mirror of
https://github.com/home-assistant/core.git
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Introduce base entity in Open Router (#148910)
This commit is contained in:
Joost Lekkerkerker
GitHub
parent
49807c9fbe
commit
e5c7e04329
@ -2,13 +2,12 @@
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import logging
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from homeassistant.const import CONF_LLM_HASS_API
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from homeassistant.const import CONF_LLM_HASS_API, CONF_PROMPT
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from homeassistant.helpers import llm
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DOMAIN = "open_router"
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LOGGER = logging.getLogger(__package__)
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CONF_PROMPT = "prompt"
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CONF_RECOMMENDED = "recommended"
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RECOMMENDED_CONVERSATION_OPTIONS = {
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@ -1,39 +1,16 @@
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"""Conversation support for OpenRouter."""
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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
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import openai
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from openai import NOT_GIVEN
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from openai.types.chat import (
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ChatCompletionAssistantMessageParam,
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ChatCompletionMessage,
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ChatCompletionMessageParam,
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ChatCompletionMessageToolCallParam,
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ChatCompletionSystemMessageParam,
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ChatCompletionToolMessageParam,
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ChatCompletionToolParam,
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ChatCompletionUserMessageParam,
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)
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from openai.types.chat.chat_completion_message_tool_call_param import Function
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from openai.types.shared_params import FunctionDefinition
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from voluptuous_openapi import convert
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from typing import Literal
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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.const import CONF_LLM_HASS_API, CONF_MODEL, MATCH_ALL
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from homeassistant.const import CONF_LLM_HASS_API, CONF_PROMPT, MATCH_ALL
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from homeassistant.core import HomeAssistant
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from homeassistant.exceptions import HomeAssistantError
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from homeassistant.helpers import llm
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from homeassistant.helpers.device_registry import DeviceEntryType, DeviceInfo
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from homeassistant.helpers.entity_platform import AddConfigEntryEntitiesCallback
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from . import OpenRouterConfigEntry
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from .const import CONF_PROMPT, DOMAIN, LOGGER
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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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from .const import DOMAIN
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from .entity import OpenRouterEntity
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async def async_setup_entry(
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@ -49,106 +26,14 @@ async def async_setup_entry(
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)
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def _format_tool(
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tool: llm.Tool,
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custom_serializer: Callable[[Any], Any] | None,
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) -> ChatCompletionToolParam:
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"""Format tool specification."""
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tool_spec = FunctionDefinition(
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name=tool.name,
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parameters=convert(tool.parameters, custom_serializer=custom_serializer),
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)
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if tool.description:
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tool_spec["description"] = tool.description
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return ChatCompletionToolParam(type="function", function=tool_spec)
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def _convert_content_to_chat_message(
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content: conversation.Content,
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) -> ChatCompletionMessageParam | None:
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"""Convert any native chat message for this agent to the native format."""
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LOGGER.debug("_convert_content_to_chat_message=%s", content)
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if isinstance(content, conversation.ToolResultContent):
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return ChatCompletionToolMessageParam(
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role="tool",
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tool_call_id=content.tool_call_id,
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content=json.dumps(content.tool_result),
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)
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role: Literal["user", "assistant", "system"] = content.role
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if role == "system" and content.content:
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return ChatCompletionSystemMessageParam(role="system", content=content.content)
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if role == "user" and content.content:
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return ChatCompletionUserMessageParam(role="user", content=content.content)
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if role == "assistant":
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param = ChatCompletionAssistantMessageParam(
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role="assistant",
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content=content.content,
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)
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if isinstance(content, conversation.AssistantContent) and content.tool_calls:
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param["tool_calls"] = [
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ChatCompletionMessageToolCallParam(
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type="function",
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id=tool_call.id,
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function=Function(
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arguments=json.dumps(tool_call.tool_args),
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name=tool_call.tool_name,
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),
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)
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for tool_call in content.tool_calls
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]
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return param
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LOGGER.warning("Could not convert message to Completions API: %s", content)
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return None
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def _decode_tool_arguments(arguments: str) -> Any:
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"""Decode tool call arguments."""
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try:
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return json.loads(arguments)
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except json.JSONDecodeError as err:
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raise HomeAssistantError(f"Unexpected tool argument response: {err}") from err
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async def _transform_response(
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message: ChatCompletionMessage,
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) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
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"""Transform the OpenRouter message to a ChatLog format."""
