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conversation.py
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conversation.py
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"""Defines the various LLM Backend Agents"""
from __future__ import annotations
import aiohttp
import asyncio
import csv
import importlib
import json
import logging
import os
import random
import re
import threading
import time
import voluptuous as vol
from typing import Literal, Any, Callable
from homeassistant.components.conversation import ConversationInput, ConversationResult, AbstractConversationAgent, ConversationEntity
from homeassistant.components import assist_pipeline, conversation as ha_conversation
from homeassistant.components.conversation.const import DOMAIN as CONVERSATION_DOMAIN
from homeassistant.components.homeassistant.exposed_entities import async_should_expose
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import ATTR_ENTITY_ID, CONF_HOST, CONF_PORT, CONF_SSL, MATCH_ALL, CONF_LLM_HASS_API
from homeassistant.core import HomeAssistant, callback
from homeassistant.exceptions import ConfigEntryNotReady, ConfigEntryError, TemplateError, HomeAssistantError
from homeassistant.helpers import config_validation as cv, intent, template, entity_registry as er, llm, \
area_registry as ar, device_registry as dr
from homeassistant.helpers.entity_platform import AddEntitiesCallback
from homeassistant.helpers.aiohttp_client import async_get_clientsession
from homeassistant.helpers.event import async_track_state_change, async_call_later
from homeassistant.components.sensor import SensorEntity
from homeassistant.util import ulid, color
import voluptuous_serialize
from .utils import closest_color, flatten_vol_schema, custom_custom_serializer, install_llama_cpp_python, \
validate_llama_cpp_python_installation, format_url
from .const import (
CONF_CHAT_MODEL,
CONF_MAX_TOKENS,
CONF_PROMPT,
CONF_TEMPERATURE,
CONF_TOP_K,
CONF_TOP_P,
CONF_TYPICAL_P,
CONF_MIN_P,
CONF_REQUEST_TIMEOUT,
CONF_BACKEND_TYPE,
CONF_DOWNLOADED_MODEL_FILE,
CONF_EXTRA_ATTRIBUTES_TO_EXPOSE,
CONF_PROMPT_TEMPLATE,
CONF_TOOL_FORMAT,
CONF_TOOL_MULTI_TURN_CHAT,
CONF_ENABLE_FLASH_ATTENTION,
CONF_USE_GBNF_GRAMMAR,
CONF_GBNF_GRAMMAR_FILE,
CONF_USE_IN_CONTEXT_LEARNING_EXAMPLES,
CONF_IN_CONTEXT_EXAMPLES_FILE,
CONF_NUM_IN_CONTEXT_EXAMPLES,
CONF_TEXT_GEN_WEBUI_PRESET,
CONF_OPENAI_API_KEY,
CONF_TEXT_GEN_WEBUI_ADMIN_KEY,
CONF_REFRESH_SYSTEM_PROMPT,
CONF_REMEMBER_CONVERSATION,
CONF_REMEMBER_NUM_INTERACTIONS,
CONF_PROMPT_CACHING_ENABLED,
CONF_PROMPT_CACHING_INTERVAL,
CONF_SERVICE_CALL_REGEX,
CONF_REMOTE_USE_CHAT_ENDPOINT,
CONF_TEXT_GEN_WEBUI_CHAT_MODE,
CONF_OLLAMA_KEEP_ALIVE_MIN,
CONF_OLLAMA_JSON_MODE,
CONF_GENERIC_OPENAI_PATH,
CONF_CONTEXT_LENGTH,
CONF_BATCH_SIZE,
CONF_THREAD_COUNT,
CONF_BATCH_THREAD_COUNT,
DEFAULT_MAX_TOKENS,
DEFAULT_PROMPT,
DEFAULT_TEMPERATURE,
DEFAULT_TOP_K,
DEFAULT_TOP_P,
DEFAULT_MIN_P,
DEFAULT_TYPICAL_P,
DEFAULT_BACKEND_TYPE,
DEFAULT_REQUEST_TIMEOUT,
DEFAULT_EXTRA_ATTRIBUTES_TO_EXPOSE,
DEFAULT_PROMPT_TEMPLATE,
DEFAULT_TOOL_FORMAT,
DEFAULT_TOOL_MULTI_TURN_CHAT,
DEFAULT_ENABLE_FLASH_ATTENTION,
DEFAULT_USE_GBNF_GRAMMAR,
DEFAULT_GBNF_GRAMMAR_FILE,
DEFAULT_USE_IN_CONTEXT_LEARNING_EXAMPLES,
DEFAULT_IN_CONTEXT_EXAMPLES_FILE,
DEFAULT_NUM_IN_CONTEXT_EXAMPLES,
DEFAULT_REFRESH_SYSTEM_PROMPT,
DEFAULT_REMEMBER_CONVERSATION,
DEFAULT_REMEMBER_NUM_INTERACTIONS,
DEFAULT_PROMPT_CACHING_ENABLED,
DEFAULT_PROMPT_CACHING_INTERVAL,
