diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..6a561b6968d532507dd9315452ace33c12942df6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +asset/external_view.pptx filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 85c1f277951dea827c35b46fd8312f1881d1d4ff..1cb4be8f25a08cf9e1bc3ae79ae37f56c60fff28 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,26 @@ ---- -title: Neo Llm Module V1.3.5 -emoji: 🐢 -colorFrom: pink -colorTo: blue -sdk: static -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# neoLLM Module + +neoAIのLLMソリューションの基盤モジュール +[neoLLM Module Document](https://www.notion.so/neoLLM-Module-Document-64399d1d1db24d92bce8f9b88472833f) + +## 準備 +[neoLLM インストール方法](https://www.notion.so/c760d96f1b4240e6880a32bee96bba35) +1. install neoLLM Module ※ Python 3.10 + ```bash + $ pip install git+https://github.com/neoAI-inc/neo-llm-module.git@v1.x.x + ``` + +2. APIキーの設定 + `.env`ファイルの配置 + - 環境変数を`.env`ファイルで定義し，実行するバスに配置 + - `project/example_env.txt`を`.env`に名前を変えて, 必要事項を記入 + +## 使用方法 +### 概要 +灰色背景の部分を開発するだけでOK +- MyLLM: 1回のLLMへのリクエストをラップできる +- MyL3M2: 複数のLLMへのリクエストをラップできる + +詳しくは、`project/neollm-tutorial.ipynb`, `project/ex_module` +![外観図](asset/external_view.png) + diff --git a/asset/external_view.png b/asset/external_view.png new file mode 100644 index 0000000000000000000000000000000000000000..d32771de8ef74fcb1c9addf07433a4363671f7b8 Binary files /dev/null and b/asset/external_view.png differ diff --git a/asset/external_view.pptx b/asset/external_view.pptx new file mode 100644 index 0000000000000000000000000000000000000000..f4addf7db533576e076d51f002e71ebcf0f015be --- /dev/null +++ b/asset/external_view.pptx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b3e9d7dbbb6f9ca5750edd9eaad8fe7ce5fcb5797e8027ae11dea90a0a47a2c +size 8728033 diff --git a/makefile b/makefile new file mode 100644 index 0000000000000000000000000000000000000000..e3ebaeaec55ca76bdcff0af0b30f519e1af5f13f --- /dev/null +++ b/makefile @@ -0,0 +1,14 @@ +.PHONY: lint +lint: ## run tests with poetry (isort, black, pflake8, mypy) + poetry run black neollm + poetry run isort neollm + poetry run pflake8 neollm + poetry run mypy neollm --explicit-package-bases + +.PHONY: test +test: + poetry run pytest + +.PHONY: unit-test +unit-test: + poetry run pytest -k "not test_neollm" diff --git a/neollm.code-workspace b/neollm.code-workspace new file mode 100644 index 0000000000000000000000000000000000000000..d856242a7f726cb27fa25cc1ecd711fb79b304a1 --- /dev/null +++ b/neollm.code-workspace @@ -0,0 +1,63 @@ +{ + "folders": [ + { + "name": "neo-llm-module", + "path": "." + } + ], + "settings": { + "editor.codeActionsOnSave": { + "source.fixAll.eslint": "explicit", + "source.fixAll.stylelint": "explicit" + }, + "editor.formatOnSave": true, + "editor.formatOnPaste": true, + "editor.formatOnType": true, + "json.format.keepLines": true, + "[javascript]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[typescript]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[typescriptreact]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[css]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[json]": { + "editor.defaultFormatter": "vscode.json-language-features" + }, + "search.exclude": { + "**/node_modules": true, + "static": true + }, + "[python]": { + "editor.defaultFormatter": "ms-python.black-formatter", + "editor.codeActionsOnSave": { + "source.organizeImports": "explicit" + } + }, + "flake8.args": [ + "--max-line-length=119", + "--max-complexity=15", + "--ignore=E203,E501,E704,W503", + "--exclude=.venv,.git,__pycache__,.mypy_cache,.hg" + ], + "isort.args": ["--settings-path=pyproject.toml"], + "black-formatter.args": ["--config=pyproject.toml"], + "mypy-type-checker.args": ["--config-file=pyproject.toml"], + "python.analysis.extraPaths": ["./backend"] + }, + "extensions": { + "recommendations": [ + "esbenp.prettier-vscode", + "dbaeumer.vscode-eslint", + "ms-python.flake8", + "ms-python.isort", + "ms-python.black-formatter", + "ms-python.mypy-type-checker" + ] + } +} diff --git a/neollm/__init__.py b/neollm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0056ff0106251f8ca7b38f52ac3141eed7dc8df6 --- /dev/null +++ b/neollm/__init__.py @@ -0,0 +1,5 @@ +from neollm.myllm.abstract_myllm import AbstractMyLLM +from neollm.myllm.myl3m2 import MyL3M2 +from neollm.myllm.myllm import MyLLM + +__all__ = ["AbstractMyLLM", "MyLLM", "MyL3M2"] diff --git a/neollm/exceptions.py b/neollm/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..2ebe003364d19a78f4d3d43454ef9f120b119fcf --- /dev/null +++ b/neollm/exceptions.py @@ -0,0 +1,2 @@ +class ContentFilterError(Exception): + pass diff --git a/neollm/llm/__init__.py b/neollm/llm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc68336613b5a985d136426b4e619a83f22273b1 --- /dev/null +++ b/neollm/llm/__init__.py @@ -0,0 +1,4 @@ +from neollm.llm.abstract_llm import AbstractLLM +from neollm.llm.get_llm import get_llm + +__all__ = ["AbstractLLM", "get_llm"] diff --git a/neollm/llm/abstract_llm.py b/neollm/llm/abstract_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..ba58b15784cbc2ee971aefbdac2739320a0bd202 --- /dev/null +++ b/neollm/llm/abstract_llm.py @@ -0,0 +1,188 @@ +from abc import ABC, abstractmethod +from typing import Any + +from neollm.llm.utils import get_entity +from neollm.types import ( + APIPricing, + ChatCompletion, + ChatCompletionMessage, + ChatCompletionMessageToolCall, + Choice, + ChoiceDeltaToolCall, + Chunk, + ClientSettings, + CompletionUsage, + Function, + FunctionCall, + LLMSettings, + Messages, + Response, + StreamResponse, +) +from neollm.utils.utils import cprint + + +# 現状、Azure, OpenAIに対応 +class AbstractLLM(ABC): + dollar_per_ktoken: APIPricing + model: str + context_window: int + _custom_price_calculation: bool = False # self.tokenではなく、self.custom_tokenを使う場合にTrue + + def __init__(self, client_settings: ClientSettings): + """LLMクラスの初期化 + + Args: + client_settings (ClientSettings): クライアント設定 + """ + self.client_settings = client_settings + + def calculate_price(self, num_input_tokens: int = 0, num_output_tokens: int = 0) -> float: + """ + 費用の計測 + + Args: + num_input_tokens (int, optional): 入力のトークン数. Defaults to 0. + num_output_tokens (int, optional): 出力のトークン数. Defaults to 0. + + Returns: + float: API利用料(USD) + """ + price = ( + self.dollar_per_ktoken.input * num_input_tokens + self.dollar_per_ktoken.output * num_output_tokens + ) / 1000 + return price + + @abstractmethod + def count_tokens(self, messages: Messages | None = None, only_response: bool = False) -> int: ... + + @abstractmethod + def encode(self, text: str) -> list[int]: ... + + @abstractmethod + def decode(self, encoded: list[int]) -> str: ... + + @abstractmethod + def generate(self, messages: Messages, llm_settings: LLMSettings) -> Response: + """生成 + + Args: + messages (Messages): OpenAI仕様のMessages(list[dict]) + + Returns: + Response: OpenAI likeなResponse + """ + + @abstractmethod + def generate_stream(self, messages: Messages, llm_settings: LLMSettings) -> StreamResponse: ... + + def __repr__(self) -> str: + return f"{self.__class__}()" + + def convert_nonstream_response( + self, chunk_list: list[Chunk], messages: Messages, functions: Any = None + ) -> Response: + # messagesとfunctionsはトークン数計測に必要 + _chunk_choices = [chunk.choices[0] for chunk in chunk_list if len(chunk.choices) > 0] + # TODO: n=2以上の場合にwarningを出したい + + # FunctionCall -------------------------------------------------- + function_call: FunctionCall | None + if all([_c.delta.function_call is None for _c in _chunk_choices]): + function_call = None + else: + function_call = FunctionCall( + arguments="".join( + [ + _c.delta.function_call.arguments + for _c in _chunk_choices + if _c.delta.function_call is not None and _c.delta.function_call.arguments is not None + ] + ), + name=get_entity( + [_c.delta.function_call.name for _c in _chunk_choices if _c.delta.function_call is not None], + default="", + ), + ) + + # ToolCalls -------------------------------------------------- + _tool_calls_dict: dict[int, list[ChoiceDeltaToolCall]] = {} # key=index + for _chunk in _chunk_choices: + if _chunk.delta.tool_calls is None: + continue + for _tool_call in _chunk.delta.tool_calls: + _tool_calls_dict.setdefault(_tool_call.index, []).append(_tool_call) + + tool_calls: list[ChatCompletionMessageToolCall] | None + if sum(len(_tool_calls) for _tool_calls in _tool_calls_dict.values()) == 0: + tool_calls = None + else: + tool_calls = [] + for _tool_calls in _tool_calls_dict.values(): + tool_calls.append( + ChatCompletionMessageToolCall( + id=get_entity([_tc.id for _tc in _tool_calls], default=""), + function=Function( + arguments="".join( + [ + _tc.function.arguments + for _tc in _tool_calls + if _tc.function is not None and _tc.function.arguments is not None + ] + ), + name=get_entity( + [_tc.function.name for _tc in _tool_calls if _tc.function is not None], default="" + ), + ), + type=get_entity([_tc.type for _tc in _tool_calls], default="function"), + ) + ) + message = ChatCompletionMessage( + content="".join([_c.delta.content for _c in _chunk_choices if _c.delta.content is not None]), + # TODO: ChoiceDeltaのroleなんで、assistant以外も許されてるの？ + role=get_entity([_c.delta.role for _c in _chunk_choices], default="assistant"), # type: ignore + function_call=function_call, + tool_calls=tool_calls, + ) + choice = Choice( + index=get_entity([_c.index for _c in _chunk_choices], default=0), + message=message, + finish_reason=get_entity([_c.finish_reason for _c in _chunk_choices], default=None), + ) + + # Usage -------------------------------------------------- + try: + for chunk in chunk_list: + if getattr(chunk, "tokens"): + prompt_tokens = int(getattr(chunk, "tokens")["input_tokens"]) + completion_tokens = int(getattr(chunk, "tokens")["output_tokens"]) + assert prompt_tokens + assert completion_tokens + except Exception: + prompt_tokens = self.count_tokens(messages) # TODO: fcなど + completion_tokens = self.count_tokens([message.to_typeddict_message()], only_response=True) + usages = CompletionUsage( + completion_tokens=completion_tokens, + prompt_tokens=prompt_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + + # ChatCompletion ------------------------------------------ + response = ChatCompletion( + id=get_entity([chunk.id for chunk in chunk_list], default=""), + object="chat.completion", + created=get_entity([getattr(chunk, "created", 0) for chunk in chunk_list], default=0), + model=get_entity([getattr(chunk, "model", "") for chunk in chunk_list], default=""), + choices=[choice], + system_fingerprint=get_entity( + [getattr(chunk, "system_fingerprint", None) for chunk in chunk_list], default=None + ), + usage=usages, + ) + + return response + + @property + def max_tokens(self) -> int: + cprint("max_tokensは非推奨です。context_windowを使用してください。") + return self.context_window diff --git a/neollm/llm/claude/abstract_claude.py b/neollm/llm/claude/abstract_claude.py new file mode 100644 index 0000000000000000000000000000000000000000..073ef53d4de530c4c442bccb6f75c8a3d268b86b --- /dev/null +++ b/neollm/llm/claude/abstract_claude.py @@ -0,0 +1,214 @@ +import time +from abc import abstractmethod +from typing import Any, Literal, cast + +from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, Stream +from anthropic.types import MessageParam as AnthropicMessageParam +from anthropic.types import MessageStreamEvent as AnthropicMessageStreamEvent +from anthropic.types.message import Message as AnthropicMessage + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.types import ( + ChatCompletion, + LLMSettings, + Message, + Messages, + Response, + StreamResponse, +) +from neollm.types.openai.chat_completion import ( + ChatCompletionMessage, + Choice, + CompletionUsage, + FinishReason, +) +from neollm.types.openai.chat_completion_chunk import ( + ChatCompletionChunk, + ChoiceDelta, + ChunkChoice, +) +from neollm.utils.utils import cprint + +DEFAULT_MAX_TOKENS = 4_096 + + +class AbstractClaude(AbstractLLM): + @property + @abstractmethod + def client(self) -> Anthropic | AnthropicVertex | AnthropicBedrock: ... + + @property + def _client_for_token(self) -> Anthropic: + """トークンカウント用のAnthropicクライアント取得 + (AnthropicBedrock, AnthropicVertexがmethodを持っていないため) + + Returns: + Anthropic: Anthropicクライアント + """ + return Anthropic() + + def encode(self, text: str) -> list[int]: + tokenizer = self._client_for_token.get_tokenizer() + encoded = cast(list[int], tokenizer.encode(text).ids) + return encoded + + def decode(self, decoded: list[int]) -> str: + tokenizer = self._client_for_token.get_tokenizer() + text = cast(str, tokenizer.decode(decoded)) + return text + + def count_tokens(self, messages: list[Message] | None = None, only_response: bool = False) -> int: + """ + トークン数の計測 + + Args: + messages (Messages): messages + + Returns: + int: トークン数 + """ + if messages is None: + return 0 + tokens = 0 + for message in messages: + content = message["content"] + if isinstance(content, str): + tokens += self._client_for_token.count_tokens(content) + continue + if isinstance(content, list): + for content_i in content: + if content_i["type"] == "text": + tokens += self._client_for_token.count_tokens(content_i["text"]) + continue + return tokens + + def _convert_finish_reason( + self, stop_reason: Literal["end_turn", "max_tokens", "stop_sequence"] | None + ) -> FinishReason | None: + if stop_reason == "max_tokens": + return "length" + if stop_reason == "stop_sequence": + return "stop" + return None + + def _convert_to_response(self, platform_response: AnthropicMessage) -> Response: + return ChatCompletion( + id=platform_response.id, + choices=[ + Choice( + index=0, + message=ChatCompletionMessage( + content=platform_response.content[0].text if len(platform_response.content) > 0 else "", + role="assistant", + ), + finish_reason=self._convert_finish_reason(platform_response.stop_reason), + ) + ], + created=int(time.time()), + model=self.model, + object="messages.create", + system_fingerprint=None, + usage=CompletionUsage( + prompt_tokens=platform_response.usage.input_tokens, + completion_tokens=platform_response.usage.output_tokens, + total_tokens=platform_response.usage.input_tokens + platform_response.usage.output_tokens, + ), + ) + + def _convert_to_platform_messages(self, messages: Messages) -> tuple[str, list[AnthropicMessageParam]]: + _system = "" + _message: list[AnthropicMessageParam] = [] + for message in messages: + if message["role"] == "system": + _system += "\n" + message["content"] + elif message["role"] == "user": + if isinstance(message["content"], str): + _message.append({"role": "user", "content": message["content"]}) + else: + cprint("WARNING: 未対応です", color="yellow", background=True) + elif message["role"] == "assistant": + if isinstance(message["content"], str): + _message.append({"role": "assistant", "content": message["content"]}) + else: + cprint("WARNING: 未対応です", color="yellow", background=True) + else: + cprint("WARNING: 未対応です", color="yellow", background=True) + return _system, _message + + def _convert_to_streamresponse( + self, platform_streamresponse: Stream[AnthropicMessageStreamEvent] + ) -> StreamResponse: + created = int(time.time()) + model = "" + id_ = "" + content: str | None = None + for chunk in platform_streamresponse: + input_tokens = 0 + output_tokens = 0 + if chunk.type == "message_stop" or chunk.type == "content_block_stop": + continue + if chunk.type == "message_start": + model = model or chunk.message.model + id_ = id_ or chunk.message.id + input_tokens = chunk.message.usage.input_tokens + output_tokens = chunk.message.usage.output_tokens + content = "".join([content_block.text for content_block in chunk.message.content]) + finish_reason = self._convert_finish_reason(chunk.message.stop_reason) + elif chunk.type == "message_delta": + content = "" + finish_reason = self._convert_finish_reason(chunk.delta.stop_reason) + output_tokens = chunk.usage.output_tokens + elif chunk.type == "content_block_start": + content = chunk.content_block.text + finish_reason = None + elif chunk.type == "content_block_delta": + content = chunk.delta.text + finish_reason = None + yield ChatCompletionChunk( + id=id_, + choices=[ + ChunkChoice( + delta=ChoiceDelta( + content=content, + role="assistant", + ), + finish_reason=finish_reason, + index=0, # 0-indexedじゃないかもしれないので0に塗り替え + ) + ], + created=created, + model=model, + object="chat.completion.chunk", + tokens={"input_tokens": input_tokens, "output_tokens": output_tokens}, # type: ignore + ) + + def generate(self, messages: Messages, llm_settings: LLMSettings) -> Response: + _system, _message = self._convert_to_platform_messages(messages) + llm_settings = self._set_max_tokens(llm_settings) + response = self.client.messages.create( + model=self.model, + system=_system, + messages=_message, + stream=False, + **llm_settings, + ) + return self._convert_to_response(platform_response=response) + + def generate_stream(self, messages: Any, llm_settings: LLMSettings) -> StreamResponse: + _system, _message = self._convert_to_platform_messages(messages) + llm_settings = self._set_max_tokens(llm_settings) + response = self.client.messages.create( + model=self.model, + system=_system, + messages=_message, + stream=True, + **llm_settings, + ) + return self._convert_to_streamresponse(platform_streamresponse=response) + + def _set_max_tokens(self, llm_settings: LLMSettings) -> LLMSettings: + # claudeはmax_tokensが必須 + if not llm_settings.get("max_tokens"): + cprint(f"max_tokens is not set. Set to {DEFAULT_MAX_TOKENS}.", color="yellow") + llm_settings["max_tokens"] = DEFAULT_MAX_TOKENS + return llm_settings diff --git a/neollm/llm/claude/anthropic_llm.py b/neollm/llm/claude/anthropic_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..7ba376c8c7fb9df3204c676ad4facdf5773e1389 --- /dev/null +++ b/neollm/llm/claude/anthropic_llm.py @@ -0,0 +1,66 @@ +from typing import Literal, cast, get_args + +from anthropic import Anthropic + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.llm.claude.abstract_claude import AbstractClaude +from neollm.types import APIPricing, ClientSettings +from neollm.utils.utils import cprint + +# price: https://www.anthropic.com/api +# models: https://docs.anthropic.com/claude/docs/models-overview + +SUPPORTED_MODELS = Literal[ + "claude-3-opus-20240229", + "claude-3-sonnet-20240229", + "claude-3-haiku-20240307", +] + + +def get_anthoropic_llm(model_name: SUPPORTED_MODELS | str, client_settings: ClientSettings) -> AbstractLLM: + # Add 日付 + replace_map_for_nodate: dict[str, SUPPORTED_MODELS] = { + "claude-3-opus": "claude-3-opus-20240229", + "claude-3-sonnet": "claude-3-sonnet-20240229", + "claude-3-haiku": "claude-3-haiku-20240307", + } + if model_name in replace_map_for_nodate: + cprint("WARNING: model_nameに日付を指定してください", color="yellow", background=True) + print(f"model_name: {model_name} -> {replace_map_for_nodate[model_name]}") + model_name = replace_map_for_nodate[model_name] + + # map to LLM + supported_model_map: dict[SUPPORTED_MODELS, AbstractLLM] = { + "claude-3-opus-20240229": AnthropicClaude3Opus20240229(client_settings), + "claude-3-sonnet-20240229": AnthropicClaude3Sonnet20240229(client_settings), + "claude-3-haiku-20240307": AnthropicClaude3Haiku20240229(client_settings), + } + if model_name in supported_model_map: + model_name = cast(SUPPORTED_MODELS, model_name) + return supported_model_map[model_name] + raise ValueError(f"model_name must be {get_args(SUPPORTED_MODELS)}, but got {model_name}.") + + +class AnthoropicLLM(AbstractClaude): + @property + def client(self) -> Anthropic: + client = Anthropic(**self.client_settings) + return client + + +class AnthropicClaude3Opus20240229(AnthoropicLLM): + dollar_per_ktoken = APIPricing(input=15 / 1000, output=75 / 1000) + model: str = "claude-3-opus-20240229" + context_window: int = 200_000 + + +class AnthropicClaude3Sonnet20240229(AnthoropicLLM): + dollar_per_ktoken = APIPricing(input=3 / 1000, output=15 / 1000) + model: str = "claude-3-sonnet-20240229" + context_window: int = 200_000 + + +class AnthropicClaude3Haiku20240229(AnthoropicLLM): + dollar_per_ktoken = APIPricing(input=0.25 / 1000, output=1.25 / 1000) + model: str = "claude-3-haiku-20240307" + context_window: int = 200_000 diff --git a/neollm/llm/claude/gcp_llm.py b/neollm/llm/claude/gcp_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..9a281d211fa6a74a7d805fbc4b58d84b756e9745 --- /dev/null +++ b/neollm/llm/claude/gcp_llm.py @@ -0,0 +1,67 @@ +from typing import Literal, cast, get_args + +from anthropic import AnthropicVertex + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.llm.claude.abstract_claude import AbstractClaude +from neollm.types import APIPricing, ClientSettings +from neollm.utils.utils import cprint + +# price: https://www.anthropic.com/api +# models: https://docs.anthropic.com/claude/docs/models-overview + +SUPPORTED_MODELS = Literal[ + "claude-3-opus@20240229", + "claude-3-sonnet@20240229", + "claude-3-haiku@20240307", +] + + +# TODO! google 動かしたいね +def get_gcp_llm(model_name: SUPPORTED_MODELS | str, client_settings: ClientSettings) -> AbstractLLM: + # Add 日付 + replace_map_for_nodate: dict[str, SUPPORTED_MODELS] = { + "claude-3-opus": "claude-3-opus@20240229", + "claude-3-sonnet": "claude-3-sonnet@20240229", + "claude-3-haiku": "claude-3-haiku@20240307", + } + if model_name in replace_map_for_nodate: + cprint("WARNING: model_nameに日付を指定してください", color="yellow", background=True) + print(f"model_name: {model_name} -> {replace_map_for_nodate[model_name]}") + model_name = replace_map_for_nodate[model_name] + + # map to LLM + supported_model_map: dict[SUPPORTED_MODELS, AbstractLLM] = { + "claude-3-opus@20240229": GCPClaude3Opus20240229(client_settings), + "claude-3-sonnet@20240229": GCPClaude3Sonnet20240229(client_settings), + "claude-3-haiku@20240307": GCPClaude3Haiku20240229(client_settings), + } + if model_name in supported_model_map: + model_name = cast(SUPPORTED_MODELS, model_name) + return supported_model_map[model_name] + raise ValueError(f"model_name must be {get_args(SUPPORTED_MODELS)}, but got {model_name}.") + + +class GoogleLLM(AbstractClaude): + @property + def client(self) -> AnthropicVertex: + client = AnthropicVertex(**self.client_settings) + return client + + +class GCPClaude3Opus20240229(GoogleLLM): + dollar_per_ktoken = APIPricing(input=15 / 1000, output=75 / 1000) + model: str = "claude-3-opus@20240229" + context_window: int = 200_000 + + +class GCPClaude3Sonnet20240229(GoogleLLM): + dollar_per_ktoken = APIPricing(input=3 / 1000, output=15 / 1000) + model: str = "claude-3-sonnet@20240229" + context_window: int = 200_000 + + +class GCPClaude3Haiku20240229(GoogleLLM): + dollar_per_ktoken = APIPricing(input=0.25 / 1000, output=1.25 / 1000) + model: str = "claude-3-haiku@20240307" + context_window: int = 200_000 diff --git a/neollm/llm/gemini/abstract_gemini.py b/neollm/llm/gemini/abstract_gemini.py new file mode 100644 index 0000000000000000000000000000000000000000..a960ce1fdc2a8e5d4900bef16ccc896371dcd6e9 --- /dev/null +++ b/neollm/llm/gemini/abstract_gemini.py @@ -0,0 +1,229 @@ +import time +from abc import abstractmethod +from typing import Iterable, cast + +from google.cloud.aiplatform_v1beta1.types import CountTokensResponse +from google.cloud.aiplatform_v1beta1.types.content import Candidate +from vertexai.generative_models import ( + Content, + GenerationConfig, + GenerationResponse, + GenerativeModel, + Part, +) +from vertexai.generative_models._generative_models import ContentsType + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.types