Generate text
Introduced in 2025.11

The function ai.text.completion can be used to generate text based on a textual input prompt. It is similar to submitting a prompt to an LLM.

Signature for `ai.text.completion`
FunctionIntroduced in 2025.11
Syntax	`ai.text.completion(prompt, provider, configuration) :: STRING`
Description	Generate text output based on the provided prompt.
Inputs	Name	Type	Description
	`prompt`	`STRING`	Textual prompt.
	`provider`	`STRING`	Name of the third party AI provider, see Providers.
	`configuration`	`MAP`	Provider specific configuration, see Providers.
Returns	Generated text based on the provided prompt.

Example

The examples on this page use the Neo4j movie recommendations dataset, focusing on the plot and title properties of Movie nodes. There are 9083 Movie nodes with a plot and title property.

To recreate the graph, download and import this dump file into an empty Neo4j database. Dump files can be imported for both Aura and self-managed instances.

The example below selects the 5 top-rated movies, joins their titles and plots into a single, newline-separated string, and asks the external AI provider to pick the most child-friendly one.

MATCH (n:Movie) WHERE n.imdbRating IS NOT NULL
ORDER BY n.imdbRating DESC LIMIT 5  (1)
WITH n.title || ': ' || n.plot AS movie  (2)
WITH
  reduce(acc = '', item IN collect(movie) | acc || item || '\n') AS movies,  (3)
  {
    token: $openaiToken,
    model: 'gpt-5-nano',
    vendorOptions: {
      max_output_tokens: 1024,
      instructions: 'Be short.'
    }
  } AS config  (4)
RETURN ai.text.completion(
    'Here is a list of movies with their titles and plots. Recommend the most child-friendly one.\n\n' || movies,  (5)
    'openai',
    config
) AS result

1	Pick the 5 top-rated movies.
2	Join title and plot as `<title>: <plot>`.
3	Join all movies into a single, newline-separated string.
4	Provider specific configuration, see Providers → OpenAI.
5	Prompt for the AI model.

result
"Cosmos. It’s an educational, non-graphic science documentary about the universe, making it the most child-friendly option here (parental guidance for younger kids)."

result

"Cosmos. It’s an educational, non-graphic science documentary about the universe, making it the most child-friendly option here (parental guidance for younger kids)."

The example below selects the 5 top-rated movies, joins their titles and plots into a single, newline-separated string, and asks the external AI provider to pick the most child-friendly one.

MATCH (n:Movie) WHERE n.imdbRating IS NOT NULL
ORDER BY n.imdbRating DESC LIMIT 5  (1)
WITH n.title || ': ' || n.plot AS movie  (2)
WITH
  reduce(acc = '', item IN collect(movie) | acc || item || '\n') AS movies,  (3)
  {
    token: $azureOpenaiToken,
    resource: '<azure-openai-resource>',
    model: 'gpt-5-nano',
    vendorOptions: {
      max_output_tokens: 1024,
      instructions: 'Be short.'
    }
  } AS config  (4)
RETURN ai.text.completion(
    'Here is a list of movies with their titles and plots. Recommend the most child-friendly one.\n\n' || movies,  (5)
    'azure-openai',
    config
) AS result

1	Pick the 5 top-rated movies.
2	Join title and plot as `<title>: <plot>`.
3	Join all movies into a single, newline-separated string.
4	Provider specific configuration, see Providers → Azure OpenAI.
5	Prompt for the AI model.

result
"Cosmos. It’s an educational, non-graphic science documentary about the universe, making it the most child-friendly option here (parental guidance for younger kids)."

result

"Cosmos. It’s an educational, non-graphic science documentary about the universe, making it the most child-friendly option here (parental guidance for younger kids)."

The example below selects the 5 top-rated movies, joins their titles and plots into a single, newline-separated string, and asks the external AI provider to pick the most child-friendly one.

