# Foundry Local ca Integrare de Instrumente

Un cadru cuprinzător pentru integrarea Microsoft Foundry Local ca instrumente apelabile în aplicații mai mari, urmând modelele oficiale Microsoft pentru integrarea AI bazată pe instrumente.

## Prezentare Generală

Acest exemplu demonstrează cum să expuneți modelele Foundry Local ca instrumente reutilizabile care pot fi integrate în aplicații, fluxuri de lucru și medii de dezvoltare existente. Prezintă modelele recomandate de Microsoft pentru integrarea instrumentelor și apelarea funcțiilor.

## Concepte Cheie

### 🔧 **Arhitectură Centrată pe Instrumente**
- Modelele Foundry Local ca funcții apelabile
- Interfețe și scheme standardizate pentru instrumente
- Integrare fără probleme în codurile existente
- Definiții de instrumente sigure din punct de vedere al tipurilor și validare

### ⚡ **Modele de Apelare a Funcțiilor**
- Implementarea apelării funcțiilor Microsoft Foundry Local
- Definiții de instrumente compatibile cu OpenAI
- Validarea automată a parametrilor și conversie
- Gestionarea erorilor și formatarea răspunsurilor

### 🔌 **Cadre de Integrare**
- **Integrare LangChain**: Suport nativ pentru instrumentele LangChain
- **Semantic Kernel**: Funcții Microsoft Semantic Kernel
- **REST API**: Endpoint-uri bazate pe HTTP
- **Instrumente CLI**: Integrare prin interfața de linie de comandă
- **Jupyter Notebooks**: Instrumente interactive de dezvoltare

### 🎯 **Modele de Utilizare**
- Instrumente pentru analiză și generare de cod
- Procesarea și sumarizarea conținutului
- Analiza și vizualizarea datelor
- Cercetare și recuperare de informații
- Sisteme de suport decizional

## Arhitectură

```
┌─────────────────────────────────────────────────────────────────┐
│                    Application Layer                            │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │  LangChain  │  │  Semantic   │  │  Custom     │            │
│  │    Tools    │  │   Kernel    │  │    Apps     │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
└─────────────────┬─────────────────┬─────────────────────────────┘
                  │                 │
                  ▼                 ▼
┌─────────────────────────────────────────────────────────────────┐
│                    Tool Integration Layer                       │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │ Function    │  │   REST      │  │    CLI      │            │
│  │ Registry    │  │  Gateway    │  │  Interface  │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
└─────────────────────────┬───────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────────┐
│                Microsoft Foundry Local Service                  │
│                                                                 │
│ • Model Management        • Function Calling Support           │
│ • Inference Engine        • Tool Schema Validation             │
│ • Context Handling        • Response Formatting                │
└─────────────────────────────────────────────────────────────────┘
```

## Cerințe Prealabile

### Cerințe de Sistem
- **Python**: 3.9+ cu suport pentru asyncio
- **Node.js**: v18+ (pentru integrări JavaScript)
- **Memorie**: Recomandat 12GB+
- **Stocare**: 10GB+ pentru modele și instrumente

### Dependențe de Bază
```bash
pip install foundry-local-sdk openai langchain semantic-kernel fastapi uvicorn typer rich
```

### Dependențe Specifice Cadrelor
```bash
# LangChain integration
pip install langchain-openai langchain-community

# Semantic Kernel integration  
pip install semantic-kernel

# Web framework integration
pip install fastapi uvicorn streamlit gradio

# Development tools
pip install jupyter ipywidgets
```

## Ghid Rapid

### 1. Crearea de Instrumente de Bază
```python
from foundry_tools import FoundryTool, FoundryToolRegistry

# Create a simple analysis tool
@FoundryTool(
    name="code_analyzer",
    description="Analyze code quality and suggest improvements",
    model="phi-4-mini"
)
async def analyze_code(code: str, language: str = "python") -> dict:
    """Analyze code and return quality metrics and suggestions."""
    pass

# Register and use the tool
registry = FoundryToolRegistry()
await registry.register(analyze_code)

result = await registry.call("code_analyzer", {
    "code": "def hello(): print('world')",
    "language": "python"
})
```

### 2. Integrarea LangChain
```python
from langchain.tools import BaseTool
from foundry_tools.langchain import FoundryLangChainTool

# Create LangChain-compatible tool
class CodeAnalyzerTool(FoundryLangChainTool):
    name = "code_analyzer"
    description = "Analyze code quality using Foundry Local"
    model = "phi-4-mini"
    
    async def _arun(self, code: str, language: str = "python") -> str:
        return await self.foundry_call({
            "code": code,
            "language": language
        })

# Use with LangChain agents
from langchain.agents import initialize_agent, AgentType

tools = [CodeAnalyzerTool()]
agent = initialize_agent(
    tools=tools,
    llm=None,  # Uses Foundry Local
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
```

### 3. Integrarea REST API
```python
from fastapi import FastAPI
from foundry_tools.rest import FoundryRESTEndpoint

app = FastAPI()

# Auto-generate REST endpoints from Foundry tools
foundry_api = FoundryRESTEndpoint()
await foundry_api.register_tool("code_analyzer", analyze_code)

# Mount endpoints
app.include_router(foundry_api.router, prefix="/foundry/v1")

# Use via HTTP
# POST /foundry/v1/code_analyzer
# {
#   "code": "def hello(): print('world')",
#   "language": "python"
# }
```

## Structura Proiectului

```
10/
├── README.md                    # This documentation
├── requirements.txt             # Python dependencies
├── foundry_tools/
│   ├── __init__.py             # Package initialization
│   ├── core/
│   │   ├── __init__.py
│   │   ├── tool_base.py        # Base tool implementation
│   │   ├── registry.py         # Tool registry
│   │   ├── validation.py       # Schema validation
│   │   └── client.py           # Foundry Local client
│   ├── integrations/
│   │   ├── __init__.py
│   │   ├── langchain.py        # LangChain integration
│   │   ├── semantic_kernel.py  # Semantic Kernel integration
│   │   ├── rest_api.py         # REST API framework
│   │   ├── cli.py              # Command-line interface
│   │   └── jupyter.py          # Jupyter notebook tools
│   ├── frameworks/
│   │   ├── __init__.py
│   │   ├── fastapi_tools.py    # FastAPI integration
│   │   ├── streamlit_tools.py  # Streamlit integration
│   │   ├── gradio_tools.py     # Gradio integration
│   │   └── flask_tools.py      # Flask integration
│   └── tools/
│       ├── __init__.py
│       ├── code_tools.py       # Code analysis tools
│       ├── content_tools.py    # Content processing tools
│       ├── data_tools.py       # Data analysis tools
│       ├── research_tools.py   # Research and retrieval tools
│       └── decision_tools.py   # Decision support tools
├── examples/
│   ├── basic_tools.py          # Simple tool examples
│   ├── langchain_demo.py       # LangChain integration
│   ├── semantic_kernel_demo.py # Semantic Kernel demo
│   ├── rest_api_server.py      # REST API server
│   ├── cli_application.py      # CLI application
│   ├── jupyter_notebook.ipynb  # Interactive notebook
│   ├── streamlit_app.py        # Streamlit application
│   └── production_deployment.py # Production patterns
├── integrations/
│   ├── vscode_extension/       # VS Code extension
│   ├── github_actions/         # CI/CD workflows
│   ├── azure_functions/        # Serverless deployment
│   └── docker_containers/      # Containerization
└── tests/
    ├── test_tools.py           # Tool tests
    ├── test_integrations.py    # Integration tests
    └── test_frameworks.py      # Framework tests
```

## Modele de Instrumente de Bază

### 1. Instrumente Bazate pe Funcții
```python
from foundry_tools import FoundryTool
from typing import List, Dict, Any

@FoundryTool(
    name="summarize_content",
    description="Summarize long-form content into key points",
    model="phi-4-mini",
    parameters={
        "content": {"type": "string", "description": "Content to summarize"},
        "max_points": {"type": "integer", "description": "Maximum summary points", "default": 5},
        "style": {"type": "string", "description": "Summary style", "enum": ["bullet", "paragraph", "outline"]}
    }
)
async def summarize_content(
    content: str, 
    max_points: int = 5, 
    style: str = "bullet"
) -> Dict[str, Any]:
    """Summarize content using Foundry Local model."""
    
    # The decorator automatically handles:
    # - Parameter validation
    # - Foundry Local client setup
    # - Error handling and logging
    # - Response formatting
    
    system_prompt = f"""
    Summarize the following content into {max_points} key points.
    Use {style} format for the summary.
    """
    
    # This gets automatically routed to Foundry Local
    return {
        "summary": "Generated summary here...",
        "points": max_points,
        "style": style,
        "word_count": len(content.split())
    }
```

### 2. Instrumente Bazate pe Clase
```python
from foundry_tools.core import BaseFoundryTool

class CodeAnalysisTool(BaseFoundryTool):
    """Advanced code analysis tool with state management."""
    
    name = "advanced_code_analyzer"
    description = "Perform comprehensive code analysis"
    model = "phi-4-mini"
    
    def __init__(self):
        super().__init__()
        self.analysis_cache = {}
        self.supported_languages = ["python", "javascript", "typescript", "java", "csharp"]
    
    async def validate_input(self, **kwargs) -> bool:
        """Custom input validation."""
        language = kwargs.get("language", "").lower()
        return language in self.supported_languages
    
    async def execute(self, code: str, language: str, analysis_type: str = "full") -> Dict[str, Any]:
        """Execute code analysis."""
        
        # Check cache
        cache_key = f"{hash(code)}_{language}_{analysis_type}"
        if cache_key in self.analysis_cache:
            return self.analysis_cache[cache_key]
        
        # Perform analysis using Foundry Local
        result = await self.foundry_call({
            "system_prompt": f"Analyze this {language} code for {analysis_type} analysis",
            "user_prompt": f"Code to analyze:\n\n```{language}\n{code}\n```",
            "max_tokens": 1000
        })
        
        # Process and cache result
        analysis_result = self.process_analysis_result(result, analysis_type)
        self.analysis_cache[cache_key] = analysis_result
        
        return analysis_result
    
    def process_analysis_result(self, raw_result: str, analysis_type: str) -> Dict[str, Any]:
        """Process the raw analysis result into structured data."""
        # Implementation here
        pass
```

### 3. Instrumente de Streaming
```python
from foundry_tools import StreamingFoundryTool
from typing import AsyncGenerator

@StreamingFoundryTool(
    name="code_generator",
    description="Generate code with real-time streaming",
    model="qwen2.5-coder-0.5b"
)
async def generate_code(
    specification: str,
    language: str = "python",
    include_tests: bool = False
) -> AsyncGenerator[Dict[str, Any], None]:
    """Generate code with streaming responses."""
    
    # Yield metadata first
    yield {
        "type": "metadata",
        "language": language,
        "include_tests": include_tests,
        "estimated_lines": 50
    }
    
    # Stream code generation
    async for chunk in foundry_stream({
        "prompt": f"Generate {language} code: {specification}",
        "stream": True
    }):
        yield {
            "type": "code_chunk",
            "content": chunk.content,
            "complete": chunk.finish_reason is not None
        }
    
    # Yield final result
    if include_tests:
        async for test_chunk in foundry_stream({
            "prompt": f"Generate unit tests for the above {language} code",
            "stream": True
        }):
            yield {
                "type": "test_chunk", 
                "content": test_chunk.content,
                "complete": test_chunk.finish_reason is not None
            }
```

## Exemple de Integrare

### Integrarea LangChain
```python
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain.prompts import ChatPromptTemplate
from foundry_tools.langchain import FoundryToolkit

# Create Foundry-powered toolkit
toolkit = FoundryToolkit()
toolkit.add_tool("code_analyzer", model="phi-4-mini")
toolkit.add_tool("content_summarizer", model="qwen2.5-0.5b") 
toolkit.add_tool("research_assistant", model="phi-3.5-mini")

# Create agent with Foundry tools
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to Foundry Local tools."),
    ("user", "{input}"),
    ("assistant", "{agent_scratchpad}")
])

agent = create_openai_functions_agent(
    llm=toolkit.get_llm(),  # Uses Foundry Local as LLM
    tools=toolkit.get_tools(),
    prompt=prompt
)

agent_executor = AgentExecutor(agent=agent, tools=toolkit.get_tools())

# Use the agent
result = await agent_executor.ainvoke({
    "input": "Analyze this Python code and summarize any issues you find"
})
```

### Integrarea Semantic Kernel
```python
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from foundry_tools.semantic_kernel import FoundryKernelPlugin

# Initialize kernel with Foundry Local
kernel = Kernel()

# Add Foundry Local as chat service
foundry_service = OpenAIChatCompletion(
    service_id="foundry_chat",
    ai_model_id="phi-4-mini",
    api_key="not-needed",
    base_url="http://localhost:5273/v1"
)
kernel.add_service(foundry_service)

# Create and add Foundry plugin
foundry_plugin = FoundryKernelPlugin()
foundry_plugin.add_function("analyze_code", model="phi-4-mini")
foundry_plugin.add_function("summarize_text", model="qwen2.5-0.5b")

kernel.add_plugin(foundry_plugin, plugin_name="foundry_tools")

# Use in Semantic Kernel workflows
result = await kernel.invoke(
    "foundry_tools", 
    "analyze_code",
    code="def hello(): print('world')",
    language="python"
)
```

### Integrarea FastAPI
```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from foundry_tools.rest import FoundryRESTFramework

app = FastAPI(title="Foundry Local Tools API")

# Initialize Foundry REST framework
foundry_framework = FoundryRESTFramework()

# Auto-register all available tools
await foundry_framework.auto_register_tools([
    "code_analyzer",
    "content_summarizer", 
    "data_processor",
    "research_assistant"
])

# Mount Foundry endpoints
app.include_router(
    foundry_framework.get_router(),
    prefix="/api/v1/foundry",
    tags=["foundry-tools"]
)

# Custom endpoint using Foundry tools
class AnalysisRequest(BaseModel):
    code: str
    language: str = "python"

@app.post("/api/v1/analyze")
async def analyze_code_endpoint(request: AnalysisRequest):
    try:
        result = await foundry_framework.call_tool(
            "code_analyzer",
            code=request.code,
            language=request.language
        )
        return {"success": True, "analysis": result}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# Health check endpoint
@app.get("/api/v1/health")
async def health_check():
    status = await foundry_framework.get_health_status()
    return {
        "foundry_status": status.foundry_running,
        "active_models": status.loaded_models,
        "available_tools": status.available_tools
    }
```

### Integrarea prin Linia de Comandă
```python
import typer
from rich.console import Console
from rich.table import Table
from foundry_tools.cli import FoundryCLI

app = typer.Typer(name="foundry-tools")
console = Console()
foundry_cli = FoundryCLI()

@app.command()
async def analyze(
    file_path: str = typer.Argument(..., help="Path to code file"),
    language: str = typer.Option("python", help="Programming language"),
    output: str = typer.Option("table", help="Output format (table, json, yaml)")
):
    """Analyze code file using Foundry Local."""
    
    try:
        with open(file_path, 'r') as f:
            code = f.read()
        
        result = await foundry_cli.call_tool(
            "code_analyzer",
            code=code,
            language=language
        )
        
        if output == "table":
            table = Table(title=f"Code Analysis: {file_path}")
            table.add_column("Metric", style="cyan")
            table.add_column("Value", style="magenta")
            
            for key, value in result.items():
                table.add_row(key, str(value))
            
            console.print(table)
        
        elif output == "json":
            console.print_json(data=result)
        
        else:
            console.print(result)
            
    except Exception as e:
        console.print(f"[red]Error: {e}[/red]")
        raise typer.Exit(1)

@app.command()
async def list_tools():
    """List all available Foundry tools."""
    
    tools = await foundry_cli.list_available_tools()
    
    table = Table(title="Available Foundry Tools")
    table.add_column("Name", style="cyan")
    table.add_column("Description", style="white")
    table.add_column("Model", style="yellow")
    
    for tool in tools:
        table.add_row(
            tool["name"],
            tool["description"][:50] + "..." if len(tool["description"]) > 50 else tool["description"],
            tool["model"]
        )
    
    console.print(table)

if __name__ == "__main__":
    app()
```

## Modele Avansate

### 1. Compoziția Instrumentelor
```python
from foundry_tools import CompositeFoundryTool

@CompositeFoundryTool(
    name="full_code_review",
    description="Comprehensive code review using multiple analysis tools"
)
async def comprehensive_code_review(code: str, language: str = "python") -> Dict[str, Any]:
    """Perform comprehensive code review using multiple tools."""
    
    # Run multiple analyses in parallel
    analyses = await asyncio.gather(
        call_tool("code_analyzer", code=code, language=language),
        call_tool("security_scanner", code=code, language=language),
        call_tool("performance_analyzer", code=code, language=language),
        call_tool("style_checker", code=code, language=language)
    )
    
    # Synthesize results
    return await call_tool("analysis_synthesizer", analyses=analyses)
```

### 2. Instrumente Conștiente de Context
```python
from foundry_tools.context import ContextAwareFoundryTool

class ProjectAnalyzerTool(ContextAwareFoundryTool):
    """Analyze entire project with context awareness."""
    
    async def execute(self, project_path: str, analysis_depth: str = "shallow") -> Dict[str, Any]:
        """Analyze project with full context."""
        
        # Build project context
        context = await self.build_project_context(project_path)
        
        # Analyze with context
        return await self.foundry_call_with_context({
            "prompt": f"Analyze this {context.language} project",
            "context": context.to_dict(),
            "analysis_depth": analysis_depth
        })
    
    async def build_project_context(self, project_path: str) -> ProjectContext:
        """Build comprehensive project context."""
        # Implementation here
        pass
```

### 3. Lanțuri de Instrumente
```python
from foundry_tools.chains import FoundryToolChain

# Define a tool chain for document processing
doc_processing_chain = FoundryToolChain([
    ("extract_text", {"input": "document_path"}),
    ("summarize_content", {"input": "extracted_text", "style": "outline"}),
    ("generate_keywords", {"input": "summary"}),
    ("create_metadata", {"input": ["summary", "keywords"]})
])

# Execute the chain
result = await doc_processing_chain.execute({
    "document_path": "/path/to/document.pdf"
})
```

## Optimizarea Performanței

### 1. Strategii de Caching
```python
from foundry_tools.cache import CacheConfig, CacheStrategy

cache_config = CacheConfig(
    strategy=CacheStrategy.LRU,
    max_size=1000,
    ttl=3600,  # 1 hour
    key_generator="content_hash"
)

# Apply to specific tools
@FoundryTool(
    name="cached_analyzer",
    cache_config=cache_config
)
async def cached_code_analyzer(code: str) -> Dict[str, Any]:
    # Expensive analysis that benefits from caching
    pass
```

### 2. Gestionarea Pool-ului de Modele
```python
from foundry_tools.pool import ModelPoolConfig

pool_config = ModelPoolConfig(
    models={
        "phi-4-mini": {"instances": 2, "priority": "high"},
        "qwen2.5-coder-0.5b": {"instances": 1, "priority": "medium"},
        "phi-3.5-mini": {"instances": 1, "priority": "low"}
    },
    load_balancing="round_robin",
    health_check_interval=30
)

# Configure tool registry with pool
registry = FoundryToolRegistry(model_pool_config=pool_config)
```

### 3. Procesare în Loturi
```python
from foundry_tools.batch import BatchProcessor

@BatchProcessor(
    batch_size=10,
    timeout=60,
    parallel_batches=3
)
async def batch_code_analysis(code_files: List[str]) -> List[Dict[str, Any]]:
    """Process multiple code files in batches."""
    results = []
    
    for code_file in code_files:
        with open(code_file, 'r') as f:
            code = f.read()
        
        result = await call_tool("code_analyzer", code=code)
        results.append(result)
    
    return results
```

## Monitorizare și Observabilitate

### 1. Metrice ale Instrumentelor
```python
from foundry_tools.monitoring import ToolMetrics

# Automatic metrics collection
metrics = await ToolMetrics.get_tool_performance("code_analyzer")
print(f"Average execution time: {metrics.avg_execution_time}s")
print(f"Success rate: {metrics.success_rate}%")
print(f"Cache hit rate: {metrics.cache_hit_rate}%")
```

### 2. Monitorizarea Sănătății
```python
from foundry_tools.health import HealthMonitor

health_monitor = HealthMonitor()

# Monitor tool health
health_status = await health_monitor.check_all_tools()
print(f"Healthy tools: {health_status.healthy_count}")
print(f"Failed tools: {health_status.failed_tools}")
```

### 3. Analiza Utilizării
```python
from foundry_tools.analytics import UsageAnalytics

analytics = UsageAnalytics()

# Track tool usage patterns
usage_report = await analytics.generate_usage_report(
    start_date="2024-01-01",
    end_date="2024-01-31"
)

print(f"Most used tool: {usage_report.most_used_tool}")
print(f"Peak usage time: {usage_report.peak_usage_time}")
```

## Rezultate ale Învățării

După finalizarea acestui exemplu, veți înțelege:

1. **Modele de Integrare a Instrumentelor**
   - Design bazat pe funcții și clase pentru instrumente
   - Modele de integrare Microsoft Foundry Local
   - Validarea schemelor și siguranța tipurilor
   - Gestionarea erorilor și recuperarea

2. **Integrarea Cadrelor**
   - Dezvoltarea instrumentelor LangChain
   - Integrarea funcțiilor Semantic Kernel
   - Integrarea cadrelor REST API
   - Dezvoltarea aplicațiilor CLI

3. **Considerații pentru Producție**
   - Strategii de optimizare a performanței
   - Gestionarea caching-ului și a resurselor
   - Monitorizare și observabilitate
   - Securitate și validare

4. **Modele Avansate de Instrumente**
   - Compoziția și lanțurile de instrumente
   - Procesare conștientă de context
   - Operațiuni în loturi și streaming
   - Dezvoltarea integrărilor personalizate

## Pași Următori

- **Proiecte de Integrare**: Construiți integrări personalizate cu cadrele preferate
- **Dezvoltarea Instrumentelor**: Creați instrumente specializate pentru domeniul dvs.
- **Ajustarea Performanței**: Optimizați pentru cazurile dvs. specifice de utilizare
- **Implementare în Producție**: Scalați instrumentele pentru utilizare la nivel de întreprindere

## Contribuții

Consultați ghidurile din depozitul principal pentru instrucțiuni de contribuție.

## Licență

Acest exemplu urmează aceeași licență ca proiectul Microsoft Foundry Local.

---