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data: conversation.AssistantContentDeltaDict = {
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"role": message.role,
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"content": message.content,
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}
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if message.tool_calls:
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data["tool_calls"] = [
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llm.ToolInput(
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id=tool_call.id,
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tool_name=tool_call.function.name,
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tool_args=_decode_tool_arguments(tool_call.function.arguments),
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)
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for tool_call in message.tool_calls
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]
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yield data
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class OpenRouterConversationEntity(conversation.ConversationEntity):
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class OpenRouterConversationEntity(OpenRouterEntity, conversation.ConversationEntity):
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"""OpenRouter conversation agent."""
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_attr_has_entity_name = True
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_attr_name = None
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def __init__(self, entry: OpenRouterConfigEntry, subentry: ConfigSubentry) -> None:
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"""Initialize the agent."""
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self.entry = entry
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self.subentry = subentry
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self.model = subentry.data[CONF_MODEL]
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self._attr_unique_id = subentry.subentry_id
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self._attr_device_info = DeviceInfo(
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identifiers={(DOMAIN, subentry.subentry_id)},
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name=subentry.title,
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entry_type=DeviceEntryType.SERVICE,
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)
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super().__init__(entry, subentry)
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if self.subentry.data.get(CONF_LLM_HASS_API):
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self._attr_supported_features = (
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conversation.ConversationEntityFeature.CONTROL
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@ -164,7 +49,7 @@ class OpenRouterConversationEntity(conversation.ConversationEntity):
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user_input: conversation.ConversationInput,
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chat_log: conversation.ChatLog,
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) -> conversation.ConversationResult:
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"""Process a sentence."""
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"""Process the user input and call the API."""
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options = self.subentry.data
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try:
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@ -177,49 +62,6 @@ class OpenRouterConversationEntity(conversation.ConversationEntity):
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except conversation.ConverseError as err:
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return err.as_conversation_result()
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tools: list[ChatCompletionToolParam] | 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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messages = [
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m
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for content in chat_log.content
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if (m := _convert_content_to_chat_message(content))
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]
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client = self.entry.runtime_data
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for _iteration in range(MAX_TOOL_ITERATIONS):
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try:
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result = await client.chat.completions.create(
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model=self.model,
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messages=messages,
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tools=tools or NOT_GIVEN,
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user=chat_log.conversation_id,
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extra_headers={
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"X-Title": "Home Assistant",
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"HTTP-Referer": "https://www.home-assistant.io/integrations/open_router",
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},
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)
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except openai.OpenAIError as err:
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LOGGER.error("Error talking to API: %s", err)
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raise HomeAssistantError("Error talking to API") from err
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result_message = result.choices[0].message
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messages.extend(
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[
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msg
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async for content in chat_log.async_add_delta_content_stream(
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user_input.agent_id, _transform_response(result_message)
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)
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if (msg := _convert_content_to_chat_message(content))
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]
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)
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if not chat_log.unresponded_tool_results:
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break
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await self._async_handle_chat_log(chat_log)
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return conversation.async_get_result_from_chat_log(user_input, chat_log)
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185
homeassistant/components/open_router/entity.py
Normal file
185
homeassistant/components/open_router/entity.py
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@ -0,0 +1,185 @@
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"""Base entity for Open Router."""
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from __future__ import annotations
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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
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import openai
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from openai import NOT_GIVEN
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from openai.types.chat import (
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ChatCompletionAssistantMessageParam,
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ChatCompletionMessage,
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ChatCompletionMessageParam,
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ChatCompletionMessageToolCallParam,
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ChatCompletionSystemMessageParam,
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ChatCompletionToolMessageParam,
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ChatCompletionToolParam,
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ChatCompletionUserMessageParam,
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)
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from openai.types.chat.chat_completion_message_tool_call_param import Function
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from openai.types.shared_params import FunctionDefinition
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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.const import CONF_MODEL
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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 OpenRouterConfigEntry
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from .const import DOMAIN, LOGGER
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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,
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custom_serializer: Callable[[Any], Any] | None,
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) -> ChatCompletionToolParam:
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"""Format tool specification."""
