Find historically similar audience moments across the screen network using embedding similarity search. Input a natural-language description of the target moment.
Moment embeddings are 768-D vectors generated from multi-modal observation data
(visual, audio, environmental, social) via the MomentEmbeddingService. This tool
embeds your query text and finds the closest real-world moments via approximate
nearest-neighbour (ANN) cosine similarity over a Lance IVF_PQ index.
CONSISTENCY: results are APPROXIMATE and EVENTUALLY CONSISTENT.
- Approximate: retrieval is ANN, not an exhaustive scan (measured recall ~0.96
against exact KNN), so an identical query may omit a borderline match.
- Eventually consistent: the index is served from a replicated pool whose
replicas refresh independently, so for up to 5 minutes after new moments are
published, two identical calls may return slightly different result sets. The
difference is confined to the VISIBILITY of newly-published moments; the
relative ranking of already-visible ones does not change.
Do not use this tool where a repeatable, exhaustive result set is required.
WHEN TO USE:
- Searching for historical moments similar to a target scenario
- Finding "moments like this one" across different venues/times
- Discovering when similar audience compositions or behaviors occurred
- Planning ad placements based on past similar contexts
RETURNS:
- data: Array of matching observations with similarity scores
- observation_id, observed_at, venue_type, device_id, screen_mongo_id
- payload: full observation data
- evidence_grade: quality of observation
- similarity: cosine similarity score (0-1, higher = more similar)
- metadata: { result_count, embedding_model, min_similarity_threshold }
- suggested_next_queries: Follow-up queries
EXAMPLE:
User: "Find moments with high engagement in evening restaurants with families"
find_similar_moments({ query: "evening restaurant venue with families present, high emotional engagement and attention" })
User: "When did we see young adults highly engaged at transit screens?"
find_similar_moments({ query: "transit venue morning commute young adults high attention" })
