# 🔍 NLP2CMD vs Azure AI Foundry & OptiMind-SFT - Analiza i Plan Ulepszeń

## 📊 Porównanie Architektur

### Podobieństwa do Azure AI Foundry (formerly Azure AI Studio)

| Cecha | Azure AI Foundry | NLP2CMD | Podobieństwo |
|-------|------------------|---------|--------------|
| **Multi-domain DSL Generation** | Tak (SQL, REST, Python) | Tak (SQL, Shell, Docker, K8s, DQL) | ✅ Wysoki |
| **Model Context Protocol (MCP)** | Tak - oficjalny standard | Brak | ❌ Brak |
| **Visual Prompt Flow** | Tak - drag & drop orchestrator | Brak GUI | ❌ Brak |
| **Action Registry** | Foundry Tools (1400+ connectors) | Action Registry (19 akcji) | 🟡 Podobny koncept |
| **Plan Executor** | Agent Service + Orchestrator | PlanExecutor z foreach/variables | ✅ Podobny |
| **Multi-turn Correction** | Tak - iterative refinement | FeedbackAnalyzer | 🟡 Podstawowy |
| **Schema Validation** | Foundry IQ + built-in | SchemaRegistry (11 formatów) | ✅ Podobny |
| **Safety Policies** | Defender + Entra ID | SafetyPolicy per adapter | 🟡 Podstawowy |

### Podobieństwa do OptiMind-SFT

| Cecha | OptiMind-SFT | NLP2CMD | Podobieństwo |
|-------|--------------|---------|--------------|
| **NL → Executable Code** | Tak (→ GurobiPy/MILP) | Tak (→ SQL/Shell/Docker/K8s) | ✅ Wysoki |
| **Domain-specific Hints** | Class-based error hints | Domain adapters | ✅ Podobny |
| **Self-correction Loop** | Multi-turn z solver feedback | FeedbackLoop + Validators | 🟡 Podstawowy |
| **Expert-aligned Training** | SFT na cleaned datasets | Brak treningu | ❌ Brak |
| **Majority Voting** | K=8 samples, best-of-N | Brak | ❌ Brak |
| **Structured Output** | Mathematical formulation + code | ExecutionPlan + DSL command | ✅ Podobny |
| **Intermediate Reasoning** | Chain-of-thought przed kodem | Brak explicit reasoning | ❌ Brak |

---

## 🚀 Lista Ulepszeń (Improvements) dla NLP2CMD v0.3.0+

### 🔴 KRYTYCZNE (High Priority)

#### 1. **Model Context Protocol (MCP) Support**
```
Status: BRAK
Priorytet: KRYTYCZNY
Opis: MCP to standard Azure/Anthropic dla łączenia AI z narzędziami
```
**Zadania:**
- [ ] Implementacja MCP server dla NLP2CMD
- [ ] MCP client do integracji z innymi toolami
- [ ] Wsparcie dla tool definitions w formacie MCP
- [ ] Auto-discovery narzędzi przez MCP

#### 2. **LLM Integration Layer**
```
Status: Częściowy (LLMPlanner stub)
Priorytet: KRYTYCZNY
Opis: Brak rzeczywistej integracji z LLM dla NL parsing
```
**Zadania:**
- [ ] Adapter dla OpenAI/Azure OpenAI API
- [ ] Adapter dla lokalnych modeli (Ollama, vLLM)
- [ ] Adapter dla Anthropic Claude API
- [ ] Structured output z JSON schema validation
- [ ] Retry logic z exponential backoff
- [ ] Token counting i cost tracking

#### 3. **Multi-turn Self-Correction (jak OptiMind)**
```
Status: Podstawowy (FeedbackAnalyzer)
Priorytet: KRYTYCZNY
Opis: Iteracyjna korekcja błędów z feedback od executora
```
**Zadania:**
- [ ] Execution feedback loop (execute → validate → correct → repeat)
- [ ] Class-specific error hints per domain
- [ ] Max turns configuration
- [ ] Early stopping na success
- [ ] Error pattern learning

