some fixes

generate_synthetic_table/flow.py

@@ -765,6 +765,7 @@ def build_synthetic_table_graph(
     llm: ChatOpenAI,
     provider: str = "openai",
     qa_only: bool = False,
+    skip_qa: bool = False,
 ) -> StateGraph:
     """
     Assemble the LangGraph pipeline.
@@ -773,6 +774,7 @@ def build_synthetic_table_graph(
         llm: LLM instance
         provider: LLM provider name
         qa_only: If True, generate QA directly from image without synthetic data generation
+        skip_qa: If True, skip QA generation after table generation (table only mode)
     """
 
     graph = StateGraph(TableState)
@@ -783,7 +785,7 @@ def build_synthetic_table_graph(
         graph.add_edge(START, "generate_qa_from_image")
         graph.add_edge("generate_qa_from_image", END)
     else:
-        # Full pipeline mode
+        # Full pipeline mode (or table-only mode if skip_qa=True)
         graph.add_node("image_to_html", image_to_html_node(llm))
         graph.add_node("pymupdf_parse", pymupdf_parse_node)
         graph.add_node("validate_parsed_table", validate_parsed_table_node(llm))
@@ -795,7 +797,9 @@ def build_synthetic_table_graph(
         graph.add_node("self_reflection", self_reflection_node(llm))
         graph.add_node("revise_synthetic_table", revise_synthetic_table_node(llm))
         graph.add_node("parse_synthetic_table", parse_synthetic_table_node(llm))
-        graph.add_node("generate_qa", generate_qa_node(llm))
+
+        if not skip_qa:
+            graph.add_node("generate_qa", generate_qa_node(llm))
 
         # Routing based on provider and input type
         def route_start(state: TableState) -> str:
@@ -842,8 +846,13 @@ def route_start(state: TableState) -> str:
         )
 
         graph.add_edge("revise_synthetic_table", "self_reflection")
-        graph.add_edge("parse_synthetic_table", "generate_qa")
-        graph.add_edge("generate_qa", END)
+
+        # Final edge: skip QA if requested
+        if skip_qa:
+            graph.add_edge("parse_synthetic_table", END)
+        else:
+            graph.add_edge("parse_synthetic_table", "generate_qa")
+            graph.add_edge("generate_qa", END)
 
     return graph
 
@@ -914,6 +923,7 @@ def run_synthetic_table_flow(
     azure_deployment: str | None = None,
     azure_endpoint: str | None = None,
     qa_only: bool = False,
+    skip_qa: bool = False,
     image_paths: List[str] | None = None,
     domain: str | None = None,
     # 체크포인팅 옵션
@@ -935,6 +945,7 @@ def run_synthetic_table_flow(
         azure_deployment: Azure OpenAI deployment name
         azure_endpoint: Azure OpenAI endpoint URL
         qa_only: If True, skip synthetic data generation and only generate QA from image
+        skip_qa: If True, generate table only without QA generation
         image_paths: Optional list of image paths for multi-image processing
         domain: Optional domain for prompt customization (e.g. 'public')
         enable_checkpointing: 체크포인팅 활성화 여부
@@ -955,7 +966,7 @@ def run_synthetic_table_flow(
         config_path=config_path,
     )
 
-    graph = build_synthetic_table_graph(llm, provider=provider, qa_only=qa_only)
+    graph = build_synthetic_table_graph(llm, provider=provider, qa_only=qa_only, skip_qa=skip_qa)
 
     # 체크포인팅 설정
     if enable_checkpointing:

generate_synthetic_table/prompts/academic.yaml

@@ -94,37 +94,79 @@ generate_qa_from_image: |
 
 generate_synthetic_table: |
   You are a Synthetic Data Generator specializing in Academic Data.
-  Your task is to generate a new HTML table that mirrors the structure of the provided original table but contains entirely new, realistic synthetic academic data.
+
+  **⚠️ CRITICAL INSTRUCTION: DO NOT COPY ORIGINAL DATA ⚠️**
+  Your task is to generate a new HTML table with the SAME STRUCTURE as the original but COMPLETELY DIFFERENT academic data values.
 
