import asyncio
from app.utils import init

init()
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.teams.magentic_one import MagenticOne
from autogen_agentchat.ui import Console


async def example_usage():
    client = OpenAIChatCompletionClient(model="gpt-4o")
    m1 = MagenticOne(client=client)
    task = "Write a Python script to fetch data from an API."
    result = await Console(m1.run_stream(task=task))
    print(result)


if __name__ == "__main__":
    asyncio.run(example_usage())


"""

/Users/welcome/anaconda3/envs/autogen_0_4/bin/python /Users/welcome/Library/Mobile Documents/com~apple~CloudDocs/Sai_Workspace/lang_server_app/DeepDive_GenAI/autogen_src_new/python/packages/autogen-studio/app/teams/mangentic_team_2.py 
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_capabilities" in BaseOpenAIClientConfigurationConfigModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_info" in BaseOpenAIClientConfigurationConfigModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_capabilities" in OpenAIClientConfigurationConfigModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_info" in OpenAIClientConfigurationConfigModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_capabilities" in AzureOpenAIClientConfigurationConfigModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_info" in AzureOpenAIClientConfigurationConfigModel has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_client" in AssistantAgentConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_context" in AssistantAgentConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_client_stream" in AssistantAgentConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_client" in SocietyOfMindAgentConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_client" in MagenticOneGroupChatConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_client" in SelectorGroupChatConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_client" in MultimodalWebSurferConfig has conflict with protected namespace "model_".

You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
  warnings.warn(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `TextMessage` with value `TextMessage(source='Magen...\n", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `TextMessage` with value `TextMessage(source='Magen...\n", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `TextMessage` with value `TextMessage(source='Magen...\n", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `TextMessage` with value `TextMessage(source='Magen...\n", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `TextMessage` with value `TextMessage(source='Magen...\n", type='TextMessage')` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_json(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `TextMessage` with value `TextMessage(source='Magen...s.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `TextMessage` with value `TextMessage(source='Magen...s.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `TextMessage` with value `TextMessage(source='Magen...s.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `TextMessage` with value `TextMessage(source='Magen...s.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `TextMessage` with value `TextMessage(source='Magen...s.', type='TextMessage')` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_json(
---------- user ----------
Write a Python script to fetch data from an API.
---------- MagenticOneOrchestrator ----------

We are working to address the following user request:

Write a Python script to fetch data from an API.


To answer this request we have assembled the following team:

FileSurfer: An agent that can handle local files.
WebSurfer: 
    A helpful assistant with access to a web browser.
    Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.).
    It can also summarize the entire page, or answer questions based on the content of the page.
    It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.
Coder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.
Executor: A computer terminal that performs no other action than running Python scripts (provided to it quoted in ```python code blocks), or sh shell scripts (provided to it quoted in ```sh code blocks).


Here is an initial fact sheet to consider:

1. GIVEN OR VERIFIED FACTS
   - There is a request for a Python script to fetch data from an API.

2. FACTS TO LOOK UP
   - Specific details about the API from which data needs to be fetched, such as the API endpoint, request type (GET, POST, etc.), authentication requirements, and any specific parameters or headers needed. This information would typically be found in the API's documentation.

3. FACTS TO DERIVE
   - If the API endpoint and authentication details are provided, one can derive the appropriate Python libraries to use (such as `requests` or `http.client`).
   - Derive the structure of the JSON response if the API documentation includes example responses.

4. EDUCATED GUESSES
   - It's common to use the `requests` library in Python for making API calls due to its simplicity and wide adoption.
   - The API likely requires some form of authentication, such as an API key, OAuth token, or similar, given the prevalence of authenticated APIs in modern applications.
   - The script will likely need to handle potential errors, such as network issues or invalid responses, using try-except blocks.


Here is the plan to follow as best as possible:

- Use the WebSurfer to search for and identify the API documentation pertinent to the task, ensuring details like the API endpoint, authentication method, and request parameters are gathered.

- Develop the script using the Coder to create a Python program that fetches data from the identified API using the information provided by the WebSurfer.

- Utilize the Executor to run the Python script to test it and ensure it performs the API call correctly and handles the results appropriately, including managing potential errors.

- If data needs to be processed or stored, potentially involve FileSurfer to handle file operations, such as reading from or writing to local files, if there are any requirements related to this in the task.

---------- MagenticOneOrchestrator ----------
Please search for API documentation relevant to fetching data, focusing on obtaining information like API endpoints, request types, authentication, and any required parameters or headers.
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `MultiModalMessage` with value `MultiModalMessage(source=...ype='MultiModalMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `MultiModalMessage` with value `MultiModalMessage(source=...ype='MultiModalMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `MultiModalMessage` with value `MultiModalMessage(source=...ype='MultiModalMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `MultiModalMessage` with value `MultiModalMessage(source=...ype='MultiModalMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `MultiModalMessage` with value `MultiModalMessage(source=...ype='MultiModalMessage')` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_json(
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/_internal/_serializers.py:42: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `TextMessage` with value `TextMessage(source='WebSu... )', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `TextMessage` with value `TextMessage(source='WebSu... )', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `TextMessage` with value `TextMessage(source='WebSu... )', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `TextMessage` with value `TextMessage(source='WebSu... )', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `TextMessage` with value `TextMessage(source='WebSu... )', type='TextMessage')` - serialized value may not be as expected
  v = handler(item, index)
---------- WebSurfer ----------
I typed 'generic API documentation' into '0 characters out of 2000'.

 Here is a screenshot of the webpage: [generic API documentation - Search](https://www.bing.com/search?q=generic+API+documentation&form=QBLH&sp=-1&lq=0&pq=&sc=0-0&qs=n&sk=&cvid=17FE297640044063ACEC272B069A10B5&ghsh=0&ghacc=0&ghpl=).
 The viewport shows 25% of the webpage, and is positioned at the top of the page 
The following metadata was extracted from the webpage:

{
    "meta_tags": {
        "referrer": "origin-when-cross-origin",
        "SystemEntropyOriginTrialToken": "A5is4nwJJVnhaJpUr1URgj4vvAXSiHoK0VBbM9fawMskbDUj9WUREpa3JzGAo6xd1Cp2voQEG1h6NQ71AsMznU8AAABxeyJvcmlnaW4iOiJodHRwczovL3d3dy5iaW5nLmNvbTo0NDMiLCJmZWF0dXJlIjoiTXNVc2VyQWdlbnRMYXVuY2hOYXZUeXBlIiwiZXhwaXJ5IjoxNzUzNzQ3MjAwLCJpc1N1YmRvbWFpbiI6dHJ1ZX0=",
        "og:description": "Intelligent search from Bing makes it easier to quickly find what you\u2019re looking for and rewards you.",
        "og:site_name": "Bing",
        "og:title": "generic API documentation - Bing",
        "og:url": "https://www.bing.com/search?q=generic+API+documentation&form=QBLH&sp=-1&lq=0&pq=&sc=0-0&qs=n&sk=&cvid=17FE297640044063ACEC272B069A10B5&ghsh=0&ghacc=0&ghpl=",
        "fb:app_id": "3732605936979161",
        "og:image": "http://www.bing.com/sa/simg/facebook_sharing_5.png",
        "og:type": "website",
        "og:image:width": "600",
        "og:image:height": "315"
    }
}

The first 50 lines of the page text is:

