AI Notes for Learners Page 9

Automating Customer Communication: Building a Production-Ready LangChain Agent for Order Notifications

In the fast-paced world of e-commerce, proactive and timely communication with customers is paramount for fostering trust and ensuring a seamless post-purchase experience. Manually tracking new orders and…

Continue Reading

Intelligent Order Monitoring Langchain LLM tools

Building Intelligent Order Monitoring: A LangChain Agent for Database ChecksIn today’s fast-paced e-commerce landscape, staying on top of new orders is crucial for efficient operations and timely fulfillment.…

Continue Reading

Loading and Indexing data into a vector database

Vector databases store data as high-dimensional vectors, which are numerical representations of data points. Loading data into a vector database involves converting your data into these vector embeddings.…

Continue Reading

Building a Hilariously Insightful Image Recognition Chatbot with Spring AI

Building a Hilariously Insightful Image Recognition Chatbot with Spring AI (and a Touch of Sass)While Spring AI’s current spotlight shines on language models, the underlying principles of integration…

Continue Reading

Spring AI chatbot with RAG and FAQ

Demonstrate the concepts of building a Spring AI chatbot with both general knowledge RAG and an FAQ section into a single comprehensive article.Building a Powerful Spring AI Chatbot…

Continue Reading

Vector Database Internals

Vector databases are specialized databases designed to store, manage, and efficiently query high-dimensional vectors. These vectors are numerical representations of data, often generated by machine learning models to…

Continue Reading

RAG to with sample FAQ and LLM

Code Explanation: RAG with FAQ and OpenAI This Python code implements a Retrieval Augmented Generation (RAG) system specifically designed to answer questions from an FAQ dataset using OpenAI’s…

Continue Reading

RAG with locally running LLM

Sample code to enable running the LLM locally. This will involve using a local LLM instead of OpenAI. Key Changes: To run this code with a local LLM:…

Continue Reading

Implementing RAG with vector database

Explanation: Key Points: Remember to:

Continue Reading

Retrieval Augmented Generation (RAG) with LLMs

Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate information from external sources during…

Continue Reading

React Hooks and class components

The introduction of React Hooks in React 16.8 marked a significant shift in how React developers manage state and side effects. Here’s a breakdown of the key differences…

Continue Reading

Redux “under the hood”

To understand Redux “under the hood,” let’s break down its core principles and how they work together: 1. The Store: 2. Actions: 3. Reducers: 4. The Data Flow:…

Continue Reading

Managing state in ReactJS

Managing state in ReactJS is crucial for building dynamic and interactive user interfaces. Here’s a breakdown of the common approaches, from simple to more complex: 1. useState Hook…

Continue Reading

ReactJS Bits

Alright, let’s dive into some ReactJS questions! To give you the most helpful answers, I’ll cover a range of topics from basic to more advanced. Basic React Questions:…

Continue Reading

Kafka Disk I/O Tuning Guide

Disk I/O is a critical bottleneck for Kafka performance. Kafka relies heavily on the file system for storing and retrieving messages, and inefficient disk I/O can lead to…

Continue Reading

Kafka Network Latency Tuning

Network latency is a critical factor in Kafka performance, especially for applications requiring near-real-time data processing. High network latency can significantly increase the time it takes for messages…

Continue Reading

Kafka CPU Tuning Guide

Optimizing CPU usage in your Kafka cluster is essential for achieving high throughput, low latency, and overall stability. Here’s a comprehensive guide to help you effectively tune Kafka…

Continue Reading

gRPC vs HTTP

gRPC (gRPC Remote Procedure Calls) and HTTP (Hypertext Transfer Protocol) are both fundamental protocols used for communication between applications, but they differ significantly in their design, features, and…

Continue Reading

Databricks scalability

Databricks is designed with scalability as a core tenet, allowing users to handle massive amounts of data and complex analytical workloads. Its scalability stems from several key architectural…

Continue Reading

Apache Spark

Let’s illustrate Apache Spark with a classic “word count” example using PySpark (the Python API for Spark). This example demonstrates the fundamental concepts of distributed data processing with…

Continue Reading

Inner workings of Apache Spark

Here’s a breakdown of key internal aspects of the inner workings of Apache Spark. : 1. Architecture: 2. Execution Model: 3. Data Partitioning: 4. Shuffle Operations: 5. Memory…

Continue Reading

MLOps pipeline

While a full-fledged MLOps pipeline involves integrating various tools and platforms, here are some illustrative code snippets demonstrating key MLOps concepts using popular Python libraries and tools. These…

Continue Reading

Workflow of MLOps

The workflow of MLOps is an iterative and cyclical process that encompasses the entire lifecycle of a machine learning model, from initial ideation to ongoing monitoring and maintenance…

Continue Reading

Developing and training machine learning models within an MLOps framework

The “MLOps training workflow” specifically focuses on the steps involved in developing and training machine learning models within an MLOps framework. It’s a subset of the broader MLOps…

Continue Reading

AI Notes For Learning

Automating Customer Communication: Building a Production-Ready LangChain Agent for Order Notifications

Intelligent Order Monitoring Langchain LLM tools

Loading and Indexing data into a vector database

Building a Hilariously Insightful Image Recognition Chatbot with Spring AI

Spring AI chatbot with RAG and FAQ

Vector Database Internals

RAG to with sample FAQ and LLM

RAG with locally running LLM

Implementing RAG with vector database

Retrieval Augmented Generation (RAG) with LLMs

React Hooks and class components

Redux “under the hood”

Managing state in ReactJS

ReactJS Bits

Kafka Disk I/O Tuning Guide

Kafka Network Latency Tuning

Kafka CPU Tuning Guide

gRPC vs HTTP

Databricks scalability

Apache Spark

Inner workings of Apache Spark

Developing and training machine learning models within an MLOps framework

Popular Posts