   **Inputs:**
-  1. **Original Table Structure:**
+  1. **Original Table Structure (for structure reference ONLY - DO NOT copy the data values):**
   {html}
 
-  2. **Table Summary:**
+  2. **Table Summary (describes the data patterns to follow):**
   {summary}
 
   **Requirements:**
-  1. **Structure:** Keep the exact same HTML structure.
-  2. **Data:** Replace ALL cell values with new, synthetic academic data.
-      - Use realistic Korean student names, university names, course titles, and grades.
-      - Contexts: Transcripts, Research Papers, Enrollment Stats, Faculty Lists.
-      - Do NOT use real private data.
-  3. **Consistency:** Ensure mathematical consistency (e.g., sum of credits, correct GPA calculations if visible).
-  4. **Output:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`.
+  1. **Structure:** Keep the exact same HTML structure (rows, columns, headers, merges, rowspan, colspan).
+  2. **Headers:** Keep header text the same (column names, category labels).
+  3. **⚠️ Data Transformation - ABSOLUTELY MANDATORY ⚠️:**
+     - **ALL data cell values MUST be replaced with completely new synthetic values.**
+     - **NEVER copy any original data values** - generate fresh, realistic alternatives.
+     - For student/model names: Generate DIFFERENT names
+     - For university names: Generate DIFFERENT names
+     - For grades/scores: Generate DIFFERENT realistic values
+     - For course/research topics: Generate DIFFERENT titles
+     - For dates: Generate DIFFERENT plausible dates
+  4. **Styling:** Use **Tailwind CSS** classes (NO inline styles). **Observe and mimic the original image's visual style:**
+      - Look at the original image's color scheme and design
+      - Use appropriate Tailwind color classes to match the original style
+      - Basic structure: `<table class="w-full border-collapse text-sm">`
+      - Headers/cells: Include `border`, `px-4 py-3`, appropriate colors
+      - Lists: `class="list-disc ml-5 space-y-1"`
+      - **DO NOT use inline style attributes**
+  5. **Domain Consistency:** Ensure academic logic (credits sum correctly, GPA valid)
+  6. **Output:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`. No markdown code blocks.
+
+  **Example Transformation (Generic):**
+  - Original name: "학생A" → Synthetic: "학생B"
+  - Original score: "4.0" → Synthetic: "3.5"
+  - Original model: "모델X" → Synthetic: "모델Y"
+
+  ⚠️ If the generated content is identical or very similar to the original, the output is INVALID.
 
 generate_synthetic_table_from_image: |
   You are a Synthetic Data Generator specializing in Academic Data.
-  Your task is to generate a new HTML table that mirrors the structure of the provided image but contains entirely new, realistic synthetic academic data.
+
+  **⚠️ CRITICAL INSTRUCTION: DO NOT TRANSCRIBE - GENERATE NEW DATA ⚠️**
+  Your task is NOT to OCR/transcribe the image. Instead, you must:
+  1. Understand the table's STRUCTURE from the image
+  2. Understand it's an ACADEMIC table
+  3. Generate COMPLETELY NEW synthetic academic data that fits the domain but uses ENTIRELY DIFFERENT values
 
   **Inputs:**
-  1. **Image:** An image of an academic table.
+  1. **Image:** An image of an academic table. Use this to understand structure and domain ONLY.
 
   **Requirements:**
-  1. **Structure Preservation:** Accurately reconstruct the table structure.
-  2. **Data Generation:** Replace ALL cell values with new, synthetic academic data.
-      - Use realistic Korean student names, course titles, grades, research topics.
-  3. **Styling:** Use **Tailwind CSS** classes (same as default).
-      - `class="border-collapse border border-slate-400 w-full text-sm text-left rtl:text-right text-gray-500"` on `<table>`.
-      - `class="border border-slate-300 p-2 bg-gray-50 font-semibold"` on `<th>`.
-      - `class="border border-slate-300 p-2"` on `<td>`.
-  4. **Output Format:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`.
+  1. **Structure Preservation:** Accurately reconstruct the table structure, including rowspan/colspan.
+  2. **Headers:** Keep header text the same as in the image.
+  3. **⚠️ Data Generation - ABSOLUTELY CRITICAL ⚠️:**
+     - **NEVER copy the data values from the image** - this is NOT an OCR task
+     - **ALL cell content must be completely NEW and DIFFERENT**
+     - For student/model names: Generate DIFFERENT names
+     - For grades/scores: Generate DIFFERENT values
+     - For course/research topics: Generate DIFFERENT titles
+  4. **Styling:** Use **Tailwind CSS** classes exclusively (NO inline styles).
+      - `<table>`: `class="w-full border-collapse text-sm"`
+      - `<thead>`: `class="bg-gradient-to-r from-indigo-700 to-indigo-800 text-white"`
+      - `<th>`: `class="border border-indigo-300 px-4 py-3 font-semibold text-left"`
+      - `<tbody>`: `class="divide-y divide-slate-200"`
+      - `<tr>` (body rows): `class="hover:bg-indigo-50 transition-colors"`
+      - `<td>`: `class="border border-slate-200 px-4 py-3 text-slate-700"`
+      - `<ul>`: `class="list-disc ml-5 space-y-1 text-slate-600"`
+      - **DO NOT use inline style attributes**
+  5. **Output Format:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`. No markdown code blocks.
+
+  **Example (Generic):**
+  - Name in image: "이름X" → Generate: "이름Y"
+  - Score in image: "점수A" → Generate: "점수B"
+
+  ⚠️ If the generated content is identical or very similar to the image, the output is INVALID.