import ( + ChatCompletion, + CompletionUsageForCustomPriceCalculation, + LLMSettings, + Message, + Messages, + Response, + StreamResponse, +) +from neollm.types.openai.chat_completion import ( + ChatCompletionMessage, + Choice, + CompletionUsage, +) +from neollm.types.openai.chat_completion import FinishReason as FinishReasonVertex +from neollm.types.openai.chat_completion_chunk import ( + ChatCompletionChunk, + ChoiceDelta, + ChunkChoice, +) +from neollm.utils.utils import cprint + + +class AbstractGemini(AbstractLLM): + + @abstractmethod + def generate_config(self, llm_settings: LLMSettings) -> GenerationConfig: ... + + # 使っていない + def encode(self, text: str) -> list[int]: + return [ord(char) for char in text] + + # 使っていない + def decode(self, decoded: list[int]) -> str: + return "".join([chr(number) for number in decoded]) + + def _count_tokens_vertex(self, contents: ContentsType) -> CountTokensResponse: + model = GenerativeModel(model_name=self.model) + return cast(CountTokensResponse, model.count_tokens(contents)) + + def count_tokens(self, messages: list[Message] | None = None, only_response: bool = False) -> int: + """ + トークン数の計測 + + Args: + messages (Messages): messages + + Returns: + int: トークン数 + """ + if messages is None: + return 0 + _system, _message = self._convert_to_platform_messages(messages) + total_tokens = 0 + if _system: + total_tokens += int(self._count_tokens_vertex(_system).total_tokens) + if _message: + total_tokens = int(self._count_tokens_vertex(_message).total_tokens) + return total_tokens + + def _convert_to_platform_messages(self, messages: Messages) -> tuple[str | None, list[Content]]: + _system = None + _message: list[Content] = [] + + for message in messages: + if message["role"] == "system": + _system = "\n" + message["content"] + elif message["role"] == "user": + if isinstance(message["content"], str): + _message.append(Content(role="user", parts=[Part.from_text(message["content"])])) + else: + try: + if isinstance(message["content"], list) and message["content"][1]["type"] == "image_url": + encoded_image = message["content"][1]["image_url"]["url"].split(",")[-1] + _message.append( + Content( + role="user", + parts=[ + Part.from_text(message["content"][0]["text"]), + Part.from_data(data=encoded_image, mime_type="image/jpeg"), + ], + ) + ) + except KeyError: + cprint("WARNING: 未対応です", color="yellow", background=True) + except IndexError: + cprint("WARNING: 未対応です", color="yellow", background=True) + except Exception as e: + cprint(e, color="red", background=True) + elif message["role"] == "assistant": + if isinstance(message["content"], str): + _message.append(Content(role="model", parts=[Part.from_text(message["content"])])) + else: + cprint("WARNING: 未対応です", color="yellow", background=True) + return _system, _message + + def _convert_finish_reason(self, stop_reason: Candidate.FinishReason) -> FinishReasonVertex | None: + """ + 参考記事 : https://ai.google.dev/api/python/google/ai/generativelanguage/Candidate/FinishReason + + 0: FINISH_REASON_UNSPECIFIED + Default value. This value is unused. + 1: STOP + Natural stop point of the model or provided stop sequence. + 2: MAX_TOKENS + The maximum number of tokens as specified in the request was reached. + 3: SAFETY + The candidate content was flagged for safety reasons. + 4: RECITATION + The candidate content was flagged for recitation reasons. + 5: OTHER + Unknown reason. + """ + + if stop_reason.value in [0, 3, 4, 5]: + return "stop" + + if stop_reason.value in [2]: + return "length" + + return None + + def _convert_to_response( + self, platform_response: GenerationResponse, system: str | None, message: list[Content] + ) -> Response: + # input 請求用文字数 + input_billable_characters = 0 + if system: + input_billable_characters += self._count_tokens_vertex(system).total_billable_characters + if message: + input_billable_characters += self._count_tokens_vertex(message).total_billable_characters + # output 請求用文字数 + output_billable_characters = 0 + if platform_response.text: + output_billable_characters += self._count_tokens_vertex(platform_response.text).total_billable_characters + return ChatCompletion( # type: ignore [call-arg] + id="", + choices=[ + Choice( + index=0, + message=ChatCompletionMessage( + content=platform_response.text, + role="assistant", + ), + finish_reason=self._convert_finish_reason(platform_response.candidates[0].finish_reason), + ) + ], + created=int(time.time()), + model=self.model, + object="messages.create", + system_fingerprint=None, + usage=CompletionUsage( + prompt_tokens=platform_response.usage_metadata.prompt_token_count, + completion_tokens=platform_response.usage_metadata.candidates_token_count, + total_tokens=platform_response.usage_metadata.prompt_token_count + + platform_response.usage_metadata.candidates_token_count, + ), + usage_for_price=CompletionUsageForCustomPriceCalculation( + prompt_tokens=input_billable_characters, + completion_tokens=output_billable_characters, + total_tokens=input_billable_characters + output_billable_characters, + ), + ) + + def _convert_to_streamresponse(self, platform_streamresponse: Iterable[GenerationResponse]) -> StreamResponse: + created = int(time.time()) + content: str | None = None + for chunk in platform_streamresponse: + content = chunk.text + yield ChatCompletionChunk( + id="", + choices=[ + ChunkChoice( + delta=ChoiceDelta( + content=content, + role="assistant", + ), + finish_reason=self._convert_finish_reason(chunk.candidates[0].finish_reason), + index=0, # 0-indexedじゃないかもしれないので0に塗り替え + ) + ], + created=created, + model=self.model, + object="chat.completion.chunk", + ) + + def generate(self, messages: Messages, llm_settings: LLMSettings) -> Response: + _system, _message = self._convert_to_platform_messages(messages) + model = GenerativeModel( + model_name=self.model, + system_instruction=_system, + ) + + response = model.generate_content( + contents=_message, + stream=False, + generation_config=self.generate_config(llm_settings), + ) + + return self._convert_to_response(platform_response=response, system=_system, message=_message) + + def generate_stream(self, messages: Messages, llm_settings: LLMSettings) -> StreamResponse: + _system, _message = self._convert_to_platform_messages(messages) + model = GenerativeModel( + model_name=self.model, + system_instruction=_system, + ) + response = model.generate_content( + contents=_message, + stream=True, + generation_config=self.generate_config(llm_settings), + ) + return self._convert_to_streamresponse(platform_streamresponse=response) diff --git a/neollm/llm/gemini/gcp_llm.py b/neollm/llm/gemini/gcp_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..86123d90663913102ebee47e97b8abb3a2df7c96 --- /dev/null +++ b/neollm/llm/gemini/gcp_llm.py @@ -0,0 +1,114 @@ +from copy import deepcopy +from typing import Literal, cast, get_args + +import vertexai +from vertexai.generative_models import GenerationConfig + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.llm.gemini.abstract_gemini import AbstractGemini +from neollm.types import APIPricing, ClientSettings, LLMSettings, StreamResponse +from neollm.types.mytypes import Messages, Response +from neollm.utils.utils import cprint + +# price: https://ai.google.dev/pricing?hl=ja +# models: https://ai.google.dev/gemini-api/docs/models/gemini?hl=ja + +SUPPORTED_MODELS = Literal["gemini-1.0-pro", "gemini-1.0-pro-vision", "gemini-1.5-pro-preview-0409"] +AVAILABLE_CONFIG_VARIABLES = [ + "candidate_count", + "stop_sequences", + "temperature", + "max_tokens", # "max_output_tokensが設定されていない場合、max_tokensを使う + "max_output_tokens", + "top_p", + "top_k", +] + + +def get_gcp_llm(model_name: SUPPORTED_MODELS | str, client_settings: ClientSettings) -> AbstractLLM: + + vertexai.init(**client_settings) + + # map to LLM + supported_model_map: dict[SUPPORTED_MODELS, AbstractLLM] = { + "gemini-1.0-pro": GCPGemini10Pro(client_settings), + "gemini-1.0-pro-vision": GCPGemini10ProVision(client_settings), + "gemini-1.5-pro-preview-0409": GCPGemini15Pro0409(client_settings), + } + if model_name in supported_model_map: + model_name = cast(SUPPORTED_MODELS, model_name) + return supported_model_map[model_name] + raise ValueError(f"model_name must be {get_args(SUPPORTED_MODELS)}, but got {model_name}.") + + +class GoogleLLM(AbstractGemini): + + def generate_config(self, llm_settings: LLMSettings) -> GenerationConfig: + """ + 参考記事 : https://ai.google.dev/api/rest/v1/GenerationConfig?hl=ja + """ + # gemini + candidate_count = llm_settings.pop("candidate_count", None) + stop_sequences = llm_settings.pop("stop_sequences", None) + temperature = llm_settings.pop("temperature", None) + max_output_tokens = llm_settings.pop("max_output_tokens", None) + top_p = llm_settings.pop("top_p", None) + top_k = llm_settings.pop("top_k", None) + + # neollmの引数でも動くようにする + if max_output_tokens is None: + max_output_tokens = llm_settings.pop("max_tokens", None) + + if len(llm_settings) > 0 and "max_tokens" not in llm_settings: + raise ValueError(f"llm_settings has unknown keys: {llm_settings}") + + return GenerationConfig( + candidate_count=candidate_count, + stop_sequences=stop_sequences, + temperature=temperature, + max_output_tokens=max_output_tokens, + top_p=top_p, + top_k=top_k, + ) + + +class GCPGemini10Pro(GoogleLLM): + dollar_per_ktoken = APIPricing(input=0.125 / 1000, output=0.375 / 1000) + model: str = "gemini-1.0-pro" + context_window: int = 32_000 + + +class GCPGemini10ProVision(GoogleLLM): + dollar_per_ktoken = APIPricing(input=0.125 / 1000, output=0.375 / 1000) + model: str = "gemini-1.0-pro-vision" + context_window: int = 32_000 + + def generate(self, messages: Messages, llm_settings: LLMSettings) -> Response: + messages = self._preprocess_message_to_use_system(messages) + return super().generate(messages, llm_settings) + + def generate_stream(self, messages: Messages, llm_settings: LLMSettings) -> StreamResponse: + messages = self._preprocess_message_to_use_system(messages) + return super().generate_stream(messages, llm_settings) + + def _preprocess_message_to_use_system(self, message: Messages) -> Messages: + if message[0]["role"] != "system": + return message + preprocessed_message = deepcopy(message) + system = preprocessed_message[0]["content"] + del preprocessed_message[0] + if ( + isinstance(system, str) + and isinstance(preprocessed_message[0]["content"], list) + and isinstance(preprocessed_message[0]["content"][0]["text"], str) + ): + preprocessed_message[0]["content"][0]["text"] = system + preprocessed_message[0]["content"][0]["text"] + else: + cprint("WARNING: 入力形式が不正です", color="yellow", background=True) + return preprocessed_message + + +class GCPGemini15Pro0409(GoogleLLM): + dollar_per_ktoken = APIPricing(input=2.5 / 1000, output=7.5 / 1000) + model: str = "gemini-1.5-pro-preview-0409" + context_window: int = 1_000_000 diff --git a/neollm/llm/get_llm.py b/neollm/llm/get_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..3edbe95892a71a44aa673b96847d8c17021f1efe --- /dev/null +++ b/neollm/llm/get_llm.py @@ -0,0 +1,47 @@ +from neollm.llm.abstract_llm import AbstractLLM +from neollm.types import ClientSettings + +from .platform import Platform + +SUPPORTED_CLAUDE_MODELS = [ + "claude-3-opus", + "claude-3-sonnet", + "claude-3-haiku", + "claude-3-opus@20240229", + "claude-3-sonnet@20240229", + "claude-3-haiku@20240307", +] + +SUPPORTED_GEMINI_MODELS = [ + "gemini-1.5-pro-preview-0409", + "gemini-1.0-pro", + "gemini-1.0-pro-vision", +] + + +def get_llm(model_name: str, platform: str, client_settings: ClientSettings) -> AbstractLLM: + platform = Platform(platform) + # llmの取得 + if platform == Platform.AZURE: + from neollm.llm.gpt.azure_llm import get_azure_llm + + return get_azure_llm(model_name, client_settings) + if