Skill v1.0.1

name: neo4j-genai-plugin-skill description: 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. version: 1.0.1 status: active allowed-tools: Bash WebFetch

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

CYPHER 25 required for all ai.* functions. Two ways to enable:

// Per-query prefix (self-managed, no admin rights needed):
CYPHER 25 MATCH (n:Chunk) ...
 
// Per-database default (admin; applies to all sessions):
ALTER DATABASE neo4j SET DEFAULT LANGUAGE CYPHER 25

Installation:

• Aura: GenAI plugin enabled by default — no action needed
• Self-managed JAR: copy plugin JAR to plugins/ directory
• Docker: --env NEO4J_PLUGINS='["genai"]'

Provider Config Quick Reference

All ai.text.* functions accept a configuration :: MAP as last argument.

Optional for all: vendorOptions :: MAP passes provider-specific extras (e.g. { dimensions: 1024 } for OpenAI).

❌ Never hardcode API key literals. ✅ Always use $param passed via driver parameters dict.

Full provider config table → references/providers.md

Embedding

Single embed [2025.11]

CYPHER 25
MATCH (c:Chunk)
WHERE c.embedding IS NULL
WITH c
CALL {
  WITH c
  SET c.embedding = ai.text.embed(c.text, 'openai', {
    token: $openaiKey,
    model: 'text-embedding-3-small'
  })
} IN TRANSACTIONS OF 500 ROWS

ai.text.embed() returns VECTOR — directly storable and queryable in a vector index.

Batch embed procedure [2025.11]

CYPHER 25
MATCH (c:Chunk) WHERE c.embedding IS NULL
WITH collect(c) AS chunks
UNWIND chunks AS c
WITH c.text AS text, c AS node
CALL ai.text.embedBatch(text, 'openai', { token: $openaiKey, model: 'text-embedding-3-small' })
YIELD index, resource, vector
MATCH (c:Chunk {text: resource})
SET c.embedding = vector

Procedure signature: CALL ai.text.embedBatch(resource, provider, config) YIELD index, resource, vector

List configured embed providers

CYPHER 25
CALL ai.text.embed.providers()
YIELD name, requiredConfigType, optionalConfigType, defaultConfig
RETURN name, requiredConfigType

Text Completion [2025.11]

CYPHER 25
RETURN ai.text.completion(
  'Summarize: ' + $text,
  'openai',
  { token: $openaiKey, model: 'gpt-4o-mini' }
) AS summary

Returns STRING.

Aggregate completion — summarize across rows [2026.03]

CYPHER 25
MATCH (c:Chunk)-[:PART_OF]->(a:Article {id: $articleId})
RETURN ai.text.aggregateCompletion(
  c.text,
  'Summarize the following article chunks in 3 sentences',
  'openai',
  { token: $openaiKey, model: 'gpt-4o-mini' }
) AS summary

value parameter = each row's STRING fed to the LLM. Uses toString() for non-string values.

Pure-Cypher GraphRAG Pattern

Embed question → vector search → graph traverse → LLM completion — all in one Cypher query:

CYPHER 25
WITH ai.text.embed($question, 'openai', { token: $openaiKey, model: 'text-embedding-3-small' }) AS qEmbedding
CALL db.index.vector.queryNodes('chunk_embedding', 10, qEmbedding) YIELD node AS chunk, score
MATCH (chunk)<-[:HAS_CHUNK]-(article:Article)
OPTIONAL MATCH path = shortestPath((article)-[*..3]-(other:Article))
WITH chunk, article, collect(DISTINCT other.title) AS related, score
ORDER BY score DESC LIMIT 5
WITH collect(chunk.text + '\n[Source: ' + article.title + ']') AS context, $question AS question
RETURN ai.text.completion(
  'Answer based on context:\n' + reduce(s='', c IN context | s + c + '\n') + '\nQuestion: ' + question,
  'openai',
  { token: $openaiKey, model: 'gpt-4o-mini' }
) AS answer

Key insight (Bergman): shortest path between seed nodes surfaces relationships not visible from direct neighbors alone.

Structured Output [2026.02]

Returns MAP — directly storable as node properties or used downstream in Cypher.

