Spaces:
Runtime error
Runtime error
File size: 9,035 Bytes
03c0888 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
# Advanced Usage of JsonCssExtractionStrategy
While the basic usage of JsonCssExtractionStrategy is powerful for simple structures, its true potential shines when dealing with complex, nested HTML structures. This section will explore advanced usage scenarios, demonstrating how to extract nested objects, lists, and nested lists.
## Hypothetical Website Example
Let's consider a hypothetical e-commerce website that displays product categories, each containing multiple products. Each product has details, reviews, and related items. This complex structure will allow us to demonstrate various advanced features of JsonCssExtractionStrategy.
Assume the HTML structure looks something like this:
```html
<div class="category">
<h2 class="category-name">Electronics</h2>
<div class="product">
<h3 class="product-name">Smartphone X</h3>
<p class="product-price">$999</p>
<div class="product-details">
<span class="brand">TechCorp</span>
<span class="model">X-2000</span>
</div>
<ul class="product-features">
<li>5G capable</li>
<li>6.5" OLED screen</li>
<li>128GB storage</li>
</ul>
<div class="product-reviews">
<div class="review">
<span class="reviewer">John D.</span>
<span class="rating">4.5</span>
<p class="review-text">Great phone, love the camera!</p>
</div>
<div class="review">
<span class="reviewer">Jane S.</span>
<span class="rating">5</span>
<p class="review-text">Best smartphone I've ever owned.</p>
</div>
</div>
<ul class="related-products">
<li>
<span class="related-name">Phone Case</span>
<span class="related-price">$29.99</span>
</li>
<li>
<span class="related-name">Screen Protector</span>
<span class="related-price">$9.99</span>
</li>
</ul>
</div>
<!-- More products... -->
</div>
```
Now, let's create a schema to extract this complex structure:
```python
schema = {
"name": "E-commerce Product Catalog",
"baseSelector": "div.category",
"fields": [
{
"name": "category_name",
"selector": "h2.category-name",
"type": "text"
},
{
"name": "products",
"selector": "div.product",
"type": "nested_list",
"fields": [
{
"name": "name",
"selector": "h3.product-name",
"type": "text"
},
{
"name": "price",
"selector": "p.product-price",
"type": "text"
},
{
"name": "details",
"selector": "div.product-details",
"type": "nested",
"fields": [
{
"name": "brand",
"selector": "span.brand",
"type": "text"
},
{
"name": "model",
"selector": "span.model",
"type": "text"
}
]
},
{
"name": "features",
"selector": "ul.product-features li",
"type": "list",
"fields": [
{
"name": "feature",
"type": "text"
}
]
},
{
"name": "reviews",
"selector": "div.review",
"type": "nested_list",
"fields": [
{
"name": "reviewer",
"selector": "span.reviewer",
"type": "text"
},
{
"name": "rating",
"selector": "span.rating",
"type": "text"
},
{
"name": "comment",
"selector": "p.review-text",
"type": "text"
}
]
},
{
"name": "related_products",
"selector": "ul.related-products li",
"type": "list",
"fields": [
{
"name": "name",
"selector": "span.related-name",
"type": "text"
},
{
"name": "price",
"selector": "span.related-price",
"type": "text"
}
]
}
]
}
]
}
```
This schema demonstrates several advanced features:
1. **Nested Objects**: The `details` field is a nested object within each product.
2. **Simple Lists**: The `features` field is a simple list of text items.
3. **Nested Lists**: The `products` field is a nested list, where each item is a complex object.
4. **Lists of Objects**: The `reviews` and `related_products` fields are lists of objects.
Let's break down the key concepts:
### Nested Objects
To create a nested object, use `"type": "nested"` and provide a `fields` array for the nested structure:
```python
{
"name": "details",
"selector": "div.product-details",
"type": "nested",
"fields": [
{
"name": "brand",
"selector": "span.brand",
"type": "text"
},
{
"name": "model",
"selector": "span.model",
"type": "text"
}
]
}
```
### Simple Lists
For a simple list of identical items, use `"type": "list"`:
```python
{
"name": "features",
"selector": "ul.product-features li",
"type": "list",
"fields": [
{
"name": "feature",
"type": "text"
}
]
}
```
### Nested Lists
For a list of complex objects, use `"type": "nested_list"`:
```python
{
"name": "products",
"selector": "div.product",
"type": "nested_list",
"fields": [
// ... fields for each product
]
}
```
### Lists of Objects
Similar to nested lists, but typically used for simpler objects within the list:
```python
{
"name": "related_products",
"selector": "ul.related-products li",
"type": "list",
"fields": [
{
"name": "name",
"selector": "span.related-name",
"type": "text"
},
{
"name": "price",
"selector": "span.related-price",
"type": "text"
}
]
}
```
## Using the Advanced Schema
To use this advanced schema with AsyncWebCrawler:
```python
import json
import asyncio
from crawl4ai import AsyncWebCrawler
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
async def extract_complex_product_data():
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(
url="https://gist.githubusercontent.com/githubusercontent/2d7b8ba3cd8ab6cf3c8da771ddb36878/raw/1ae2f90c6861ce7dd84cc50d3df9920dee5e1fd2/sample_ecommerce.html",
extraction_strategy=extraction_strategy,
bypass_cache=True,
)
assert result.success, "Failed to crawl the page"
product_data = json.loads(result.extracted_content)
print(json.dumps(product_data, indent=2))
asyncio.run(extract_complex_product_data())
```
This will produce a structured JSON output that captures the complex hierarchy of the product catalog, including nested objects, lists, and nested lists.
## Tips for Advanced Usage
1. **Start Simple**: Begin with a basic schema and gradually add complexity.
2. **Test Incrementally**: Test each part of your schema separately before combining them.
3. **Use Chrome DevTools**: The Element Inspector is invaluable for identifying the correct selectors.
4. **Handle Missing Data**: Use the `default` key in your field definitions to handle cases where data might be missing.
5. **Leverage Transforms**: Use the `transform` key to clean or format extracted data (e.g., converting prices to numbers).
6. **Consider Performance**: Very complex schemas might slow down extraction. Balance complexity with performance needs.
By mastering these advanced techniques, you can use JsonCssExtractionStrategy to extract highly structured data from even the most complex web pages, making it a powerful tool for web scraping and data analysis tasks. |