add docstrings for tools package

motleycrew/tools/autogen_chat_tool.py

@@ -1,3 +1,4 @@
+""" Module description """
 from typing import Optional, Type, Callable, Any
 
 from langchain_core.tools import StructuredTool
@@ -16,6 +17,14 @@
 
 
 def get_last_message(chat_result: ChatResult) -> str:
+    """ Description
+
+    Args:
+        chat_result (ChatResult):
+
+    Returns:
+        str:
+    """
     for message in reversed(chat_result.chat_history):
         if message.get("content") and "TERMINATE" not in message["content"]:
             return message["content"]
@@ -32,6 +41,17 @@ def __init__(
         result_extractor: Callable[[ChatResult], Any] = get_last_message,
         input_schema: Optional[Type[BaseModel]] = None,
     ):
+        """ Description
+
+        Args:
+            name (str):
+            description (str):
+            prompt (:obj:`str`, :obj:`BasePromptTemplate`):
+            initiator (ConversableAgent):
+            recipient (ConversableAgent):
+            result_extractor (:obj:`Callable[[ChatResult]`, :obj:`Any`, optional):
+            input_schema (:obj:`Type[BaseModel]`, optional):
+        """
         ensure_module_is_installed("autogen")
         langchain_tool = create_autogen_chat_tool(
             name=name,
@@ -54,6 +74,20 @@ def create_autogen_chat_tool(
     result_extractor: Callable[[ChatResult], Any],
     input_schema: Optional[Type[BaseModel]] = None,
 ):
+    """ Description
+
+    Args:
+        name (str):
+        description (str):
+        prompt (:obj:`str`, :obj:`BasePromptTemplate`):
+        initiator (ConversableAgent):
+        recipient (ConversableAgent):
+        result_extractor (:obj:`Callable[[ChatResult]`, :obj:`Any`, optional):
+        input_schema (:obj:`Type[BaseModel]`, optional):
+
+    Returns:
+
+    """
     if not isinstance(prompt, BasePromptTemplate):
         prompt = PromptTemplate.from_template(prompt)
 

motleycrew/tools/image/dall_e.py

@@ -1,3 +1,9 @@
+""" Module description
+
+Attributes:
+    prompt_template (str):
+    dall_e_template (str):
+"""
 from typing import Optional
 
 import os
@@ -17,6 +23,15 @@
 
 
 def download_image(url: str, file_path: str) -> Optional[str]:
+    """ Description
+
+    Args:
+        url (str):
+        file_path (str):
+
+    Returns:
+        :obj:`str`, None:
+    """
     response = requests.get(url, stream=True)
     if response.status_code == requests.codes.ok:
         content_type = response.headers.get("content-type")
@@ -46,6 +61,16 @@ def __init__(
         size: str = "1024x1024",
         style: Optional[str] = None,
     ):
+        """ Description
+
+        Args:
+            images_directory (:obj:`str`, optional):
+            refine_prompt_with_llm (:obj:`bool`, optional):
+            model (:obj:`str`, optional):
+            quality (:obj:`str`, optional):
+            size (:obj:`str`, optional):
+            style (:obj:`str`, optional):
+        """
         langchain_tool = create_dalle_image_generator_langchain_tool(
             images_directory=images_directory,
             refine_prompt_with_llm=refine_prompt_with_llm,
@@ -58,7 +83,11 @@ def __init__(
 
 
 class DallEToolInput(BaseModel):
-    """Input for the Dall-E tool."""
+    """Input for the Dall-E tool.
+
+    Attributes:
+        description (str):
+    """
 
     description: str = Field(description="image description")
 
@@ -83,6 +112,21 @@ def run_dalle_and_save_images(
     style: Optional[str] = None,
     file_name_length: int = 8,
 ) -> Optional[list[str]]:
+    """ Description
+
+    Args:
+        description (str):
+        images_directory (:obj:`str`, optional):
+        refine_prompt_with_llm(:obj:`bool`, optional):
+        model (:obj:`str`, optional):
+        quality (:obj:`str`, optional):
+        size (:obj:`str`, optional):
+        style (:obj:`str`, optional):
+        file_name_length (:obj:`int`, optional):
+
+    Returns:
+        :obj:`list` of :obj:`str`:
+    """
 
     dall_e_prompt = PromptTemplate.from_template(dall_e_template)
 