CYPHER 25
MATCH (p:Product {id: $productId})
WITH p,
  ai.text.structuredCompletion(
    'Extract key attributes from: ' + p.description,
    {
      type: 'object',
      properties: {
        category: { type: 'string' },
        tags: { type: 'array', items: { type: 'string' } },
        priceRange: { type: 'string', enum: ['budget', 'mid', 'premium'] }
      },
      required: ['category', 'tags', 'priceRange'],
      additionalProperties: false
    },
    'openai',
    { token: $openaiKey, model: 'gpt-4o-mini' }
  ) AS extracted
SET p.category = extracted.category,
    p.priceRange = extracted.priceRange
WITH p, extracted.tags AS tags
UNWIND tags AS tag
MERGE (t:Tag {name: tag})
MERGE (p)-[:TAGGED]->(t)

Aggregate structured completion — extract across multiple rows [2026.03]

CYPHER 25
MATCH (:User {id: $userId})-[:ORDERED]->(o:Order)-[:CONTAINS]->(p:Product)
RETURN ai.text.aggregateStructuredCompletion(
  p.name + ': ' + p.category,
  'Build a shopping profile for this user',
  {
    type: 'object',
    properties: {
      preferredCategories: { type: 'array', items: { type: 'string' } },
      spendingTier: { type: 'string', enum: ['economy', 'standard', 'premium'] }
    },
    required: ['preferredCategories', 'spendingTier']
  },
  'openai',
  { token: $openaiKey, model: 'gpt-4o-mini' }
) AS profile

Chat [2025.12]

Supported providers: openai and azure-openai only.

// Start new conversation (chatId = null → new session)
CYPHER 25
WITH ai.text.chat(
  'Hello, who are you?',
  null,
  'openai',
  { token: $openaiKey, model: 'gpt-4o-mini' }
) AS result
RETURN result.message AS reply, result.chatId AS sessionId
 
// Continue conversation (pass returned chatId)
CYPHER 25
WITH ai.text.chat(
  'What did I just ask you?',
  $chatId,
  'openai',
  { token: $openaiKey, model: 'gpt-4o-mini' }
) AS result
RETURN result.message AS reply, result.chatId AS sessionId

Returns MAP { message: STRING, chatId: STRING }. Store chatId to continue session.

Tokenization & Chunking [2026.04]

// Count tokens before sending to LLM
CYPHER 25
RETURN ai.text.tokenCount($text, 'openai', { token: $openaiKey, model: 'gpt-4o-mini' }) AS tokenCount
 
// Chunk text by token limit (no external dependencies)
CYPHER 25
UNWIND ai.text.chunkByTokenLimit($longText, 512, 'gpt-4', 50) AS chunk
MERGE (c:Chunk { text: chunk })
 
// List providers supporting tokenCount
CYPHER 25
CALL ai.text.tokenCount.providers() YIELD name, requiredConfigType
RETURN name, requiredConfigType

Signatures:

• ai.text.tokenCount(input, provider, configuration = {}) :: INTEGER — provider-driven tokenizer; uses provider config (token/model).
• ai.text.chunkByTokenLimit(input, limit, model = 'gpt-4', overlap = 0) :: LIST<STRING> — local tokenizer keyed off model; no provider call, no token required.

Write Gate

SET node.embedding = ai.text.embed(...) and SET node.* = ai.text.structuredCompletion(...) write to the graph.

Before bulk writes:

1. Count nodes first: MATCH (c:Chunk) WHERE c.embedding IS NULL RETURN count(c)
2. Verify config with one test node before batch
3. Use CALL { ... } IN TRANSACTIONS OF 500 ROWS for batches > 1000 nodes
4. Require explicit confirmation before executing

Deprecated — Do NOT Use

Common Errors

Checklist

• [ ] CYPHER 25 prefix present on every ai.text.* query
• [ ] GenAI plugin installed (Aura: automatic; self-managed: JAR in plugins/)
• [ ] API key passed as $param, never as literal string
• [ ] model key explicit in config (no silent defaults)
• [ ] Provider string lowercase ('openai', 'vertexai', 'bedrock-titan')
• [ ] Bulk writes use IN TRANSACTIONS OF 500 ROWS; count target nodes first
• [ ] genai.vector.encode() replaced with ai.text.embed() [2025.11+]
• [ ] Chat sessions: store returned chatId for continuation; only openai/azure-openai supported
• [ ] Structured output schema uses additionalProperties: false to prevent hallucination keys

References

• Full provider config — all required/optional keys per provider
• Official docs
• API reference