mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-06-21 03:41:55 +03:00
b0b00fe1d0
* Ensure runtime options are passed * Add attribute info to prompt when configured * Move GenAI plugins to dedicated directory * Migrate prompts to dedicated folder * Move chat prompts to prompts * Implement reasoning traces in the UI * Cleanup * Make azure a subclass of openai * Implement reasoning for other providers * mypy * Cleanup
655 lines
26 KiB
Python
655 lines
26 KiB
Python
"""Gemini Provider for Frigate AI."""
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import json
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import logging
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from typing import Any, AsyncGenerator, Optional
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from google import genai
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from google.genai import errors, types
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from google.genai.types import FunctionCallingConfigMode
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from frigate.config import GenAIProviderEnum
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from frigate.genai import GenAIClient, register_genai_provider
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logger = logging.getLogger(__name__)
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def _stats_from_gemini_usage(usage: Any) -> Optional[dict[str, Any]]:
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"""Build a stats dict from a Gemini usage_metadata object."""
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prompt_tokens = getattr(usage, "prompt_token_count", None)
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completion_tokens = getattr(usage, "candidates_token_count", None)
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if prompt_tokens is None and completion_tokens is None:
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return None
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stats: dict[str, Any] = {}
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if isinstance(prompt_tokens, int):
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stats["prompt_tokens"] = prompt_tokens
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if isinstance(completion_tokens, int):
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stats["completion_tokens"] = completion_tokens
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return stats or None
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@register_genai_provider(GenAIProviderEnum.gemini)
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class GeminiClient(GenAIClient):
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"""Generative AI client for Frigate using Gemini."""
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provider: genai.Client
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def _init_provider(self) -> genai.Client:
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"""Initialize the client."""
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# Merge provider_options into HttpOptions
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http_options_dict: dict[str, Any] = {
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"timeout": int(self.timeout * 1000), # requires milliseconds
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"retry_options": types.HttpRetryOptions(
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attempts=3,
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initial_delay=1.0,
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max_delay=60.0,
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exp_base=2.0,
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jitter=1.0,
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http_status_codes=[429, 500, 502, 503, 504],
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),
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}
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if isinstance(self.genai_config.provider_options, dict):
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http_options_dict.update(self.genai_config.provider_options)
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return genai.Client(
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api_key=self.genai_config.api_key,
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http_options=types.HttpOptions(**http_options_dict),
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)
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def _send(
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self,
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prompt: str,
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images: list[bytes],
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response_format: Optional[dict] = None,
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) -> Optional[str]:
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"""Submit a request to Gemini."""
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contents = [prompt] + [
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types.Part.from_bytes(data=img, mime_type="image/jpeg") for img in images
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]
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try:
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# Merge runtime_options into generation_config if provided
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generation_config_dict: dict[str, Any] = {"candidate_count": 1}
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generation_config_dict.update(self.genai_config.runtime_options)
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if response_format and response_format.get("type") == "json_schema":
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generation_config_dict["response_mime_type"] = "application/json"
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schema = response_format.get("json_schema", {}).get("schema")
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if schema:
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generation_config_dict["response_schema"] = schema
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response = self.provider.models.generate_content(
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model=self.genai_config.model,
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contents=contents, # type: ignore[arg-type]
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config=types.GenerateContentConfig(
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**generation_config_dict,
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),
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)
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except errors.APIError as e:
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logger.warning("Gemini returned an error: %s", str(e))
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return None
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except Exception as e:
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logger.warning("An unexpected error occurred with Gemini: %s", str(e))
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return None
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try:
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if response.text is None:
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return None
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description = response.text.strip()
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except (ValueError, AttributeError):
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# No description was generated
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return None
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return description
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def list_models(self) -> list[str]:
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"""Return available model names from Gemini."""
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try:
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return sorted(m.name or "" for m in self.provider.models.list())
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except Exception as e:
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logger.warning("Failed to list Gemini models: %s", e)
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return []
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def get_context_size(self) -> int:
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"""Get the context window size for Gemini."""
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# Gemini Pro Vision has a 1M token context window
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return 1000000
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def chat_with_tools(
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self,
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messages: list[dict[str, Any]],
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tools: Optional[list[dict[str, Any]]] = None,
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tool_choice: Optional[str] = "auto",
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) -> dict[str, Any]:
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"""
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Send chat messages to Gemini with optional tool definitions.
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Implements function calling/tool usage for Gemini models.
