AQL_OOP_EXTENSION_PROPOSAL

AQL Language Extension Proposal - OOP & Best Practices

Datum: 22. Dezember 2025
Version: v1.3.1 Proposal
Status: 📋 Vorschlag zur Diskussion
Autor: AI Code Review Agent

---

Zusammenfassung

Dieses Dokument schlägt Erweiterungen der AQL (Advanced Query Language) vor, die auf objektorientierten Prinzipien (OOP) und Best Practices basieren. Die Vorschläge berücksichtigen insbesondere die neuen LLM-Erweiterungen (llama und llama.cpp vision) in v1.3.0 und zielen darauf ab, die Sprache wartbarer, wiederverwendbarer und ausdrucksstärker zu machen.

Hauptziele

1. Modularität: Namespace-System für bessere Code-Organisation
2. Wiederverwendbarkeit: User-defined Functions (UDFs) und Typen
3. Komposition: Method Chaining und Fluent Interfaces
4. Vision-Integration: Erweiterte Multimodal-Operationen
5. Typsicherheit: Stärkere Typsysteme für LLM-Operationen

---

1. Namespace/Module-System

Problem

Aktuell existiert nur ein flacher Namespace für alle Funktionen. Bei wachsender Funktionalität (LLM, Vision, Graph, Geo, etc.) wird dies unübersichtlich und fehleranfällig.

Vorschlag: Hierarchische Namespaces

-- Namespace-Definition
NAMESPACE llm.vision;

-- Function in Namespace
FUNCTION analyze_image(img_path: String) -> Object {
  LET result = LLM VISION ANALYZE img_path
    USING MODEL 'llava-7b'
    OPTIONS { detail: 'high' }
  RETURN result
}

-- Verwendung mit qualifiziertem Namen
LET analysis = llm.vision::analyze_image('/images/drone_photo.jpg');

-- Import für kürzere Notation
IMPORT llm.vision.*;
LET analysis = analyze_image('/images/drone_photo.jpg');

Implementierung

namespace_decl ::= 
  "NAMESPACE" qualified_identifier ";"

qualified_identifier ::= 
  identifier ("." identifier)*

import_stmt ::=
  "IMPORT" qualified_identifier ("." "*")? ";"

qualified_function_call ::=
  qualified_identifier "::" function_name "(" argument_list? ")"

Vorteile

• ✅ Bessere Code-Organisation
• ✅ Vermeidung von Namenskonflikten
• ✅ Klare Zuordnung von Funktionalität
• ✅ Ermöglicht Plugin-Erweiterungen

---

2. User-Defined Types (UDTs)

Problem

Komplexe Datenstrukturen werden als generische Objects dargestellt, ohne Typsicherheit oder Validierung.

Vorschlag: Strukturierte Typen

-- Type Definition
TYPE VisionAnalysis {
  objects: Array<DetectedObject>,
  scene: String,
  confidence: Float,
  embeddings: Array<Float>
}

TYPE DetectedObject {
  label: String,
  confidence: Float,
  bbox: BoundingBox,
  attributes: Object?  -- Optional field
}

TYPE BoundingBox {
  x: Int,
  y: Int,
  width: Int,
  height: Int
}

-- Usage
FUNCTION process_image(path: String) -> VisionAnalysis {
  LET raw_result = LLM VISION ANALYZE path
  
  -- Type-safe construction
  RETURN VisionAnalysis {
    objects: raw_result.detections MAP (d -> 
      DetectedObject {
        label: d.label,
        confidence: d.score,
        bbox: BoundingBox(d.box),
        attributes: d.attrs
      }
    ),
    scene: raw_result.scene_type,
    confidence: raw_result.overall_confidence,
    embeddings: raw_result.embedding
  }
}

