ow.ai
$gpt
$gpt(aModel) : $gpt
Creates a GPT AI model of aType (e.g. "openai" or "ollama") with aOptions.
$gpt.close
$gpt.close()
Closes the current GPT model.
$gpt.iniPrompt
$gpt.iniPrompt(aPrompt, aRole, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor) after cleaning the current conversation.
$gpt.prompt
$gpt.prompt(aPrompt, aRole, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor).
$gpt.promptBool
$gpt.promptBool(aPrompt, aRole, aModel, aTemperature) : boolean
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor) returning a boolean.
$gpt.promptImage
$gpt.promptImage(aPrompt, aImage, aDetailLevel, aRole, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) with aImage (a file path or a base64 string representation), aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
$gpt.promptJSON
$gpt.promptJSON(aPrompt, aModel, aTemperature)
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor) returning a Javascript function.
$gpt.promptMD
$gpt.promptMD(aPrompt, aRole, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor) returning a markdown string.
$gpt.promptPath
$gpt.promptPath(aPrompt, aJSONSchemaDef, aModel, aTemperature)
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor) returning a JMESPath query.
$gpt.promptSQL
$gpt.promptSQL(aPrompt, aTableDefs, aDBName, aModel, aTemperature)
Tries to prompt aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor) returning a SQL query.
$gpt.sysPrompt
$gpt.sysPrompt(aPrompt, aModel, aTemperature) : String
Tries to prompt system aPrompt (a string or an array of strings) and aModel (defaults to the one provided on the constructor).
$gpt.withContext
$gpt.withContext(anObject, aContext) : ow.ai.gpt
Adds a context to the current conversation.
$gpt.withJSONAssert
$gpt.withJSONAssert(aPath, anAssert) : ow.ai.gpt
Adds a JSON path aPath to be asserted with anAssert (e.g. isArray or isMap).
$gpt.withSQLTables
$gpt.withSQLTables(aDBName, aTablesDefs) : ow.ai.gpt
Adds aDBName with aTableDefs to be used with promptSQL.
ow.ai.cluster
ow.ai.cluster(args) : Object
Wraps access to clustering of data. The result will be an object with a classify method that will return the clustering result given the provided data. Args expects different arguments depending on type of clustering:
args.type (String) "kmeans" (default)
args.numberOfClusters (Number) number of clusters to use (default to 5)
classify(normalizedData) (Map) returns a map of centroids and cluster assignments
ow.ai.decisionTree
ow.ai.decisionTree(aMap) : Object
Provides a wrapper to access the existing decision tree algorithms included:
ID3:
type 'id3'
trainingSet (array of maps) The training data
categoryAttr (key name) The map key to build the decision tree on
ignoredAttributes (array of keys) The list of keys to be ignored in each map
RandomForest:
type 'randomforest'
trainingSet (array of maps) The training data
categoryAttr (key name) The map key to build the decision tree on
ignoredAttributes (array of keys) The list of keys to be ignored in each map
treesNumber (number) The number of decision trees to use
C45:
type 'c45'
data (array of arrays) The training data
features (arrays of keys) The keys name by order of each array data value
featureTypes (arrays of types) Categorization of each attribute between 'category' and 'number'
target (key) The target key name (the last of each array data value)
ow.ai.gpt
ow.ai.gpt(aType, aOptions) : ow.ai.gpt
Creates a GPT AI model of aType (e.g. "openai" or "ollama") with aOptions.
ow.ai.gpt.addPrompt
ow.ai.gpt.addPrompt(aPrompt, aRole) : ow.ai.gpt
Adds aPrompt (a string or an array of strings) with aRole (defaults to "user") to the current conversation.
ow.ai.gpt.addSystemPrompt
ow.ai.gpt.addSystemPrompt(aPrompt) : ow.ai.gpt
Adds aPrompt (a string or an array of strings) with aRole (defaults to "user") to the current conversation.
