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Extract Google LLM base entity class (#146817)
This commit is contained in:
Paulus Schoutsen
GitHub
parent
9f19c4250a
commit
ec02f6d010
@ -2,63 +2,18 @@
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from __future__ import annotations
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from __future__ import annotations
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import codecs
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from typing import Literal
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from collections.abc import AsyncGenerator, Callable
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from dataclasses import replace
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from typing import Any, Literal, cast
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from google.genai.errors import APIError, ClientError
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from google.genai.types import (
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AutomaticFunctionCallingConfig,
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Content,
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FunctionDeclaration,
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GenerateContentConfig,
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GenerateContentResponse,
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GoogleSearch,
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HarmCategory,
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Part,
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SafetySetting,
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Schema,
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Tool,
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)
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from voluptuous_openapi import convert
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from homeassistant.components import assist_pipeline, conversation
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from homeassistant.components import assist_pipeline, conversation
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from homeassistant.config_entries import ConfigEntry
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from homeassistant.config_entries import ConfigEntry
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from homeassistant.const import CONF_LLM_HASS_API, MATCH_ALL
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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.core import HomeAssistant
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from homeassistant.exceptions import HomeAssistantError
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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 homeassistant.helpers.entity_platform import AddConfigEntryEntitiesCallback
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from .const import (
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from .const import CONF_PROMPT, DOMAIN, LOGGER
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CONF_CHAT_MODEL,
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from .entity import ERROR_GETTING_RESPONSE, GoogleGenerativeAILLMBaseEntity
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CONF_DANGEROUS_BLOCK_THRESHOLD,
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CONF_HARASSMENT_BLOCK_THRESHOLD,
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CONF_HATE_BLOCK_THRESHOLD,
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CONF_MAX_TOKENS,
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CONF_PROMPT,
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CONF_SEXUAL_BLOCK_THRESHOLD,
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CONF_TEMPERATURE,
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CONF_TOP_K,
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CONF_TOP_P,
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CONF_USE_GOOGLE_SEARCH_TOOL,
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DOMAIN,
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LOGGER,
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RECOMMENDED_CHAT_MODEL,
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RECOMMENDED_HARM_BLOCK_THRESHOLD,
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RECOMMENDED_MAX_TOKENS,
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RECOMMENDED_TEMPERATURE,
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RECOMMENDED_TOP_K,
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RECOMMENDED_TOP_P,
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)
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# Max number of back and forth with the LLM to generate a response
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MAX_TOOL_ITERATIONS = 10
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ERROR_GETTING_RESPONSE = (
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"Sorry, I had a problem getting a response from Google Generative AI."
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)
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async def async_setup_entry(
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async def async_setup_entry(
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@ -71,267 +26,18 @@ async def async_setup_entry(
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async_add_entities([agent])
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async_add_entities([agent])
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SUPPORTED_SCHEMA_KEYS = {
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# Gemini API does not support all of the OpenAPI schema
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# SoT: https://ai.google.dev/api/caching#Schema
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"type",
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"format",
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"description",
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"nullable",
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"enum",
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"max_items",
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"min_items",
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"properties",
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"required",
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"items",
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}
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def _camel_to_snake(name: str) -> str:
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"""Convert camel case to snake case."""
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return "".join(["_" + c.lower() if c.isupper() else c for c in name]).lstrip("_")
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def _format_schema(schema: dict[str, Any]) -> Schema:
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"""Format the schema to be compatible with Gemini API."""
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if subschemas := schema.get("allOf"):
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for subschema in subschemas: # Gemini API does not support allOf keys
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if "type" in subschema: # Fallback to first subschema with 'type' field
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return _format_schema(subschema)
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return _format_schema(
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subschemas[0]
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) # Or, if not found, to any of the subschemas
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result = {}
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for key, val in schema.items():
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key = _camel_to_snake(key)
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if key not in SUPPORTED_SCHEMA_KEYS:
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continue
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if key == "type":
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val = val.upper()
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elif key == "format":
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# Gemini API does not support all formats, see: https://ai.google.dev/api/caching#Schema
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# formats that are not supported are ignored
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if schema.get("type") == "string" and val not in ("enum", "date-time"):
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continue
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if schema.get("type") == "number" and val not in ("float", "double"):
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continue
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if schema.get("type") == "integer" and val not in ("int32", "int64"):
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continue
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if schema.get("type") not in ("string", "number", "integer"):
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continue
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elif key == "items":
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val = _format_schema(val)
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elif key == "properties":
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val = {k: _format_schema(v) for k, v in val.items()}
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result[key] = val
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if result.get("enum") and result.get("type") != "STRING":
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# enum is only allowed for STRING type. This is safe as long as the schema
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# contains vol.Coerce for the respective type, for example:
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# vol.All(vol.Coerce(int), vol.In([1, 2, 3]))
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result["type"] = "STRING"
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result["enum"] = [str(item) for item in result["enum"]]
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if result.get("type") == "OBJECT" and not result.get("properties"):
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# An object with undefined properties is not supported by Gemini API.
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# Fallback to JSON string. This will probably fail for most tools that want it,
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# but we don't have a better fallback strategy so far.
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result["properties"] = {"json": {"type": "STRING"}}
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result["required"] = []
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return cast(Schema, result)
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def _format_tool(
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tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
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) -> Tool:
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"""Format tool specification."""
