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# Phoenix Trace Decorator Documentation
## Introduction
Welcome to the documentation for the `phoenix_trace_decorator` module. This module provides a convenient decorator for tracing Python functions and capturing exceptions using Phoenix, a versatile tracing and monitoring tool. Phoenix allows you to gain insights into the execution of your code, capture errors, and monitor performance.
## Table of Contents
1. [Installation](#installation)
2. [Getting Started](#getting-started)
3. [Decorator Usage](#decorator-usage)
4. [Examples](#examples)
5. [Best Practices](#best-practices)
6. [References](#references)
## 1. Installation <a name="installation"></a>
Before using the `phoenix_trace_decorator`, you need to install the Swarms library. You can install Phoenix using pip:
```bash
pip install swarms
```
## 2. Getting Started <a name="getting-started"></a>
Phoenix is a powerful tracing and monitoring tool, and the `phoenix_trace_decorator` simplifies the process of tracing functions and capturing exceptions within your Python code. To begin, ensure that Phoenix is installed, and then import the `phoenix_trace_decorator` module into your Python script.
```python
from swarms import phoenix_trace_decorator
```
## 3. Decorator Usage <a name="decorator-usage"></a>
The `phoenix_trace_decorator` module provides a decorator, `phoenix_trace_decorator`, which can be applied to functions you want to trace. The decorator takes a single argument, a docstring that describes the purpose of the function being traced.
Here is the basic structure of using the decorator:
```python
@phoenix_trace_decorator("Description of the function")
def my_function(param1, param2):
# Function implementation
pass
```
## 4. Examples <a name="examples"></a>
Let's explore some practical examples of using the `phoenix_trace_decorator` in your code.
### Example 1: Basic Tracing
In this example, we'll trace a simple function and print a message.
```python
@phoenix_trace_decorator("Tracing a basic function")
def hello_world():
print("Hello, World!")
# Call the decorated function
hello_world()
```
### Example 2: Tracing a Function with Parameters
You can use the decorator with functions that have parameters.
```python
@phoenix_trace_decorator("Tracing a function with parameters")
def add_numbers(a, b):
result = a + b
print(f"Result: {result}")
# Call the decorated function with parameters
add_numbers(2, 3)
```
### Example 3: Tracing Nested Calls
The decorator can also trace nested function calls.
```python
@phoenix_trace_decorator("Outer function")
def outer_function():
print("Outer function")
@phoenix_trace_decorator("Inner function")
def inner_function():
print("Inner function")
inner_function()
# Call the decorated functions
outer_function()
```
### Example 4: Exception Handling
Phoenix can capture exceptions and provide detailed information about them.
```python
@phoenix_trace_decorator("Function with exception handling")
def divide(a, b):
try:
result = a / b
except ZeroDivisionError as e:
raise ValueError("Division by zero") from e
# Call the decorated function with an exception
try:
divide(5, 0)
except ValueError as e:
print(f"Error: {e}")
```
## 5. Best Practices <a name="best-practices"></a>
When using the `phoenix_trace_decorator`, consider the following best practices:
- Use meaningful docstrings to describe the purpose of the traced functions.
- Keep your tracing focused on critical parts of your code.
- Make sure Phoenix is properly configured and running before using the decorator.
## 6. References <a name="references"></a>
For more information on Phoenix and advanced usage, please refer to the [Phoenix Documentation](https://phoenix-docs.readthedocs.io/en/latest/).
---
By following this documentation, you can effectively use the `phoenix_trace_decorator` to trace your Python functions, capture exceptions, and gain insights into the execution of your code. This tool is valuable for debugging, performance optimization, and monitoring the health of your applications.

@ -111,7 +111,6 @@ nav:
- PGVectorStore: "swarms/memory/pg.md"
- ShortTermMemory: "swarms/memory/short_term_memory.md"
- swarms.utils:
- phoenix_trace_decorator: "swarms/utils/phoenix_tracer.md"
- math_eval: "swarms/utils/math_eval.md"
- Guides:
- Overview: "examples/index.md"

@ -12,7 +12,6 @@ from swarms.prompts.logistics import (
Efficiency_Agent_Prompt,
)
# from swarms.utils.phoenix_handler import phoenix_trace_decorator
# from swarms.utils.banana_wrapper import banana
load_dotenv()
@ -26,7 +25,6 @@ llm = GPT4VisionAPI(openai_api_key=api_key)
factory_image = "factory_image1.jpg"
# Initialize agents with respective prompts
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
health_security_agent = Agent(
llm=llm,
sop=Health_Security_Agent_Prompt,
@ -34,7 +32,6 @@ health_security_agent = Agent(
multi_modal=True,
)
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
quality_control_agent = Agent(
llm=llm,
sop=Quality_Control_Agent_Prompt,
@ -42,7 +39,6 @@ quality_control_agent = Agent(
multi_modal=True,
)
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
productivity_agent = Agent(
llm=llm,
sop=Productivity_Agent_Prompt,
@ -50,17 +46,14 @@ productivity_agent = Agent(
multi_modal=True,
)
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
safety_agent = Agent(
llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True
)
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
security_agent = Agent(
llm=llm, sop=Security_Agent_Prompt, max_loops=1, multi_modal=True
)
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
sustainability_agent = Agent(
llm=llm,
sop=Sustainability_Agent_Prompt,
@ -68,7 +61,6 @@ sustainability_agent = Agent(
multi_modal=True,
)
# @phoenix_trace_decorator("This function is an agent and is traced by Phoenix.")
efficiency_agent = Agent(
llm=llm,
sop=Efficiency_Agent_Prompt,

@ -5,7 +5,6 @@ from dotenv import load_dotenv
from swarms.models import OpenAIChat
from swarms.structs import Agent
# from swarms.utils.phoenix_handler import phoenix_trace_decorator
# import modal
load_dotenv()
@ -22,9 +21,6 @@ llm = OpenAIChat(
# Agent
# @phoenix_trace_decorator(
# "This function is an agent and is traced by Phoenix."
# )
# @stub.function(gpu="any")
agent = Agent(
llm=llm,

@ -1,52 +0,0 @@
import functools
import traceback
import phoenix as px
def phoenix_trace_decorator(doc_string):
"""Phoenix trace decorator.
Args:
doc_string (_type_): doc string for the function
Example:
>>> @phoenix_trace_decorator(
>>> "This is a doc string"
>>> )
>>> def test_function():
>>> print("Hello world")
>>>
>>> test_function()
# Example of using the decorator
@phoenix_trace_decorator("This function does XYZ and is traced by Phoenix.")
def my_function(param1, param2):
# Function implementation
pass
"""
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
# Start phoenix session for tracing
session = px.active_session() or px.launch_app()
try:
# Attempt to execute the function
result = func(*args, **kwargs)
return result
except Exception as error:
error_info = traceback.format_exc()
session.trace_exception(
exception=error, error_info=error_info
)
raise
# Atteach docs to wrapper func
wrapper.__doc__ = doc_string
return wrapper
return decorator
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