function-calling

# Function Calling

Enable LLMs to use external tools and return structured data.

## Basic Tool Definition (2026 Best Practice)

```python
# OpenAI format with strict mode (2026 recommended)
tools = [{
    "type": "function",
    "function": {
        "name": "search_documents",
        "description": "Search the document database for relevant content",
        "strict": True,  # ← 2026: Enables structured output validation
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query"
                },
                "limit": {
                    "type": "integer",
                    "description": "Max results to return"
                }
            },
            "required": ["query", "limit"],  # All props required when strict
            "additionalProperties": False     # ← 2026: Required for strict mode
        }
    }
}]

# Note: With strict=True:
# - All properties must be listed in "required"
# - additionalProperties must be False
# - No "default" values (provide via code instead)
```

## Tool Execution Loop

```python
async def run_with_tools(messages: list, tools: list) -> str:
    """Execute tool calls until LLM returns final answer."""
    while True:
        response = await llm.chat(messages=messages, tools=tools)

        # Check if LLM wants to call tools
        if not response.tool_calls:
            return response.content

        # Execute each tool call
        for tool_call in response.tool_calls:
            result = await execute_tool(
                tool_call.function.name,
                json.loads(tool_call.function.arguments)
            )

            # Add tool result to conversation
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })

        # Continue loop (LLM will proces