neo4j-genai-plugin-skill

What this skill does

## 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:

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

---

## Text Completion [2025.11]

```cypher
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
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
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
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
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.

```cypher
// 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]

```cypher
// 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