Apply formatting to docs_src (#217)

docs/docs/en/getting-started/index.md

@@ -120,14 +120,14 @@ Depending on the interface you choose, you'll need to import different modules.
 
 === "Console"
     ```python hl_lines="7"
-    {!> docs_src/getting_started/main_console.py [ln:1-8] !}
+    {!> docs_src/getting_started/main_console.py [ln:1-7] !}
     ```
 
     For Console applications, import `ConsoleUI` to handle command-line input and output.
 
 === "Mesop"
     ```python hl_lines="7"
-    {!> docs_src/getting_started/main_mesop.py [ln:1-8] !}
+    {!> docs_src/getting_started/main_mesop.py [ln:1-7] !}
     ```
 
     For Mesop applications, import `MesopUI` to integrate with the Mesop web interface.
@@ -137,7 +137,7 @@ Depending on the interface you choose, you'll need to import different modules.
 You need to define the workflow that your application will use. This is where you specify how the agents interact and what they do. Here's a simple example of a workflow definition:
 
 ```python
-{! docs_src/getting_started/main_console.py [ln:10-43] !}
+{! docs_src/getting_started/main_console.py [ln:10-45] !}
 ```
 
 This code snippet sets up a simple learning chat between a student and a teacher. You define the agents and how they should interact, specifying how the conversation should be summarized.

docs/docs/en/user-guide/api/openapi/index.md

@@ -21,15 +21,15 @@ pip install "fastagency[autogen,openapi]"
 These imports are similar to the imports section we have already covered, with the only difference being the additional imports of the `OpenAPI` Client and `UserProxyAgent`:
 
 ```python
-{! docs_src/user_guide/external_rest_apis/main.py [ln:1-11] !}
+{! docs_src/user_guide/external_rest_apis/main.py [ln:1-9] !}
 ```
 
 ## Define Workflow
 
 In this workflow, the only difference is that we create a Python client for the external REST API by passing the URL of the `openapi.json` to the `Client.create` method. Then, we register the generated client with the agent using the methods `register_for_llm` and `register_for_execution`. Here's a simple example of a workflow definition:
 
 ```python
-{! docs_src/user_guide/external_rest_apis/main.py [ln:12-60] !}
+{! docs_src/user_guide/external_rest_apis/main.py [ln:11-57] !}
 ```
 
 This code snippet sets up a simple weather agent that calls an external weather API using the registered functions generated from the `openapi.json` URL.
@@ -39,7 +39,7 @@ This code snippet sets up a simple weather agent that calls an external weather
 Next, define your FastAgency application.
 
 ```python
-{! docs_src/user_guide/external_rest_apis/main.py [ln:61] !}
+{! docs_src/user_guide/external_rest_apis/main.py [ln:60] !}
 ```
 
 ## Complete Application Code

docs/docs/en/user-guide/api/security.md

@@ -19,7 +19,7 @@ pip install "fastagency[autogen,openapi]"
 The imports are the same as in the [previous chapter](./index.md){.internal-link}, except here we also import `APIKeyHeader` to set the security value in the header:
 
 ```python hl_lines="11"
-{! docs_src/user_guide/external_rest_apis/security.py [ln:1-11] !}
+{! docs_src/user_guide/external_rest_apis/security.py [ln:1-10] !}
 ```
 
 ## Define Workflow
@@ -29,13 +29,13 @@ In this workflow, we create a Python client for the external REST API by passing
 Additionally, we set the API key for the API using the `set_security_params` method:
 
 ```python hl_lines="2"
-{! docs_src/user_guide/external_rest_apis/security.py [ln:26,27] !}
+{! docs_src/user_guide/external_rest_apis/security.py [ln:25,26] !}
 ```
 
 Here's a simple example of a workflow definition:
 
 ```python hl_lines="15"
-{! docs_src/user_guide/external_rest_apis/security.py [ln:13-66] !}
+{! docs_src/user_guide/external_rest_apis/security.py [ln:12-69] !}
 ```
 
 This code snippet sets up a simple weather agent that calls an external weather API with security, using the registered functions generated from the `openapi.json` URL.
@@ -45,7 +45,7 @@ This code snippet sets up a simple weather agent that calls an external weather
 Next, define your FastAgency application.
 
 ```python
-{! docs_src/user_guide/external_rest_apis/security.py [ln:68] !}
+{! docs_src/user_guide/external_rest_apis/security.py [ln:72] !}
 ```
 
 ## Complete Application Code

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

@@ -113,7 +113,7 @@ Depending on the interface you choose, you'll need to import different modules.
 
