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| ai_audit_analysis_categories = { | |
| "AI Audit": [ | |
| "sentiment_analysis", | |
| "emotion_detection", | |
| "political_bias_detection", | |
| "stress_level_detection", | |
| "empathy_level_assessment", | |
| "mood_detection", | |
| "toxicity_detection" | |
| ], | |
| "GDPR": [ | |
| "Data Handling and Processing", | |
| "Consent and Transparency", | |
| "Data Security", | |
| "Environmental Impact"], | |
| "Toxicity": [ | |
| "Content Moderation", | |
| "Reporting Mechanism", | |
| "Content guidelines", | |
| "User Education"], | |
| "Legal": [ | |
| "Privacy Policy", | |
| "Data Retention", | |
| "Consent Mechanism", | |
| "GDPR Compliance"], | |
| "Context": [ | |
| "Ethical AI", | |
| "Bais Mitigation", | |
| "Fairness Assestment", | |
| "Explainability"], | |
| "Governance": [ | |
| "Model development", | |
| "Data Quality", | |
| "Bais Mitigation", | |
| "Fairness Assestment" | |
| "Explainability" | |
| "User Input"], | |
| "RiskManagement": [ | |
| "Corporate Ethics", | |
| "Board Management", | |
| "Stakeholder Engagement", | |
| "Risk Management"], | |
| "Robustness": [ | |
| "System Reliability", | |
| "Quality Assurance", "Stress Testing", | |
| "Fail-Safe Procedures"], | |
| "Sustainability": [ | |
| "Renewable Resources", | |
| "Waste Reduction", | |
| "Energy Efficiency", | |
| "Sustainable Practices"] | |
| } | |
| # Define a standard template for prompts | |
| STANDARD_PROMPT_TEMPLATE = "You are a data analysis assistant capable of {analysis_type} analysis. {specific_instruction} Respond with your analysis in JSON format. The JSON schema should include '{json_schema}'." | |
| def get_system_prompt(analysis_type: str) -> str: | |
| specific_instruction = ANALYSIS_TYPES.get(analysis_type, "Perform the analysis as per the specified type.") | |
| json_schema = JSON_SCHEMAS.get(analysis_type, {}) | |
| json_schema_str = ', '.join([f"'{key}': {value}" for key, value in json_schema.items()]) | |
| return (f"You are a data analyst API capable of {analysis_type} analysis. " | |
| f"{specific_instruction} Please respond with your analysis directly in JSON format " | |
| f"(without using Markdown code blocks or any other formatting). Always include confidence_score:number (0-1) with two decimals for result based on analysis" | |
| f"The JSON schema should include: {{{json_schema_str}}}.") | |
| ANALYSIS_TYPES = { | |
| "sentiment_analysis": "Analyze the sentiment of the provided text. Determine whether the sentiment is positive, negative, or neutral and provide a confidence score.", | |
| "emotion_detection": "Detect and identify the primary emotions expressed in the provided text. Provide a score for the intensity of the detected emotion.", | |
| "political_bias_detection": "Detect any political bias in the provided text, identifying leaning towards particular ideologies or parties.", | |
| "stress_level_detection": "Analyze the text to assess stress levels, identifying triggers and intensity of stress.", | |
| "empathy_level_assessment": "Assess the level of empathy expressed in the text, identifying empathetic responses and tendencies.", | |
| "mood_detection": "Detect the mood of the individual based on textual cues, ranging from happy to sad, calm to angry.", | |
| "toxicity_detection": "Identify and assess the level of toxicity in the provided text. Determine whether the text contains harmful, offensive, or inappropriate content and provide a score indicating the severity of the toxicity" | |
| } | |
| JSON_SCHEMAS = { | |
| "sentiment_analysis": { | |
| "sentiment": "string (positive, negative, neutral)", | |
| "confidence_score": "number (0-1)", | |
| "text_snippets": "array of strings (specific text portions contributing to sentiment)" | |
| }, | |
| "emotion_detection": { | |
| "emotion": "string (primary emotion detected)", | |
| "confidence_score": "number (0-1)", | |
| "secondary_emotions": "array of objects (secondary emotions and their scores)" | |
| }, | |
| "political_bias_detection": { | |
| "bias": "string (left, right, neutral)", | |
| "confidence_score": "number (0-1)", | |
| "bias_indicators": "array of strings (elements indicating bias)", | |
| "political_alignment_score": "number (quantifying degree of political bias)" | |
| }, | |
| "stress_level_detection": { | |
| "stress_level": "string", | |
| "stress_triggers": "array of strings" | |
| }, | |
| "empathy_level_assessment": { | |
| "empathy_level": "string", | |
| "empathetic_responses": "array of strings" | |
| }, | |
| "mood_detection": { | |
| "mood": "string", | |
| "mood_intensity": "number" | |
| }, | |
| "toxicity_detection": { | |
| "toxicity_level": "string (none, low, medium, high)", | |
| "toxicity_flags": "array of strings (specific words or phrases contributing to toxicity)", | |
| "contextual_factors": "array of objects (additional contextual elements influencing toxicity interpretation)" | |
| } | |
| } | |