ow.ai.gpt.addUserPrompt
ow.ai.gpt.addUserPrompt(aPrompt) : ow.ai.gpt
Adds aPrompt (a string or an array of strings) with aRole (defaults to "user") to the current conversation.
ow.ai.gpt.booleanPrompt
ow.ai.gpt.booleanPrompt(aPrompt, aModel, aTemperature) : boolean
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.cleanPrompt
ow.ai.gpt.cleanPrompt() : ow.ai.gpt
Cleans the current conversation.
ow.ai.gpt.codePrompt
ow.ai.gpt.codePrompt(aPrompt, aModel, aTemperature, aCommentChars) : String
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.getConversation
ow.ai.gpt.getConversation() : Array
Returns the current conversation.
ow.ai.gpt.jsonPrompt
ow.ai.gpt.jsonPrompt(aPrompt, aModel, aTemperature) : Object
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.parseCode
ow.ai.gpt.parseCode(anAnswer) : String
Tries to parse anAnswer and return the code between \``` and \```.
ow.ai.gpt.pathPrompt
ow.ai.gpt.pathPrompt(aPrompt, aJSONSchemaDef, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.prompt
ow.ai.gpt.prompt(aPrompt, aRole, aModel, aTemperature, aJsonFlag) : String
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.promptImage
ow.ai.gpt.promptImage(aPrompt, aImage, aDetailLevel, aRole, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) with aImage (a file path or a base64 string representation), aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.rawPrompt
ow.ai.gpt.rawPrompt(aPrompt, aRole, aModel, aTemperature, aJsonFlag) : String
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.gpt.setConversation
ow.ai.gpt.setConversation(aConversation) : ow.ai.gpt
Sets the current conversation to aConversation.
ow.ai.gpt.setInstructions
ow.ai.gpt.setInstructions(aType) : ow.ai.gpt
Sets the instructions for the current conversation. aType can be a string (e.g. json, boolean, sql, js and path) or an array of strings.
ow.ai.gpt.sqlPrompt
ow.ai.gpt.sqlPrompt(aPrompt, aTableDefs, aDBName, aModel, aTemperature) : String
Tries to prompt aPrompt (a string or an array of strings) with aRole (defaults to "user") and aModel (defaults to the one provided on the constructor).
ow.ai.network
ow.ai.network(aMap) : ow.ai.network
Creates a neural network given the parameters in aMap. aMap should contain a "type" parameter to indicate the type of network (perceptron, lstm, liquid or hopfield). Then aMap should contain a "args" parameter to provide each network inialization parameters. Please see "help ow.ai.network.[type of network]" for more details about each one.
ow.ai.network.fromJson
ow.ai.network.fromJson(aMap)
Tries to rebuild the network from aMap returned previously by a toJson function.
ow.ai.network.get
ow.ai.network.get(inputArray) : Array
Given an inputArray of decimal values, normalize between 0 and 1, will activate the current network and return an output array of decimal values between 0 and 1.
ow.ai.network.hopfield
ow.ai.network.hopfield(args) : ow.ai.network
Hopfield serves as a content-addressable memory remembering patterns and when feed with new patterns the network returns the most similar one from the patterns it was trained to remember. You need to provide then number of input patterns args = [ 10 ].
ow.ai.network.liquid
ow.ai.network.liquid(args) : ow.ai.network
Liquid state machines are neural networks where neurons are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. You need to provide args = [number of inputs, size of pool of neurons, number of outputs, number of random connections, number of random gates] (e.g. 2, 20, 1, 30, 10).
ow.ai.network.lstm
ow.ai.network.lstm(args) : ow.ai.network
LSTM (Long short-term memory) are well-suited to learn from experience to classify, process and predict time series when there are very long time lags of unknown size between important events. There is a minimum of 3 layers (input, memory block (input, memory cell, forget gate, output gate), output). args = [2, 6, 1] means 2 input, 6 memory blocks, 1 output; args = [2, 4, 4, 4, 1] means 2 input neurons, 3 memory blocks and 1 output.