MATCH (n:Movie) WHERE n.imdbRating IS NOT NULL
ORDER BY n.imdbRating DESC LIMIT 5  (1)
WITH n.title || ': ' || n.plot AS movie  (2)
WITH
  reduce(acc = '', item IN collect(movie) | acc || item || '\n') AS movies,  (3)
  {
    token: $vertexaiToken,
    model: 'gemini-2.5-flash-lite',
    publisher: 'google',
    project: '<google-cloud-project>',
    region: '<gcp-region>',
    vendorOptions: {
      systemInstruction: 'Be short.',
      generationConfig: {
        maxOutputTokens: 1024
      }
    }
  } AS conf  (4)
RETURN ai.text.completion(
    'Here is a list of movies with their titles and plots. Recommend the most child-friendly one.\n\n' || movies,  (5)
    'vertexai',
    config
) AS result

1	Pick the 5 top-rated movies.
2	Join title and plot as `<title>: <plot>`.
3	Join all movies into a single, newline-separated string.
4	Provider specific configuration, see Providers → VertexAI.
5	Prompt for the AI model.

result
"The most child-friendly movie is Cosmos. It is an educational documentary about space, lacking the violence and mature themes of the other options. Would you like to know the recommended age range for Cosmos?"

result

"The most child-friendly movie is Cosmos.

It is an educational documentary about space, lacking the violence and mature themes of the other options.

Would you like to know the recommended age range for Cosmos?"

The example below selects the 5 top-rated movies, joins their titles and plots into a single, newline-separated string, and asks the external AI provider to pick the most child-friendly one.

MATCH (n:Movie) WHERE n.imdbRating IS NOT NULL
ORDER BY n.imdbRating DESC LIMIT 5  (1)
WITH n.title || ': ' || n.plot AS movie  (2)
WITH
  reduce(acc = '', item IN collect(movie) | acc || item || '\n') AS movies,  (3)
  {
    accessKeyId: $awsAccessKeyId,
    secretAccessKey: $secretAccessKey,
    model: 'amazon.nova-micro-v1:0',
    region: '<region>',
    vendorOptions: {
      system: [{ text: 'Be short' }],
      inferenceConfig: { maxTokens: 1024 }
    }
  } AS conf  (4)
RETURN ai.text.completion(
    'Here is a list of movies with their titles and plots. Recommend the most child-friendly one.\n\n' || movies,  (5)
    'bedrock-nova',
    config
) AS result

1	Pick the 5 top-rated movies.
2	Join title and plot as `<title>: <plot>`.
3	Join all movies into a single, newline-separated string.
4	Provider specific configuration, see Providers → Amazon Bedrock Nova.
5	Prompt for the AI model.

result
"Cosmos is the most child-friendly. It’s an educational series about space and science, perfect for young minds."

result

"Cosmos is the most child-friendly. It’s an educational series about space and science, perfect for young minds."

Run multiple requests in parallel

To run text completion for several prompts in parallel, you have two options:

Use CALL {…} IN CONCURRENT TRANSACTIONS, with 1 row per transaction:

WITH ['Name a movie', 'Name a book'] AS prompts
UNWIND prompts AS prompt
CALL(prompt) {
  WITH
  {
    token: $openaiToken,
    model: 'gpt-5-nano'
  } AS config
  RETURN ai.text.completion(prompt, 'openai', config) AS response
} IN CONCURRENT TRANSACTIONS OF 1 ROW
RETURN response

Use Cypher’s parallel runtime: Enteprise Edition

CYPHER runtime=parallel
WITH
  {
    token: $openaiToken,
    model: 'gpt-5-nano'
  } AS config,
  ['Name a movie', 'Name a book'] AS prompts
UNWIND prompts AS prompt
RETURN ai.text.completion(prompt, 'openai', config) AS result

Providers

You can generate text via the following providers:

OpenAI (openai)
Azure OpenAI (azure-openai)
Google Vertex AI (vertexai)
Amazon Bedrock Nova Models (bedrock-nova)

The query CALL ai.text.completion.providers() (see reference) shows the list of supported providers in the installed version of the plugin.