Skip to content
generic API documentation
Deep search
ALLCOPILOTIMAGESVIDEOSMAPSNEWS
MORE
TOOLS
About 2,790,000 results
Postman
https://www.postman.com › api-docs
Streamline API Documentation | Build Better API Documentation
SponsoredEmpower your enterprise to create, update, and securely share API documentation. Generate auto-updated API docs, synced with your codebase using Postman documentation tool
Site visitors: Over 10K in the past month
Return On Investment · Identity Management · Take Charge
Manage API Docs At Scale
With API docs Postman enables
faster onboarding, reducing time...
Sign Up Today
Collaborate in real-time: API
design, testing, and deployment...
Elevate Your API Testing
Simplify API testing and debugging
with Postmans built-in...
See more at postman.com
API documentation provides detailed information about an API's available endpoints, methods, resources, authentication protocols, parameters, and headers, as well as examples of common requests and responses123.
Learn more:
1
API Documentation: How to Write, Examples & Be…
postman.com
2
API Documentation Made Easy with O…
swagger.io
3
The Definitive Guide to Creating API Document…
assets.madcapsoft…
Feedback
Django REST Framework
https://www.django-rest-framework.org › api-guide › generic-views
Generic views - Django REST framework
Returns the queryset that should be used for list views, and that should be used as the base for lookups in detail views. Defaults to returning the queryset specified by the querysetattribute. This method should always be used rather than accessing self.queryset directly, as self.querysetgets evaluated only once, … See more
Get_object
Returns an object instance that should be used for detail views. Defaults to using the lookup_fieldparameter to filter the base queryset. May be overridden to … See more
Filter_Queryset
<image>
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `TextMessage` with value `TextMessage(source='Magen...'.", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `TextMessage` with value `TextMessage(source='Magen...'.", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `TextMessage` with value `TextMessage(source='Magen...'.", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `TextMessage` with value `TextMessage(source='Magen...'.", type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `TextMessage` with value `TextMessage(source='Magen...'.", type='TextMessage')` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_json(
---------- MagenticOneOrchestrator ----------
Draft a basic Python script using the 'requests' library to make a GET request to a placeholder API, such as 'https://jsonplaceholder.typicode.com/posts'.
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `TextMessage` with value `TextMessage(source='Coder...e.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `TextMessage` with value `TextMessage(source='Coder...e.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `TextMessage` with value `TextMessage(source='Coder...e.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `TextMessage` with value `TextMessage(source='Coder...e.', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `TextMessage` with value `TextMessage(source='Coder...e.', type='TextMessage')` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_json(
---------- Coder ----------
Here is a simple Python script using the `requests` library to make a GET request to the placeholder API `https://jsonplaceholder.typicode.com/posts`:

```python
import requests

def fetch_data_from_api():
    url = 'https://jsonplaceholder.typicode.com/posts'
    
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raises an HTTPError for bad responses
        
        # Print the JSON response from the API
        data = response.json()
        for post in data:
            print(f"ID: {post['id']}, Title: {post['title']}")
        
    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err}")
    except Exception as err:
        print(f"Other error occurred: {err}")

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

### How It Works:
- **URL**: The script targets `https://jsonplaceholder.typicode.com/posts`.
- **GET Request**: It sends a GET request to the API.
- **Error Handling**: It includes error handling to manage HTTP errors and other exceptions.
- **Response Processing**: It prints out the ID and title of each post in the response.
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:
  PydanticSerializationUnexpectedValue: Expected `ToolCallRequestEvent` but got `TextMessage` with value `TextMessage(source='Magen...e!', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ToolCallExecutionEvent` but got `TextMessage` with value `TextMessage(source='Magen...e!', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `MemoryQueryEvent` but got `TextMessage` with value `TextMessage(source='Magen...e!', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `UserInputRequestedEvent` but got `TextMessage` with value `TextMessage(source='Magen...e!', type='TextMessage')` - serialized value may not be as expected
  PydanticSerializationUnexpectedValue: Expected `ModelClientStreamingChunkEvent` but got `TextMessage` with value `TextMessage(source='Magen...e!', type='TextMessage')` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_json(
---------- MagenticOneOrchestrator ----------
Here is a simple Python script that uses the `requests` library to fetch data from a placeholder API, `https://jsonplaceholder.typicode.com/posts`. This script makes a GET request and handles any potential errors:

```python
import requests

def fetch_data_from_api():
    url = 'https://jsonplaceholder.typicode.com/posts'
    
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raises an HTTPError for bad responses
        
        # Print the JSON response from the API
        data = response.json()
        for post in data:
            print(f"ID: {post['id']}, Title: {post['title']}")
        
    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err}")
    except Exception as err:
        print(f"Other error occurred: {err}")