DEFAULT_SERVICE_CALL_REGEX,
DEFAULT_REMOTE_USE_CHAT_ENDPOINT,
DEFAULT_TEXT_GEN_WEBUI_CHAT_MODE,
DEFAULT_OLLAMA_KEEP_ALIVE_MIN,
DEFAULT_OLLAMA_JSON_MODE,
DEFAULT_GENERIC_OPENAI_PATH,
DEFAULT_CONTEXT_LENGTH,
DEFAULT_BATCH_SIZE,
DEFAULT_THREAD_COUNT,
DEFAULT_BATCH_THREAD_COUNT,
TEXT_GEN_WEBUI_CHAT_MODE_CHAT,
TEXT_GEN_WEBUI_CHAT_MODE_INSTRUCT,
TEXT_GEN_WEBUI_CHAT_MODE_CHAT_INSTRUCT,
DOMAIN,
HOME_LLM_API_ID,
SERVICE_TOOL_NAME,
PROMPT_TEMPLATE_DESCRIPTIONS,
TOOL_FORMAT_FULL,
TOOL_FORMAT_REDUCED,
TOOL_FORMAT_MINIMAL,
ALLOWED_SERVICE_CALL_ARGUMENTS,
CONF_BACKEND_TYPE,
DEFAULT_BACKEND_TYPE,
BACKEND_TYPE_LLAMA_HF,
BACKEND_TYPE_LLAMA_EXISTING,
BACKEND_TYPE_TEXT_GEN_WEBUI,
BACKEND_TYPE_GENERIC_OPENAI,
BACKEND_TYPE_LLAMA_CPP_PYTHON_SERVER,
BACKEND_TYPE_OLLAMA,
)
# make type checking work for llama-cpp-python without importing it directly at runtime
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from llama_cpp import Llama as LlamaType
else:
LlamaType = Any
_LOGGER = logging.getLogger(__name__)
CONFIG_SCHEMA = cv.config_entry_only_config_schema(DOMAIN)
async def update_listener(hass: HomeAssistant, entry: ConfigEntry):
"""Handle options update."""
hass.data[DOMAIN][entry.entry_id] = entry
# call update handler
agent: LocalLLMAgent = ha_conversation.get_agent_manager(hass).async_get_agent(entry.entry_id)
await hass.async_add_executor_job(agent._update_options)
return True
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback) -> bool:
"""Set up Local LLM Conversation from a config entry."""
def create_agent(backend_type):
agent_cls = None
if backend_type in [ BACKEND_TYPE_LLAMA_HF, BACKEND_TYPE_LLAMA_EXISTING ]:
agent_cls = LlamaCppAgent
elif backend_type == BACKEND_TYPE_GENERIC_OPENAI:
agent_cls = GenericOpenAIAPIAgent
elif backend_type == BACKEND_TYPE_TEXT_GEN_WEBUI:
agent_cls = TextGenerationWebuiAgent
elif backend_type == BACKEND_TYPE_LLAMA_CPP_PYTHON_SERVER:
agent_cls = LlamaCppPythonAPIAgent
elif backend_type == BACKEND_TYPE_OLLAMA:
agent_cls = OllamaAPIAgent
return agent_cls(hass, entry)
# create the agent in an executor job because the constructor calls `open()`
backend_type = entry.data.get(CONF_BACKEND_TYPE, DEFAULT_BACKEND_TYPE)
agent = await hass.async_add_executor_job(create_agent, backend_type)
# call load model
await agent._async_load_model(entry)
# handle updates to the options
entry.async_on_unload(entry.add_update_listener(update_listener))
async_add_entities([agent])
return True
class LocalLLMAgent(ConversationEntity, AbstractConversationAgent):
"""Base Local LLM conversation agent."""
hass: HomeAssistant
entry_id: str
history: dict[str, list[dict]]
in_context_examples: list[dict]
_attr_has_entity_name = True
def __init__(self, hass: HomeAssistant, entry: ConfigEntry) -> None:
"""Initialize the agent."""
self._attr_name = entry.title
self._attr_unique_id = entry.entry_id
self.hass = hass
self.entry_id = entry.entry_id
self.history = {}
self.backend_type = entry.data.get(
CONF_BACKEND_TYPE, DEFAULT_BACKEND_TYPE
)
if self.entry.options.get(CONF_LLM_HASS_API):
self._attr_supported_features = (
ha_conversation.ConversationEntityFeature.CONTROL
)
self.in_context_examples = None
if entry.options.get(CONF_USE_IN_CONTEXT_LEARNING_EXAMPLES, DEFAULT_USE_IN_CONTEXT_LEARNING_EXAMPLES):
self._load_icl_examples(entry.options.get(CONF_IN_CONTEXT_EXAMPLES_FILE, DEFAULT_IN_CONTEXT_EXAMPLES_FILE))
async def async_added_to_hass(self) -> None:
"""When entity is added to Home Assistant."""
