Building a Dynamic RAG System | Job API + ChromaDB + Gemini | GenAI Series Ep 8

RAG Data Ingestion Tutorial | Learn how to build a ingestion pipeline for a Retrieval-Augmented Generation (RAG )system using Job APIs, Vector Databases, and Google Gemini. Welcome to Episode 8 of the “Generative AI & Agentic AI” Series 🚀 In the previous episode, we implemented a RAG-powered Resume Analyzer using static knowledge documents stored in a Vector Database. Now it's time to make our RAG system dynamic by ingesting job postings from external Job APIs 🔥 In this video, we will extend our Resume Analyzer and build a complete data ingestion pipeline that fetches real-time job data, processes it, and stores it inside a Vector Database for retrieval. In this video, we will explore: 👉 Fetching jobs from external Job APIs 👉 Building a data ingestion pipeline 👉 Data cleaning and normalization 👉 Creating embeddings from job descriptions 👉 Storing job data in ChromaDB 👉 Updating the Vector Database automatically 👉 Using job data during resume analysis Topics Covered: 📌 RAG Data Ingestion Pipeline 📌 Adzuna API Integration 📌 Data Cleaning and Preprocessing 📌 Embedding Generation 📌 Vector Database Updates 📌 Dynamic Knowledge Bases 📌 Semantic Search 📌 Context-Aware Resume Evaluation Project Overview: This project is an AI-powered Resume Analyzer that extracts resume content from PDF and DOCX files and uses Google Gemini to generate: ✅ Resume Scores ✅ Eligible Job Roles ✅ Improvement Suggestions To make the analysis more accurate and aligned with current market opportunities, we implement Retrieval-Augmented Generation (RAG) using ChromaDB and build a data ingestion pipeline that fetches live job postings from external APIs. The job data is cleaned, normalized, converted into embeddings, and stored in a Vector Database. When a resume is uploaded, the system retrieves relevant job information and domain knowledge from the Vector Database, enabling more accurate and context-aware evaluations instead of relying solely on the LLM's general knowledge. By the end of this video, you will understand how production-grade RAG systems keep their knowledge bases updated using automated data ingestion pipelines. This video is beginner-friendly and highly useful for developers learning: ✅ Generative AI ✅ RAG Implementation ✅ Data Ingestion Pipelines ✅ Google Gemini ✅ Vector Databases ✅ AI Engineering ✅ Enterprise AI Applications Links: GitHub Repository: https://github.com/the-systems-mind/Resume... Adzuna link: https://developer.adzuna.com/?utm_source=c... Adzuna login link: https://developer.adzuna.com/login