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if tool.parameters.schema:
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parameters = _format_schema(
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convert(tool.parameters, custom_serializer=custom_serializer)
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)
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else:
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parameters = None
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return Tool(
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function_declarations=[
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FunctionDeclaration(
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name=tool.name,
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description=tool.description,
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parameters=parameters,
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)
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]
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)
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def _escape_decode(value: Any) -> Any:
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"""Recursively call codecs.escape_decode on all values."""
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if isinstance(value, str):
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return codecs.escape_decode(bytes(value, "utf-8"))[0].decode("utf-8") # type: ignore[attr-defined]
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if isinstance(value, list):
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return [_escape_decode(item) for item in value]
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if isinstance(value, dict):
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return {k: _escape_decode(v) for k, v in value.items()}
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return value
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def _create_google_tool_response_parts(
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parts: list[conversation.ToolResultContent],
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) -> list[Part]:
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"""Create Google tool response parts."""
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return [
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Part.from_function_response(
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name=tool_result.tool_name, response=tool_result.tool_result
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)
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for tool_result in parts
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]
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def _create_google_tool_response_content(
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content: list[conversation.ToolResultContent],
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) -> Content:
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"""Create a Google tool response content."""
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return Content(
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role="user",
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parts=_create_google_tool_response_parts(content),
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)
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def _convert_content(
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content: (
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conversation.UserContent
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| conversation.AssistantContent
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| conversation.SystemContent
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),
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) -> Content:
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"""Convert HA content to Google content."""
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if content.role != "assistant" or not content.tool_calls:
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role = "model" if content.role == "assistant" else content.role
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return Content(
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role=role,
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parts=[
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Part.from_text(text=content.content if content.content else ""),
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],
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)
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# Handle the Assistant content with tool calls.
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assert type(content) is conversation.AssistantContent
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parts: list[Part] = []
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if content.content:
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parts.append(Part.from_text(text=content.content))
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if content.tool_calls:
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parts.extend(
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[
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Part.from_function_call(
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name=tool_call.tool_name,
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args=_escape_decode(tool_call.tool_args),
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)
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for tool_call in content.tool_calls
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]
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)
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return Content(role="model", parts=parts)
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async def _transform_stream(
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result: AsyncGenerator[GenerateContentResponse],
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) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
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new_message = True
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try:
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async for response in result:
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LOGGER.debug("Received response chunk: %s", response)
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chunk: conversation.AssistantContentDeltaDict = {}
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if new_message:
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chunk["role"] = "assistant"
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new_message = False
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# According to the API docs, this would mean no candidate is returned, so we can safely throw an error here.
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if response.prompt_feedback or not response.candidates:
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reason = (
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response.prompt_feedback.block_reason_message
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if response.prompt_feedback
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else "unknown"
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)
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raise HomeAssistantError(
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f"The message got blocked due to content violations, reason: {reason}"
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)
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candidate = response.candidates[0]
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if (
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candidate.finish_reason is not None
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and candidate.finish_reason != "STOP"
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):
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# The message ended due to a content error as explained in: https://ai.google.dev/api/generate-content#FinishReason
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LOGGER.error(
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"Error in Google Generative AI response: %s, see: https://ai.google.dev/api/generate-content#FinishReason",
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candidate.finish_reason,
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)
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raise HomeAssistantError(
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f"{ERROR_GETTING_RESPONSE} Reason: {candidate.finish_reason}"
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)
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response_parts = (
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candidate.content.parts
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if candidate.content is not None and candidate.content.parts is not None
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else []
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)
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content = "".join([part.text for part in response_parts if part.text])
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tool_calls = []
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for part in response_parts:
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if not part.function_call:
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continue
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tool_call = part.function_call
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tool_name = tool_call.name if tool_call.name else ""
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tool_args = _escape_decode(tool_call.args)
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tool_calls.append(
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llm.ToolInput(tool_name=tool_name, tool_args=tool_args)
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)
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if tool_calls:
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chunk["tool_calls"] = tool_calls
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chunk["content"] = content
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yield chunk
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except (
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APIError,
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ValueError,
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) as err:
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LOGGER.error("Error sending message: %s %s", type(err), err)
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if isinstance(err, APIError):
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message = err.message
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else:
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message = type(err).__name__
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error = f"{ERROR_GETTING_RESPONSE}: {message}"
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raise HomeAssistantError(error) from err
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class GoogleGenerativeAIConversationEntity(
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class GoogleGenerativeAIConversationEntity(
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conversation.ConversationEntity, conversation.AbstractConversationAgent
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conversation.ConversationEntity,
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conversation.AbstractConversationAgent,
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GoogleGenerativeAILLMBaseEntity,
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):
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):
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"""Google Generative AI conversation agent."""
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"""Google Generative AI conversation agent."""
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_attr_has_entity_name = True
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_attr_name = None
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_attr_supports_streaming = True
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_attr_supports_streaming = True
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def __init__(self, entry: ConfigEntry) -> None:
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def __init__(self, entry: ConfigEntry) -> None:
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"""Initialize the agent."""
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"""Initialize the agent."""
