mirror of
https://github.com/home-assistant/core.git
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Split Ollama entity (#147769)
This commit is contained in:
Paulus Schoutsen
GitHub
parent
70856bd92a
commit
bf74ba990a
@ -2,41 +2,18 @@
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from __future__ import annotations
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from collections.abc import AsyncGenerator, AsyncIterator, Callable
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import json
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import logging
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from typing import Any, Literal
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import ollama
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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 assist_pipeline, conversation
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from homeassistant.config_entries import ConfigEntry, ConfigSubentry
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from homeassistant.const import CONF_LLM_HASS_API, 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 device_registry as dr, intent, llm
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from homeassistant.helpers import intent
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from homeassistant.helpers.entity_platform import AddConfigEntryEntitiesCallback
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from . import OllamaConfigEntry
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from .const import (
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CONF_KEEP_ALIVE,
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CONF_MAX_HISTORY,
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CONF_MODEL,
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CONF_NUM_CTX,
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CONF_PROMPT,
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CONF_THINK,
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DEFAULT_KEEP_ALIVE,
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DEFAULT_MAX_HISTORY,
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DEFAULT_NUM_CTX,
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DOMAIN,
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)
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from .models import MessageHistory, MessageRole
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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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_LOGGER = logging.getLogger(__name__)
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from .const import CONF_PROMPT, DOMAIN
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from .entity import OllamaBaseLLMEntity
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async def async_setup_entry(
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@ -55,129 +32,10 @@ async def async_setup_entry(
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)
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def _format_tool(
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tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
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) -> dict[str, Any]:
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"""Format tool specification."""
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tool_spec = {
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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 {"type": "function", "function": tool_spec}
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def _fix_invalid_arguments(value: Any) -> Any:
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"""Attempt to repair incorrectly formatted json function arguments.
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Small models (for example llama3.1 8B) may produce invalid argument values
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which we attempt to repair here.
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"""
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if not isinstance(value, str):
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return value
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if (value.startswith("[") and value.endswith("]")) or (
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value.startswith("{") and value.endswith("}")
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):
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try:
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return json.loads(value)
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except json.decoder.JSONDecodeError:
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pass
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return value
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def _parse_tool_args(arguments: dict[str, Any]) -> dict[str, Any]:
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"""Rewrite ollama tool arguments.
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This function improves tool use quality by fixing common mistakes made by
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small local tool use models. This will repair invalid json arguments and
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omit unnecessary arguments with empty values that will fail intent parsing.
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"""
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return {k: _fix_invalid_arguments(v) for k, v in arguments.items() if v}
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def _convert_content(
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chat_content: (
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conversation.Content
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| conversation.ToolResultContent
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| conversation.AssistantContent
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),
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) -> ollama.Message:
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"""Create tool response content."""
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if isinstance(chat_content, conversation.ToolResultContent):
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return ollama.Message(
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role=MessageRole.TOOL.value,
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content=json.dumps(chat_content.tool_result),
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)
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if isinstance(chat_content, conversation.AssistantContent):
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return ollama.Message(
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role=MessageRole.ASSISTANT.value,
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content=chat_content.content,
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tool_calls=[
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ollama.Message.ToolCall(
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function=ollama.Message.ToolCall.Function(
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name=tool_call.tool_name,
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arguments=tool_call.tool_args,
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)
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)
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for tool_call in chat_content.tool_calls or ()
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],
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)
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if isinstance(chat_content, conversation.UserContent):
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return ollama.Message(
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role=MessageRole.USER.value,
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content=chat_content.content,
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)
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if isinstance(chat_content, conversation.SystemContent):
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return ollama.Message(
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role=MessageRole.SYSTEM.value,
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content=chat_content.content,
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)
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raise TypeError(f"Unexpected content type: {type(chat_content)}")
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async def _transform_stream(
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result: AsyncIterator[ollama.ChatResponse],
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) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
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"""Transform the response stream into HA format.
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An Ollama streaming response may come in chunks like this:
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response: message=Message(role="assistant", content="Paris")
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response: message=Message(role="assistant", content=".")
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response: message=Message(role="assistant", content=""), done: True, done_reason: "stop"
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response: message=Message(role="assistant", tool_calls=[...])
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response: message=Message(role="assistant", content=""), done: True, done_reason: "stop"
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This generator conforms to the chatlog delta stream expectations in that it
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yields deltas, then the role only once the response is done.
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"""
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new_msg = True
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async for response in result:
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_LOGGER.debug("Received response: %s", response)
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response_message = response["message"]
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chunk: conversation.AssistantContentDeltaDict = {}
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if new_msg:
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new_msg = False
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chunk["role"] = "assistant"
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if (tool_calls := response_message.get("tool_calls")) is not None:
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chunk["tool_calls"] = [
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llm.ToolInput(
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tool_name=tool_call["function"]["name"],
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tool_args=_parse_tool_args(tool_call["function"]["arguments"]),
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)
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for tool_call in tool_calls
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]
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if (content := response_message.get("content")) is not None:
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chunk["content"] = content
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if response_message.get("done"):
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new_msg = True
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yield chunk
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class OllamaConversationEntity(
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conversation.ConversationEntity, conversation.AbstractConversationAgent
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conversation.ConversationEntity,
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conversation.AbstractConversationAgent,
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OllamaBaseLLMEntity,
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):
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"""Ollama conversation agent."""
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@ -185,17 +43,7 @@ class OllamaConversationEntity(
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def __init__(self, entry: OllamaConfigEntry, 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._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="Ollama",
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model=entry.data[CONF_MODEL],
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entry_type=dr.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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@ -255,89 +103,6 @@ class OllamaConversationEntity(
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continue_conversation=chat_log.continue_conversation,
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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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settings = {**self.entry.data, **self.subentry.data}
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client = self.entry.runtime_data
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model = settings[CONF_MODEL]
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tools: list[dict[str, Any]] | 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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message_history: MessageHistory = MessageHistory(
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[_convert_content(content) for content in chat_log.content]
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)
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max_messages = int(settings.get(CONF_MAX_HISTORY, DEFAULT_MAX_HISTORY))
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self._trim_history(message_history, max_messages)
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# Get response
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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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try:
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response_generator = await client.chat(
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model=model,
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# Make a copy of the messages because we mutate the list later
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messages=list(message_history.messages),
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tools=tools,
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stream=True,
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# keep_alive requires specifying unit. In this case, seconds
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keep_alive=f"{settings.get(CONF_KEEP_ALIVE, DEFAULT_KEEP_ALIVE)}s",
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options={CONF_NUM_CTX: settings.get(CONF_NUM_CTX, DEFAULT_NUM_CTX)},
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think=settings.get(CONF_THINK),
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)
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except (ollama.RequestError, ollama.ResponseError) as err:
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_LOGGER.error("Unexpected error talking to Ollama server: %s", err)
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raise HomeAssistantError(
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f"Sorry, I had a problem talking to the Ollama server: {err}"
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) from err
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message_history.messages.extend(
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[
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_convert_content(content)
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async for content in chat_log.async_add_delta_content_stream(
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self.entity_id, _transform_stream(response_generator)
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)
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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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def _trim_history(self, message_history: MessageHistory, max_messages: int) -> None:
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"""Trims excess messages from a single history.
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This sets the max history to allow a configurable size history may take
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up in the context window.
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Note that some messages in the history may not be from ollama only, and
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may come from other anents, so the assumptions here may not strictly hold,
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but generally should be effective.
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"""
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if max_messages < 1:
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# Keep all messages
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return
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# Ignore the in progress user message
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num_previous_rounds = message_history.num_user_messages - 1
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if num_previous_rounds >= max_messages:
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# Trim history but keep system prompt (first message).
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# Every other message should be an assistant message, so keep 2x
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# message objects. Also keep the last in progress user message
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num_keep = 2 * max_messages + 1
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drop_index = len(message_history.messages) - num_keep
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message_history.messages = [
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message_history.messages[0],
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*message_history.messages[drop_index:],
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]
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async def _async_entry_update_listener(
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self, hass: HomeAssistant, entry: ConfigEntry
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) -> None:
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258
homeassistant/components/ollama/entity.py
Normal file
258
homeassistant/components/ollama/entity.py
Normal file
@ -0,0 +1,258 @@
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"""Base entity for the Ollama integration."""
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from __future__ import annotations
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from collections.abc import AsyncGenerator, AsyncIterator, Callable
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import json
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import logging
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from typing import Any
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import ollama
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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 OllamaConfigEntry
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from .const import (
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CONF_KEEP_ALIVE,
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CONF_MAX_HISTORY,
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CONF_MODEL,
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CONF_NUM_CTX,
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CONF_THINK,
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DEFAULT_KEEP_ALIVE,
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DEFAULT_MAX_HISTORY,
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DEFAULT_NUM_CTX,
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DOMAIN,
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)
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from .models import MessageHistory, MessageRole
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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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_LOGGER = logging.getLogger(__name__)
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def _format_tool(
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tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
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) -> dict[str, Any]:
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"""Format tool specification."""
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tool_spec = {
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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 {"type": "function", "function": tool_spec}
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def _fix_invalid_arguments(value: Any) -> Any:
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"""Attempt to repair incorrectly formatted json function arguments.
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Small models (for example llama3.1 8B) may produce invalid argument values
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which we attempt to repair here.
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"""
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if not isinstance(value, str):
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return value
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if (value.startswith("[") and value.endswith("]")) or (
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value.startswith("{") and value.endswith("}")
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):
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try:
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return json.loads(value)
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except json.decoder.JSONDecodeError:
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pass
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return value
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def _parse_tool_args(arguments: dict[str, Any]) -> dict[str, Any]:
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"""Rewrite ollama tool arguments.
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This function improves tool use quality by fixing common mistakes made by
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small local tool use models. This will repair invalid json arguments and
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omit unnecessary arguments with empty values that will fail intent parsing.
|
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"""
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return {k: _fix_invalid_arguments(v) for k, v in arguments.items() if v}
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def _convert_content(
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chat_content: (
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conversation.Content
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| conversation.ToolResultContent
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| conversation.AssistantContent
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),
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) -> ollama.Message:
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"""Create tool response content."""
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if isinstance(chat_content, conversation.ToolResultContent):
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return ollama.Message(
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role=MessageRole.TOOL.value,
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content=json.dumps(chat_content.tool_result),
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)
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if isinstance(chat_content, conversation.AssistantContent):
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return ollama.Message(
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role=MessageRole.ASSISTANT.value,
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content=chat_content.content,
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tool_calls=[
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ollama.Message.ToolCall(
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function=ollama.Message.ToolCall.Function(
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name=tool_call.tool_name,
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arguments=tool_call.tool_args,
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)
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)
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for tool_call in chat_content.tool_calls or ()
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],
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)
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if isinstance(chat_content, conversation.UserContent):
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return ollama.Message(
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role=MessageRole.USER.value,
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content=chat_content.content,
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)
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if isinstance(chat_content, conversation.SystemContent):
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return ollama.Message(
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role=MessageRole.SYSTEM.value,
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content=chat_content.content,
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)
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raise TypeError(f"Unexpected content type: {type(chat_content)}")
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async def _transform_stream(
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result: AsyncIterator[ollama.ChatResponse],
|
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) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
|
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"""Transform the response stream into HA format.
|
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|
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An Ollama streaming response may come in chunks like this:
|
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|
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response: message=Message(role="assistant", content="Paris")
|
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response: message=Message(role="assistant", content=".")
|
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response: message=Message(role="assistant", content=""), done: True, done_reason: "stop"
|
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response: message=Message(role="assistant", tool_calls=[...])
|
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response: message=Message(role="assistant", content=""), done: True, done_reason: "stop"
|
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|
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This generator conforms to the chatlog delta stream expectations in that it
|
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yields deltas, then the role only once the response is done.
|
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"""
|
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|
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new_msg = True
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async for response in result:
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_LOGGER.debug("Received response: %s", response)
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response_message = response["message"]
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chunk: conversation.AssistantContentDeltaDict = {}
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if new_msg:
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new_msg = False
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chunk["role"] = "assistant"
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if (tool_calls := response_message.get("tool_calls")) is not None:
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chunk["tool_calls"] = [
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llm.ToolInput(
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tool_name=tool_call["function"]["name"],
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tool_args=_parse_tool_args(tool_call["function"]["arguments"]),
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)
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for tool_call in tool_calls
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]
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if (content := response_message.get("content")) is not None:
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chunk["content"] = content
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if response_message.get("done"):
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new_msg = True
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yield chunk
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class OllamaBaseLLMEntity(Entity):
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"""Ollama base LLM entity."""
