Learning Tracks
Structured Skill Tracks
Data Analytics & SQL Mastery
From foundational SQL queries to advanced CTEs, window functions (`RANK`, `DENSE_RANK`), Pandas dataframe pipelines, and data warehouse modeling.
GenAI & RAG System Architecture
Build production RAG pipelines with LangChain, PyTorch embeddings, ChromaDB vector stores, and vLLM inference serving.
FastAPI, Docker & Microservices
Build production REST APIs with Python FastAPI, SQLAlchemy async ORM, PostgreSQL connection pools, and containerized Docker compose setups.
🤖 Building a Complete RAG Pipeline in Python with LangChain & Vector Search
Step-by-step code walkthrough from document chunking to context retrieval and LLM response generation.
Retrieval-Augmented Generation (RAG) is the industry-standard architecture for grounding Large Language Models (LLMs) with private enterprise data. Below is the step-by-step breakdown and runnable Python code.
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
# 1. Load & Chunk Technical Documentation
loader = TextLoader("knowledge_base.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=50
)
chunks = text_splitter.split_documents(documents)
# 2. Generate HuggingFace Vector Embeddings
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
# 3. Store in Local ChromaDB Vector Store
vector_db = Chroma.from_documents(chunks, embeddings, persist_directory="./chroma_db")
# 4. Perform Similarity Search Query
query = "How do we handle API rate limiting in production?"
matching_docs = vector_db.similarity_search(query, k=3)
print(f"Top Matched Context: {matching_docs[0].page_content}")Splitting raw text into 500-character chunks prevents exceeding LLM context windows.
Dense vector math computes semantic distance using Cosine similarity.
ChromaDB provides fast HNSW indexing for real-time document retrieval.
📊 Mastering Advanced SQL Window Functions: ROW_NUMBER vs RANK vs DENSE_RANK
Real interview question walkthrough: Finding the Top 3 Highest Earning Employees per Department.
Window functions compute aggregations across a set of rows related to the current row without collapsing the dataset.
WITH RankedSalaries AS (
SELECT
employee_id,
first_name,
department_name,
salary,
DENSE_RANK() OVER (
PARTITION BY department_name
ORDER BY salary DESC
) AS salary_rank
FROM employees
)
SELECT
employee_id,
first_name,
department_name,
salary
FROM RankedSalaries
WHERE salary_rank <= 3;More Developer Tutorials
Explore our complete library of technical guides.
Deploying Async FastAPI Services to AWS EC2 with Docker Compose
Write clean async REST APIs with Pydantic v2 validation, Gunicorn/Uvicorn workers, and PostgreSQL container configuration.
Designing a Distributed Rate Limiter with Redis & Token Bucket
Implement sliding-window and token bucket rate limiters in Python/Node.js backed by Redis memory clusters.