19 docs tagged with "Python"

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

Log business events as immutable records separate from current state so teams can reconstruct what happened, not just what is true now.

Use your own embedding model to generate vectors and store them in RushDB, then search with queryVector instead of query text.

Expose RushDB through an application API with query translation, safe filtering, and response shaping patterns for production use.

Ingest tickets, docs, decisions, incidents, and feature requests into a connected graph so your team can retrieve context instead of isolated documents.

Implement expiration, archival, and field-level redaction for GDPR, CCPA, and other data lifecycle requirements in RushDB without breaking graph structure.

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

Store goals, intermediate observations, tool outputs, and decisions as linked records so long-running agents can resume with context instead of stateless prompts.

Handle partial, repeated, and out-of-order events from webhooks or message queues without corrupting connected graph state.

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

Retrieve chunks semantically, then traverse the knowledge graph to assemble author, topic, and source provenance as richer LLM context.

Combine structured where-clause filtering with vector semantic search to narrow candidates by business constraints, then rank by relevance.

Model operational incidents as graph structures to answer root cause, blast radius, and resolution timeline questions in a single query.

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

Build a scholarly graph supporting citation traversal, topical clustering, and author-centric discovery for research workflows.

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.

Compare in-place mutation, append-only versions, and hybrid versioning approaches — and how to query latest state while preserving historical analysis.