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self.entry = entry
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super().__init__(entry)
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self._genai_client = entry.runtime_data
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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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name=entry.title,
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manufacturer="Google",
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model="Generative AI",
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entry_type=dr.DeviceEntryType.SERVICE,
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)
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if self.entry.options.get(CONF_LLM_HASS_API):
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if self.entry.options.get(CONF_LLM_HASS_API):
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self._attr_supported_features = (
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self._attr_supported_features = (
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conversation.ConversationEntityFeature.CONTROL
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conversation.ConversationEntityFeature.CONTROL
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@ -358,13 +64,6 @@ class GoogleGenerativeAIConversationEntity(
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conversation.async_unset_agent(self.hass, self.entry)
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conversation.async_unset_agent(self.hass, self.entry)
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await super().async_will_remove_from_hass()
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await super().async_will_remove_from_hass()
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def _fix_tool_name(self, tool_name: str) -> str:
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"""Fix tool name if needed."""
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# The Gemini 2.0+ tokenizer seemingly has a issue with the HassListAddItem tool
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# name. This makes sure when it incorrectly changes the name, that we change it
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# back for HA to call.
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return tool_name if tool_name != "HasListAddItem" else "HassListAddItem"
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async def _async_handle_message(
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async def _async_handle_message(
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self,
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self,
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user_input: conversation.ConversationInput,
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user_input: conversation.ConversationInput,
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@ -399,163 +98,6 @@ class GoogleGenerativeAIConversationEntity(
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continue_conversation=chat_log.continue_conversation,
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continue_conversation=chat_log.continue_conversation,
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)
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)
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async def _async_handle_chat_log(
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self,
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chat_log: conversation.ChatLog,
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) -> None:
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"""Generate an answer for the chat log."""
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options = self.entry.options
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tools: list[Tool | Callable[..., 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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# Using search grounding allows the model to retrieve information from the web,
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# however, it may interfere with how the model decides to use some tools, or entities
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# for example weather entity may be disregarded if the model chooses to Google it.
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if options.get(CONF_USE_GOOGLE_SEARCH_TOOL) is True:
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tools = tools or []
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tools.append(Tool(google_search=GoogleSearch()))
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model_name = self.entry.options.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL)
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|
||||||
# Avoid INVALID_ARGUMENT Developer instruction is not enabled for <model>
|
|
||||||
supports_system_instruction = (
|
|
||||||
"gemma" not in model_name
|
|
||||||
and "gemini-2.0-flash-preview-image-generation" not in model_name
|
|
||||||
)
|
|
||||||
|
|
||||||
prompt_content = cast(
|
|
||||||
conversation.SystemContent,
|
|
||||||
chat_log.content[0],
|
|
||||||
)
|
|
||||||
|
|
||||||
if prompt_content.content:
|
|
||||||
prompt = prompt_content.content
|
|
||||||
else:
|
|
||||||
raise HomeAssistantError("Invalid prompt content")
|
|
||||||
|
|
||||||
messages: list[Content] = []
|
|
||||||
|
|
||||||
# Google groups tool results, we do not. Group them before sending.
|
|
||||||
tool_results: list[conversation.ToolResultContent] = []
|
|
||||||
|
|
||||||
for chat_content in chat_log.content[1:-1]:
|
|
||||||
if chat_content.role == "tool_result":
|
|
||||||
tool_results.append(chat_content)
|
|
||||||
continue
|
|
||||||
|
|
||||||
if (
|
|
||||||
not isinstance(chat_content, conversation.ToolResultContent)
|
|
||||||
and chat_content.content == ""
|
|
||||||
):
|
|
||||||
# Skipping is not possible since the number of function calls need to match the number of function responses
|
|
||||||
# and skipping one would mean removing the other and hence this would prevent a proper chat log
|
|
||||||
chat_content = replace(chat_content, content=" ")
|
|
||||||
|
|
||||||
if tool_results:
|
|
||||||