await super().async_added_to_hass()
assist_pipeline.async_migrate_engine(
self.hass, "conversation", self.entry.entry_id, self.entity_id
)
ha_conversation.async_set_agent(self.hass, self.entry, self)
async def async_will_remove_from_hass(self) -> None:
"""When entity will be removed from Home Assistant."""
ha_conversation.async_unset_agent(self.hass, self.entry)
await super().async_will_remove_from_hass()
def _load_icl_examples(self, filename: str):
"""Load info used for generating in context learning examples"""
try:
icl_filename = os.path.join(os.path.dirname(__file__), filename)
with open(icl_filename, encoding="utf-8-sig") as f:
self.in_context_examples = list(csv.DictReader(f))
if set(self.in_context_examples[0].keys()) != set(["type", "request", "tool", "response" ]):
raise Exception("ICL csv file did not have 2 columns: service & response")
if len(self.in_context_examples) == 0:
_LOGGER.warning(f"There were no in context learning examples found in the file '{filename}'!")
self.in_context_examples = None
else:
_LOGGER.debug(f"Loaded {len(self.in_context_examples)} examples for ICL")
except Exception:
_LOGGER.exception("Failed to load in context learning examples!")
self.in_context_examples = None
def _update_options(self):
if self.entry.options.get(CONF_LLM_HASS_API):
self._attr_supported_features = (
ha_conversation.ConversationEntityFeature.CONTROL
)
if self.entry.options.get(CONF_USE_IN_CONTEXT_LEARNING_EXAMPLES, DEFAULT_USE_IN_CONTEXT_LEARNING_EXAMPLES):
self._load_icl_examples(self.entry.options.get(CONF_IN_CONTEXT_EXAMPLES_FILE, DEFAULT_IN_CONTEXT_EXAMPLES_FILE))
else:
self.in_context_examples = None
@property
def entry(self) -> ConfigEntry:
try:
return self.hass.data[DOMAIN][self.entry_id]
except KeyError as ex:
raise Exception("Attempted to use self.entry during startup.") from ex
@property
def supported_languages(self) -> list[str] | Literal["*"]:
"""Return a list of supported languages."""
return MATCH_ALL
def _load_model(self, entry: ConfigEntry) -> None:
"""Load the model on the backend. Implemented by sub-classes"""
raise NotImplementedError()
async def _async_load_model(self, entry: ConfigEntry) -> str:
"""Default implementation is to call _load_model() which probably does blocking stuff"""
return await self.hass.async_add_executor_job(
self._load_model, entry
)
def _generate(self, conversation: dict) -> str:
"""Call the backend to generate a response from the conversation. Implemented by sub-classes"""
raise NotImplementedError()
async def _async_generate(self, conversation: dict) -> str:
"""Default implementation is to call _generate() which probably does blocking stuff"""
return await self.hass.async_add_executor_job(
self._generate, conversation
)
def _warn_context_size(self):
num_entities = len(self._async_get_exposed_entities()[0])
context_size = self.entry.options.get(CONF_CONTEXT_LENGTH, DEFAULT_CONTEXT_LENGTH)
_LOGGER.error("There were too many entities exposed when attempting to generate a response for " +
f"{self.entry.data[CONF_CHAT_MODEL]} and it exceeded the context size for the model. " +
f"Please reduce the number of entities exposed ({num_entities}) or increase the model's context size ({int(context_size)})")
async def async_process(
self, user_input: ConversationInput
) -> ConversationResult:
"""Process a sentence."""
