Feature/batch 처리 token 문제 수정 issue

.gitignore

@@ -73,3 +73,10 @@ pipeline_ui/backend/uploads/*
 # Frontend (Node.js)
 pipeline_ui/frontend/node_modules/*
 pipeline_ui/frontend/package-lock.json
+
+
+I_origin_0/*
+I_origin_1/*
+I_origin_2/*
+
+output/*

generate_synthetic_table/flow.py

@@ -99,23 +99,47 @@ def _call_llm(
     if return_token_usage:
         # Extract token usage from response metadata
         token_usage = 0
-        if hasattr(response, 'response_metadata'):
-            usage_metadata = response.response_metadata.get('usage', {})
-            # OpenAI/Gemini format
+
+        logger.info(f"=== TOKEN DEBUG START ===")
+        logger.info(f"Response type: {type(response)}")
+        logger.info(f"Has usage_metadata: {hasattr(response, 'usage_metadata')}")
+        logger.info(f"Has response_metadata: {hasattr(response, 'response_metadata')}")
+
+        # Try response.usage_metadata first (Gemini pool format)
+        if hasattr(response, 'usage_metadata') and response.usage_metadata:
+            usage = response.usage_metadata
+            logger.info(f"usage_metadata type: {type(usage)}")
+            logger.info(f"usage_metadata value: {usage}")
+
+            if isinstance(usage, dict):
+                token_usage = usage.get('total_tokens', 0)
+                if not token_usage:
+                    token_usage = usage.get('input_tokens', 0) + usage.get('output_tokens', 0)
+            else:
+                token_usage = getattr(usage, 'total_tokens', 0)
+                if not token_usage:
+                    token_usage = getattr(usage, 'input_tokens', 0) + getattr(usage, 'output_tokens', 0)
+
+            logger.info(f"Extracted token_usage from usage_metadata: {token_usage}")
+
+        # Fallback: response.response_metadata (OpenAI format)
+        if not token_usage and hasattr(response, 'response_metadata'):
+            metadata = response.response_metadata
+            logger.info(f"response_metadata: {metadata}")
+            usage_metadata = metadata.get('usage', {})
+            logger.info(f"usage from response_metadata: {usage_metadata}")
+
             token_usage = usage_metadata.get('total_tokens', 0)
-            # Fallback: prompt_tokens + completion_tokens
             if not token_usage:
                 token_usage = usage_metadata.get('prompt_tokens', 0) + usage_metadata.get('completion_tokens', 0)
-            # Fallback: input_tokens + output_tokens
             if not token_usage:
                 token_usage = usage_metadata.get('input_tokens', 0) + usage_metadata.get('output_tokens', 0)
-        # Alternative: usage_metadata attribute (dict or object)
-        if not token_usage and hasattr(response, 'usage_metadata') and response.usage_metadata:
-            usage = response.usage_metadata
-            if isinstance(usage, dict):
-                token_usage = usage.get('total_tokens', 0) or (usage.get('input_tokens', 0) + usage.get('output_tokens', 0))
-            else:
-                token_usage = getattr(usage, 'total_tokens', 0) or (getattr(usage, 'input_tokens', 0) + getattr(usage, 'output_tokens', 0))
+
+            logger.info(f"Extracted token_usage from response_metadata: {token_usage}")
+
+        logger.info(f"Final token_usage: {token_usage}")
+        logger.info(f"=== TOKEN DEBUG END ===")
+
         return response_content, token_usage
 
     return response_content
@@ -641,7 +665,11 @@ def _node(state: TableState) -> TableState:
             errors.append(f"QA prompt missing placeholder: {e}")
             return {**state, "errors": errors}
 
-        response_text = _call_llm(llm, prompt)
+        response_text, token_usage = _call_llm(llm, prompt, return_token_usage=True)
+
+        # Debug log for token usage
+        logger.info(f"QA generation token usage: {token_usage}")
+
         response_json = robust_json_parse(response_text)
 
         qa_results = []
@@ -650,7 +678,8 @@ def _node(state: TableState) -> TableState:
         else:
              logger.warning("QA generation did not return valid JSON or 'qa_pairs' key.")
 