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def __init__(self, entry: OllamaConfigEntry, 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="Ollama",
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model=entry.data[CONF_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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settings = {**self.entry.data, **self.subentry.data}
|
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|
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client = self.entry.runtime_data
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model = settings[CONF_MODEL]
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tools: list[dict[str, Any]] | 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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|
||||
message_history: MessageHistory = MessageHistory(
|
||||
[_convert_content(content) for content in chat_log.content]
|
||||
)
|
||||
max_messages = int(settings.get(CONF_MAX_HISTORY, DEFAULT_MAX_HISTORY))
|
||||
self._trim_history(message_history, max_messages)
|
||||
|
||||
# Get response
|
||||
# To prevent infinite loops, we limit the number of iterations
|
||||
for _iteration in range(MAX_TOOL_ITERATIONS):
|
||||
try:
|
||||
response_generator = await client.chat(
|
||||
model=model,
|
||||
# Make a copy of the messages because we mutate the list later
|
||||
messages=list(message_history.messages),
|
||||
tools=tools,
|
||||
stream=True,
|
||||
# keep_alive requires specifying unit. In this case, seconds
|
||||
keep_alive=f"{settings.get(CONF_KEEP_ALIVE, DEFAULT_KEEP_ALIVE)}s",
|
||||
options={CONF_NUM_CTX: settings.get(CONF_NUM_CTX, DEFAULT_NUM_CTX)},
|
||||
think=settings.get(CONF_THINK),
|
||||
)
|
||||
except (ollama.RequestError, ollama.ResponseError) as err:
|
||||
_LOGGER.error("Unexpected error talking to Ollama server: %s", err)
|
||||
raise HomeAssistantError(
|
||||
f"Sorry, I had a problem talking to the Ollama server: {err}"
|
||||
) from err
|
||||
|
||||
message_history.messages.extend(
|
||||
[
|
||||
_convert_content(content)
|
||||
async for content in chat_log.async_add_delta_content_stream(
|
||||
self.entity_id, _transform_stream(response_generator)
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
if not chat_log.unresponded_tool_results:
|
||||
break
|
||||
|
||||
def _trim_history(self, message_history: MessageHistory, max_messages: int) -> None:
|
||||
"""Trims excess messages from a single history.
|
||||
|
||||
This sets the max history to allow a configurable size history may take
|
||||
up in the context window.
|
||||
|
||||
Note that some messages in the history may not be from ollama only, and
|
||||
may come from other anents, so the assumptions here may not strictly hold,
|
||||
but generally should be effective.
|
||||
"""
|
||||
if max_messages < 1:
|
||||
# Keep all messages
|
||||
return
|
||||
|
||||
# Ignore the in progress user message
|
||||
num_previous_rounds = message_history.num_user_messages - 1
|
||||
if num_previous_rounds >= max_messages:
|
||||
# Trim history but keep system prompt (first message).
|
||||
# Every other message should be an assistant message, so keep 2x
|
||||
# message objects. Also keep the last in progress user message
|
||||
num_keep = 2 * max_messages + 1
|
||||
drop_index = len(message_history.messages) - num_keep
|
||||
message_history.messages = [
|
||||
message_history.messages[0],
|
||||
*message_history.messages[drop_index:],
|
||||
]
|
||||
@ -206,7 +206,7 @@ async def test_template_variables(
|
||||
),
|
||||
],
|
||||
)
|
||||
@patch("homeassistant.components.ollama.conversation.llm.AssistAPI._async_get_tools")
|
||||
@patch("homeassistant.components.ollama.entity.llm.AssistAPI._async_get_tools")
|
||||
async def test_function_call(
|
||||
mock_get_tools,
|
||||
hass: HomeAssistant,
|
||||
@ -293,7 +293,7 @@ async def test_function_call(
|
||||
)
|
||||
|
||||
|
||||
@patch("homeassistant.components.ollama.conversation.llm.AssistAPI._async_get_tools")
|
||||
@patch("homeassistant.components.ollama.entity.llm.AssistAPI._async_get_tools")
|
||||
async def test_function_exception(
|
||||
mock_get_tools,
|
||||
hass: HomeAssistant,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user