further streamlining

Author: kazmer97

config_library/pattern-2/bank-statement-sample/config.yaml

@@ -452,117 +452,6 @@ assessment:
     - Provide tight, accurate bounding boxes around the actual text
     </assessment-guidelines>
 
-    <spatial-localization-guidelines>
-    For each field, provide bounding box coordinates:
-    - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
-    - page: Page number where the field appears (starting from 1)
-
-    Coordinate system:
-    - The document images have ruler markings along all edges showing the 0-1000 coordinate scale
-    - Use these ruler markings to determine precise coordinates for bounding boxes
-    - Use normalized scale 0-1000 for both x and y axes
-    - x1, y1 = top-left corner of bounding box  
-    - x2, y2 = bottom-right corner of bounding box
-    - Ensure x2 > x1 and y2 > y1
-    - Make bounding boxes tight around the actual text content
-    - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-    - Reference the ruler markings on the image edges to provide accurate coordinates
-    </spatial-localization-guidelines>
-
-    <final-instructions>
-    Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
-    For SIMPLE attributes: 
-    {
-      "simple_attribute_name": {
-        "confidence": 0.85,
-        "bbox": [100, 200, 300, 250],
-        "page": 1
-      }
-    }
-
-    For GROUP attributes (nested object structure): 
-    {
-      "group_attribute_name": {
-        "sub_attribute_1": {
-          "confidence": 0.90,
-          "bbox": [150, 300, 250, 320],
-          "page": 1
-        },
-        "sub_attribute_2": {
-          "confidence": 0.75,
-          "bbox": [150, 325, 280, 345],
-          "page": 1
-        }
-      }
-    }
-
-    For LIST attributes (array of assessed items): 
-    {
-      "list_attribute_name": [
-        {
-          "item_attribute_1": {
-            "confidence": 0.95,
-            "bbox": [100, 400, 200, 420],
-            "page": 1
-          },
-          "item_attribute_2": {
-            "confidence": 0.88,
-            "bbox": [250, 400, 350, 420],
-            "page": 1
-          }
-        },
-        {
-          "item_attribute_1": {
-            "confidence": 0.92,
-            "bbox": [100, 425, 200, 445],
-            "page": 1
-          },
-          "item_attribute_2": {
-            "confidence": 0.70,
-            "bbox": [250, 425, 350, 445],
-            "page": 1
-          }
-        }
-      ]
-    }
-
-    IMPORTANT:  
-    - The <task-schema> contains raw JSON schema for the attributes you should assess
-    - The <extraction-results> contains the FULL extraction data (you have complete context for cross-referencing)
-    - The <task-instruction> specifies which attributes to focus on
-    - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
-    - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
-    - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
-    - Include assessments AND bounding boxes for ALL attributes present in the extraction results
-    - Match the exact structure of the extracted data
-    - Provide page numbers for all bounding boxes (starting from 1)
-    </final-instructions>
-
-    <<CACHEPOINT>>
-
-    <document-image>
-    {DOCUMENT_IMAGE}
-    </document-image>
-
-    <ocr-text-confidence-results>
-    {OCR_TEXT_CONFIDENCE}
-    </ocr-text-confidence-results>
-
-    <<CACHEPOINT>>
-
-    <task-schema>
-    {TASK_SCHEMA}
-    </task-schema>
-
-    <task-instruction>
-    {TASK_INSTRUCTION}
-    </task-instruction>
-
-    <extraction-results>
-    {EXTRACTION_RESULTS}
-    </extraction-results>
-
 evaluation:
   enabled: true
   llm_method:

config_library/pattern-2/lending-package-sample/config.yaml

@@ -1478,116 +1478,6 @@ assessment:
     - Provide tight, accurate bounding boxes around the actual text
     </assessment-guidelines>
 