messages.append(_create_google_tool_response_content(tool_results))
|
|
||||||
tool_results.clear()
|
|
||||||
|
|
||||||
messages.append(_convert_content(chat_content))
|
|
||||||
|
|
||||||
# The SDK requires the first message to be a user message
|
|
||||||
# This is not the case if user used `start_conversation`
|
|
||||||
# Workaround from https://github.com/googleapis/python-genai/issues/529#issuecomment-2740964537
|
|
||||||
if messages and messages[0].role != "user":
|
|
||||||
messages.insert(
|
|
||||||
0,
|
|
||||||
Content(role="user", parts=[Part.from_text(text=" ")]),
|
|
||||||
)
|
|
||||||
|
|
||||||
if tool_results:
|
|
||||||
messages.append(_create_google_tool_response_content(tool_results))
|
|
||||||
generateContentConfig = GenerateContentConfig(
|
|
||||||
temperature=self.entry.options.get(
|
|
||||||
CONF_TEMPERATURE, RECOMMENDED_TEMPERATURE
|
|
||||||
),
|
|
||||||
top_k=self.entry.options.get(CONF_TOP_K, RECOMMENDED_TOP_K),
|
|
||||||
top_p=self.entry.options.get(CONF_TOP_P, RECOMMENDED_TOP_P),
|
|
||||||
max_output_tokens=self.entry.options.get(
|
|
||||||
CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS
|
|
||||||
),
|
|
||||||
safety_settings=[
|
|
||||||
SafetySetting(
|
|
||||||
category=HarmCategory.HARM_CATEGORY_HATE_SPEECH,
|
|
||||||
threshold=self.entry.options.get(
|
|
||||||
CONF_HATE_BLOCK_THRESHOLD, RECOMMENDED_HARM_BLOCK_THRESHOLD
|
|
||||||
),
|
|
||||||
),
|
|
||||||
SafetySetting(
|
|
||||||
category=HarmCategory.HARM_CATEGORY_HARASSMENT,
|
|
||||||
threshold=self.entry.options.get(
|
|
||||||
CONF_HARASSMENT_BLOCK_THRESHOLD,
|
|
||||||
RECOMMENDED_HARM_BLOCK_THRESHOLD,
|
|
||||||
),
|
|
||||||
),
|
|
||||||
SafetySetting(
|
|
||||||
category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
|
|
||||||
threshold=self.entry.options.get(
|
|
||||||
CONF_DANGEROUS_BLOCK_THRESHOLD, RECOMMENDED_HARM_BLOCK_THRESHOLD
|
|
||||||
),
|
|
||||||
),
|
|
||||||
SafetySetting(
|
|
||||||
category=HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
|
|
||||||
threshold=self.entry.options.get(
|
|
||||||
CONF_SEXUAL_BLOCK_THRESHOLD, RECOMMENDED_HARM_BLOCK_THRESHOLD
|
|
||||||
),
|
|
||||||
),
|
|
||||||
],
|
|
||||||
tools=tools or None,
|
|
||||||
system_instruction=prompt if supports_system_instruction else None,
|
|
||||||
automatic_function_calling=AutomaticFunctionCallingConfig(
|
|
||||||
disable=True, maximum_remote_calls=None
|
|
||||||
),
|
|
||||||
)
|
|
||||||
|
|
||||||
if not supports_system_instruction:
|
|
||||||
messages = [
|
|
||||||
Content(role="user", parts=[Part.from_text(text=prompt)]),
|
|
||||||
Content(role="model", parts=[Part.from_text(text="Ok")]),
|
|
||||||
*messages,
|
|
||||||
]
|
|
||||||
chat = self._genai_client.aio.chats.create(
|
|
||||||
model=model_name, history=messages, config=generateContentConfig
|
|
||||||
)
|
|
||||||
user_message = chat_log.content[-1]
|
|
||||||
assert isinstance(user_message, conversation.UserContent)
|
|
||||||
chat_request: str | list[Part] = user_message.content
|
|
||||||
# To prevent infinite loops, we limit the number of iterations
|
|
||||||
for _iteration in range(MAX_TOOL_ITERATIONS):
|
|
||||||
try:
|
|
||||||
chat_response_generator = await chat.send_message_stream(
|
|
||||||
message=chat_request
|
|
||||||
)
|
|
||||||
except (
|
|
||||||
APIError,
|
|
||||||
ClientError,
|
|
||||||
ValueError,
|
|
||||||
) as err:
|
|
||||||
LOGGER.error("Error sending message: %s %s", type(err), err)
|
|
||||||
error = ERROR_GETTING_RESPONSE
|
|
||||||
raise HomeAssistantError(error) from err
|
|
||||||
|
|
||||||
chat_request = _create_google_tool_response_parts(
|
|
||||||
[
|
|
||||||
content
|
|
||||||
async for content in chat_log.async_add_delta_content_stream(
|
|
||||||
self.entity_id,
|
|
||||||
_transform_stream(chat_response_generator),
|
|
||||||
)
|
|
||||||
if isinstance(content, conversation.ToolResultContent)
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
if not chat_log.unresponded_tool_results:
|
|
||||||
break
|
|
||||||
|
|
||||||
async def _async_entry_update_listener(
|
async def _async_entry_update_listener(
|
||||||
self, hass: HomeAssistant, entry: ConfigEntry
|
self, hass: HomeAssistant, entry: ConfigEntry
|
||||||
) -> None:
|
) -> None:
|
||||||
|
|||||||
@ -0,0 +1,475 @@
|
|||||||
|
"""Conversation support for the Google Generative AI Conversation integration."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import codecs
|
||||||
|
from collections.abc import AsyncGenerator, Callable
|
||||||
|
from dataclasses import replace
|
||||||
|
from typing import Any, cast
|
||||||
|
|
||||||
|
from google.genai.errors import APIError, ClientError
|
||||||
|
from google.genai.types import (
|
||||||
|
AutomaticFunctionCallingConfig,
|
||||||
|
Content,
|
||||||
|
FunctionDeclaration,
|
||||||
|
GenerateContentConfig,
|
||||||
|
GenerateContentResponse,
|
||||||
|
GoogleSearch,
|
||||||
|
HarmCategory,
|
||||||
|
Part,
|
||||||
|
SafetySetting,
|
||||||
|
Schema,
|
||||||
|
Tool,
|
||||||
|
)
|
||||||
|
from voluptuous_openapi import convert
|
||||||
|
|
||||||
|
from homeassistant.components import conversation
|
||||||
|
from homeassistant.config_entries import ConfigEntry
|
||||||
|
from homeassistant.exceptions import HomeAssistantError
|
||||||
|
from homeassistant.helpers import device_registry as dr, llm
|
||||||
|
from homeassistant.helpers.entity import Entity
|
||||||
|
|
||||||
|
from .const import (
|
||||||
|
CONF_CHAT_MODEL,
|
||||||
|
CONF_DANGEROUS_BLOCK_THRESHOLD,
|
||||||
|
CONF_HARASSMENT_BLOCK_THRESHOLD,
|
||||||
|
CONF_HATE_BLOCK_THRESHOLD,
|
||||||
|
CONF_MAX_TOKENS,
|
||||||
|
CONF_SEXUAL_BLOCK_THRESHOLD,
|
||||||
|
CONF_TEMPERATURE,
|
||||||
|
CONF_TOP_K,
|
||||||
|
CONF_TOP_P,
|
||||||
|
CONF_USE_GOOGLE_SEARCH_TOOL,
|
||||||
|
DOMAIN,
|
||||||
|
LOGGER,
|
||||||
|
RECOMMENDED_CHAT_MODEL,
|
||||||
|
RECOMMENDED_HARM_BLOCK_THRESHOLD,
|
||||||
|
RECOMMENDED_MAX_TOKENS,
|
||||||
|
RECOMMENDED_TEMPERATURE,
|
||||||
|
RECOMMENDED_TOP_K,
|
||||||
|
RECOMMENDED_TOP_P,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Max number of back and forth with the LLM to generate a response
|
||||||
|
MAX_TOOL_ITERATIONS = 10
|
||||||
|
|
||||||
|
ERROR_GETTING_RESPONSE = (
|
||||||
|
"Sorry, I had a problem getting a response from Google Generative AI."
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
SUPPORTED_SCHEMA_KEYS = {
|
||||||
|
# Gemini API does not support all of the OpenAPI schema
|
||||||
|
# SoT: https://ai.google.dev/api/caching#Schema
|
||||||
|
"type",
|
||||||
|
"format",
|
||||||
|
"description",
|
||||||
|
"nullable",
|
||||||
|
"enum",
|
||||||
|
"max_items",
|
||||||
|
"min_items",
|
||||||
|
"properties",
|
||||||
|
"required",
|
||||||
|
"items",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def _camel_to_snake(name: str) -> str:
|
||||||
|
"""Convert camel case to snake case."""
|
||||||
|
return "".join(["_" + c.lower() if c.isupper() else c for c in name]).lstrip("_")
|
||||||
|
|
||||||
|
|
||||||
|
def _format_schema(schema: dict[str, Any]) -> Schema:
|
||||||
|
"""Format the schema to be compatible with Gemini API."""
|
||||||
|
if subschemas := schema.get("allOf"):
|
||||||
|
for subschema in subschemas: # Gemini API does not support allOf keys
|
||||||
|
if "type" in subschema: # Fallback to first subschema with 'type' field
|
||||||
|
return _format_schema(subschema)
|
||||||
|
return _format_schema(
|
||||||
|
subschemas[0]
|
||||||
|
) # Or, if not found, to any of the subschemas
|
||||||
|
|
||||||
|
result = {}
|
||||||
|
for key, val in schema.items():
|
||||||
|
key = _camel_to_snake(key)
|
||||||
|
if key not in SUPPORTED_SCHEMA_KEYS:
|
||||||
|
continue
|
||||||
|
if key == "type":
|
||||||
|
val = val.upper()
|
||||||
|
elif key == "format":
|
||||||
|
# Gemini API does not support all formats, see: https://ai.google.dev/api/caching#Schema
|
||||||
|
# formats that are not supported are ignored
|
||||||
|
if schema.get("type") == "string" and val not in ("enum", "date-time"):
|
||||||
|
continue
|
||||||
|
if schema.get("type") == "number" and val not in ("float", "double"):
|
||||||
|
continue
|
||||||
|
if schema.get("type") == "integer" and val not in ("int32", "int64"):
|
||||||
|
continue
|
||||||
|
if schema.get("type") not in ("string", "number", "integer"):
|
||||||
|
continue
|
||||||
|
elif key == "items":
|
||||||
|
val = _format_schema(val)
|
||||||
|
elif key == "properties":
|
||||||
|
val = {k: _format_schema(v) for k, v in val.items()}
|
||||||
|
result[key] = val
|
||||||
|
|
||||||
|
if result.get("enum") and result.get("type") != "STRING":
|
||||||
|
# enum is only allowed for STRING type. This is safe as long as the schema
|
||||||
|
# contains vol.Coerce for the respective type, for example:
|
||||||
|
# vol.All(vol.Coerce(int), vol.In([1, 2, 3]))
|
||||||
|
result["type"] = "STRING"
|
||||||
|
result["enum"] = [str(item) for item in result["enum"]]
|
||||||
|
|
||||||
|
if result.get("type") == "OBJECT" and not result.get("properties"):
|
||||||
|
# An object with undefined properties is not supported by Gemini API.
|
||||||
|
# Fallback to JSON string. This will probably fail for most tools that want it,
|
||||||
|
# but we don't have a better fallback strategy so far.
|
||||||
|
result["properties"] = {"json": {"type": "STRING"}}
|
||||||
|
result["required"] = []
|
||||||
|
return cast(Schema, result)
|
||||||
|
|
||||||
|
|
||||||
|
def _format_tool(
|
||||||
|
tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
|
||||||
|
) -> Tool:
|
||||||
|
"""Format tool specification."""
|
||||||
|
|
||||||
|
if tool.parameters.schema:
|
||||||
|
parameters = _format_schema(
|
||||||
|
convert(tool.parameters, custom_serializer=custom_serializer)
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
parameters = None
|
||||||
|
|
||||||
|
return Tool(
|
||||||
|
function_declarations=[
|
||||||
|
FunctionDeclaration(
|
||||||
|
name=tool.name,
|
||||||
|
description=tool.description,
|
||||||
|
parameters=parameters,
|
||||||
|
)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def _escape_decode(value: Any) -> Any:
|
||||||
|
"""Recursively call codecs.escape_decode on all values."""