raw_prompt = self.entry.options.get(CONF_PROMPT, DEFAULT_PROMPT)
prompt_template = self.entry.options.get(CONF_PROMPT_TEMPLATE, DEFAULT_PROMPT_TEMPLATE)
template_desc = PROMPT_TEMPLATE_DESCRIPTIONS[prompt_template]
refresh_system_prompt = self.entry.options.get(CONF_REFRESH_SYSTEM_PROMPT, DEFAULT_REFRESH_SYSTEM_PROMPT)
remember_conversation = self.entry.options.get(CONF_REMEMBER_CONVERSATION, DEFAULT_REMEMBER_CONVERSATION)
remember_num_interactions = self.entry.options.get(CONF_REMEMBER_NUM_INTERACTIONS, DEFAULT_REMEMBER_NUM_INTERACTIONS)
service_call_regex = self.entry.options.get(CONF_SERVICE_CALL_REGEX, DEFAULT_SERVICE_CALL_REGEX)
try:
service_call_pattern = re.compile(service_call_regex)
except Exception as err:
_LOGGER.exception("There was a problem compiling the service call regex")
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.UNKNOWN,
f"Sorry, there was a problem compiling the service call regex: {err}",
)
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
llm_api: llm.APIInstance | None = None
if self.entry.options.get(CONF_LLM_HASS_API):
try:
llm_api = await llm.async_get_api(
self.hass,
self.entry.options[CONF_LLM_HASS_API],
llm_context=llm.LLMContext(
platform=DOMAIN,
context=user_input.context,
user_prompt=user_input.text,
language=user_input.language,
assistant=ha_conversation.DOMAIN,
device_id=user_input.device_id,
)
)
except HomeAssistantError as err:
_LOGGER.error("Error getting LLM API: %s", err)
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.UNKNOWN,
f"Error preparing LLM API: {err}",
)
return ConversationResult(
response=intent_response, conversation_id=user_input.conversation_id
)
if user_input.conversation_id in self.history:
conversation_id = user_input.conversation_id
conversation = self.history[conversation_id] if remember_conversation else [self.history[conversation_id][0]]
else:
conversation_id = ulid.ulid()
conversation = []
if len(conversation) == 0 or refresh_system_prompt:
try:
message = self._generate_system_prompt(raw_prompt, llm_api)
except TemplateError as err:
_LOGGER.error("Error rendering prompt: %s", err)
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.UNKNOWN,
f"Sorry, I had a problem with my template: {err}",
)
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
system_prompt = { "role": "system", "message": message }
if len(conversation) == 0:
conversation.append(system_prompt)
if not remember_conversation:
self.history[conversation_id] = conversation
else:
conversation[0] = system_prompt
conversation.append({"role": "user", "message": user_input.text})
# generate a response
try:
_LOGGER.debug(conversation)
response = await self._async_generate(conversation)
_LOGGER.debug(response)
except Exception as err:
_LOGGER.exception("There was a problem talking to the backend")
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.FAILED_TO_HANDLE,
f"Sorry, there was a problem talking to the backend: {repr(err)}",
)
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
# remove end of text token if it was returned
response = response.replace(template_desc["assistant"]["suffix"], "")
conversation.append({"role": "assistant", "message": response})
if remember_conversation:
if remember_num_interactions and len(conversation) > (remember_num_interactions * 2) + 1:
for i in range(0,2):
conversation.pop(1)
self.history[conversation_id] = conversation
if llm_api is None:
# return the output without messing with it if there is no API exposed to the model
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_speech(response.strip())
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
# parse response
to_say = service_call_pattern.sub("", response.strip())
for block in service_call_pattern.findall(response.strip()):
parsed_tool_call: dict = json.loads(block)
if llm_api.api.id == HOME_LLM_API_ID:
schema_to_validate = vol.Schema({
vol.Required('service'): str,
vol.Required('target_device'): str,
vol.Optional('rgb_color'): str,
vol.Optional('brightness'): float,
vol.Optional('temperature'): float,
vol.Optional('humidity'): float,
vol.Optional('fan_mode'): str,
vol.Optional('hvac_mode'): str,
vol.Optional('preset_mode'): str,
vol.Optional('duration'): str,
vol.Optional('item'): str,
})
else:
schema_to_validate = vol.Schema({
vol.Required("name"): str,
vol.Required("arguments"): dict,
})
try:
schema_to_validate(parsed_tool_call)
except vol.Error as ex:
_LOGGER.info(f"LLM produced an improperly formatted response: {repr(ex)}")
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.NO_INTENT_MATCH,
f"I'm sorry, I didn't produce a correctly formatted tool call! Please see the logs for more info.",
)
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
_LOGGER.info(f"calling tool: {block}")
# try to fix certain arguments
args_dict = parsed_tool_call if llm_api.api.id == HOME_LLM_API_ID else parsed_tool_call["arguments"]
# make sure brightness is 0-255 and not a percentage
if "brightness" in args_dict and 0.0 < args_dict["brightness"] <= 1.0:
args_dict["brightness"] = int(args_dict["brightness"] * 255)
# convert string "tuple" to a list for RGB colors
if "rgb_color" in args_dict and isinstance(args_dict["rgb_color"], str):
args_dict["rgb_color"] = [ int(x) for x in args_dict["rgb_color"][1:-1].split(",") ]
if llm_api.api.id == HOME_LLM_API_ID:
to_say = to_say + parsed_tool_call.pop("to_say", "")
tool_input = llm.ToolInput(
tool_name=SERVICE_TOOL_NAME,
tool_args=parsed_tool_call,
)
else:
tool_input = llm.ToolInput(
tool_name=parsed_tool_call["name"],
tool_args=parsed_tool_call["arguments"],
)
tool_response = None
try:
tool_response = await llm_api.async_call_tool(tool_input)
_LOGGER.debug("Tool response: %s", tool_response)
except (HomeAssistantError, vol.Invalid) as e:
tool_response = {"error": type(e).__name__}
if str(e):
tool_response["error_text"] = str(e)
_LOGGER.debug("Tool response: %s", tool_response)
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.NO_INTENT_MATCH,
f"I'm sorry! I encountered an error calling the tool. See the logs for more info.",
)
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
# handle models that generate a function call and wait for the result before providing a response
if self.entry.options.get(CONF_TOOL_MULTI_TURN_CHAT, DEFAULT_TOOL_MULTI_TURN_CHAT):
conversation.append({"role": "tool", "message": json.dumps(tool_response)})
# generate a response based on the tool result
try:
_LOGGER.debug(conversation)
to_say = await self._async_generate(conversation)
_LOGGER.debug(to_say)
except Exception as err:
_LOGGER.exception("There was a problem talking to the backend")
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_error(
intent.IntentResponseErrorCode.FAILED_TO_HANDLE,
f"Sorry, there was a problem talking to the backend: {repr(err)}",
)
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
conversation.append({"role": "assistant", "message": response})
# generate intent response to Home Assistant
intent_response = intent.IntentResponse(language=user_input.language)
intent_response.async_set_speech(to_say.strip())
return ConversationResult(
response=intent_response, conversation_id=conversation_id
)
def _async_get_exposed_entities(self) -> tuple[dict[str, str], list[str]]:
"""Gather exposed entity states"""
entity_states = {}
domains = set()
entity_registry = er.async_get(self.hass)
device_registry = dr.async_get(self.hass)
area_registry = ar.async_get(self.hass)
for state in self.hass.states.async_all():
if not async_should_expose(self.hass, CONVERSATION_DOMAIN, state.entity_id):
continue
entity = entity_registry.async_get(state.entity_id)
device = None
if entity and entity.device_id:
device = device_registry.async_get(entity.device_id)
attributes = dict(state.attributes)
attributes["state"] = state.state
if entity:
if entity.aliases:
attributes["aliases"] = entity.aliases
if entity.unit_of_measurement:
attributes["state"] = attributes["state"] + " " + entity.unit_of_measurement
# area could be on device or entity. prefer device area
area_id = None
if device and device.area_id:
area_id = device.area_id
if entity and entity.area_id:
area_id = entity.area_id
if area_id:
area = area_registry.async_get_area(entity.area_id)
if area:
attributes["area_id"] = area.id
attributes["area_name"] = area.name
entity_states[state.entity_id] = attributes
domains.add(state.domain)
return entity_states, list(domains)
def _format_prompt(
self, prompt: list[dict], include_generation_prompt: bool = True
) -> str:
"""Format a conversation into a raw text completion using the model's prompt template"""
formatted_prompt = ""
prompt_template = self.entry.options.get(CONF_PROMPT_TEMPLATE, DEFAULT_PROMPT_TEMPLATE)
template_desc = PROMPT_TEMPLATE_DESCRIPTIONS[prompt_template]
# handle models without a system prompt
if prompt[0]["role"] == "system" and "system" not in template_desc:
system_prompt = prompt.pop(0)
prompt[0]["message"] = system_prompt["message"] + prompt[0]["message"]
for message in prompt:
role = message["role"]
message = message["message"]
# fall back to the "user" role for unknown roles
role_desc = template_desc.get(role, template_desc["user"])
formatted_prompt = (
formatted_prompt + f"{role_desc['prefix']}{message}{role_desc['suffix']}\n"
)
if include_generation_prompt:
formatted_prompt = formatted_prompt + template_desc["generation_prompt"]
_LOGGER.debug(formatted_prompt)
return formatted_prompt
def _format_tool(self, name: str, parameters: vol.Schema, description: str):
style = self.entry.options.get(CONF_TOOL_FORMAT, DEFAULT_TOOL_FORMAT)
if style == TOOL_FORMAT_MINIMAL:
result = f"{name}({','.join(flatten_vol_schema(parameters))})"
if description:
result = result + f" - {description}"
return result
raw_parameters: list = voluptuous_serialize.convert(
parameters, custom_serializer=custom_custom_serializer)
# handle vol.Any in the key side of things
processed_parameters = []
for param in raw_parameters:
if isinstance(param["name"], vol.Any):
for possible_name in param["name"].validators:
actual_param = param.copy()
actual_param["name"] = possible_name
actual_param["required"] = False
processed_parameters.append(actual_param)
else:
processed_parameters.append(param)
if style == TOOL_FORMAT_REDUCED:
return {
"name": name,
"description": description,
"parameters": {
"properties": {
x["name"]: x.get("type", "string") for x in processed_parameters
},
"required": [
x["name"] for x in processed_parameters if x.get("required")
]
}
}
elif style == TOOL_FORMAT_FULL:
return {
"type": "function",
"function": {
"name": name,
"description": description,
"parameters": {
"type": "object",
"properties": {
x["name"]: {
"type": x.get("type", "string"),
"description": x.get("description", ""),
} for x in processed_parameters
},
"required": [
x["name"] for x in processed_parameters if x.get("required")
]
}
}
}
raise Exception(f"Unknown tool format {style}")
def _generate_icl_examples(self, num_examples, entity_names):
entity_names = entity_names[:]
entity_domains = set([x.split(".")[0] for x in entity_names])
area_registry = ar.async_get(self.hass)
all_areas = list(area_registry.async_list_areas())
in_context_examples = [
x for x in self.in_context_examples
if x["type"] in entity_domains
]
random.shuffle(in_context_examples)
random.shuffle(entity_names)
num_examples_to_generate = min(num_examples, len(in_context_examples))
if num_examples_to_generate < num_examples:
_LOGGER.warning(f"Attempted to generate {num_examples} ICL examples for conversation, but only {len(in_context_examples)} are available!")
