# Refactoring Plan: Integrate Intent Analysis into Existing Prompts and Simplify AI Loop Mode

## Overview
**Integrate** IntentAnalyzer logic into existing prompts (userintention, taskplan, dynamic) instead of making separate AI calls (saves 3 AI calls). Simplify AI loop logic to: **Complete JSON = Stop, Cut-off JSON = Continue**. Remove Definition of Done (DoD) logic entirely.

**Key Change**: Keep intent analysis logic (dataType, expectedFormats, qualityRequirements, etc.) but merge it into existing prompts instead of separate calls.

**Overlap Analysis**:
- **UserIntention prompt** already does: language detection, normalization, intent extraction
- **Intent Analysis prompt** does: primaryGoal, dataType, expectedFormats, qualityRequirements, successCriteria, language detection
- **Overlap**: Language detection (both do it)
- **Solution**: Merge intent analysis fields into userintention prompt (one call instead of two)

## Changes Required

### Phase 1: Integrate Intent Analysis into Existing Prompts

#### 1. Update UserIntention Prompt (Integrate Intent Analysis)
- **File**: `gateway/modules/workflows/workflowManager.py`
- **Lines**: 353-378
- **Changes**: 
  - Merge intent analysis fields (from intentAnalyzer.py lines 2-31) into userintention prompt
  - Add fields: `primaryGoal`, `dataType`, `expectedFormats`, `qualityRequirements`, `successCriteria`
  - Keep existing fields: `detectedLanguage`, `normalizedRequest`, `intent`, `contextItems`
  - **Integration**: Combine both prompts into one - userintention already does language detection and normalization, now also does full intent analysis
  - **Note**: Adding 5 items is no problem for AI - prompt complexity is acceptable
  - **CRITICAL**: Intent check should be different on workflow, task, and action levels (keep separate)
  - **New Prompt Structure**:
    ```python
    analyzerPrompt = (
        "You are an input analyzer. From the user's message, perform ALL of the following in one pass:\n"
        "1) detectedLanguage: detect ISO 639-1 language code (e.g., de, en).\n"
        "2) normalizedRequest: full, explicit restatement of the user's request in the detected language; do NOT summarize; preserve ALL constraints and details.\n"
        "3) intent: concise single-paragraph core request in the detected language for high-level routing.\n"
        "4) contextItems: supportive data blocks to attach as separate documents if significantly larger than the intent.\n"
        "5) primaryGoal: The main objective the user wants to achieve.\n"
        "6) dataType: What type of data/content they want (numbers|text|documents|analysis|code|unknown).\n"
        "7) expectedFormats: What file format(s) they expect - provide matching file format extensions list (e.g., [\"xlsx\", \"pdf\"]). If format is unclear or not specified, use empty list [].\n"
        "8) qualityRequirements: Quality requirements they have (accuracy, completeness) as {accuracyThreshold: 0.0-1.0, completenessThreshold: 0.0-1.0}.\n"
        "9) successCriteria: Specific success criteria that define completion (array of strings).\n\n"
        "Rules:\n"
        "- If total content (intent + data) is < 10% of model max tokens, do not extract; return empty contextItems and keep intent compact and self-contained.\n"
        "- If content exceeds that threshold, move bulky parts into contextItems; keep intent short and clear.\n"
        "- Preserve critical references (URLs, filenames) in intent.\n"
        "- Normalize to the primary detected language if mixed-language.\n\n"
        "Return ONLY JSON (no markdown) with this shape:\n"
        "{\n"
        "  \"detectedLanguage\": \"de|en|fr|it|...\",\n"
        "  \"normalizedRequest\": \"Full explicit instruction in detected language\",\n"
        "  \"intent\": \"Concise normalized request...\",\n"
        "  \"contextItems\": [...],\n"
        "  \"primaryGoal\": \"The main objective the user wants to achieve\",\n"
        "  \"dataType\": \"numbers|text|documents|analysis|code|unknown\",\n"
        "  \"expectedFormats\": [\"pdf\", \"docx\", \"xlsx\", ...],\n"
        "  \"qualityRequirements\": {\n"
        "    \"accuracyThreshold\": 0.0-1.0,\n"
        "    \"completenessThreshold\": 0.0-1.0\n"
        "  },\n"
        "  \"successCriteria\": [\"specific criterion 1\", \"specific criterion 2\"]\n"
        "}\n\n"
        f"User message:\n{self.services.utils.sanitizePromptContent(userInput.prompt, 'userinput')}"
    )
    ```
  - **Update parsing** (lines 397-402): Extract new fields and store in workflow object
  - Store as `workflow._workflowIntent` for reuse