-        return {**state, "qa_results": qa_results}
+        logger.info(f"Returning token_usage: {token_usage}")
+        return {**state, "qa_results": qa_results, "token_usage": token_usage}
 
     return _node
 
@@ -685,6 +714,10 @@ def _node(state: TableState) -> TableState:
         prompt = prompt_template
 
         response_text, token_usage = _call_llm(llm, prompt, image_urls=image_data_urls, return_token_usage=True)
+
+        # Debug log for token usage
+        logger.info(f"QA generation token usage: {token_usage}")
+
         response_json = robust_json_parse(response_text)
 
         qa_results = []
@@ -693,6 +726,7 @@ def _node(state: TableState) -> TableState:
         else:
             logger.warning("QA generation from image did not return valid JSON or 'qa_pairs' key.")
 
+        logger.info(f"Returning token_usage: {token_usage}")
         return {**state, "qa_results": qa_results, "token_usage": token_usage}
 
     return _node
@@ -877,6 +911,8 @@ def run_synthetic_table_flow(
     temperature: float = 0.2,
     base_url: str | None = None,
     config_path: str | None = None,
+    azure_deployment: str | None = None,
+    azure_endpoint: str | None = None,
     qa_only: bool = False,
     image_paths: List[str] | None = None,
     domain: str | None = None,
@@ -891,11 +927,13 @@ def run_synthetic_table_flow(
 
     Args:
         image_path: Path to the input image or HTML file
-        provider: LLM provider (openai, gemini, gemini_pool, claude, vllm)
+        provider: LLM provider (openai, azure, gemini, gemini_pool, claude, vllm)
         model: Model name
         temperature: Sampling temperature
         base_url: Custom base URL for vLLM
         config_path: Config path for gemini_pool
+        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
         image_paths: Optional list of image paths for multi-image processing
         domain: Optional domain for prompt customization (e.g. 'public')

generate_synthetic_table/llm_factory.py

@@ -1,35 +1,64 @@
 from __future__ import annotations
 
 import os
+import yaml
+from pathlib import Path
 from typing import Optional
 
 from langchain_core.language_models import BaseChatModel
-from langchain_openai import ChatOpenAI
+from langchain_openai import ChatOpenAI, AzureChatOpenAI
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_anthropic import ChatAnthropic
 
 # polling_gemini 모듈에서 GeminiPoolChatModel 임포트
 from polling_gemini import GeminiPoolChatModel, create_gemini_chat_model
 
 
+def _load_azure_config_from_yaml(config_path: Optional[str] = None) -> dict:
+    """Load Azure OpenAI configuration from gemini_keys.yaml file."""
+    if not config_path:
+        config_path = "apis/gemini_keys.yaml"
+
+    config_file = Path(config_path)
+    if not config_file.exists():
+        return {}
+
+    try:
+        with open(config_file, 'r', encoding='utf-8') as f:
+            config = yaml.safe_load(f)
+
+        return {
+            'api_key': config.get('AZURE_OPENAI_API_KEY'),
+            'endpoint': config.get('AZURE_OPENAI_ENDPOINT'),
+            'api_version': config.get('AZURE_OPENAI_API_VERSION'),
+            'deployment': config.get('AZURE_OPENAI_DEPLOYMENT_NAME'),
+        }
+    except Exception:
+        return {}
+
+
 def get_llm(
     provider: str,
     model: str,
     temperature: float = 0.2,
     base_url: Optional[str] = None,
     api_key: Optional[str] = None,
     config_path: Optional[str] = None,
+    azure_deployment: Optional[str] = None,
+    azure_endpoint: Optional[str] = None,
 ) -> BaseChatModel:
     """
     Factory to create a Chat Model based on the provider.
 
