> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/BHARTIYAYASH/TRUTH-MAPPER/llms.txt > Use this file to discover all available pages before exploring further. # Blueprint Output Schema > Complete output structure from argument blueprint generation ## Overview The `GenerateArgumentBlueprintOutput` schema defines the complete output from the `generateArgumentBlueprint` flow. It contains the argument blueprint, analysis, social pulse, and detected fallacies. This is the primary data structure used throughout Argument Cartographer. ## Schema Definition ```typescript theme={null} const GenerateArgumentBlueprintOutputSchema = z.object({ blueprint: z.array(ArgumentNodeSchema).describe('A structured JSON blueprint of the arguments.'), summary: z.string().describe("A concise, neutral summary of the overall state of the debate."), analysis: z.string().describe("AI-driven meta-analysis providing novel insights, identifying emerging themes, logical gaps, or the overall state of the debate."), credibilityScore: z.number().min(1).max(10).describe("A score from 1-10 rating the overall quality, diversity, and reliability of the sources found."), brutalHonestTake: z.string().describe("A candid, slightly cynical, 'no-BS' summary of the situation, written in simple, conversational language."), keyPoints: z.array(z.string()).describe("A list of 3-5 key takeaways or summary points."), socialPulse: z.string().describe("A summary of the public sentiment and key discussion points on the topic from social media platform X (Twitter)."), tweets: z.array(TweetSchema).describe('An array of relevant tweets from X/Twitter.'), fallacies: z.array(DetectedFallacySchema).optional().describe('An array of logical fallacies detected in the source material.'), }); type GenerateArgumentBlueprintOutput = z.infer; ``` **Source:** `src/ai/flows/generate-argument-blueprint.ts:77` ## Fields ### Core Analysis A structured JSON blueprint of the arguments. Array of argument nodes forming a tree structure. See [Argument Node Schema](/api/schemas/argument-node). A concise, neutral summary of the overall state of the debate. Typically 2-4 paragraphs providing an objective overview. AI-driven meta-analysis providing novel insights, identifying emerging themes, logical gaps, or the overall state of the debate. Deeper analysis than summary, includes: * Emerging themes and patterns * Logical gaps in arguments * Quality of evidence on both sides * State of the debate overall ### Quality Metrics A score from 1-10 rating the overall quality, diversity, and reliability of the sources found. Factors considered: * Source diversity (number of unique outlets) * Source reputation (trusted vs unknown) * Evidence quality (primary vs secondary sources) * Balanced coverage (both sides represented) ### Additional Insights A candid, slightly cynical, "no-BS" summary of the situation, written in simple, conversational language. Example: "Look, both sides have points, but most of the debate is just people yelling past each other. The real issue is \[X], but nobody wants to talk about that." A list of 3-5 key takeaways or summary points. Bullet-point style insights that capture the essence of the debate. ### Social Context A summary of the public sentiment and key discussion points on the topic from social media platform X (Twitter). Generated from analyzing recent tweets, written in a "Grok-style" conversational tone. An array of relevant tweets from X/Twitter (up to 5 tweets). Sorted by engagement (like count). Each tweet includes: * `id`: Tweet ID * `text`: Full tweet text * `author`: Author details (name, username, profile image) * `created_at`: Timestamp * `public_metrics`: Engagement metrics (likes, retweets, replies, impressions) ### Fallacy Detection An array of logical fallacies detected in the source material. Optional field. See [Fallacy Detection Schema](/api/schemas/fallacy-detection) for structure. ## Example Output ```json theme={null} { "blueprint": [ { "id": "1", "parentId": null, "type": "thesis", "side": "for", "content": "Should governments implement carbon taxes?", "sourceText": "User query", "source": "https://user-input.local/query", "fallacies": [], "logicalRole": "Central debate question" }, { "id": "2", "parentId": "1", "type": "claim", "side": "for", "content": "Carbon taxes effectively reduce emissions", "sourceText": "Countries with carbon taxes saw 15% emission reduction...", "source": "https://example.com/study", "fallacies": [], "logicalRole": "Primary economic argument" } ], "summary": "The debate over carbon taxes centers on economic vs environmental priorities...", "analysis": "What emerges from the sources is a fundamental tension between short-term economic concerns and long-term environmental sustainability...", "credibilityScore": 7, "brutalHonestTake": "Both sides cherry-pick data to support their position. The real question isn't whether carbon taxes work, but whether we have the political will to implement them properly.", "keyPoints": [ "Carbon taxes have proven effective in some countries", "Implementation varies widely affecting outcomes", "Economic impact depends on how revenue is used", "Political feasibility remains the biggest challenge" ], "socialPulse": "Twitter discourse is predictably polarized. Environmental advocates tout success stories while critics focus on economic hardship. Few nuanced takes.", "tweets": [ { "id": "1234567890", "text": "Carbon taxes work. Just look at Sweden's emissions...", "author": { "name": "Climate Analyst", "username": "climate_data", "profile_image_url": "https://..." }, "created_at": "2024-03-08T12:00:00Z", "public_metrics": { "retweet_count": 45, "reply_count": 12, "like_count": 230, "impression_count": 5600 } } ], "fallacies": [ { "id": "f1", "name": "Appeal to Emotion", "severity": "Major", "category": "Emotional Fallacy", "confidence": 0.9, "problematicText": "This tax will destroy families...", "explanation": "Uses emotional language without supporting evidence", "definition": "Appeal to Emotion manipulates emotions rather than using logic", "avoidance": "Focus on evidence-based arguments", "example": "Instead of 'this will destroy families', say 'data shows economic impact of X%'", "suggestion": "Replace emotional claims with specific economic data", "location": "Node 3" } ] } ``` ## Usage This output is used throughout the application: * **Visualization**: Blueprint rendered in various graph layouts * **Analysis Display**: Summary and analysis shown to users * **Quality Indicator**: Credibility score displayed * **Social Context**: Social pulse and tweets provide public sentiment * **Fallacy Highlighting**: Detected fallacies marked in UI * **Chat Context**: Full output passed to Ask More flow ## Validation The schema enforces: * `blueprint` must be a non-empty array * `credibilityScore` must be between 1 and 10 * `keyPoints` should contain 3-5 items (recommended) * `tweets` limited to 5 items in practice * All required fields must be present ## Post-Processing The flow applies these transformations: * **Credibility Score**: Converts decimal (0.9) to 1-10 scale (9) if needed * **Source URLs**: Replaces "null" strings with placeholder URLs * **Parent IDs**: Converts "null" strings to actual `null` * **Tweet Sorting**: Sorts by like count before limiting to 5 ## Related * [Argument Node Schema](/api/schemas/argument-node) * [Fallacy Detection Schema](/api/schemas/fallacy-detection) * [Generate Blueprint Flow](/api/flows/generate-blueprint) * [Ask More Flow](/api/flows/ask-more) - uses this output as context