---

### 🟡 WAŻNE (Medium Priority)

#### 4. **Majority Voting / Self-Consistency**
```
Status: BRAK
Priorytet: WYSOKI
Opis: Generowanie K kandydatów, wybór najczęstszego
```
**Zadania:**
- [ ] Multiple candidate generation (K samples)
- [ ] Result clustering/grouping
- [ ] Consensus voting mechanism
- [ ] Confidence boosting przez agreement
- [ ] Configurable K parameter

#### 5. **Chain-of-Thought Reasoning**
```
Status: BRAK
Priorytet: WYSOKI
Opis: Intermediate reasoning przed generowaniem kodu
```
**Zadania:**
- [ ] Structured reasoning format
- [ ] Step-by-step problem decomposition
- [ ] Domain-specific reasoning templates
- [ ] Reasoning trace w wynikach

#### 6. **Visual Prompt Flow (GUI)**
```
Status: BRAK
Priorytet: ŚREDNI
Opis: Drag & drop interface dla budowania workflows
```
**Zadania:**
- [ ] Web UI z React/Vue
- [ ] Visual plan builder
- [ ] Step connections i dependencies
- [ ] Real-time execution preview
- [ ] Export do Python/YAML

#### 7. **Expanded Action Registry**
```
Status: 19 akcji
Priorytet: ŚREDNI
Cel: 100+ akcji (jak Azure 1400 connectors)
```
**Zadania:**
- [ ] Git actions (commit, push, branch, merge)
- [ ] HTTP/REST actions (GET, POST, PUT, DELETE)
- [ ] File system actions (read, write, copy, move)
- [ ] Cloud actions (AWS CLI, GCP, Azure)
- [ ] Database actions (migrations, backups)
- [ ] Monitoring actions (metrics, alerts)
- [ ] CI/CD actions (GitHub Actions, GitLab CI)

#### 8. **Streaming Output**
```
Status: BRAK
Priorytet: ŚREDNI
Opis: Real-time streaming wyników
```
**Zadania:**
- [ ] Async generators dla step results
- [ ] WebSocket support
- [ ] Progress callbacks
- [ ] Partial results display

---

### 🟢 ULEPSZENIA (Nice to Have)

#### 9. **Plugin System**
```
Status: BRAK
Priorytet: NISKI
Opis: Dynamiczne ładowanie nowych adapterów/akcji
```
**Zadania:**
- [ ] Plugin discovery mechanism
- [ ] Plugin manifest format
- [ ] Hot-reload capability
- [ ] Plugin marketplace concept

#### 10. **Telemetry & Observability**
```
Status: Podstawowy (trace_id, duration)
Priorytet: NISKI
Opis: Pełna integracja z OpenTelemetry
```
**Zadania:**
- [ ] OpenTelemetry spans i traces
- [ ] Metrics export (Prometheus)
- [ ] Structured logging (JSON)
- [ ] Dashboard templates (Grafana)

#### 11. **Cost Estimation**
```
Status: BRAK
Priorytet: NISKI
Opis: Szacowanie kosztów przed wykonaniem
```
**Zadania:**
- [ ] Token cost estimation dla LLM calls
- [ ] Resource usage prediction
- [ ] Billing alerts

#### 12. **Caching Layer**
```
Status: BRAK
Priorytet: NISKI
Opis: Cache dla powtarzalnych zapytań
```
**Zadania:**
- [ ] Semantic similarity cache
- [ ] TTL-based invalidation
- [ ] Redis/Memory backends

#### 13. **Batch Processing**
```
Status: BRAK
Priorytet: NISKI
Opis: Przetwarzanie wielu requestów naraz
```
**Zadania:**
- [ ] Batch API endpoint
- [ ] Parallel execution
- [ ] Rate limiting
- [ ] Progress tracking

