Functions/GenXdev.AI.Queries/Get-Fallacy.ps1
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################################################################################ <# .SYNOPSIS Analyzes text to identify logical fallacies using AI-powered detection. .DESCRIPTION This function analyzes provided text to detect logical fallacies using an AI model trained on Wikipedia's List of Fallacies. It returns detailed information about each fallacy found, including the specific quote, fallacy name, description, explanation, and formal classification. The function uses a structured response format to ensure consistent output. .PARAMETER Text The text content to analyze for logical fallacies. Can accept multiple text inputs through pipeline or array. .PARAMETER Instructions Additional instructions for the AI model on how to analyze the text or focus on specific types of fallacies. .PARAMETER Attachments Array of file paths to attach to the analysis request for additional context. .PARAMETER Functions Array of custom function definitions to make available to the AI model during analysis. .PARAMETER ImageDetail Detail level for image processing when attachments include images. Options are low, medium, or high. .PARAMETER NoConfirmationToolFunctionNames Array of function names that don't require user confirmation before execution when used by the AI model. .PARAMETER ExposedCmdLets Array of PowerShell command definitions that the AI model can use as tools during analysis. .PARAMETER Temperature Controls the randomness of the AI response. Lower values (0.0-0.3) provide more focused analysis, higher values (0.7-1.0) allow more creative interpretation. .PARAMETER LLMQueryType The type of LLM query to perform for the analysis. .PARAMETER Model The model identifier or pattern to use for AI operations. .PARAMETER HuggingFaceIdentifier The LM Studio specific model identifier. .PARAMETER MaxToken The maximum number of tokens to use in AI operations. .PARAMETER Cpu The number of CPU cores to dedicate to AI operations. .PARAMETER Gpu How much to offload to the GPU. If 'off', GPU offloading is disabled. If 'max', all layers are offloaded to GPU. If a number between 0 and 1, that fraction of layers will be offloaded to the GPU. -1 = LM Studio will decide how much to offload to the GPU. -2 = Auto. .PARAMETER ApiEndpoint The API endpoint URL for AI operations. .PARAMETER ApiKey The API key for authenticated AI operations. .PARAMETER TimeoutSeconds The timeout in seconds for AI operations. .PARAMETER PreferencesDatabasePath Database path for preference data files. .PARAMETER ContinueLast Switch to continue from the last conversation context instead of starting a new analysis session. .PARAMETER Force Forces LM Studio to stop and restart before initializing the model for analysis. .PARAMETER IncludeThoughts Switch to include the model's reasoning process in the output alongside the final analysis. .PARAMETER NoSessionCaching Switch to disable storing the analysis session in the session cache for privacy or performance reasons. .PARAMETER OpenInImdb Switch to open IMDB searches for each detected fallacy result (legacy parameter from inherited function structure). .PARAMETER ShowWindow Switch to display the LM Studio window during processing for monitoring purposes. .PARAMETER Speak Switch to enable text-to-speech output for the AI analysis results. .PARAMETER SpeakThoughts Switch to enable text-to-speech output for the AI model's reasoning process when IncludeThoughts is enabled. .PARAMETER SessionOnly Use alternative settings stored in session for AI preferences. .PARAMETER ClearSession Clear alternative settings stored in session for AI preferences. .PARAMETER SkipSession Store settings only in persistent preferences without affecting session. .EXAMPLE Get-Fallacy -Text ("All politicians are corrupt because John was corrupt " + "and he was a politician") Analyzes the provided text for logical fallacies and returns structured information about any fallacies detected. .EXAMPLE "This product is the best because everyone uses it" | Get-Fallacy -Temperature 0.1 Uses pipeline input to analyze text with low temperature for focused analysis. .EXAMPLE dispicetext "Everyone knows this is true" Uses the alias to analyze text for logical fallacies. #> ################################################################################ function Get-Fallacy { [CmdletBinding()] [OutputType([object[]])] [Alias("dispicetext")] [System.Diagnostics.CodeAnalysis.SuppressMessageAttribute("PSUseSingularNouns", "")] param( ####################################################################### [Parameter( ValueFromPipeline = $true, Position = 0, Mandatory = $true, HelpMessage = "Text to parse to find Fallacies