spring-ai-playground

title: Vector Database Observability description: RAG-side observability - query rate, top_k distribution, similarity threshold bands, per-DB returned-doc counts. The indicators that determine RAG quality.

Vector Database

Vector Database - Similarity threshold distribution buckets thresholds into 6 bands (0.0-0.1, 0.1-0.3, …, 0.9-1.0) so an over-strict threshold is visible without per-query inspection.

Purpose - RAG-side observability. Beyond simple “did the agent retrieve” questions, this tab surfaces retrieval parameters (top_k, similarity threshold) and per-DB returned-doc counts - the indicators that determine RAG quality.

When to look here

• “Did the agent actually use RAG?” - Traces with RAG count + Vector queries count.
• “Are queries slow against the configured store?” - Query latency p50 / p95 / p99.
• “Is top_k being set sensibly?” - top_k distribution (a spike at 1 or above 50 usually indicates an agent miscalibration).
• “Is the similarity threshold too strict?” - Similarity threshold distribution (everything in 0.9-1.0 → almost no recall).
• “Multi-store mix?” - DB systems donut (Chroma / Pinecone / SimpleVectorStore / etc.).
• “How many docs per query, on average?” - Avg returned docs by db.

Span filter

db.vector.client.operation spans.

Controls

All dashboards share the Observability global settings - time window, refresh interval, custom range. Vector Database has no tab-specific controls beyond those.

KPI cards (four)

Card	Shows	Source
Traces with RAG	Number of chat turns that included a vector query	TraceRecord.hasRag true count
Vector queries	Total vector store operations (query + add + delete)	db.vector.client.operation span count
Avg top_k	Mean of the requested top_k parameter	db.vector.query.top_k attribute averaged
Avg query latency	Mean query duration	Span duration

Charts (six)

Chart	Type	Reading
Operations / minute	Stacked bar (query / add / delete)	Add bursts during document ingestion; query bursts during chat
Query latency p50 / p95 / p99	Multi-line, ms	Tail latency on remote vector stores can be ten-fold higher than SimpleVectorStore
top_k distribution	Histogram	Distribution of requested k values
Similarity threshold distribution	Histogram, 6 bands ([0.0, 0.1) … [0.9, 1.0])	Where the agent is anchoring relevance - strictness vs recall
DB systems	Donut by db.system	One backend = single configuration; multiple = mixed stores
Avg returned docs by db	Horizontal bar	Combined with similarity threshold, this is the retrieval-recall signal

Cross-references

• Vector Database (feature) - how documents are ingested and indexed
• Tutorial 3 - Index a Document - end-to-end ingestion walkthrough
• Tutorial 5 - Chat with RAG - consuming the indexed corpus

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