Name: Muhammad Suleman
Type: User
Company: Cloud Data Silicon
Bio: I'm Muhammad Suleman, a seasoned software engineer with 18 years of diverse experience in software engineering, DevOps, Currently working Associate Sr Manager
Location: Canada
Blog: https://uk.linkedin.com/in/sulemanmuhammad
Muhammad Suleman's Projects
very basic project in go lang for the go server with form submission
Chat with Sql Server using google gemini generative ai
Provide the Url or html, it will convert it into PDF
python code to upload the csv dataset into kaggle
Unlock the power of LangChain & ChatGPT: Load your data (text, PDF, etc.), query with prompts, gain insights! Get started now.
Learning Solidity
my portfolio
This project aims to automate the process of generating multiple-choice questions (MCQs) using Generative AI techniques. Users can upload a text file containing relevant content, and our system utilizes Streamlit for the frontend, Python for backend processing, LangChain, LLM (Large Language Model)
Website-to-MCQs is an application built in Python that utilizes generative AI, Langchain, embedding techniques, and ChatGPT to automatically generate multiple-choice questions (MCQs) from website content.
MongoDB Express React Node
mooro youtube channel ai chat bot
create a real REST API with database integration and token authentication using Node.js with Restify, Mongoose/MongoDB and JWT json web token. In this part we will create basic database CRUD and in the next we will implement users and authentication
how to create a local RAG (Retrieval Augmented Generation) pipeline that processes and allows you to chat with your PDF file(s) using Ollama and LangChain!
how to implement fake api testing with IEnumerableDisposable<out T> : IEnumerable<T>, IDisposable, Generics
Paint Shop code challenge
using Llama Parse to read pdf and convert into mark down or text
Microsoft Phi-3 Vision-the first Multimodal model By Microsoft- Demo With Huggingface
Prompt Engineering with langchain
Question and Answer Similarity Search Application using pinecone vector database
This project is dedicated to creating a robust Question and Answer (Q&A) similarity search application using Python including Jupyter Notebook, Chroma Database, Vector Database, LangChain, large language models, RetrievalQA, ChatGPT, OpenAI Embeddings, PyPDFDirectoryLoader, and RecursiveCharacterTextSplitter
Question Answer application using ObjectBox vector database, Gpt4o and langchain
This project is an end-to-end data engineering pipeline that orchestrates data ingestion, processing, and storage using a suite of powerful tools and technologies. The pipeline is designed for ease of deployment and scalability, leveraging Docker to containerize all components.