examples = []
for _ in range(num_examples_to_generate):
chosen_example = in_context_examples.pop()
request = chosen_example["request"]
response = chosen_example["response"]
random_device = [ x for x in entity_names if x.split(".")[0] == chosen_example["type"] ][0]
random_area = random.choice(all_areas).name
random_brightness = round(random.random(), 2)
random_color = random.choice(list(color.COLORS.keys()))
tool_arguments = {}
if "<area>" in request:
request = request.replace("<area>", random_area)
response = response.replace("<area>", random_area)
tool_arguments["area"] = random_area
if "<name>" in request:
request = request.replace("<name>", random_device)
response = response.replace("<name>", random_device)
tool_arguments["name"] = random_device
if "<brightness>" in request:
request = request.replace("<brightness>", str(random_brightness))
response = response.replace("<brightness>", str(random_brightness))
tool_arguments["brightness"] = random_brightness
if "<color>" in request:
request = request.replace("<color>", random_color)
response = response.replace("<color>", random_color)
tool_arguments["color"] = random_color
examples.append({
"request": request,
"response": response,
"tool": {
"name": chosen_example["tool"],
"arguments": tool_arguments
}
})
return examples
def _generate_system_prompt(self, prompt_template: str, llm_api: llm.APIInstance) -> str:
"""Generate the system prompt with current entity states"""
entities_to_expose, domains = self._async_get_exposed_entities()
extra_attributes_to_expose = self.entry.options \
.get(CONF_EXTRA_ATTRIBUTES_TO_EXPOSE, DEFAULT_EXTRA_ATTRIBUTES_TO_EXPOSE)
def expose_attributes(attributes) -> list[str]:
result = []
for attribute_name in extra_attributes_to_expose:
if attribute_name not in attributes:
continue
_LOGGER.debug(f"{attribute_name} = {attributes[attribute_name]}")
value = attributes[attribute_name]
if value is not None:
if attribute_name == "temperature":
value = int(value)
if value > 50:
value = f"{value}F"
else:
value = f"{value}C"
elif attribute_name == "rgb_color":
value = F"{closest_color(value)} {value}"
elif attribute_name == "volume_level":
value = f"vol={int(value*100)}"
elif attribute_name == "brightness":
value = f"{int(value/255*100)}%"
elif attribute_name == "humidity":
value = f"{value}%"
result.append(str(value))
return result
devices = []
formatted_devices = ""
# expose devices and their alias as well
for name, attributes in entities_to_expose.items():
state = attributes["state"]
exposed_attributes = expose_attributes(attributes)
str_attributes = ";".join([state] + exposed_attributes)
formatted_devices = formatted_devices + f"{name} '{attributes.get('friendly_name')}' = {str_attributes}\n"
devices.append({
"entity_id": name,
"name": attributes.get('friendly_name'),
"state": state,
"attributes": exposed_attributes,
"area_name": attributes.get("area_name"),
"area_id": attributes.get("area_id"),
"is_alias": False
})
if "aliases" in attributes:
for alias in attributes["aliases"]:
formatted_devices = formatted_devices + f"{name} '{alias}' = {str_attributes}\n"
devices.append({
"entity_id": name,
"name": alias,
"state": state,
"attributes": exposed_attributes,
"area_name": attributes.get("area_name"),
"area_id": attributes.get("area_id"),
"is_alias": True
})
if llm_api:
if llm_api.api.id == HOME_LLM_API_ID:
service_dict = self.hass.services.async_services()
all_services = []
scripts_added = False
for domain in domains:
# scripts show up as individual services
if domain == "script" and not scripts_added:
all_services.extend([
("script.reload", vol.Schema({}), ""),
("script.turn_on", vol.Schema({}), ""),
("script.turn_off", vol.Schema({}), ""),
("script.toggle", vol.Schema({}), ""),
])
scripts_added = True
continue
for name, service in service_dict.get(domain, {}).items():
args = flatten_vol_schema(service.schema)
args_to_expose = set(args).intersection(ALLOWED_SERVICE_CALL_ARGUMENTS)
service_schema = vol.Schema({
vol.Optional(arg): str for arg in args_to_expose
})
all_services.append((f"{domain}.{name}", service_schema, ""))
tools = [
self._format_tool(*tool)
for tool in all_services
]
else:
tools = [
self._format_tool(tool.name, tool.parameters, tool.description)
for tool in llm_api.tools
]
if self.entry.options.get(CONF_TOOL_FORMAT, DEFAULT_TOOL_FORMAT) == TOOL_FORMAT_MINIMAL:
formatted_tools = ", ".join(tools)
else:
formatted_tools = json.dumps(tools)
else:
tools = ["No tools were provided. If the user requests you interact with a device, tell them you are unable to do so."]