platform == Platform.OPENAI: + from neollm.llm.gpt.openai_llm import get_openai_llm + + return get_openai_llm(model_name, client_settings) + if platform == Platform.ANTHROPIC: + from neollm.llm.claude.anthropic_llm import get_anthoropic_llm + + return get_anthoropic_llm(model_name, client_settings) + if platform == Platform.GCP: + if model_name in SUPPORTED_CLAUDE_MODELS: + from neollm.llm.claude.gcp_llm import get_gcp_llm as get_gcp_llm_for_claude + + return get_gcp_llm_for_claude(model_name, client_settings) + elif model_name in SUPPORTED_GEMINI_MODELS: + from neollm.llm.gemini.gcp_llm import get_gcp_llm as get_gcp_llm_for_gemini + + return get_gcp_llm_for_gemini(model_name, client_settings) + else: + raise ValueError(f"{model_name} is not supported in GCP.") diff --git a/neollm/llm/gpt/abstract_gpt.py b/neollm/llm/gpt/abstract_gpt.py new file mode 100644 index 0000000000000000000000000000000000000000..87e4e9bc49b77b9964967aca2d00295cf6f05665 --- /dev/null +++ b/neollm/llm/gpt/abstract_gpt.py @@ -0,0 +1,81 @@ +import tiktoken + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.types import ( + ChatCompletion, + ChatCompletionChunk, + Message, + Messages, + OpenAIMessages, + OpenAIResponse, + OpenAIStreamResponse, + Response, + StreamResponse, +) + + +class AbstractGPT(AbstractLLM): + def encode(self, text: str) -> list[int]: + tokenizer = tiktoken.encoding_for_model(self.model or "gpt-3.5-turbo") + return tokenizer.encode(text) + + def decode(self, encoded: list[int]) -> str: + tokenizer = tiktoken.encoding_for_model(self.model or "gpt-3.5-turbo") + return tokenizer.decode(encoded) + + def count_tokens(self, messages: list[Message] | None = None, only_response: bool = False) -> int: + """ + トークン数の計測 + + Args: + messages (Messages): messages + + Returns: + int: トークン数 + """ + if messages is None: + return 0 + + # count tokens + num_tokens: int = 0 + # messages ---------------------------------------------------------------------------v + for message in messages: + # per message ------------------------------------------- + num_tokens += 4 + # content ----------------------------------------------- + content = message.get("content", None) + if content is None: + num_tokens += 0 + elif isinstance(content, str): + num_tokens += len(self.encode(content)) + continue + elif isinstance(content, list): + for content_params in content: + if content_params["type"] == "text": + num_tokens += len(self.encode(content_params["text"])) + # TODO: ChatCompletionFunctionMessageParam.name + # tokens_per_name = 1 + # tool calls ------------------------------------------------ + # TODO: ChatCompletionAssistantMessageParam.function_call + # TODO: ChatCompletionAssistantMessageParam.tool_calls + + if only_response: + if len(messages) != 1: + raise ValueError("only_response=Trueの場合、messagesは1つのみにしてください。") + num_tokens -= 4 # per message分を消す + else: + num_tokens += 3 # every reply is primed with <|start|>assistant<|message|> + + return num_tokens + + def _convert_to_response(self, platform_response: OpenAIResponse) -> Response: + return ChatCompletion(**platform_response.model_dump()) + + def _convert_to_platform_messages(self, messages: Messages) -> OpenAIMessages: + # OpenAIのMessagesをデフォルトに置いているため、変換は不要 + platform_messages: OpenAIMessages = messages + return platform_messages + + def _convert_to_streamresponse(self, platform_streamresponse: OpenAIStreamResponse) -> StreamResponse: + for chunk in platform_streamresponse: + yield ChatCompletionChunk(**chunk.model_dump()) diff --git a/neollm/llm/gpt/azure_llm.py b/neollm/llm/gpt/azure_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..c1f74d76a5e14b243518305c5785e88e33fefbde --- /dev/null +++ b/neollm/llm/gpt/azure_llm.py @@ -0,0 +1,215 @@ +from typing import Literal, cast + +from openai import AzureOpenAI + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.llm.gpt.abstract_gpt import AbstractGPT +from neollm.types import ( + APIPricing, + ClientSettings, + LLMSettings, + Messages, + Response, + StreamResponse, +) +from neollm.utils.utils import cprint, ensure_env_var, suport_unrecomended_env_var + +suport_unrecomended_env_var(old_key="AZURE_API_BASE", new_key="AZURE_OPENAI_ENDPOINT") +suport_unrecomended_env_var(old_key="AZURE_API_VERSION", new_key="OPENAI_API_VERSION") +# 0613なし +suport_unrecomended_env_var(old_key="AZURE_ENGINE_GPT35", new_key="AZURE_ENGINE_GPT35T_0613") +suport_unrecomended_env_var(old_key="AZURE_ENGINE_GPT35_16k", new_key="AZURE_ENGINE_GPT35T_16K_0613") +suport_unrecomended_env_var(old_key="AZURE_ENGINE_GPT4", new_key="AZURE_ENGINE_GPT4_0613") +suport_unrecomended_env_var(old_key="AZURE_ENGINE_GPT4_32k", new_key="AZURE_ENGINE_GPT4_32K_0613") +# turbo抜け +suport_unrecomended_env_var(old_key="AZURE_ENGINE_GPT35_0613", new_key="AZURE_ENGINE_GPT35T_0613") +suport_unrecomended_env_var(old_key="AZURE_ENGINE_GPT35_16K_0613", new_key="AZURE_ENGINE_GPT35T_16K_0613") + +# Pricing: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/ + +SUPPORTED_MODELS = Literal[ + "gpt-4o-2024-05-13", + "gpt-4-turbo-2024-04-09", + "gpt-3.5-turbo-0125", + "gpt-4-turbo-0125", + "gpt-3.5-turbo-1106", + "gpt-4-turbo-1106", + "gpt-4v-turbo-1106", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0613", + "gpt-4-32k-0613", +] + + +def get_azure_llm(model_name: SUPPORTED_MODELS | str, client_settings: ClientSettings) -> AbstractLLM: + # 表記変更 + model_name = model_name.replace("gpt-35-turbo", "gpt-3.5-turbo") + # Add 日付 + replace_map_for_nodate: dict[str, SUPPORTED_MODELS] = { + "gpt-4o": "gpt-4o-2024-05-13", + "gpt-3.5-turbo": "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k": "gpt-3.5-turbo-16k-0613", + "gpt-4": "gpt-4-0613", + "gpt-4-32k": "gpt-4-32k-0613", + "gpt-4-turbo": "gpt-4-turbo-1106", + "gpt-4v-turbo": "gpt-4v-turbo-1106", + } + if model_name in replace_map_for_nodate: + cprint("WARNING: model_nameに日付を指定してください", color="yellow", background=True) + print(f"model_name: {model_name} -> {replace_map_for_nodate[model_name]}") + model_name = replace_map_for_nodate[model_name] + + # map to LLM + supported_model_map: dict[SUPPORTED_MODELS, AbstractLLM] = { + "gpt-4o-2024-05-13": AzureGPT4O_20240513(client_settings), + "gpt-4-turbo-2024-04-09": AzureGPT4T_20240409(client_settings), + "gpt-3.5-turbo-0125": AzureGPT35T_0125(client_settings), + "gpt-4-turbo-0125": AzureGPT4T_0125(client_settings), + "gpt-3.5-turbo-1106": AzureGPT35T_1106(client_settings), + "gpt-4-turbo-1106": AzureGPT4T_1106(client_settings), + "gpt-4v-turbo-1106": AzureGPT4VT_1106(client_settings), + "gpt-3.5-turbo-0613": AzureGPT35T_0613(client_settings), + "gpt-3.5-turbo-16k-0613": AzureGPT35T16k_0613(client_settings), + "gpt-4-0613": AzureGPT4_0613(client_settings), + "gpt-4-32k-0613": AzureGPT432k_0613(client_settings), + } + # 通常モデル + if model_name in supported_model_map: + model_name = cast(SUPPORTED_MODELS, model_name) + return supported_model_map[model_name] + # FTモデル + return AzureGPT35FT(model_name, client_settings) + + +class AzureLLM(AbstractGPT): + _engine_name_env_key: str | None = None + + @property + def client(self) -> AzureOpenAI: + client: AzureOpenAI = AzureOpenAI(**self.client_settings) + # api_key: str | None = (None,) + # timeout: httpx.Timeout(timeout=600.0, connect=5.0) + # max_retries: int = 2 + return client + + @property + def engine(self) -> str: + return ensure_env_var(self._engine_name_env_key) + + def generate(self, messages: Messages, llm_settings: LLMSettings) -> Response: + openai_response = self.client.chat.completions.create( + model=self.engine, + messages=self._convert_to_platform_messages(messages), + stream=False, + **llm_settings, + ) + response = self._convert_to_response(openai_response) + return response + + def generate_stream(self, messages: Messages, llm_settings: LLMSettings) -> StreamResponse: + platform_stream_response = self.client.chat.completions.create( + model=self.engine, + messages=self._convert_to_platform_messages(messages), + stream=True, + **llm_settings, + ) + stream_response = self._convert_to_streamresponse(platform_stream_response) + return stream_response + + +# omni 2024-05-13 -------------------------------------------------------------------------------------------- +class AzureGPT4O_20240513(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.005, output=0.015) # 30倍/45倍 + model: str = "gpt-4o-2024-05-13" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4O_20240513" + context_window: int = 128_000 + + +# 2024-04-09 -------------------------------------------------------------------------------------------- +class AzureGPT4T_20240409(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) + model: str = "gpt-4-turbo-2024-04-09" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4T_20240409" + context_window: int = 128_000 + + +# 0125 -------------------------------------------------------------------------------------------- +class AzureGPT35T_0125(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.0005, output=0.0015) + model: str = "gpt-3.5-turbo-0125" + _engine_name_env_key: str = "AZURE_ENGINE_GPT35T_0125" + context_window: int = 16_385 + + +class AzureGPT4T_0125(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) + model: str = "gpt-4-turbo-0125" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4T_0125" + context_window: int = 128_000 + + +# 1106 -------------------------------------------------------------------------------------------- +class AzureGPT35T_1106(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.001, output=0.002) + model: str = "gpt-3.5-turbo-1106" + _engine_name_env_key: str = "AZURE_ENGINE_GPT35T_1106" + context_window: int = 16_385 + + +class AzureGPT4VT_1106(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) # 10倍/15倍 + model: str = "gpt-4-1106-vision-preview" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4VT_1106" + context_window: int = 128_000 + + +class AzureGPT4T_1106(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) + model: str = "gpt-4-turbo-1106" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4T_1106" + context_window: int = 128_000 + + +# FT -------------------------------------------------------------------------------------------- +class AzureGPT35FT(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.0005, output=0.0015) # 1倍 + セッション稼働時間 + model: str = "gpt-3.5-turbo-ft" + context_window: int = 4_096 + + def __init__(self, model_name: str, client_setting: ClientSettings) -> None: + super().