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1.5 Upgrading From Previous Versions
Upgrading to 4.1.0
Upgrading Servers to 4.1.0 from any version 3.3.x through 4.0.x
Notable changes in 4.1.0
Apache Kafka 4.1 ships with a preview of Queues for Kafka (KIP-932). This feature introduces a new kind of group called share groups, as an alternative to consumer groups. Consumers in a share group cooperatively consume records from topics, without assigning each partition to just one consumer. Share groups also introduce per-record acknowledgement and counting of delivery attempts. Use share groups in cases where records are processed one at a time, rather than as part of an ordered stream. To enable share groups, use the kafka-features.sh tool to upgrade to share.version=1. For more information, please read the release notes.
Common
The logger class name for LogCleaner has been updated from kafka.log.LogCleaner to org.apache.kafka.storage.internals.log.LogCleaner in the log4j2.yaml configuration file. Added loggers for org.apache.kafka.storage.internals.log.LogCle

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def image_to_ascii(img, width=100, height=50):
    # Convert to numpy array if it's not already
    if not isinstance(img, np.ndarray):
        img = np.array(img)
    
    # Ensure the image is in the correct format (H, W, C)
    if img.ndim == 3 and img.shape[0] == 3:
        img = np.transpose(img, (1, 2, 0))
    
    # Normalize the image if it's not in [0, 1] range

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---

title: "Supercharging Your AI: Setting Up RAG with PostgreSQL and pgvector" date: 2024-09-30T21:28:28+05:30 draft: false ShowToc: true TocOpen: false summary: "Learn how to implement a powerful Retrieval-Augmented Generation (RAG) system using PostgreSQL and pgvector. This comprehensive guide covers everything from setting up a custom PostgreSQL Docker image to creating a fully functional RAG query system with vector embeddings and language model inference." tags: ["PostgreSql", "PGVector", "RAG", "Vector Database", "AI", "NLP", "Docker", "GPT", "LLM"] categories: ["Database", "Artificial Intelligence", "Tutorial"] cover:

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{
  "title": "Supercharging Your AI: Setting Up RAG with PostgreSQL and pgvector",
  "content": "## Introduction\n\nImagine having a conversation with an AI that not only understands your questions but also draws upon a vast repository of knowledge to provide accurate, contextually relevant answers. This isn't science fiction—it's the power of Retrieval-Augmented Generation (RAG) systems. In this blog post, we'll dive into the exciting world of RAG and show you how to harness this technology using PostgreSQL and pgvector.\n\n## What is RAG, and Why Should You Care?\n\nRAG is like giving your AI a photographic memory and the ability to instantly recall relevant information. It combines the power of large language models with a knowledge base, allowing the AI to provide more accurate, up-to-date, and contextually relevant responses. Whether you're building a chatbot, a question-answering system, or any AI application that requires access to specific knowledge, RAG is your secret weapon.\n\n## Prerequisites\n\nB

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import argparse
import json
import re
import sys
from datetime import datetime

import frontmatter


def convert_markdown_to_medium(input_file, output_file):

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---

title: "Git Aliases to Supercharge Your Workflow" date: 2024-03-21 draft: false ShowToc: true TocOpen: false math: true summary: "Git aliases are a powerful workflow tool that create shortcuts to frequently used Git commands." tags: [ "git", "version-control", "workflow", "development", "productivity", "command-line", "aliases", "git-commands", "git-aliases", "coding",

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1. Get details about this Connect worker and the id of the Kafka cluster it is connected to

curl -X GET "http://localhost:8083/" -H "accept: application/json"

2. List the current loggers and their log levels

curl -X GET "http://localhost:8083/admin/loggers" -H "accept: application/json"

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"""
- based no splits 1000 splits
- based on time eg 10s
- based on time interval 2:10 -> 2:50
- 10 people upload 1 gb 2 file daily
- we have design a system to split it into 1000 partitions


Solution:

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def fibonacci(n):
    if n <= 2:
        return 1
    return fibonacci(n - 1) + fibonacci(n - 2)

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from django.urls import path, include
from rest_framework import routers
from .views import UploadViewSet

router = routers.DefaultRouter()
router.register(r'upload', UploadViewSet, basename="upload")

# Wire up our API using automatic URL routing.
urlpatterns = [
    path('', include(router.urls)),