]
)
```
### Custom Styling
The Mermaid diagrams include custom CSS classes based on facet groups, which you can override in your rendering environment.
## Data Format
The taxonomy follows a strict JSON Schema (see `/data/topics.schema.json`):
```json
{
"id": "string (kebab-case)",
"label": "Human-readable name",
"facet": "Category type",
"facet_group": "conceptual | technical | contextual | communicative",
"parents": ["parent-topic-ids"],
"relations": {
"depends_on": ["topic-ids"],
"implements": ["topic-ids"],
"influences": ["topic-ids"]
},
"rank_hint": 0.0-1.0
}
```
## Export Formats
### Mermaid
Best for documentation and GitHub/GitLab READMEs.
### JSON Graph
Compatible with D3.js, Cytoscape.js, and other graph visualization libraries:
```json
{
"nodes": [
{
"id": "ai",
"label": "Artificial Intelligence",
"facet": "domain",
"facet_group": "conceptual"
}
],
"links": [
{
"source": "ai",
"target": "ml",
"type": "parent"
}
]
}
```
### DOT (Graphviz)
For high-quality static diagrams (requires Graphviz):
```bash
# Generate DOT file
python -c "
from ai_web_feeds.taxonomy import load_taxonomy, TaxonomyVisualizer
viz = TaxonomyVisualizer(load_taxonomy())
print(viz.to_dot())
" > taxonomy.dot
# Render with Graphviz
dot -Tpng taxonomy.dot -o taxonomy.png
```
## Contributing
To add or modify topics:
1. Edit `/data/topics.yaml`
2. Validate against `/data/topics.schema.json`
3. Run `aiwebfeeds validate data/topics.yaml`
4. Generate updated visualizations
5. Submit a pull request
## API Reference
See the [Python API documentation](/docs/api/taxonomy) for complete details on:
* `TopicNode` - Topic model
* `TopicsTaxonomy` - Taxonomy container
* `TaxonomyVisualizer` - Visualization generator
* `load_taxonomy()` - Load from YAML
* `export_mermaid()` - Export Mermaid diagram
* `export_json_graph()` - Export JSON graph
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TITLE: Math Test
URL: https://ai-web-feeds.w4w.dev/docs/test-math
MARKDOWN: https://ai-web-feeds.w4w.dev/docs/test-math.mdx
DESCRIPTION: Test page for verifying KaTeX math rendering
PATH: /test-math
--------------------------------------------------------------------------------
CONTENT
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# Math Test (/docs/test-math)
# Math Rendering Test
## Inline Math
The Pythagorean theorem: $a^2 + b^2 = c^2$
Einstein's mass-energy equivalence: $E = mc^2$
## Block Math
### Simple Equation
```math
\frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
```
### Complex Equation
```math
\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi}
```
### Matrix
```math
\begin{bmatrix}
1 & 2 & 3 \\
4 & 5 & 6 \\
7 & 8 & 9
\end{bmatrix}
```
If you can see properly formatted mathematical equations above, KaTeX is working correctly! ✅
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PAGE 5 OF 57
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TITLE: Components
URL: https://ai-web-feeds.w4w.dev/docs/test
MARKDOWN: https://ai-web-feeds.w4w.dev/docs/test.mdx
DESCRIPTION: Components
PATH: /test
--------------------------------------------------------------------------------
CONTENT
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# Components (/docs/test)
## Code Block
```js
console.log("Hello World");
```
## Cards