__init__(client_setting) + self._engine = model_name + + @property + def engine(self) -> str: + return self._engine + + +# 0613 -------------------------------------------------------------------------------------------- +class AzureGPT35T_0613(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.0015, output=0.002) + model: str = "gpt-3.5-turbo-0613" + _engine_name_env_key: str = "AZURE_ENGINE_GPT35T_0613" + context_window: int = 4_096 + + +class AzureGPT35T16k_0613(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.003, output=0.004) # 2倍 + model: str = "gpt-3.5-turbo-16k-0613" + _engine_name_env_key: str = "AZURE_ENGINE_GPT35T_16K_0613" + context_window: int = 16_385 + + +class AzureGPT4_0613(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.03, output=0.06) # 20倍/30倍 + model: str = "gpt-4-0613" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4_0613" + context_window: int = 8_192 + + +class AzureGPT432k_0613(AzureLLM): + dollar_per_ktoken = APIPricing(input=0.06, output=0.12) # 40倍/60倍 + model: str = "gpt-4-32k-0613" + _engine_name_env_key: str = "AZURE_ENGINE_GPT4_32K_0613" + context_window: int = 32_768 diff --git a/neollm/llm/gpt/openai_llm.py b/neollm/llm/gpt/openai_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..d9663680e701cc162ce2edafb91af5731ee060b9 --- /dev/null +++ b/neollm/llm/gpt/openai_llm.py @@ -0,0 +1,222 @@ +from typing import Literal, cast + +from openai import OpenAI + +from neollm.llm.abstract_llm import AbstractLLM +from neollm.llm.gpt.abstract_gpt import AbstractGPT +from neollm.types import ( + APIPricing, + ClientSettings, + LLMSettings, + Messages, + Response, + StreamResponse, +) +from neollm.utils.utils import cprint + +# Models: https://platform.openai.com/docs/models/continuous-model-upgrades +# Pricing: https://openai.com/pricing + +SUPPORTED_MODELS = Literal[ + "gpt-4o-2024-05-13", + "gpt-4-turbo-2024-04-09", + "gpt-3.5-turbo-0125", + "gpt-4-turbo-0125", + "gpt-3.5-turbo-1106", + "gpt-4-turbo-1106", + "gpt-4v-turbo-1106", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0613", + "gpt-4-32k-0613", +] + + +def get_openai_llm(model_name: SUPPORTED_MODELS | str, client_settings: ClientSettings) -> AbstractLLM: + # Add 日付 + replace_map_for_nodate: dict[str, SUPPORTED_MODELS] = { + "gpt-4o": "gpt-4o-2024-05-13", + "gpt-3.5-turbo": "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k": "gpt-3.5-turbo-16k-0613", + "gpt-4": "gpt-4-0613", + "gpt-4-32k": "gpt-4-32k-0613", + "gpt-4-turbo": "gpt-4-turbo-1106", + "gpt-4v-turbo": "gpt-4v-turbo-1106", + } + if model_name in replace_map_for_nodate: + cprint("WARNING: model_nameに日付を指定してください", color="yellow", background=True) + print(f"model_name: {model_name} -> {replace_map_for_nodate[model_name]}") + model_name = replace_map_for_nodate[model_name] + + # map to LLM + supported_model_map: dict[SUPPORTED_MODELS, AbstractLLM] = { + "gpt-4o-2024-05-13": OpenAIGPT4O_20240513(client_settings), + "gpt-4-turbo-2024-04-09": OpenAIGPT4T_20240409(client_settings), + "gpt-3.5-turbo-0125": OpenAIGPT35T_0125(client_settings), + "gpt-4-turbo-0125": OpenAIGPT4T_0125(client_settings), + "gpt-3.5-turbo-1106": OpenAIGPT35T_1106(client_settings), + "gpt-4-turbo-1106": OpenAIGPT4T_1106(client_settings), + "gpt-4v-turbo-1106": OpenAIGPT4VT_1106(client_settings), + "gpt-3.5-turbo-0613": OpenAIGPT35T_0613(client_settings), + "gpt-3.5-turbo-16k-0613": OpenAIGPT35T16k_0613(client_settings), + "gpt-4-0613": OpenAIGPT4_0613(client_settings), + "gpt-4-32k-0613": OpenAIGPT432k_0613(client_settings), + } + # 通常モデル + if model_name in supported_model_map: + model_name = cast(SUPPORTED_MODELS, model_name) + return supported_model_map[model_name] + # FTモデル + if "gpt-3.5-turbo-1106" in model_name: + return OpenAIGPT35TFT_1106(model_name, client_settings) + if "gpt-3.5-turbo-0613" in model_name: + return OpenAIGPT35TFT_0613(model_name, client_settings) + if "gpt-3.5-turbo-0125" in model_name: + return OpenAIGPT35TFT_0125(model_name, client_settings) + if "gpt4" in model_name.replace("-", ""): # TODO! もっといい条件に修正 + return OpenAIGPT4FT_0613(model_name, client_settings) + + cprint( + f"WARNING: このFTモデルは何？: {model_name} -> OpenAIGPT35TFT_1106として設定", color="yellow", background=True + ) + return OpenAIGPT35TFT_1106(model_name, client_settings) + + +class OpenAILLM(AbstractGPT): + model: str + + @property + def client(self) -> OpenAI: + client: OpenAI = OpenAI(**self.client_settings) + # api_key: str | None = (None,) + # timeout: httpx.Timeout(timeout=600.0, connect=5.0) + # max_retries: int = 2 + return client + + def generate(self, messages: Messages, llm_settings: LLMSettings) -> Response: + openai_response = self.client.chat.completions.create( + model=self.model, + messages=self._convert_to_platform_messages(messages), + stream=False, + **llm_settings, + ) + response = self._convert_to_response(openai_response) + return response + + def generate_stream(self, messages: Messages, llm_settings: LLMSettings) -> StreamResponse: + platform_stream_response = self.client.chat.completions.create( + model=self.model, + messages=self._convert_to_platform_messages(messages), + stream=True, + **llm_settings, + ) + stream_response = self._convert_to_streamresponse(platform_stream_response) + return stream_response + + +# omni 2024-05-13 -------------------------------------------------------------------------------------------- +class OpenAIGPT4O_20240513(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.005, output=0.015) + model: str = "gpt-4o-2024-05-13" + context_window: int = 128_000 + + +# 2024-04-09 -------------------------------------------------------------------------------------------- +class OpenAIGPT4T_20240409(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) # 10倍/15倍 + model: str = "gpt-4-turbo-2024-04-09" + # model: str = "gpt-4-turbo-2024-04-09" + context_window: int = 128_000 + + +# 0125 -------------------------------------------------------------------------------------------- +class OpenAIGPT35T_0125(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.0005, output=0.0015) + model: str = "gpt-3.5-turbo-0125" + context_window: int = 16_385 + + +class OpenAIGPT4T_0125(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) + model: str = "gpt-4-0125-preview" + context_window: int = 128_000 + + +class OpenAIGPT35TFT_0125(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.003, output=0.006) + context_window: int = 16_385 + + def __init__(self, model_name: str, client_setting: ClientSettings) -> None: + super().__init__(client_setting) + self.model = model_name + + +# 1106 -------------------------------------------------------------------------------------------- +class OpenAIGPT35T_1106(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.0010, output=0.0020) + model: str = "gpt-3.5-turbo-1106" + context_window: int = 16_385 + + +class OpenAIGPT4T_1106(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) + model: str = "gpt-4-1106-preview" + context_window: int = 128_000 + + +class OpenAIGPT4VT_1106(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.01, output=0.03) + model: str = "gpt-4-1106-vision-preview" + context_window: int = 128_000 + + +class OpenAIGPT35TFT_1106(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.003, output=0.006) + context_window: int = 4_096 + + def __init__(self, model_name: str, client_setting: ClientSettings) -> None: + super().__init__(client_setting) + self.model = model_name + + +# 0613 -------------------------------------------------------------------------------------------- +class OpenAIGPT35T_0613(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.0015, output=0.002) + model: str = "gpt-3.5-turbo-0613" + context_window: int = 4_096 + + +class OpenAIGPT35T16k_0613(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.003, output=0.004) + model: str = "gpt-3.5-turbo-16k-0613" + context_window: int = 16_385 + + +class OpenAIGPT4_0613(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.03, output=0.06) + model: str = "gpt-4-0613" + context_window: int = 8_192 + + +class OpenAIGPT432k_0613(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.06, output=0.12) + model: str = "gpt-4-32k-0613" + context_window: int = 32_768 + + +class OpenAIGPT35TFT_0613(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.003, output=0.006) + context_window: int = 4_096 + + def __init__(self, model_name: str, client_setting: ClientSettings) -> None: + super().__init__(client_setting) + self.model = model_name + + +class OpenAIGPT4FT_0613(OpenAILLM): + dollar_per_ktoken = APIPricing(input=0.045, output=0.090) + context_window: int = 8_192 + + def __init__(self, model_name: str, client_setting: ClientSettings) -> None: + super().__init__(client_setting) + self.model = model_name diff --git a/neollm/llm/gpt/token.py b/neollm/llm/gpt/token.py new file mode 100644 index 0000000000000000000000000000000000000000..c9b5c7ce07476e2d69857bb49f9d539824c2f238 --- /dev/null +++ b/neollm/llm/gpt/token.py @@ -0,0 +1,247 @@ +import json +import textwrap +from typing import Any, Iterator, overload + +import tiktoken + +from neollm.types import Function +from neollm.utils.utils import cprint # , Functions, Messages + +DEFAULT_MODEL_NAME = "gpt-3.5-turbo" + + +def get_tokenizer(model_name: str) -> tiktoken.Encoding: + # 参考: https://platform.openai.com/docs/models/gpt-3-5 + MODEL_NAME_MAP = [ + ("gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613"), + ("gpt-3.5-turbo", "gpt-3.5-turbo-0613"), + ("gpt-4-32k", "gpt-4-32k-0613"), + ("gpt-4", "gpt-4-0613"), + ] + ALL_VERSION_MODELS = [ + # gpt-3.5-turbo + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-3.5-turbo-0301", # Legacy + # gpt-4 + "gpt-4o-2024-05-13", + "gpt-4-turbo-0125", + "gpt-4-turbo-1106", + "gpt-4-0613", + "gpt-4-32k-0613", + "gpt-4-0314", # Legacy + "gpt-4-32k-0314", # Legacy + ] + # Azure表記 → OpenAI表記に統一 + model_name = model_name.replace("gpt-35", "gpt-3.5") + # 最新モデルを正式名称に & 新モデル, FTモデルをキャッチ + if model_name not in ALL_VERSION_MODELS: + for key, model_name_version in MODEL_NAME_MAP: + if key in model_name: + model_name = model_name_version + break + try: + return tiktoken.encoding_for_model(model_name) + except Exception as e: + cprint(f"WARNING: Tokenizerの取得に失敗。{model_name}: {e}", color="yellow", background=True) + return tiktoken.encoding_for_model("gpt-3.5-turbo") + + +@overload +def count_tokens(messages: str, model_name: str | None = None) -> int: ... + + +@overload +def count_tokens( + messages: Iterator[dict[str, str]], model_name: str | None = None, functions: Any | None = None +) -> int: ... + + +def count_tokens( + messages: Iterator[dict[str, str]] | str, + model_name: str | None = None, + functions: Any | None = None, +) -> int: + if isinstance(messages, str): + tokenizer = get_tokenizer(model_name or DEFAULT_MODEL_NAME) + encoded = tokenizer.encode(messages) + return len(encoded) + return _count_messages_and_function_tokens(messages, model_name, functions) + + +def _count_messages_and_function_tokens( + messages: Iterator[dict[str, str]], model_name: str | None = None, functions: Any | None = None +) -> int: + """トークン数計測 + + Args: + messages (Messages): GPTAPIの入力のmessages + model_name (str | None, optional): モデル名. Defaults to None. + functions (Functions | None, optional): GPTAPIの入力のfunctions. Defaults to None. + + Returns: + int: トークン数 + """ + num_tokens = _count_messages_tokens(messages, model_name or DEFAULT_MODEL_NAME) + if functions is not None: + num_tokens += _count_functions_tokens(functions, model_name) + return num_tokens + + +# https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb +def _count_messages_tokens(messages: Iterator[dict[str, str]] | None, model_name: str) -> int: + """メッセージのトークン数を計算 + + Args: + messages (Messages): ChatGPT等APIに入力するmessages + model_name (str, optional): 使用するモデルの名前 + "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-4-0314", "gpt-4-32k-0314" + "gpt-4-0613", "gpt-4-32k-0613", "gpt-3.5-turbo", "gpt-4" + + Returns: + int: トークン数の合計 + """ + if messages is None: + return 0 + # setting model + encoding_model = get_tokenizer(model_name) + + # config + if "gpt-3.5-turbo-0301" in model_name: + tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n + tokens_per_name = -1 # if there's a name, the role is omitted + else: + tokens_per_message = 3 + tokens_per_name = 1 + + # count tokens + num_tokens = 3 # every reply is primed with <|start|>assistant<|message|> + for message in messages: + num_tokens += tokens_per_message + for key, value in message.items(): + if isinstance(value, str): + num_tokens += len(encoding_model.encode(value)) + if key == "name": + num_tokens += tokens_per_name + return num_tokens + + +# https://gist.github.com/CGamesPlay/dd4f108f27e2eec145eedf5c717318f5 +def _count_functions_tokens(functions: Any, model_name: str | None = None) -> int: + """ + functionsのトークン数計測 + + Args: + functions (Functions): GPTAPIの入力のfunctions + model_name (str | None, optional): モデル名. Defaults to None. + + Returns: + _type_: トークン数 + """ + encoding_model = encoding_model = get_tokenizer(model_name or