formatted_tools = tools[0]
render_variables = {
"devices": devices,
"formatted_devices": formatted_devices,
"tools": tools,
"formatted_tools": formatted_tools,
"response_examples": []
}
# only pass examples if there are loaded examples + an API was exposed
if self.in_context_examples and llm_api:
num_examples = int(self.entry.options.get(CONF_NUM_IN_CONTEXT_EXAMPLES, DEFAULT_NUM_IN_CONTEXT_EXAMPLES))
render_variables["response_examples"] = self._generate_icl_examples(num_examples, list(entities_to_expose.keys()))
return template.Template(prompt_template, self.hass).async_render(
render_variables,
parse_result=False,
)
class LlamaCppAgent(LocalLLMAgent):
model_path: str
llm: LlamaType
grammar: Any
llama_cpp_module: Any
remove_prompt_caching_listener: Callable
model_lock: threading.Lock
last_cache_prime: float
last_updated_entities: dict[str, float]
cache_refresh_after_cooldown: bool
loaded_model_settings: dict[str, Any]
def _load_model(self, entry: ConfigEntry) -> None:
self.model_path = entry.data.get(CONF_DOWNLOADED_MODEL_FILE)
_LOGGER.info(
"Using model file '%s'", self.model_path
)
if not self.model_path:
raise Exception(f"Model was not found at '{self.model_path}'!")
validate_llama_cpp_python_installation()
# don't import it until now because the wheel is installed by config_flow.py
try:
self.llama_cpp_module = importlib.import_module("llama_cpp")
except ModuleNotFoundError:
# attempt to re-install llama-cpp-python if it was uninstalled for some reason
install_result = install_llama_cpp_python(self.hass.config.config_dir)
if not install_result == True:
raise ConfigEntryError("llama-cpp-python was not installed on startup and re-installing it led to an error!")
validate_llama_cpp_python_installation()
self.llama_cpp_module = importlib.import_module("llama_cpp")
Llama = getattr(self.llama_cpp_module, "Llama")
_LOGGER.debug(f"Loading model '{self.model_path}'...")
self.loaded_model_settings = {}
self.loaded_model_settings[CONF_CONTEXT_LENGTH] = entry.options.get(CONF_CONTEXT_LENGTH, DEFAULT_CONTEXT_LENGTH)
self.loaded_model_settings[CONF_BATCH_SIZE] = entry.options.get(CONF_BATCH_SIZE, DEFAULT_BATCH_SIZE)
self.loaded_model_settings[CONF_THREAD_COUNT] = entry.options.get(CONF_THREAD_COUNT, DEFAULT_THREAD_COUNT)
self.loaded_model_settings[CONF_BATCH_THREAD_COUNT] = entry.options.get(CONF_BATCH_THREAD_COUNT, DEFAULT_BATCH_THREAD_COUNT)
self.loaded_model_settings[CONF_ENABLE_FLASH_ATTENTION] = entry.options.get(CONF_ENABLE_FLASH_ATTENTION, DEFAULT_ENABLE_FLASH_ATTENTION)
self.llm = Llama(
model_path=self.model_path,
n_ctx=int(self.loaded_model_settings[CONF_CONTEXT_LENGTH]),
n_batch=int(self.loaded_model_settings[CONF_BATCH_SIZE]),
n_threads=int(self.loaded_model_settings[CONF_THREAD_COUNT]),
n_threads_batch=int(self.loaded_model_settings[CONF_BATCH_THREAD_COUNT]),
flash_attn=self.loaded_model_settings[CONF_ENABLE_FLASH_ATTENTION],
)
_LOGGER.debug("Model loaded")
self.grammar = None
if entry.options.get(CONF_USE_GBNF_GRAMMAR, DEFAULT_USE_GBNF_GRAMMAR):
self._load_grammar(entry.options.get(CONF_GBNF_GRAMMAR_FILE, DEFAULT_GBNF_GRAMMAR_FILE))
# TODO: check about disk caching
# self.llm.set_cache(self.llama_cpp_module.LlamaDiskCache(
# capacity_bytes=(512 * 10e8),
# cache_dir=os.path.join(self.hass.config.media_dirs.get("local", self.hass.config.path("media")), "kv_cache")
# ))
self.remove_prompt_caching_listener = None
self.last_cache_prime = None
self.last_updated_entities = {}
self.cache_refresh_after_cooldown = False
self.model_lock = threading.Lock()
self.loaded_model_settings[CONF_PROMPT_CACHING_ENABLED] = entry.options.get(CONF_PROMPT_CACHING_ENABLED, DEFAULT_PROMPT_CACHING_ENABLED)
if self.loaded_model_settings[CONF_PROMPT_CACHING_ENABLED]:
@callback
async def enable_caching_after_startup(_now) -> None:
self._set_prompt_caching(enabled=True)
await self._async_cache_prompt(None, None, None)
async_call_later(self.hass, 5.0, enable_caching_after_startup)
def _load_grammar(self, filename: str):
LlamaGrammar = getattr(self.llama_cpp_module, "LlamaGrammar")
_LOGGER.debug(f"Loading grammar {filename}...")
