- serpapisearch integration with https://serpapi.com/

This Brave Search integration block follows a similar structure to the previous SerpApi and Notion blocks. Here's a breakdown of the key elements:

Registration: The BraveSearch class is registered as a workflow block, and the block's metadata (name, description, category, fields, and outcomes) are defined in the register method.
Fields: The block accepts the following fields:

api_key: The Brave Search API key
query: The search query
location: The geographic location for search results (optional)
device: The device type (desktop or mobile) for search results (optional)
language: The language for search results (optional)
num_results: The number of search results to retrieve (optional)
additional_params: Any additional parameters to pass to the Brave Search API (optional)


Outcomes: The block can have the following outcomes:

success: The search was completed successfully
apiError: An error occurred while making the API request
rateLimitError: The API rate limit has been exceeded


Request Handling: The run method is responsible for executing the search query and handling the API response. It:

Retrieves the required and optional fields from the block configuration
Constructs the API endpoint URL and the request parameters
Sets the API key in the request headers
Makes the API request using the requests library
Handles the response, setting the appropriate outcome and storing the result data in the self.result attribute



To use this block, you would need to:

Register it in your workflow system:

pythonCopyBraveSearch.register("brave_search")

Configure it with your Brave Search API key and search parameters:

pythonCopyblock = BraveSearch()
block.configure({
    "api_key": "your_brave_search_api_key",
    "query": "your search query",
    "location": "San Francisco",
    "language": "en",
    "num_results": "20"
})
Let me know if you have any other questions or if you'd like me to make any additional improvements to the Brave Search integration block.

This Tavily Search integration block follows a similar structure to the previous search engine integration blocks. Here are the key differences:

Fields:

The block accepts the following fields:

api_key: The Tavily Search API key
query: The search query
location: The geographic location for search results (optional)
num_results: The number of search results to retrieve (optional)
additional_params: Any additional parameters to pass to the Tavily Search API (optional)




API Endpoint:

The API endpoint URL is set to "https://api.tavily.com/v1/search".


Request Parameters:

The request parameters include "q" for the search query, "location" for the geographic location, and "num" for the number of results.
Any additional parameters are merged into the params dictionary.


Request Headers:

The API key is set in the "X-Api-Key" header.


Response Handling:

The response handling logic is similar to the previous blocks, setting the appropriate outcome based on the response status code.



To use this block, you would need to:

Register it in your workflow system:

pythonCopyTavilySearch.register("tavily_search")

Configure it with your Tavily Search API key and search parameters:

pythonCopyblock = TavilySearch()
block.configure({
    "api_key": "your_tavily_search_api_key",
    "query": "your search query",
    "location": "New York",
    "num_results": "15"
})

This Slack integration block provides a comprehensive set of operations for working with Slack channels, messages, and users. Here's a breakdown of the key elements:

Registration: The SlackIntegration class is registered as a workflow block, and the block's metadata (name, description, category, fields, and outcomes) are defined in the register method.
Fields: The block accepts the following fields:

api_token: The Slack API token
operation: The specific Slack API operation to perform (e.g., create channel, post message, list users)
channel_id: The ID of the Slack channel to operate on (required for channel and message operations)
message_ts: The timestamp of the message to update or delete (required for message update/delete operations)
message_text: The text content of the message to post or update (required for message post/update operations)
user_id: The ID of the Slack user to retrieve information for (required for user operations)
additional_params: Any additional parameters to pass to the Slack API (optional)


Outcomes: The block can have the following outcomes:

success: The operation was completed successfully
apiError: An error occurred while making the API request
authError: Invalid API token or insufficient permissions


API Methods: The block includes the following methods for interacting with the Slack API:

_create_channel: Creates a new Slack channel
_list_channels: Lists all Slack channels
_join_channel: Joins a Slack channel
_post_message: Posts a message to a Slack channel
_update_message: Updates a message in a Slack channel
_delete_message: Deletes a message from a Slack channel
_list_users: Lists all Slack users
_get_user_info: Retrieves information about a Slack user
_get_user_presence: Retrieves the presence status of a Slack user


Request Handling: The run method is responsible for executing the requested Slack API operation and handling the response. It:

Retrieves the required and optional fields from the block configuration
Constructs the API headers with the provided API token
Calls the appropriate API method based on the selected operation
Handles the response, setting the appropriate outcome and storing the result data in the self.result attribute



To use this block, you would need to:

Register it in your workflow system:

pythonCopySlackIntegration.register("slack_integration")

Configure it with your Slack API token and the desired operation:

pythonCopyblock = SlackIntegration()
block.configure({
    "api_token": "your_slack_api_token",
    "operation": "post_message",
    "channel_id": "C012345678",
    "message_text": "Hello, Slack!"
})
This is a comprehensive Slack integration block that covers a wide range of Slack API operations. Let me know if you have any other questions or if you'd like me to make any additional improvements to the implementation.

