GenAI Assistant Procedures

Query with natural language

This procedure apoc.ml.query takes a question in natural language and returns the results of that query.

It uses the chat/completions API which is documented here.

Query call
CALL apoc.ml.query("What movies did Tom Hanks play in?") yield value, query
RETURN *
Example response
+------------------------------------------------------------------------------------------------------------------------------+
| value                                 | query                                                                                |
+------------------------------------------------------------------------------------------------------------------------------+
| {m.title -> "You've Got Mail"}        | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "Apollo 13"}              | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "Joe Versus the Volcano"} | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "That Thing You Do"}      | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "Cloud Atlas"}            | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "The Da Vinci Code"}      | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "Sleepless in Seattle"}   | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "A League of Their Own"}  | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "The Green Mile"}         | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "Charlie Wilson's War"}   | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "Cast Away"}              | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
| {m.title -> "The Polar Express"}      | "cypher
MATCH (m:Movie)<-[:ACTED_IN]-(p:Person {name: 'Tom Hanks'})
RETURN m.title
" |
+------------------------------------------------------------------------------------------------------------------------------+
12 rows
Table 1. Input Parameters
name description

question

The question in the natural language

conf

An optional configuration map, please check the next section

Table 2. Configuration map
name description mandatory

retries

The number of retries in case of API call failures

no, default 3

retryWithError

If true, in case of error retry the api adding the following messages to the body request: {"role":"user", "content": "The previous Cypher Statement throws the following error, consider it to return the correct statement: `<errorMessage>`"}, {"role":"assistant", "content":"Cypher Statement (in backticks):"}

no, default false

apiKey

OpenAI API key

in case apoc.openai.key is not defined

model

The Open AI model

no, default gpt-3.5-turbo

sample

The number of nodes to skip, e.g. a sample of 1000 will read every 1000th node. It’s used as a parameter to apoc.meta.data procedure that computes the schema

no, default is a random number

Table 3. Results
name description

value

the result of the query

cypher

the query used to compute the result

Describe the graph model with natural language

This procedure apoc.ml.schema returns a description, in natural language, of the underlying dataset.

It uses the chat/completions API which is documented here.

Query call
CALL apoc.ml.schema() yield value
RETURN *
Example response
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| value                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "The graph database schema represents a system where users can follow other users and review movies. Users (:Person) can either follow other users (:Person) or review movies (:Movie). The relationships allow users to express their preferences and opinions about movies. This schema can be compared to social media platforms where users can follow each other and leave reviews or ratings for movies they have watched. It can also be related to movie recommendation systems where user preferences and reviews play a crucial role in generating personalized recommendations." |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row
Table 4. Input Parameters
name description

conf

An optional configuration map, please check the next section

Table 5. Configuration map
name description mandatory

apiKey

OpenAI API key

in case apoc.openai.key is not defined

model

The Open AI model

no, default gpt-3.5-turbo

sample

The number of nodes to skip, e.g. a sample of 1000 will read every 1000th node. It’s used as a parameter to apoc.meta.data procedure that computes the schema

no, default is a random number

Table 6. Results
name description

value

the description of the dataset

Create cypher queries from a natural language query

This procedure apoc.ml.cypher takes a natural language question and transforms it into a number of requested cypher queries.

It uses the chat/completions API which is documented here.

Query call
CALL apoc.ml.cypher("Who are the actors which also directed a movie?", {count: 4}) yield cypher
RETURN *
Example response
+----------------------------------------------------------------------------------------------------------------+
| query                                                                                                          |
+----------------------------------------------------------------------------------------------------------------+
| "
MATCH (a:Person)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d:Person)
RETURN a.name as actor, d.name as director
" |
| "cypher
MATCH (a:Person)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(a)
RETURN a.name
"                               |
| "
MATCH (a:Person)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d:Person)
RETURN a.name
"                              |
| "cypher
MATCH (a:Person)-[:ACTED_IN]->(:Movie)<-[:DIRECTED]-(a)
RETURN DISTINCT a.name
"                       |
+----------------------------------------------------------------------------------------------------------------+
4 rows
Table 7. Input Parameters
name description mandatory

