Use modern Python for OpenAI

homeassistant/components/openai_conversation/__init__.py

@@ -117,7 +117,8 @@ async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
             )
 
         # Get first conversation subentry for options
-        conversation_subentry = next(
+        if not (
+            conversation_subentry := next(
                 (
                     sub
                     for sub in entry.subentries.values()
@@ -125,7 +126,7 @@ async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
                 ),
                 None,
             )
-        if not conversation_subentry:
+        ):
             raise ServiceValidationError("No conversation configuration found")
 
         model: str = conversation_subentry.data.get(
@@ -138,10 +139,17 @@ async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
         ]
 
         if filenames := call.data.get(CONF_FILENAMES):
-            for filename in filenames:
-                if not hass.config.is_allowed_path(filename):
+            # Check access to all files first
+            if blocked_file := next(
+                (
+                    filename
+                    for filename in filenames
+                    if not hass.config.is_allowed_path(filename)
+                ),
+                None,
+            ):
                 raise HomeAssistantError(
-                        f"Cannot read `{filename}`, no access to path; "
+                    f"Cannot read `{blocked_file}`, no access to path; "
                     "`allowlist_external_dirs` may need to be adjusted in "
                     "`configuration.yaml`"
                 )
@@ -244,10 +252,12 @@ async def async_setup_entry(hass: HomeAssistant, entry: OpenAIConfigEntry) -> bo
 
     try:
         await hass.async_add_executor_job(client.with_options(timeout=10.0).models.list)
-    except openai.AuthenticationError as err:
+    except openai.OpenAIError as err:
+        match err:
+            case openai.AuthenticationError():
                 LOGGER.error("Invalid API key: %s", err)
                 return False
-    except openai.OpenAIError as err:
+            case _:
                 raise ConfigEntryNotReady(err) from err
 
     entry.runtime_data = client

homeassistant/components/openai_conversation/config_flow.py

@@ -114,10 +114,15 @@ class OpenAIConfigFlow(ConfigFlow, domain=DOMAIN):
         self._async_abort_entries_match(user_input)
         try:
             await validate_input(self.hass, user_input)
-        except openai.APIConnectionError:
+        except openai.OpenAIError as err:
+            match err:
+                case openai.APIConnectionError():
                     errors["base"] = "cannot_connect"
-        except openai.AuthenticationError:
+                case openai.AuthenticationError():
                     errors["base"] = "invalid_auth"
+                case _:
+                    _LOGGER.exception("Unexpected OpenAI error")
+                    errors["base"] = "unknown"
         except Exception:
             _LOGGER.exception("Unexpected exception")
             errors["base"] = "unknown"
@@ -293,8 +298,12 @@ class OpenAISubentryFlowHandler(ConfigSubentryFlow):
 
         if user_input is not None:
             options.update(user_input)
-            if user_input.get(CONF_CHAT_MODEL) in UNSUPPORTED_MODELS:
+
+            match user_input.get(CONF_CHAT_MODEL):
+                case model if model in UNSUPPORTED_MODELS:
                     errors[CONF_CHAT_MODEL] = "model_not_supported"
+                case _:
+                    pass
 
             if not errors:
                 return await self.async_step_model()

homeassistant/components/openai_conversation/const.py

@@ -1,6 +1,8 @@
 """Constants for the OpenAI Conversation integration."""
 
+from dataclasses import dataclass, field
 import logging
+from typing import Any
 
 from homeassistant.const import CONF_LLM_HASS_API
 from homeassistant.helpers import llm
@@ -58,6 +60,50 @@ UNSUPPORTED_WEB_SEARCH_MODELS: list[str] = [
     "o3-mini",
 ]
 