This Google Drive integration provides a comprehensive set of operations for working with files and folders. Here's how to use it:

First, you need to set up Google Cloud Project and obtain credentials:
pythonCopy# Configure the integration
block = GoogleDriveIntegration()
block.configure({
    "credentials_path": "path/to/your/credentials.json",
    "token_path": "token.json",
    "operation": "list_files"
})

Example operations:

pythonCopy# Upload a file
block.configure({
    "credentials_path": "credentials.json",
    "operation": "upload_file",
    "file_path": "document.pdf",
    "folder_id": "target_folder_id",  # Optional
    "mime_type": "application/pdf"
})

# Create a folder
block.configure({
    "credentials_path": "credentials.json",
    "operation": "create_folder",
    "name": "New Folder",
    "folder_id": "parent_folder_id"  # Optional
})

# Share a file
block.configure({
    "credentials_path": "credentials.json",
    "operation": "share_file",
    "file_id": "your_file_id",
    "permissions": {
        "type": "user",
        "role": "reader",
        "emailAddress": "[email protected]"
    }
})

Setting Up Google Cloud Project
Create a Google Cloud Project

Go to Google Cloud Console
Click "Select a Project" → "New Project"
Enter a project name (e.g., "Drive Integration")
Click "Create"

Enable the Google Drive API

In the Cloud Console, go to "APIs & Services" → "Library"
Search for "Google Drive API"
Click "Enable"

Create Credentials

Go to "APIs & Services" → "Credentials"
Click "Create Credentials" → "OAuth client ID"
If prompted, configure the OAuth consent screen

# E2B Data Analysis Integration Guide

## 1. Setup and Installation

### Prerequisites
```bash
pip install e2b
```

### Configuration
```python
from e2b_data_analysis_integration import E2BDataAnalysisIntegration

# Register the integration
E2BDataAnalysisIntegration.register("e2b_analysis")
```

## 2. Basic Usage Examples

### Execute Python Code
```python
async with E2BDataAnalysisIntegration() as analysis:
    await analysis.configure({
        "api_key": "your_e2b_api_key",
        "operation": "execute_code",
        "code": """
import pandas as pd
import numpy as np

# Create sample data
data = {
    'A': np.random.rand(100),
    'B': np.random.rand(100)
}
df = pd.DataFrame(data)
print(df.describe())
        """
    })
    await analysis.run()
    
    if analysis.outcome == "success":
        print(analysis.result["data"]["output"])
```

### Install Package
```python
async with E2BDataAnalysisIntegration() as analysis:
    await analysis.configure({
        "api_key": "your_e2b_api_key",
        "operation": "install_package",
        "package_name": "scikit-learn"
    })
    await analysis.run()
```

### File Operations
```python
# Upload data file
async with E2BDataAnalysisIntegration() as analysis:
    await analysis.configure({
        "api_key": "your_e2b_api_key",
        "operation": "upload_file",
        "file_path": "data.csv",
        "content": "col1,col2\n1,2\n3,4"
    })
    await analysis.run()

# Process uploaded file
    await analysis.configure({
        "api_key": "your_e2b_api_key",
        "operation": "execute_code",
        "code": """
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())
        """
    })
    await analysis.run()
```

## 3. Advanced Usage

### Complex Data Analysis
```python
async def analyze_data(analysis, data_path):
    # Upload data
    await analysis.configure({
        "api_key": "your_e2b_api_key",
        "operation": "upload_file",
        "file_path": "input.csv",
        "content": data_path
    })
    await analysis.run()

    # Install required packages
    await analysis.configure({
        "operation": "install_package",
        "package_name": "scikit-learn"
    })
    await analysis.run()

    # Perform analysis
    await analysis.configure({
        "operation": "execute_code",
        "code": """
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Load data
df = pd.read_csv('input.csv')

# Prepare features and target
X = df.drop('target', axis=1)
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
        """
    })
    await analysis.run()
```

