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Generative AI client has been designed to allow running custom Groovy scripts. The scripts can do many different tasks but they are most useful when Generative AI services are needed to be called for crawled documents and the results from those calls are to be put in the document for later indexing. When writing Groovy scripts we can use so called binding variables to simplify AI related tasks:
- REST client comes configured with required authentication methods based on UI parameters
- REST client can be configured how to handle automatically typical throttling issues - like 429 errors
- Methods for creating POST body for typical AI tasks are present
- Methods for allowing REST calls for typical AI URLs are present
- The REST call response comes in typical fashion for easy parsing and using it in the document
- Support for general template scripts is here to allow selection of already prepared scripts with the support of template script properties
- If secret information must be used in Groovy script they can be defined in UI, stored as encrypted and use as automatically decrypted in the scripts
- Typical scenarios for running embeddings and prompts related tasks are built in and can be used with just several lines of Groovy script
- Support for different AI services providers is supported - we support Azure and Google Palm in the present version
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Binding variables
| Name in script | Description | Aspire type | Init script | Process script |
|---|---|---|---|---|
| doc | Crawled document | AspireObject | false | true |
| component | Aspire workflow component running Groovy scripts | ComponentImpl | true | true |
| connection.client | REST client component for making AI calls | GenAIRestRequester | true | true |
utilities.azure.embeddings utilities.google.embeddings | Methods related to "embeddings" processing | Embeddings | true | true |
job | Job containing the crawled document | Job | false | true |
secrets | Map of secrets provided in UI | Map<String,String> | true | true |
template | Map of selected script template variables | Map<String,String> | true | true |
utilities.azure.prompts utilities.google.prompts | Methods related to "prompts" processing | Prompts | true | true |
utilities.textSplitter | Method related to text splitting | TextSplitterComponent | true | true |
variables | Map of variables provided in initialize script | Map<String,Object> | true | false |
utilities | Various helper methods | Utils | true | true |
Document
The crawled document can be used for accessing metadata and the content and also for storing a new metadata acquired from AI:
| Code Block | ||
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doc.add(embeddings.toAspireObject()); |
Component
The component can typically be used as a logger:
| Code Block | ||
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component.info(" %s","${doc.id}: Got embeddings for sentence: ${currentSentence}") |
Connection
// TODO
Authentication
Request
Response
Throttling, 429 Policy
Embeddings
// TODO
| utilities.azure.embeddings, utilities.google.embeddings |
|---|
| Method | Syntax | Init script | Process script | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Initialize Azure | void utilities.azure.initialize(AspireObject config) | true | false | |||||||||||||||
config:
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| Initialize Google Palm | void utilities.google.initialize(AspireObject config) | true | false | |||||||||||||||
config:
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| Process | VectorEmbeddingResult utilities.azure.embeddings.process(List<String> splitText) VectorEmbeddingResult utilities.google.embeddings.process(List<String> splitText) VectorEmbeddingResult: TODO splitText: TODO | false | true | |||||||||||||||
| Convert response | VectorEmbeddingsResult utilities.azure.embeddings.convertResponse(String text, AspireObjectResponse response) VectorEmbeddingsResult utilities.google.embeddings.convertResponse(String text, AspireObjectResponse response) VectorEmbeddingsResult: TODO response: TODO | |||||||||||||||||
| Create POST body | AspireObject utilities.azure.embeddings.createPostBody(String text) | |||||||||||||||||
| Create Sub document | AspireObject utilities.azure.embeddings.createSubDoc(VectorEmbeddingsResult vectorEmbeddingsResult, AspireObject doc, int chunkCount) |
Example init script:
| Code Block | ||
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import com.accenture.aspire.services.AspireObject;
utilities.textSplitter.initialize(getTextSplitterConfig("sentence"))
utilities.azure.embeddings.initialize(getEmbeddingsConfig())
def getEmbeddingsConfig() {
AspireObject returnValue = new AspireObject("config");
returnValue.add("endpoint", "${template.endpoint}");
returnValue.add("model", "${template.model}");
returnValue.add("apiVersion", "${template.apiVersion}");
returnValue.add("apiKey", "${secrets.apiKey}");
return returnValue;
}
def getTextSplitterConfig(String splitType) {
.....
} |
Example script:
| Code Block | ||
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def sentences = utilities.textSplitter.process(doc); embeddings = utilities.azure.embeddings.process(sentences); doc.add(embeddings.toAspireObject()); |
Example script:
| Code Block | ||
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import com.accenture.aspire.services.AspireException
// split field "content" and create "sentences"
def sentences = utilities.textSplitter.process(doc);
// url
endpointEmbeddings = "${template.endpoint}/openai/deployments/${template.model}/embeddings?api-version=${template.apiVersion}"
// generate and publish embeddings
sentences.eachWithIndex {currentSentence, sentencesCount ->
embeddingVector = getEmbeddingsFromSentence(endpointEmbeddings, currentSentence)
subJobAO = utilities.azure.embeddings.createSubDoc(embeddingVector, doc, sentencesCount);
utilities.createSubJob(job, subJobAO)
}
def getEmbeddingsFromSentence(endpointEmbeddings, sentence) {
response = connection.client.executePost(endpointEmbeddings, utilities.azure.embeddings.createPostBody(sentence));
def embeddings = utilities.azure.embeddings.convertResponse(sentence, response)
return embeddings
} |
Job
Job can be used when required as a parameter for other methods:
| Code Block | ||
|---|---|---|
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utilities.createSubJob(job, subJobAO) |
Secrets
Secrets defined in UI which are stored as encrypted can be accessed in scripts:. They are automatically decrypted before using them.
| Code Block | ||
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client.addHeader("api-key", "${secrets.apiKey}"); |
Template
If in UI a template script with properties has been selected we can access those properties in the script:
| Code Block | ||
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def getEmbeddingsConfig() {
AspireObject returnValue = new AspireObject("config");
returnValue.add("endpoint", "${template.endpoint}");
....
} |
| Code Block | ||
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// url
endpointEmbeddings = "${template.endpoint}/openai/deployments/${template.model}/embeddings?api-version=${template.apiVersion}" |
Prompts
// TODO
Text Splitter
// TODO
| Text splitter | utilities.textSplitter |
|---|
| Method | Syntax | Init script | Process script | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Initialize | void utilities.textSplitter.initialize(AspireObject config) | true | false | |||||||||||||||
config:
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| Process | List<String> utilities.textSplitter.process(AspireObject doc) List<String>: TODO doc: TODO | false | true |
Example init of initialization script:
| Code Block | ||
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import com.accenture.aspire.services.AspireObject;
utilities.textSplitter.initialize(getTextSplitterConfig("sentence"))
def getTextSplitterConfig(String splitType) {
AspireObject returnValue = new AspireObject("config");
returnValue.add("splitType", splitType);
returnValue.add("fieldsToSplit", "content");
returnValue.add("customSplitRegex", "\\|+");
returnValue.add("characterThreshold", 4);
return returnValue;
} |
Example script:
| Code Block | ||
|---|---|---|
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def sentences = utilities.textSplitter.process(doc); |
Variables
// TODO
Utilities
REST connection
Authentication
Request
Response
Throttling, 429 Policy// TODO
Overview
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