+
+@dataclass(frozen=True)
+class ConversationOptions:
+    """Configuration options for conversation."""
+
+    recommended: bool = True
+    llm_hass_api: list[str] = field(default_factory=lambda: [llm.LLM_API_ASSIST])
+    prompt: str = llm.DEFAULT_INSTRUCTIONS_PROMPT
+    chat_model: str = RECOMMENDED_CHAT_MODEL
+    max_tokens: int = RECOMMENDED_MAX_TOKENS
+    temperature: float = RECOMMENDED_TEMPERATURE
+    top_p: float = RECOMMENDED_TOP_P
+    reasoning_effort: str = RECOMMENDED_REASONING_EFFORT
+    web_search: bool = RECOMMENDED_WEB_SEARCH
+    code_interpreter: bool = RECOMMENDED_CODE_INTERPRETER
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary format."""
+        return {
+            CONF_RECOMMENDED: self.recommended,
+            CONF_LLM_HASS_API: self.llm_hass_api,
+            CONF_PROMPT: self.prompt,
+            CONF_CHAT_MODEL: self.chat_model,
+            CONF_MAX_TOKENS: self.max_tokens,
+            CONF_TEMPERATURE: self.temperature,
+            CONF_TOP_P: self.top_p,
+            CONF_REASONING_EFFORT: self.reasoning_effort,
+            CONF_WEB_SEARCH: self.web_search,
+            CONF_CODE_INTERPRETER: self.code_interpreter,
+        }
+
+
+@dataclass(frozen=True)
+class AITaskOptions:
+    """Configuration options for AI tasks."""
+
+    recommended: bool = True
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary format."""
+        return {CONF_RECOMMENDED: self.recommended}
+
+
+# Maintain backward compatibility with existing dictionary format
 RECOMMENDED_CONVERSATION_OPTIONS = {
     CONF_RECOMMENDED: True,
     CONF_LLM_HASS_API: [llm.LLM_API_ASSIST],

homeassistant/components/openai_conversation/entity.py

@@ -184,30 +184,33 @@ async def _transform_stream(
     async for event in result:
         LOGGER.debug("Received event: %s", event)
 
-        if isinstance(event, ResponseOutputItemAddedEvent):
-            if isinstance(event.item, ResponseOutputMessage):
-                yield {"role": event.item.role}
-            elif isinstance(event.item, ResponseFunctionToolCall):
+        match event:
+            case ResponseOutputItemAddedEvent(item=ResponseOutputMessage() as msg):
+                yield {"role": msg.role}
+            case ResponseOutputItemAddedEvent(
+                item=ResponseFunctionToolCall() as tool_call
+            ):
                 # OpenAI has tool calls as individual events
                 # while HA puts tool calls inside the assistant message.
                 # We turn them into individual assistant content for HA
                 # to ensure that tools are called as soon as possible.
                 yield {"role": "assistant"}
-                current_tool_call = event.item
-        elif isinstance(event, ResponseOutputItemDoneEvent):
-            item = event.item.model_dump()
+                current_tool_call = tool_call
+            case ResponseOutputItemDoneEvent() as done_event:
+                item = done_event.item.model_dump()
                 item.pop("status", None)
-            if isinstance(event.item, ResponseReasoningItem):
+                match done_event.item:
+                    case ResponseReasoningItem():
                         messages.append(cast(ResponseReasoningItemParam, item))
-            elif isinstance(event.item, ResponseOutputMessage):
+                    case ResponseOutputMessage():
                         messages.append(cast(ResponseOutputMessageParam, item))
-            elif isinstance(event.item, ResponseFunctionToolCall):
+                    case ResponseFunctionToolCall():
                         messages.append(cast(ResponseFunctionToolCallParam, item))
-        elif isinstance(event, ResponseTextDeltaEvent):
-            yield {"content": event.delta}
-        elif isinstance(event, ResponseFunctionCallArgumentsDeltaEvent):
-            current_tool_call.arguments += event.delta
-        elif isinstance(event, ResponseFunctionCallArgumentsDoneEvent):
+            case ResponseTextDeltaEvent(delta=delta):
+                yield {"content": delta}
+            case ResponseFunctionCallArgumentsDeltaEvent(delta=delta):
+                current_tool_call.arguments += delta
+            case ResponseFunctionCallArgumentsDoneEvent():
                 current_tool_call.status = "completed"
                 yield {
                     "tool_calls": [
@@ -218,57 +221,60 @@ async def _transform_stream(
                         )
                     ]
                 }
-        elif isinstance(event, ResponseCompletedEvent):
-            if event.response.usage is not None:
-                chat_log.async_trace(
-                    {
-                        "stats": {
-                            "input_tokens": event.response.usage.input_tokens,
-                            "output_tokens": event.response.usage.output_tokens,
-                        }
-                    }
-                )
-        elif isinstance(event, ResponseIncompleteEvent):
-            if event.response.usage is not None:
-                chat_log.async_trace(
-                    {
-                        "stats": {
-                            "input_tokens": event.response.usage.input_tokens,
-                            "output_tokens": event.response.usage.output_tokens,
-                        }
-                    }
-                )
-
-            if (
-                event.response.incomplete_details
-                and event.response.incomplete_details.reason
+            case ResponseCompletedEvent(response=response) if (
+                response.usage is not None
             ):
-                reason: str = event.response.incomplete_details.reason
-            else:
-                reason = "unknown reason"
-
-            if reason == "max_output_tokens":
-                reason = "max output tokens reached"
-            elif reason == "content_filter":
-                reason = "content filter triggered"
-
-            raise HomeAssistantError(f"OpenAI response incomplete: {reason}")
-        elif isinstance(event, ResponseFailedEvent):
-            if event.response.usage is not None:
                 chat_log.async_trace(
                     {
                         "stats": {
-                            "input_tokens": event.response.usage.input_tokens,
-                            "output_tokens": event.response.usage.output_tokens,
+                            "input_tokens": response.usage.input_tokens,
+                            "output_tokens": response.usage.output_tokens,
                         }
                     }
                 )
-            reason = "unknown reason"
-            if event.response.error is not None:
-                reason = event.response.error.message
-            raise HomeAssistantError(f"OpenAI response failed: {reason}")
-        elif isinstance(event, ResponseErrorEvent):
-            raise HomeAssistantError(f"OpenAI response error: {event.message}")
+            case ResponseIncompleteEvent(response=response):
+                if response.usage is not None:
+                    chat_log.async_trace(
+                        {
+                            "stats": {
+                                "input_tokens": response.usage.input_tokens,
+                                "output_tokens": response.usage.output_tokens,
+                            }
+                        }
+                    )
+
+                reason = (
+                    response.incomplete_details.reason
+                    if response.incomplete_details
+                    and response.incomplete_details.reason
+                    else "unknown reason"
+                )
+
+                match reason:
+                    case "max_output_tokens":
+                        error_message = "max output tokens reached"
+                    case "content_filter":
+                        error_message = "content filter triggered"
+                    case _:
+                        error_message = reason
+
+                raise HomeAssistantError(f"OpenAI response incomplete: {error_message}")
+            case ResponseFailedEvent(response=response):
+                if response.usage is not None:
+                    chat_log.async_trace(
+                        {
+                            "stats": {
+                                "input_tokens": response.usage.input_tokens,
+                                "output_tokens": response.usage.output_tokens,
+                            }
+                        }
+                    )
+                error_message = (
+                    response.error.message if response.error else "unknown reason"
+                )
+                raise HomeAssistantError(f"OpenAI response failed: {error_message}")
+            case ResponseErrorEvent(message=message):
+                raise HomeAssistantError(f"OpenAI response error: {message}")
 
