10 docs tagged with "SearchQuery"

A repeatable agent pattern — ontology first, query spec second, constrained execution, and failure recovery when labels or fields are wrong.

Model users, accounts, subscriptions, invoices, touchpoints, and support interactions as a connected graph so customer context becomes retrievable instead of siloed.

A practical workflow for exploring a RushDB project you did not design — using ontology tools, label listing, and progressive query refinement before building reliable retrieval.

Pair raw search results with related evidence, summary metrics, and traversal paths so users and agents can understand why a result was returned.

Three common graph shapes — trees, many-to-many networks, and cyclic systems — with guidance on how to query each without flattening away meaning.

Shape SearchQuery, traversal breadth, aggregation strategy, and batch patterns to reduce compute cost and improve throughput.

Combine structured filters, semantic ranking, and contextual fields to build explainable, user-facing search experiences on top of RushDB.

Model suppliers, batches, products, shipments, and incidents so teams can answer upstream-impact and downstream-blast-radius questions for recalls.

Write parity-driven tests that prove one query intent behaves identically across every RushDB surface.

Map the same product, customer, and order dataset from relational and document mental models into RushDB's graph model, then translate common business questions into multi-hop queries.