From 2c6e1e4a41b4718b60c0362a261bb50c10050095 Mon Sep 17 00:00:00 2001 From: clement Date: Sun, 7 Jul 2024 23:43:04 +0200 Subject: [PATCH] feat(provider): add initial support for Gemini (#111) * build(dep): adding google-generativeai package * fix(base_engine): remove useless params in abstract method * feat(gemini_provider): Implement the gemini provider without stream * feat(gemini_provider): reference gemini provider as available provider * feat(gemini_engine): pass advanced parameters to the generate_content method * fix(standalone_executable): update package build for google generativeai * feat(gemini_engine): implement the streaming response for gemini * style(isort): fix import order * feat(gemini_engine): Implement local file image support for gemini * fix(base_engine): Fix parameter name completion_response_with_steam * feat(gemini_engine): reorder gemini models --- basilisk/provider.py | 11 + basilisk/provider_engine/base_engine.py | 6 +- basilisk/provider_engine/gemini_engine.py | 247 ++++++++++++++++ poetry.lock | 340 +++++++++++++++++++++- pyproject.toml | 15 +- 5 files changed, 607 insertions(+), 12 deletions(-) create mode 100644 basilisk/provider_engine/gemini_engine.py diff --git a/basilisk/provider.py b/basilisk/provider.py index daf00bf0..c3b7e787 100644 --- a/basilisk/provider.py +++ b/basilisk/provider.py @@ -17,6 +17,7 @@ class ProviderAPIType(Enum): OPENAI = "openai" ANTHROPIC = "anthropic" OLLAMA = "ollama" + GEMINI = "gemini" class Provider(BaseModel): @@ -100,6 +101,16 @@ def engine_cls(self) -> Type[BaseEngine]: env_var_name_api_key="OPENROUTER_API_KEY", engine_cls_path="basilisk.provider_engine.openrouter_engine.OpenRouterEngine", ), + Provider( + id="gemini", + name="Gemini", + base_url="https://https://generativelanguage.googleapis.com", + api_type=ProviderAPIType.GEMINI, + organization_mode_available=False, + require_api_key=True, + env_var_name_api_key="GOOGLE_API_KEY", + engine_cls_path="basilisk.provider_engine.gemini_engine.GeminiEngine", + ), ] diff --git a/basilisk/provider_engine/base_engine.py b/basilisk/provider_engine/base_engine.py index ef6ebca8..4b5858ed 100644 --- a/basilisk/provider_engine/base_engine.py +++ b/basilisk/provider_engine/base_engine.py @@ -74,9 +74,7 @@ def completion( pass @abstractmethod - def completion_response_with_stream( - self, response: Any, new_block: MessageBlock, debug: bool, **kwargs - ) -> MessageBlock: + def completion_response_with_stream(self, stream: Any, **kwargs): """ Response with stream """ @@ -84,7 +82,7 @@ def completion_response_with_stream( @abstractmethod def completion_response_without_stream( - self, response: Any, new_block: MessageBlock, debug: bool, **kwargs + self, response: Any, new_block: MessageBlock, **kwargs ) -> MessageBlock: """ Response without stream diff --git a/basilisk/provider_engine/gemini_engine.py b/basilisk/provider_engine/gemini_engine.py new file mode 100644 index 00000000..be51b6e6 --- /dev/null +++ b/basilisk/provider_engine/gemini_engine.py @@ -0,0 +1,247 @@ +from __future__ import annotations + +import logging +from base64 import b64decode +from functools import cached_property +from typing import TYPE_CHECKING + +import google.generativeai as genai + +from basilisk.conversation import ( + Conversation, + ImageUrlMessageContent, + Message, + MessageBlock, + MessageRoleEnum, + TextMessageContent, +) + +from .base_engine import BaseEngine, ProviderAIModel, ProviderCapability + +if TYPE_CHECKING: + from basilisk.account import Account + +logger = logging.getLogger(__name__) + + +class GeminiEngine(BaseEngine): + capabilities: set[ProviderCapability] = { + ProviderCapability.TEXT, + ProviderCapability.IMAGE, + } + + def __init__(self, account: Account) -> None: + super().__init__(account) + genai.configure(api_key=self.account.api_key.get_secret_value()) + + @property + def client(self) -> None: + """ + Property to return the client object + """ + raise NotImplementedError("Getting client not supported for Gemini") + + @cached_property + def models(self) -> list[ProviderAIModel]: + """ + Get models + """ + return [ + ProviderAIModel( + id="gemini-1.5-flash-latest", + # Translators: This is a ProviderAIModel description + description=_( + 'Fast and versatile multimodal model for scaling across diverse tasks' + ), + context_window=1048576, + max_output_tokens=8192, + vision=True, + default_temperature=1.0, + ), + ProviderAIModel( + id="gemini-1.5-pro-latest", + # Translators: This is a ProviderAIModel description + description=_( + "Mid-size multimodal model that supports up to 1 million tokens" + ), + context_window=2097152, + max_output_tokens=8192, + vision=True, + default_temperature=1.0, + ), + ProviderAIModel( + id="gemini-1.5-flash", + # Translators: This is a ProviderAIModel description + description=_( + "Fast and versatile multimodal model for scaling across diverse tasks" + ), + context_window=1048576, + max_output_tokens=8192, + vision=True, + default_temperature=1.0, + ), + ProviderAIModel( + id="gemini-1.5-pro", + # Translators: This is a ProviderAIModel description + description=_( + "Mid-size multimodal model that supports up to 1 million tokens" + ), + context_window=2097152, + max_output_tokens=8192, + vision=True, + default_temperature=1.0, + ), + ProviderAIModel( + id="gemini-1.0-pro-latest", + # Translators: This is a ProviderAIModel description + description=_( + "The best model for scaling across a wide range of tasks. This is the latest model." + ), + context_window=30720, + max_output_tokens=2048, + default_temperature=0.9, + ), + ProviderAIModel( + id="gemini-1.0-pro", + # Translators: This is a ProviderAIModel description + description=_( + "The best model for scaling across a wide range of tasks" + ), + context_window=30720, + max_output_tokens=2048, + default_temperature=0.9, + ), + ProviderAIModel( + id="gemini-1.0-pro-vision-latest", + # Translators: This is a ProviderAIModel description + description=_( + 'The best image understanding model to handle a broad range of applications' + ), + context_window=12288, + max_output_tokens=4096, + vision=True, + default_temperature=0.4, + ), + ] + + def convert_role(self, role: MessageRoleEnum) -> str: + if role == MessageRoleEnum.ASSISTANT: + return "model" + elif role == MessageRoleEnum.USER: + return "user" + elif role == MessageRoleEnum.SYSTEM: + raise NotImplementedError( + "System role must be set on the model instance" + ) + + def convert_image(self, image: dict[str, str]) -> genai.protos.Part: + if image["url"].startswith("data:"): + image_media_type, image_data = image["url"].split(";", 1) + image_media_type = image_media_type.split(":", 1)[1] + image_data = image_data.split(",", 1)[1] + return genai.protos.Part( + inline_data=genai.protos.Blob( + mime_type=image_media_type, data=b64decode(image_data) + ) + ) + else: + raise ValueError("Unsupported content type") + + def convert_message_content( + self, message: Message + ) -> list[genai.protos.Part]: + parts = [] + for content in message.content: + if isinstance(content, TextMessageContent): + parts.append(genai.protos.Part(text=content.text)) + elif isinstance(content, ImageUrlMessageContent): + parts.append(self.convert_image(content.image_url)) + elif isinstance(content, str): + parts.append(genai.protos.Part(text=content)) + else: + raise NotImplementedError( + f"Content type {type(content)} not supported" + ) + return parts + + def get_messages( + self, new_block: MessageBlock, conversation: Conversation, **kwargs + ) -> list[genai.types.ContentsType]: + """ + Get messages + """ + messages = [] + for message_block in conversation.messages: + if not message_block.response: + continue + messages.extend( + [ + genai.protos.Content( + role=self.convert_role(message_block.request.role), + parts=self.convert_message_content( + message_block.request + ), + ), + genai.protos.Content( + role=self.convert_role(message_block.response.role), + parts=self.convert_message_content( + message_block.response + ), + ), + ] + ) + messages.append( + genai.protos.Content( + role=self.convert_role(new_block.request.role), + parts=self.convert_message_content(new_block.request), + ) + ) + return messages + + def completion( + self, + new_block: MessageBlock, + conversation: Conversation, + system_message: Message | None, + **kwargs, + ) -> genai.types.GenerateContentResponse: + super().completion(new_block, conversation, system_message, **kwargs) + model = genai.GenerativeModel( + model_name=new_block.model.id, + system_instruction=system_message.content + if system_message + else None, + ) + + generation_config = genai.GenerationConfig( + max_output_tokens=new_block.max_tokens + if new_block.max_tokens + else None, + temperature=new_block.temperature, + top_p=new_block.top_p, + ) + return model.generate_content( + contents=self.get_messages(new_block, conversation), + generation_config=generation_config, + stream=new_block.stream, + ) + + def completion_response_without_stream( + self, + response: genai.types.GenerateContentResponse, + new_block: MessageBlock, + **kwargs, + ) -> MessageBlock: + new_block.response = Message( + role=MessageRoleEnum.ASSISTANT, + content=self.normalize_linesep(response.text), + ) + return new_block + + def completion_response_with_stream( + self, stream: genai.types.GenerateContentResponse, **kwargs + ): + for chunk in stream: + chunk_text = chunk.text + if chunk_text: + yield 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