 
 class OpenAIBaseLLMEntity(Entity):
@@ -436,30 +442,31 @@ async def async_prepare_files_for_prompt(
             if not file_path.exists():
                 raise HomeAssistantError(f"`{file_path}` does not exist")
 
-            mime_type, _ = guess_file_type(file_path)
-
-            if not mime_type or not mime_type.startswith(("image/", "application/pdf")):
+            if not (
+                mime_type := guess_file_type(file_path)[0]
+            ) or not mime_type.startswith(("image/", "application/pdf")):
                 raise HomeAssistantError(
                     "Only images and PDF are supported by the OpenAI API,"
                     f"`{file_path}` is not an image file or PDF"
                 )
 
-            base64_file = base64.b64encode(file_path.read_bytes()).decode("utf-8")
+            base64_data = f"data:{mime_type};base64,{base64.b64encode(file_path.read_bytes()).decode('utf-8')}"
 
-            if mime_type.startswith("image/"):
+            match mime_type.split("/")[0]:
+                case "image":
                     content.append(
                         ResponseInputImageParam(
                             type="input_image",
-                        image_url=f"data:{mime_type};base64,{base64_file}",
+                            image_url=base64_data,
                             detail="auto",
                         )
                     )
-            elif mime_type.startswith("application/pdf"):
+                case "application" if mime_type == "application/pdf":
                     content.append(
                         ResponseInputFileParam(
                             type="input_file",
                             filename=str(file_path),
-                        file_data=f"data:{mime_type};base64,{base64_file}",
+                            file_data=base64_data,
                         )
                     )