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"""
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try:
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# Convert messages to Gemini format
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gemini_messages: list[types.Content] = []
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for msg in messages:
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role = msg.get("role", "user")
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content = msg.get("content", "")
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# Map roles to Gemini format
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if role == "system":
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# Gemini doesn't have system role, prepend to first user message
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if (
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gemini_messages
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and gemini_messages[0].role == "user"
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and gemini_messages[0].parts
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):
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gemini_messages[0].parts[
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0
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].text = f"{content}\n\n{gemini_messages[0].parts[0].text}"
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else:
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gemini_messages.append(
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types.Content(
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role="user", parts=[types.Part.from_text(text=content)]
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)
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)
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elif role == "assistant":
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parts: list[types.Part] = []
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if content:
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parts.append(types.Part.from_text(text=content))
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for tc in msg.get("tool_calls") or []:
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func = tc.get("function") or {}
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tc_name = func.get("name") or ""
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tc_args: Any = func.get("arguments")
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if isinstance(tc_args, str):
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try:
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tc_args = json.loads(tc_args)
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except (json.JSONDecodeError, TypeError):
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tc_args = {}
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if not isinstance(tc_args, dict):
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tc_args = {}
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if tc_name:
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parts.append(
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types.Part.from_function_call(
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name=tc_name, args=tc_args
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)
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)
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if not parts:
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parts.append(types.Part.from_text(text=" "))
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gemini_messages.append(types.Content(role="model", parts=parts))
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elif role == "tool":
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# Handle tool response
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response_payload = (
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content if isinstance(content, dict) else {"result": content}
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)
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gemini_messages.append(
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types.Content(
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role="function",
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parts=[
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types.Part.from_function_response(
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name=msg.get("name")
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or msg.get("tool_call_id")
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or "",
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response=response_payload,
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)
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],
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)
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)
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else: # user
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gemini_messages.append(
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types.Content(
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role="user", parts=[types.Part.from_text(text=content)]
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)
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)
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# Convert tools to Gemini format
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gemini_tools = None
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if tools:
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gemini_tools = []
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for tool in tools:
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if tool.get("type") == "function":
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func = tool.get("function", {})
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gemini_tools.append(
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types.Tool(
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function_declarations=[
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types.FunctionDeclaration(
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name=func.get("name", ""),
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description=func.get("description", ""),
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parameters=func.get("parameters", {}),
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)
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]
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)
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)
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# Configure tool choice
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tool_config = None
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if tool_choice:
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if tool_choice == "none":
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=FunctionCallingConfigMode.NONE
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)
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)
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elif tool_choice == "auto":
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=FunctionCallingConfigMode.AUTO
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)
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)
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elif tool_choice == "required":
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=FunctionCallingConfigMode.ANY
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)
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)
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# Build request config
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config_params: dict[str, Any] = {"candidate_count": 1}
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if gemini_tools:
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config_params["tools"] = gemini_tools
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if tool_config:
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config_params["tool_config"] = tool_config
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# Ask thinking-capable models (Gemini 2.5+) to include their
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# reasoning trace as separate `thought` parts so we can surface
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# it on the reasoning channel. Older models ignore this field.
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config_params["thinking_config"] = types.ThinkingConfig(
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include_thoughts=True
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)
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# Merge runtime_options
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if isinstance(self.genai_config.runtime_options, dict):
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config_params.update(self.genai_config.runtime_options)
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response = self.provider.models.generate_content(
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model=self.genai_config.model,
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contents=gemini_messages, # type: ignore[arg-type]
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config=types.GenerateContentConfig(**config_params),
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)
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# Check if response is valid
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if not response or not response.candidates:
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return {
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"content": None,
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"reasoning": None,
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"tool_calls": None,
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"finish_reason": "error",
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}
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candidate = response.candidates[0]
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content = None
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reasoning_parts: list[str] = []
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tool_calls = None
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# Extract content, reasoning, and tool calls from response
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if candidate.content and candidate.content.parts:
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for part in candidate.content.parts:
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if part.text:
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if getattr(part, "thought", False):
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reasoning_parts.append(part.text)
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else:
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content = part.text.strip()
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elif part.function_call:
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# Handle function call
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if tool_calls is None:
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tool_calls = []
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try:
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arguments = (
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dict(part.function_call.args)
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if part.function_call.args
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else {}
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)
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except Exception:
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arguments = {}
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tool_calls.append(
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{
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"id": part.function_call.name or "",
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"name": part.function_call.name or "",
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"arguments": arguments,
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}
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)
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reasoning = "".join(reasoning_parts).strip() or None
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# Determine finish reason
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finish_reason = "error"
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if hasattr(candidate, "finish_reason") and candidate.finish_reason:
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from google.genai.types import FinishReason
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if candidate.finish_reason == FinishReason.STOP:
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finish_reason = "stop"
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elif candidate.finish_reason == FinishReason.MAX_TOKENS:
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finish_reason = "length"
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elif candidate.finish_reason in [
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FinishReason.SAFETY,
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FinishReason.RECITATION,
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]:
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finish_reason = "error"
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elif tool_calls:
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finish_reason = "tool_calls"
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elif content:
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finish_reason = "stop"
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elif tool_calls:
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finish_reason = "tool_calls"
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elif content:
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finish_reason = "stop"
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return {
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"content": content,
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"reasoning": reasoning,
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"tool_calls": tool_calls,
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"finish_reason": finish_reason,
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}
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except errors.APIError as e:
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logger.warning("Gemini API error during chat_with_tools: %s", str(e))
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return {
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"content": None,
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"reasoning": None,
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"tool_calls": None,
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"finish_reason": "error",
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}
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except Exception as e:
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logger.warning(
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"Gemini returned an error during chat_with_tools: %s", str(e)
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)
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return {
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"content": None,
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"reasoning": None,
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"tool_calls": None,
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"finish_reason": "error",
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}
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async def chat_with_tools_stream(
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self,
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messages: list[dict[str, Any]],
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tools: Optional[list[dict[str, Any]]] = None,
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tool_choice: Optional[str] = "auto",
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) -> AsyncGenerator[tuple[str, Any], None]:
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"""
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Stream chat with tools; yields content deltas then final message.
