Tutorials
Hands-on guides to build real things with RushDB
Importing data from external sources
Learn how to import your data to RushDB
Reusable SearchQuery
Learn the canonical SearchQuery shape reused across records, properties, labels, relationships, and values
Importing from MongoDB
A step-by-step guide to migrating MongoDB collections to RushDB — nested documents, embedded arrays, upsert, and change streams
Semantic Search in 5 Minutes
Create embedding indexes, wait for backfill, and run your first semantic search query in TypeScript, Python, or REST.
SearchQuery Deep Dive: Advanced Patterns
Build confidence with advanced SearchQuery patterns through realistic RushDB examples
Is RushDB Right for My Problem? A Practical Decision Guide
A scenario-driven catalog showing seven problems RushDB is designed for, where it outperforms fragmented stacks, and where it isn't the right fit.
RAG Pipeline in Minutes
Chunk Markdown files, store them in RushDB, and build a retrieval-augmented generation pipeline in TypeScript, Python, or REST.
Thinking in Graphs: From Tables to Traversals
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.
RushDB as a Memory Layer: Facts, Episodes, and References
Model persistent facts, episodic interactions, and linked reference material as a graph so agents and applications can retrieve and reason over connected context.
End-to-End Data Lineage: From Source to Answer
Model imported records, transformation steps, derived summaries, and final outputs so every answer can be traced back to its upstream source.
Using RushDB Agent Skills in OpenClaw
End-to-end guide — connect RushDB to OpenClaw, install the RushDB skills pack, and let your AI assistant query, store, and model structured data without writing a single line of code.
Semantic Search for Multi-Tenant Products
Build tenant-safe semantic retrieval using RushDB's project-scoped prefilter and exact cosine similarity ranking — without global index assumptions.
Choosing Relationship Types That Age Well
When to use generic nesting-driven edges versus explicit typed relationships, and how that choice affects readability, search, and analytics downstream.
Modeling Hierarchies, Networks, and Feedback Loops
Three common graph shapes — trees, many-to-many networks, and cyclic systems — with guidance on how to query each without flattening away meaning.
Temporal Graphs: Modeling State and Event Time Together
Represent durable entities alongside time-stamped events so you can answer both current-state and historical questions without losing lineage.
Building a Graph-Backed API Layer
Expose RushDB through an application API with query translation, safe filtering, and response shaping patterns for production use.
Hybrid Retrieval: Filters Plus Semantic Search
Combine structured where-clause filtering with vector semantic search to narrow candidates by business constraints, then rank by relevance.
Discovery Queries: Exploring an Unknown Schema
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.
Building Team Memory for Product and Support Workflows
Ingest tickets, docs, decisions, incidents, and feature requests into a connected graph so your team can retrieve context instead of isolated documents.
Agent-Safe Query Planning with Ontology First
A repeatable agent pattern — ontology first, query spec second, constrained execution, and failure recovery when labels or fields are wrong.
Episodic Memory for Multi-Step Agents
Store goals, intermediate observations, tool outputs, and decisions as linked records so long-running agents can resume with context instead of stateless prompts.
Audit Trails with Immutable Events and Derived State
Log business events as immutable records separate from current state so teams can reconstruct what happened, not just what is true now.
Versioning Records Without Losing Queryability
Compare in-place mutation, append-only versions, and hybrid versioning approaches — and how to query latest state while preserving historical analysis.
Customer 360 as a Connected Graph
Model users, accounts, subscriptions, invoices, touchpoints, and support interactions as a connected graph so customer context becomes retrievable instead of siloed.
Compliance and Retention Patterns
Implement expiration, archival, and field-level redaction for GDPR, CCPA, and other data lifecycle requirements in RushDB without breaking graph structure.
Research Knowledge Graph: Papers, Authors, Topics, Citations
Build a scholarly graph supporting citation traversal, topical clustering, and author-centric discovery for research workflows.
Incident Response Graphs
Model operational incidents as graph structures to answer root cause, blast radius, and resolution timeline questions in a single query.
Supply Chain Traceability and Recall Analysis
Model suppliers, batches, products, shipments, and incidents so teams can answer upstream-impact and downstream-blast-radius questions for recalls.
Event-Driven Ingestion from Webhooks and Queues
Handle partial, repeated, and out-of-order events from webhooks or message queues without corrupting connected graph state.
Search UX Patterns
Combine structured filters, semantic ranking, and contextual fields to build explainable, user-facing search experiences on top of RushDB.
Webhook Ingestion from Third-Party Tools (Clay, Supabase, Superglue)
A beginner-friendly guide to send JSON or CSV from no-code and data platforms into RushDB using custom HTTP requests.
Query Optimization
Shape SearchQuery, traversal breadth, aggregation strategy, and batch patterns to reduce compute cost and improve throughput.
Testing SearchQuery Across TypeScript, Python, and REST
Write parity-driven tests that prove one query intent behaves identically across every RushDB surface.
Explainable Results
Pair raw search results with related evidence, summary metrics, and traversal paths so users and agents can understand why a result was returned.
Bring Your Own Vectors (BYOV) — External Embeddings
Use your own embedding model to generate vectors and store them in RushDB, then search with queryVector instead of query text.
BYOV in Practice: When and Why to Bring Your Own Vectors
A case study showing exactly when BYOV makes sense, when it doesn't, and a complete walkthrough of a real scenario where you already have vectors from your own pipeline.
GraphRAG — Graph-Enriched Retrieval Augmented Generation
Retrieve chunks semantically, then traverse the knowledge graph to assemble author, topic, and source provenance as richer LLM context.
BYOC vs Managed vs Self-Hosted
Compare RushDB's three deployment models — Managed, BYOC (Bring Your Own Cloud), and Self-Hosted — with a feature matrix, decision guide, and step-by-step migration path.
Multi-Source RAG
Ingest PDFs, web pages, and database records as distinct labels, then search across all sources in a single vector query with source-aware citations.
RAG Evaluation
Measure Precision@k and Recall@k for your retrieval pipeline, detect score drift after model updates, and add a CI regression gate that fails on quality drops.
RAG Reranking
Improve retrieval precision with two-stage search — over-fetch candidates with vector similarity, then rerank with LLM scoring or Reciprocal Rank Fusion before sending to the LLM.