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315 lines
12 KiB
Python
315 lines
12 KiB
Python
"""Base entity for OpenAI."""
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from collections.abc import AsyncGenerator, Callable
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import json
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from typing import Any, Literal, cast
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import openai
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from openai._streaming import AsyncStream
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from openai.types.responses import (
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EasyInputMessageParam,
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FunctionToolParam,
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ResponseCompletedEvent,
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ResponseErrorEvent,
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ResponseFailedEvent,
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ResponseFunctionCallArgumentsDeltaEvent,
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ResponseFunctionCallArgumentsDoneEvent,
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ResponseFunctionToolCall,
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ResponseFunctionToolCallParam,
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ResponseIncompleteEvent,
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ResponseInputParam,
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ResponseOutputItemAddedEvent,
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ResponseOutputItemDoneEvent,
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ResponseOutputMessage,
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ResponseOutputMessageParam,
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ResponseReasoningItem,
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ResponseReasoningItemParam,
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ResponseStreamEvent,
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ResponseTextDeltaEvent,
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ToolParam,
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WebSearchToolParam,
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)
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from openai.types.responses.response_input_param import FunctionCallOutput
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from openai.types.responses.web_search_tool_param import UserLocation
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from voluptuous_openapi import convert
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from homeassistant.components import conversation
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from homeassistant.config_entries import ConfigSubentry
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from homeassistant.exceptions import HomeAssistantError
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from homeassistant.helpers import device_registry as dr, llm
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from homeassistant.helpers.entity import Entity
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from . import OpenAIConfigEntry
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from .const import (
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CONF_CHAT_MODEL,
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CONF_MAX_TOKENS,
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CONF_REASONING_EFFORT,
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CONF_TEMPERATURE,
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CONF_TOP_P,
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CONF_WEB_SEARCH,
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CONF_WEB_SEARCH_CITY,
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CONF_WEB_SEARCH_CONTEXT_SIZE,
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CONF_WEB_SEARCH_COUNTRY,
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CONF_WEB_SEARCH_REGION,
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CONF_WEB_SEARCH_TIMEZONE,
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CONF_WEB_SEARCH_USER_LOCATION,
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DOMAIN,
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LOGGER,
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RECOMMENDED_CHAT_MODEL,
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RECOMMENDED_MAX_TOKENS,
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RECOMMENDED_REASONING_EFFORT,
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RECOMMENDED_TEMPERATURE,
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RECOMMENDED_TOP_P,
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RECOMMENDED_WEB_SEARCH_CONTEXT_SIZE,
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)
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# Max number of back and forth with the LLM to generate a response
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MAX_TOOL_ITERATIONS = 10
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def _format_tool(
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tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
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) -> FunctionToolParam:
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"""Format tool specification."""
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return FunctionToolParam(
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type="function",
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name=tool.name,
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parameters=convert(tool.parameters, custom_serializer=custom_serializer),
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description=tool.description,
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strict=False,
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)
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def _convert_content_to_param(
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content: conversation.Content,
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) -> ResponseInputParam:
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"""Convert any native chat message for this agent to the native format."""
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messages: ResponseInputParam = []
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if isinstance(content, conversation.ToolResultContent):
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return [
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FunctionCallOutput(
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type="function_call_output",
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call_id=content.tool_call_id,
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output=json.dumps(content.tool_result),
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)
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]
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if content.content:
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role: Literal["user", "assistant", "system", "developer"] = content.role
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if role == "system":
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role = "developer"
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messages.append(
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EasyInputMessageParam(type="message", role=role, content=content.content)
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)
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if isinstance(content, conversation.AssistantContent) and content.tool_calls:
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messages.extend(
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ResponseFunctionToolCallParam(
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type="function_call",
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name=tool_call.tool_name,
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arguments=json.dumps(tool_call.tool_args),
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call_id=tool_call.id,
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)
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for tool_call in content.tool_calls
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)
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return messages
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async def _transform_stream(
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chat_log: conversation.ChatLog,
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result: AsyncStream[ResponseStreamEvent],
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messages: ResponseInputParam,
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) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
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"""Transform an OpenAI delta stream into HA format."""
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async for event in result:
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LOGGER.debug("Received event: %s", event)
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if isinstance(event, ResponseOutputItemAddedEvent):
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if isinstance(event.item, ResponseOutputMessage):
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yield {"role": event.item.role}
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elif isinstance(event.item, ResponseFunctionToolCall):
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# OpenAI has tool calls as individual events
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# while HA puts tool calls inside the assistant message.
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# We turn them into individual assistant content for HA
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# to ensure that tools are called as soon as possible.
