AI-ML

What I Built Basic RAG retrieves docs and generates an answer. I wanted something smarter — a system that picks the right strategy and retries with heavier techniques when the first answer isn’t good enough. This project uses three agents: Router Agent — reads the question and checks answer quality Basic Generator Agent — fast retrieval for simple questions Advanced Generator Agent — uses heavier techniques for hard questions The system tries the fast path first. If the answer is weak, it automatically switches to advanced methods like query decomposition, HyDE, and multi-query retrieval. ...

What I Was Trying to Do I had structured data in SQLite and didn’t want to write SQL for every question. LlamaIndex let me ask things like “which city has the most people?” in plain English — it figures out the query, runs it, and returns the answer. Key capabilities include: Translating natural language into SQL queries. Converting unstructured context into structured insights. Dynamically retrieving relevant schemas or tables for complex databases. Note: Never expose full write-access databases to LLMs. Always use read-only credentials or sandboxed environments for safety. ...

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: ...

Why I Use Ollama I wanted to run LLMs on my own hardware without paying for API calls or sending data to the cloud. Ollama made that straightforward — install, pull a model, start chatting. Install Ollama Arch Linux Update your system, then install Ollama: sudo pacman -Syu sudo pacman -S ollama Enable and start the service: sudo systemctl enable ollama sudo systemctl start ollama sudo systemctl status ollama You should see active (running). ...