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Overview

OmniScraperGraph is a scraping pipeline that automates the process of extracting information from web pages including text and images. It uses vision-capable language models to describe and analyze images alongside text content.

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Class Signature

class OmniScraperGraph(AbstractGraph):
    def __init__(
        self,
        prompt: str,
        source: str,
        config: dict,
        schema: Optional[Type[BaseModel]] = None,
    )

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Constructor Parameters

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prompt
str
required
The natural language prompt describing what information to extract from the page, including text and images.
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source
str
required
The source to scrape. Can be:
  • A URL starting with http:// or https://
  • A local directory path for offline HTML files
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config
dict
required
Configuration parameters for the graph. Must include:
  • llm: LLM configuration with vision support (e.g., {"model": "openai/gpt-4o"})
Optional parameters:
  • max_images (int): Maximum number of images to process (default: 5)
  • verbose (bool): Enable detailed logging
  • headless (bool): Run browser in headless mode
  • additional_info (str): Extra context for the LLM
  • loader_kwargs (dict): Parameters for page loading
  • storage_state (str): Browser state file path
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schema
Type[BaseModel]
default:"None"
Optional Pydantic model defining the expected output structure.

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Attributes

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prompt
str
The user’s extraction prompt.
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source
str
The source URL or local directory path.
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config
dict
Configuration dictionary for the graph.
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schema
BaseModel
Optional output schema for structured data extraction.
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llm_model
object
The configured language model instance (must support vision).
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max_images
int
Maximum number of images to process and analyze.
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input_key
str
Either “url” or “local_dir” based on the source type.

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Methods

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run()

Executes the scraping process including image analysis and returns the answer. 
def run(self) -> str
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return
str
The extracted information including text and image descriptions, or “No answer found.” if extraction fails.

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Basic Usage

from scrapegraphai.graphs import OmniScraperGraph

graph_config = {
    "llm": {
        "model": "openai/gpt-4o",  # Vision-capable model
        "api_key": "your-api-key"
    },
    "max_images": 5
}

omni_scraper = OmniScraperGraph(
    prompt="List all the attractions in Chioggia and describe their pictures.",
    source="https://en.wikipedia.org/wiki/Chioggia",
    config=graph_config
)

result = omni_scraper.run()
print(result)

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Vision-Capable Models

OmniScraperGraph requires LLM models with vision capabilities:

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OpenAI GPT-4 Vision

config = {
    "llm": {
        "model": "openai/gpt-4o",
        "api_key": "your-api-key"
    },
    "max_images": 10
}

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Anthropic Claude with Vision

config = {
    "llm": {
        "model": "anthropic/claude-3-opus-20240229",
        "api_key": "your-api-key"
    },
    "max_images": 8
}

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Google Gemini

config = {
    "llm": {
        "model": "google_genai/gemini-pro-vision",
        "api_key": "your-api-key"
    },
    "max_images": 5
}

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Structured Output with Schema

from pydantic import BaseModel, Field
from typing import List

class ImageDescription(BaseModel):
    url: str = Field(description="Image URL")
    description: str = Field(description="What the image shows")
    relevance: str = Field(description="How it relates to the query")

class Attraction(BaseModel):
    name: str = Field(description="Attraction name")
    description: str = Field(description="Text description")
    images: List[ImageDescription] = Field(description="Related images")

class Attractions(BaseModel):
    attractions: List[Attraction]
    summary: str = Field(description="Overall summary")

omni_scraper = OmniScraperGraph(
    prompt="Extract attractions with their descriptions and images",
    source="https://en.wikipedia.org/wiki/Chioggia",
    config=graph_config,
    schema=Attractions
)

result = omni_scraper.run()

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Graph Workflow

The OmniScraperGraph uses the following node pipeline: 
FetchNode → ParseNode → ImageToTextNode → GenerateAnswerOmniNode
  1. FetchNode: Fetches the web page content
  2. ParseNode: Parses HTML and extracts image URLs (parse_urls=True)
  3. ImageToTextNode: Analyzes images using vision model
  4. GenerateAnswerOmniNode: Combines text and image descriptions to answer the prompt

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Controlling Image Processing

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Limit Number of Images

config = {
    "llm": {"model": "openai/gpt-4o"},
    "max_images": 3  # Process only first 3 images
}

omni_scraper = OmniScraperGraph(
    prompt="Describe the main product images",
    source="https://example.com/product",
    config=config
)

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Process More Images

config = {
    "llm": {"model": "openai/gpt-4o"},
    "max_images": 20  # Process up to 20 images
}

omni_scraper = OmniScraperGraph(
    prompt="Analyze all gallery images and categorize them",
    source="https://example.com/gallery",
    config=config
)

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Use Cases

  1. E-commerce Product Analysis: Extract product info with image descriptions
  2. Real Estate Listings: Scrape property details with photo analysis
  3. Art Gallery Cataloging: Document artwork with descriptions
  4. News Articles: Extract articles with image context
  5. Travel Guides: Collect destination info with visual descriptions

