Gemini AI를 사용하여 훈련 계획 PDF를 Hevy 앱 훈련 계획으로 변환합니다.

• •대상 API의 인증 정보가 필요할 수 있음

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        "content": "## Scan Any Workout Plan into the Hevy App with AI\n\nThis workflow extracts a workout plan from a PDF, uses AI to match the exercises to those available in the Hevy app, and automatically creates the routine for you.\nSetup:\n\nConfigure the trigger.\nAdd your OpenRouter & Hevy credentials.\nActivate and test!"
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        "content": "These nodes fetch all available exercises from your Hevy account and combine them into a single list. This list provides the context for the AI to know which exercises are valid.\nRequires: https://api.hevyapp.com/docs/ credentials."
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        "content": "This is the core of the workflow.\nIt uses Google's Gemini model to:\n\nRead the messy text from the PDF.\nCompare the exercises mentioned against the official Hevy exercise list.\nCreate a clean, structured JSON output ready for the Hevy API.\nThe prompt and the Schema in the Structured Output Parser are critical here."
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        "content": "This final node takes the structured data from the AI and makes a POST request to the Hevy API, creating the complete workout routine in your account."
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        "text": "=# GOAL\nYour task is to analyze the provided OCR data of a workout routine, interpret its contents, and convert it into a structured JSON object. You must strictly adhere to the provided JSON schema.\n\n# CONTEXT\n- The OCR data is imperfect and may contain typos or formatting quirks.\n- You will be given a list of available exercises from an application database (`Base Exercise Data`).\n\n# INPUT DATA\n1.  **OCR Data:**\n    ```\n    {{ $('Extract Text from PDF via AI').item.json.choices[0].message.content }}\n    ```\n2.  **Base Exercise Data:**\n    ```json\n    {{ $json.exercise_templates.toJsonString() }}\n    ```\n\n# EXTRACTION RULES & SPECIFICS\nThis section contains specific instructions to guide the extraction process.\n\n- **Exercise Matching:** For each exercise name found in the OCR data, you MUST find the most semantically similar exercise in the `Base Exercise Data`. Use its `id` for the `exercise_template_id` in the final JSON. Do not invent new IDs.\n- **Default Folder:** If no folder is specified, use the default `folder_id` of `1352963`.\n- **Interpreting Sets:** A line like \"3x10 @ 50kg\" means 3 sets of 10 repetitions with 50 kilograms.\n- **Rest Times:** Look for terms like \"Pause,\" \"Rest,\" or time values (e.g., \"90s,\" \"1 min\") between exercises to determine the `rest_seconds`.\n- **Supersets:** If exercises are grouped with labels like \"A1, A2\" or listed together without a rest period, they are a superset. Assign them the same, incrementing `superset_id` (e.g., the first superset pair gets `superset_id: 1`, the next pair gets `superset_id: 2`).\n- **Tempo Information (optional):** Look for a 3 or 4-digit number (e.g., `4010`, `31X0`, `311`), often labeled \"Tempo\". If found for an exercise, capture this and include it in that exercise's `notes` field (e.g., `notes: \"Tempo: 4010\"`).\n\n\n\n# OUTPUT INSTRUCTIONS\n- Produce ONLY the final JSON object.\n- Do not include any explanatory text, markdown formatting, or apologies in your response.\n- The output must be a single, valid JSON object conforming to the schema.\n",
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