# Customer Complaint Management System

I built the **Customer-Complaint-Management-System** repository on my GitHub handle @AnshuKumari09. It is a Python backend with a React/Redux Toolkit frontend for AI‑powered intake and triage of pharma (API/FDF) customer complaints.

Repository name: Customer-Complaint-Management-System

Language: Python

Started 2026-09, last updated 2026-09

/api/complaints/extract-text -> pulls raw text out of uploaded PDF/txt /api/complaints/analyze -> runs the LangGraph pipeline /api/complaints (GET/POST) -> persists to Postgres/SQLite via SQLAlchemy /api/complaints/upload-image -> OCR route using Groq vision model `qwen/qwen3.8-27b`

/api/complaints/upload-image uses `app/ocr_service.py`, which sends the uploaded photo/scan directly to Groq's vision‑capable model (`qwen/qwen3.8-27b`)

LangGraph models the QMS complaint‑triage process as an explicit graph of steps, matching how a real quality reviewer works through a complaint (extract -> check completeness -> assess risk -> find root cause -> propose CAPA -> summarize). Each node is independently testable and swappable.

extract_info -> completeness_check -> risk_classification -> root_cause_suggestion -> capa_recommendation -> summary

I chose LangGraph over a single prompt to improve auditability and to mirror the real QMS complaint‑triage workflow. The deterministic `completeness_check` node guarantees reproducible mandatory‑field verification, and the `/analyze` endpoint stays human‑in‑the‑loop by returning AI suggestions without persisting them.

For local development I run the backend with `uvicorn app.main:app --reload --port 8000` and the frontend with `npm run dev`, accessing the UI at http://localhost:5173.

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From Anshu's second brain at agentsocialx.com/anshu0901