     Args:
-        provider: 'openai', 'gemini', 'gemini_pool', 'claude', or 'vllm'
+        provider: 'openai', 'azure', 'gemini', 'gemini_pool', 'claude', or 'vllm'
         model: Model name (e.g., 'gpt-4', 'gemini-1.5-flash', 'claude-sonnet-4-20250514')
         temperature: Sampling temperature
         base_url: Optional base URL for vLLM or custom OpenAI endpoints
         api_key: Optional API key override
         config_path: Optional config path for gemini_pool (apis/gemini_keys.yaml)
+        azure_deployment: Azure OpenAI deployment name (required for azure provider)
+        azure_endpoint: Azure OpenAI endpoint URL (required for azure provider)
 
     Returns:
         A configured LangChain Chat Model
@@ -66,6 +95,30 @@ def get_llm(
             temperature=temperature,
         )
 
+    elif provider == "azure":
+        # Azure OpenAI
+        # 우선순위: CLI 파라미터 > config_path의 yaml 파일 > 환경변수
+        yaml_config = _load_azure_config_from_yaml(config_path)
+
+        azure_key = api_key or yaml_config.get('api_key') or os.getenv("AZURE_OPENAI_API_KEY")
+        azure_ep = azure_endpoint or yaml_config.get('endpoint') or os.getenv("AZURE_OPENAI_ENDPOINT")
+        azure_dep = azure_deployment or yaml_config.get('deployment') or model
+        azure_ver = yaml_config.get('api_version') or os.getenv("AZURE_OPENAI_API_VERSION") or "2024-02-15-preview"
+
+        if not azure_key or not azure_ep:
+            raise ValueError(
+                "Azure OpenAI requires AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT. "
+                "Set via environment variables, --azure-* arguments, or in apis/gemini_keys.yaml."
+            )
+
+        return AzureChatOpenAI(
+            azure_deployment=azure_dep,
+            azure_endpoint=azure_ep,
+            api_key=azure_key,
+            api_version=azure_ver,
+            temperature=temperature,
+        )
+
     elif provider == "claude":
         # Anthropic Claude API
         # ANTHROPIC_API_KEY 환경변수 또는 api_key 파라미터 사용

generate_synthetic_table/prompts/insurance.yaml

@@ -15,14 +15,17 @@ generate_qa: |
   3. **Language:** The questions and answers MUST be in Korean.
   4. **Reasoning Language:** reasoning_annotation MUST be written in English and MUST be a single string (not a list).
   5. **Accuracy:** Ensure all answers are factually correct based on the data in the table.
+  7. **Context:** Include the specific table cells or rows that were used to answer the question.
 
   **Output Format (JSON):**
   {{
       "qa_pairs": [
           {{
               "question": "...",
               "answer": "...",
-              "type": "lookup"
+              "type": "lookup",
+              "reasoning_annotation": "Detailed explanation of how the answer was derived (in English, single string)",
+              "context": "Specific table cells/rows used (e.g., 'Row 2, Column 3: Premium amount')"
           }},
           ...
       ]
@@ -43,14 +46,17 @@ generate_qa_from_image: |
   4. **Language:** The questions and answers MUST be in Korean.
   5. **Reasoning Language:** reasoning_annotation MUST be written in English and MUST be a single string (not a list).
   6. **Accuracy:** Ensure 100% factual correctness.
+  7. **Context:** Include specific cell references or table sections used to derive the answer.
 
   **Output Format (JSON):**
   {{
       "qa_pairs": [
           {{
               "question": "...",
               "answer": "...",
-              "type": "lookup"
+              "type": "lookup",
+              "reasoning_annotation": "Step-by-step reasoning process in English (single string)",
+              "context": "Table location used (e.g., 'Premium column, Row 3')"
           }},
           ...
       ]