-    <spatial-localization-guidelines>
-    For each field, provide bounding box coordinates:
-    - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
-    - page: Page number where the field appears (starting from 1)
-
-    Coordinate system:
-    - The document images have ruler markings along all edges showing the 0-1000 coordinate scale
-    - Use these ruler markings to determine precise coordinates for bounding boxes
-    - Use normalized scale 0-1000 for both x and y axes
-    - x1, y1 = top-left corner of bounding box  
-    - x2, y2 = bottom-right corner of bounding box
-    - Ensure x2 > x1 and y2 > y1
-    - Make bounding boxes tight around the actual text content
-    - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-    - Reference the ruler markings on the image edges to provide accurate coordinates
-    </spatial-localization-guidelines>
-
-    <final-instructions>
-    Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
-    For SIMPLE attributes: 
-    {
-      "simple_attribute_name": {
-        "confidence": 0.85,
-        "bbox": [100, 200, 300, 250],
-        "page": 1
-      }
-    }
-
-    For GROUP attributes (nested object structure): 
-    {
-      "group_attribute_name": {
-        "sub_attribute_1": {
-          "confidence": 0.90,
-          "bbox": [150, 300, 250, 320],
-          "page": 1
-        },
-        "sub_attribute_2": {
-          "confidence": 0.75,
-          "bbox": [150, 325, 280, 345],
-          "page": 1
-        }
-      }
-    }
-
-    For LIST attributes (array of assessed items): 
-    {
-      "list_attribute_name": [
-        {
-          "item_attribute_1": {
-            "confidence": 0.95,
-            "bbox": [100, 400, 200, 420],
-            "page": 1
-          },
-          "item_attribute_2": {
-            "confidence": 0.88,
-            "bbox": [250, 400, 350, 420],
-            "page": 1
-          }
-        },
-        {
-          "item_attribute_1": {
-            "confidence": 0.92,
-            "bbox": [100, 425, 200, 445],
-            "page": 1
-          },
-          "item_attribute_2": {
-            "confidence": 0.70,
-            "bbox": [250, 425, 350, 445],
-            "page": 1
-          }
-        }
-      ]
-    }
-
-    IMPORTANT:  
-    - The <task-schema> contains raw JSON schema for the attributes you should assess
-    - The <extraction-results> contains the FULL extraction data (you have complete context for cross-referencing)
-    - The <task-instruction> specifies which attributes to focus on
-    - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
-    - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
-    - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
-    - Include assessments AND bounding boxes for ALL attributes present in the extraction results
-    - Match the exact structure of the extracted data
-    - Provide page numbers for all bounding boxes (starting from 1)
-    </final-instructions>
-
-    <<CACHEPOINT>>
-
-    <document-image>
-    {DOCUMENT_IMAGE}
-    </document-image>
-
-    <ocr-text-confidence-results>
-    {OCR_TEXT_CONFIDENCE}
-    </ocr-text-confidence-results>
-
-    <<CACHEPOINT>>
-
-    <task-schema>
-    {TASK_SCHEMA}
-    </task-schema>
-
-    <task-instruction>
-    {TASK_INSTRUCTION}
-    </task-instruction>
-
-    <extraction-results>
-    {EXTRACTION_RESULTS}
-    </extraction-results>
 evaluation:
   enabled: true
   llm_method:

config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml

@@ -1026,116 +1026,6 @@ assessment:
     - Provide tight, accurate bounding boxes around the actual text
     </assessment-guidelines>
 
-    <spatial-localization-guidelines>
-    For each field, provide bounding box coordinates:
-    - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
-    - page: Page number where the field appears (starting from 1)
-
-    Coordinate system:
-    - The document images have ruler markings along all edges showing the 0-1000 coordinate scale
-    - Use these ruler markings to determine precise coordinates for bounding boxes
-    - Use normalized scale 0-1000 for both x and y axes
-    - x1, y1 = top-left corner of bounding box  
-    - x2, y2 = bottom-right corner of bounding box
-    - Ensure x2 > x1 and y2 > y1
-    - Make bounding boxes tight around the actual text content
-    - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-    - Reference the ruler markings on the image edges to provide accurate coordinates
-    </spatial-localization-guidelines>
-
-    <final-instructions>
-    Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
-    For SIMPLE attributes: 
-    {
-      "simple_attribute_name": {
-        "confidence": 0.85,
-        "bbox": [100, 200, 300, 250],
-        "page": 1
-      }
-    }
-
-    For GROUP attributes (nested object structure): 
-    {
-      "group_attribute_name": {
-        "sub_attribute_1": {
-          "confidence": 0.90,
-          "bbox": [150, 300, 250, 320],
-          "page": 1
-        },
-        "sub_attribute_2": {
-          "confidence": 0.75,
-          "bbox": [150, 325, 280, 345],
-          "page": 1
-        }
-      }
-    }
-
-    For LIST attributes (array of assessed items): 
-    {
-      "list_attribute_name": [
-        {
-          "item_attribute_1": {
-            "confidence": 0.95,
-            "bbox": [100, 400, 200, 420],
-            "page": 1
-          },
-          "item_attribute_2": {
-            "confidence": 0.88,
-            "bbox": [250, 400, 350, 420],
-            "page": 1
-          }
-        },
-        {
-          "item_attribute_1": {
-            "confidence": 0.92,
-            "bbox": [100, 425, 200, 445],
-            "page": 1
-          },
-          "item_attribute_2": {
-            "confidence": 0.70,
-            "bbox": [250, 425, 350, 445],
-            "page": 1
-          }
-        }
-      ]
-    }
-
-    IMPORTANT:  
-    - The <task-schema> contains raw JSON schema for the attributes you should assess
-    - The <extraction-results> contains the FULL extraction data (you have complete context for cross-referencing)
-    - The <task-instruction> specifies which attributes to focus on
-    - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
-    - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
-    - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
-    - Include assessments AND bounding boxes for ALL attributes present in the extraction results
-    - Match the exact structure of the extracted data
-    - Provide page numbers for all bounding boxes (starting from 1)
-    </final-instructions>
-
-    <<CACHEPOINT>>
-
-    <document-image>
-    {DOCUMENT_IMAGE}
-    </document-image>
-
-    <ocr-text-confidence-results>
-    {OCR_TEXT_CONFIDENCE}
-    </ocr-text-confidence-results>
-
-    <<CACHEPOINT>>
-
-    <task-schema>
-    {TASK_SCHEMA}
-    </task-schema>
-
-    <task-instruction>
-    {TASK_INSTRUCTION}
-    </task-instruction>
-
-    <extraction-results>
-    {EXTRACTION_RESULTS}
-    </extraction-results>
 evaluation:
   enabled: true
   llm_method:
@@ -1452,7 +1342,7 @@ agents:
     parameters:
       max_log_events: 5
       time_range_hours_default: 24
-  
+
   chat_companion:
     model_id: us.anthropic.claude-haiku-4-5-20251001-v1:0
 pricing: