# Intégration de Foundry Local en tant qu'outils

Un cadre complet pour intégrer Microsoft Foundry Local en tant qu'outils appelables dans des applications plus vastes, conformément aux modèles officiels de Microsoft pour l'intégration d'IA basée sur des outils.

## Aperçu

Cet exemple montre comment exposer les modèles Foundry Local en tant qu'outils réutilisables pouvant être intégrés dans des applications, des flux de travail et des environnements de développement existants. Il illustre les modèles recommandés par Microsoft pour l'intégration d'outils et l'appel de fonctions.

## Concepts clés

### 🔧 **Architecture centrée sur les outils**
- Modèles Foundry Local en tant que fonctions appelables
- Interfaces et schémas d'outils standardisés
- Intégration fluide avec les bases de code existantes
- Définitions d'outils sécurisées par type et validation

### ⚡ **Modèles d'appel de fonctions**
- Implémentation de l'appel de fonctions Microsoft Foundry Local
- Définitions d'outils compatibles avec OpenAI
- Validation et conversion automatique des paramètres
- Gestion des erreurs et formatage des réponses

### 🔌 **Cadres d'intégration**
- **Intégration LangChain** : Support natif des outils LangChain
- **Semantic Kernel** : Fonctions Microsoft Semantic Kernel
- **API REST** : Points de terminaison basés sur HTTP
- **Outils CLI** : Intégration via interface en ligne de commande
- **Jupyter Notebooks** : Outils de développement interactifs

### 🎯 **Modèles de cas d'utilisation**
- Outils d'analyse et de génération de code
- Traitement et synthèse de contenu
- Analyse et visualisation de données
- Recherche et récupération d'informations
- Systèmes d'aide à la décision

## Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                    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                │
└─────────────────────────────────────────────────────────────────┘
```

## Prérequis

### Configuration système
- **Python** : 3.9+ avec support asyncio
- **Node.js** : v18+ (pour les intégrations JavaScript)
- **Mémoire** : 12 Go ou plus recommandé
- **Stockage** : 10 Go ou plus pour les modèles et outils

### Dépendances principales
```bash
pip install foundry-local-sdk openai langchain semantic-kernel fastapi uvicorn typer rich
```

### Dépendances spécifiques au cadre
```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
```

## Démarrage rapide

### 1. Création d'un outil de base
```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. Intégration 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. Intégration API REST
```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"
# }
```

## Structure du projet

```
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
```

## Modèles d'outils principaux

### 1. Outils basés sur des fonctions
```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. Outils basés sur des classes
```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. Outils 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
            }
```

## Exemples d'intégration

### Intégration 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"
})
```

### Intégration 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"
)
```

### Intégration 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
    }
```

### Intégration en ligne de commande
```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()
```

## Modèles avancés

### 1. Composition d'outils
```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. Outils sensibles au contexte
```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. Chaînage d'outils
```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"
})
```

## Optimisation des performances

### 1. Stratégies de mise en cache
```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. Gestion des pools de modèles
```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. Traitement par lots
```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
```

## Surveillance et observabilité

### 1. Métriques des outils
```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. Surveillance de la santé
```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. Analyse de l'utilisation
```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}")
```

## Résultats d'apprentissage

Après avoir complété cet exemple, vous comprendrez :

1. **Modèles d'intégration d'outils**
   - Conception d'outils basés sur des fonctions et des classes
   - Modèles d'intégration Microsoft Foundry Local
   - Validation des schémas et sécurité des types
   - Gestion des erreurs et récupération

2. **Intégration des cadres**
   - Développement d'outils LangChain
   - Intégration des fonctions Semantic Kernel
   - Intégration des cadres API REST
   - Développement d'applications CLI

3. **Considérations pour la production**
   - Stratégies d'optimisation des performances
   - Gestion de la mise en cache et des ressources
   - Surveillance et observabilité
   - Sécurité et validation

4. **Modèles d'outils avancés**
   - Composition et chaînage d'outils
   - Traitement sensible au contexte
   - Opérations par lots et en streaming
   - Développement d'intégrations personnalisées

## Prochaines étapes

- **Projets d'intégration** : Construisez des intégrations personnalisées avec vos cadres préférés
- **Développement d'outils** : Créez des outils spécialisés pour votre domaine
- **Optimisation des performances** : Adaptez les outils à vos cas d'utilisation spécifiques
- **Déploiement en production** : Faites évoluer les outils pour un usage en entreprise

## Contribution

Consultez les directives du dépôt principal pour les instructions de contribution.

## Licence

Cet exemple suit la même licence que le projet Microsoft Foundry Local.

---