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+# Dynamic Generic AI Calls Implementation Strategy
+
+## Overview
+
+This document outlines the implementation strategy for a robust, model-agnostic AI service that handles both planning and text processing calls with intelligent fallbacks, size management, and integration with the existing ExtractionService architecture.
+
+## Core Principles
+
+1. **Model Fallback Strategy**: Iterative model trying for maximum reliability
+2. **Size Management**: 90% token limit with configurable safety margins
+3. **Separation of Concerns**: Planning vs. text calls have different parameter sets and processing logic
+4. **TypeGroup-Aware Processing**: Leverage existing chunking and merging logic based on content types
+5. **Capability-Based Model Selection**: Only use models capable of handling specific operations
+
+## Call Type Distinction
+
+### Planning Calls
+**Criteria**: `no documents AND (operationType in ["generate_plan", "analyse_content"])`
+
+**Characteristics**:
+- Use placeholder system for selective content summarization
+- Prompt integrity is critical (can be protected with `compressPrompt=False`)
+- Placeholders can be summarized while preserving prompt structure
+- Examples: Task planning, action definition, validation, decision making
+
+### Text Calls
+**Criteria**: `has documents OR operationType not in ["generate_plan", "analyse_content"]`
+
+**Characteristics**:
+- Process documents through ExtractionService
+- Use typeGroup-aware chunking and merging
+- Can process documents individually or as a group
+- Examples: Document analysis, content generation, format conversion
+
+## Enhanced Data Models
+
+### AiCallOptions
+```python
+class AiCallOptions(BaseModel):
+    # Existing fields
+    operationType: OperationType
+    priority: Priority = Priority.BALANCED
+    compressPrompt: bool = True
+    compressContext: bool = True
+    processDocumentsIndividually: bool = False
+    maxContextBytes: Optional[int] = None
+    
+    # New fields for dynamic strategy
+    callType: Literal["planning", "text"] = Field(default_factory=lambda: "text")
+    safetyMargin: float = Field(default=0.1, ge=0.0, le=0.5)
+    modelCapabilities: Optional[List[str]] = None  # e.g., ["text", "image", "vision"]
+```
+
+### Model Capabilities
+```python
+class ModelCapabilities(BaseModel):
+    name: str
+    maxTokens: int
+    capabilities: List[str]  # ["text", "image", "vision", "reasoning", "analysis"]
+    costPerToken: float
+    processingTime: float
+    isAvailable: bool = True
+```
+
+## Implementation Architecture
+
+### 1. Unified AI Call Interface
+
+```python
+async def callAi(
+    self,
+    prompt: str,
+    documents: Optional[List[ChatDocument]] = None,
+    placeholders: Optional[Dict[str, str]] = None,
+    options: AiCallOptions
+) -> str:
+    """
+    Unified AI call interface that automatically routes to appropriate handler.
+    
+    Args:
+        prompt: The main prompt for the AI call
+        documents: Optional list of documents to process
+        placeholders: Optional dictionary of placeholder replacements for planning calls
+        options: AI call configuration options
+    
+    Returns:
+        AI response as string
+        
+    Raises:
+        Exception: If all available models fail
+    """
+    # Auto-determine call type based on documents and operation type
+    call_type = self._determineCallType(documents, options.operationType)
+    
+    if call_type == "planning":
+        return await self._callAiPlanning(prompt, placeholders, options)
+    else:
+        return await self._callAiText(prompt, documents, options)
+```
+
+### 2. Planning Call Implementation
+
+```python
+async def _callAiPlanning(
+    self,
+    prompt: str,
+    placeholders: Optional[Dict[str, str]],
+    options: AiCallOptions
+) -> str:
+    """
+    Handle planning calls with placeholder system and selective summarization.