DEFAULT_MODEL_NAME) + num_tokens = 3 + len(encoding_model.encode(__functions2string(functions))) + return num_tokens + + +# functionsのstring化、補助関数 --------------------------------------------------------------------------- +def __functions2string(functions: Any) -> str: + """functionsの文字列化 + + Args: + functions (Functions): GPTAPIの入力のfunctions + + Returns: + str: functionsの文字列 + """ + prefix = "# Tools\n\n## functions\n\nnamespace functions {\n\n} // namespace functions\n" + functions_string = prefix + "".join(__function2string(function) for function in functions) + return functions_string + + +def __function2string(function: Function) -> str: + """functionの文字列化 + + Args: + function (Function): GPTAPIのfunctionの要素 + + Returns: + str: functionの文字列 + """ + object_string = __format_object(function["parameters"]) + if object_string is not None: + object_string = "_: " + object_string + else: + object_string = "" + + functions_string: str = ( + f"// {function['description']}\ntype {function['name']} = (" + object_string + ") => any;\n\n" + ) + return functions_string + + +def __format_object(schema: dict[str, Any], indent: int = 0) -> str | None: + if "properties" not in schema or len(schema["properties"]) == 0: + if schema.get("additionalProperties", False): + return "object" + return None + + result = "{\n" + for key, value in dict(schema["properties"]).items(): + # value <- resolve_ref(value) + value_rendered = __format_schema(value, indent + 1) + if value_rendered is None: + continue + # description + if "description" in value: + description = "".join( + " " * indent + f"// {description_i}\n" + for description_i in textwrap.dedent(value["description"]).strip().split("\n") + ) + # optional + optional = "" if key in schema.get("required", {}) else "?" + # default + default_comment = "" if "default" not in value else f" // default: {__format_default(value)}" + # add string + result += description + " " * indent + f"{key}{optional}: {value_rendered},{default_comment}\n" + result += (" " * (indent - 1)) + "}" + return result + + +# よくわからん +# def resolve_ref(schema): +# if schema.get("$ref") is not None: +# ref = schema["$ref"][14:] +# schema = json_schema["definitions"][ref] +# return schema + + +def __format_schema(schema: dict[str, Any], indent: int) -> str | None: + # schema <- resolve_ref(schema) + if "enum" in schema: + return __format_enum(schema) + elif schema["type"] == "object": + return __format_object(schema, indent) + elif schema["type"] in {"integer", "number"}: + return "number" + elif schema["type"] in {"string"}: + return "string" + elif schema["type"] == "array": + return str(__format_schema(schema["items"], indent)) + "[]" + else: + raise ValueError("unknown schema type " + schema["type"]) + + +def __format_enum(schema: dict[str, Any]) -> str: + # "A" | "B" | "C" + return " | ".join(json.dumps(element, ensure_ascii=False) for element in schema["enum"]) + + +def __format_default(schema: dict[str, Any]) -> str: + default = schema["default"] + if schema["type"] == "number" and float(default).is_integer(): + # numberの時、0 → 0.0 + return f"{default:.1f}" + else: + return str(default) diff --git a/neollm/llm/platform.py b/neollm/llm/platform.py new file mode 100644 index 0000000000000000000000000000000000000000..960dfc2ce6b2b9f3d8df0470c0a6bd98e55ad773 --- /dev/null +++ b/neollm/llm/platform.py @@ -0,0 +1,16 @@ +from enum import Enum + + +class Platform(str, Enum): + AZURE = "azure" + OPENAI = "openai" + ANTHROPIC = "anthropic" + GCP = "gcp" + + @classmethod + def from_string(cls, platform: str) -> "Platform": + platform = platform.lower().strip() + try: + return cls(platform) + except Exception: + raise ValueError(f"platform must be {cls.__members__}, but got {platform}.") diff --git a/neollm/llm/utils.py b/neollm/llm/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..83a69945e292e59284c51589ebf299d2627d4b8d --- /dev/null +++ b/neollm/llm/utils.py @@ -0,0 +1,72 @@ +from typing import Any, TypeVar + +from neollm.utils.utils import cprint + +Immutable = tuple[Any, ...] | str | int | float | bool +_T = TypeVar("_T") +_TD = TypeVar("_TD") + + +def _to_immutable(x: Any) -> Immutable: + """list, dictをtupleに変換して, setに格納できるようにする + + Args: + x (Any): 要素 + + Returns: + Immutable: Immutableな要素(dict, listはtupleに変換) + """ + if isinstance(x, list): + return tuple(map(_to_immutable, x)) + if isinstance(x, dict): + return tuple((key, _to_immutable(value)) for key, value in sorted(x.items())) + if isinstance(x, (set, frozenset)): + return tuple(sorted(map(_to_immutable, x))) + if isinstance(x, (str, int, float, bool)): + return x + cprint("_to_immutable: not supported: 無理やりstr(*)", color="yellow", background=True) + return str(x) + + +def _remove_duplicate(arr: list[_T | None]) -> list[_T]: + """listの重複と初期値を削除する + + Args: + arr (list[Any]): リスト + + Returns: + list[Any]: 重複削除済みのlist + """ + seen_set: set[Immutable] = set() + unique_list: list[_T] = [] + for x in arr: + if x is None or bool(x) is False: + continue + x_immutable = _to_immutable(x) + if x_immutable not in seen_set: + unique_list.append(x) + seen_set.add(x_immutable) + return unique_list + + +def get_entity(arr: list[_T | None], default: _TD, index: int | None = None) -> _T | _TD: + """listから必要な1要素を取得する + + Args: + arr (list[Any]): list + default (Any): 初期値 + index (int | None, optional): 複数ある場合、指定のindex. Defaults to None. + + Returns: + Any: 要素 + """ + arr_cleaned = _remove_duplicate(arr) + if len(arr_cleaned) == 0: + return default + if len(arr_cleaned) == 1: + return arr_cleaned[0] + if index is not None: + return arr_cleaned[index] + cprint("get_entity: not unique", color="yellow", background=True) + cprint(arr_cleaned, color="yellow", background=True) + return arr_cleaned[0] diff --git a/neollm/myllm/abstract_myllm.py b/neollm/myllm/abstract_myllm.py new file mode 100644 index 0000000000000000000000000000000000000000..620c5e7d78244272a05d3ef418ac24187aa0a6a7 --- /dev/null +++ b/neollm/myllm/abstract_myllm.py @@ -0,0 +1,148 @@ +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Generator, Generic, Optional, TypeAlias, cast + +from neollm.myllm.print_utils import print_inputs, print_metadata, print_outputs +from neollm.types import ( + InputType, + OutputType, + PriceInfo, + StreamOutputType, + TimeInfo, + TokenInfo, +) +from neollm.utils.utils import cprint + +if TYPE_CHECKING: + from typing import Any + + from neollm.myllm.myl3m2 import MyL3M2 + + _MyL3M2: TypeAlias = MyL3M2[Any, Any] + + +class AbstractMyLLM(ABC, Generic[InputType, OutputType]): + """MyLLM, MyL3M2の抽象クラス""" + + inputs: InputType | None + outputs: OutputType | None + silent_set: set[str] + verbose: bool + time: float = 0.0 + time_detail: TimeInfo = TimeInfo() + parent: Optional["_MyL3M2"] = None + do_stream: bool + + @property + @abstractmethod + def token(self) -> TokenInfo: + """LLMの利用トークン数 + + Returns: + TokenInfo: トークン数 (入力, 出力, 合計) + >>> TokenInfo(input=1588, output=128, total=1716) + """ + + @property + def custom_token(self) -> TokenInfo | None: + """料金計算用トークン（Gemini用）""" + return None + + @property + @abstractmethod + def price(self) -> PriceInfo: + """LLMの利用料金 (USD) + + Returns: + PriceInfo: 利用料金 (USD) (入力, 出力, 合計) + >>> PriceInfo(input=0.002382, output=0.000256, total=0.002638) + """ + + @abstractmethod + def _call(self, inputs: InputType, stream: bool = False) -> Generator[StreamOutputType, None, OutputType]: + """MyLLMの子クラスのメインロジック + + streamとnon-streamの両方のコードを書く必要がある + + Args: + inputs (InputType): LLMへの入力 + stream (bool, optional): streamの有無. Defaults to False. + + Yields: + Generator[StreamOutputType, None, OutputType]: LLMのstream出力 + + Returns: + OutputType: LLMの出力 + """ + + def __call__(self, inputs: InputType) -> OutputType: + """MyLLMのメインロジック + + Args: + inputs (InputType): LLMへの入力 + + Returns: + OutputType: LLMの出力 + """ + it: Generator[StreamOutputType, None, OutputType] = self._call(inputs, stream=self.do_stream) + while True: + try: + next(it) + except StopIteration as e: + outputs = cast(OutputType, e.value) + return outputs + except Exception as e: + raise e + + def call_stream(self, inputs: InputType) -> Generator[StreamOutputType, None, OutputType]: + """MyLLMのメインロジック(stream処理) + + Args: + inputs (InputType): LLMへの入力 + + Yields: + Generator[StreamOutputType, None, OutputType]: LLMのstream出力 + + Returns: + LLMの出力 + """ + it: Generator[StreamOutputType, None, OutputType] = self._call(inputs, stream=True) + while True: + try: + delta_content = next(it) + yield delta_content + except StopIteration as e: + outputs = cast(OutputType, e.value) + return outputs + except Exception as e: + raise e + + def _print_inputs(self) -> None: + if self.inputs is None: + return + if not ("inputs" not in self.silent_set and self.verbose): + return + print_inputs(self.inputs) + + def _print_outputs(self) -> None: + if self.outputs is None: + return + if not ("outputs" not in self.silent_set and self.verbose): + return + print_outputs(self.outputs) + + def _print_metadata(self) -> None: + if not ("metadata" not in self.silent_set and self.verbose): + return + print_metadata(self.time, self.token, self.price) + + def _print_start(self, sep: str = "-") -> None: + if not self.verbose: + return + if self.parent is None: + cprint("PARENT", color="red", background=True) + print(self, sep * (99 - len(str(self)))) + + def _print_end(self, sep: str = "-") -> None: + if not self.verbose: + return + print(sep * 100) diff --git a/neollm/myllm/myl3m2.py b/neollm/myllm/myl3m2.py new file mode 100644 index 0000000000000000000000000000000000000000..a84f69c450ae68354f7c768af99f608f5d93b171 --- /dev/null +++ b/neollm/myllm/myl3m2.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import time +from typing import Any, Generator, Literal, Optional, cast + +from neollm.myllm.abstract_myllm import AbstractMyLLM +from neollm.myllm.myllm import MyLLM +from neollm.myllm.print_utils import TITLE_COLOR +from neollm.types import ( + InputType, + OutputType, + PriceInfo, + StreamOutputType, + TimeInfo, + TokenInfo, +) +from neollm.utils.utils import cprint + + +class MyL3M2(AbstractMyLLM[InputType, OutputType]): + """LLMの複数リクエストをまとめるクラス""" + + do_stream: bool = False # stream_verboseがないため、__call__ではstreamを使わない + + def __init__( + self, + parent: Optional["MyL3M2[Any, Any]"] = None, + verbose: bool = False, + silent_list: list[Literal["inputs", "outputs", "metadata", "all_myllm"]] | None = None, + ) -> None: + """ + MyL3M2の初期化 + + Args: + parent (MyL3M2, optional): + 親のMyL3M2のインスタンス(self or None) + verbose (bool, optional): + 出力をするかどうかのフラグ. Defaults to False. + sileznt_list (list[Literal["inputs", "outputs", "metadata", "all_myllm"]], optional): + サイレントモードのリスト。