Implementierung

type_definition ::=
  "TYPE" identifier "{" field_list "}"

field_list ::=
  field_decl ("," field_decl)*

field_decl ::=
  identifier ":" type_spec ("?")?  -- ? marks optional

type_spec ::=
  primitive_type
  | "Array" "<" type_spec ">"
  | "Map" "<" type_spec "," type_spec ">"
  | qualified_identifier  -- User-defined type

Vorteile

• ✅ Typsicherheit zur Compile-Zeit
• ✅ Bessere Dokumentation
• ✅ IDE-Unterstützung (Autocomplete)
• ✅ Frühe Fehlererkennung

---

3. User-Defined Functions (UDFs)

Problem

Keine Möglichkeit, wiederverwendbare Funktionen zu definieren. Code-Duplikation bei komplexen LLM-Workflows.

Vorschlag: Function Definitions

-- Simple Function
FUNCTION calculate_similarity(vec1: Array<Float>, vec2: Array<Float>) -> Float {
  RETURN COSINE_SIMILARITY(vec1, vec2)
}

-- Function with Default Parameters
FUNCTION generate_summary(
  text: String, 
  model: String = 'mistral-7b',
  max_tokens: Int = 150
) -> String {
  RETURN LLM INFER CONCAT('Summarize: ', text)
    USING MODEL model
    OPTIONS { max_tokens: max_tokens }
}

-- Recursive Function
FUNCTION traverse_graph_recursive(
  start_id: String, 
  depth: Int = 3
) -> Array<Object> {
  IF depth <= 0 THEN
    RETURN []
  ENDIF
  
  LET neighbors = (
    FOR v IN 1..1 OUTBOUND start_id edges
      RETURN v
  )
  
  LET recursive_results = (
    FOR n IN neighbors
      RETURN traverse_graph_recursive(n._id, depth - 1)
  )
  
  RETURN UNION(neighbors, FLATTEN(recursive_results))
}

Implementierung

function_definition ::=
  "FUNCTION" identifier "(" parameter_list? ")" "->" type_spec
  "{" function_body "}"

parameter_list ::=
  parameter ("," parameter)*

parameter ::=
  identifier ":" type_spec ("=" expression)?  -- Default value

function_body ::=
  statement_list

Vorteile

• ✅ Code-Wiederverwendung
• ✅ Reduzierung von Duplikation
• ✅ Bessere Testbarkeit
• ✅ Modulare Entwicklung

---

4. Method Chaining & Fluent Interfaces

Problem

Verschachtelte Funktionsaufrufe sind schwer lesbar, besonders bei komplexen LLM-Pipelines.

Vorschlag: Pipeline-Operator

-- Traditional nested style (hard to read)
LET result = UPPER(TRIM(SUBSTRING(doc.title, 0, 50)));

-- Pipeline style (readable, left-to-right)
LET result = doc.title 
  |> SUBSTRING(_, 0, 50)
  |> TRIM(_)
  |> UPPER(_);

-- LLM Pipeline Example
LET analysis = image_path
  |> LLM VISION ANALYZE _ 
       USING MODEL 'llava-7b'
  |> _.description
  |> LLM INFER CONCAT('Translate to German: ', _)
       USING MODEL 'mistral-7b'
  |> TRIM(_);

-- Complex RAG Pipeline
LET answer = user_query
  |> LLM EMBED _ USING MODEL 'e5-base'
  |> VECTOR_SEARCH(documents, _, 10)
  |> MAP(_, d -> d.content)
  |> JOIN(_, '\n\n')
  |> LLM RAG user_query FROM CONTEXT _
       WITH LORA 'domain-qa';

Implementierung

pipeline_expr ::=
  expression ("|>" pipeline_stage)+

pipeline_stage ::=
  function_call_with_placeholder
  | llm_operation_with_placeholder

-- '_' represents the piped value
placeholder ::= "_"

Vorteile

• ✅ Verbesserte Lesbarkeit
• ✅ Intuitiver Datenfluss
• ✅ Weniger Verschachtelung
• ✅ Funktionale Programmierung

---

5. Vision-Spezifische Erweiterungen

Problem

Aktuelle LLM VISION-Syntax ist zu generisch. Benötigt spezifischere Operationen für Vision Tasks.

