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Add typing for OpenAI client and fallout (#118514)
* typing for client and consequences * Update homeassistant/components/openai_conversation/conversation.py --------- Co-authored-by: Paulus Schoutsen <paulus@home-assistant.io>
This commit is contained in:
Josef Zweck
GitHub
parent
2bd142d3a6
commit
eae04bf2e9
@ -1,9 +1,22 @@
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"""Conversation support for OpenAI."""
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import json
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from typing import Any, Literal
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from typing import Literal
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import openai
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from openai._types 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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import voluptuous as vol
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from voluptuous_openapi import convert
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@ -45,13 +58,12 @@ async def async_setup_entry(
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async_add_entities([agent])
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def _format_tool(tool: llm.Tool) -> dict[str, Any]:
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def _format_tool(tool: llm.Tool) -> ChatCompletionToolParam:
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"""Format tool specification."""
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tool_spec = {"name": tool.name}
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tool_spec = FunctionDefinition(name=tool.name, parameters=convert(tool.parameters))
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if tool.description:
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tool_spec["description"] = tool.description
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tool_spec["parameters"] = convert(tool.parameters)
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return {"type": "function", "function": tool_spec}
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return ChatCompletionToolParam(type="function", function=tool_spec)
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class OpenAIConversationEntity(
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@ -65,7 +77,7 @@ class OpenAIConversationEntity(
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def __init__(self, entry: ConfigEntry) -> None:
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"""Initialize the agent."""
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self.entry = entry
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self.history: dict[str, list[dict]] = {}
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self.history: dict[str, list[ChatCompletionMessageParam]] = {}
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self._attr_unique_id = entry.entry_id
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self._attr_device_info = dr.DeviceInfo(
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identifiers={(DOMAIN, entry.entry_id)},
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@ -100,7 +112,7 @@ class OpenAIConversationEntity(
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options = self.entry.options
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intent_response = intent.IntentResponse(language=user_input.language)
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llm_api: llm.APIInstance | None = None
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tools: list[dict[str, Any]] | None = None
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tools: list[ChatCompletionToolParam] | None = None
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if options.get(CONF_LLM_HASS_API):
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try:
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@ -164,16 +176,18 @@ class OpenAIConversationEntity(
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response=intent_response, conversation_id=conversation_id
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)
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messages = [{"role": "system", "content": prompt}]
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messages = [ChatCompletionSystemMessageParam(role="system", content=prompt)]
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messages.append({"role": "user", "content": user_input.text})
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messages.append(
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ChatCompletionUserMessageParam(role="user", content=user_input.text)
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)
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LOGGER.debug("Prompt: %s", messages)
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trace.async_conversation_trace_append(
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trace.ConversationTraceEventType.AGENT_DETAIL, {"messages": messages}
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)
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client = self.hass.data[DOMAIN][self.entry.entry_id]
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client: openai.AsyncClient = self.hass.data[DOMAIN][self.entry.entry_id]
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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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@ -181,7 +195,7 @@ class OpenAIConversationEntity(
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result = await client.chat.completions.create(
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model=options.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL),
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messages=messages,
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tools=tools or None,
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tools=tools or NOT_GIVEN,
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max_tokens=options.get(CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS),
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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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@ -199,7 +213,31 @@ class OpenAIConversationEntity(
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LOGGER.debug("Response %s", result)
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response = result.choices[0].message
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messages.append(response)
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def message_convert(
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message: ChatCompletionMessage,
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) -> ChatCompletionMessageParam:
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"""Convert from class to TypedDict."""
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tool_calls: list[ChatCompletionMessageToolCallParam] = []
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if message.tool_calls:
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tool_calls = [
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ChatCompletionMessageToolCallParam(
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id=tool_call.id,
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function=Function(
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arguments=tool_call.function.arguments,
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name=tool_call.function.name,
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),
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type=tool_call.type,
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)
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for tool_call in message.tool_calls
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]
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return ChatCompletionAssistantMessageParam(
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role=message.role,
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tool_calls=tool_calls,
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content=message.content,
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)
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messages.append(message_convert(response))
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tool_calls = response.tool_calls
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if not tool_calls or not llm_api:
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@ -223,18 +261,17 @@ class OpenAIConversationEntity(
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LOGGER.debug("Tool response: %s", tool_response)
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messages.append(
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{
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_call.function.name,
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"content": json.dumps(tool_response),
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}
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ChatCompletionToolMessageParam(
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role="tool",
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tool_call_id=tool_call.id,
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content=json.dumps(tool_response),
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)
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)
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self.history[conversation_id] = messages
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intent_response = intent.IntentResponse(language=user_input.language)
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intent_response.async_set_speech(response.content)
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intent_response.async_set_speech(response.content or "")
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return conversation.ConversationResult(
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response=intent_response, conversation_id=conversation_id
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)
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@ -184,7 +184,6 @@ async def test_function_call(
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assert mock_create.mock_calls[1][2]["messages"][3] == {
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"role": "tool",
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"tool_call_id": "call_AbCdEfGhIjKlMnOpQrStUvWx",
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"name": "test_tool",
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"content": '"Test response"',
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}
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mock_tool.async_call.assert_awaited_once_with(
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@ -317,7 +316,6 @@ async def test_function_exception(
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assert mock_create.mock_calls[1][2]["messages"][3] == {
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"role": "tool",
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"tool_call_id": "call_AbCdEfGhIjKlMnOpQrStUvWx",
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"name": "test_tool",
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"content": '{"error": "HomeAssistantError", "error_text": "Test tool exception"}',
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}
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mock_tool.async_call.assert_awaited_once_with(
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