added documentation for chat with workflow

vision_agent/agent/easytool_v2.py

@@ -544,7 +544,7 @@ def chat_with_workflow(
  visualize_output: Optional[bool] = False,
  self_reflection: Optional[bool] = True,
  ) -> Tuple[str, List[Dict]]:
- """Chat with the vision agent and return the final answer and all tool results.
+ """Chat with EasyToolV2 and return the final answer and all tool results.
 
  Parameters:
  chat: A conversation in the format of

vision_agent/agent/vision_agent.py

@@ -245,6 +245,18 @@ def retrieve_tools(
 
 
 class VisionAgent(Agent):
+ """Vision Agent is an agentic framework that can output code based on a user
+ request. It can plan tasks, retrieve relevant tools, write code, write tests and
+ reflect on failed test cases to debug code. It is inspired by AgentCoder
+ https://arxiv.org/abs/2312.13010 and Data Interpeter
+ https://arxiv.org/abs/2402.18679
+
+ Example
+ -------
+ >>> from vision_agent import VisionAgent
+ >>> agent = VisionAgent()
+ >>> code = agent("What percentage of the area of the jar is filled with coffee beans?", media="jar.jpg")
+ """
  def __init__(
  self,
  planner: Optional[LLM] = None,
@@ -255,6 +267,22 @@ def __init__(
  verbosity: int = 0,
  report_progress_callback: Optional[Callable[[Dict[str, Any]], None]] = None,
  ) -> None:
+ """Initialize the Vision Agent.
+
+ Parameters:
+ planner (Optional[LLM]): The planner model to use. Defaults to OpenAILLM.
+ coder (Optional[LLM]): The coder model to use. Defaults to OpenAILLM.
+ tester (Optional[LLM]): The tester model to use. Defaults to OpenAILLM.
+ debugger (Optional[LLM]): The debugger model to
+ tool_recommender (Optional[Sim]): The tool recommender model to use.
+ verbosity (int): The verbosity level of the agent. Defaults to 0. 2 is the
+ highest verbosity level which will output all intermediate debugging
+ code.
+ report_progress_callback: a callback to report the progress of the agent.
+ This is useful for streaming logs in a web application where multiple
+ VisionAgent instances are running in parallel. This callback ensures
+ that the progress are not mixed up.
+ """
  self.planner = (
  OpenAILLM(temperature=0.0, json_mode=True) if planner is None else planner
  )
@@ -278,6 +306,17 @@ def __call__(
  input: Union[List[Dict[str, str]], str],
  media: Optional[Union[str, Path]] = None,
  ) -> str:
+ """Chat with Vision Agent and return intermediate information regarding the task.
+
+ Parameters:
+ chat (List[Dict[str, str]]): A conversation in the format of
+ [{"role": "user", "content": "describe your task here..."}].
+ media (Optional[Union[str, Path]]): The media file to be used in the task.
+ self_reflection (bool): Whether to reflect on the task and debug the code.
+
+ Returns:
+ str: The code output by the Vision Agent.
+ """
  if isinstance(input, str):
  input = [{"role": "user", "content": input}]
  results = self.chat_with_workflow(input, media)
@@ -290,6 +329,18 @@ def chat_with_workflow(
  media: Optional[Union[str, Path]] = None,
  self_reflection: bool = False,
  ) -> Dict[str, Any]:
+ """Chat with Vision Agent and return intermediate information regarding the task.
+
+ Parameters:
+ chat (List[Dict[str, str]]): A conversation in the format of
+ [{"role": "user", "content": "describe your task here..."}].
+ media (Optional[Union[str, Path]]): The media file to be used in the task.
+ self_reflection (bool): Whether to reflect on the task and debug the code.
+
+ Returns:
+ Dict[str, Any]: A dictionary containing the code, test, test result, plan,
+ and working memory of the agent.
+ """
  if len(chat) == 0:
  raise ValueError("Chat cannot be empty.")
 
@@ -373,7 +424,7 @@ def chat_with_workflow(
 
  self.log_progress(
  {
- "log": f"The Vision Agent V3 has concluded this chat.\nSuccess: {success}",
+ "log": f"Vision Agent has concluded this chat.\nSuccess: {success}",
  "finished": True,
  }
  )