|
||||||
|
if isinstance(value, str):
|
||||||
|
return codecs.escape_decode(bytes(value, "utf-8"))[0].decode("utf-8") # type: ignore[attr-defined]
|
||||||
|
if isinstance(value, list):
|
||||||
|
return [_escape_decode(item) for item in value]
|
||||||
|
if isinstance(value, dict):
|
||||||
|
return {k: _escape_decode(v) for k, v in value.items()}
|
||||||
|
return value
|
||||||
|
|
||||||
|
|
||||||
|
def _create_google_tool_response_parts(
|
||||||
|
parts: list[conversation.ToolResultContent],
|
||||||
|
) -> list[Part]:
|
||||||
|
"""Create Google tool response parts."""
|
||||||
|
return [
|
||||||
|
Part.from_function_response(
|
||||||
|
name=tool_result.tool_name, response=tool_result.tool_result
|
||||||
|
)
|
||||||
|
for tool_result in parts
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def _create_google_tool_response_content(
|
||||||
|
content: list[conversation.ToolResultContent],
|
||||||
|
) -> Content:
|
||||||
|
"""Create a Google tool response content."""
|
||||||
|
return Content(
|
||||||
|
role="user",
|
||||||
|
parts=_create_google_tool_response_parts(content),
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def _convert_content(
|
||||||
|
content: (
|
||||||
|
conversation.UserContent
|
||||||
|
| conversation.AssistantContent
|
||||||
|
| conversation.SystemContent
|
||||||
|
),
|
||||||
|
) -> Content:
|
||||||
|
"""Convert HA content to Google content."""
|
||||||
|
if content.role != "assistant" or not content.tool_calls:
|
||||||
|
role = "model" if content.role == "assistant" else content.role
|
||||||
|
return Content(
|
||||||
|
role=role,
|
||||||
|
parts=[
|
||||||
|
Part.from_text(text=content.content if content.content else ""),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
# Handle the Assistant content with tool calls.
|
||||||
|
assert type(content) is conversation.AssistantContent
|
||||||
|
parts: list[Part] = []
|
||||||
|
|
||||||
|
if content.content:
|
||||||
|
parts.append(Part.from_text(text=content.content))
|
||||||
|
|
||||||
|
if content.tool_calls:
|
||||||
|
parts.extend(
|
||||||
|
[
|
||||||
|
Part.from_function_call(
|
||||||
|
name=tool_call.tool_name,
|
||||||
|
args=_escape_decode(tool_call.tool_args),
|
||||||
|
)
|
||||||
|
for tool_call in content.tool_calls
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
return Content(role="model", parts=parts)
|
||||||
|
|
||||||
|
|
||||||
|
async def _transform_stream(
|
||||||
|
result: AsyncGenerator[GenerateContentResponse],
|
||||||
|
) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
|
||||||
|
new_message = True
|
||||||
|
try:
|
||||||
|
async for response in result:
|
||||||
|
LOGGER.debug("Received response chunk: %s", response)
|
||||||
|
chunk: conversation.AssistantContentDeltaDict = {}
|
||||||
|
|
||||||
|
if new_message:
|
||||||
|
chunk["role"] = "assistant"
|
||||||
|
new_message = False
|
||||||
|
|
||||||
|
# According to the API docs, this would mean no candidate is returned, so we can safely throw an error here.
|
||||||
|
if response.prompt_feedback or not response.candidates:
|
||||||
|
reason = (
|
||||||
|
response.prompt_feedback.block_reason_message
|
||||||
|
if response.prompt_feedback
|
||||||
|
else "unknown"
|
||||||
|
)
|
||||||
|
raise HomeAssistantError(
|
||||||
|
f"The message got blocked due to content violations, reason: {reason}"
|
||||||
|
)
|
||||||
|
|
||||||
|
candidate = response.candidates[0]
|
||||||
|
|
||||||
|
if (
|
||||||
|
candidate.finish_reason is not None
|
||||||
|
and candidate.finish_reason != "STOP"
|
||||||
|
):
|
||||||
|
# The message ended due to a content error as explained in: https://ai.google.dev/api/generate-content#FinishReason
|
||||||
|
LOGGER.error(
|
||||||
|
"Error in Google Generative AI response: %s, see: https://ai.google.dev/api/generate-content#FinishReason",
|
||||||
|
candidate.finish_reason,
|
||||||
|
)
|
||||||
|
raise HomeAssistantError(
|
||||||
|
f"{ERROR_GETTING_RESPONSE} Reason: {candidate.finish_reason}"
|
||||||
|
)
|
||||||
|
|
||||||
|
response_parts = (
|
||||||
|
candidate.content.parts
|
||||||
|
if candidate.content is not None and candidate.content.parts is not None
|
||||||
|
else []
|
||||||
|
)
|
||||||
|
|
||||||
|
content = "".join([part.text for part in response_parts if part.text])
|
||||||
|
tool_calls = []
|
||||||
|
for part in response_parts:
|
||||||
|
if not part.function_call:
|
||||||
|
continue
|
||||||
|
tool_call = part.function_call
|
||||||
|
tool_name = tool_call.name if tool_call.name else ""
|
||||||
|
tool_args = _escape_decode(tool_call.args)
|
||||||
|
tool_calls.append(
|
||||||
|
llm.ToolInput(tool_name=tool_name, tool_args=tool_args)
|
||||||
|
)
|
||||||
|
|
||||||
|
if tool_calls:
|
||||||
|
chunk["tool_calls"] = tool_calls
|
||||||
|
|
||||||
|
chunk["content"] = content
|
||||||
|
yield chunk
|
||||||
|
except (
|
||||||
|
APIError,
|
||||||
|
ValueError,
|
||||||
|
) as err:
|
||||||
|
LOGGER.error("Error sending message: %s %s", type(err), err)
|
||||||
|
if isinstance(err, APIError):
|
||||||
|
message = err.message
|
||||||
|
else:
|
||||||
|
message = type(err).__name__
|
||||||
|
error = f"{ERROR_GETTING_RESPONSE}: {message}"
|
||||||
|
raise HomeAssistantError(error) from err
|
||||||
|
|
||||||
|
|
||||||
|
class GoogleGenerativeAILLMBaseEntity(Entity):
|
||||||
|
"""Google Generative AI base entity."""
