Build semantic search with embeddings, hybrid retrieval, rerankers, and feedback analytics
No sign-up required • Free to try
Blend lexical, semantic, and behavioral signals to improve search ranking
Map ingestion, embeddings, vector stores, and LLM orchestration for grounded generation
Diagram ingestion, embeddings, metadata filters, and hybrid dense + sparse search
Common questions about ai semantic search platform generator
Parse and chunk content, generate embeddings, attach metadata, and index into vector and keyword stores.
Use hybrid retrieval and rerank with learned models that consider text, metadata, and behavior.
Filter candidates by ACLs before scoring. Keep tenant and role metadata with each document.
Use relevance labels, offline metrics (NDCG/Recall), and online A/B tests for CTR/zero results.
Track latency, query volume, zero-result rate, and satisfaction signals. Alert on degradation.
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