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This is implemented from scratch rather than using a dedicated SDK, good API documentation is available [here](https://ai.google.dev/api). Apart from `__init__`, all methods are private or match those of the base class. """ client: Client = field(repr=False) _model_name: GoogleModelName = field(repr=False) _provider: Provider[Client] = field(repr=False) def __init__( self, model_name: GoogleModelName, *, provider: Literal['google-gla', 'google-vertex', 'gateway'] | Provider[Client] = 'google-gla', profile: ModelProfileSpec | None = None, settings: ModelSettings | None = None, ): """Initialize a Gemini model. Args: model_name: The name of the model to use. provider: The provider to use for authentication and API access. Can be either the string 'google-gla' or 'google-vertex' or an instance of `Provider[google.genai.AsyncClient]`. Defaults to 'google-gla'. profile: The model profile to use. Defaults to a profile picked by the provider based on the model name. settings: The model settings to use. Defaults to None. """ self._model_name = model_name if isinstance(provider, str): provider = infer_provider('gateway/google-vertex' if provider == 'gateway' else provider) self._provider = provider self.client = provider.client super().__init__(settings=settings, profile=profile or provider.model_profile) @property def base_url(self) -> str: return self._provider.base_url @property def model_name(self) -> GoogleModelName: """The model name.""" return self._model_name @property def system(self) -> str: """The model provider.""" return self._provider.name def prepare_request( self, model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters ) -> tuple[ModelSettings | None, ModelRequestParameters]: supports_native_output_with_builtin_tools = GoogleModelProfile.from_profile( self.profile ).google_supports_native_output_with_builtin_tools if model_request_parameters.builtin_tools and model_request_parameters.output_tools: if model_request_parameters.output_mode == 'auto': output_mode = 'native' if supports_native_output_with_builtin_tools else 'prompted' model_request_parameters = replace(model_request_parameters, output_mode=output_mode) else: output_mode = 'NativeOutput' if supports_native_output_with_builtin_tools else 'PromptedOutput' raise UserError( f'Google does not support output tools and built-in tools at the same time. Use `output_type={output_mode}(...)` instead.' ) return super().prepare_request(model_settings, model_request_parameters) async def request( self, messages: list[ModelMessage], model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters, ) -> ModelResponse: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) model_settings = cast(GoogleModelSettings, model_settings or {}) response = await self._generate_content(messages, False, model_settings, model_request_parameters) return self._process_response(response) async def count_tokens( self, messages: list[ModelMessage], model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters, ) -> usage.RequestUsage: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) model_settings = cast(GoogleModelSettings, model_settings or {}) contents, generation_config = await self._build_content_and_config( messages, model_settings, model_request_parameters ) # Annoyingly, the type of `GenerateContentConfigDict.get` is "partially `Unknown`" because `response_schema` includes `typing._UnionGenericAlias`, # so without this we'd need `pyright: ignore[reportUnknownMemberType]` on every line and wouldn't get type checking anyway. generation_config = cast(dict[str, Any], generation_config) config = CountTokensConfigDict( http_options=generation_config.get('http_options'), ) if self._provider.name != 'google-gla': # The fields are not supported by the Gemini API per https://github.com/googleapis/python-genai/blob/7e4ec284dc6e521949626f3ed54028163ef9121d/google/genai/models.py#L1195-L1214 config.update( # pragma: lax no cover system_instruction=generation_config.get('system_instruction'), tools=cast(list[ToolDict], generation_config.get('tools')), # Annoyingly, GenerationConfigDict has fewer fields than GenerateContentConfigDict, and no extra fields are allowed. generation_config=GenerationConfigDict( temperature=generation_config.get('temperature'), top_p=generation_config.get('top_p'), max_output_tokens=generation_config.get('max_output_tokens'), stop_sequences=generation_config.get('stop_sequences'), presence_penalty=generation_config.get('presence_penalty'), frequency_penalty=generation_config.get('frequency_penalty'), seed=generation_config.get('seed'), thinking_config=generation_config.get('thinking_config'), media_resolution=generation_config.get('media_resolution'), response_mime_type=generation_config.get('response_mime_type'), response_json_schema=generation_config.get('response_json_schema'), ), ) response = await self.client.aio.models.count_tokens( model=self._model_name, contents=contents, config=config, ) if response.total_tokens is None: raise UnexpectedModelBehavior( # pragma: no cover 'Total tokens missing from Gemini response', str(response) ) return usage.RequestUsage( input_tokens=response.total_tokens, ) @asynccontextmanager async def request_stream( self, messages: list[ModelMessage], model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters, run_context: RunContext[Any] | None = None, ) -> AsyncIterator[StreamedResponse]: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) model_settings = cast(GoogleModelSettings, model_settings or {}) response = await self._generate_content(messages, True, model_settings, model_request_parameters) yield await self._process_streamed_response(response, model_request_parameters) # type: ignore def _get_tools(self, model_request_parameters: ModelRequestParameters) -> list[ToolDict] | None: tools: list[ToolDict] = [ ToolDict(function_declarations=[_function_declaration_from_tool(t)]) for t in model_request_parameters.tool_defs.values() ] if model_request_parameters.builtin_tools: if model_request_parameters.function_tools: raise UserError('Google does not support function tools and built-in tools at the same time.') for tool in model_request_parameters.builtin_tools: if isinstance(tool, WebSearchTool): tools.append(ToolDict(google_search=GoogleSearchDict())) elif isinstance(tool, WebFetchTool): tools.append(ToolDict(url_context=UrlContextDict())) elif isinstance(tool, CodeExecutionTool): tools.append(ToolDict(code_execution=ToolCodeExecutionDict())) elif isinstance(tool, ImageGenerationTool): # pragma: no branch if not self.profile.supports_image_output: raise UserError( "`ImageGenerationTool` is not supported by this model. Use a model with 'image' in the name instead." ) else: # pragma: no cover raise UserError( f'`{tool.__class__.__name__}` is not supported by `GoogleModel`. If it should be, please file an issue.' ) return tools or None def _get_tool_config( self, model_request_parameters: ModelRequestParameters, tools: list[ToolDict] | None ) -> ToolConfigDict | None: if not model_request_parameters.allow_text_output and tools: names: list[str] = [] for tool in tools: for function_declaration in tool.get('function_declarations') or []: if name := function_declaration.get('name'): # pragma: no branch names.append(name) return _tool_config(names) else: return None @overload async def _generate_content( self, messages: list[ModelMessage], stream: Literal[False], model_settings: GoogleModelSettings, model_request_parameters: ModelRequestParameters, ) -> GenerateContentResponse: ... @overload async def _generate_content( self, messages: list[ModelMessage], stream: Literal[True], model_settings: GoogleModelSettings, model_request_parameters: ModelRequestParameters, ) -> Awaitable[AsyncIterator[GenerateContentResponse]]: ... async def _generate_content( self, messages: list[ModelMessage], stream: bool, model_settings: GoogleModelSettings, model_request_parameters: ModelRequestParameters, ) -> GenerateContentResponse | Awaitable[AsyncIterator[GenerateContentResponse]]: contents, config = await self._build_content_and_config(messages, model_settings, model_request_parameters) func = self.client.aio.models.generate_content_stream if stream else self.client.aio.models.generate_content try: return await func(model=self._model_name, contents=contents, config=config) # type: ignore except errors.APIError as e: if (status_code := e.code) >= 400: raise ModelHTTPError( status_code=status_code, model_name=self._model_name, body=cast(Any, e.details), # pyright: ignore[reportUnknownMemberType] ) from e raise ModelAPIError(model_name=self._model_name, message=str(e)) from e async def _build_content_and_config( self, messages: list[ModelMessage], model_settings: GoogleModelSettings, model_request_parameters: ModelRequestParameters, ) -> tuple[list[ContentUnionDict], GenerateContentConfigDict]: tools = self._get_tools(model_request_parameters) if tools and not self.profile.supports_tools: raise UserError('Tools are not supported by this model.') response_mime_type = None response_schema = None if model_request_parameters.output_mode == 'native': if model_request_parameters.function_tools: raise UserError( 'Google does not support `NativeOutput` and function tools at the same time. Use `output_type=ToolOutput(...)` instead.' ) response_mime_type = 'application/json' output_object = model_request_parameters.output_object assert output_object is not None response_schema = self._map_response_schema(output_object) elif model_request_parameters.output_mode == 'prompted' and not tools: if not self.profile.supports_json_object_output: raise UserError('JSON output is not supported by this model.') response_mime_type = 'application/json' tool_config = self._get_tool_config(model_request_parameters, tools) system_instruction, contents = await self._map_messages(messages, model_request_parameters) modalities = [Modality.TEXT.value] if self.profile.supports_image_output: modalities.append(Modality.IMAGE.value) http_options: HttpOptionsDict = { 'headers': {'Content-Type': 'application/json', 'User-Agent': get_user_agent()} } if timeout := model_settings.get('timeout'): if isinstance(timeout, int | float): http_options['timeout'] = int(1000 * timeout) else: raise UserError('Google does not support setting ModelSettings.timeout to a httpx.Timeout') config = GenerateContentConfigDict( http_options=http_options, system_instruction=system_instruction, temperature=model_settings.get('temperature'), top_p=model_settings.get('top_p'), max_output_tokens=model_settings.get('max_tokens'), stop_sequences=model_settings.get('stop_sequences'), presence_penalty=model_settings.get('presence_penalty'), frequency_penalty=model_settings.get('frequency_penalty'), seed=model_settings.get('seed'), safety_settings=model_settings.get('google_safety_settings'), thinking_config=model_settings.get('google_thinking_config'), labels=model_settings.get('google_labels'), media_resolution=model_settings.get('google_video_resolution'), cached_content=model_settings.get('google_cached_content'), tools=cast(ToolListUnionDict, tools), tool_config=tool_config, response_mime_type=response_mime_type, response_json_schema=response_schema, response_modalities=modalities, ) return contents, config def _process_response(self, response: GenerateContentResponse) -> ModelResponse: if not response.candidates: raise UnexpectedModelBehavior('Expected at least one candidate in Gemini response') # pragma: no cover candidate = response.candidates[0] vendor_id = response.response_id vendor_details: dict[str, Any] | None = None finish_reason: FinishReason | None = None raw_finish_reason = candidate.finish_reason if raw_finish_reason: # pragma: no branch vendor_details = {'finish_reason': raw_finish_reason.value} finish_reason = _FINISH_REASON_MAP.get(raw_finish_reason) if candidate.content is None or candidate.content.parts is None: if finish_reason == 'content_filter' and raw_finish_reason: raise UnexpectedModelBehavior( f'Content filter {raw_finish_reason.value!r} triggered', response.model_dump_json() ) parts = [] # pragma: no cover else: parts = candidate.content.parts or [] usage = _metadata_as_usage(response, provider=self._provider.name, provider_url=self._provider.base_url) return _process_response_from_parts( parts, candidate.grounding_metadata, response.model_version or self._model_name, self._provider.name, usage, vendor_id=vendor_id, vendor_details=vendor_details, finish_reason=finish_reason, url_context_metadata=candidate.url_context_metadata, ) async def _process_streamed_response( self, response: AsyncIterator[GenerateContentResponse], model_request_parameters: ModelRequestParameters ) -> StreamedResponse: """Process a streamed response, and prepare a streaming response to return.""" peekable_response = _utils.PeekableAsyncStream(response) first_chunk = await peekable_response.peek() if isinstance(first_chunk, _utils.Unset): raise UnexpectedModelBehavior('Streamed response ended without content or tool calls') # pragma: no cover return GeminiStreamedResponse( model_request_parameters=model_request_parameters, _model_name=first_chunk.model_version or self._model_name, _response=peekable_response, _timestamp=first_chunk.create_time or _utils.now_utc(), _provider_name=self._provider.name, _provider_url=self._provider.base_url, ) async def _map_messages( self, messages: list[ModelMessage], model_request_parameters: ModelRequestParameters ) -> tuple[ContentDict | None, list[ContentUnionDict]]: contents: list[ContentUnionDict] = [] system_parts: list[PartDict] = [] for m in messages: if isinstance(m, ModelRequest): message_parts: list[PartDict] = [] for part in m.parts: if isinstance(part, SystemPromptPart): system_parts.append({'text': part.content}) elif isinstance(part, UserPromptPart): message_parts.extend(await self._map_user_prompt(part)) elif isinstance(part, ToolReturnPart): message_parts.append( { 'function_response': { 'name': part.tool_name, 'response': part.model_response_object(), 'id': part.tool_call_id, } } ) elif isinstance(part, RetryPromptPart): if part.tool_name is None: message_parts.append({'text': part.model_response()}) # pragma: no cover else: message_parts.append( { 'function_response': { 'name': part.tool_name, 'response': {'call_error': part.model_response()}, 'id': part.tool_call_id, } } ) else: assert_never(part) if message_parts: contents.append({'role': 'user', 'parts': message_parts}) elif isinstance(m, ModelResponse): maybe_content = _content_model_response(m, self.system) if maybe_content: contents.append(maybe_content) else: assert_never(m) # Google GenAI requires at least one part in the message. if not contents: contents = [{'role': 'user', 'parts': [{'text': ''}]}] if instructions := self._get_instructions(messages, model_request_parameters): system_parts.insert(0, {'text': instructions}) system_instruction = ContentDict(role='user', parts=system_parts) if system_parts else None return system_instruction, contents async def _map_user_prompt(self, part: UserPromptPart) -> list[PartDict]: if isinstance(part.content, str): return [{'text': part.content}] else: content: list[PartDict] = [] for item in part.content: if isinstance(item, str): content.append({'text': item}) elif isinstance(item, BinaryContent): inline_data_dict: BlobDict = {'data': item.data, 'mime_type': item.media_type} part_dict: PartDict = {'inline_data': inline_data_dict} if item.vendor_metadata: part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata) content.append(part_dict) elif isinstance(item, VideoUrl) and item.is_youtube: file_data_dict: FileDataDict = {'file_uri': item.url, 'mime_type': item.media_type} part_dict: PartDict = {'file_data': file_data_dict} if item.vendor_metadata: # pragma: no branch part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata) content.append(part_dict) elif isinstance(item, FileUrl): if item.force_download or ( # google-gla does not support passing file urls directly, except for youtube videos # (see above) and files uploaded to the file API (which cannot be downloaded anyway) self.system == 'google-gla' and not item.url.startswith(r'https://generativelanguage.googleapis.com/v1beta/files') ): downloaded_item = await download_item(item, data_format='bytes') inline_data: BlobDict = { 'data': downloaded_item['data'], 'mime_type': downloaded_item['data_type'], } content.append({'inline_data': inline_data}) else: file_data_dict: FileDataDict = {'file_uri': item.url, 'mime_type': item.media_type} content.append({'file_data': file_data_dict}) # pragma: lax no cover elif isinstance(item, CachePoint): # Google Gemini doesn't support prompt caching via CachePoint pass else: assert_never(item) return content def _map_response_schema(self, o: OutputObjectDefinition) -> dict[str, Any]: response_schema = o.json_schema.copy() if o.name: response_schema['title'] = o.name if o.description: response_schema['description'] = o.description return response_schema
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