---

## 📋 Priorytety Implementacji

### Faza 1: v0.3.0 (Core LLM Integration)
1. ✅ LLM Integration Layer (OpenAI, Claude, Ollama)
2. ✅ Multi-turn Self-Correction
3. ✅ Majority Voting

### Faza 2: v0.4.0 (Protocol Support)
1. ✅ MCP Support
2. ✅ Chain-of-Thought Reasoning
3. ✅ Streaming Output

### Faza 3: v0.5.0 (Scale)
1. ✅ Expanded Action Registry (100+ akcji)
2. ✅ Plugin System
3. ✅ Telemetry & Observability

### Faza 4: v1.0.0 (Production)
1. ✅ Visual Prompt Flow GUI
2. ✅ Cost Estimation
3. ✅ Caching Layer
4. ✅ Batch Processing

---

## 🎯 Kluczowe Różnice do Zaadresowania

### vs Azure AI Foundry
| Gap | Wpływ | Rozwiązanie |
|-----|-------|-------------|
| Brak MCP | Izolacja od ekosystemu | Implementacja MCP server/client |
| Brak GUI | Słaba UX dla non-devs | React-based visual builder |
| 19 vs 1400 akcji | Ograniczone use cases | Plugin system + community |
| Brak governance | Enterprise concerns | Role-based access + audit logs |

### vs OptiMind-SFT
| Gap | Wpływ | Rozwiązanie |
|-----|-------|-------------|
| Brak fine-tuned model | Niższa accuracy | LLM integration + domain hints |
| Brak majority voting | Single point of failure | K-sample voting |
| Brak CoT reasoning | Błędy w złożonych przypadkach | Structured reasoning templates |
| Brak expert hints | Generic errors | Domain-specific error catalogs |

---

## 📊 Metryki Sukcesu

| Metryka | Obecna | Cel v0.3.0 | Cel v1.0.0 |
|---------|--------|------------|------------|
| Akcje w Registry | 19 | 50 | 200+ |
| Wspierane LLM | 0 | 3 | 10+ |
| Test coverage | ~75% | 85% | 95% |
| Self-correction turns | 0 | 5 | 10 |
| Accuracy (estimated) | ~60% | 75% | 90% |
| Execution time | <1s | <2s | <5s |

---

## 🔗 Inspiracje z Innych Projektów

1. **LangChain** - chain composition, tool calling
2. **LlamaIndex** - structured data extraction
3. **Instructor** - pydantic-based LLM outputs
4. **Outlines** - constrained generation
5. **DSPy** - programmatic LLM pipelines
6. **Marvin** - AI functions for Python

---

## 📝 Notatki Techniczne

### MCP Implementation Sketch
```python
class MCPServer:
    """NLP2CMD as MCP tool provider."""
    
    def list_tools(self) -> list[ToolDefinition]:
        return self.registry.to_mcp_format()
    
    def call_tool(self, name: str, params: dict) -> ToolResult:
        action = self.registry.get(name)
        return self.executor.execute_single(action, params)
```

### Multi-turn Correction Sketch
```python
class SelfCorrector:
    def correct(self, plan: ExecutionPlan, max_turns: int = 5):
        for turn in range(max_turns):
            result = self.executor.execute(plan)
            if result.success:
                return result
            
            errors = self.analyzer.extract_errors(result)
            hints = self.hint_generator.for_domain(plan.domain)
            plan = self.llm.regenerate(plan, errors, hints)
        
        return result  # Best effort
```

### Majority Voting Sketch
```python
class MajorityVoter:
    def vote(self, prompt: str, k: int = 8) -> Result:
        candidates = [self.llm.generate(prompt) for _ in range(k)]
        groups = self.cluster_by_similarity(candidates)
        best_group = max(groups, key=len)
        return self.select_representative(best_group)
```