OpenAI

OpenAI parameters
Name	Type	Default	Description
`model`	`STRING`	-	Model ID (see OpenAI → Models).
`token`	`STRING`	-	OpenAI API key (see OpenAI → API Keys).
`vendorOptions`	`MAP`	`{}`	Optional vendor options that will be passed on as-is in the request to OpenAI (see OpenAI → Create a model response).

Usage example

WITH
  {
    token: $openaiToken,
    model: 'gpt-5-nano',
    vendorOptions: {
      max_output_tokens: 1024,
      instructions: 'Be short.'
    }
  } AS conf
RETURN ai.text.completion('Name a movie', 'openai', conf) AS result

Azure OpenAI

Azure OpenAI parameters
Name	Type	Default	Description
`model`	`STRING`	-	Model ID (see Azure → Azure OpenAI in Foundry Models).
`resource`	`STRING`	-	Azure resource name.
`token`	`STRING`	-	Azure OAuth2 bearer token.
`vendorOptions`	`MAP`	`{}`	Optional vendor options that will be passed on as-is in the request to Azure.

Usage example

WITH
  {
    token: $azureToken,
    resource: 'my-azure-openai-resource',
    model: 'gpt-5-nano',
    vendorOptions: {
      max_output_tokens: 1024,
      instructions: 'Be short.'
    }
  } AS conf
RETURN ai.text.completion('Name a movie', 'azure-openai', conf) AS result

Google VertexAI

VertexAI parameters
Name	Type	Default	Description
`model`	`STRING`	-	Model resource name (see Vertex AI → Model Garden).
`project`	`STRING`	-	Google cloud project ID.
`region`	`STRING`	-	Google cloud region (see Vertex AI → Locations).
`publisher`	`STRING`	'google'	Model publisher.
`token`	`STRING`	-	Vertex API access token.
`vendorOptions`	`MAP`	`{}`	Optional vendor options that will be passed on as-is in the request to VertexAI (see Vertex AI → Method: models.generateContent).

Usage example

WITH
  {
    token: $vertexaiApiAccessKey,
    model: 'gemini-2.5-flash-lite',
    publisher: 'google',
    project: 'my-google-cloud-project',
    region: 'asia-northeast1',
    vendorOptions: {
      systemInstruction: 'Be short.'
    }
  } AS conf
RETURN ai.text.completion('Name a movie', 'vertexai', conf) AS result

Amazon Bedrock Nova Models

This provider supports all models that use the same request parameters and response fields as the Nova text models.

Amazon Bedrock Nova parameters
Name	Type	Default	Description
`model`	`STRING`	-	Model ID or its ARN.
`region`	`STRING`	-	Amazon region (see Amazon Bedrock → Model Support).
`accessKeyId`	`STRING`	-	Amazon access key ID.
`secretAccessKey`	`STRING`	-	Amazon secret access key.
`vendorOptions`	`MAP`	`{}`	Optional vendor options that will be passed on as-is in the request to Bedrock (see Amazon Bedrock → Inference request parameters and response fields).

Usage example

WITH
  {
    accessKeyId: $awsAccessKeyId,
    secretAccessKey: $secretAccessKey,
    model: 'amazon.nova-micro-v1:0',
    region: 'eu-west-2',
    vendorOptions: {
      system: [{ text: 'Be short' }],
      inferenceConfig: { maxTokens: 1024 }
    }
  } AS conf
RETURN ai.text.completion('Name a movie', 'bedrock-nova', conf) AS result

Amazon Bedrock Titan Models

This provider supports all models that use the same request parameters and response fields as the Titan text models. Configuration and usage is similar to Bedrock Nova Models.

Titan models have different vendorOptions, see Amazon Bedrock → Titan Text Models.

Generate textIntroduced in 2025.11

Example

Run multiple requests in parallel

Providers

OpenAI

Azure OpenAI

Google VertexAI

Amazon Bedrock Nova Models

Amazon Bedrock Titan Models

Generate text
Introduced in 2025.11