question

The question in the natural language

yes

conf

An optional configuration map, please check the next section

no

Table 8. Configuration map
name description mandatory

count

The number of queries to retrieve

no, default 1

apiKey

OpenAI API key

in case apoc.openai.key is not defined

model

The Open AI model

no, default gpt-3.5-turbo

sample

The number of nodes to skip, e.g. a sample of 1000 will read every 1000th node. It’s used as a parameter to apoc.meta.data procedure that computes the schema

no, default is a random number

Table 9. Results
name description

value

the description of the dataset

Create a natural language query explanation from a cypher query

This procedure apoc.ml.fromCypher takes a natural language question and transforms it into natural language query explanation.

It uses the chat/completions API which is documented here.

Query call
CALL apoc.ml.cypher("MATCH (p:Person {name: "Tom Hanks"})-[:ACTED_IN]->(m:Movie) RETURN m", {}) yield value
RETURN *
Table 10. Example response
value

this database schema represents a simplified version of a common movie database model. the movie node represents a movie entity with attributes such as the year it was released, a tagline, and the movie title. the person node represents a person involved in the movie industry, with attributes for the person’s year of birth and name. the relationship directed connects a person node to a movie node, indicating that the person directed the movie. in terms of domains, this schema can be related to the entertainment industry, specifically the movie industry. movies and people involved in creating those movies are fundamental entities in this domain. the directed relationship captures the directed-by relationship between a person and a movie. this type of model can be extended to include other relationships like acted_in, produced, wrote, etc., to capture more complex connections within the movie industry. overall, this graph database schema provides a simple yet powerful representation of entities and relationships in the movie domain, allowing for querying and analysis of connections within the industry.

Table 11. Input Parameters
name description mandatory

cypher

The question in the natural language

yes

conf

An optional configuration map, please check the next section

no

Table 12. Configuration map
name description mandatory

retries

The number of retries in case of API call failures

no, default 3

apiKey

OpenAI API key

in case apoc.openai.key is not defined

model

The Open AI model

no, default gpt-3.5-turbo

sample

The number of nodes to skip, e.g. a sample of 1000 will read every 1000th node. It’s used as a parameter to apoc.meta.data procedure that computes the schema

no, default is a random number

Table 13. Results
name description

value

the description of the dataset

Create explanation of the subgraph from a set of queries

This procedure apoc.ml.fromQueries returns an explanation, in natural language, of the given set of queries.

It uses the chat/completions API which is documented here.

Query call
CALL apoc.ml.fromQueries(['MATCH (n:Movie) RETURN n', 'MATCH (n:Person) RETURN n'],
    {apiKey: <apiKey>})
YIELD value
RETURN *
Example response
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| value                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "The database represents movies and people, like in a movie database or social network.
    There are no defined relationships between nodes, allowing flexibility for future connections.
    The Movie node includes properties like title, tagline, and release year." |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row
Query call with path
CALL apoc.ml.fromQueries(['MATCH (n:Movie) RETURN n', 'MATCH p=(n:Movie)--() RETURN p'],
    {apiKey: <apiKey>})
YIELD value
RETURN *
Example response
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| value                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "models relationships in the movie industry, connecting :Person nodes to :Movie nodes.
    It represents actors, directors, writers, producers, and reviewers connected to movies they are involved with.
    Similar to a social network graph but specialized for the entertainment industry.
    Each relationship type corresponds to common roles in movie production and reviewing.
    Allows for querying and analyzing connections and collaborations within the movie business." |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row
Table 14. Input Parameters
name description

queries

The list of queries

conf

An optional configuration map, please check the next section

Table 15. Configuration map
name description mandatory

apiKey

OpenAI API key

in case apoc.openai.key is not defined

model

The Open AI model

no, default gpt-3.5-turbo

sample

The number of nodes to skip, e.g. a sample of 1000 will read every 1000th node. It’s used as a parameter to apoc.meta.data procedure that computes the schema

no, default is a random number

Table 16. Results
name description

value

the description of the dataset