Slava Ukraine



Hi my name is
Spas Cholakov
web developer.

Software Developer (Full Stack) / Data Scientist

React / NextJS / NodeJS / TypeScript / Golang / Python / PHP

LangChain Seminar  Hosted by Spas Cholakov & Soft University 

Slava Ukraine



React
5+ years
More Details

NodeJS
5+ years
More Details

GO Lang
2+ years
More Details

PHP
14+ years
More Details

Mongo DB
4+ years
More Details

MySQL
14+ years
More Details

Typescript
5+ years
More Details

HTML & CSS
14+ years
Portfolio

Lang Chain Seminar

  • Whether you're a developer, data scientist, or AI enthusiast, this session gives you the practical foundation to start building smart, dynamic AI solutions.

LangChain Seminar  Hosted by Spas Cholakov & SoftUni

Discover how to build powerful AI applications using LangChain — a framework designed to connect Large Language Models (LLMs) like GPT-4 with memory, tools, and external data.
In this seminar, we explore:
How LangChain simplifies AI app development
Real-world use cases with agents, prompts, and vector databases
Integrating tools like Qdrant, OpenAI, and Retrieval-Augmented Generation (RAG)
Best practices in production-ready LLM systems

Read More

Latest From AI section

  • Sentence Transformers Explained: SBERT, MiniLM, RoBERTa + Benchmarking & Model Comparison!

Sentence Transformers Explained

Sentence Transformers — a powerful family of models, designed for text embeddings!
This model family creates sentence-level embeddings, preserving the full meaning of a sentence, rather than just individual words.
Built on top of BERT, SBERT, and other transformer architectures, it excels in tasks like text similarity, clustering, and retrieval.

Read More




Latest From The AI-Powered App Tutorials

  • Build an AI-Powered App with FastAPI, Qdrant, NumPy, and Pydantic | Step-by-Step Tutorial #9

Build an AI-Powered App with FastAPI, Qdrant, NumPy, and Pydantic | Step-by-Step Tutorial #9

FastAPI with Custom Decorators & Embeddings In this tutorial, we take FastAPI development to the next level by implementing custom decorators with error logging and enhancing vector generation with SentenceTransformers.
🔹 Key Topics Covered:
✅ Creating custom decorators for structured logging
✅ Handling exceptions globally with decorators
✅ Implementing automatic error handling across classes
✅ Refactoring project structure for better maintainability
✅ Integrating MPNet embeddings to generate semantic vectors

Read More




Recommendation System AI

  • PyTorch DataLoader for Recommendation System: Loading and Preparing User-Item Data | Tutorial #1

Recommendation System AI

In this first video of the series, we tackle one of the most crucial steps in building a recommendation system: data loading and preparation. Using PyTorch, you’ll learn how to handle user-item interaction data, split datasets for training and validation, and efficiently batch data for your models.
What you'll learn:
✔️ Custom PyTorch Dataset class creation
✔️ Efficient data loading and batching with DataLoader
✔️ Dataset splitting for training and validation
✔️ Data preprocessing for recommendation systems

Read More