+    
+    Process:
+    1. Get models capable of planning operations
+    2. Build full prompt with placeholders
+    3. Check token limits and reduce if needed
+    4. Try each model until one succeeds
+    """
+    # Get available models for planning (text + reasoning capabilities)
+    models = self._getModelsForOperation("planning", options)
+    
+    for model in models:
+        try:
+            # Build full prompt with placeholders
+            full_prompt = self._buildPromptWithPlaceholders(prompt, placeholders)
+            
+            # Check size and reduce if needed
+            if self._exceedsTokenLimit(full_prompt, model, options.safetyMargin):
+                full_prompt = self._reducePlanningPrompt(full_prompt, placeholders, model, options)
+            
+            # Make AI call
+            result = await self._callModel(model, full_prompt, options)
+            return result
+            
+        except Exception as e:
+            logger.warning(f"Planning model {model.name} failed: {e}")
+            continue
+    
+    raise Exception("All planning models failed - check model availability and capabilities")
+```
+
+### 3. Text Call Implementation
+
+```python
+async def _callAiText(
+    self,
+    prompt: str,
+    documents: Optional[List[ChatDocument]],
+    options: AiCallOptions
+) -> str:
+    """
+    Handle text calls with document processing through ExtractionService.
+    
+    Process:
+    1. Get models capable of text operations
+    2. Extract and process documents using ExtractionService
+    3. Check token limits and reduce if needed
+    4. Try each model until one succeeds
+    """
+    # Get available models for text processing
+    models = self._getModelsForOperation("text", options)
+    
+    for model in models:
+        try:
+            # Extract and process documents using ExtractionService
+            context = ""
+            if documents:
+                extracted_content = await self.extractionService.extractDocuments(
+                    documentList=[{
+                        "id": d.id,
+                        "bytes": d.fileData,
+                        "fileName": d.fileName,
+                        "mimeType": d.mimeType
+                    } for d in documents],
+                    options={
+                        "prompt": prompt,
+                        "operationType": options.operationType.value,
+                        "processDocumentsIndividually": options.processDocumentsIndividually,
+                        "maxSize": options.maxContextBytes or int(model.maxTokens * 0.9),
+                        "chunkAllowed": not options.compressContext,
+                        "mergeStrategy": {"groupBy": "typeGroup"}
+                    }
+                )
+                
+                # Get text content from extracted parts using typeGroup-aware processing
+                context = self._extractTextFromContentParts(extracted_content)
+            
+            # Check size and reduce if needed
+            full_prompt = prompt + "\n\n" + context if context else prompt
+            if self._exceedsTokenLimit(full_prompt, model, options.safetyMargin):
+                full_prompt = self._reduceTextPrompt(prompt, context, model, options)
+            
+            # Make AI call
+            result = await self._callModel(model, full_prompt, options)
+            return result
+            
+        except Exception as e:
+            logger.warning(f"Text model {model.name} failed: {e}")
+            continue
+    
+    raise Exception("All text models failed - check model availability and capabilities")
+```
+
+### 4. Model Selection Strategy
+
+```python
+def _getModelsForOperation(self, operation_type: str, options: AiCallOptions) -> List[Model]:
+    """
+    Get models capable of handling the specific operation with capability filtering.
+    
+    Args:
+        operation_type: "planning" or "text"
+        options: AI call options including required capabilities
+    
+    Returns:
+        List of models sorted by priority and capability match
+    """
+    all_models = self._getAvailableModels()
+    
+    # Filter by operation type capabilities
+    if operation_type == "planning":
+        capable_models = [m for m in all_models 
+                         if "text" in m.capabilities and "reasoning" in m.capabilities]
+    elif operation_type == "text":
+        capable_models = [m for m in all_models if "text" in m.capabilities]
+    else:
+        capable_models = all_models
+    
+    # Filter by specific capabilities if requested
+    if options.modelCapabilities:
+        capable_models = [m for m in capable_models 
+                         if all(cap in m.capabilities for cap in options.modelCapabilities)]
+    
+    # Sort by priority preference (quality > balanced > speed > cost)
+    return self._sortModelsByPriority(capable_models, options.priority)
+```
+
+### 5. Size Management with TypeGroup-Aware Chunking
+
+```python
+def _reduceTextPrompt(
+    self,
+    prompt: str,
+    context: str,
+    model: Model,
+    options: AiCallOptions
+) -> str:
+    """
+    Reduce text prompt size using typeGroup-aware chunking and merging.