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

You can use this script to fetch and display data from the specified API endpoint. Let me know if you need any further assistance!
TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Write a Python script to fetch data from an API.', type='TextMessage'), TextMessage(source='MagenticOneOrchestrator', models_usage=None, content="\nWe are working to address the following user request:\n\nWrite a Python script to fetch data from an API.\n\n\nTo answer this request we have assembled the following team:\n\nFileSurfer: An agent that can handle local files.\nWebSurfer: \n    A helpful assistant with access to a web browser.\n    Ask them to perform web searches, open pages, and interact with content (e.g., clicking links, scrolling the viewport, etc., filling in form fields, etc.).\n    It can also summarize the entire page, or answer questions based on the content of the page.\n    It can also be asked to sleep and wait for pages to load, in cases where the pages seem to be taking a while to load.\nCoder: A helpful and general-purpose AI assistant that has strong language skills, Python skills, and Linux command line skills.\nExecutor: A computer terminal that performs no other action than running Python scripts (provided to it quoted in ```python code blocks), or sh shell scripts (provided to it quoted in ```sh code blocks).\n\n\nHere is an initial fact sheet to consider:\n\n1. GIVEN OR VERIFIED FACTS\n   - There is a request for a Python script to fetch data from an API.\n\n2. FACTS TO LOOK UP\n   - Specific details about the API from which data needs to be fetched, such as the API endpoint, request type (GET, POST, etc.), authentication requirements, and any specific parameters or headers needed. This information would typically be found in the API's documentation.\n\n3. FACTS TO DERIVE\n   - If the API endpoint and authentication details are provided, one can derive the appropriate Python libraries to use (such as `requests` or `http.client`).\n   - Derive the structure of the JSON response if the API documentation includes example responses.\n\n4. EDUCATED GUESSES\n   - It's common to use the `requests` library in Python for making API calls due to its simplicity and wide adoption.\n   - The API likely requires some form of authentication, such as an API key, OAuth token, or similar, given the prevalence of authenticated APIs in modern applications.\n   - The script will likely need to handle potential errors, such as network issues or invalid responses, using try-except blocks.\n\n\nHere is the plan to follow as best as possible:\n\n- Use the WebSurfer to search for and identify the API documentation pertinent to the task, ensuring details like the API endpoint, authentication method, and request parameters are gathered.\n\n- Develop the script using the Coder to create a Python program that fetches data from the identified API using the information provided by the WebSurfer.\n\n- Utilize the Executor to run the Python script to test it and ensure it performs the API call correctly and handles the results appropriately, including managing potential errors.\n\n- If data needs to be processed or stored, potentially involve FileSurfer to handle file operations, such as reading from or writing to local files, if there are any requirements related to this in the task.\n", type='TextMessage'), TextMessage(source='MagenticOneOrchestrator', models_usage=None, content='Please search for API documentation relevant to fetching data, focusing on obtaining information like API endpoints, request types, authentication, and any required parameters or headers.', type='TextMessage'), MultiModalMessage(source='WebSurfer', models_usage=RequestUsage(prompt_tokens=2749, completion_tokens=50), content=['I typed \'generic API documentation\' into \'0 characters out of 2000\'.\n\n Here is a screenshot of the webpage: [generic API documentation - Search](https://www.bing.com/search?q=generic+API+documentation&form=QBLH&sp=-1&lq=0&pq=&sc=0-0&qs=n&sk=&cvid=17FE297640044063ACEC272B069A10B5&ghsh=0&ghacc=0&ghpl=).\n The viewport shows 25% of the webpage, and is positioned at the top of the page \nThe following metadata was extracted from the webpage:\n\n{\n    "meta_tags": {\n        "referrer": "origin-when-cross-origin",\n        "SystemEntropyOriginTrialToken": "A5is4nwJJVnhaJpUr1URgj4vvAXSiHoK0VBbM9fawMskbDUj9WUREpa3JzGAo6xd1Cp2voQEG1h6NQ71AsMznU8AAABxeyJvcmlnaW4iOiJodHRwczovL3d3dy5iaW5nLmNvbTo0NDMiLCJmZWF0dXJlIjoiTXNVc2VyQWdlbnRMYXVuY2hOYXZUeXBlIiwiZXhwaXJ5IjoxNzUzNzQ3MjAwLCJpc1N1YmRvbWFpbiI6dHJ1ZX0=",\n        "og:description": "Intelligent search from Bing makes it easier to quickly find what you\\u2019re looking for and rewards you.",\n        "og:site_name": "Bing",\n        "og:title": "generic API documentation - Bing",\n        "og:url": "https://www.bing.com/search?q=generic+API+documentation&form=QBLH&sp=-1&lq=0&pq=&sc=0-0&qs=n&sk=&cvid=17FE297640044063ACEC272B069A10B5&ghsh=0&ghacc=0&ghpl=",\n        "fb:app_id": "3732605936979161",\n        "og:image": "http://www.bing.com/sa/simg/facebook_sharing_5.png",\n        "og:type": "website",\n        "og:image:width": "600",\n        "og:image:height": "315"\n    }\n}\n\nThe first 50 lines of the page text is:\n\nSkip to content\ngeneric API documentation\nDeep search\nALLCOPILOTIMAGESVIDEOSMAPSNEWS\nMORE\nTOOLS\nAbout 2,790,000 results\nPostman\nhttps://www.postman.com › api-docs\nStreamline API Documentation | Build Better API Documentation\nSponsoredEmpower your enterprise to create, update, and securely share API documentation. Generate auto-updated API docs, synced with your codebase using Postman documentation tool\nSite visitors: Over 10K in the past month\nReturn On Investment\xa0· Identity Management\xa0· Take Charge\nManage API Docs At Scale\nWith API docs Postman enables\nfaster onboarding, reducing time...