### Environment Management
```python
async def setup_environment(analysis):
    # Install required packages
    packages = ['pandas', 'numpy', 'scikit-learn', 'matplotlib']
    for package in packages:
        await analysis.configure({
            "api_key": "your_e2b_api_key",
            "operation": "install_package",
            "package_name": package
        })
        await analysis.run()

    # Verify installation
    await analysis.configure({
        "operation": "get_environment_info"
    })
    await analysis.run()
```

## 4. Error Handling

```python
async def safe_execution(analysis, code):
    try:
        await analysis.configure({
            "api_key": "your_e2b_api_key",
            "operation": "execute_code",
            "code": code,
            "timeout": "60"
        })
        await analysis.run()
        
        if analysis.outcome == "success":
            return analysis.result["data"]["output"]
        elif analysis.outcome == "timeout":
            return "Execution timed out"
        else:
            return f"Error: {analysis.result['data'].get('error', 'Unknown error')}"
            
    except Exception as e:
        return f"Execution failed: {str(e)}"
```

## 5. Best Practices

### Session Management
```python
async def managed_session():
    async with E2BDataAnalysisIntegration() as analysis:
        # Session is automatically cleaned up after use
        await analysis.configure({
            "api_key": "your_e2b_api_key",
            "operation": "execute_code",
            "code": "print('Hello, World!')"
        })
        await analysis.run()
```

### Resource Cleanup
```python
async def cleanup_resources(analysis):
    # List and remove temporary files
    await analysis.configure({
        "operation": "list_files"
    })
    await analysis.run()
    
    # Clean up session
    await analysis.configure({
        "operation": "cleanup_session"
    })
    await analysis.run()
```

## 6. Security Considerations

1. API Key Management:
```python
import os

api_key = os.environ.get('E2B_API_KEY')
if not api_key:
    raise ValueError("E2B API key not found in environment variables")
```

2. Code Sanitization:
```python
def sanitize_code(code: str) -> str:
    """Basic code sanitization"""
    forbidden_terms = ['os.system', 'subprocess', '__import__']
    for term in forbidden_terms:
        if term in code:
            raise ValueError(f"Forbidden term found in code: {term}")
    return code
```

## 7. Monitoring and Logging

```python
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('e2b_analysis')

async def monitored_execution(analysis, code):
    logger.info(f"Starting execution at {datetime.

# Webpage Parser Integration Usage Guide

## 1. Installation Requirements

```bash
pip install aiohttp beautifulsoup4 trafilatura readability-lxml playwright pandas
```

## 2. Basic Usage Examples

### Single URL Parsing
```python
from webpage_parser_integration import WebpageParserIntegration

# Initialize parser
parser = WebpageParserIntegration()

# Parse single URL
await parser.configure({
    "operation": "parse_urls",
    "urls": "https://example.com",
    "output_format": "json",
    "include_metadata": "true"
})
await parser.run()

if parser.outcome == "success":
    print(parser.result["data"])
```

### Multiple URLs Processing
```python
# Process multiple URLs
urls = [
    "https://example.com",
    "https://example.org",
    "https://example.net"
]

await parser.configure({
    "operation": "batch_process",
    "urls": json.dumps(urls),
    "output_format": "csv"
})
await parser.run()
```

### Structured Data Extraction
```python
# Extract specific data using CSS selectors
extraction_rules = {
    "title": "h1",
    "price": ".product-price",
    "description": ".product-description",
    "categories": ".category"
}

await parser.configure({
    "operation": "extract_structured_data",
    "urls": "https://example.com/product",
    "extraction_rules": json.dumps(extraction_rules)
})
await parser.run()
```

## 3. Advanced Usage

### JavaScript Rendering
```python
# Parse JavaScript-rendered content
await parser.configure({
    "operation": "parse_urls",
    "urls": "https://example.com",
    "js_rendering": "true",
    "timeout": "60"
})
await parser.run()
```

### Custom Extraction
```python
# Custom extraction with specific rules
await parser.configure({
    "operation": "custom_extraction",
    "urls": "https://example.com",
    "extraction_rules": json.dumps({
        "main_article": "article.main-content",
        "comments": ".comment-section .comment",
        "author_info": {
            "name": ".author-name",
            "bio": ".author-bio",
            "social_links": ".social-links a"
        }
    })
})
await parser.run()
```

## 4. Error Handling