Vorschlag: Erweiterte Vision-Befehle

-- 5.1 Image Analysis with structured output
LLM VISION ANALYZE 'image.jpg'
  USING MODEL 'llava-7b'
  DETECT [
    objects,
    text,
    faces,
    landmarks
  ]
  OPTIONS {
    detail: 'high',
    language: 'de'
  }
  RETURN AS VisionAnalysisResult;

-- 5.2 Batch Image Processing
LLM VISION BATCH ANALYZE
  FOR image IN images
    DETECT objects
    USING MODEL 'llava-7b'
  RETURN {
    image_id: image._id,
    analysis: _.objects,
    timestamp: DATE_NOW()
  };

-- 5.3 Image-to-Image Operations
LLM VISION TRANSFORM 'input.jpg'
  USING MODEL 'stable-diffusion'
  OPERATIONS [
    { type: 'enhance', strength: 0.8 },
    { type: 'style_transfer', style: 'artistic' }
  ]
  OUTPUT TO 'output.jpg';

-- 5.4 Visual Question Answering (VQA)
LLM VISION QUESTION 'What objects are in this image?'
  ABOUT IMAGE 'photo.jpg'
  USING MODEL 'llava-7b'
  OPTIONS { format: 'structured' };

-- 5.5 Image Comparison
LLM VISION COMPARE
  IMAGES ['img1.jpg', 'img2.jpg']
  USING MODEL 'clip'
  RETURN {
    similarity: _.score,
    differences: _.diff_areas
  };

-- 5.6 Multimodal RAG with Images
LLM VISION RAG 'Explain this medical scan'
  FROM COLLECTION medical_images
  WHERE image.modality == 'CT'
  WITH IMAGE 'patient_scan.jpg'
  TOP 5
  USING MODEL 'llava-med'
  WITH LORA 'radiology-qa';

Implementierung

llm_vision_stmt ::=
    llm_vision_analyze
  | llm_vision_batch
  | llm_vision_transform
  | llm_vision_question
  | llm_vision_compare
  | llm_vision_rag

llm_vision_analyze ::=
  "LLM" "VISION" "ANALYZE" string_literal
  "USING" "MODEL" string_literal
  ("DETECT" "[" detection_list "]")?
  ("OPTIONS" object_literal)?
  ("RETURN" "AS" type_spec)?

detection_list ::=
  detection_type ("," detection_type)*

detection_type ::=
  "objects" | "text" | "faces" | "landmarks" | 
  "emotions" | "brands" | "celebrities"

llm_vision_question ::=
  "LLM" "VISION" "QUESTION" string_literal
  "ABOUT" "IMAGE" string_literal
  "USING" "MODEL" string_literal
  ("OPTIONS" object_literal)?

llm_vision_compare ::=
  "LLM" "VISION" "COMPARE"
  "IMAGES" array_literal
  "USING" "MODEL" string_literal
  ("OPTIONS" object_literal)?

Vorteile

• ✅ Spezialisierte Vision-Operations
• ✅ Strukturierte Ausgaben
• ✅ Batch-Verarbeitung
• ✅ Multimodale Integration

---

6. Error Handling & Try-Catch

Problem

Keine strukturierte Fehlerbehandlung. LLM-Operationen können fehlschlagen.

