Python

What This Project Is I built a backend that scrapes websites, stores data in PostgreSQL and MongoDB, and exposes REST APIs through FastAPI and Flask. I made it as a portfolio piece — something I could demo and actually run on my machine. 1. What Does This Project Do? (Simple Explanation) This project is a data aggregator backend. It can: Scrape product or article data from websites Store that data in two databases (PostgreSQL for structured data, MongoDB for flexible logs) Expose REST APIs so other apps can create, read, update, and delete data Call external APIs and return the results You can think of it as: ...

What I Built I hate writing SQL for simple questions. This project lets me ask things like “how many customers are from Brazil?” in plain English — it generates the SQL, runs it, and gives me a readable answer. All local, using Ollama and ChromaDB. Easy to use — ask questions in everyday language Private — everything runs locally on your machine Fast feedback — generates and runs SQL in seconds Schema-aware — understands table names and relationships Interactive — ask follow-up questions in a chat-style flow Error handling — catches common issues like wrong table name casing System Architecture My RAG Text-to-SQL system consists of five core components: ...

Why I Built This I wanted a chatbot that answers questions from my own documents — without sending data to a cloud API. This RAG setup runs fully offline with Ollama and ChromaDB on my machine. Privacy — your data stays on your machine No API costs — no usage fees or rate limits Speed — no network delay for each query Control — you choose what goes into the knowledge base Offline use — works without internet after setup Architecture Overview My local RAG system consists of four main components: ...

What I Built I wanted to paste a YouTube URL and get a summary — or ask questions about the video — without watching the whole thing. This project uses LangGraph for the workflow and Gemini for the actual summarizing and Q&A. Key Features of Agentic Workflows Modularity — each step can be built and tested on its own State — data flows from step to step in a structured way Reusability — the same steps can be reused in other projects Autonomy — each step can decide what to do based on the input it receives Popular Tools for Agentic Workflows Tool Description Language Best For LangGraph Graph-based AI workflow framework with state management. Python Custom step-by-step pipelines CrewAI Agent orchestration tool inspired by human teams and role delegation. Python Role-based agent collaboration Autogen Microsoft’s multi-agent framework for goal-oriented dialogue and tasks. Python Conversational multi-agent systems LangChain General framework for chaining LLMs with tools, memory, and logic. Python Broader LLM apps beyond just agents AgentOps Infra layer for deploying and monitoring agentic systems. Platform Scaling, testing, and managing agents Why I Chose LangGraph For this project, I chose LangGraph because: ...