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Implements streaming function calling/tool usage for Gemini models.
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"""
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try:
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# Convert messages to Gemini format
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gemini_messages: list[types.Content] = []
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for msg in messages:
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role = msg.get("role", "user")
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content = msg.get("content", "")
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# Map roles to Gemini format
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if role == "system":
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# Gemini doesn't have system role, prepend to first user message
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if (
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gemini_messages
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and gemini_messages[0].role == "user"
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and gemini_messages[0].parts
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):
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gemini_messages[0].parts[
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0
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].text = f"{content}\n\n{gemini_messages[0].parts[0].text}"
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else:
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gemini_messages.append(
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types.Content(
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role="user", parts=[types.Part.from_text(text=content)]
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)
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)
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elif role == "assistant":
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parts: list[types.Part] = []
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if content:
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parts.append(types.Part.from_text(text=content))
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for tc in msg.get("tool_calls") or []:
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func = tc.get("function") or {}
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tc_name = func.get("name") or ""
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tc_args: Any = func.get("arguments")
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if isinstance(tc_args, str):
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try:
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tc_args = json.loads(tc_args)
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except (json.JSONDecodeError, TypeError):
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tc_args = {}
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if not isinstance(tc_args, dict):
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tc_args = {}
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if tc_name:
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parts.append(
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types.Part.from_function_call(
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name=tc_name, args=tc_args
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)
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)
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if not parts:
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parts.append(types.Part.from_text(text=" "))
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gemini_messages.append(types.Content(role="model", parts=parts))
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elif role == "tool":
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# Handle tool response
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response_payload = (
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content if isinstance(content, dict) else {"result": content}
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)
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gemini_messages.append(
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types.Content(
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role="function",
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parts=[
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types.Part.from_function_response(
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name=msg.get("name")
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or msg.get("tool_call_id")
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or "",
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response=response_payload,
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)
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],
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)
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)
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else: # user
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gemini_messages.append(
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types.Content(
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role="user", parts=[types.Part.from_text(text=content)]
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)
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)
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# Convert tools to Gemini format
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gemini_tools = None
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if tools:
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gemini_tools = []
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for tool in tools:
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if tool.get("type") == "function":
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func = tool.get("function", {})
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gemini_tools.append(
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types.Tool(
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function_declarations=[
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types.FunctionDeclaration(
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name=func.get("name", ""),
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description=func.get("description", ""),
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parameters=func.get("parameters", {}),
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)
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]
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)
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)
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# Configure tool choice
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tool_config = None
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if tool_choice:
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if tool_choice == "none":
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=FunctionCallingConfigMode.NONE
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)
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)
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elif tool_choice == "auto":
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=FunctionCallingConfigMode.AUTO
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)
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)
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elif tool_choice == "required":
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=FunctionCallingConfigMode.ANY
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)
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)
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# Build request config
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config_params: dict[str, Any] = {"candidate_count": 1}
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if gemini_tools:
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config_params["tools"] = gemini_tools
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|
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if tool_config:
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config_params["tool_config"] = tool_config
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# Ask thinking-capable models to include their reasoning trace
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# as separate `thought` parts (Gemini 2.5+; ignored elsewhere).
