Skip to content

Vector Database

Vector Database dashboard — four KPI cards (Traces with RAG, Vector queries, Avg top_k, Avg query latency) and charts for Operations / minute stacked, Query latency p50/p95/p99, top_k distribution histogram, Similarity threshold distribution, DB systems donut, Avg returned docs by db

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

Was this page helpful? Ask on Discussions · Report a docs issue