Retrieval-Augmented Generation (RAG) system using LangChain, ChromaDB, and local LLMs.
The Problem: The "Documentation Drain" We’ve all been there: you need a specific sql syntax or a complex join optimization strategy, and you're stuck searching a 200-page PDF. Standard AI models li...
Source: DEV Community
The Problem: The "Documentation Drain" We’ve all been there: you need a specific sql syntax or a complex join optimization strategy, and you're stuck searching a 200-page PDF. Standard AI models like ChatGPT are great, but they don't know the specifics of your project's internal documentation. The goal was to build a system that: Reads the entire PDF. Indexes it for instant retrieval. Answers complex queries using a local model for privacy and speed. The Tech Stack (2026 Edition) To keep the project modern and efficient, I used a modular stack: Language: Python 3.12+ managed by uv (the fastest package manager). Orchestration: LangChain and LangChain-Classic for the RAG pipeline. Vector Database: ChromaDB for persistent, local storage. Models: Google Gemini 2.5 Flash (for heavy lifting) and Qwen 3: 0.6B-F16 (running locally via Docker). Frontend: Streamlit for a clean, browser-based chat interface. Implementation: Step-by-Step 1. Data Ingestion & Chunking A 200-page PDF is too large