#### 2. Remove IntentAnalyzer Class and Calls
- **File**: `gateway/modules/workflows/processing/adaptive/intentAnalyzer.py`
- **Action**: Delete entire file (logic now integrated into prompts)
- **File**: `gateway/modules/workflows/processing/adaptive/__init__.py`
- **Action**: Remove `IntentAnalyzer` from exports

- **File**: `gateway/modules/workflows/processing/core/taskPlanner.py`
  - **Line 56**: Remove `workflowIntent = await intentAnalyzer.analyzeUserIntent(actualUserPrompt, None)`
  - **Line 60**: Use `workflowIntent` from workflow object (set in workflowManager)
  - **Lines 167-173**: Use workflowIntent from workflow object for dataType/expectedFormats/qualityRequirements

- **File**: `gateway/modules/workflows/processing/modes/modeDynamic.py`
  - **Line 36**: Remove `self.intentAnalyzer = IntentAnalyzer(services)`
  - **Line 66**: Use `workflow._workflowIntent` from workflow object (already set)
  - **Line 72**: Remove `self.taskIntent = await self.intentAnalyzer.analyzeUserIntent(taskStep.objective, context)`
  - **Line 362**: Remove `actionIntent = await self.intentAnalyzer.analyzeUserIntent(actionObjective, context)`
  - **Lines 60-67**: Use existing workflowIntent from workflow object
  - **Lines 359-373**: Remove actionIntent creation logic (will be integrated into dynamic prompts)

#### 3. Remove IntentAnalyzer Imports
- **File**: `taskPlanner.py` (line 12): Remove `from modules.workflows.processing.adaptive import IntentAnalyzer`
- **File**: `modeDynamic.py` (line 25): Remove `IntentAnalyzer` from imports

### Phase 2: Integrate Information Gathering into Existing Prompts

#### 4. Update Taskplan Prompt (Use Workflow Intent from UserIntention, Allow Override)
- **File**: `gateway/modules/workflows/processing/shared/promptGenerationTaskplan.py`
- **Changes**:
  - Use `workflowIntent` from workflow object (already set in workflowManager from userintention analysis)
  - Pass workflowIntent fields as context to taskplan prompt via placeholders
  - **CRITICAL**: Allow taskplan to **override workflow intent** if task-specific needs differ
  - Example: Workflow wants PDF, but task needs CSV for intermediate step
  - Taskplan prompt can reference workflow intent but can override with task-specific values
  - **If taskplan needs task-specific intent analysis, add fields to task JSON**:
    ```json
    {
        "overview": "...",
        "userMessage": "...",
        "tasks": [
            {
                "id": "task_1",
                "objective": "...",
                "dataType": "numbers|text|documents|analysis|code|unknown",  // Inherit from workflow or task-specific
                "expectedFormats": ["pdf", "docx", ...],  // Inherit from workflow or task-specific
                "qualityRequirements": {...},  // Inherit from workflow or task-specific
                ...
            }
        ]
    }
    ```
  - Update `taskPlanner.py` to use workflowIntent from workflow object (line 60)
  - Extract task-specific fields from task plan response if provided

#### 5. Update Dynamic Plan Selection Prompt (Integrate Action Intent Analysis)
- **File**: `gateway/modules/workflows/processing/shared/promptGenerationActionsDynamic.py`
- **Function**: `generateDynamicPlanSelectionPrompt`
- **Changes**:
  - Integrate intent analysis into action selection prompt
  - Add intent analysis instructions to prompt template
  - Add to JSON response structure:
    ```json
    {
        "action": "...",
        "actionObjective": "...",
        "dataType": "numbers|text|documents|analysis|code|unknown",  // Analyze from actionObjective
        "expectedFormats": ["pdf", "docx", "xlsx", ...],  // Analyze from actionObjective
        "qualityRequirements": {
            "accuracyThreshold": 0.0-1.0,
            "completenessThreshold": 0.0-1.0
        },  // Analyze from actionObjective
        "successCriteria": ["specific criterion 1", ...],  // Analyze from actionObjective
        "userMessage": "...",
        "learnings": [...],
        "requiredInputDocuments": [...],
        "requiredConnection": "...",
        "parametersContext": "..."
    }
    ```
  - Update prompt instructions to analyze actionObjective for these fields
  - Extract these fields in `modeDynamic.py` when processing selection
  - Store as `workflow._actionIntent` for use in AI loop (but without DoD)