出力を抑制する要素を指定する。. Defaults to None(=[]). + """ + self.parent = parent + self.verbose = verbose + self.silent_set = set(silent_list or []) + self.myllm_list: list["MyL3M2[Any, Any]" | MyLLM[Any, Any]] = [] + self.inputs: InputType | None = None + self.outputs: OutputType | None = None + self.called: bool = False + + def _link(self, inputs: InputType) -> OutputType: + """複数のLLMの処理を行う + + Args: + inputs (InputType): 入力データを保持する辞書 + + Returns: + OutputType: 処理結果の出力データ + """ + raise NotImplementedError("_link(self, inputs: InputType) -> OutputType:を実装してください") + + def _stream_link(self, inputs: InputType) -> Generator[StreamOutputType, None, OutputType]: + """複数のLLMの処理を行う(stream処理) + + Args: + inputs (InputType): 入力データを保持する辞書 + + Yields: + Generator[StreamOutputType, None, OutputType]: 処理結果の出力データ(stream) + + Returns: + self.outputsに入れたいもの + """ + raise NotImplementedError( + "_stream_link(self, inputs: InputType) -> Generator[StreamOutputType, None, None]を実装してください" + ) + + def _call(self, inputs: InputType, stream: bool = False) -> Generator[StreamOutputType, None, OutputType]: + if self.called: + raise RuntimeError("MyLLMは1回しか呼び出せない") + + self._print_start(sep="=") + + # main ----------------------------------------------------------- + t_start = time.time() + self.inputs = inputs + # [stream] + if stream: + it = self._stream_link(inputs) + while True: + try: + yield next(it) + except StopIteration as e: + self.outputs = cast(OutputType, e.value) + break + except Exception as e: + raise e + # [non-stream] + else: + self.outputs = self._link(inputs) + self._print_inputs() + self._print_outputs() + self._print_all_myllm() + self.time = time.time() - t_start + self.time_detail = TimeInfo(total=self.time, main=self.time) + + # metadata ----------------------------------------------------------- + self._print_metadata() + self._print_end(sep="=") + + # 親MyL3M2にAppend ----------------------------------------------------------- + if self.parent is not None: + self.parent.myllm_list.append(self) + self.called = True + + return self.outputs + + @property + def token(self) -> TokenInfo: + token = TokenInfo(input=0, output=0, total=0) + for myllm in self.myllm_list: + # TODO: token += myllm.token + token.input += myllm.token.input + token.output += myllm.token.output + token.total += myllm.token.total + return token + + @property + def price(self) -> PriceInfo: + price = PriceInfo(input=0.0, output=0.0, total=0.0) + for myllm in self.myllm_list: + # TODO: price += myllm.price + price.input += myllm.price.input + price.output += myllm.price.output + price.total += myllm.price.total + return price + + @property + def logs(self) -> list[Any]: + logs: list[Any] = [] + for myllm in self.myllm_list: + if isinstance(myllm, MyLLM): + logs.append(myllm.log) + elif isinstance(myllm, MyL3M2): + logs.extend(myllm.logs) + return logs + + def _print_all_myllm(self, prefix: str = "", title: bool = True) -> None: + if not ("all_myllm" not in self.silent_set and self.verbose): + return + try: + if title: + cprint("[all_myllm]", color=TITLE_COLOR) + print(" ", end="") + cprint(f"{self}", color="magenta", bold=True, underline=True) + for myllm in self.myllm_list: + if isinstance(myllm, MyLLM): + cprint(f" {prefix}- {myllm}", color="cyan") + elif isinstance(myllm, MyL3M2): + cprint(f" {prefix}- {myllm}", color="magenta") + myllm._print_all_myllm(prefix=prefix + " ", title=False) + except Exception as e: + cprint(e, color="red", background=True) + + def __repr__(self) -> str: + return f"MyL3M2({self.__class__.__name__})" diff --git a/neollm/myllm/myllm.py b/neollm/myllm/myllm.py new file mode 100644 index 0000000000000000000000000000000000000000..ab0178f4f20f77e487cdcff88ee322edc960564d --- /dev/null +++ b/neollm/myllm/myllm.py @@ -0,0 +1,449 @@ +import os +import time +from abc import abstractmethod +from typing import TYPE_CHECKING, Any, Final, Generator, Literal, Optional + +from neollm.exceptions import ContentFilterError +from neollm.llm import AbstractLLM, get_llm +from neollm.llm.gpt.azure_llm import AzureLLM +from neollm.myllm.abstract_myllm import AbstractMyLLM +from neollm.myllm.print_utils import ( + print_client_settings, + print_delta, + print_llm_settings, + print_messages, +) +from neollm.types import ( + Chunk, + ClientSettings, + Functions, + InputType, + LLMSettings, + Message, + Messages, + OutputType, + PriceInfo, + Response, + StreamOutputType, + TimeInfo, + TokenInfo, + Tools, +) +from neollm.types.openai.chat_completion import CompletionUsageForCustomPriceCalculation +from neollm.utils.preprocess import dict2json +from neollm.utils.utils import cprint + +if TYPE_CHECKING: + from neollm.myllm.myl3m2 import MyL3M2 + + _MyL3M2 = MyL3M2[Any, Any] + _State = dict[Any, Any] + +DEFAULT_LLM_SETTINGS: LLMSettings = {"temperature": 0} +DEFAULT_PLATFORM: Final[str] = "azure" + + +class MyLLM(AbstractMyLLM[InputType, OutputType]): + """LLMの単一リクエストをまとめるクラス""" + + def __init__( + self, + model: str, + parent: Optional["_MyL3M2"] = None, + llm_settings: LLMSettings | None = None, + client_settings: ClientSettings | None = None, + platform: str | None = None, + verbose: bool = False, + stream_verbose: bool = False, + silent_list: list[Literal["llm_settings", "inputs", "outputs", "messages", "metadata"]] | None = None, + log_dir: str | None = None, + ) -> None: + """ + MyLLMクラスの初期化 + + Args: + model (Optional[str]): LLMモデル名 + parent (Optional[MyL3M2]): 親のMyL3M2のインスタンス (self or None) + llm_settings (LLMSettings): LLMの設定パラメータ + client_settings (ClientSettings): llmのclientの設定パラメータ + platform (Optional[str]): LLMのプラットフォーム名 (デフォルト: os.environ["PLATFORM"] or "azure") + (enum: openai, azure) + verbose (bool): 出力をするかどうかのフラグ + stream_verbose (bool): assitantをstreamで出力するか(verbose=False, message in "messages"の時、無効) + silent_list (list[Literal["llm_settings", "inputs", "outputs", "messages", "metadata"]]): + verbose=True時, 出力を抑制する要素のリスト + log_dir (Optional[str]): ログを保存するディレクトリのパス Noneの時、保存しない + """ + self.parent: _MyL3M2 | None = parent + self.llm_settings = llm_settings or DEFAULT_LLM_SETTINGS + self.client_settings = client_settings or {} + self.model: str = model + self.platform: str = platform or os.environ.get("LLM_PLATFORM", DEFAULT_PLATFORM) or DEFAULT_PLATFORM + self.verbose: bool = verbose & (True if self.parent is None else self.parent.verbose) # 親に合わせる + self.silent_set = set(silent_list or []) + self.stream_verbose: bool = stream_verbose if verbose and ("messages" not in self.silent_set) else False + self.log_dir: str | None = log_dir + + self.inputs: InputType | None = None + self.outputs: OutputType | None = None + self.messages: Messages | None = None + self.functions: Functions | None = None + self.tools: Tools | None = None + self.response: Response | None = None + self.called: bool = False + self.do_stream: bool = self.stream_verbose + + self.llm: AbstractLLM = get_llm( + model_name=self.model, platform=self.platform, client_settings=self.client_settings + ) + + @abstractmethod + def _preprocess(self, inputs: InputType) -> Messages: + """ + inputs を API入力 の messages に前処理する + + Args: + inputs (InputType): 入力 + + Returns: + Messages: API入力 の messages + >>> [{"role": "system", "content": "system_prompt"}, {"role": "user", "content": "user_prompt"}] + """ + + @abstractmethod + def _postprocess(self, response: Response) -> OutputType: + """ + API の response を outputs に後処理する + + Args: + response (Response): API の response + >>> {"choices": [{"message": {"role": "assistant", + >>> "content": "This is a test!"}}]} + >>> {"choices": [{"message": {"role": "assistant", + >>> "function_call": {"name": "func", "arguments": "{a: 1}"}}]} + + Returns: + OutputType: 出力 + """ + + def _ruleprocess(self, inputs: InputType) -> OutputType | None: + """ + ルールベース処理 or APIリクエスト の判断 + + Args: + inputs (InputType): MyLLMの入力 + + Returns: + RuleOutputs: + ルールベース処理の時、MyLLMの出力を返す + APIリクエストの時、Noneを返す + """ + return None + + def _update_settings(self) -> None: + """ + APIの設定の更新 + Note: + messageのトークン数 + >>> self.llm.count_tokens(self.messsage) + + モデル変更 + >>> self.model = "gpt-3.5-turbo-16k" + + パラメータ変更 + >>> self.llm_settings = {"temperature": 0.2} + """ + return None + + def _add_tools(self, inputs: InputType) -> Tools | None: + return None + + def _add_functions(self, inputs: InputType) -> Functions | None: + """ + functions の追加 + + Args: + inputs (InputType): 入力 + + Returns: + Functions | None: functions。追加しない場合None + https://json-schema.org/understanding-json-schema/reference/index.html + >>> { + >>> "name": "関数名", + >>> "description": "関数の動作の説明。GPTは説明を見て利用するか選ぶ", + >>> "parameters": { + >>> "type": "object", "properties": {"city_name": {"type": "string", "description": "都市名"}}, + >>> json-schema[https://json-schema.org/understanding-json-schema/reference/index.html] + >>> } + >>> } + """ + return None + + def _stream_postprocess( + self, + new_chunk: Chunk, + state: "_State", + ) -> StreamOutputType: + """call_streamのGeneratorのpostprocess + + Args: + new_chunk (OpenAIChunkResponse): 新しいchunk + state (dict[Any, Any]): 状態を持てるdict. 初めは、default {}. 状態が消えてしまうのでoverwriteしない。 + + Returns: + StreamOutputType: 一時的なoutput + """ + if len(new_chunk.choices) == 0: + return "" + return new_chunk.choices[0].delta.content + + def _generate(self, stream: bool) -> Generator[StreamOutputType, None, None]: + """ + LLMの出力を得て、`self.response`に格納する + + Args: + messages (list[dict[str, str]]): LLMの入力メッセージ + """ + # 例外処理 ----------------------------------------------------------- + if self.messages is None: + raise ValueError("MessagesがNoneです。") + + # kwargs ----------------------------------------------------------- + generate_kwargs = dict(**self.llm_settings) + if self.functions is not None: + generate_kwargs["functions"] = self.functions + if self.functions is not None: + generate_kwargs["tools"] = self.tools + + # generate ---------------------------------------------------------- + self._print_messages() # verbose + self.llm = get_llm(model_name=self.model, platform=self.platform, client_settings=self.client_settings) + # [stream] + if stream or self.stream_verbose: + it = self.llm.generate_stream(messages=self.messages, llm_settings=generate_kwargs) + chunk_list: list[Chunk] = [] + state: "_State" = {} + for chunk in it: + chunk_list.append(chunk) + self._print_delta(chunk=chunk) # verbose: stop→改行、conent, TODO: fc→出力 + yield self._stream_postprocess(new_chunk=chunk, state=state) + self.response = self.llm.convert_nonstream_response(chunk_list, self.messages, self.functions) + # [non-stream] + else: + try: + self.response = self.llm.generate(messages=self.messages, llm_settings=generate_kwargs) + self._print_message_assistant() + except Exception as e: + raise e + + # ContentFilterError ------------------------------------------------- + if len(self.response.choices) == 0: + cprint(self.response, color="red", background=True) + raise ContentFilterError("入力のコンテンツフィルターに引っかかりました。") + if self.response.choices[0].finish_reason == "content_filter": + cprint(self.response, color="red", background=True) + raise ContentFilterError("出力のコンテンツフィルターに引っかかりました。") + + def _call(self, inputs: InputType, stream: bool = False) -> Generator[StreamOutputType, None, OutputType]: + """ + LLMの処理を行う (preprocess, check_input, generate, postprocess) + + Args: + inputs (InputType): 入力データを保持する辞書 + + Returns: + OutputType: 処理結果の出力データ + + Raises: + RuntimeError: 既に呼び出されている場合に発生 + """ + if self.called: + raise RuntimeError("MyLLMは1回しか呼び出せない") + + self._print_start(sep="-") + + # main ----------------------------------------------------------- + t_start = time.time() + self.inputs = inputs + self._print_inputs() + rulebase_output = self._ruleprocess(inputs) + if rulebase_output is None: # API リクエストを送る場合 + self._update_settings() + self.messages = self._preprocess(inputs) + self.functions = self._add_functions(inputs) + self.tools = self._add_tools(inputs) + t_preprocessed = time.time() + # [generate] + it = self._generate(stream=stream) + for delta_content in it: # stream=Falseの時、空のGenerator + yield delta_content + if self.response is None: + raise ValueError("responseがNoneです。") + t_generated = time.time() + # [postprocess] + self.outputs = self._postprocess(self.response) + t_postprocessed = time.time() + else: # ルールベースの場合 + self.outputs = rulebase_output + t_preprocessed = t_generated = t_postprocessed = time.time() + self.time_detail = TimeInfo( + total=t_postprocessed - t_start, + preprocess=t_preprocessed - t_start, + main=t_generated - t_preprocessed, + postprocess=t_postprocessed - t_generated, + ) + self.time = t_postprocessed - t_start + + # print ----------------------------------------------------------- + self._print_outputs() + self._print_client_settings() + self._print_llm_settings() + self._print_metadata() + self._print_end(sep="-") + + # 親MyL3M2にAppend ----------------------------------------------------------- + if