+    
+    Args:
+        prompt: Original prompt
+        context: Extracted document context
+        model: Target model with token limits
+        options: AI call options
+    
+    Returns:
+        Reduced prompt that fits within token limits
+    """
+    max_size = int(model.maxTokens * (1 - options.safetyMargin))
+    
+    if options.compressPrompt:
+        # Reduce both prompt and context
+        target_size = max_size
+        current_size = len(prompt) + len(context)
+        reduction_factor = (target_size * 0.7) / current_size
+        
+        if reduction_factor < 1.0:
+            prompt = self._reduceText(prompt, reduction_factor)
+            context = self._reduceTextWithTypeGroups(context, reduction_factor, options)
+    else:
+        # Only reduce context, preserve prompt integrity
+        max_context_size = max_size - len(prompt)
+        if len(context) > max_context_size:
+            reduction_factor = max_context_size / len(context)
+            context = self._reduceTextWithTypeGroups(context, reduction_factor, options)
+    
+    return prompt + "\n\n" + context if context else prompt
+
+def _reduceTextWithTypeGroups(
+    self,
+    context: str,
+    reduction_factor: float,
+    options: AiCallOptions
+) -> str:
+    """
+    Reduce text using typeGroup-aware chunking and merging strategies.
+    
+    Leverages existing chunking/merging modules:
+    - text_chunker.py / text_merger.py
+    - table_chunker.py / table_merger.py  
+    - structure_chunker.py / default_merger.py
+    """
+    if options.compressContext:
+        # Summarize content using AI
+        return await self._summarizeContent(context, reduction_factor)
+    else:
+        # Chunk content using typeGroup-aware chunkers
+        return await self._chunkContent(context, reduction_factor, options)
+```
+
+## Integration Points
+
+### 1. ExtractionService Integration
+- Use `extractionService.extractDocuments()` for all document processing
+- Leverage existing 3-pass pipeline (Extract → Chunk → Merge)
+- Utilize typeGroup-based processing for different content types
+
+### 2. Existing Chunking/Merging Logic
+- **Text Content**: `text_chunker.py` / `text_merger.py`
+- **Table Content**: `table_chunker.py` / `table_merger.py`
+- **Structured Content**: `structure_chunker.py` / `default_merger.py`
+
+### 3. Model Capability Management
+- Maintain model capability registry
+- Filter models based on operation requirements
+- Support dynamic model availability
+
+## Error Handling Strategy
+
+### Model Failure Handling
+1. **Individual Model Failure**: Log warning, try next model
+2. **All Models Failed**: Return error with diagnostic information including:
+   - List of attempted models
+   - Failure reasons for each model
+   - Suggested alternatives or parameter adjustments
+
+### Size Management Failures
+1. **Token Limit Exceeded**: Apply reduction strategies
+2. **Reduction Failed**: Fall back to emergency chunking
+3. **Critical Content Lost**: Return error with size analysis
+
+## Configuration and Tuning
+
+### Safety Margins
+- **Default**: 10% safety margin (0.1)
+- **Configurable**: Per-call basis via `AiCallOptions.safetyMargin`
+- **Range**: 0.0 to 0.5 (0% to 50% safety margin)
+
+### Model Selection Priority
+1. **Quality**: Best model for accuracy
+2. **Balanced**: Good balance of speed and quality
+3. **Speed**: Fastest available model
+4. **Cost**: Most cost-effective model
+
+### Size Reduction Strategies
+- **Prompt Compression**: When `compressPrompt=True`
+- **Context Summarization**: When `compressContext=True`
+- **Document Chunking**: When `processDocumentsIndividually=True`
+
+## Migration Strategy
+
+### Phase 1: Enhanced AiCallOptions
+- Add new fields to `AiCallOptions` model
+- Update existing AI calls to use new options
+
+### Phase 2: Unified Interface
+- Implement `callAi()` as unified entry point
+- Maintain backward compatibility with existing `callAiText()`
+
+### Phase 3: Model Management
+- Implement model capability registry
+- Add model selection and fallback logic
+
+### Phase 4: Size Management
+- Integrate with ExtractionService
+- Implement typeGroup-aware reduction strategies
+
+### Phase 5: Full Migration
+- Migrate all AI calls to use unified interface
+- Remove legacy AI call methods
+
+## Benefits
+
+1. **Reliability**: Multiple model fallbacks ensure high success rate
+2. **Efficiency**: Intelligent size management prevents token limit issues
+3. **Flexibility**: TypeGroup-aware processing handles diverse content types
+4. **Maintainability**: Centralized logic reduces code duplication
+5. **Scalability**: Easy to add new models and capabilities
+6. **Integration**: Seamless integration with existing ExtractionService
+
+## Future Enhancements
+
+1. **Dynamic Model Loading**: Load models on-demand based on requirements
+2. **Performance Monitoring**: Track model performance and optimize selection
+3. **Cost Optimization**: Balance quality vs. cost based on use case
+4. **Caching**: Cache processed content for repeated operations
+5. **Streaming**: Support for streaming responses from models
diff --git a/poweron/implementation/implementation_extraction.md b/poweron/implementation/implementation_extraction.md
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+## PowerON Extraction Service – Concept and Architecture
+
+### Goals
+- **Normalize** any document into a small set of processing‑ready typeGroups: `text`, `table`, `structure`, `image`, `binary`, `metadata`, `container`.