#### 6. Update Dynamic Parameters Prompt
- **File**: `gateway/modules/workflows/processing/shared/promptGenerationActionsDynamic.py`
- **Function**: `generateDynamicParametersPrompt`
- **Changes**:
  - Add completion criteria description (natural language, not DoD metrics)
  - Ask AI to describe what "complete" means for this action
  - Example: "This action is complete when: [description]"
  - Use natural language completion criteria instead of DoD metrics

### Phase 3: Simplify AI Loop Logic

#### 7. Simplify `_shouldContinueGeneration`
- **File**: `gateway/modules/services/serviceAi/mainServiceAi.py`
- **Lines**: 904-943
- **Changes**:
  - Remove DoD/KPI checking logic
  - Remove `workflowIntent` parameter usage for DoD
  - Remove `_analyzeTaskCompletion` call (verified: not called anywhere)
  - **CRITICAL**: JSON completeness is determined by **parsing**, NOT by checking last character!
  - Last character check (line 860) is **WRONG** - `}` or `]` could be by chance, JSON still incomplete
  - **New Logic**:
    ```python
    def _shouldContinueGeneration(
        self,
        allSections: List[Dict[str, Any]],
        iteration: int,
        wasJsonComplete: bool,
        rawResponse: str = None
    ) -> bool:
        """
        Determine if AI generation loop should continue.
        
        Simple logic:
        - If JSON parsing failed or incomplete → continue (needs more content)
        - If JSON parses successfully and is complete → stop (all content delivered)
        - Loop detection prevents infinite loops
        
        CRITICAL: JSON completeness is determined by parsing, NOT by last character check!
        Returns True if we should continue, False if AI Loop is done.
        """
        if len(allSections) == 0:
            return True  # No sections yet, continue
        
        # CRITERION 1: If JSON was incomplete/broken (parsing failed or incomplete) - continue to repair/complete
        if not wasJsonComplete:
            logger.info(f"Iteration {iteration}: JSON incomplete/broken - continuing to complete")
            return True
        
        # CRITERION 2: JSON is complete (parsed successfully) - check for loop detection
        if self._isStuckInLoop(allSections, iteration):
            logger.warning(f"Iteration {iteration}: Detected potential infinite loop - stopping AI loop")
            return False
        
        # JSON is complete and not stuck in loop - done
        logger.info(f"Iteration {iteration}: JSON complete - AI loop done")
        return False
    ```
  - Remove `userPrompt` and `workflowIntent` parameters (no longer needed)
  - **Update `_extractSectionsFromResponse`**: Remove last character check (line 860), rely only on JSON parsing

#### 8. Remove `_analyzeTaskCompletion` and Check DoD Usage
- **File**: `gateway/modules/services/serviceAi/mainServiceAi.py`
- **Lines**: 945-1090
- **Action**: Delete entire method (verified: not called anywhere in codebase)
- **Action**: Remove all references to this method
- **CRITICAL**: Check deeply in code how completeness checks are handled:
  - `_refineDecide` (modeDynamic.py line 693) uses content validation and analysis
  - ContentValidator checks content quality and requirements
  - ProgressTracker tracks progress state
  - **Verify**: DoD checking happens in refinement/validation phase, NOT in AI loop
  - **Action**: Ensure validation/refinement phase still checks requirements after DoD removal

#### 9. Revise `buildContinuationContext`
- **File**: `gateway/modules/shared/jsonUtils.py`
- **Lines**: 448-1016
- **Changes**:

