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RAG Text-to-SQL: Asking My Database Questions in Plain English

Sat, 25 Oct 2025 00:00:00 +0000

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:

Local RAG with Ollama and ChromaDB: Running AI Offline

Sun, 29 Jun 2025 00:00:00 +0000

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:

YouTube Summarizer: What I Built with LangGraph and Gemini

Fri, 06 Jun 2025 00:00:00 +0000

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: