antigravity_workflow.json

{
    "name": "Antigravity Image Research Extractor",
    "nodes": [
        {
            "parameters": {
                "path": "C:/Users/Nauti/Desktop/LOGOS CURSOR/LOGOS Notes",
                "fileExtensions": "jpg,jpeg,png,pdf,heic",
                "options": {
                    "recurse": true
                }
            },
            "id": "image_scanner",
            "name": "Scan Images (Notes)",
            "type": "n8n-nodes-base.readFilesFromFolder",
            "position": [
                250,
                300
            ]
        },
        {
            "parameters": {
                "jsCode": "// ROUTER: Classify images for specialized analysis\nconst images = items.map(item => {\n  const fileName = item.json.fileName;\n  const fileSize = item.json.size || 0;\n  \n  let taskType = 'general_vision';\n  let priority = 1;\n  \n  // Route by filename patterns and size\n  if (fileName.includes('diagram') || fileName.includes('sketch')) {\n    taskType = 'diagram_analysis';\n    priority = 3;\n  } else if (fileName.includes('note') || fileName.includes('handwritten')) {\n    taskType = 'handwriting_ocr';\n    priority = 2;\n  } else if (fileName.includes('ui') || fileName.includes('interface')) {\n    taskType = 'ui_analysis';\n    priority = 3;\n  } else if (fileSize < 500000) {\n    taskType = 'handwriting_ocr'; // Smaller files likely notes\n    priority = 2;\n  } else {\n    taskType = 'diagram_analysis'; // Larger files likely detailed diagrams\n    priority = 3;\n  }\n  \n  return {\n    json: {\n      fileName,\n      taskType,\n      priority,\n      fullPath: item.json.directory + '/' + fileName,\n      fileSize\n    },\n    binary: item.binary\n  };\n});\n\nreturn images;"
            },
            "id": "router",
            "name": "Neural Router",
            "type": "n8n-nodes-base.code",
            "position": [
                450,
                300
            ]
        },
        {
            "parameters": {
                "conditions": {
                    "options": {
                        "caseSensitive": false
                    },
                    "conditions": [
                        {
                            "id": "handwriting_path",
                            "leftValue": "={{ $json.taskType }}",
                            "rightValue": "handwriting_ocr",
                            "operator": {
                                "type": "string",
                                "operation": "equals"
                            }
                        },
                        {
                            "id": "diagram_path",
                            "leftValue": "={{ $json.taskType }}",
                            "rightValue": "diagram_analysis",
                            "operator": {
                                "type": "string",
                                "operation": "equals"
                            }
                        },
                        {
                            "id": "ui_path",
                            "leftValue": "={{ $json.taskType }}",
                            "rightValue": "ui_analysis",
                            "operator": {
                                "type": "string",
                                "operation": "equals"
                            }
                        }
                    ]
                },
                "options": {}
            },
            "id": "switch",
            "name": "Task Switch",
            "type": "n8n-nodes-base.switch",
            "position": [
                650,
                300
            ]
        },
        {
            "parameters": {
                "method": "POST",
                "url": "https://api-inference.huggingface.co/models/microsoft/trocr-base-handwritten",
                "authentication": "genericCredentialType",
                "genericAuthType": "httpHeaderAuth",
                "sendHeaders": true,
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "Content-Type",
                            "value": "application/json"
                        }
                    ]
                },
                "sendBody": true,
                "specifyBody": "json",
                "jsonBody": "={\n  \"inputs\": \"{{ $binary.data.toString('base64') }}\"\n}",
                "options": {}
            },
            "id": "ocr_analyst",
            "name": "OCR Handwriting (TrOCR)",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
                850,
                200
            ]
        },
        {
            "parameters": {
                "method": "POST",
                "url": "http://localhost:1234/v1/chat/completions",
                "authentication": "none",
                "sendHeaders": true,
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "Content-Type",
                            "value": "application/json"
                        }
                    ]
                },
                "sendBody": true,
                "specifyBody": "json",
                "jsonBody": "={\n  \"model\": \"llava-v1.6-mistral-7b\",\n  \"messages\": [\n    {\n      \"role\": \"system\",\n      \"content\": \"You are a technical diagram analyst specializing in geometry, polyforms, and compression systems. Identify: 1) Mathematical concepts shown, 2) Geometric shapes/polyhedra types, 3) Compression techniques mentioned, 4) UI/workflow elements. Output structured JSON.\"\n    },\n    {\n      \"role\": \"user\",\n      \"content\": [\n        {\n          \"type\": \"text\",\n          \"text\": \"Analyze this diagram from {{ $json.fileName }}. Focus on polyform development, compression methods, and UI design.\"\n        },\n        {\n          \"type\": \"image_url\",\n          \"image_url\": {\n            \"url\": \"data:image/jpeg;base64,{{ $binary.data.toString('base64') }}\"\n          }\n        }\n      ]\n    }\n  ],\n  \"temperature\": 0.2,\n  \"max_tokens\": 1500\n}",
                "options": {}
            },
            "id": "diagram_analyst",
            "name": "Diagram Analyst (LLaVA)",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
                850,
                300
            ]
        },
        {
            "parameters": {
                "method": "POST",
                "url": "http://localhost:1234/v1/chat/completions",
                "authentication": "none",
                "sendHeaders": true,
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "Content-Type",
                            "value": "application/json"
                        }
                    ]
                },
                "sendBody": true,
                "specifyBody": "json",
