Neo4j Genai Plugin Skill

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.

| Provider string | Required keys | Notes | |---|---|---| | 'openai' | token, model | token = OpenAI API key | | 'azure-openai' | token, resource, model | token = OAuth2 bearer; resource = Azure resource name | | 'vertexai' | model, project, region, token or apiKey | publisher defaults to 'google' | | 'bedrock-titan' | model, region, accessKeyId, secretAccessKey | Embedding only | | 'bedrock-nova' | model, region, accessKeyId, secretAccessKey | Completion only |

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](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)(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 — 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

| Old function | Replacement | |---|---| | genai.vector.encode() [deprecated] | ai.text.embed() | | genai.vector.encodeBatch() [deprecated] | CALL ai.text.embedBatch() | | genai.vector.listEncodingProviders() [deprecated] | CALL ai.text.embed.providers() |

---

Common Errors

| Error | Cause | Fix | |---|---|---| | Unknown function 'ai.text.embed' | Missing CYPHER 25 prefix OR plugin not installed | Add CYPHER 25 prefix; verify plugin installed | | Cypher version not supported | Using CYPHER 25 on Neo4j < 5.20 or missing plugin | Upgrade Neo4j; ensure GenAI plugin loaded | | Configuration key 'token' missing | Provider config map incomplete | Check required keys for provider (see table above) | | null returned from embed | Wrong model name or provider auth failed | Test with RETURN ai.text.embed('test', 'openai', {token:$k, model:'text-embedding-3-small'}) standalone | | Unsupported provider | Provider string typo (case-sensitive, lowercase) | Use 'openai' not 'OpenAI'; run CALL ai.text.embed.providers() | | ai.text.chat fails on VertexAI | Chat only supported on openai/azure-openai | Switch to openai/azure-openai for chat |

---

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](references/providers.md) — all required/optional keys per provider
• Official docs
• API reference

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: neo4j-contrib
• Source: neo4j-contrib/neo4j-skills
• License: MIT
• Homepage: https://neo4j.com/llms.txt

Install and usage instructions live in the source repository linked above.