  **A. New Summary Format** (per-section counts):
  ```
  Following data has already been delivered:
  
  - heading "id" <list of elements.level . elements.text>
  - paragraph with <count> texts
  - bullet_list with <count> items
  - table "id" with <count> rows
  - code_block "id" with <count> code lines
  
  Check if with already delivered data and the last delivered data part the full response is delivered. 
  If not, deliver the remaining part.
  ```
  
  **Rules**:
  - If section has no ID, **omit it** from summary (don't show "unknown")
  - If summary is too long (exceeds token limit), truncate: show **first 100 items and last 100 items** (remove middle)

  **B. New Extraction Algorithm**:
  1. Loop over all sections of the JSON (as it is, cut off), until a section is not complete
  2. **CRITICAL**: There is always only **one section incomplete** (JSON cut-off point)
  3. In the cut off section, loop through all elements, until an element is cut off
  4. **Edge case**: If cut-off is in first element, just show cut-off element (no element before exists)
  5. **Normal case**: Return cut-off element AND the element before it to give to the next iteration prompt
  6. **CRITICAL**: In 99% of cases, JSON is cut off mid-string or mid-number - deliver the cut-off part **as-is** (don't try to "complete" it)
  7. **Performance**: No problem - we only parse one AI response, not all accumulated sections

  **C. Implementation**:
  ```python
  def buildContinuationContext(allSections: List[Dict[str, Any]], lastRawResponse: Optional[str] = None) -> Dict[str, Any]:
      """
      Build context information from accumulated sections for continuation prompt.
      
      Returns summary of delivered data and cut-off point for continuation.
      """
      context = {
          "section_count": len(allSections),
      }
      
      # Build summary of delivered data (per-section counts)
      summary_lines = []
      summary_lines.append("Following data has already been delivered:\n")
      
      for section in allSections:
          section_id = section.get("id")
          # CRITICAL: If section has no ID, omit it from summary
          if not section_id:
              continue
              
          content_type = section.get("content_type", "")
          elements = section.get("elements", [])
          
          if isinstance(elements, list) and elements:
              elem = elements[-1] if elements else {}
          else:
              elem = elements if isinstance(elements, dict) else {}
          
          if isinstance(elem, dict):
              if content_type == "heading":
                  level = elem.get("level", "")
                  text = elem.get("text", "")
                  summary_lines.append(f'- heading "{section_id}" level {level}: {text}')
              
              elif content_type == "paragraph":
                  # Count text elements
                  text_count = sum(1 for e in (elements if isinstance(elements, list) else [elem]) 
                                  if isinstance(e, dict) and e.get("text"))
                  summary_lines.append(f'- paragraph with {text_count} text(s)')
              
              elif content_type in ["bullet_list", "numbered_list"]:
                  items = elem.get("items", [])
                  item_count = len(items) if isinstance(items, list) else 0
                  summary_lines.append(f'- bullet_list with {item_count} items')
              
              elif content_type == "table":
                  rows = elem.get("rows", [])
                  row_count = len(rows) if isinstance(rows, list) else 0
                  summary_lines.append(f'- table "{section_id}" with {row_count} rows')
              
              elif content_type == "code_block":
                  code = elem.get("code", "")
                  if code:
                      lines = [l for l in code.split('\n') if l.strip()]
                      line_count = len(lines)
                      summary_lines.append(f'- code_block "{section_id}" with {line_count} code lines")
      
      # CRITICAL: If summary is too long, truncate: show first 100 and last 100 items
      summary_text = "\n".join(summary_lines)
      if len(summary_lines) > 200:  # More than 200 lines
          first_100 = "\n".join(summary_lines[:100])
          last_100 = "\n".join(summary_lines[-100:])
          summary_text = f"{first_100}\n... (truncated {len(summary_lines) - 200} items) ...\n{last_100}"
      
      context["delivered_summary"] = summary_text
      
      # Extract cut-off point using new algorithm
      # 1. Loop over all sections until finding incomplete section
      incomplete_section = None
      incomplete_section_index = -1
      for i, section in enumerate(allSections):
          if self._isSectionIncomplete(section):
              incomplete_section = section
              incomplete_section_index = i
              break
      
      # 2. In incomplete section, loop through elements until finding cut-off element
      # CRITICAL: There is always only ONE section incomplete (JSON cut-off point)
      cut_off_element = None
      element_before_cutoff = None
      
      if incomplete_section:
          elements = incomplete_section.get("elements", [])
          if isinstance(elements, list):
              for i, elem in enumerate(elements):
                  if self._isElementIncomplete(elem):
                      cut_off_element = elem
                      # Edge case: If cut-off is in first element, no element before exists
                      if i > 0:
                          element_before_cutoff = elements[i-1]
                      break
      
      # 3. Extract from lastRawResponse if available
      # CRITICAL: In 99% of cases, JSON is cut off mid-string or mid-number
      # Deliver the cut-off part AS-IS (don't try to "complete" it)
      if lastRawResponse and not cut_off_element:
          # Try to extract cut-off element from raw response
          # Extract exactly as-is, even if mid-string/mid-number
          cut_off_element = self._extractCutOffElementFromRaw(lastRawResponse, incomplete_section)
      
      context["element_before_cutoff"] = element_before_cutoff
      context["cut_off_element"] = cut_off_element
      
      return context
  ```