                "jsonBody": "={\n  \"model\": \"llava-v1.6-mistral-7b\",\n  \"messages\": [\n    {\n      \"role\": \"system\",\n      \"content\": \"You are a UI/UX analyst. Extract: 1) Interface components shown, 2) Interaction patterns, 3) Data visualization methods, 4) Success indicators mentioned, 5) User workflow steps. Output structured JSON.\"\n    },\n    {\n      \"role\": \"user\",\n      \"content\": [\n        {\n          \"type\": \"text\",\n          \"text\": \"Analyze UI design from {{ $json.fileName }}. Identify successful patterns for polyform visualization and user interaction.\"\n        },\n        {\n          \"type\": \"image_url\",\n          \"image_url\": {\n            \"url\": \"data:image/jpeg;base64,{{ $binary.data.toString('base64') }}\"\n          }\n        }\n      ]\n    }\n  ],\n  \"temperature\": 0.3,\n  \"max_tokens\": 1200\n}",
                "options": {}
            },
            "id": "ui_analyst",
            "name": "UI Analyst (LLaVA)",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
                850,
                400
            ]
        },
        {
            "parameters": {
                "mode": "mergeByPosition",
                "options": {}
            },
            "id": "merge",
            "name": "Merge Analysis",
            "type": "n8n-nodes-base.merge",
            "position": [
                1050,
                300
            ]
        },
        {
            "parameters": {
                "jsCode": "// SYNTHESIS: Parse vision model responses and structure results\nconst results = items.map(item => {\n  let analysis = {};\n  \n  try {\n    // Handle TrOCR response (array format)\n    if (Array.isArray(item.json)) {\n      analysis = {\n        extracted_text: item.json[0]?.generated_text || item.json.toString(),\n        confidence: item.json[0]?.score || 0.8\n      };\n    }\n    // Handle LLaVA response (OpenAI format)\n    else if (item.json.choices) {\n      const content = item.json.choices[0]?.message?.content || '{}';\n      analysis = JSON.parse(content);\n    }\n    // Handle direct JSON response\n    else if (typeof item.json === 'object') {\n      analysis = item.json;\n    }\n    else {\n      analysis = { raw_response: JSON.stringify(item.json) };\n    }\n  } catch (e) {\n    analysis = {\n      raw_response: JSON.stringify(item.json),\n      parse_error: true,\n      error_detail: e.message\n    };\n  }\n  \n  return {\n    json: {\n      file: item.json.fileName || 'unknown',\n      taskType: item.json.taskType,\n      analysis,\n      timestamp: new Date().toISOString()\n    }\n  };\n});\n\nreturn results;"
            },
            "id": "synthesizer",
            "name": "Synthesizer",
            "type": "n8n-nodes-base.code",
            "position": [
                1250,
                300
            ]
        },
        {
            "parameters": {
                "method": "POST",
                "url": "http://localhost:1234/v1/chat/completions",
                "authentication": "none",
                "sendHeaders": true,
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "Content-Type",
                            "value": "application/json"
                        }
                    ]
                },
                "sendBody": true,
                "specifyBody": "json",
                "jsonBody": "={\n  \"model\": \"nvidia/nemotron-3-nano\",\n  \"messages\": [\n    {\n      \"role\": \"system\",\n      \"content\": \"You are the RESEARCH SYNTHESIZER for a polyform compression project. Analyze handwritten notes and diagrams to extract: 1) POLYFORM TYPES (Platonic, Archimedean, Johnson, near-miss solids, geodesics), 2) COMPRESSION METHODS (vertex encoding, edge compression, spheroid nets), 3) SUCCESSFUL UI PATTERNS from iterations, 4) MATHEMATICAL INSIGHTS (topology, manifolds, optimization), 5) CRITICAL GAPS to address. Output: Executive Summary, Key Findings by Category, Priority Next Steps, Integration Opportunities.\"\n    },\n    {\n      \"role\": \"user\",\n      \"content\": \"Synthesize these image analyses from research notes:\\n\\n{{ JSON.stringify($json) }}\\n\\nFocus on actionable insights for building the polyform generator and compression library.\"\n    }\n  ],\n  \"temperature\": 0.3,\n  \"max_tokens\": 3000\n}",
                "options": {}
            },
            "id": "jury",
            "name": "Jury Consensus (Nemotron)",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
                1450,
                300
            ]
        },
        {
            "parameters": {
                "operation": "write",
                "fileName": "=/tmp/polyform_research_{{ DateTime.now().toFormat('yyyyMMdd_HHmmss') }}.json",
                "options": {}
            },
            "id": "save_results",
            "name": "Save Research Synthesis",
            "type": "n8n-nodes-base.writeFile",
            "position": [
                1650,
                300
            ]
        }
    ],
    "connections": {
        "image_scanner": {
            "main": [
                [
                    {
                        "node": "router",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "router": {
            "main": [
                [
                    {
                        "node": "switch",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "switch": {
            "main": [
                [
                    {
                        "node": "ocr_analyst",
                        "type": "main",
                        "index": 0
                    }
                ],
                [
                    {
                        "node": "diagram_analyst",
                        "type": "main",
                        "index": 0
                    }
                ],
                [
                    {
                        "node": "ui_analyst",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "ocr_analyst": {
            "main": [
                [
                    {
                        "node": "merge",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "diagram_analyst": {
            "main": [
                [
                    {
                        "node": "merge",
                        "type": "main",
                        "index": 1
                    }
                ]
            ]
        },
        "ui_analyst": {
            "main": [
                [
                    {
                        "node": "merge",
                        "type": "main",
                        "index": 2
                    }
                ]
            ]
        },
        "merge": {
            "main": [
                [
                    {
                        "node": "synthesizer",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "synthesizer": {
            "main": [
                [
                    {
                        "node": "jury",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "jury": {
            "main": [
                [
                    {
                        "node": "save_results",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        }
    },
    "settings": {
        "executionOrder": "v1"
    }
}