generate_synthetic_table/prompts/business.yaml

@@ -94,37 +94,90 @@ generate_qa_from_image: |
 
 generate_synthetic_table: |
   You are a Synthetic Data Generator specializing in Business Data.
-  Your task is to generate a new HTML table that mirrors the structure of the provided original table but contains entirely new, realistic synthetic business data.
+
+  **⚠️ CRITICAL INSTRUCTION: DO NOT COPY ORIGINAL DATA ⚠️**
+  Your task is to generate a new HTML table with the SAME STRUCTURE as the original but COMPLETELY DIFFERENT business data values.
+  The goal is to create realistic synthetic business data that looks like it could come from the same domain, but with entirely different companies, employees, products, and metrics.
 
   **Inputs:**
-  1. **Original Table Structure:**
+  1. **Original Table Structure (for structure reference ONLY - DO NOT copy the data values):**
   {html}
 
-  2. **Table Summary:**
+  2. **Table Summary (describes the data patterns to follow):**
   {summary}
 
   **Requirements:**
-  1. **Structure:** Keep the exact same HTML structure.
-  2. **Data:** Replace ALL cell values with new, synthetic business data.
-      - Use realistic Korean company names, department names, product lines, and financial metrics.
-      - Contexts: Sales Reports, Inventory, HR Employee Lists, Marketing Campaigns.
-      - Do NOT use real private data.
-  3. **Consistency:** Ensure mathematical consistency (e.g., Q1 + Q2 + Q3 + Q4 = Total).
-  4. **Output:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`.
+  1. **Structure:** Keep the exact same HTML structure (rows, columns, headers, merges, rowspan, colspan).
+  2. **Headers:** Keep header text the same (column names, category labels like 기업경쟁력, 시장경쟁력).
+  3. **⚠️ Data Transformation - ABSOLUTELY MANDATORY ⚠️:**
+     - **ALL data cell values MUST be replaced with completely new synthetic values.**
+     - **NEVER copy any original data values** - generate fresh, realistic alternatives.
+     - For company/team names: Generate DIFFERENT names (e.g., "A팀" → "B팀")
+     - For employee names: Generate DIFFERENT Korean names (e.g., "김OO" → "박OO")
+     - For business metrics: Generate DIFFERENT numbers (e.g., "100억" → "150억")
+     - For strategy/description text: Write DIFFERENT content with similar structure
+     - For bullet point items: Create DIFFERENT but domain-appropriate content
+  4. **Styling:** Use **Tailwind CSS** classes (NO inline styles). **Observe and mimic the original image's visual style:**
+      - Look at the original image's color scheme and design
+      - Use appropriate Tailwind color classes to match the original style
+      - Basic structure: `<table class="w-full border-collapse text-sm">`
+      - Headers/cells: Include `border`, `px-4 py-3`, appropriate colors
+      - Lists: `class="list-disc ml-5 space-y-1"`
+      - **DO NOT use inline style attributes**
+  5. **Domain Consistency:**
+     - Ensure business logic (Q1+Q2+Q3+Q4=Total, percentages add up)
+     - Use realistic Korean business terminology
+     - Contexts: Sales Reports, Inventory, HR Employee Lists, Marketing Campaigns
+  6. **Output:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`. No markdown code blocks.
+
+  **Example Transformation (Generic):**
+  - Original name: "A팀" → Synthetic: "B팀"
+  - Original amount: "5억원" → Synthetic: "7.3억원"
+  - Original description: "신규 사업 추진" → Synthetic: "해외 시장 진출"
+
+  ⚠️ If the generated content is identical or very similar to the original, the output is INVALID.
+  Remember: The synthetic table should look like a completely different business dataset from the same domain.
 