ow.ai.network.perceptron
ow.ai.network.perceptron(args) : ow.ai.network
Perceptron or feed-forward neural networks. There is a minimum of 3 layers (input, hidden and output) and a any nmumber of hidden layers. args = [2, 3, 1] means 2 input neurons, 3 hidden neurons and 1 output neuron; args = [2, 10, 10, 10, 10, 1] means 2 input neurons, 4 layers of 10 neurons and 1 output neuron.
ow.ai.network.put
ow.ai.network.put(inputArray, outputArray, learningRate)
Given an inputArray of decimal values, normalize between 0 and 1, will activate the current network and then the outputArray of decimal values, normalize between 0 and 1, with an optionial learningRate (defaults to 0.3).
ow.ai.network.readFile
ow.ai.network.readFile(aFile)
Rebuilds a network from a map stored in aFile previously with ow.ai.network.writeFile.
ow.ai.network.toJson
ow.ai.network.toJson() : Map
Returns a map representation of the current network to be later rebuilt with the fromJson function.
ow.ai.network.train
ow.ai.network.train(trainingData, trainArgs)
Trains the current network with the trainingData provided. trainingData should be an array of maps. Each map entry should have a input and output keys. Each input and output entries should be an array for the entry values and output values normalized to a decimal number between 0 and 1. Example:
[{input: [0,0], output: [0]}, {input: [0,1], output: [1]}, {input: [1,0], output: [1]}, {input: [1,1], output :[0]}]\.
ow.ai.network.writeFile
ow.ai.network.writeFile(aFile)
Writes a compressed file with the map representation of the current network.
ow.ai.normalize.intArray
ow.ai.normalize.intArray(anArray) : Array
Returns anArray where all numbers have been rounded to an integer value.
ow.ai.normalize.scaleArray
ow.ai.normalize.scaleArray(anArray, aMax, aMin) : Array
Given anArray of numbers tries to normalize returning an array of values between 0 and 1. If aMax or aMin are not provided they will be infered from the provided anArray.
ow.ai.normalize.toFeaturesArray
ow.ai.normalize.toFeaturesArray(anArrayOfObjects, ignoredAttrs) : Map
Tries to convert anArrayOfObjects into an array of array of values where each value is positioned in the resulting array by the corresponding key sorted. The result will be a map with the resulting array in 'data' (with the features values ignoring any key on ignoredAttrs), the 'ignoredAttrs' and keys with all the 'keys' identified.
ow.ai.normalize.withSchema
ow.ai.normalize.withSchema(aSimpleMapOfData, aMapSchema, convertBools) : Array
Tries to normalize and return aSimpleMapOfData normalized according with aMapSchema provided. Each element of aMapSchema should be a map describing how to normalize aSimpleMapOfData. Example:
var ar = [
{name:'workout', duration:'120', enjoy: true, time:1455063275, tags:['gym', 'weights'], crucial: false },
{name:'lunch', duration:'45', enjoy: false, time:1455063275, tags:['salad', 'wine'], crucial: true },
{name:'sleep', duration:'420', enjoy: true, time:1455063275, tags:['bed', 'romance'], crucial: true}
];
var sar = {
name : { col: 0, oneOf: [ 'workout', 'lunch', 'sleep' ] },
duration: { col: 1, min: 0, max: 1000 },
enjoy : { col: 2 },
tags : { col: 3, anyOf: [ 'gym', 'weights', 'salad', 'wine', 'bed', 'romance' ] },
crucial : { col: 4, scaleOf: [
{ val: true, weight: 0.85 },
{ val: false, weight: 0.15 }
]},
};
$from(ar).sort("time").select((r) => { return normalize(r, sar); });
ow.ai.regression
ow.ai.regression() : Regression
Returns a Regression with the following functions:
linear(data, options) : Map
power(data, options) : Map
exponential(data, options) : Map
logarithmic(data, options) : Map
polynomial(data, options) : Map
data - an array of arrays of x, y values ([[0,1],[1,3],[2,5]])
options - map to determine the order and precision ({ order: 2, precision: 5})