|
||||||
|
|
||||||
|
_attr_has_entity_name = True
|
||||||
|
_attr_name = None
|
||||||
|
|
||||||
|
def __init__(self, entry: ConfigEntry) -> None:
|
||||||
|
"""Initialize the agent."""
|
||||||
|
self.entry = entry
|
||||||
|
self._genai_client = entry.runtime_data
|
||||||
|
self._attr_unique_id = entry.entry_id
|
||||||
|
self._attr_device_info = dr.DeviceInfo(
|
||||||
|
identifiers={(DOMAIN, entry.entry_id)},
|
||||||
|
name=entry.title,
|
||||||
|
manufacturer="Google",
|
||||||
|
model="Generative AI",
|
||||||
|
entry_type=dr.DeviceEntryType.SERVICE,
|
||||||
|
)
|
||||||
|
|
||||||
|
async def _async_handle_chat_log(
|
||||||
|
self,
|
||||||
|
chat_log: conversation.ChatLog,
|
||||||
|
) -> None:
|
||||||
|
"""Generate an answer for the chat log."""
|
||||||
|
options = self.entry.options
|
||||||
|
|
||||||
|
tools: list[Tool | Callable[..., Any]] | None = None
|
||||||
|
if chat_log.llm_api:
|
||||||
|
tools = [
|
||||||
|
_format_tool(tool, chat_log.llm_api.custom_serializer)
|
||||||
|
for tool in chat_log.llm_api.tools
|
||||||
|
]
|
||||||
|
|
||||||
|
# Using search grounding allows the model to retrieve information from the web,
|
||||||
|
# however, it may interfere with how the model decides to use some tools, or entities
|
||||||
|
# for example weather entity may be disregarded if the model chooses to Google it.
|
||||||
|
if options.get(CONF_USE_GOOGLE_SEARCH_TOOL) is True:
|
||||||
|
tools = tools or []
|
||||||
|
tools.append(Tool(google_search=GoogleSearch()))
|
||||||
|
|
||||||
|
model_name = self.entry.options.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL)
|
||||||
|
# Avoid INVALID_ARGUMENT Developer instruction is not enabled for <model>
|
||||||
|
supports_system_instruction = (
|
||||||
|
"gemma" not in model_name
|
||||||
|
and "gemini-2.0-flash-preview-image-generation" not in model_name
|
||||||
|
)
|
||||||
|
|
||||||
|
prompt_content = cast(
|
||||||
|
conversation.SystemContent,
|
||||||
|
chat_log.content[0],
|
||||||
|
)
|
||||||
|
|
||||||
|
if prompt_content.content:
|
||||||
|
prompt = prompt_content.content
|
||||||
|
else:
|
||||||
|
raise HomeAssistantError("Invalid prompt content")
|
||||||
|
|
||||||
|
messages: list[Content] = []
|
||||||
|
|
||||||
|
# Google groups tool results, we do not. Group them before sending.
|
||||||
|
tool_results: list[conversation.ToolResultContent] = []
|
||||||
|
|
||||||
|
for chat_content in chat_log.content[1:-1]:
|
||||||
|
if chat_content.role == "tool_result":
|
||||||
|
tool_results.append(chat_content)
|
||||||
|
continue
|
||||||
|
|
||||||
|
if (
|
||||||
|
not isinstance(chat_content, conversation.ToolResultContent)
|
||||||
|
and chat_content.content == ""
|
||||||
|
):
|
||||||
|
# Skipping is not possible since the number of function calls need to match the number of function responses
|
||||||
|
# and skipping one would mean removing the other and hence this would prevent a proper chat log
|
||||||
|
chat_content = replace(chat_content, content=" ")
|
||||||
|
|
||||||
|
if tool_results:
|
||||||
|
messages.append(_create_google_tool_response_content(tool_results))
|
||||||
|
tool_results.clear()
|
||||||
|
|
||||||
|
messages.append(_convert_content(chat_content))
|
||||||
|
|
||||||
|
# The SDK requires the first message to be a user message
|
||||||
|
# This is not the case if user used `start_conversation`
|
||||||
|
# Workaround from https://github.com/googleapis/python-genai/issues/529#issuecomment-2740964537
|
||||||
|
if messages and messages[0].role != "user":
|
||||||
|
messages.insert(
|
||||||
|
0,
|
||||||
|
Content(role="user", parts=[Part.from_text(text=" ")]),
|
||||||
|
)
|
||||||
|
|
||||||
|
if tool_results:
|
||||||
|
messages.append(_create_google_tool_response_content(tool_results))
|
||||||
|
generateContentConfig = GenerateContentConfig(
|
||||||
|
temperature=self.entry.options.get(
|
||||||
|
CONF_TEMPERATURE, RECOMMENDED_TEMPERATURE
|
||||||
|
),
|
||||||
|
top_k=self.entry.options.get(CONF_TOP_K, RECOMMENDED_TOP_K),
|
||||||
|
top_p=self.entry.options.get(CONF_TOP_P, RECOMMENDED_TOP_P),
|
||||||
|
max_output_tokens=self.entry.options.get(
