| Name | llama-index-tools-scrapegraphai JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | llama-index tools integrating ScrapegraphAI |
| upload_time | 2025-09-08 20:47:59 |
| maintainer | Vincigit00 |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
scraping
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# LlamaIndex Tool - Scrapegraph
This tool integrates [Scrapegraph](https://scrapegraphai.com) with LlamaIndex, providing intelligent web scraping capabilities with structured data extraction.
## Installation
```bash
pip install llama-index-tools-scrapegraph
```
## Usage
First, import and initialize the ScrapegraphToolSpec:
```python
from llama_index.tools.scrapegraph import ScrapegraphToolSpec
scrapegraph_tool = ScrapegraphToolSpec()
```
### Available Functions
The tool provides the following capabilities:
1. **Smart Scraper**
```python
from pydantic import BaseModel
# Define your schema (optional)
class ProductSchema(BaseModel):
name: str
price: float
description: str
schema = [ProductSchema]
# Perform the scraping
result = scrapegraph_tool.scrapegraph_smartscraper(
prompt="Extract product information",
url="https://example.com/product",
api_key="your-api-key",
schema=schema, # Optional
)
```
2. **Markdownify**
Convert webpage content to markdown format:
```python
markdown_content = scrapegraph_tool.scrapegraph_markdownify(
url="https://example.com", api_key="your-api-key"
)
```
3. **Local Scrape**
Extract structured data from raw text:
```python
text = """
Your raw text content here...
"""
structured_data = scrapegraph_tool.scrapegraph_local_scrape(
text=text, api_key="your-api-key"
)
```
## Requirements
- Python 3.8+
- `scrapegraph-py` package
- Valid Scrapegraph API key
Raw data
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"description": "# LlamaIndex Tool - Scrapegraph\n\nThis tool integrates [Scrapegraph](https://scrapegraphai.com) with LlamaIndex, providing intelligent web scraping capabilities with structured data extraction.\n\n## Installation\n\n```bash\npip install llama-index-tools-scrapegraph\n```\n\n## Usage\n\nFirst, import and initialize the ScrapegraphToolSpec:\n\n```python\nfrom llama_index.tools.scrapegraph import ScrapegraphToolSpec\n\nscrapegraph_tool = ScrapegraphToolSpec()\n```\n\n### Available Functions\n\nThe tool provides the following capabilities:\n\n1. **Smart Scraper**\n\n```python\nfrom pydantic import BaseModel\n\n\n# Define your schema (optional)\nclass ProductSchema(BaseModel):\n name: str\n price: float\n description: str\n\n\nschema = [ProductSchema]\n\n# Perform the scraping\nresult = scrapegraph_tool.scrapegraph_smartscraper(\n prompt=\"Extract product information\",\n url=\"https://example.com/product\",\n api_key=\"your-api-key\",\n schema=schema, # Optional\n)\n```\n\n2. **Markdownify**\n\nConvert webpage content to markdown format:\n\n```python\nmarkdown_content = scrapegraph_tool.scrapegraph_markdownify(\n url=\"https://example.com\", api_key=\"your-api-key\"\n)\n```\n\n3. **Local Scrape**\n\nExtract structured data from raw text:\n\n```python\ntext = \"\"\"\nYour raw text content here...\n\"\"\"\n\nstructured_data = scrapegraph_tool.scrapegraph_local_scrape(\n text=text, api_key=\"your-api-key\"\n)\n```\n\n## Requirements\n\n- Python 3.8+\n- `scrapegraph-py` package\n- Valid Scrapegraph API key\n",
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