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from transformers import BertTokenizer

# Assuming you have a tokenizer function
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Iterate over objects in Weaviate (pseudo-code)
for object in weaviate_objects:
    # Tokenize the text of the object
    tokenized = tokenizer(object['text'], padding=True, truncation=True, return_tensors="pt")
    

Cdaprod

Batch Subscribing to Slack GitHub Intergration Over Webhook

Prerequisites

• Slack Workspace: You need access to a Slack workspace where you can add apps.
• Python Environment: Ensure you have Python installed on your machine. This guide assumes you are using Python 3.x.
• iOS Device: An iPhone or iPad with the Shortcuts app and Working Copy app installed.

Step 1: Create a Slack App and Incoming Webhook

1. Create a Slack App:

• Go to the Slack API page and click "Create New App".

Cdaprod

#!/bin/bash

# WLEDFlasher - Effortless WLED Firmware Flashing Tool

cat << EOF
  ____ ____    _    ____  ____   ___  ____  
 / ___|  _ \  / \  |  _ \|  _ \ / _ \|  _ \ 
| |   | | | |/ _ \ | |_) | |_) | | | | | | |
| |___| |_| / ___ \|  __/|  _ <| |_| | |_| |
 \____|____/_/   \_\_|   |_| \_\\___/|____/ 

Cdaprod

To create an abstracted LangGraph-like state machine using Pydantic, we will design a system that allows for the creation of nodes (agents) and edges (transitions) in a graph that represents a workflow. The agents will have specific roles or functions, and the transitions between these agents will be dictated by the outcomes of their actions. Pydantic will be used for data validation and settings management, ensuring that the components of our state machine are well-defined and interact correctly.

Step 1: Define the Base Models with Pydantic

First, we need to define our base models for Nodes (Agents) and Edges (Transitions). Pydantic models will ensure that each component of our graph adheres to a specific structure and type, facilitating error handling and data consistency.

Step 2: Implement the Graph Structure

We will create a class for the graph itself, which holds the nodes and edges and manages the workflow's execution. This includes methods for adding nodes, adding edges, and determining the

Cdaprod

To create dynamic ETL (Extract, Transform, Load) pipes with dynamic edges and nodes using Pydantic for data validation, we will expand upon the previously discussed abstracted LangGraph-like state machine. This ETL pipeline will dynamically adjust its flow based on the data passed through each node, allowing for flexible data processing scenarios.

Step 1: Define the Data Model and Node Functionality

First, define Pydantic models for the data that will flow through the ETL pipeline. These models ensure that each stage of the ETL process receives and produces data in the expected format.

from pydantic import BaseModel

class DataModel(BaseModel):

Cdaprod

Creating an iOS Shortcuts workflow that integrates with the VirusTotal API to test URLs directly from the Share Sheet involves a series of steps. This workflow will capture URLs shared via the Share Sheet, submit them to the VirusTotal API for safety analysis, and then display the analysis results to you. Below is a guide on setting up this workflow, including the necessary scripts to publish this as a gist or integrate it into your iOS device's Shortcuts app.

Preparing Your Environment:

1. Obtain a VirusTotal API Key:

   • Sign up for a free account at VirusTotal.
   • Navigate to your profile settings to find your API key.

2. Ensure You Have the Shortcuts App Installed:

   • The Shortcuts app is available by default on iOS devices. Update it to the latest version for the best experience.

Cdaprod

from typing import Optional
from pydantic import BaseModel, Field
from minio import Minio

class MinIoConfig(BaseModel):
    endpoint: str
    access_key: str
    secret_key: str
    secure: bool = Field(default=True, title="Use SSL?")
    bucket: Optional[str] = None

Cdaprod

Data collection using Python

See output

Cdaprod

import os
import sys
import io
import tarfile
import uuid
from urllib.parse import urlparse

import requests
from pydantic import BaseModel
from rich.console import Console

Cdaprod

To remove newline characters (\n) or punctuation from strings within your Pydantic models, you can enhance your validators to include these additional checks and transformations. Python's str methods and the string module can be very helpful for such tasks.

Here's an example of how you might adjust a Pydantic model validator to remove newline characters and punctuation from each item in a list attribute:

Extending the Pydantic Model with Custom Validation

from pydantic import BaseModel, validator
from typing import List
import string