📌 How to Use This Document
Each topic is structured as:
-
Outcome-first summary
-
Exact README excerpts (verbatim)
-
Representative code / usage
-
Simple architecture diagram
🔴 Kafka — Event-driven order ingestion demo
🟣 AI-Agents — Prompt-to-code AI Agent CLI, RAG Agent to answer PDFs
🟢 Data-Science-IIITB — R-based academic data science work
🟡 Web-Projects — Collection of small TypeScript/CSS web projects
Event-driven order ingestion with Apache Kafka
Repository: nitinsriv/Kafka
🎯 High-level Outcome
✅ Simple, reliable demonstration of file-driven order ingestion into Apache Kafka using Python.
⚡ Quick Summary (from README)
💬 "This repository provides example producer and consumer scripts using Apache Kafka for simple order processing based on files containing XML order data."
📄 README Excerpts & Usage (Verbatim)
- producer.py
"Watches an
inputdirectory for new XML files describing orders, extracts their order number and name, and sends these [...]"
- consumer.py
"Consumes messages from the
ordersKafka topic, deserializes the order data, and prints the received messages."
<order>
<number>12345</number>
<name>BWG1</name>
</order>python producer.py
python consumer.py📝 File Summary
-
Monitors the input directory for XML files
-
Parses order number and name
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Publishes JSON to Kafka topic orders
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Subscribes to topic orders
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Prints received JSON messages
🛠 Technical Highlights
- Language: Python 3.x
- Dependency: kafka-python
- Environment Kafka broker :: localhost:9092
Production patterns illustrated:
File watching → parsing → Kafka publish Consumer subscription → deserialization → processing
🧱 Architecture Diagram
graph TD;
FileSystem[Input XML files] --> Producer[producer.py]
Producer --> Kafka[Kafka Broker - orders topic]
Kafka --> Consumer[consumer.py]
Consumer --> Terminal[Console Output]
A simplistic programmable implemetation of AI-Agent
Repository: nitinsriv/ai-agents
🎯 High-level Outcome
✅ A compact CLI-based Prompt-to-Code AI Agent
✅ Demonstrates agent-driven code generation using LangChain + OpenAI
✅ Extracts and persists generated code automatically
📄 Description
"An AI Agent which provides a simple command-line interface for generating code snippets using langchain."
✨ Features
- Interactive User Prompt
- AI Agent Integration
- Markdown Code Extraction
- Automatic Code Saving (output.txt)
🧰 Dependencies
- LangChain
- OpenAI API key via .env
- Python
python play_webUI.py
📌 Example Output
Java - implement binary search on a sorted array
Java Code:
public class BinarySearch {
...
}
🛠 Representative Flow & Notes
- Extracts first fenced markdown code block
- Saves to output.txt Uses
- regex-based parsing
🧱 Architecture Diagram
graph TD;
User --> CLI[play_webUI.py]
CLI --> Agent[LangChain + OpenAI]
Agent --> AIResponse[AI Text + Code]
AIResponse --> Extractor[Regex Extractor]
Extractor --> Output[output.txt]
Retrieval-Augmented Generation (RAG) for answering questions over PDF documents
Repository: nitinsriv/rag-based-pdf
🎯 High-level Outcome
✅ Pipeline to ingest PDF documents, build a vector index of text chunks, and answer user questions by retrieving relevant chunks and conditioning an LLM (RAG).
✅ Demonstrates PDF parsing, chunking, embedding generation, vector store (FAISS/Chroma), and a retriever+LLM query flow.
✅ Example usage for document search, Q&A and knowledge-base style assistants.
📄 Description
RAG-based PDF demo: extract text from PDFs, create embeddings, store in a vector DB and run retrieval-augmented queries against an LLM.
✨ Features
PDF ingestion Text chunking Embeddings (OpenAI) Retriever + LLM composition (context + generation)
Academic Data Science in R
Repository: nitinsriv/Data-Science-IIITB
🎯 High-level Outcome
📊 Data science coursework and labs authored in R, supporting reproducible analysis and use-cases.
📦 Repository Metadata
Language: R (100%)
Description: R
📚 Expected Contents & Workflow
✅ R scripts
✅ Visualizations using tidyverse / ggplot2
🧪 Representative R Snippet
library(tidyverse)
data <- read_csv('data/student_scores.csv') %>%
mutate(passed = score >= 50)
summary_stats <- data %>%
group_by(course) %>%
summarize(
mean_score = mean(score, na.rm = TRUE),
pass_rate = mean(passed, na.rm = TRUE)
)
ggplot(summary_stats, aes(x = course, y = mean_score)) +
geom_col() +
theme_minimal()
Collection of small TypeScript & CSS web projects
Repository: nitinsriv/web-projects
🎯 High-level Outcome
✅ A set of compact web projects and demos, primarily implemented in TypeScript with CSS styling — useful as learning exercises and small deployable apps.
📄 Description
This repository contains multiple front-end projects showcasing TypeScript, modern tooling, and CSS techniques. Use it to explore examples, clone demos, or adapt them for quick prototypes.
✨ Notable points
- Language composition: TypeScript-focused projects (approx 85% TypeScript, 14% CSS)
- Small, self-contained apps and UI demos