LCORE-390: Update documentation for /query endpoint handler module

src/app/endpoints/query.py

@@ -152,7 +152,21 @@ async def query_endpoint_handler(
     auth: Annotated[AuthTuple, Depends(auth_dependency)],
     mcp_headers: dict[str, dict[str, str]] = Depends(mcp_headers_dependency),
 ) -> QueryResponse:
-    """Handle request to the /query endpoint."""
+    """
+    Handle request to the /query endpoint.
+
+    Processes a POST request to the /query endpoint, forwarding the
+    user's query to a selected Llama Stack LLM or agent and
+    returning the generated response.
+
+    Validates configuration and authentication, selects the appropriate model
+    and provider, retrieves the LLM response, updates metrics, and optionally
+    stores a transcript of the interaction. Handles connection errors to the
+    Llama Stack service by returning an HTTP 500 error.
+
+    Returns:
+        QueryResponse: Contains the conversation ID and the LLM-generated response.
+    """
     check_configuration_loaded(configuration)
 
     llama_stack_config = configuration.llama_stack_configuration
@@ -242,7 +256,24 @@ async def query_endpoint_handler(
 def select_model_and_provider_id(
     models: ModelListResponse, model_id: str | None, provider_id: str | None
 ) -> tuple[str, str, str]:
-    """Select the model ID and provider ID based on the request or available models."""
+    """
+    Select the model ID and provider ID based on the request or available models.
+
+    Determine and return the appropriate model and provider IDs for
+    a query request.
+
+    If the request specifies both model and provider IDs, those are used.
+    Otherwise, defaults from configuration are applied. If neither is
+    available, selects the first available LLM model from the provided model
+    list. Validates that the selected model exists among the available models.
+
+    Returns:
+        A tuple containing the combined model ID (in the format
+        "provider/model") and the provider ID.
+
+    Raises:
+        HTTPException: If no suitable LLM model is found or the selected model is not available.
+    """
     # If model_id and provider_id are provided in the request, use them
 
     # If model_id is not provided in the request, check the configuration
@@ -303,16 +334,44 @@ def select_model_and_provider_id(
 
 
 def _is_inout_shield(shield: Shield) -> bool:
+    """
+    Determine if the shield identifier indicates an input/output shield.
+
+    Parameters:
+        shield (Shield): The shield to check.
+
+    Returns:
+        bool: True if the shield identifier starts with "inout_", otherwise False.
+    """
     return shield.identifier.startswith("inout_")
 
 
 def is_output_shield(shield: Shield) -> bool:
-    """Determine if the shield is for monitoring output."""
+    """
+    Determine if the shield is for monitoring output.
+
+    Return True if the given shield is classified as an output or
+    inout shield.
+
+    A shield is considered an output shield if its identifier
+    starts with "output_" or "inout_".
+    """
     return _is_inout_shield(shield) or shield.identifier.startswith("output_")
 
 
 def is_input_shield(shield: Shield) -> bool:
-    """Determine if the shield is for monitoring input."""
+    """
+    Determine if the shield is for monitoring input.
+
+    Return True if the shield is classified as an input or inout
+    shield.
+
+    Parameters:
+        shield (Shield): The shield identifier to classify.
+
+    Returns:
+        bool: True if the shield is for input or both input/output monitoring; False otherwise.
+    """
     return _is_inout_shield(shield) or not is_output_shield(shield)
 
 
@@ -323,7 +382,31 @@ async def retrieve_response(  # pylint: disable=too-many-locals
     token: str,
     mcp_headers: dict[str, dict[str, str]] | None = None,
 ) -> tuple[str, str]:
-    """Retrieve response from LLMs and agents."""
+    """
+    Retrieve response from LLMs and agents.
+
+    Retrieves a response from the Llama Stack LLM or agent for a
+    given query, handling shield configuration, tool usage, and
+    attachment validation.
+
+    This function configures input/output shields, system prompts,
+    and toolgroups (including RAG and MCP integration) as needed
+    based on the query request and system configuration. It
+    validates attachments, manages conversation and session
+    context, and processes MCP headers for multi-component
+    processing. Shield violations in the response are detected and
+    corresponding metrics are updated.
+
+    Parameters:
+        model_id (str): The identifier of the LLM model to use.
+        query_request (QueryRequest): The user's query and associated metadata.
+        token (str): The authentication token for authorization.
+        mcp_headers (dict[str, dict[str, str]], optional): Headers for multi-component processing.
+
+    Returns:
+        tuple[str, str]: A tuple containing the LLM or agent's response content
+        and the conversation ID.
+    """
     available_input_shields = [
         shield.identifier
         for shield in filter(is_input_shield, await client.shields.list())
@@ -416,7 +499,9 @@ async def retrieve_response(  # pylint: disable=too-many-locals
 def validate_attachments_metadata(attachments: list[Attachment]) -> None:
     """Validate the attachments metadata provided in the request.
 
-    Raises HTTPException if any attachment has an improper type or content type.
+    Raises:
+        HTTPException: If any attachment has an invalid type or content type,
+        an HTTP 422 error is raised.
     """
     for attachment in attachments:
         if attachment.attachment_type not in constants.ATTACHMENT_TYPES:
@@ -444,7 +529,19 @@ def validate_attachments_metadata(attachments: list[Attachment]) -> None:
 
 
 def construct_transcripts_path(user_id: str, conversation_id: str) -> Path:
-    """Construct path to transcripts."""
+    """
+    Construct path to transcripts.
+
+    Constructs a sanitized filesystem path for storing transcripts
+    based on the user ID and conversation ID.
+
+    Parameters:
+        user_id (str): The user identifier, which will be normalized and sanitized.
+        conversation_id (str): The conversation identifier, which will be normalized and sanitized.
+
+    Returns:
+        Path: The constructed path for storing transcripts for the specified user and conversation.
+    """
     # these two normalizations are required by Snyk as it detects
     # this Path sanitization pattern
     uid = os.path.normpath("/" + user_id).lstrip("/")
@@ -468,7 +565,14 @@ def store_transcript(  # pylint: disable=too-many-arguments,too-many-positional-
     truncated: bool,
     attachments: list[Attachment],
 ) -> None:
-    """Store transcript in the local filesystem.
+    """
+    Store transcript in the local filesystem.
+
+    Constructs a sanitized filesystem path for storing transcripts
+    based on the user ID and conversation ID.
+
+    Returns:
+        Path: The constructed path for storing transcripts for the specified user and conversation.
 
     Args:
         user_id: The user ID (UUID).
@@ -513,7 +617,19 @@ def store_transcript(  # pylint: disable=too-many-arguments,too-many-positional-
 def get_rag_toolgroups(
     vector_db_ids: list[str],
 ) -> list[Toolgroup] | None:
-    """Return a list of RAG Tool groups if the given vector DB list is not empty."""
+    """
+    Return a list of RAG Tool groups if the given vector DB list is not empty.
+
+    Generate a list containing a RAG knowledge search toolgroup if
+    vector database IDs are provided.
+
+    Parameters:
+        vector_db_ids (list[str]): List of vector database identifiers to include in the toolgroup.
+
+    Returns:
+        list[Toolgroup] | None: A list with a single RAG toolgroup if
+        vector_db_ids is non-empty; otherwise, None.
+    """
     return (
         [
             ToolgroupAgentToolGroupWithArgs(