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Text-to-Cypher uses a language model to translate a user question into a Cypher query grounded in the graph schema. The database executes this structured query and returns targeted records as answer context. The pattern supports precise relational questions but requires schema grounding, query validation, and controlled execution permissions.
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Text-to-Cypher translates a natural-language question into a Cypher query for a graph database.
Text-to-Cypher grew at the intersection of natural-language processing and graph databases from the need to make graph queries accessible without complete Cypher expertise. Neo4j describes tools that connect language models with graph schema and query execution for this purpose.
A model receives the question and relevant information about graph labels, relationships and properties. It generates Cypher, which should be checked before execution and run with restricted permissions. The query result can then become structured context or a direct answer.
Natural-language intent is translated into a graph-oriented query language.
Schema context guides generation; validation and controlled execution limit misinterpretation and risk.
The pattern supports exploratory questions but needs accurate schema descriptions, permissions and checks.
Text-to-Cypher lowers the entry barrier to graph queries and supports search or RAG applications when generated queries are controlled.
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