 === "Console"
     ```python
-    {!> docs_src/getting_started/main_console.py [ln:1-8] !}
+    {!> docs_src/getting_started/main_console.py [ln:1-7] !}
     ```
 
     For Console applications, import `ConsoleUI` to handle command-line input and output.
@@ -138,7 +138,7 @@ Depending on the interface you choose, you'll need to import different modules.
 You need to define the workflow that your application will use. This is where you specify how the agents interact and what they do. Here's a simple example of a workflow definition:
 
 ```python
-{! docs_src/getting_started/main_console.py [ln:10-43] !}
+{! docs_src/getting_started/main_console.py [ln:10-45] !}
 ```
 
 This code snippet sets up a simple learning chat between a student and a teacher. You define the agents and how they should interact, specifying how the conversation should be summarized.

docs/docs/en/user-guide/runtime/autogen/index.md

@@ -22,21 +22,21 @@ This installation includes the AutoGen runtime, allowing you to build multi-agen
 The example starts by importing the necessary modules from **AutoGen** and **FastAgency**. These imports lay the foundation for building and running multi-agent workflows.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:1-11] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:1-9] !}
 ```
 
 #### 2. **Configure the Language Model (LLM)**
 Here, the large language model is configured to use the `gpt-4o` model, and the API key is retrieved from the environment. This setup ensures that both the user and weather agents can interact effectively.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:12-22] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:11-19] !}
 ```
 
 #### 3. **Set Up the Weather API**
 We define the OpenAPI specification URL for the weather service. This API will later be used by the weather agent to fetch real-time weather data.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:23-26] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:21-23] !}
 ```
 
 #### 4. **Define the Workflow and Agents**
@@ -49,28 +49,28 @@ In this step, we create two agents:
 The workflow is registered using **[AutoGenWorkflows](../../../api/fastagency/runtime/autogen/AutoGenWorkflows/)**.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:27-45] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:28-43] !}
 ```
 
 #### 5. **Register API Functions with the Agents**
 In this step, we register the weather API functions to ensure that the weather agent can call the correct functions, such as `get_daily_weather` and `get_daily_weather_weekly_get`, to retrieve the required weather data.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:46-60] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:45-58] !}
 ```
 
 #### 6. **Enable Agent Interaction and Chat**
 Here, the user agent initiates a chat with the weather agent, which queries the API and returns the weather information. The conversation is summarized using a method provided by the LLM.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:61-69] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:60-67] !}
 ```
 
 #### 7. **Create and Run the Application**
 Finally, we create the FastAgency application and launch it using the console interface.
 
 ```python
-{! docs_src/user_guide/runtime/autogen/main.py [ln:71-72] !}
+{! docs_src/user_guide/runtime/autogen/main.py [ln:70] !}
 ```
 
 ## Complete Application Code

docs/docs/en/user-guide/runtime/autogen/interactions.md

@@ -23,23 +23,23 @@ Let's define three functions which will be available to the agents:
 `TextInput` is suitable for free-form text messages, ideal for open-ended queries and dialogues. This function allows the student to request exam questions from the teacher and provides some suggestions using `TextInput`.
 
 ```python
-{! docs_src/user_guide/custom_user_interactions/main.py [ln:52.5,53.5,54.5,55.5,56.5,57.5,58.5,59.5,60.5,61.5,62.5,63.5,64.5,66.5,67.5,68.5,69.5,70.5] !}
+{! docs_src/user_guide/custom_user_interactions/main.py [ln:50.5,51.5,52.5,53.5,54.5,55.5,56.5,57.5,58.5,59.5,60.5,61.5,62.5,63.5,64.5,66.5,67.5,68.5] !}
 ```
 