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config_params["thinking_config"] = types.ThinkingConfig(
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include_thoughts=True
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)
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# Merge runtime_options
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if isinstance(self.genai_config.runtime_options, dict):
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config_params.update(self.genai_config.runtime_options)
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# Use streaming API
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content_parts: list[str] = []
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reasoning_parts: list[str] = []
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tool_calls_by_index: dict[int, dict[str, Any]] = {}
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finish_reason = "stop"
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usage_stats: Optional[dict[str, Any]] = None
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stream = await self.provider.aio.models.generate_content_stream(
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model=self.genai_config.model,
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contents=gemini_messages, # type: ignore[arg-type]
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config=types.GenerateContentConfig(**config_params),
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)
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async for chunk in stream:
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chunk_usage = getattr(chunk, "usage_metadata", None)
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if chunk_usage is not None:
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maybe_stats = _stats_from_gemini_usage(chunk_usage)
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if maybe_stats is not None:
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usage_stats = maybe_stats
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if not chunk or not chunk.candidates:
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continue
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candidate = chunk.candidates[0]
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# Check for finish reason
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if hasattr(candidate, "finish_reason") and candidate.finish_reason:
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from google.genai.types import FinishReason
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if candidate.finish_reason == FinishReason.STOP:
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finish_reason = "stop"
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elif candidate.finish_reason == FinishReason.MAX_TOKENS:
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finish_reason = "length"
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elif candidate.finish_reason in [
|
|
FinishReason.SAFETY,
|
|
FinishReason.RECITATION,
|
|
]:
|
|
finish_reason = "error"
|
|
|
|
# Extract content, reasoning, and tool calls from chunk
|
|
if candidate.content and candidate.content.parts:
|
|
for part in candidate.content.parts:
|
|
if part.text:
|
|
if getattr(part, "thought", False):
|
|
reasoning_parts.append(part.text)
|
|
yield ("reasoning_delta", part.text)
|
|
else:
|
|
content_parts.append(part.text)
|
|
yield ("content_delta", part.text)
|
|
elif part.function_call:
|
|
# Handle function call
|
|
try:
|
|
arguments = (
|
|
dict(part.function_call.args)
|
|
if part.function_call.args
|
|
else {}
|
|
)
|
|
except Exception:
|
|
arguments = {}
|
|
|
|
# Store tool call
|
|
tool_call_id = part.function_call.name or ""
|
|
tool_call_name = part.function_call.name or ""
|
|
|
|
# Check if we already have this tool call
|
|
found_index = None
|
|
for idx, tc in tool_calls_by_index.items():
|
|
if tc["name"] == tool_call_name:
|
|
found_index = idx
|
|
break
|
|
|
|
if found_index is None:
|
|
found_index = len(tool_calls_by_index)
|
|
tool_calls_by_index[found_index] = {
|
|
"id": tool_call_id,
|
|
"name": tool_call_name,
|
|
"arguments": "",
|
|
}
|
|
|
|
# Accumulate arguments
|
|
if arguments:
|
|
tool_calls_by_index[found_index]["arguments"] += (
|
|
json.dumps(arguments)
|
|
if isinstance(arguments, dict)
|
|
else str(arguments)
|
|
)
|
|
|
|
# Build final message
|
|
full_content = "".join(content_parts).strip() or None
|
|
full_reasoning = "".join(reasoning_parts).strip() or None
|
|
|
|
# Convert tool calls to list format
|
|
tool_calls_list = None
|
|
if tool_calls_by_index:
|
|
tool_calls_list = []
|
|
for tc in tool_calls_by_index.values():
|
|
try:
|
|
# Try to parse accumulated arguments as JSON
|
|
parsed_args = json.loads(tc["arguments"])
|
|
except (json.JSONDecodeError, Exception):
|
|
parsed_args = tc["arguments"]
|
|
|
|
tool_calls_list.append(
|
|
{
|
|
"id": tc["id"],
|
|
"name": tc["name"],
|
|
"arguments": parsed_args,
|
|
}
|
|
)
|
|
finish_reason = "tool_calls"
|
|
|
|
if usage_stats is not None:
|
|
yield ("stats", usage_stats)
|
|
|
|
yield (
|
|
"message",
|
|
{
|
|
"content": full_content,
|
|
"reasoning": full_reasoning,
|
|
"tool_calls": tool_calls_list,
|
|
"finish_reason": finish_reason,
|
|
},
|
|
)
|
|
|
|
except errors.APIError as e:
|
|
logger.warning("Gemini API error during streaming: %s", str(e))
|
|
yield (
|
|
"message",
|
|
{
|
|
"content": None,
|
|
"reasoning": None,
|
|
"tool_calls": None,
|
|
"finish_reason": "error",
|
|
},
|
|
)
|
|
except Exception as e:
|
|
logger.warning(
|
|
"Gemini returned an error during chat_with_tools_stream: %s", str(e)
|
|
)
|
|
yield (
|
|
"message",
|
|
{
|
|
"content": None,
|
|
"reasoning": None,
|
|
"tool_calls": None,
|
|
"finish_reason": "error",
|
|
},
|
|
)
|