OpenAI API Access

You need to acquire an OpenAI API key to use these procedures. Using them will incur costs on your OpenAI account. You can set the api key globally by defining the apoc.openai.key configuration in apoc.conf

Generate Embeddings API

This procedure apoc.ml.openai.embedding can take a list of text strings, and will return one row per string, with the embedding data as a 1536 element vector. It uses the /embeddings/create API which is documented here.

Additional configuration is passed to the API, the default model used is text-embedding-ada-002.

Generate Embeddings Call

CALL apoc.ml.openai.embedding(['Some Text'], $apiKey, {}) yield index, text, embedding;

Table 1. Generate Embeddings Response
index	text	embedding
0	"Some Text"	[-0.0065358975, -7.9563365E-4, …. -0.010693862, -0.005087272]

Table 2. Parameters
name	description
texts	List of text strings
apiKey	OpenAI API key
configuration	optional map for entries like model and other request parameters

Table 3. Results
name	description
index	index entry in original list
text	line of text from original list
embedding	1536 element floating point embedding vector for ada-002 model

Text Completion API

This procedure apoc.ml.openai.completion can continue/complete a given text.

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

Additional configuration is passed to the API, the default model used is text-davinci-003.

Text Completion Call

CALL apoc.ml.openai.completion('What color is the sky? Answer in one word: ', $apiKey, {config}) yield value;

Text Completion Response

{ created=1684248202, model="text-davinci-003", id="cmpl-7GqBWwX49yMJljdmnLkWxYettZoOy",
  usage={completion_tokens=2, prompt_tokens=12, total_tokens=14},
  choices=[{finish_reason="stop", index=0, text="Blue", logprobs=null}], object="text_completion"}

Table 4. Parameters
name	description
prompt	Text to complete
apiKey	OpenAI API key
configuration	optional map for entries like model, temperature, and other request parameters

Table 5. Results
name	description
value	result entry from OpenAI (containing)

Chat Completion API

This procedure apoc.ml.openai.chat takes a list of maps of chat exchanges between assistant and user (with optional system message), and will return the next message in the flow.

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

Additional configuration is passed to the API, the default model used is gpt-3.5-turbo.

Chat Completion Call

CALL apoc.ml.openai.chat([
{role:"system", content:"Only answer with a single word"},
{role:"user", content:"What planet do humans live on?"}
],  $apiKey) yield value

Chat Completion Response

{created=1684248203, id="chatcmpl-7GqBXZr94avd4fluYDi2fWEz7DIHL",
object="chat.completion", model="gpt-3.5-turbo-0301",
usage={completion_tokens=2, prompt_tokens=26, total_tokens=28},
choices=[{finish_reason="stop", index=0, message={role="assistant", content="Earth."}}]}

Table 6. Parameters
name	description
messages	List of maps of instructions with `{role:"assistant
user	system", content:"text}`
apiKey	OpenAI API key
configuration	optional map for entries like model, temperature, and other request parameters

Table 7. Results
name	description
value	result entry from OpenAI (containing created, id, model, object, usage(tokens), choices(message, index, finish_reason))

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 8. Input Parameters
name	description
question	The question in the natural language
conf	An optional configuration map, please check the next section

Table 9. 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 10. 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 11. Input Parameters
name	description
conf	An optional configuration map, please check the next section

Table 12. 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 13. 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?", 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 14. Input Parameters
name	description	mandatory
question	The question in the natural language	yes

Table 15. 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 16. Results
name	description
value	the description of the dataset