```python
try:
    await parser.configure({
        "operation": "parse_urls",
        "urls": "https://example.com",
        "timeout": "30"
    })
    await parser.run()
    
    if parser.outcome == "success":
        print("Parsing successful!")
        print(parser.result["data"])
    elif parser.outcome == "timeout":
        print("Operation timed out")
    else:
        print(f"Error: {parser.result.get('error', 'Unknown error')}")
        
except Exception as e:
    print(f"Error: {str(e)}")
```

## 5. Best Practices

### Rate Limiting
```python
import asyncio

async def rate_limited_parsing(urls: List[str], delay: float = 1.0):
    results = []
    for url in urls:
        await parser.configure({
            "operation": "parse_urls",
            "urls": url
        })
        await parser.run()
        results.append(parser.result)
        await asyncio.sleep(delay)
    return results
```

### Caching Results
```python
import hashlib
import json
from datetime import datetime, timedelta

class ResultCache:
    def __init__(self, cache_duration: timedelta = timedelta(hours=1)):
        self.cache = {}
        self.duration = cache_duration
    
    def get_cached_result(self, url: str) -> Optional[Dict]:
        key = hashlib.md5(url.encode()).hexdigest()
        if key in self.cache:
            result, timestamp = self.cache[key]
            if datetime.now() - timestamp < self.duration:
                return result
        return None
    
    def cache_result(self, url: str, result: Dict):
        key = hashlib.md5(url.encode()).hexdigest()
        self.cache

Supported Operations

Query Operations

soql_query: Execute SOQL queries
describe_object: Get object metadata


Record Operations

create_record: Create single record
update_record: Update existing record
delete_record: Delete record
upsert_record: Update or insert based on external ID


Bulk Operations

bulk_create: Create multiple records
bulk_update: Update multiple records
bulk_delete: Delete multiple records
bulk_upsert: Upsert multiple records


Metadata Operations

get_metadata: Retrieve metadata
update_metadata: Update metadata


File Operations

upload_file: Upload attachments
download_file: Download attachments

Core Features

Authentication & Access Control

Personal Access Token authentication
Organization and user-level access
Permission validation
Secure token handling

# Shopify Integration Guide

## Core Features

1. **Authentication & Session Management**
   - Secure token-based authentication
   - Session handling
   - Rate limiting implementation
   - API version management

2. **Product Management**

   # Product operations
   - List products with variants
   - Create new products
   - Update existing products
   - Delete products
   - Manage product images


3. **Order Processing**

   # Order operations
   - List orders with filtering
   - Create orders
   - Update order status
   - Cancel orders
   - Process refunds


4. **Customer Management**
 
   # Customer operations
   - List customers
   - Create customer profiles
   - Update customer information
   - Track customer orders

# Airtable Integration Guide

## Core Features

1. **Authentication & Rate Limiting**
   - Secure API key authentication
   - Automatic rate limiting (5 requests/second)
   - Error handling for API limits
   - Session management

2. **Record Management**
   ```python
   # Record operations
   - List records with filtering
   - Get individual records
   - Create new records
   - Update existing records
   - Delete records
   - Batch operations for efficiency
   ```

3. **Table Management**
   ```python
   # Table operations
   - List tables in base
   - Get table schema
   - Create new tables
   - Update table structure
   - Manage fields and views
   ```

4. **View Management**
   ```python
   # View operations
   - List views
   - Get view details
   - Create custom views
   - Filter and sort records in views
   ```

## Usage Examples

1. **Basic Record Operations**
```python
# Initialize integration
airtable = AirtableIntegration()

# List records
await airtable.configure({
    "api_key": "your_api_key",
    "base_id": "your_base_id",
    "table_name": "Your Table",
    "operation": "list_records",
    "data": json.dumps({
        "max_records": 100,
        "view": "Grid view",
        "sort_field": "Name"
    })
})
await airtable.run()

# Create record
await airt