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neo4j-genai-plugin-skill

neo4j-contrib/n…j-skills

Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

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安装命令

npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-genai-plugin-skill

SKILL.md

When to Use

  • Generating embeddings inside Cypher without external Python (ai.text.embed())
  • Batch-embedding nodes/chunks during ingestion (ai.text.embedBatch())
  • Calling LLMs directly in Cypher for completions or GraphRAG (ai.text.completion())
  • Extracting structured JSON maps from LLM inside Cypher (ai.text.structuredCompletion())
  • Aggregating LLM summaries over grouped rows (ai.text.aggregateCompletion())
  • Stateful chat sessions in Cypher (ai.text.chat())
  • Counting tokens or chunking text by token limit (ai.text.tokenCount(), ai.text.chunkByTokenLimit())

When NOT to Use

  • Python-based GraphRAG pipelines (VectorCypherRetriever, HybridCypherRetriever) → neo4j-graphrag-skill
  • Vector index CREATE / kNN search / SEARCH clause → neo4j-vector-index-skill
  • GDS embeddings (FastRP, Node2Vec) → neo4j-gds-skill
  • Fulltext / keyword search → neo4j-cypher-skill

Prerequisites