Remove docstrings from nodes

src/enrichment_agent/graph.py

@@ -31,21 +31,6 @@ async def call_agent_model(
     3. Initializes and configures the LLM with available tools.
     4. Invokes the LLM and processes its response.
     5. Handles the LLM's decision to either continue research or submit final info.
-
-    Args:
-        state (State): The current state of the research process, including topic and extraction schema.
-        config (Optional[RunnableConfig]): Configuration for the LLM, if provided.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing:
-            - 'messages': List of response messages from the LLM.
-            - 'info': Extracted information if the LLM decided to submit final info, else None.
-            - 'loop_step': Incremented step count for the research loop.
-
-    Note:
-        - The function uses three tools: scrape_website, search, and a dynamic 'Info' tool.
-        - If the LLM calls the 'Info' tool, it's considered as submitting the final answer.
-        - If the LLM doesn't call any tool, a prompt to use a tool is appended to the messages.
     """
     # Load configuration from the provided RunnableConfig
     configuration = Configuration.from_runnable_config(config)
@@ -121,24 +106,6 @@ async def reflect(
     4. Initializes and configures a language model with structured output.
     5. Invokes the model to assess the quality of the gathered information.
     6. Processes the model's response and determines if the info is satisfactory.
-
-    Args:
-        state (State): The current state of the research process, including topic,
-                       extraction schema, and gathered information.
-        config (Optional[RunnableConfig]): Configuration for the language model, if provided.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing either:
-            - 'info': The presumed info if it's deemed satisfactory.
-            - 'messages': A list with a ToolMessage indicating an error or unsatisfactory result.
-
-    Raises:
-        ValueError: If the last message in the state is not an AIMessage with tool calls.
-
-    Note:
-        - This function acts as a quality check for the information gathered by the agent.
-        - It uses a separate language model invocation to critique the gathered info.
-        - The function can either approve the gathered info or request further research.
     """
     p = prompts.MAIN_PROMPT.format(
         info=json.dumps(state.extraction_schema, indent=2), topic=state.topic
@@ -202,24 +169,6 @@ def route_after_agent(
     1. Error recovery: If the last message is unexpectedly not an AIMessage.
     2. Info submission: If the agent has called the "Info" tool to submit findings.
     3. Continued research: If the agent has called any other tool.
-
-    Args:
-        state (State): The current state of the research process, including
-                       the message history.
-
-    Returns:
-        Literal["reflect", "tools", "call_agent_model", "__end__"]:
-            - "reflect": If the agent has submitted info for review.
-            - "tools": If the agent has called a tool other than "Info".
-            - "call_agent_model": If an unexpected message type is encountered.
-
-    Note:
-        - The function assumes that normally, the last message should be an AIMessage.
-        - The "Info" tool call indicates that the agent believes it has gathered
-          sufficient information to answer the query.
-        - Any other tool call indicates that the agent needs to continue research.
-        - The error recovery path (returning "call_agent_model" for non-AIMessages)
-          serves as a safeguard against unexpected states.
     """
     last_message = state.messages[-1]
 
@@ -242,29 +191,6 @@ def route_after_checker(
 
     This function determines whether to continue the research process or end it
     based on the checker's evaluation and the current state of the research.
-
-    Args:
-        state (State): The current state of the research process, including
-                       the message history, info gathered, and loop step count.
-        config (RunnableConfig): Configuration object containing settings like
-                                 the maximum number of allowed loops.
-
-    Returns:
-        Literal["__end__", "call_agent_model"]:
-            - "__end__": If the research process should terminate.
-            - "call_agent_model": If further research is needed.
-
-    The function makes decisions based on the following criteria:
-    1. If the maximum number of loops has been reached, it ends the process.
-    2. If no info has been gathered yet, it continues the research.
-    3. If the last message indicates an error or unsatisfactory result, it continues the research.
-    4. If none of the above conditions are met, it assumes the result is satisfactory and ends the process.
-
-    Note:
-        - The function relies on a Configuration object derived from the RunnableConfig.
-        - It checks the loop_step against max_loops to prevent infinite loops.
-        - The presence of info and its quality (determined by the checker) influence the decision.
-        - An error status in the last message triggers additional research.
     """
     configurable = Configuration.from_runnable_config(config)
     last_message = state.messages