in" )] [string[]]$Text, ####################################################################### [Parameter( Position = 1, Mandatory = $false, HelpMessage = ("Instructions for the AI model on how to generate " + "the string list") )] [string]$Instructions = "", ####################################################################### [Parameter( Position = 2, Mandatory = $false, HelpMessage = "Array of file paths to attach" )] [string[]]$Attachments = @(), ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Array of function definitions" )] [hashtable[]]$Functions = @(), ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Image detail level" )] [ValidateSet("low", "medium", "high")] [string]$ImageDetail = "low", ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("Array of command names that don't require " + "confirmation") )] [Alias("NoConfirmationFor")] [string[]]$NoConfirmationToolFunctionNames = @(), ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("Array of PowerShell command definitions to use " + "as tools") )] [GenXdev.Helpers.ExposedCmdletDefinition[]]$ExposedCmdLets = @(), ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Temperature for response randomness (0.0-1.0)" )] [ValidateRange(0.0, 1.0)] [double]$Temperature = 0.2, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The type of LLM query" )] [ValidateSet( "SimpleIntelligence", "Knowledge", "Pictures", "TextTranslation", "Coding", "ToolUse" )] [string]$LLMQueryType = "Knowledge", ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("The model identifier or pattern to use for AI " + "operations") )] [string]$Model, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The LM Studio specific model identifier" )] [Alias("ModelLMSGetIdentifier")] [string]$HuggingFaceIdentifier, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The maximum number of tokens to use in AI operations" )] [int]$MaxToken, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The number of CPU cores to dedicate to AI operations" )] [int]$Cpu, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("How much to offload to the GPU. If 'off', GPU " + "offloading is disabled. If 'max', all layers are " + "offloaded to GPU. If a number between 0 and 1, " + "that fraction of layers will be offloaded to the " + "GPU. -1 = LM Studio will decide how much to " + "offload to the GPU. -2 = Auto") )] [ValidateRange(-2, 1)] [int]$Gpu = -1, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The API endpoint URL for AI operations" )] [string]$ApiEndpoint, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The API key for authenticated AI operations" )] [string]$ApiKey, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "The timeout in seconds for AI operations" )] [int]$TimeoutSeconds, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Database path for preference data files" )] [string]$PreferencesDatabasePath, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Continue from last conversation" )] [switch]$ContinueLast, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Force stop LM Studio before initialization" )] [switch]$Force, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Include model's thoughts in output" )] [switch]$IncludeThoughts, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Don't store session in session cache" )] [switch]$NoSessionCaching, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Opens IMDB searches for each result" )] [Alias("imdb")] [switch]$OpenInImdb, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Show the LM Studio window" )] [switch]$ShowWindow, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Enable text-to-speech for AI responses" )] [switch]$Speak, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = "Enable text-to-speech for AI thought responses" )] [switch]$SpeakThoughts, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("Use alternative settings stored in session for AI " + "preferences") )] [switch]$SessionOnly, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("Clear alternative settings stored in session for " + "AI preferences") )] [switch]$ClearSession, ####################################################################### [Parameter( Mandatory = $false, HelpMessage = ("Store settings only in persistent preferences " + "without affecting session") )] [Alias("FromPreferences")] [switch]$SkipSession ####################################################################### ) begin { # output verbose information about starting fallacy analysis Microsoft.PowerShell.Utility\Write-Verbose ` "Starting fallacy analysis for provided text" # initialize results array to store detected fallacies [object[]]$results = @() # create comprehensive instructions for ai model fallacy detection $instructions = @" You