Vorschlag: Exception Handling

-- Try-Catch Block
TRY {
  LET result = LLM INFER 'Complex prompt'
    USING MODEL 'large-model'
    OPTIONS { max_tokens: 1000 }
  RETURN result
}
CATCH (error) {
  CASE error.type
    WHEN 'LLM_MODEL_NOT_FOUND' THEN
      RETURN LLM INFER 'Complex prompt'
        USING MODEL 'fallback-model'
    WHEN 'LLM_QUEUE_FULL' THEN
      WAIT(5000)  -- Wait 5 seconds
      RETRY
    WHEN 'LLM_TIMEOUT' THEN
      RETURN { error: 'Timeout', message: error.message }
    ELSE
      THROW error  -- Re-throw unknown errors
  END
}

-- Optional Chaining (Elvis Operator)
LET value = risky_operation()?.result?.nested?.value ?? 'default';

-- Error Propagation
FUNCTION safe_llm_call(prompt: String) -> Result<String, Error> {
  TRY {
    RETURN Ok(LLM INFER prompt)
  } CATCH (e) {
    RETURN Err(e)
  }
}

Implementierung

try_catch_stmt ::=
  "TRY" "{" statement_list "}"
  "CATCH" "(" identifier ")" "{" catch_body "}"

catch_body ::=
  case_stmt | statement_list

case_stmt ::=
  "CASE" expression
  ("WHEN" expression "THEN" statement_list)+
  ("ELSE" statement_list)?
  "END"

optional_chaining ::=
  expression ("?." identifier)*
  (?? expression)?  -- Null coalescing

---

7. Classes & Methods für LLM-Workflows

Problem

Komplexe LLM-Workflows erfordern State Management und wiederverwendbare Komponenten.

Vorschlag: Class-basierte Workflows

-- Class Definition
CLASS MultimodalRAGPipeline {
  -- Properties
  PRIVATE collection: String;
  PRIVATE model: String;
  PRIVATE lora: String?;
  
  -- Constructor
  CONSTRUCTOR(collection: String, model: String = 'llava-7b') {
    THIS.collection = collection;
    THIS.model = model;
  }
  
  -- Methods
  PUBLIC METHOD setLoRA(lora_id: String) -> SELF {
    THIS.lora = lora_id;
    RETURN THIS;  -- Enable chaining
  }
  
  PUBLIC METHOD analyzeImage(img_path: String) -> VisionAnalysis {
    RETURN LLM VISION ANALYZE img_path
      USING MODEL THIS.model
      WITH LORA THIS.lora;
  }
  
  PUBLIC METHOD searchSimilar(embedding: Array<Float>, top_k: Int = 5) -> Array<Object> {
    FOR doc IN THIS.collection
      LET similarity = COSINE_SIMILARITY(doc.embedding, embedding)
      FILTER similarity > 0.7
      SORT similarity DESC
      LIMIT top_k
      RETURN doc;
  }
  
  PUBLIC METHOD executeRAG(query: String, image: String?) -> String {
    LET context = IF image THEN
      LET vision_result = THIS.analyzeImage(image);
      LET similar = THIS.searchSimilar(vision_result.embeddings, 5);
      CONCAT_SEPARATOR('\n', similar[*].content)
    ELSE
      LET query_embedding = LLM EMBED query;
      LET similar = THIS.searchSimilar(query_embedding, 5);
      CONCAT_SEPARATOR('\n', similar[*].content)
    ENDIF;
    
    RETURN LLM INFER CONCAT(
      'Context: ', context, '\n\n',
      'Question: ', query
    )
    USING MODEL THIS.model
    WITH LORA THIS.lora;
  }
}

-- Usage
LET pipeline = NEW MultimodalRAGPipeline('documents', 'llava-7b')
  .setLoRA('medical-qa');

LET answer = pipeline.executeRAG(
  'What is visible in this scan?',
  'patient_scan.jpg'
);

Implementierung

class_definition ::=
  "CLASS" identifier "{" class_body "}"

class_body ::=
  (property_decl | constructor_decl | method_decl)*

property_decl ::=
  ("PUBLIC" | "PRIVATE") identifier ":" type_spec ";"

constructor_decl ::=
  "CONSTRUCTOR" "(" parameter_list? ")" "{" statement_list "}"

method_decl ::=
  ("PUBLIC" | "PRIVATE") "METHOD" identifier 
  "(" parameter_list? ")" "->" type_spec
  "{" statement_list "}"

object_instantiation ::=
  "NEW" qualified_identifier "(" argument_list? ")"

method_call ::=
  expression "." identifier "(" argument_list? ")"

this_reference ::= "THIS"

self_reference ::= "SELF"  -- For method chaining

---

8. Pattern Matching für LLM-Ergebnisse

Problem

LLM-Antworten sind oft unstrukturiert. Parsing und Validierung ist umständlich.

