Cypher
Cypher is a declarative graph query language for expressing patterns and retrieving, creating, or modifying property-graph data.
Graph Algorithms for Data Science
Cypher syntax is the grammatical system governing how Cypher queries represent graph elements and operations.
- How it is used
- Supports modeling, importing, querying, and exploratory analysis of labeled-property graph data.
- How deep it goes
- Explains node and relationship notation, variables, major clauses, aggregation, filtering, and pattern-counting queries without optimization details.
- Examples
- Uses a Thomas–Elaine friendship, sampled Twitter FOLLOWS paths, and ranked co-occurring hashtag pairs.
- What you can do after
- Apply it via Neo4j to create, retrieve, filter, aggregate, update, and visualize graph patterns.
Read how Graph Algorithms for Data Science explains Cypher →
Knowledge Graphs and LLMs in Action
Cypher is a declarative graph query language for matching patterns and reading or modifying property-graph data.
- How it is used
- Supports readable querying, ingestion, enrichment, cleanup, and natural-language question answering over property-graph knowledge graphs.
- How deep it goes
- Explains pattern matching, filtering, returning, loading, conditional updates, batching, and deletion, but does not detail language internals.
- Examples
- Uses disease–phenotype metadata queries, HPOA tabular ingestion, relationship enrichment, graph cleanup, and structured query-generation prompts.
- What you can do after
- Builds and queries annotated LPG knowledge graphs, including loading data, enriching relationships, and generating queries from structured questions.
Read how Knowledge Graphs and LLMs in Action explains Cypher →
Data Analysis with LLMs
A Cypher query is a declarative statement written in Cypher that requests or manipulates graph data.
- How it is used
- Uses graph databases to answer natural-language questions by expressing graph-data operations as executable queries.
- How deep it goes
- Explains MATCH, RETURN, labels, properties, relationships, and direction through simple examples, while deferring full language coverage.
- Examples
- Demonstrates Person and Movie nodes, ACTED_IN relationships, Tom Cruise filtering, returned titles, and result counting.
- What you can do after
- Evaluate/understand simple Cypher queries and inspect queries generated from natural-language questions.
AI Agents and Applications
Cypher is a declarative graph query language associated with Neo4j for querying and manipulating connected data.
- How it is used
- Translates natural-language questions into graph queries for knowledge-graph retrieval and routed data-access systems.
- How deep it goes
- Defines graph querying and demonstrates one generated statement, but defers broader Cypher syntax and database operations.
- Examples
- Uses a Top Gun actor-count query, graph nodes and edges, schema-constrained prompts, and routed graph questions.
- What you can do after
- Apply it via Neo4j integration by generating schema-constrained queries and routing graph questions to the appropriate retriever.
Related concepts
- graph databaseuses · A graph database is a database system that represents data as entities and their relationships, typically using nodes and edges, and supports queries over that structure.
- property graphuses · A property graph is a graph data model in which nodes and the edges connecting them can have labels and key-value properties.
- automated query generationused by · Automated query generation is the process of producing an executable database query from natural-language requests, structured intent, declarations, or other specifications.
- traversalused by · Traversal is the systematic process of visiting elements or nodes in a data structure according to its links, ordering, or a specified rule.
- similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
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