are an expert in logical reasoning and fallacy detection, trained on the Wikipedia "List of Fallacies" page (as provided). Your task is to analyze a given text for logical fallacies and return each occurrence with the following details: Partial Quote: The specific segment of the text where the fallacy occurs. Fallacy Name: The formal name of the fallacy as listed in the Wikipedia "List of Fallacies". Description: A brief description of the fallacy based on its definition from the Wikipedia page. Explanation: An explanation of why this instance qualifies as the identified fallacy, tailored to the context of the text. Formal Classification: The category under which the fallacy falls (e.g., Formal Fallacies, Informal Fallacies > Faulty Generalizations, etc.), as per the Wikipedia structure. Instructions: $Instructions Analyze the entire text provided by the user. Identify every instance of a logical fallacy, even if multiple fallacies occur in the same sentence or paragraph. If no fallacies are present, return a fallacies property holding an empty array. Use the Wikipedia "List of Fallacies" page as the sole reference for fallacy definitions and classifications. Do not invent fallacies or use external sources beyond the provided Wikipedia content. Present the results in a clear, structured format (e.g., numbered list or table). If the text is ambiguous, make a reasonable interpretation and explain your reasoning. Input Format: The user will provide a text sample (e.g., a paragraph, argument, or statement) for analysis. Output Format: For each detected fallacy: Fallacy Occurrence #X: - Partial Quote: "[quote from text]" - Fallacy Name: [name of fallacy] - Description: [brief description from Wikipedia] - Explanation: [context-specific explanation] - Formal Classification: [e.g., Informal Fallacies > Relevance Fallacies] If no fallacies are found: Return nothing. "@ # define json schema for structured fallacy detection response $responseSchema = @{ type = "json_schema" json_schema = @{ name = "fallacy_detection_response" strict = "true" schema = @{ type = "object" properties = @{ fallacies = @{ type = "array" items = @{ type = "object" properties = @{ PartialQuote = @{ type = "string" } FallacyName = @{ type = "string" } Description = @{ type = "string" } Explanation = @{ type = "string" } FormalClassification = @{ type = "string" } } required = @("PartialQuote", "FallacyName", "Description", "Explanation", "FormalClassification") } } } required = @("fallacies") } } } | Microsoft.PowerShell.Utility\ConvertTo-Json -Depth 10 # output verbose confirmation of schema initialization Microsoft.PowerShell.Utility\Write-Verbose ` "Initialized response schema for fallacy detection" } ####################################################################### process { # iterate through each text part provided for analysis foreach ($textPart in $Text) { # output verbose information about parameter preparation Microsoft.PowerShell.Utility\Write-Verbose ` "Preparing LLM invocation parameters" try { # copy matching parameters from bound parameters to invocation $invocationParams = GenXdev.Helpers\Copy-IdenticalParamValues ` -BoundParameters $PSBoundParameters ` -FunctionName "GenXdev.AI\Invoke-LLMQuery" # set specific parameters for fallacy detection analysis $invocationParams.Query = $textPart $invocationParams.Instructions = $instructions $invocationParams.IncludeThoughts = $false $invocationParams.ResponseFormat = $responseSchema $invocationParams.Temperature = $Temperature # configure chat mode if default tools are allowed if ($AllowDefaultTools) { $invocationParams.ChatMode = "textprompt" $invocationParams.ChatOnce = $true } # invoke language model query for fallacy detection $response = GenXdev.AI\Invoke-LLMQuery @invocationParams | Microsoft.PowerShell.Utility\ConvertFrom-Json # process and store fallacy detection results if ($response.fallacies) { # add detected fallacies to results collection $results += $response.fallacies } else { # output verbose message when no fallacies are detected Microsoft.PowerShell.Utility\Write-Verbose ` "No fallacies detected in the provided text" } } catch { # output error message if fallacy analysis fails Microsoft.PowerShell.Utility\Write-Error ` "Failed to analyze text for fallacies: $_" } } } ####################################################################### end { # output appropriate verbose message based on results if ($results.Count -gt 0) { Microsoft.PowerShell.Utility\Write-Verbose ` "Returning detected fallacies" } else { Microsoft.PowerShell.Utility\Write-Verbose ` "No fallacies detected in any provided text" } # return the collected fallacy detection results $results } } ################################################################################ |