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nyasalohiya/README.md

Hi, I'm Nyasa Lohiya 👋

AI Engineer | Graph Neural Networks | RAG Systems | LLM Applications

🚀 Building intelligent AI systems using GraphRAG, GNNs, and LLMs 🎓 MSc IT Student 📍 Ahmedabad, India

🧠 About Me

  • 🔬 Working on Graph Neural Networks (GCN, GraphSAGE, GIN)
  • 🤖 Building RAG-based AI systems
  • 📊 AI-powered Stock Market Analysis
  • 🧩 Developing LLM applications
  • 🏗️ Designing Graph-based Retrieval Systems

🔥 Featured Projects

🧠 GraphRAG using GNN

End-to-end GraphRAG pipeline using Graph Neural Networks for graph-based retrieval

  • Node ranking
  • Context-aware query answering
  • Knowledge graph retrieval

🔗 https://github.com/nyasalohiya/graphrag_gnn

📈 NSE LLM Stock Analysis

LLM-powered stock analysis using Python

  • Technical indicators
  • AI-based insights
  • Portfolio analysis

🔗 https://github.com/nyasalohiya/nse-llm-stock-analysis

📄 RAG Document QA

RAG system using LangChain and vector DB

  • PDF ingestion
  • Embeddings
  • Context-aware answering

🔗 https://github.com/nyasalohiya/RAG-Document-QA-LangChain

🛠️ Tech Stack

AI / ML

Python • PyTorch • Scikit-learn • Pandas • NumPy

LLM & RAG

LangChain • Vector DB • Embeddings • GraphRAG

Graph ML

GNN • GCN • GraphSAGE • GIN • NetworkX

Web

Django • FastAPI • React • HTML • CSS

Database

SQL • MySQL • MongoDB

🚀 Currently Working On

  • GraphRAG Research
  • AI Financial Advisor
  • GNN-based Retrieval Systems
  • LLM + Graph Hybrid Models

🌐 Connect With Me

LinkedIn: https://linkedin.com/in/nyasa-lohiya Email: nyasalohiya1007@gmail.com

⚡ Fun Fact

I build AI systems that combine graphs + LLMs for smarter retrieval.

Pinned Loading

  1. RAG-Document-QA-LangChain RAG-Document-QA-LangChain Public

    Retrieval-Augmented Generation (RAG) system for answering questions from PDF documents using LangChain and vector databases.

    Jupyter Notebook

  2. Building-a-Personal-AI-From-Data-to-Digital-Twin-blog- Building-a-Personal-AI-From-Data-to-Digital-Twin-blog- Public

  3. GraphRAG-GNN GraphRAG-GNN Public

    End-to-end GraphRAG pipeline using Graph Neural Networks (GCN, GAT, GraphSAGE, GIN) for graph-based retrieval, node ranking, and context-aware query answering.

    Jupyter Notebook

  4. rag-fastapi rag-fastapi Public

    FastAPI RAG document question answering system using embeddings and semantic search

    Python