try:
with open(os.path.join(os.path.dirname(__file__), filename)) as f:
grammar_str = "".join(f.readlines())
self.grammar = LlamaGrammar.from_string(grammar_str)
self.loaded_model_settings[CONF_GBNF_GRAMMAR_FILE] = filename
_LOGGER.debug("Loaded grammar")
except Exception:
_LOGGER.exception("Failed to load grammar!")
self.grammar = None
def _update_options(self):
LocalLLMAgent._update_options(self)
model_reloaded = False
if self.loaded_model_settings[CONF_CONTEXT_LENGTH] != self.entry.options.get(CONF_CONTEXT_LENGTH, DEFAULT_CONTEXT_LENGTH) or \
self.loaded_model_settings[CONF_BATCH_SIZE] != self.entry.options.get(CONF_BATCH_SIZE, DEFAULT_BATCH_SIZE) or \
self.loaded_model_settings[CONF_THREAD_COUNT] != self.entry.options.get(CONF_THREAD_COUNT, DEFAULT_THREAD_COUNT) or \
self.loaded_model_settings[CONF_BATCH_THREAD_COUNT] != self.entry.options.get(CONF_BATCH_THREAD_COUNT, DEFAULT_BATCH_THREAD_COUNT) or \
self.loaded_model_settings[CONF_ENABLE_FLASH_ATTENTION] != self.entry.options.get(CONF_ENABLE_FLASH_ATTENTION, DEFAULT_ENABLE_FLASH_ATTENTION):
_LOGGER.debug(f"Reloading model '{self.model_path}'...")
self.loaded_model_settings[CONF_CONTEXT_LENGTH] = self.entry.options.get(CONF_CONTEXT_LENGTH, DEFAULT_CONTEXT_LENGTH)
self.loaded_model_settings[CONF_BATCH_SIZE] = self.entry.options.get(CONF_BATCH_SIZE, DEFAULT_BATCH_SIZE)
self.loaded_model_settings[CONF_THREAD_COUNT] = self.entry.options.get(CONF_THREAD_COUNT, DEFAULT_THREAD_COUNT)
self.loaded_model_settings[CONF_BATCH_THREAD_COUNT] = self.entry.options.get(CONF_BATCH_THREAD_COUNT, DEFAULT_BATCH_THREAD_COUNT)
self.loaded_model_settings[CONF_ENABLE_FLASH_ATTENTION] = self.entry.options.get(CONF_ENABLE_FLASH_ATTENTION, DEFAULT_ENABLE_FLASH_ATTENTION)
Llama = getattr(self.llama_cpp_module, "Llama")
self.llm = Llama(
model_path=self.model_path,
n_ctx=int(self.loaded_model_settings[CONF_CONTEXT_LENGTH]),
n_batch=int(self.loaded_model_settings[CONF_BATCH_SIZE]),
n_threads=int(self.loaded_model_settings[CONF_THREAD_COUNT]),
n_threads_batch=int(self.loaded_model_settings[CONF_BATCH_THREAD_COUNT]),
flash_attn=self.loaded_model_settings[CONF_ENABLE_FLASH_ATTENTION],
)
_LOGGER.debug("Model loaded")
model_reloaded = True
if self.entry.options.get(CONF_USE_GBNF_GRAMMAR, DEFAULT_USE_GBNF_GRAMMAR):
current_grammar = self.entry.options.get(CONF_GBNF_GRAMMAR_FILE, DEFAULT_GBNF_GRAMMAR_FILE)
if not self.grammar or self.loaded_model_settings[CONF_GBNF_GRAMMAR_FILE] != current_grammar:
self._load_grammar(current_grammar)
else:
self.grammar = None
if self.entry.options.get(CONF_PROMPT_CACHING_ENABLED, DEFAULT_PROMPT_CACHING_ENABLED):
self._set_prompt_caching(enabled=True)
if self.loaded_model_settings[CONF_PROMPT_CACHING_ENABLED] != self.entry.options.get(CONF_PROMPT_CACHING_ENABLED, DEFAULT_PROMPT_CACHING_ENABLED) or \
model_reloaded:
self.loaded_model_settings[CONF_PROMPT_CACHING_ENABLED] = self.entry.options.get(CONF_PROMPT_CACHING_ENABLED, DEFAULT_PROMPT_CACHING_ENABLED)