Google Cloud Vertex.AI API Access

This is the APOC Extended documentation.

APOC Extended is not supported by Neo4j. For the officially supported APOC Core, go to the APOC Core page.

You need to create a Google Cloud project in your account an enable the Vertex.AI services. As an access-token you can run gcloud auth print-access-token. Using these services will incurr costs on your Google Cloud account.

Generate Embeddings API

This procedure apoc.ml.vertexai.embedding can take a list of text strings, and will return one row per string, with the embedding data as a 768 element vector. It uses the embedding endpoint which is documented here.

Additional configuration is passed to the API, the default model used is textembedding-gecko.

Generate Embeddings Call

CALL apoc.ml.vertexai.embedding(['Some Text'], $accessToken, $project, {}) 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
accessToken	Vertex.AI API access token
project	Google Cloud project
configuration	optional map for entries like model and other request parameters like `region`

Table 3. Results
name	description
index	index entry in original list
text	line of text from original list
embedding	768 element floating point embedding vector for the textembedding-gecko model

Text Completion API

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

It uses the completion model API which is documented here.

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

Text Completion Call

CALL apoc.ml.vertexai.completion('What color is the sky? Answer in one word: ', $apiKey, $project, {})

Text Completion Response

{value={safetyAttributes={blocked=false, scores=[0.1], categories=[Sexual]},
recitationResult={recitations=[], recitationAction=NO_ACTION}, content=blue}}

Table 4. Parameters
name	description
prompt	Text to complete
accessToken	Vertex.AI API access token
project	Google Cloud project
configuration	optional map for entries like model, region, temperature, topK, topP, maxOutputTokens, and other request parameters

Table 5. Results
name	description
value	result entry from Vertex.AI (content, safetyAttributes(blocked, categories, scores), recitationResult(recitationAction, recitations))

Chat Completion API

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

It uses the chat model API which is documented here.

Additional configuration is passed to the API, the default model used is chat-bison.

Chat Completion Call

CALL apoc.ml.vertexai.chat(
/*messages*/
[{author:"user", content:"What planet do timelords live on?"}],
$apiKey, $project,
{temperature:0},
/*context*/ "Fictional universe of Doctor Who. Only answer with a single word!",
/*examples*/ [{input:{content:"What planet do humans live on?"}, output:{content:"Earth"}}])
yield value

Chat Completion Response

{value={candidates=[{author=1, content=Gallifrey.}], safetyAttributes={blocked=false, scores=[0.1, 0.1, 0.1], categories=[Religion & Belief, Sexual, Toxic]}, recitationResults=[{recitations=[], recitationAction=NO_ACTION}]}}

Table 6. Parameters
name	description
messages	List of maps of instructions with `{author:"bot
user", content:"text"}`	accessToken
Vertex.AI API access token	project
Google Cloud project	configuration
optional map for entries like region, model, temperature, topK, topP, maxOutputTokens and other parameters	context
optional context and system prompt for the completion	examples

Table 7. Results
name	description
value	result entry from Vertex.AI (containing candidates(author, content), safetyAttributes(categories, scores, blocked), recitationResults(recitationAction, recitations))