AI工程SynapseB类

Passive Storage to Active Indexing for Agent Knowledge Bases

TL;DR

  • OBS knowledge base upgraded to auto-index every 2 days instead of on-demand scans
  • Active indexing reduces Agent retrieval latency from seconds to near-instant lookup
  • Index generation decoupled from real-time queries to prevent blocking
  • Eliminates manual tag maintenance when document count exceeds 200
  • Transforms knowledge from “stored and forgotten” to “ready when needed”

Problem, Impact, and Solution

In our Synapse Multi-Agent System, knowledge base retrieval latency was killing system responsiveness. When an Agent needed historical project decisions or domain-specific terminology explanations, locating relevant content from OBS storage sometimes exceeded 3 seconds per query. Under high concurrency, multiple Agents waiting in queue for knowledge retrieval caused latency to compound, degrading end-to-end user experience.

We initially suspected OBS storage performance and considered adding a caching layer. After deeper investigation, we identified the real culprit: our knowledge base operated in passive storage mode. Documents were uploaded and left untouched—no processing, no structure. Every retrieval required scanning the entire original document collection, like searching a library without a catalog system.

Simple fixes like metadata tagging proved inadequate. Tags required manual maintenance, and consistency broke down once the knowledge base grew beyond 200 documents. More critically, our retrieval needs were dynamic—different task scenarios required different access patterns that static labels could never cover.

The solution was redesigning our knowledge base from passive storage to active indexing. When documents are uploaded, the system now automatically triggers an indexing job that extracts key entities, relationships, and summaries, writing them to a separate index store. During retrieval, Agents query the index first to locate target documents, then fetch the original content. The IndexScheduler class manages this workflow, with schedule_full_reindex() running every 2 days to keep the index current. This changes our retrieval complexity from O(N) scanning to O(1) lookup, while index generation runs independently without impacting real-time queries.

Key Takeaways

  • If you are diagnosing knowledge retrieval latency, verify whether your system performs full scans without index acceleration—storage speed is irrelevant if the retrieval path lacks indexing.
  • If you are considering metadata tagging for knowledge organization, calculate manual maintenance overhead—automation ROI grows exponentially once document volume exceeds 200.
  • If your retrieval dimensions vary across task scenarios, skip static labels entirely and implement active indexing instead.
  • If you are designing knowledge retrieval architecture, recognize that passive storage to active indexing represents a paradigm shift, not merely a performance tweak.
  • If Agents report “document not found” despite knowing content exists, investigate whether missing indexes are causing false negatives in retrieval.

Read the Full Article (Chinese)

This is an abstract. The full technical walkthrough with implementation details and code samples is available in Chinese at the original article.