  **D. Keep Existing Merge Logic**: 
  - Keep `_mergeSectionsIntelligently` (works well)
  - Keep `_mergeSectionContent` (works well)
  - Keep `_mergeCodeBlocks` (works well)

#### 10. Update `buildGenerationPrompt`
- **File**: `gateway/modules/services/serviceGeneration/subPromptBuilderGeneration.py`
- **Lines**: 48-171
- **Changes**:
  - Remove DoD references (line 78)
  - Update continuation prompt to use new summary format:
    ```python
    if hasContinuation:
        delivered_summary = continuationContext.get("delivered_summary", "")
        element_before_cutoff = continuationContext.get("element_before_cutoff")
        cut_off_element = continuationContext.get("cut_off_element")
        
        continuationText = f"""{delivered_summary}

Check if with already delivered data and the last delivered data part the full response is delivered. 
If not, deliver the remaining part.

Last complete element before cut-off: {element_before_cutoff}
Cut-off element (incomplete): {cut_off_element}

Continue from the incomplete element above - complete it first, then add NEW items."""
    ```
  - Remove progress stats based on DoD
  - Use simple section counts instead

#### 11. Clean Up `_callAiWithLooping`
- **File**: `gateway/modules/services/serviceAi/mainServiceAi.py`
- **Lines**: 162-365
- **Changes**:
  - Remove `workflowIntent` parameter usage for DoD (line 223-224)
  - Remove `_analyzeTaskCompletion` call (if any)
  - Simplify `_shouldContinueGeneration` call (remove workflowIntent parameter)
  - Update continuation context building:
    ```python
    continuationContext = buildContinuationContext(allSections, lastRawResponse)
    # Remove taskIntent from continuationContext - no longer needed
    ```

### Phase 4: Clean Up References

#### 12. Remove DoD References
- **File**: `gateway/modules/workflows/processing/modes/modeDynamic.py`
  - Remove DoD extraction from intents (line 365)
  - Remove workflowIntent/taskIntent/actionIntent DoD usage
  
- **File**: `gateway/modules/services/serviceGeneration/subPromptBuilderGeneration.py`
  - Remove DoD from continuation context (line 78)
  
- **File**: `gateway/modules/services/serviceAi/mainServiceAi.py`
  - Remove all DoD-related code

#### 13. Update Workflow Intent Storage
- **File**: `modeDynamic.py`
- **Changes**:
  - Remove `workflow._workflowIntent` storage (or keep empty structure if needed elsewhere)
  - Remove `workflow._taskIntent` storage
  - Remove `workflow._actionIntent` storage
  - Or keep them but remove DoD fields

