Practical, task-oriented cookbooks for Fluree's key features. Each guide shows working patterns you can adapt to your use case.
If you're new to Fluree, start with the Getting Started section first.
Recipes for the list-value and path operators: dense / gap-filled series with unwind + range, collecting values into lists, the collect→unwind round-trip, working with list values, and shortest-path queries.
Querying and writing with openCypher: modeling a property graph, querying relationships, MERGE find-or-create, updates and deletes, paths and shortest path, aggregation, and cross-surface round-trips with JSON-LD/SPARQL.
Enable and configure the Bolt listener, connect with official Neo4j drivers (Python/JavaScript examples), transaction retry semantics, and troubleshooting.
The Fluree-specific SPARQL surface (the 1.1 basics assumed): time travel with FROM @t:, fact history via << s p o >> f:t/f:op, RDF 1.2 edge annotations ({| |}, ~, rdf:reifies), cross-ledger FROM/GRAPH queries, and cross-surface round-trips.
Set up BM25 full-text search and vector similarity. Insert searchable data, write relevance-ranked queries, combine search with graph patterns, and build hybrid text+vector search.
Practical patterns for temporal queries: audit trails, point-in-time comparison, compliance snapshots, recovering deleted data, and transaction metadata.
Git-like workflows for data: safe experimentation, review-before-merge, multi-environment setups, feature branches, and rebase strategies.
Set up fine-grained access control: department isolation, role-based access, property redaction, multi-tenant isolation, and default-deny patterns.
Serve your ledgers to other teams and organizations: choosing between query serving (your compute, row-level policy) and peer/block serving (their compute, whole-ledger), minting scoped tokens, declaring per-ledger participation with f:servingDefaults, identity-bound permissioning end to end, and the consumer-side CLI workflow.
Define data quality constraints: required properties, datatype validation, value ranges, string patterns, cardinality, and allowed values.
Attach properties to a relationship: model property-graph edges, record statement-level provenance, represent parallel relationships, query inline or annotation-rooted, and understand the retract cascade — in JSON-LD and SPARQL 1.2.