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Advanced Usage

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E-commerce Product Scraping

from pydantic import BaseModel
from typing import List

class ProductImage(BaseModel):
    url: str
    shows: str = Field(description="What the image shows")
    angle: str = Field(description="Camera angle or perspective")

class Product(BaseModel):
    name: str
    price: float
    description: str
    images: List[ProductImage]
    features_visible: List[str] = Field(description="Features visible in images")

config = {
    "llm": {"model": "openai/gpt-4o"},
    "max_images": 8,
    "additional_info": "Focus on product features visible in images"
}

omni_scraper = OmniScraperGraph(
    prompt="Extract product information including detailed analysis of all product images",
    source="https://example.com/product/12345",
    config=config,
    schema=Product
)

result = omni_scraper.run()

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Real Estate Listings

from pydantic import BaseModel
from typing import List

class RoomImage(BaseModel):
    room_type: str = Field(description="Type of room shown")
    description: str = Field(description="What's visible in the image")
    condition: str = Field(description="Condition/quality assessment")
    features: List[str] = Field(description="Notable features visible")

class Property(BaseModel):
    address: str
    price: str
    bedrooms: int
    bathrooms: int
    description: str
    room_images: List[RoomImage]
    property_highlights: str

config = {
    "llm": {"model": "openai/gpt-4o"},
    "max_images": 15,
    "additional_info": "Analyze room conditions and features from images"
}

omni_scraper = OmniScraperGraph(
    prompt="Extract complete property information with detailed room analysis from images",
    source="https://example.com/property/listing",
    config=config,
    schema=Property
)

result = omni_scraper.run()

from pydantic import BaseModel
from typing import List, Optional

class Artwork(BaseModel):
    title: str
    artist: str
    year: Optional[str] = None
    medium: str
    image_description: str = Field(description="Detailed description of the artwork from image")
    style: str = Field(description="Artistic style identified from image")
    colors: List[str] = Field(description="Dominant colors")
    subject: str = Field(description="Subject matter")

class Exhibition(BaseModel):
    name: str
    artworks: List[Artwork]
    curator_notes: Optional[str] = None

config = {
    "llm": {"model": "anthropic/claude-3-opus-20240229"},
    "max_images": 10,
    "additional_info": "Provide detailed art analysis including style, technique, and composition"
}

omni_scraper = OmniScraperGraph(
    prompt="Catalog all artworks with detailed visual analysis",
    source="https://example.com/exhibition",
    config=config,
    schema=Exhibition
)

result = omni_scraper.run()

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Accessing Results

result = omni_scraper.run()

# Get the answer
print("Answer:", result)

# Access full state
final_state = omni_scraper.get_state()
answer = final_state.get("answer")
img_descriptions = final_state.get("img_desc")
img_urls = final_state.get("img_urls")
parsed_doc = final_state.get("parsed_doc")

print(f"\nProcessed {len(img_urls)} images")
print(f"\nImage Descriptions:")
for i, desc in enumerate(img_descriptions, 1):
    print(f"{i}. {desc}")

# Execution info
exec_info = omni_scraper.get_execution_info()
for node_info in exec_info:
    print(f"{node_info['node_name']}: {node_info['exec_time']:.2f}s")
    print(f"Tokens: {node_info['total_tokens']}")
    print(f"Cost: ${node_info['total_cost_USD']:.4f}")

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Cost Considerations

Vision model API calls are typically more expensive than text-only: 
# Estimate cost
final_state = omni_scraper.get_state()
num_images = len(final_state.get("img_urls", []))

# OpenAI GPT-4 Vision pricing (example)
per_image_cost = 0.01  # Approximate
estimated_image_cost = num_images * per_image_cost

print(f"Processed {num_images} images")
print(f"Estimated image analysis cost: ${estimated_image_cost:.2f}")

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Performance Tips

  1. Limit max_images: Process only necessary images to reduce cost
  2. Use appropriate models: GPT-4o for quality, gpt-4o-mini for speed
  3. Provide context: Use additional_info to guide image analysis
  4. Image quality: Higher resolution images provide better analysis
  5. Test with small batches: Start with few images and scale up

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Error Handling

try:
    result = omni_scraper.run()
    
    if result == "No answer found.":
        print("Extraction failed")
        
        # Check if images were found
        final_state = omni_scraper.get_state()
        img_urls = final_state.get("img_urls", [])
        
        if not img_urls:
            print("No images found on the page")
        else:
            print(f"Found {len(img_urls)} images but analysis failed")
    else:
        print(f"Success: {result}")
        
except Exception as e:
    print(f"Error during scraping: {e}")

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Comparison with SmartScraperGraph

FeatureOmniScraperGraphSmartScraperGraph
Text ExtractionYesYes
Image AnalysisYesNo
LLM RequirementVision-capableAny
CostHigherLower
Use CaseVisual contentText content
SpeedSlowerFaster

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Limitations

  • Requires vision-capable LLM models
  • More expensive than text-only scraping
  • Slower due to image processing
  • Image quality affects analysis accuracy
  • Some images may fail to load or process