Add tool/function call structure extraction

Automatically extracts and captures full tool call structure from LLM responses:

**What's Captured:**

OpenAI Format:
- gen_ai.tool_calls.count - Number of tool calls
- gen_ai.tool.name - First tool name (for filtering)
- Events for each tool call with full structure:
  - tool_call.id (e.g., "call_abc123")
  - tool_call.type ("function")
  - tool_call.function.name ("get_weather")
  - tool_call.function.arguments (JSON string)

Anthropic Format:
- gen_ai.tool_calls.count - Number of tool uses
- gen_ai.tool.name - First tool name
- Events for each tool use:
  - tool_call.id
  - tool_call.name
  - tool_call.input

**Usage:**

```python
@observe()
def chat_with_tools(prompt: str):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        tools=[{
            "type": "function",
            "function": {
                "name": "get_weather",
                "parameters": {...}
            }
        }]
    )
    return response  # Tool calls automatically extracted!
```

**Result in Last9:**
- Attribute: gen_ai.tool_calls.count = 2
- Attribute: gen_ai.tool.name = "get_weather"
- Event: gen_ai.tool.call with full tool call structure
- Queryable: Filter by tool name, see all tool call details

This completes tool call observability matching Langfuse capabilities!

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

Author: prathamesh-sonpatki

last9_genai/decorators.py

@@ -456,6 +456,9 @@ def _add_llm_attributes(
         if finish_reason:
             span.set_attribute(GenAIAttributes.RESPONSE_FINISH_REASONS, finish_reason)
 
+        # Extract tool calls (if present)
+        _extract_tool_calls(span, result)
+
     except Exception:
         # If attribute extraction fails, just skip
         pass
@@ -569,3 +572,60 @@ def _extract_request_parameters(span, kwargs: dict) -> None:
         span.set_attribute(
             GenAIAttributes.REQUEST_PRESENCE_PENALTY, float(kwargs["presence_penalty"])
         )
+
+
+def _extract_tool_calls(span, result: Any) -> None:
+    """Extract and add tool call information from LLM response."""
+    try:
+        # OpenAI format - response.choices[0].message.tool_calls
+        if hasattr(result, "choices") and len(result.choices) > 0:
+            message = result.choices[0].message
+            if hasattr(message, "tool_calls") and message.tool_calls:
+                # Add tool call count
+                span.set_attribute("gen_ai.tool_calls.count", len(message.tool_calls))
+
+                # Add each tool call as an event with structure
+                for i, tool_call in enumerate(message.tool_calls):
+                    tool_call_data = {
+                        "tool_call.index": i,
+                        "tool_call.id": tool_call.id,
+                        "tool_call.type": tool_call.type,
+                        "tool_call.function.name": tool_call.function.name,
+                        "tool_call.function.arguments": tool_call.function.arguments,
+                    }
+                    span.add_event(f"gen_ai.tool.call", tool_call_data)
+
+                # Also add as attributes for easy filtering
+                # First tool call name (most common use case)
+                span.set_attribute(GenAIAttributes.TOOL_NAME, message.tool_calls[0].function.name)
+
+                return
+
+        # Anthropic format - response.content with tool_use blocks
+        if hasattr(result, "content") and isinstance(result.content, list):
+            tool_uses = [
+                block
+                for block in result.content
+                if hasattr(block, "type") and block.type == "tool_use"
+            ]
+
+            if tool_uses:
+                # Add tool call count
+                span.set_attribute("gen_ai.tool_calls.count", len(tool_uses))
+
+                # Add each tool call as an event
+                for i, tool_use in enumerate(tool_uses):
+                    tool_call_data = {
+                        "tool_call.index": i,
+                        "tool_call.id": tool_use.id,
+                        "tool_call.name": tool_use.name,
+                        "tool_call.input": str(tool_use.input),
+                    }
+                    span.add_event(f"gen_ai.tool.call", tool_call_data)
+
+                # First tool name
+                span.set_attribute(GenAIAttributes.TOOL_NAME, tool_uses[0].name)
+
+    except Exception:
+        # If extraction fails, just skip
+        pass