Voice Assistant Knowledge Pipeline: Holidays and Animals

Context

You are designing an end-to-end knowledge pipeline for a global voice assistant (e.g., Alexa) to answer user questions about holidays worldwide and animal-related queries. Your design should support high accuracy, low latency, and continuous freshness across locales and languages.

Part 1 — Holidays

Design an end-to-end data pipeline that enables the assistant to answer holiday-related questions worldwide. Describe:

1. Data sources (official/government, religious/lunar, public datasets) and how to evaluate reliability and licenses.
2. Ingestion architecture (batch/stream, change detection, versioning, schema validation).
3. Normalization and reconciliation across calendar systems (Gregorian, lunar/lunisolar, federal/country-specific), including:
   • Date rules (e.g., "first Monday in September"; observance shifts when a date falls on a weekend).
   • Multi-day holidays, regional variants, and time zones.
   • Conversion from lunar/lunisolar to Gregorian per year and locale.
4. Storage layers (canonical knowledge graph, precomputed expansions, search index, cache) and data models.
5. Query/answer layer (NLU intents, entity resolution, localization, latency budget, fallbacks) and how you will keep content current (freshness SLAs, monitoring, editorial overrides).

Part 2 — Animals

Extend the pipeline so the assistant can answer animal-related questions. Describe:

1. Additional data elements and taxonomies (e.g., scientific classification, common names, habitats, conservation status).
2. Required ML/NLP models (domain classification, entity linking, attribute extraction, summarization) and a feature store.
3. How you will detect and correct misrouted queries such as "Peppa Pig" (a cartoon character) that are falsely labeled as animal questions. Include confidence thresholds, intent reclassification, and human-in-the-loop.

Part 3 — Debugging Framework

You receive error logs where the assistant fails on specific animal questions. Propose a framework to:

1. Categorize errors end-to-end (ASR, language detection, intent, entity linking, knowledge gaps, freshness, rendering).
2. Trace root causes with observability and reproducible pipelines.
3. Prioritize fixes using an impact-severity-effort framework.

Notes

• Calendars differ by locale—normalize date formats, offsets, time zones, and observance rules.
• Implement calibrated confidence thresholds and intent reclassification to handle ambiguous queries like "Peppa Pig".