Vorschlag: Pattern Matching

-- Match Expression
LET result = LLM INFER 'Classify this text: ' + doc.content
  USING MODEL 'mistral-7b';

LET classification = MATCH result
  WHEN /^Category:\s*(\w+)/ THEN $1
  WHEN /^Type:\s*(\w+)/ THEN $1
  ELSE 'unknown'
END;

-- Structural Pattern Matching
LET analysis = LLM VISION ANALYZE 'image.jpg';

LET action = MATCH analysis
  WHEN { objects: [{ label: 'person', confidence: > 0.9 }, ...rest] } THEN
    'High confidence person detected'
  WHEN { objects: [...objects], scene: 'outdoor' } THEN
    'Outdoor scene with ' + LENGTH(objects) + ' objects'
  WHEN { objects: [] } THEN
    'No objects detected'
  ELSE
    'Unknown pattern'
END;

-- Type-based Pattern Matching
FUNCTION handle_llm_result(result: Any) -> String {
  MATCH result
    WHEN String s THEN 'Got string: ' + s
    WHEN Array<Object> arr THEN 'Got ' + LENGTH(arr) + ' objects'
    WHEN VisionAnalysis v THEN 'Got vision analysis with ' + LENGTH(v.objects) + ' detections'
    ELSE 'Unknown type'
  END
}

Implementierung

match_expr ::=
  "MATCH" expression
  ("WHEN" pattern "THEN" expression)+
  ("ELSE" expression)?
  "END"

pattern ::=
    regex_pattern
  | structural_pattern
  | type_pattern
  | guard_pattern

regex_pattern ::=
  "/" regex_body "/" ("THEN" capture_reference)?

structural_pattern ::=
  object_pattern | array_pattern

type_pattern ::=
  type_spec identifier

guard_pattern ::=
  pattern ("WHERE" | "IF") expression

---

9. Async/Await für LLM-Operationen

Problem

LLM-Inferenzen sind langsam. Blockierende Calls verschwenden Zeit bei parallelen Operationen.

Vorschlag: Asynchrone Ausführung

-- Async Function
ASYNC FUNCTION analyze_documents_parallel(doc_ids: Array<String>) -> Array<String> {
  LET tasks = FOR doc_id IN doc_ids
    RETURN ASYNC {
      LET doc = DOCUMENT('documents', doc_id);
      RETURN LLM INFER doc.content
        USING MODEL 'mistral-7b'
        WITH LORA 'summarization';
    };
  
  -- Wait for all tasks
  LET results = AWAIT ALL tasks;
  RETURN results;
}

-- Async with timeout
LET result = AWAIT TIMEOUT(5000) {
  LLM INFER 'Complex analysis'
    USING MODEL 'large-model'
} OR 'Timeout - using cache';

-- Parallel execution with limit
LET results = AWAIT PARALLEL LIMIT 5 {
  FOR doc IN documents
    RETURN LLM EMBED doc.content
};

Implementierung

async_function ::=
  "ASYNC" function_definition

async_block ::=
  "ASYNC" "{" statement_list "}"

await_expr ::=
  "AWAIT" expression
  | "AWAIT" "ALL" array_expr
  | "AWAIT" "TIMEOUT" "(" integer ")" expression

parallel_expr ::=
  "AWAIT" "PARALLEL" ("LIMIT" integer)? expression

---

10. Macros & Code Generation

Problem

Repetitive Patterns bei LLM-Aufrufen. Boilerplate Code.

