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Semantic Search

SearchTool

The search tool can be accessed using $ai.search

Search an index with a content/query

This example searches the blog blogIndex embedding index for matching content:

## Run a semantic query
#set($results = $ai.search.query("Where can I find the Best beaches?", "blogIndex"))
total: $results.count
limit: $results.limit
offset: $results.offset
threshold: $results.threshold

$results.query
#foreach($result in $results.dotCMSResults)
    $result.title
    #foreach($m in $result.matches)
        - $m.distance : $m.extractedText
    #end
#end

Search with parameters

You can also build a search map by hand by passing a Map.

## A semantic quer using a Map of Parameters
#set($query = {})
$!query.put("query", "Where can I find the Best beaches?")
$!query.put("contentType", "Blog")
$!query.put("indexName", "blogIndex")
$!query.put("limit", "100")
$!query.put("offset", "0")
## $!query.put("host", "SYSTEM_HOST")
## $!query.put("language", "1")
## $!query.put("fieldVar", "content")
#set($results = $ai.search.query($query ))
total: $results.count
limit: $results.limit
offset: $results.offset
threshold: $results.threshold

$results.query
#foreach($result in $results.dotCMSResults)
    $result.title
    #foreach($m in $result.matches)
        - $m.distance : $m.extractedText
    #end
#end

Find Semantically Related Content

You can easily use this to automatically find related content based on an existing content. The method will try to find the most "content-rich" field from the content you have passed in and use it to find other content that is semantically related to the original.

##  Finding related Content
#set($content = $dotcontent.find("48ec3192-5f04-466b-b7cd-7134f3ea4d67"))

## send in a contentlet to find related content in the index "blog"
#set($relatedContent = $ai.search.related($content, "blogIndex"))

#foreach($result in $relatedContent.dotCMSResults)
    $result.title : $result.inode
    #foreach($m in $result.matches)
        - $m.distance : $m.extractedText
    #end
#end

Embedding Utils

Count the tokens in a string

$ai.embeddings.countTokens("this should be about 7 tokens")

Generate the Embeddings

This checks cache to see if we have already generated the embeddings for this string and if not, makes a call to openai to get the embeddings

$ai.embeddings.generateEmbeddings("this should be about 7 tokens")

See existing content index information

$ai.embeddings.indexCount

Completions

Config

Show the current config for completions, including the model and the prompt templates

$ai.completions.config

Summarize Content

This method takes a search, and using the configured model and prompt templates, will return a summary object as json.

#set($summary = $ai.completions.summarize("Where can I find the Best beaches?", "blogIndex")))

model: $summary.model
prompt: $summary.usage.prompt_tokens
tokens: $summary.usage.total_tokens
$summary.choices.get(0).message.content

RAW Prompting

You can also do "raw" prompting using the .raw method

#set($prompt = '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "You are a chatbot providing travel advice to people who visit a travel website; provide an enticing description of the beaches of Costa Rica"
}
]
}')

#set($chat = $ai.completions.raw($prompt))

Or using a Map to build your json

#set($prompt = {})
$!prompt.put("model", "gpt-3.5-turbo")
#set($messages =[])
#set($message ={})
$!message.put("role", "user")
$!message.put("content", "You are a chatbot providing travel advice to people who visit a travel website; provide an enticing description of the beaches of Costa Rica")
$messages.add($message)
$prompt.put("messages", $messages)

#set($chat = $ai.completions.raw($prompt))