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tool_spec = FunctionDefinition(
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name=tool.name,
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parameters=convert(tool.parameters, custom_serializer=custom_serializer),
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)
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if tool.description:
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tool_spec["description"] = tool.description
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return ChatCompletionToolParam(type="function", function=tool_spec)
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def _convert_content_to_chat_message(
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content: conversation.Content,
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) -> ChatCompletionMessageParam | None:
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"""Convert any native chat message for this agent to the native format."""
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LOGGER.debug("_convert_content_to_chat_message=%s", content)
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if isinstance(content, conversation.ToolResultContent):
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return ChatCompletionToolMessageParam(
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role="tool",
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tool_call_id=content.tool_call_id,
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content=json.dumps(content.tool_result),
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)
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role: Literal["user", "assistant", "system"] = content.role
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if role == "system" and content.content:
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return ChatCompletionSystemMessageParam(role="system", content=content.content)
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if role == "user" and content.content:
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return ChatCompletionUserMessageParam(role="user", content=content.content)
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if role == "assistant":
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param = ChatCompletionAssistantMessageParam(
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role="assistant",
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content=content.content,
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)
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if isinstance(content, conversation.AssistantContent) and content.tool_calls:
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param["tool_calls"] = [
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ChatCompletionMessageToolCallParam(
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type="function",
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id=tool_call.id,
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function=Function(
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arguments=json.dumps(tool_call.tool_args),
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name=tool_call.tool_name,
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),
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)
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for tool_call in content.tool_calls
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]
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return param
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LOGGER.warning("Could not convert message to Completions API: %s", content)
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return None
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def _decode_tool_arguments(arguments: str) -> Any:
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"""Decode tool call arguments."""
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try:
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return json.loads(arguments)
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except json.JSONDecodeError as err:
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raise HomeAssistantError(f"Unexpected tool argument response: {err}") from err
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async def _transform_response(
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message: ChatCompletionMessage,
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) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
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"""Transform the OpenRouter message to a ChatLog format."""
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data: conversation.AssistantContentDeltaDict = {
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"role": message.role,
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"content": message.content,
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}
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if message.tool_calls:
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data["tool_calls"] = [
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llm.ToolInput(
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id=tool_call.id,
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tool_name=tool_call.function.name,
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tool_args=_decode_tool_arguments(tool_call.function.arguments),
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)
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for tool_call in message.tool_calls
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]
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yield data
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class OpenRouterEntity(Entity):
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"""Base entity for Open Router."""
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_attr_has_entity_name = True
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def __init__(self, entry: OpenRouterConfigEntry, 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.model = subentry.data[CONF_MODEL]
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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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entry_type=dr.DeviceEntryType.SERVICE,
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)
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async def _async_handle_chat_log(self, chat_log: conversation.ChatLog) -> None:
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"""Generate an answer for the chat log."""
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tools: list[ChatCompletionToolParam] | 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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messages = [
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m
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for content in chat_log.content
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if (m := _convert_content_to_chat_message(content))
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]
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client = self.entry.runtime_data
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for _iteration in range(MAX_TOOL_ITERATIONS):
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try:
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result = await client.chat.completions.create(
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model=self.model,
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messages=messages,
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tools=tools or NOT_GIVEN,
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user=chat_log.conversation_id,
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extra_headers={
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"X-Title": "Home Assistant",
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"HTTP-Referer": "https://www.home-assistant.io/integrations/open_router",
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},
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)
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except openai.OpenAIError as err:
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LOGGER.error("Error talking to API: %s", err)
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raise HomeAssistantError("Error talking to API") from err
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result_message = result.choices[0].message
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messages.extend(
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[
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msg
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async for content in chat_log.async_add_delta_content_stream(
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self.entity_id, _transform_response(result_message)
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)
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if (msg := _convert_content_to_chat_message(content))
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]
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)
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if not chat_log.unresponded_tool_results:
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break
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@ -25,7 +25,7 @@
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"description": "Configure the new conversation agent",
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"data": {
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"model": "Model",
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"prompt": "Instructions",
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"prompt": "[%key:common::config_flow::data::prompt%]",
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"llm_hass_api": "[%key:common::config_flow::data::llm_hass_api%]"
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},
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"data_description": {
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