+- **Decouple** extraction (split/normalize) from chunking and merging.
+- **Scale** to multi‑part/container formats (pdf, office) using recursive splitting.
+- **Control** cost/latency by honoring `maxSize` and `chunkAllowed` with AI only when needed.
+- **Integrate** with AI Prompt Builder entrypoint and support `operationType` behavior.
+
+### New Service Location
+- Base: `gateway/modules/services/serviceExtraction/mainServiceExtraction.py`
+- Sub‑modules:
+  - `gateway/modules/services/serviceExtraction/subRegistry.py` (extractor and chunker registries)
+  - `gateway/modules/services/serviceExtraction/subPipeline.py` (3‑pass orchestration)
+  - `gateway/modules/services/serviceExtraction/formats/` (per‑format extractors)
+  - `gateway/modules/services/serviceExtraction/chunking/` (per‑typeGroup chunkers)
+  - `gateway/modules/services/serviceExtraction/merging/` (per‑typeGroup mergers)
+  - `gateway/modules/services/serviceExtraction/utils/` (encoding, mime, helpers)
+
+No backwards compatibility is required; this is a clean introduction.
+
+### Core Data Model (standardized outputs)
+- ContentPart
+  - `id: str`
+  - `parentId: Optional[str]` (preserve hierarchy; root has `None`)
+  - `label: str` (e.g., "page_2", "sheet_Jan", "table_1")
+  - `typeGroup: Literal["text","table","structure","image","binary","metadata","container"]`
+  - `mimeType: str`
+  - `data: str` (utf‑8 text for `text|table|structure`; base64 for `image|binary`; empty for `container`)
+  - `metadata: Dict[str, Any]` (size, pages, width/height, pageIndex, sheetName, sourceRanges, checksum, confidence, warnings)
+
+- ExtractedContent
+  - `id: str` (document id)
+  - `parts: List[ContentPart]` (flat list; hierarchy via `parentId`)
+  - `summary: Optional[Dict[str, Any]]`
+
+Notes:
+- `metadata.sourceRanges` or page/sheet indices allow provenance for merges/summaries.
+- `metadata.confidence` and `metadata.warnings` guide downstream AI/UX decisions.
+
+### MIME → typeGroup mapping (deterministic first)
+- `text/plain`, `text/markdown` → `text`
+- `text/csv` → `table`
+- `application/json`, `application/xml`, `text/html`, `image/svg+xml` → `structure`
+- `image/*` → `image`
+- `application/pdf`, `application/vnd.openxmlformats-officedocument.*` → `container`
+- otherwise → `binary`
+
+Container extractors are responsible for disaggregating into basic typeGroups.
+
+### 3‑Pass Pipeline
+1) Identify and normalize (Split/Extract)
+   - Start with a root `container` part representing the raw file.
+   - Resolve extractor by `mimeType`/extension via registry.
+   - Recursively split container formats into child parts until only basic typeGroups remain (`text|table|structure|image|binary|metadata`).
+   - Output a single `ExtractedContent` per input document.
+
+2) Chunk
+   - Route each basic typeGroup to its chunker:
+     - `text` → size‑bounded line/paragraph aware
+     - `table` → row‑bounded (CSV lines), schema aware optional
+     - `structure` → JSON object/XML subtree/HTML block aware
+     - `image`, `binary`, `metadata`, `container` → no chunking by default
+   - Chunkers return `chunks: List[Dict]` with back‑references (`partId`, `order`).
