Enterprise RAG Chatbot — Job Finder Assistant
Built and publicly deployed an enterprise-grade Retrieval-Augmented Generation (RAG) chatbot for searching job listings via natural language queries. Built with a three-person team (Thenmozhi Boopathy, Seth Grace, and Luke Johnson) as part of the ISA-632 course at Miami University's Farmer School of Business, and deployed publicly on Streamlit Community Cloud.
Key features
A dual knowledge base combines 37 curated job PDFs (Deloitte, JPMorgan Chase, Amazon) with 2,530 scraped Data Engineer roles. Users search with natural language — e.g. “Show me remote managerial data analyst roles” — and get verified results; when nothing matches, the system explicitly explains what's available instead. A feedback loop collects thumbs up/down ratings with optional tags and comments (89% positive across 18 responses to date).
Architecture
Data ingestion parses PDFs via ai_parse_document() and transforms CSV data into a Delta table.
Databricks Vector Search indexes the knowledge base, and a LangChain agent using the Llama 4 Maverick LLM with
a VectorSearchRetrieverTool handles retrieval and reasoning. The model is served via a Databricks Model Serving
endpoint, with a Streamlit UI that preserves chat history and collects user feedback, tracked with
mlflow.genai.evaluate() for performance benchmarking.
Known trade-offs
Adding the CSV dataset improved coverage for entry-level roles but introduced retrieval noise, showing up as a drop in precision metrics during evaluation. The identified fix is hybrid search — combining semantic similarity with structured metadata filters on seniority, location, and skills.