 generate_synthetic_table_from_image: |
   You are a Synthetic Data Generator specializing in Business Data.
-  Your task is to generate a new HTML table that mirrors the structure of the provided image but contains entirely new, realistic synthetic business data.
+
+  **⚠️ CRITICAL INSTRUCTION: DO NOT TRANSCRIBE - GENERATE NEW DATA ⚠️**
+  Your task is NOT to OCR/transcribe the image. Instead, you must:
+  1. Understand the table's STRUCTURE from the image (rows, columns, merged cells, nested structures)
+  2. Understand it's a BUSINESS table (기업경쟁력, 시장경쟁력, 매출, 실적 등)
+  3. Generate COMPLETELY NEW synthetic business data that fits the domain but uses ENTIRELY DIFFERENT values
 
   **Inputs:**
-  1. **Image:** An image of a business table.
+  1. **Image:** An image of a business table. Use this to understand structure and domain ONLY.
 
   **Requirements:**
-  1. **Structure Preservation:** Accurately reconstruct the table structure.
-  2. **Data Generation:** Replace ALL cell values with new, synthetic business data.
-      - Use realistic Korean company names, products, sales figures.
-  3. **Styling:** Use **Tailwind CSS** classes (same as default).
-      - `class="border-collapse border border-slate-400 w-full text-sm text-left rtl:text-right text-gray-500"` on `<table>`.
-      - `class="border border-slate-300 p-2 bg-gray-50 font-semibold"` on `<th>`.
-      - `class="border border-slate-300 p-2"` on `<td>`.
-  4. **Output Format:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`.
+  1. **Structure Preservation:** Accurately reconstruct the table structure, including `rowspan` and `colspan` for merged cells.
+  2. **Headers:** Keep header text (column names, category labels like 기업경쟁력, 차별화 요소) the same as in the image.
+  3. **⚠️ Data Generation - ABSOLUTELY CRITICAL ⚠️:**
+     - **NEVER copy the data values from the image** - this is NOT an OCR task
+     - **ALL cell content must be completely NEW and DIFFERENT from the original**
+     - Generate COMPLETELY NEW synthetic business values for all data cells:
+       * For company/team names: Generate DIFFERENT names (e.g., "A팀" → "B팀")
+       * For business metrics: Generate DIFFERENT numbers (e.g., "100억" → "150억")
+       * For strategy/description text: Write DIFFERENT content with similar structure
+       * For bullet point items: Create DIFFERENT but domain-appropriate items
+       * For employee names: Generate DIFFERENT Korean names (e.g., "김OO" → "박OO")
+     - The synthetic table should look like a COMPLETELY DIFFERENT business report from the same industry
+  4. **Styling:** Use **Tailwind CSS** classes (NO inline styles). **Observe and mimic the original image's visual style:**
+      - Look at the original image's color scheme and design
+      - Use appropriate Tailwind color classes to match the original style
+      - Basic structure: `<table class="w-full border-collapse text-sm">`
+      - Headers/cells: Include `border`, `px-4 py-3`, appropriate colors
+      - Lists: `class="list-disc ml-5 space-y-1"`
+      - **DO NOT use inline style attributes**
+  5. **Output Format:** Return ONLY the raw HTML string starting with `<table>` and ending with `</table>`. No markdown code blocks.
+
+  **Example of Expected Behavior (Generic):**
+  If the image shows a business table with:
+  - Team name: "영업팀" → Generate different: "마케팅팀"
+  - Revenue: "10억원" → Generate different: "15억원"
+  - Strategy: "시장 확대" → Generate different: "신규 진출"
+  - Bullet point items → Generate completely different items
+
+  ⚠️ If the generated content is identical or very similar to the image, the output is INVALID.
+  Remember: The output should be a new synthetic business dataset, not a transcription of the original.