## Implementation Order

1. ✅ **Remove IntentAnalyzer class and all calls** (Phase 1)
2. ✅ **Update prompts to gather info directly** (Phase 2)
   - Update UserIntention prompt (integrate intent analysis)
   - Update Taskplan prompt (allow workflow intent override)
   - Update Dynamic prompts (integrate action-level intent)
3. ✅ **Fix JSON completeness check** (Phase 3, Step 7)
   - Remove last character check (line 860)
   - Use JSON parsing only (`json.loads()`)
4. ✅ **Simplify `_shouldContinueGeneration`** (Phase 3, Step 7)
5. ✅ **Remove `_analyzeTaskCompletion`** (Phase 3, Step 8)
   - Verify DoD checking in refinement phase (`_refineDecide`, ContentValidator)
6. ✅ **Revise `buildContinuationContext`** (Phase 3, Step 9)
   - Implement new summary format
   - Implement new extraction algorithm
   - Handle edge cases (first element, mid-string/number cuts)
   - Omit sections without ID
   - Truncate summary if too long (first 100 + last 100)
7. ✅ **Update `buildGenerationPrompt`** (Phase 3, Step 10)
8. ✅ **Clean up `_callAiWithLooping`** (Phase 3, Step 11)
9. ✅ **Remove all DoD references** (Phase 4)
10. ✅ **Testing and validation** (Post-implementation)
    - Test UserIntention prompt quality (verify all fields extracted correctly)
    - Test extraction edge cases
    - Test task-level intent override
    - Add comprehensive unit tests
11. ✅ **Documentation** (Post-implementation)
    - Update docs to explain new loop behavior
    - Document JSON completeness check (parsing-based)
    - Document continuation summary format

## Expected Benefits

- **Saves 3 AI calls per workflow** (workflow intent, task intent, action intent - now integrated into existing calls)
- **Simpler loop logic** (JSON completeness only, no DoD checking)
- **Clearer continuation prompts** (section counts instead of DoD metrics)
- **More precise cut-off detection** (element-level detection)
- **Better performance** (fewer AI calls = faster execution)
- **Same information gathered** (intent analysis logic preserved, just integrated)

## Testing Checklist

### Functional Tests
- [ ] Task planning works without intent analysis
- [ ] Dynamic mode action selection works without intent analysis
- [ ] Dynamic mode parameter generation works without intent analysis
- [ ] AI loop stops when JSON is complete (parsing-based check)
- [ ] AI loop continues when JSON is cut off (parsing fails)
- [ ] Continuation prompts show correct section counts
- [ ] Cut-off element extraction works correctly
- [ ] Merge logic still works correctly
- [ ] No DoD references remain in codebase

### Edge Case Tests (from Critical Analysis)
- [ ] JSON completeness: Nested structures that don't parse correctly
- [ ] JSON completeness: Mid-string cuts (e.g., `"text": "incomplete`)
- [ ] JSON completeness: Mid-number cuts (e.g., `"value": 1234`)
- [ ] Extraction: Cut-off in first element (no element before)
- [ ] Extraction: Only one incomplete section (not multiple)
- [ ] Summary: Sections without ID are omitted
- [ ] Summary: Truncation works (first 100 + last 100 if > 200 items)
- [ ] Task-level intent override works (task can override workflow intent)

### Quality Tests
- [ ] UserIntention prompt: All 9 fields extracted correctly
- [ ] UserIntention prompt: Quality maintained after adding 5 fields
- [ ] Taskplan prompt: Can override workflow intent when needed
- [ ] Dynamic prompts: Action-level intent analysis works correctly

## Notes

### Implementation Notes
- **Keep existing merge logic** - it works well and doesn't need changes
- **New extraction algorithm** focuses on finding the cut-off point more precisely
- **Summary format** provides clear progress without DoD thresholds
- **Intent analysis logic preserved** - just integrated into existing prompts instead of separate calls
- **UserIntention prompt** already does language detection and normalization - now also does full intent analysis
- **Taskplan prompt** can use workflowIntent from userintention (no re-analysis needed), but can override if task-specific needs differ
- **Dynamic prompts** integrate action-level intent analysis (no separate call needed)
- **DoD removed** - but dataType/expectedFormats/qualityRequirements still gathered for other purposes

### Critical Requirements (from Critical Analysis)
- ✅ **JSON completeness**: Use parsing (`json.loads()`), NOT last character check
- ✅ **Intent levels**: Keep separate checks for workflow, task, and action levels
- ✅ **Task-level override**: Critical requirement - allow taskplan to override workflow intent
- ✅ **Sections without ID**: Omit from summary (don't show "unknown")
- ✅ **Summary truncation**: Show first 100 and last 100 items if > 200 items
- ✅ **Extraction edge cases**: Handle first element, only one incomplete section, deliver mid-string/number as-is
- ✅ **DoD checking**: Verified in refinement phase (`_refineDecide`, ContentValidator), not in AI loop