Layered Information Source Retriever

Problem It Solves

When retrieval targets extend beyond web pages to local files, internal knowledge bases, APIs, and real-time data, a single flat query can mix low-signal results into context and steer model answers toward noise. The layered information source retriever is positioned to separate “which source classes to query, in what priority, and when to stop” before execution, rather than merging all sources into one batch.

How It Works and Limits

Based on its name and knowledge-management category, it behaves more like a retrieval orchestration layer:
- Layered sources: group sources by confidence, freshness, or ownership, such as internal docs, external pages, or live APIs;
- Search strategy: query high-confidence sources first, then expand to secondary sources;
- Result annotation: label candidates by source tier so models or developers can rank and filter them.
The provided material does not specify concrete source configuration, scoring rules, or API details. When integrating it, confirm source definitions, permission boundaries, deduplication policy, and whether external network calls are allowed, instead of treating layered retrieval as automatic fact-checking.