 ### System Info Messages
 
 `SystemMessage` is used for operational or system-related instructions, such as logging data, and is not part of the agent dialogue. This function logs the final answers after the student completes the discussion using `SystemMessage` to log the event.
 
 ```python
-{! docs_src/user_guide/custom_user_interactions/main.py [ln:72.5,73.5,74.5,75.5,76.5,77.5,78.5,79.5,80.5,81.5,82.5,83.5,84.5,85.5] !}
+{! docs_src/user_guide/custom_user_interactions/main.py [ln:70.5,71.5,72.5,73.5,74.5,75.5,76.5,77.5,78.5,79.5,80.5,81.5,82.5,83.5] !}
 ```
 
 ### Multiple Choice
 
 `MultipleChoice` is used for structured responses where the user must select one of several predefined options. This function retrieves the final grade for the student's submitted answers using `MultipleChoice`, presenting the user with grading options.
 
 ```python
-{! docs_src/user_guide/custom_user_interactions/main.py [ln:87.5,88.5,89.5,90.5,91.5,92.5,93.5,94.5,96.5,97.5,98.5,99.5] !}
+{! docs_src/user_guide/custom_user_interactions/main.py [ln:85.5,86.5,87.5,88.5,89.5,90.5,91.5,92.5,93.5,94.5,96.5,97.5] !}
 ```
 
 ### Other Types of Messages
@@ -50,7 +50,7 @@ All supported messages are subclasses of the [IOMessage](../../../../api/fastage
 We now register these functions with the workflow, linking the `student_agent` as the caller and the `teacher_agent` as the executor.
 
 ```python
-{! docs_src/user_guide/custom_user_interactions/main.py [ln:101.5,102.5,103.5,104.5,105.5,106.5,107.5,108.5,109.5,110.5,111.5,112.5,113.5,114.5,115.5,116.5,117.5,118.5,119.5,120.5,121.5,122.5,123.5] !}
+{! docs_src/user_guide/custom_user_interactions/main.py [ln:99.5,100.5,101.5,102.5,103.5,104.5,105.5,106.5,107.5,108.5,109.5,110.5,111.5,112.5,113.5,114.5,115.5,116.5,117.5,118.5,119.5,120.5,121.5] !}
 ```
 
 ## Complete Application Code

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

@@ -14,7 +14,7 @@ This example demonstrates how to create a workflow where a student agent interac
 We begin by importing the necessary modules from **FastAgency** and **AutoGen**. These imports provide the essential building blocks for creating agents, workflows, and integrating the ConsoleUI.
 
 ```python
-{! docs_src/getting_started/main_console.py [ln:1-8] !}
+{! docs_src/getting_started/main_console.py [ln:1-7] !}
 ```
 
 - **ConversableAgent**: This class allows the creation of agents that can engage in conversational tasks.
@@ -35,7 +35,7 @@ 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/main_console.py [ln:20-44] !}
+{! docs_src/getting_started/main_console.py [ln:22-44] !}
 ```
 
 - **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.
@@ -45,7 +45,7 @@ Here, we define a simple workflow where the **Student Agent** interacts with the
 Finally, we instantiate **[ConsoleUI](../../../../api/fastagency/ui/console/ConsoleUI/)** to link the workflow to a text-based console interface. This allows the user to interact with the agents via the terminal.
 
 ```python
-{! docs_src/getting_started/main_console.py [ln:47-48] !}
+{! docs_src/getting_started/main_console.py [ln:47] !}
 ```
 
 - **Explanation**: Here, we set up the **ConsoleUI** as the user interface for the workflow, which will allow the entire agent interaction to take place within the terminal.

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

@@ -22,7 +22,7 @@ This example shows how to create a simple learning chat where a student agent in
 We begin by importing the necessary modules from **FastAgency** and **AutoGen**. These imports provide the essential building blocks for creating agents, workflows, and integrating MesopUI.
 
