Scrapegraph Llama Index

Types of Schemas

1. Simple Schema
   Use this when you want to extract straightforward information, such as a single piece of content.

	from pydantic import BaseModel, Field

# Simple schema for a single webpage
class PageInfoSchema(BaseModel):
    title: str = Field(description="The title of the webpage")
    description: str = Field(description="The description of the webpage")

# Example Output JSON after AI extraction
{
    "title": "ScrapeGraphAI: The Best Content Extraction Tool",
    "description": "ScrapeGraphAI provides powerful tools for structured content extraction from websites."
}

2. Complex Schema (Nested)
   If you need to extract structured information with multiple related items (like a list of repositories), you can nest schemas.

	from pydantic import BaseModel, Field
from typing import List

# Define a schema for a single repository
class RepositorySchema(BaseModel):
    name: str = Field(description="Name of the repository (e.g., 'owner/repo')")
    description: str = Field(description="Description of the repository")
    stars: int = Field(description="Star count of the repository")
    forks: int = Field(description="Fork count of the repository")
    today_stars: int = Field(description="Stars gained today")
    language: str = Field(description="Programming language used")

# Define a schema for a list of repositories
class ListRepositoriesSchema(BaseModel):
    repositories: List[RepositorySchema] = Field(description="List of GitHub trending repositories")

# Example Output JSON after AI extraction
{
    "repositories": [
        {
            "name": "google-gemini/cookbook",
            "description": "Examples and guides for using the Gemini API",
            "stars": 8036,
            "forks": 1001,
            "today_stars": 649,
            "language": "Jupyter Notebook"
        },
        {
            "name": "TEN-framework/TEN-Agent",
            "description": "TEN Agent is a conversational AI powered by TEN, integrating Gemini 2.0 Multimodal Live API, OpenAI Realtime API, RTC, and more.",
            "stars": 3224,
            "forks": 311,
            "today_stars": 361,
            "language": "Python"
        }
    ]
}

Key Takeaways

• Simple Schema: Perfect for small, straightforward extractions.
• Complex Schema: Use nesting to extract lists or structured data, like "a list of repositories."

Both approaches give the AI a clear structure to follow, ensuring that the extracted content matches exactly what you need.