|
||||||
|
CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS
|
||||||
|
),
|
||||||
|
safety_settings=[
|
||||||
|
SafetySetting(
|
||||||
|
category=HarmCategory.HARM_CATEGORY_HATE_SPEECH,
|
||||||
|
threshold=self.entry.options.get(
|
||||||
|
CONF_HATE_BLOCK_THRESHOLD, RECOMMENDED_HARM_BLOCK_THRESHOLD
|
||||||
|
),
|
||||||
|
),
|
||||||
|
SafetySetting(
|
||||||
|
category=HarmCategory.HARM_CATEGORY_HARASSMENT,
|
||||||
|
threshold=self.entry.options.get(
|
||||||
|
CONF_HARASSMENT_BLOCK_THRESHOLD,
|
||||||
|
RECOMMENDED_HARM_BLOCK_THRESHOLD,
|
||||||
|
),
|
||||||
|
),
|
||||||
|
SafetySetting(
|
||||||
|
category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
|
||||||
|
threshold=self.entry.options.get(
|
||||||
|
CONF_DANGEROUS_BLOCK_THRESHOLD, RECOMMENDED_HARM_BLOCK_THRESHOLD
|
||||||
|
),
|
||||||
|
),
|
||||||
|
SafetySetting(
|
||||||
|
category=HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
|
||||||
|
threshold=self.entry.options.get(
|
||||||
|
CONF_SEXUAL_BLOCK_THRESHOLD, RECOMMENDED_HARM_BLOCK_THRESHOLD
|
||||||
|
),
|
||||||
|
),
|
||||||
|
],
|
||||||
|
tools=tools or None,
|
||||||
|
system_instruction=prompt if supports_system_instruction else None,
|
||||||
|
automatic_function_calling=AutomaticFunctionCallingConfig(
|
||||||
|
disable=True, maximum_remote_calls=None
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|
||||||
|
if not supports_system_instruction:
|
||||||
|
messages = [
|
||||||
|
Content(role="user", parts=[Part.from_text(text=prompt)]),
|
||||||
|
Content(role="model", parts=[Part.from_text(text="Ok")]),
|
||||||
|
*messages,
|
||||||
|
]
|
||||||
|
chat = self._genai_client.aio.chats.create(
|
||||||
|
model=model_name, history=messages, config=generateContentConfig
|
||||||
|
)
|
||||||
|
user_message = chat_log.content[-1]
|
||||||
|
assert isinstance(user_message, conversation.UserContent)
|
||||||
|
chat_request: str | list[Part] = user_message.content
|
||||||
|
# To prevent infinite loops, we limit the number of iterations
|
||||||
|
for _iteration in range(MAX_TOOL_ITERATIONS):
|
||||||
|
try:
|
||||||
|
chat_response_generator = await chat.send_message_stream(
|
||||||
|
message=chat_request
|
||||||
|
)
|
||||||
|
except (
|
||||||
|
APIError,
|
||||||
|
ClientError,
|
||||||
|
ValueError,
|
||||||
|
) as err:
|
||||||
|
LOGGER.error("Error sending message: %s %s", type(err), err)
|
||||||
|
error = ERROR_GETTING_RESPONSE
|
||||||
|
raise HomeAssistantError(error) from err
|
||||||
|
|
||||||
|
chat_request = _create_google_tool_response_parts(
|
||||||
|
[
|
||||||
|
content
|
||||||
|
async for content in chat_log.async_add_delta_content_stream(
|
||||||
|
self.entity_id,
|
||||||
|
_transform_stream(chat_response_generator),
|
||||||
|
)
|
||||||
|
if isinstance(content, conversation.ToolResultContent)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
if not chat_log.unresponded_tool_results:
|
||||||
|
break
|
||||||
@ -4,7 +4,7 @@ from unittest.mock import Mock, patch
|
|||||||
|
|
||||||
import pytest
|
import pytest
|
||||||
|
|
||||||
from homeassistant.components.google_generative_ai_conversation.conversation import (
|
from homeassistant.components.google_generative_ai_conversation.entity import (
|
||||||
CONF_USE_GOOGLE_SEARCH_TOOL,
|
CONF_USE_GOOGLE_SEARCH_TOOL,
|
||||||
)
|
)
|
||||||
from homeassistant.config_entries import ConfigEntry
|
from homeassistant.config_entries import ConfigEntry
|
||||||
|
|||||||
@ -9,7 +9,7 @@ import pytest
|
|||||||
|
|
||||||
from homeassistant.components import conversation
|
from homeassistant.components import conversation
|
||||||
from homeassistant.components.conversation import UserContent
|
from homeassistant.components.conversation import UserContent
|
||||||
from homeassistant.components.google_generative_ai_conversation.conversation import (
|
from homeassistant.components.google_generative_ai_conversation.entity import (
|
||||||
ERROR_GETTING_RESPONSE,
|
ERROR_GETTING_RESPONSE,
|
||||||
_escape_decode,
|
_escape_decode,
|
||||||
_format_schema,
|
_format_schema,
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user