RAG Customer Support System

This project frames a production-ready RAG workflow for customer support: ingest documents, chunk and embed content, retrieve relevant context, generate a draft answer, and expose citations for human review.

Support knowledge changes frequently and usually lives across different sources. A plain chatbot can hallucinate, while manual search slows down response time.

Build a grounded assistant that helps agents answer faster while preserving traceability, confidence, and escalation paths.

• Document ingestion worker for PDFs, markdown, and internal knowledge pages.
• Embedding pipeline with metadata for source, product area, version, and access level.
• FastAPI service for retrieval, answer generation, feedback capture, and audit logs.
• Next.js dashboard for asking questions, reviewing citations, and flagging weak answers.

• Citation-first answer layout.
• Knowledge source filters by product area and document type.
• Human feedback capture for incorrect, stale, or incomplete answers.
• Fallback path when retrieval confidence is low.

• Chunking and embedding strategy for searchable support knowledge.
• Vector search with metadata filtering.
• LLM prompt layer that separates retrieved facts from generated response.
• Evaluation set placeholder for answer quality and citation relevance.

1. 1Admin uploads or syncs knowledge documents.
2. 2Worker extracts text, chunks content, generates embeddings, and stores metadata.
3. 3Agent asks a question from the dashboard.
4. 4API retrieves relevant chunks, sends grounded context to the LLM, and returns answer plus citations.
5. 5Agent accepts, edits, escalates, or flags the answer.

• Keeping generated answers grounded in retrieved content.
• Handling stale knowledge and conflicting document versions.
• Designing a useful confidence and escalation experience.

• Prioritize cited draft answers over fully automated replies for the MVP.
• Use metadata filtering before prompt complexity to reduce irrelevant context.
• Keep evaluation samples editable so domain experts can improve quality over time.

Result metrics are not filled yet. Add real response-time, adoption, and answer-quality data after deployment or user testing.

• Add offline evaluation reports for retrieval quality.
• Add role-based knowledge access.
• Add conversation analytics for unresolved topics.