# RAG Support System

## Docs

- [POST /answer](https://mintlify.wiki/JoAmps/rgt-assignment/api/answer.md): Query the RAG system for intelligent ticket answers
- [GET /health](https://mintlify.wiki/JoAmps/rgt-assignment/api/health.md): Check API health status
- [POST /ingest](https://mintlify.wiki/JoAmps/rgt-assignment/api/ingest.md): Ingest documents into the knowledge base
- [Request Models](https://mintlify.wiki/JoAmps/rgt-assignment/api/models/requests.md): API request models for the RAG Support System
- [Response Models](https://mintlify.wiki/JoAmps/rgt-assignment/api/models/responses.md): API response models for the RAG Support System
- [API Overview](https://mintlify.wiki/JoAmps/rgt-assignment/api/overview.md): Introduction to the RAG Support System API
- [POST /triage](https://mintlify.wiki/JoAmps/rgt-assignment/api/triage.md): Run ML triage classification on support tickets
- [Architecture](https://mintlify.wiki/JoAmps/rgt-assignment/architecture.md): Deep dive into system components, request flow, and design decisions for the RAG Support System
- [Knowledge Base](https://mintlify.wiki/JoAmps/rgt-assignment/concepts/knowledge-base.md): Explore document ingestion, chunking, embeddings, and vector storage in Chroma
- [RAG Pipeline](https://mintlify.wiki/JoAmps/rgt-assignment/concepts/rag-pipeline.md): Understand the end-to-end retrieval-augmented generation flow in the support system
- [Structured Outputs](https://mintlify.wiki/JoAmps/rgt-assignment/concepts/structured-outputs.md): Understand citations, internal next steps, and human review flags in generated responses
- [Triage Models](https://mintlify.wiki/JoAmps/rgt-assignment/concepts/triage-models.md): Learn how ML models classify tickets by category and priority with confidence scoring
- [Docker deployment](https://mintlify.wiki/JoAmps/rgt-assignment/deployment/docker.md): Deploy the RAG Support System using Docker and Docker Compose
- [Environment variables](https://mintlify.wiki/JoAmps/rgt-assignment/deployment/environment-variables.md): Configure API keys and system settings for the RAG Support System
- [Production deployment](https://mintlify.wiki/JoAmps/rgt-assignment/deployment/production.md): Best practices for deploying the RAG Support System in production environments
- [Data Ingestion](https://mintlify.wiki/JoAmps/rgt-assignment/guides/data-ingestion.md): Learn how to ingest documents into the RAG system using CLI or API methods
- [Evaluation](https://mintlify.wiki/JoAmps/rgt-assignment/guides/evaluation.md): Evaluate RAG system performance with offline metrics and adversarial testing
- [Running Predictions](https://mintlify.wiki/JoAmps/rgt-assignment/guides/running-predictions.md): Use trained models to classify tickets and generate predictions
- [Testing](https://mintlify.wiki/JoAmps/rgt-assignment/guides/testing.md): Run unit tests with pytest for the RAG Support System
- [Training Models](https://mintlify.wiki/JoAmps/rgt-assignment/guides/training-models.md): Train category and priority classification models for ticket triage
- [Installation](https://mintlify.wiki/JoAmps/rgt-assignment/installation.md): Comprehensive setup guide for Python environment, dependencies, API keys, and development tools
- [RAG Support System](https://mintlify.wiki/JoAmps/rgt-assignment/introduction.md): A production-ready Retrieval-Augmented Generation system for intelligent customer support with ML triage, semantic retrieval, and LLM-based answer generation
- [Quickstart](https://mintlify.wiki/JoAmps/rgt-assignment/quickstart.md): Get from zero to your first RAG query in under 5 minutes with this step-by-step guide