Merge pull request #575 from airtai/update-cc-files

Update cc files

docs/docs/en/user-guide/ui/console/basics.md

@@ -40,7 +40,7 @@ We begin by importing the necessary modules from **FastAgency** and **AutoGen**.
 Next, we configure the language model that will power the agents. In this case, we're using **gpt-4o-mini**, and the API key is retrieved from the environment.
 
 ```python
-{! docs_src/getting_started/no_auth/mesop/my_fastagency_app/my_fastagency_app/workflow.py [ln:9-17] !}
+{! docs_src/getting_started/no_auth/mesop/my_fastagency_app/my_fastagency_app/workflow.py [ln:8-17] !}
 ```
 
 - **Explanation**: The configuration specifies the LLM model and API key used for powering the conversation between agents. The temperature is set to `0.0` to ensure deterministic responses from the agents, making interactions consistent and reliable. This is particularly useful for scenarios where repeatability and predictability are required, such as testing.
@@ -49,7 +49,7 @@ Next, we configure the language model that will power the agents. In this case,
 Here, we define a simple workflow where the **Student Agent** interacts with the **Teacher Agent**. The student asks questions, and the teacher responds as a math teacher. The workflow is registered using **AutoGenWorkflows**.
 
 ```python
-{! docs_src/getting_started/no_auth/mesop/my_fastagency_app/my_fastagency_app/workflow.py [ln:22-51] !}
+{! docs_src/getting_started/no_auth/mesop/my_fastagency_app/my_fastagency_app/workflow.py [ln:18-48] !}
 ```
 
 - **Agent Overview**: The **Student Agent** is configured with a system message, "You are a student willing to learn," and will initiate questions during the interaction. The **Teacher Agent**, on the other hand, is set up as a math teacher and will respond to the student's questions.