+
+3) Merge
+   - Strategy driven by call options and workflow:
+     - `text` → concatenate by logical order (page/section) or keep per part
+     - `table` → keep separate per table/sheet; optional schema merge
+     - `structure` → preserve keys/paths; avoid lossy merges
+     - `image|binary` → usually pass‑through
+     - `metadata|container` → excluded by default
+
+### Registries
+- ExtractorRegistry (in `subRegistry.py`)
+  - Maps `mimeType`/extension to an `Extractor` instance.
+  - Fallbacks: content sniffing, default binary extractor.
+
+- ChunkerRegistry (in `subRegistry.py`)
+  - Maps `typeGroup` to a `Chunker`.
+
+### Base Interfaces
+Use camelCase and prefix internal methods with `_`.
+
+```python
+class Extractor:
+    def detect(self, fileName: str, mimeType: str, headBytes: bytes) -> bool: ...
+    def extract(self, fileBytes: bytes, context: Dict[str, Any]) -> List[ContentPart]: ...
+
+class Chunker:
+    def chunk(self, part: ContentPart, options: Dict[str, Any]) -> List[Dict[str, Any]]: ...
+
+class Merger:
+    def merge(self, parts: List[ContentPart], strategy: Dict[str, Any]) -> List[ContentPart]: ...
+```
+
+### Format Extractors (under `formats/`)
+- `text_extractor.py` → emits one `text` part
+- `csv_extractor.py` → emits one `table` part (CSV payload)
+- `json_extractor.py`, `xml_extractor.py`, `html_extractor.py`, `svg_extractor.py` → emit `structure` parts
+- `image_extractor.py` → emits one `image` part; optional OCR is handled by AI post‑processing
+- `pdf_extractor.py` → emits `container` root with children:
+  - per page: `text` part if text found
+  - per page: extracted images as `image` parts
+  - per page/section metadata as `metadata`
+- `docx_extractor.py` → `container` + children: headings `structure`, paragraphs `text`, tables `table`, comments `metadata`
+- `xlsx_extractor.py` → `container` + children: each sheet as `table` CSV; properties `metadata`; charts as `image` or `structure`
+- `pptx_extractor.py` → `container` + slides: text boxes `text`, tables `table`, images `image`, notes `metadata`
+- `legacy_*_extractor.py` → `metadata` + `binary` with clear limitations
+- `binary_extractor.py` → single `binary` part
+
+### Chunkers (under `chunking/`)
+- `text_chunker.py` → size/paragraph aware; configurable sizes
+- `table_chunker.py` → split by row count/bytes, keep header propagation
+- `structure_chunker.py` → JSON object buckets, XML subtree buckets, HTML block buckets
+- `binary_chunker.py` → byte slicing when explicitly requested
+- `noop_chunker.py` → for image/metadata/container
+
+### Mergers (under `merging/`)
+- `text_merger.py` → page/section aware concatenation
+- `table_merger.py` → per sheet/table; optional schema merge
+- `structure_merger.py` → key/path preserving grouping
+- `default_merger.py` → pass‑through
+
+### Orchestration (in `subPipeline.py`)
+High‑level flow for one document:
+
+```python
+def runExtraction(document: bytes, fileName: str, mimeType: str, options: Dict[str, Any]) -> ExtractedContent:
+    # Pass 1: extract/normalize
+    parts = _extractAll(document, fileName, mimeType, options)
+
+    # Pass 2: chunk if allowed
+    if options.get("chunkAllowed", False):
+        chunks = _chunkParts(parts, options)
+    else:
+        chunks = []
+
+    # Pass 3: merge per strategy
+    merged = _merge(parts, chunks, options.get("mergeStrategy", {}))
+
+    return ExtractedContent(id=_makeId(), parts=merged, summary=_buildSummary(parts))
+```
+
+### Entry Point and Options (in `mainServiceExtraction.py`)
+The service is invoked by AI Prompt Builder with `(documentList, options)`.
+
+Supported options and effects:
+- `prompt: str`
+  - If present, enables optional AI augmentation on extracted content/chunks based on `operationType`.
+- `operationType: Literal["general","generate_plan","analyse_content","generate_content","web_research"]`
+  - `general`/`analyse_content`: prefer deterministic extraction; AI can summarize or answer over chunks.
