Design a RAG system for vehicle documents

Design a Retrieval-Augmented Generation (RAG) architecture to power a Q&A chatbot over **vehicle documentation**. ### Document types & characteristics - Sources: engine specification manuals, warranty documents, repair documents, vehicle datasets. - Formats: PDFs (1 to 1,000 pages), CSVs (potentially large), HTML, Markdown. - Workload: **read-heavy**. - Updates: documents are **static** (assume no edits after publishing, but new document versions may be added). ### Core requirements 1. Users ask natural-language questions (e.g., “What torque spec for …?”, “Is this covered under warranty?”). 2. System retrieves relevant passages/snippets and generates an answer grounded in the documents. 3. Provide citations (doc + page/section/row reference where possible). 4. Handle both unstructured text (PDF manuals) and structured/semi-structured data (CSV datasets). ### Non-functional requirements (discuss and propose targets) - Latency and throughput goals for chat requests. - Quality: relevance, correctness, citation accuracy. - Security: document access control if documents differ by vehicle model, region, or user entitlements. - Observability: tracing, retrieval diagnostics. - Scalability: growing corpus size. ### Deliverables - High-level architecture (ingestion/indexing + query-time flow). - Data model for documents/chunks/metadata. - Vector database choice/strategy and retrieval approach. - How to process PDFs/CSVs for chunking and citations. - Key tradeoffs and failure modes.