\nSign Up Today\nCollaborate in real-time: API\ndesign, testing, and deployment...\nElevate Your API Testing\nSimplify API testing and debugging\nwith Postmans built-in...\nSee more at postman.com\nAPI documentation provides detailed information about an API\'s available endpoints, methods, resources, authentication protocols, parameters, and headers, as well as examples of common requests and responses123.\nLearn more:\n1\nAPI Documentation: How to Write, Examples & Be…\npostman.com\n2\nAPI Documentation Made Easy with O…\nswagger.io\n3\nThe Definitive Guide to Creating API Document…\nassets.madcapsoft…\nFeedback\nDjango REST Framework\nhttps://www.django-rest-framework.org › api-guide › generic-views\nGeneric views - Django REST framework\nReturns the queryset that should be used for list views, and that should be used as the base for lookups in detail views. Defaults to returning the queryset specified by the querysetattribute. This method should always be used rather than accessing self.queryset directly, as self.querysetgets evaluated only once, … See more\nGet_object\nReturns an object instance that should be used for detail views. Defaults to using the lookup_fieldparameter to filter the base queryset. May be overridden to … See more\nFilter_Queryset', <autogen_core._image.Image object at 0x2819eb850>], type='MultiModalMessage'), TextMessage(source='MagenticOneOrchestrator', models_usage=None, content="Draft a basic Python script using the 'requests' library to make a GET request to a placeholder API, such as 'https://jsonplaceholder.typicode.com/posts'.", type='TextMessage'), TextMessage(source='Coder', models_usage=RequestUsage(prompt_tokens=3216, completion_tokens=259), content='Here is a simple Python script using the `requests` library to make a GET request to the placeholder API `https://jsonplaceholder.typicode.com/posts`:\n\n```python\nimport requests\n\ndef fetch_data_from_api():\n    url = \'https://jsonplaceholder.typicode.com/posts\'\n    \n    try:\n        response = requests.get(url)\n        response.raise_for_status()  # Raises an HTTPError for bad responses\n        \n        # Print the JSON response from the API\n        data = response.json()\n        for post in data:\n            print(f"ID: {post[\'id\']}, Title: {post[\'title\']}")\n        \n    except requests.exceptions.HTTPError as http_err:\n        print(f"HTTP error occurred: {http_err}")\n    except Exception as err:\n        print(f"Other error occurred: {err}")\n\nif __name__ == "__main__":\n    fetch_data_from_api()\n```\n\n### How It Works:\n- **URL**: The script targets `https://jsonplaceholder.typicode.com/posts`.\n- **GET Request**: It sends a GET request to the API.\n- **Error Handling**: It includes error handling to manage HTTP errors and other exceptions.\n- **Response Processing**: It prints out the ID and title of each post in the response.', type='TextMessage'), TextMessage(source='MagenticOneOrchestrator', models_usage=None, content='Here is a simple Python script that uses the `requests` library to fetch data from a placeholder API, `https://jsonplaceholder.typicode.com/posts`. This script makes a GET request and handles any potential errors:\n\n```python\nimport requests\n\ndef fetch_data_from_api():\n    url = \'https://jsonplaceholder.typicode.com/posts\'\n    \n    try:\n        response = requests.get(url)\n        response.raise_for_status()  # Raises an HTTPError for bad responses\n        \n        # Print the JSON response from the API\n        data = response.json()\n        for post in data:\n            print(f"ID: {post[\'id\']}, Title: {post[\'title\']}")\n        \n    except requests.exceptions.HTTPError as http_err:\n        print(f"HTTP error occurred: {http_err}")\n    except Exception as err:\n        print(f"Other error occurred: {err}")\n\nif __name__ == "__main__":\n    fetch_data_from_api()\n```\n\nYou can use this script to fetch and display data from the specified API endpoint. Let me know if you need any further assistance!', type='TextMessage')], stop_reason='The basic Python script to fetch data from a placeholder API has been provided.')
/Users/welcome/anaconda3/envs/autogen_0_4/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=9>
  _warn(f"unclosed transport {self!r}", ResourceWarning, source=self)
ResourceWarning: Enable tracemalloc to get the object allocation traceback

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