Vorschlag: Makro-System

-- Macro Definition
MACRO rag_query(collection, query, model, lora) {
  LET embedding = LLM EMBED $query USING MODEL $model;
  
  LET context = (
    FOR doc IN $collection
      LET sim = COSINE_SIMILARITY(doc.embedding, embedding)
      FILTER sim > 0.7
      SORT sim DESC
      LIMIT 5
      RETURN doc.content
  );
  
  RETURN LLM INFER CONCAT(
    'Context: ', JOIN(context, '\n\n'),
    '\n\nQuestion: ', $query
  )
  USING MODEL $model
  WITH LORA $lora;
}

-- Usage
LET answer = rag_query!(
  documents,
  'What is ThemisDB?',
  'mistral-7b',
  'tech-qa'
);

-- Conditional Compilation
#IF FEATURE_VISION_ENABLED
  LET vision_result = LLM VISION ANALYZE image;
#ELSE
  LET vision_result = { error: 'Vision not available' };
#ENDIF

Implementierung

macro_definition ::=
  "MACRO" identifier "(" parameter_list? ")" "{" macro_body "}"

macro_body ::=
  (statement | macro_variable_ref)*

macro_variable_ref ::=
  "$" identifier

macro_invocation ::=
  identifier "!" "(" argument_list? ")"

conditional_compilation ::=
  "#IF" condition
  statement_list
  ("#ELSE" statement_list)?
  "#ENDIF"

---

11. Best Practices - Implementierung

Coding Standards

-- 1. Verwende Namespaces für Gruppierung
NAMESPACE llm.medical;

-- 2. Definiere klare Typen
TYPE PatientScan {
  scan_id: String,
  modality: String,
  image_path: String,
  metadata: ScanMetadata
}

-- 3. Dokumentiere Funktionen
/**
 * Analyzes a medical scan using LLM vision.
 * 
 * @param scan - The patient scan to analyze
 * @param model - Vision model to use (default: 'llava-med')
 * @return Analysis result with findings
 */
FUNCTION analyze_scan(
  scan: PatientScan, 
  model: String = 'llava-med'
) -> ScanAnalysis {
  -- Implementation
}

-- 4. Behandle Fehler explizit
TRY {
  LET result = risky_operation();
} CATCH (e) {
  LOG_ERROR('Operation failed', e);
  RETURN default_value;
}

-- 5. Verwende Pipeline für Lesbarkeit
LET result = input
  |> validate(_)
  |> transform(_)
  |> enrich_with_llm(_)
  |> store(_);

-- 6. Vermeide Code-Duplikation mit UDFs
FUNCTION standard_rag(query: String, collection: String) -> String {
  -- Reusable RAG implementation
}

Performance Best Practices

-- 1. Batch LLM-Calls
LET summaries = AWAIT PARALLEL LIMIT 10 {
  FOR doc IN documents
    RETURN LLM INFER doc.content
};

-- 2. Cache häufige Queries
LET cached_embedding = CACHE_GET('user_query_' + query_hash)
  ?? LLM EMBED query;

-- 3. Verwende LoRA für Domain-Anpassung
LET result = LLM INFER prompt
  WITH LORA 'domain-specific'  -- Smaller, faster
  INSTEAD OF USING MODEL 'giant-model';

-- 4. Limitiere Token-Ausgabe
LET summary = LLM INFER doc
  OPTIONS { max_tokens: 100 };  -- Prevents runaway costs

---

12. Migration Guide

Von v1.3.0 zu v1.3.1

-- BEFORE (v1.3.0)
LET summary = (
  LET embedding = LLM EMBED doc.content;
  LET similar = (
    FOR d IN documents
      FILTER COSINE_SIMILARITY(d.embedding, embedding) > 0.8
      LIMIT 5
      RETURN d
  );
  RETURN LLM INFER CONCAT(
    'Context: ', JOIN(similar[*].content, '\n'),
    '\nQuestion: ', query
  );
);

-- AFTER (v1.3.1) - Using new features
NAMESPACE app.rag;

