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Update all documentation to import extraction strategies directly from crawl4ai.

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This commit is contained in:
UncleCode
2025-06-10 18:08:27 +08:00
parent cab457e9c7
commit c0fd36982d
43 changed files with 7811 additions and 7803 deletions
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2
docs/md_v2/core/browser-crawler-config.md
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@@ -274,7 +274,7 @@ In a typical scenario, you define **one** `BrowserConfig` for your crawler sessi
```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
async def main():
# 1) Browser config: headless, bigger viewport, no proxy

6
docs/md_v2/core/content-selection.md
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@@ -191,7 +191,7 @@ You can combine content selection with a more advanced extraction strategy. For
import asyncio
import json
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
async def main():
# Minimal schema for repeated items
@@ -243,7 +243,7 @@ import asyncio
import json
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from crawl4ai import LLMExtractionStrategy
class ArticleData(BaseModel):
headline: str
@@ -288,7 +288,7 @@ Below is a short function that unifies **CSS selection**, **exclusion** logic, a
import asyncio
import json
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
async def extract_main_articles(url: str):
schema = {

2
docs/md_v2/core/crawler-result.md
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@@ -138,7 +138,7 @@ If you run a JSON-based extraction strategy (CSS, XPath, LLM, etc.), the structu
import asyncio
import json
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
async def main():
schema = {

2
docs/md_v2/core/page-interaction.md
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@@ -296,7 +296,7 @@ if __name__ == "__main__":
Once dynamic content is loaded, you can attach an **`extraction_strategy`** (like `JsonCssExtractionStrategy` or `LLMExtractionStrategy`). For example:
```python
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
schema = {
"name": "Commits",

8
docs/md_v2/core/quickstart.md
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@@ -127,7 +127,7 @@ Crawl4AI can also extract structured data (JSON) using CSS or XPath selectors. B
> **New!** Crawl4AI now provides a powerful utility to automatically generate extraction schemas using LLM. This is a one-time cost that gives you a reusable schema for fast, LLM-free extractions:
```python
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
from crawl4ai import LLMConfig
# Generate a schema (one-time cost)
@@ -157,7 +157,7 @@ Here's a basic extraction example:
import asyncio
import json
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
async def main():
schema = {
@@ -212,7 +212,7 @@ import json
import asyncio
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from crawl4ai import LLMExtractionStrategy
class OpenAIModelFee(BaseModel):
model_name: str = Field(..., description="Name of the OpenAI model.")
@@ -328,7 +328,7 @@ Some sites require multiple “page clicks” or dynamic JavaScript updates. Bel
```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
async def extract_structured_data_using_css_extractor():
print("\n--- Using JsonCssExtractionStrategy for Fast Structured Output ---")