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yield {"role": "assistant"}
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current_tool_call = event.item
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elif isinstance(event, ResponseOutputItemDoneEvent):
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item = event.item.model_dump()
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item.pop("status", None)
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if isinstance(event.item, ResponseReasoningItem):
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messages.append(cast(ResponseReasoningItemParam, item))
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elif isinstance(event.item, ResponseOutputMessage):
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messages.append(cast(ResponseOutputMessageParam, item))
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elif isinstance(event.item, ResponseFunctionToolCall):
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messages.append(cast(ResponseFunctionToolCallParam, item))
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elif isinstance(event, ResponseTextDeltaEvent):
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yield {"content": event.delta}
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elif isinstance(event, ResponseFunctionCallArgumentsDeltaEvent):
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current_tool_call.arguments += event.delta
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elif isinstance(event, ResponseFunctionCallArgumentsDoneEvent):
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current_tool_call.status = "completed"
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yield {
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"tool_calls": [
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llm.ToolInput(
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id=current_tool_call.call_id,
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tool_name=current_tool_call.name,
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tool_args=json.loads(current_tool_call.arguments),
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)
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]
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}
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elif isinstance(event, ResponseCompletedEvent):
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if event.response.usage is not None:
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chat_log.async_trace(
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{
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"stats": {
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"input_tokens": event.response.usage.input_tokens,
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"output_tokens": event.response.usage.output_tokens,
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}
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}
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)
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elif isinstance(event, ResponseIncompleteEvent):
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if event.response.usage is not None:
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chat_log.async_trace(
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{
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"stats": {
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"input_tokens": event.response.usage.input_tokens,
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"output_tokens": event.response.usage.output_tokens,
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}
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}
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)
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if (
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event.response.incomplete_details
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and event.response.incomplete_details.reason
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):
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reason: str = event.response.incomplete_details.reason
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else:
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reason = "unknown reason"
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if reason == "max_output_tokens":
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reason = "max output tokens reached"
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elif reason == "content_filter":
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reason = "content filter triggered"
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raise HomeAssistantError(f"OpenAI response incomplete: {reason}")
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elif isinstance(event, ResponseFailedEvent):
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if event.response.usage is not None:
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chat_log.async_trace(
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{
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"stats": {
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"input_tokens": event.response.usage.input_tokens,
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"output_tokens": event.response.usage.output_tokens,
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}
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}
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)
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reason = "unknown reason"
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if event.response.error is not None:
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reason = event.response.error.message
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raise HomeAssistantError(f"OpenAI response failed: {reason}")
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elif isinstance(event, ResponseErrorEvent):
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raise HomeAssistantError(f"OpenAI response error: {event.message}")
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class OpenAIBaseLLMEntity(Entity):
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"""OpenAI conversation agent."""
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def __init__(self, entry: OpenAIConfigEntry, subentry: ConfigSubentry) -> None:
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"""Initialize the entity."""
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self.entry = entry
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self.subentry = subentry
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self._attr_name = subentry.title
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self._attr_unique_id = subentry.subentry_id
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self._attr_device_info = dr.DeviceInfo(
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identifiers={(DOMAIN, subentry.subentry_id)},
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name=subentry.title,
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manufacturer="OpenAI",
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model=subentry.data.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL),
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entry_type=dr.DeviceEntryType.SERVICE,
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)
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async def _async_handle_chat_log(
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self,
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chat_log: conversation.ChatLog,
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) -> None:
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"""Generate an answer for the chat log."""
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options = self.subentry.data
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tools: list[ToolParam] | None = None
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if chat_log.llm_api:
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tools = [
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_format_tool(tool, chat_log.llm_api.custom_serializer)
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for tool in chat_log.llm_api.tools
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]
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if options.get(CONF_WEB_SEARCH):
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web_search = WebSearchToolParam(
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type="web_search_preview",
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search_context_size=options.get(
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CONF_WEB_SEARCH_CONTEXT_SIZE, RECOMMENDED_WEB_SEARCH_CONTEXT_SIZE
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),
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)
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if options.get(CONF_WEB_SEARCH_USER_LOCATION):
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web_search["user_location"] = UserLocation(
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type="approximate",
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city=options.get(CONF_WEB_SEARCH_CITY, ""),
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region=options.get(CONF_WEB_SEARCH_REGION, ""),
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country=options.get(CONF_WEB_SEARCH_COUNTRY, ""),
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timezone=options.get(CONF_WEB_SEARCH_TIMEZONE, ""),
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)
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if tools is None:
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tools = []
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tools.append(web_search)
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model = options.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL)
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messages = [
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m
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for content in chat_log.content
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for m in _convert_content_to_param(content)
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]
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client = self.entry.runtime_data
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# To prevent infinite loops, we limit the number of iterations
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for _iteration in range(MAX_TOOL_ITERATIONS):
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model_args = {
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"model": model,
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"input": messages,
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"max_output_tokens": options.get(
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CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS
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),
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"top_p": options.get(CONF_TOP_P, RECOMMENDED_TOP_P),
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"temperature": options.get(CONF_TEMPERATURE, RECOMMENDED_TEMPERATURE),
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"user": chat_log.conversation_id,
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"stream": True,
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}
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if tools:
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model_args["tools"] = tools
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if model.startswith("o"):
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model_args["reasoning"] = {
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"effort": options.get(
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CONF_REASONING_EFFORT, RECOMMENDED_REASONING_EFFORT
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)
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}
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else:
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model_args["store"] = False
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try:
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result = await client.responses.create(**model_args)
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except openai.RateLimitError as err:
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LOGGER.error("Rate limited by OpenAI: %s", err)
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raise HomeAssistantError("Rate limited or insufficient funds") from err
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except openai.OpenAIError as err:
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LOGGER.error("Error talking to OpenAI: %s", err)
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raise HomeAssistantError("Error talking to OpenAI") from err
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async for content in chat_log.async_add_delta_content_stream(
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self.entity_id, _transform_stream(chat_log, result, messages)
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):
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if not isinstance(content, conversation.AssistantContent):
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messages.extend(_convert_content_to_param(content))
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if not chat_log.unresponded_tool_results:
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break
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