@@ -133,6 +177,19 @@ def create_dalle_image_generator_langchain_tool(
     size: str = "1024x1024",
     style: Optional[str] = None,
 ):
+    """ Description
+
+    Args:
+        images_directory (:obj:`str`, optional):
+        refine_prompt_with_llm (:obj:`bool`, optional):
+        model (:obj:`str`, optional):
+        quality (:obj:`str`, optional):
+        size (:obj:`str`, optional):
+        style (:obj:`str`, optional):
+
+    Returns:
+        Tool:
+    """
     def run_dalle_and_save_images_partial(description: str):
         return run_dalle_and_save_images(
             description=description,

motleycrew/tools/llm_tool.py

@@ -1,3 +1,4 @@
+""" Module description"""
 from typing import Optional, Type
 
 from langchain_core.tools import StructuredTool
@@ -20,6 +21,15 @@ def __init__(
         llm: Optional[BaseLanguageModel] = None,
         input_schema: Optional[Type[BaseModel]] = None,
     ):
+        """ Description
+
+        Args:
+            name (str):
+            description (str):
+            prompt (:obj:`str`, :obj:`BasePromptTemplate`):
+            llm (:obj:`BaseLanguageModel`, optional):
+            input_schema (:obj:`Type[BaseModel]`, optional):
+        """
         langchain_tool = create_llm_langchain_tool(
             name=name,
             description=description,
@@ -37,6 +47,18 @@ def create_llm_langchain_tool(
     llm: Optional[BaseLanguageModel] = None,
     input_schema: Optional[Type[BaseModel]] = None,
 ):
+    """ Description
+
+    Args:
+        name (str):
+        description (str):
+        prompt (:obj:`str`, :obj:`BasePromptTemplate`):
+        llm (:obj:`BaseLanguageModel`, optional):
+        input_schema (:obj:`Type[BaseModel]`, optional):
+
+    Returns:
+
+    """
     if llm is None:
         llm = init_llm(llm_framework=LLMFramework.LANGCHAIN)
 

motleycrew/tools/mermaid_evaluator_tool.py

@@ -1,3 +1,4 @@
+""" Module description """
 # https://nodejs.org/en/download
 # npm install -g @mermaid-js/mermaid-cli
 import os.path
@@ -13,6 +14,11 @@
 
 class MermaidEvaluatorTool(MotleyTool):
     def __init__(self, format: Optional[str] = "svg"):
+        """ Description
+
+        Args:
+            format (:obj:`str`, None):
+        """
         def eval_mermaid_partial(mermaid_code: str):
             return eval_mermaid(mermaid_code, format)
 
@@ -29,6 +35,15 @@ def eval_mermaid_partial(mermaid_code: str):
 
 
 def eval_mermaid(mermaid_code: str, format: Optional[str] = "svg") -> io.BytesIO:
+    """ Description
+
+    Args:
+        mermaid_code (str):
+        format (:obj:`str`, optional):
+
+    Returns:
+        io.BytesIO:
+    """
     with tempfile.NamedTemporaryFile(delete=True, mode="w+", suffix=".mmd") as temp_in:
         temp_in.write(mermaid_code)
         temp_in.flush()  # Ensure all data is written to disk

motleycrew/tools/python_repl.py

@@ -1,3 +1,4 @@
+""" Module description """
 from langchain.agents import Tool
 from langchain_experimental.utilities import PythonREPL
 from langchain_core.pydantic_v1 import BaseModel, Field
@@ -7,18 +8,30 @@
 
 class PythonREPLTool(MotleyTool):
     def __init__(self):
+        """ Description
+
+        """
         langchain_tool = create_repl_tool()
         super().__init__(langchain_tool)
 
 
 class REPLToolInput(BaseModel):
-    """Input for the REPL tool."""
+    """Input for the REPL tool.
+
+    Attributes:
+        command (str):
+    """
 
     command: str = Field(description="code to execute")
 
 
 # You can create the tool to pass to an agent
 def create_repl_tool():
+    """ Description
+
+    Returns:
+        Tool:
+    """
     return Tool.from_function(
         func=PythonREPL().run,
         name="python_repl",

motleycrew/tools/simple_retriever_tool.py

@@ -1,3 +1,4 @@
+""" Module description """
 import os.path
 
 from langchain_core.pydantic_v1 import BaseModel, Field
@@ -19,21 +20,43 @@
 
 class SimpleRetrieverTool(MotleyTool):
     def __init__(self, DATA_DIR, PERSIST_DIR, return_strings_only: bool = False):
+        """ Description
+
+        Args:
+            DATA_DIR (str):
+            PERSIST_DIR (str):
+            return_strings_only (:obj:`bool`, optional):
+        """
         tool = make_retriever_langchain_tool(
             DATA_DIR, PERSIST_DIR, return_strings_only=return_strings_only
         )
         super().__init__(tool)
 
 
 class RetrieverToolInput(BaseModel, arbitrary_types_allowed=True):
-    """Input for the Retriever Tool."""
+    """Input for the Retriever Tool.
+
+    Attributes:
+        question (Question):
+
+    """
 
     question: Question = Field(
         description="The input question for which to retrieve relevant data."
     )
 
 
 def make_retriever_langchain_tool(DATA_DIR, PERSIST_DIR, return_strings_only: bool = False):
+    """ Description
+
+    Args:
+        DATA_DIR (str):
+        PERSIST_DIR (str):
+        return_strings_only (:obj:`bool`, optional):
+
+    Returns:
+
+    """
     text_embedding_model = "text-embedding-ada-002"
     embeddings = OpenAIEmbedding(model=text_embedding_model)