 ```python
-{! docs_src/getting_started/main_mesop.py [ln:1-8] !}
+{! docs_src/getting_started/main_mesop.py [ln:1-7] !}
 ```
 
 - **ConversableAgent**: This class allows the creation of agents that can engage in conversational tasks.
@@ -43,7 +43,7 @@ Next, we configure the language model that powers the agents. In this case, we'r
 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/main_mesop.py [ln:20-44] !}
+{! docs_src/getting_started/main_mesop.py [ln:22-44] !}
 ```
 
 - **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.

docs/docs_src/getting_started/main_console.py

@@ -2,10 +2,9 @@
 
 from autogen.agentchat import ConversableAgent
 
-from fastagency import FastAgency, Workflows
-from fastagency import UI
-from fastagency.ui.console import ConsoleUI
+from fastagency import UI, FastAgency, Workflows
 from fastagency.runtime.autogen.base import AutoGenWorkflows
+from fastagency.ui.console import ConsoleUI
 
 llm_config = {
     "config_list": [
@@ -21,7 +20,9 @@
 
 
 @wf.register(name="simple_learning", description="Student and teacher learning chat")
-def simple_workflow(wf: Workflows, ui: UI, initial_message: str, session_id: str) -> str:
+def simple_workflow(
+    wf: Workflows, ui: UI, initial_message: str, session_id: str
+) -> str:
     student_agent = ConversableAgent(
         name="Student_Agent",
         system_message="You are a student willing to learn.",
@@ -43,5 +44,4 @@ def simple_workflow(wf: Workflows, ui: UI, initial_message: str, session_id: str
     return chat_result.summary  # type: ignore[no-any-return]
 
 
-
 app = FastAgency(wf=wf, ui=ConsoleUI())

docs/docs_src/getting_started/main_mesop.py

@@ -2,10 +2,9 @@
 
 from autogen.agentchat import ConversableAgent
 
-from fastagency import FastAgency, Workflows
-from fastagency import UI
-from fastagency.ui.mesop import MesopUI
+from fastagency import UI, FastAgency, Workflows
 from fastagency.runtime.autogen.base import AutoGenWorkflows
+from fastagency.ui.mesop import MesopUI
 
 llm_config = {
     "config_list": [
@@ -21,7 +20,9 @@
 
 
 @wf.register(name="simple_learning", description="Student and teacher learning chat")
-def simple_workflow(wf: Workflows, ui: UI, initial_message: str, session_id: str) -> str:
+def simple_workflow(
+    wf: Workflows, ui: UI, initial_message: str, session_id: str
+) -> str:
     student_agent = ConversableAgent(
         name="Student_Agent",
         system_message="You are a student willing to learn.",
@@ -43,5 +44,4 @@ def simple_workflow(wf: Workflows, ui: UI, initial_message: str, session_id: str
     return chat_result.summary  # type: ignore[no-any-return]
 
 
-
 app = FastAgency(wf=wf, ui=MesopUI())

docs/docs_src/user_guide/custom_user_interactions/main.py

@@ -1,14 +1,13 @@
 import os
-from typing import Annotated, Any, Dict, Optional
+from typing import Annotated, Any, Optional
 
 from autogen import register_function
 from autogen.agentchat import ConversableAgent
 
-from fastagency import FastAgency, Workflows
-from fastagency import UI
+from fastagency import UI, FastAgency, Workflows
 from fastagency.base import MultipleChoice, SystemMessage, TextInput
-from fastagency.ui.console import ConsoleUI
 from fastagency.runtime.autogen.base import AutoGenWorkflows
+from fastagency.ui.console import ConsoleUI
 
 llm_config = {
     "config_list": [
@@ -25,7 +24,6 @@
 
 @wf.register(name="exam_practice", description="Student and teacher chat")
 def exam_learning(wf: Workflows, ui: UI, initial_message: str, session_id: str) -> str:
-
     def is_termination_msg(msg: dict[str, Any]) -> bool:
         return msg["content"] is not None and "TERMINATE" in msg["content"]
 
@@ -50,7 +48,7 @@ def is_termination_msg(msg: dict[str, Any]) -> bool:
     )
 
     def retrieve_exam_questions(
-        message: Annotated[str, "Message for examiner"]
+        message: Annotated[str, "Message for examiner"],
     ) -> Optional[str]:
         try:
             msg = TextInput(
@@ -85,7 +83,7 @@ def write_final_answers(message: Annotated[str, "Message for examiner"]) -> str:
             return f"write_final_answers() FAILED! {e}"
 
     def get_final_grade(
-        message: Annotated[str, "Message for examiner"]
+        message: Annotated[str, "Message for examiner"],
     ) -> Optional[str]:
         try:
             msg = MultipleChoice(