+  - `generate_plan`: produce structured `structure` outputs (bullet points, tasks) from `text` chunks.
+  - `generate_content`: allow AI synthesis over merged `text` parts within `maxSize`.
+  - `web_research`: treat extracted `structure` and `text` as context; AI orchestrator may fetch more docs upstream.
+- `processDocumentsIndividually: bool`
+  - `True`: run the 3‑pass pipeline per document; apply `maxSize` per document; return list of results.
+  - `False`: extract all docs → pool parts → global chunk/merge → apply `maxSize` across the pool; keep provenance by `parentId` and `documentId`.
+- `maxSize: int` and `chunkAllowed: bool`
+  - Hard cap on total size of content passed to AI.
+  - If `chunkAllowed=True` → prefer chunking to stay under `maxSize`; process chunks iteratively in priority order (e.g., text before images, or by page order).
+  - If `chunkAllowed=False` → do not chunk; instead summarize down (per part, then hierarchical) until under `maxSize`.
+
+Size governance policy:
+1) Compute sizes for candidate parts/chunks.
+2) If total ≤ `maxSize` → pass through.
+3) If total > `maxSize` and `chunkAllowed` → progressively include highest‑value chunks until the cap; optionally add a final global summary.
+4) If total > `maxSize` and not chunkAllowed → summarize per part, then merge summaries; ensure final text ≤ cap.
+
+### AI Integration
+- AI is optional and strictly after extraction.
+- Recommended placements:
+  - OCR/VLM for `image` parts when requested.
+  - LLM summarization for large `text|structure|table` parts to respect `maxSize` when `chunkAllowed=False`.
+  - LLM question answering (`analyse_content`) over selected chunks.
+- All AI calls must respect budget/time guards and the size cap.
+
+### Error Handling
+- Every extractor must return either valid parts or a `metadata` part with `warnings/error` plus a `binary` fallback when applicable.
+- Include enough context in `metadata` to diagnose issues (library missing, parse error details) without leaking sensitive content.
+
+### Ordering and Provenance
+- Preserve logical order within a document (page index, slide index, sheet index).
+- Maintain `parentId` links to reconstruct hierarchy during merge and summarization.
+
+### Testing Strategy
+- Unit tests per extractor on small fixtures for each format.
+- Contract tests for the 3‑pass pipeline (end‑to‑end) with mixed multi‑part documents.
+- Size‑cap tests validating chunking vs summarization paths.
+
+### Migration Notes
+- Existing monolithic logic can be moved into `formats/*` and `utils/*` preserving robust decoding and Office/PDF heuristics, while removing AI calls from extractors.
+- `ContentItem` usage should shift to `ContentPart` (no backward compatibility required).
+
+### Minimal Pseudocode – processDocumentsIndividually
+```python
+def extractDocuments(documentList: List[Dict], options: Dict[str, Any]):
+    if options.get("processDocumentsIndividually", True):
+        results = []
+        for doc in documentList:
+            ec = runExtraction(doc.bytes, doc.fileName, doc.mimeType, options)
+            ec = _applyAiIfRequested(ec, options)  # respects maxSize + chunkAllowed
+            results.append(ec)
+        return results
+    else:
+        # global pool
+        parts = []
+        for doc in documentList:
+            ec = runExtraction(doc.bytes, doc.fileName, doc.mimeType, options)
+            parts.extend(_tagWithDocumentId(ec.parts, doc.id))
+        pooled = _poolAndLimit(parts, options)  # chunk/summarize to cap
+        pooled = _applyAiIfRequestedOverPool(pooled, options)
+        return pooled
+```
+
+### Defaults and Configuration
+- Chunk sizes per typeGroup are centralized and configurable.
+- Merge strategies (text concat policy, table schema inference) are pluggable.
+- Registries support runtime extension (new formats) without touching the pipeline.
+
+### Summary
+This design introduces a small, stable contract (`ContentPart` with `typeGroup`) and a 3‑pass pipeline that:
+- normalizes diverse documents into uniform parts,
+- chunks only what benefits from chunking,
+- merges predictably for downstream AI and workflow steps,
+while strictly enforcing `maxSize` and honoring `chunkAllowed` and `processDocumentsIndividually`.
+
+