TYPE RAGContext {
  query: String,
  documents: Array<Object>,
  embedding: Array<Float>
}

CLASS RAGPipeline {
  CONSTRUCTOR(collection: String) {
    THIS.collection = collection;
  }
  
  PUBLIC METHOD execute(query: String) -> String {
    RETURN query
      |> LLM EMBED _
      |> THIS.findSimilar(_, 5)
      |> THIS.generateAnswer(query, _);
  }
  
  PRIVATE METHOD findSimilar(emb: Array<Float>, k: Int) -> Array<Object> {
    FOR doc IN THIS.collection
      FILTER COSINE_SIMILARITY(doc.embedding, emb) > 0.8
      LIMIT k
      RETURN doc;
  }
  
  PRIVATE METHOD generateAnswer(query: String, docs: Array<Object>) -> String {
    RETURN LLM INFER CONCAT(
      'Context: ', JOIN(docs[*].content, '\n'),
      '\nQuestion: ', query
    );
  }
}

-- Usage
LET pipeline = NEW app.rag::RAGPipeline('documents');
LET answer = pipeline.execute('What is ThemisDB?');

---

13. Zusammenfassung der Vorschläge

Feature	Priorität	Komplexität	Nutzen	Status
Namespace System	🔴 Hoch	Mittel	Sehr hoch	📋 Vorschlag
User-Defined Types	🔴 Hoch	Hoch	Sehr hoch	📋 Vorschlag
User-Defined Functions	🔴 Hoch	Mittel	Sehr hoch	📋 Vorschlag
Method Chaining	🟡 Mittel	Niedrig	Hoch	📋 Vorschlag
Vision Extensions	🔴 Hoch	Mittel	Sehr hoch	📋 Vorschlag
Error Handling	🟡 Mittel	Mittel	Hoch	📋 Vorschlag
Classes & Methods	🟢 Niedrig	Sehr hoch	Mittel	📋 Optional
Pattern Matching	🟢 Niedrig	Hoch	Mittel	📋 Optional
Async/Await	🟡 Mittel	Sehr hoch	Hoch	📋 Future
Macros	🟢 Niedrig	Hoch	Niedrig	📋 Optional

---

14. Implementierungsplan

Phase 1: Grundlagen (v1.3.1) - Q1 2026

• ✅ Namespace System
• ✅ User-Defined Functions
• ✅ Basic Type System
• ✅ Method Chaining (Pipeline Operator)

Phase 2: Vision & Error Handling (v1.3.2) - Q2 2026

• ✅ Extended Vision Commands
• ✅ Try-Catch Error Handling
• ✅ Optional Chaining

Phase 3: Advanced Features (v1.4.0) - Q3 2026

• ✅ User-Defined Types (Full)
• ✅ Pattern Matching
• ✅ Classes (Basic)

Phase 4: Performance (v1.5.0) - Q4 2026

• ✅ Async/Await
• ✅ Parallel Execution
• ✅ Advanced Optimization

---

15. Nächste Schritte

1. Review & Diskussion

   • Team-Review dieses Proposals
   • Priorisierung der Features
   • Feedback von Early Adopters

2. Prototyping

   • Parser-Erweiterungen
   • Einfache Implementierung für kritische Features
   • Benchmark & Performance-Tests

3. Dokumentation

   • Erweiterte EBNF-Grammatik
   • API-Dokumentation
   • Tutorial & Beispiele

4. Implementation

   • Schrittweise Umsetzung nach Priorität
   • Backward Compatibility sicherstellen
   • Comprehensive Testing

---

16. Feedback & Beiträge

Dieses Dokument ist ein lebender Vorschlag. Feedback und Verbesserungsvorschläge sind willkommen:

• 📧 GitHub Issues: Diskussion einzelner Features
• 📝 Pull Requests: Konkrete Verbesserungen
• 💬 Community Forum: Allgemeines Feedback

---

Lizenz

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