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{"embedding_dim": 1024, "data": [{"__id__": "chunk-88802c5bf0fb62d03fef5793d48b3f44", "__created_at__": 1751887451, "content": "三角形面积的常见应用如下图所示:", "full_doc_id": "doc-88802c5bf0fb62d03fef5793d48b3f44", "file_path": "带图片的测试.docx"}, {"__id__": "chunk-d1aaab53663fdf2b726baaa1c24479c5", "__created_at__": 1751887472, "content": "\nImage Content Analysis:\nImage Path: images/5a2d2fce2e4e99870fe570e654c7ee292140a3bde544b7001f77874247751a92.jpg\nCaptions: None\nFootnotes: None\n\nVisual Analysis: The image is a diagrammatic representation illustrating common applications of triangle area calculations. The composition features one or more geometric triangles with labeled dimensions (likely base and height) and accompanying mathematical formulas. The visual style is technical and educational, with clean lines, neutral background colors, and clear typography for labels. The triangles may be shown in practical contexts (e.g., architectural structures, land measurement diagrams) or as abstract geometric figures with calculation examples. Arrows or annotations likely connect the visual elements to their corresponding formulas (e.g., Area = ½ × base × height). The lighting is uniform for clarity, with possible color-coding to differentiate parts of the diagram.", "full_doc_id": "chunk-d1aaab53663fdf2b726baaa1c24479c5", "file_path": "带图片的测试.docx"}], "matrix": 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"}