self.parent is not None: + self.parent.myllm_list.append(self) + self.called = True + + # log ----------------------------------------------------------- + self._save_log() + + return self.outputs + + @property + def log(self) -> dict[str, Any]: + return { + "inputs": self.inputs, + "outputs": self.outputs, + "resposnse": self.response.model_dump() if self.response is not None else None, + "input_token": self.token.input, + "output_token": self.token.output, + "total_token": self.token.total, + "input_price": self.price.input, + "output_price": self.price.output, + "total_price": self.price.total, + "time": self.time, + "time_stamp": time.time(), + "llm_settings": self.llm_settings, + "client_settings": self.client_settings, + "model": self.model, + "platform": self.platform, + "verbose": self.verbose, + "messages": self.messages, + "assistant_message": self.assistant_message, + "functions": self.functions, + "tools": self.tools, + } + + def _save_log(self) -> None: + if self.log_dir is None: + return + try: + log = self.log + json_string = dict2json(log) + + save_log_path = os.path.join(self.log_dir, f"{log['time_stamp']}.json") + os.makedirs(self.log_dir, exist_ok=True) + with open(save_log_path, mode="w") as f: + f.write(json_string) + except Exception as e: + cprint(e, color="red", background=True) + + @property + def token(self) -> TokenInfo: + if self.response is None or self.response.usage is None: + return TokenInfo(input=0, output=0, total=0) + return TokenInfo( + input=self.response.usage.prompt_tokens, + output=self.response.usage.completion_tokens, + total=self.response.usage.total_tokens, + ) + + @property + def custom_token(self) -> TokenInfo | None: + if not self.llm._custom_price_calculation: + return None + if self.response is None: + return TokenInfo(input=0, output=0, total=0) + usage_for_price = getattr(self.response, "usage_for_price", None) + if not isinstance(usage_for_price, CompletionUsageForCustomPriceCalculation): + cprint("usage_for_priceがNoneです。正しくトークン計算できません", color="red", background=True) + return TokenInfo(input=0, output=0, total=0) + return TokenInfo( + input=usage_for_price.prompt_tokens, + output=usage_for_price.completion_tokens, + total=usage_for_price.total_tokens, + ) + + @property + def price(self) -> PriceInfo: + if self.response is None: + return PriceInfo(input=0.0, output=0.0, total=0.0) + if self.llm._custom_price_calculation: + # Geniniの時は必ずcustom_tokenがある想定 + if self.custom_token is None: + cprint("custom_tokenがNoneです。正しくトークン計算できません", color="red", background=True) + else: + return PriceInfo( + input=self.llm.calculate_price(num_input_tokens=self.custom_token.input), + output=self.llm.calculate_price(num_output_tokens=self.custom_token.output), + total=self.llm.calculate_price( + num_input_tokens=self.custom_token.input, num_output_tokens=self.custom_token.output + ), + ) + return PriceInfo( + input=self.llm.calculate_price(num_input_tokens=self.token.input), + output=self.llm.calculate_price(num_output_tokens=self.token.output), + total=self.llm.calculate_price(num_input_tokens=self.token.input, num_output_tokens=self.token.output), + ) + + @property + def assistant_message(self) -> Message | None: + if self.response is None or len(self.response.choices) == 0: + return None + return self.response.choices[0].message.to_typeddict_message() + + @property + def chat_history(self) -> Messages: + chat_history: Messages = [] + if self.messages: + chat_history += self.messages + if self.assistant_message is not None: + chat_history.append(self.assistant_message) + return chat_history + + def _print_llm_settings(self) -> None: + if not ("llm_settings" not in self.silent_set and self.verbose): + return + print_llm_settings( + llm_settings=self.llm_settings, + model=self.model, + platform=self.platform, + engine=self.llm.engine if isinstance(self.llm, AzureLLM) else None, + ) + + def _print_messages(self) -> None: + if not ("messages" not in self.silent_set and self.verbose): + return + print_messages(self.messages, title=True) + + def _print_message_assistant(self) -> None: + if self.response is None or len(self.response.choices) == 0: + return + if not ("messages" not in self.silent_set and self.verbose): + return + print_messages(messages=[self.response.choices[0].message], title=False) + + def _print_delta(self, chunk: Chunk) -> None: + if not ("messages" not in self.silent_set and self.verbose): + return + print_delta(chunk) + + def _print_client_settings(self) -> None: + if not ("client_settings" not in self.silent_set and self.verbose): + return + print_client_settings(self.llm.client_settings) + + def __repr__(self) -> str: + return f"MyLLM({self.__class__.__name__})" diff --git a/neollm/myllm/print_utils.py b/neollm/myllm/print_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c19e6054886a83c07389ffe9c5231621e43d2d04 --- /dev/null +++ b/neollm/myllm/print_utils.py @@ -0,0 +1,235 @@ +import json +from typing import Any + +from openai.types.chat import ChatCompletionAssistantMessageParam +from openai.types.chat.chat_completion_assistant_message_param import FunctionCall +from openai.types.chat.chat_completion_message_tool_call_param import ( + ChatCompletionMessageToolCallParam, + Function, +) + +from neollm.types import ( + ChatCompletionMessage, + Chunk, + ClientSettings, + InputType, + LLMSettings, + Message, + Messages, + OutputType, + PriceInfo, + PrintColor, + Role, + TokenInfo, +) +from neollm.utils.postprocess import json2dict +from neollm.utils.utils import CPrintParam, cprint + +TITLE_COLOR: PrintColor = "blue" +YEN_PAR_DOLLAR: float = 140.0 # 150円になってしまったぴえん(231027) + + +def _ChatCompletionMessage2dict(message: ChatCompletionMessage) -> Message: + message_dict = ChatCompletionAssistantMessageParam(content=message.content, role=message.role) + if message.function_call is not None: + message_dict["function_call"] = FunctionCall( + arguments=message.function_call.arguments, name=message.function_call.name + ) + if message.tool_calls is not None: + message_dict["tool_calls"] = [ + ChatCompletionMessageToolCallParam( + id=tool_call.id, + function=Function(arguments=tool_call.function.arguments, name=tool_call.function.name), + type=tool_call.type, + ) + for tool_call in message.tool_calls + ] + return message_dict + + +def _get_tool_calls(message_dict: Message) -> list[ChatCompletionMessageToolCallParam]: + tool_calls: list[ChatCompletionMessageToolCallParam] = [] + if "tool_calls" in message_dict: + _tool_calls = message_dict.get("tool_calls", None) + if _tool_calls is not None and isinstance(_tool_calls, list): # isinstance(_tool_calls, list)ないと通らん,,, + for _tool_call in _tool_calls: + tool_call = ChatCompletionMessageToolCallParam( + id=_tool_call["id"], + function=Function( + arguments=_tool_call["function"]["arguments"], + name=_tool_call["function"]["name"], + ), + type=_tool_call["type"], + ) + tool_calls.append(tool_call) + if "function_call" in message_dict: + function_call = message_dict.get("function_call", None) + if function_call is not None and isinstance( + function_call, dict + ): # isinstance(function_call, dict)ないと通らん,,, + tool_calls.append( + ChatCompletionMessageToolCallParam( + id="", + function=Function( + arguments=function_call["arguments"], + name=function_call["name"], + ), + type="function", + ) + ) + return tool_calls + + +def print_metadata(time: float, token: TokenInfo, price: PriceInfo) -> None: + try: + cprint("[metadata]", color=TITLE_COLOR, kwargs={"end": " "}) + print( + f"{time:.1f}s; " + f"{token.total:,}({token.input:,}+{token.output:,})tokens; " + f"${price.total:.2g}; ¥{price.total*YEN_PAR_DOLLAR:.2g}" + ) + except Exception as e: + cprint(e, color="red", background=True) + + +def print_inputs(inputs: InputType) -> None: + try: + cprint("[inputs]", color=TITLE_COLOR) + print(json.dumps(_arange_dumpable_object(inputs), indent=2, ensure_ascii=False)) + except Exception as e: + cprint(e, color="red", background=True) + + +def print_outputs(outputs: OutputType) -> None: + try: + cprint("[outputs]", color=TITLE_COLOR) + print(json.dumps(_arange_dumpable_object(outputs), indent=2, ensure_ascii=False)) + except Exception as e: + cprint(e, color="red", background=True) + + +def print_messages(messages: list[ChatCompletionMessage] | Messages | None, title: bool = True) -> None: + if messages is None: + cprint("Not yet running _preprocess", color="red") + return + # try: + if title: + cprint("[messages]", color=TITLE_COLOR) + role2prarams: dict[Role, CPrintParam] = { + "system": {"color": "green"}, + "user": {"color": "green"}, + "assistant": {"color": "green"}, + "function": {"color": "green", "background": True}, + "tool": {"color": "green", "background": True}, + } + for message in messages: + message_dict: Message + if isinstance(message, ChatCompletionMessage): + message_dict = _ChatCompletionMessage2dict(message) + else: + message_dict = message + + # roleの出力 ---------------------------------------- + print(" ", end="") + role = message_dict["role"] + cprint(role, **role2prarams[role]) + + # contentの出力 ---------------------------------------- + content = message_dict.get("content", None) + if isinstance(content, str): + print(" " + content.replace("\n", "\n ")) + elif isinstance(content, list): + for content_part in content: + if content_part["type"] == "text": + print(" " + content_part["text"].replace("\n", "\n ")) + elif content_part["type"] == "image_url": + cprint(" ", color="green", kwargs={"end": " "}) + print(content_part["image_url"]) + # TODO: 画像出力 + # TODO: Preview用、content_part["image"]: str, dict両方いけてしまう + else: + # TODO: 未対応のcontentの出力 + pass + + # tool_callの出力 ---------------------------------------- + for tool_call in _get_tool_calls(message_dict): + print(" ", end="") + cprint(tool_call["function"]["name"], color="green", background=True) + print(" " + str(json2dict(tool_call["function"]["arguments"], error_key=None)).replace("\n", "\n ")) + + # except Exception as e: + # cprint(e, color="red", background=True) + + +def print_delta(chunk: Chunk) -> None: + if len(chunk.choices) == 0: + return + choice = chunk.choices[0] # TODO: n>2の対応 + if choice.delta.role is not None: + print(" ", end="") + cprint(choice.delta.role, color="green") + print(" ", end="") + if choice.delta.content is not None: + print(choice.delta.content.replace("\n", "\n "), end="") + if choice.delta.function_call is not None: + if choice.delta.function_call.name is not None: + cprint(choice.delta.function_call.name, color="green", background=True) + print(" ", end="") + if choice.delta.function_call.arguments is not None: + print(choice.delta.function_call.arguments.replace("\n", "\n "), end="") + if choice.delta.tool_calls is not None: + for tool_call in choice.delta.tool_calls: + if tool_call.function is not None: + if tool_call.function.name is not None: + if tool_call.index != 0: + print("\n ", end="") + cprint(tool_call.function.name, color="green", background=True) + print(" ", end="") + if tool_call.function.arguments is not None: + print(tool_call.function.arguments.replace("\n", "\n "), end="") + if choice.finish_reason is not None: + print() + + +def print_llm_settings(llm_settings: LLMSettings, model: str, engine: str | None, platform: str) -> None: + try: + cprint("[llm_settings]", color=TITLE_COLOR, kwargs={"end": " "}) + llm_settings_copy = dict(platform=platform, **llm_settings) + llm_settings_copy["model"] = model + # Azureの場合 + if platform == "azure": + llm_settings_copy["engine"] = engine # engineを追加 + print(llm_settings_copy or "-") + except Exception as e: + cprint(e, color="red", background=True) + + +def print_client_settings(client_settings: ClientSettings) -> None: + try: + cprint("[client_settings]", color=TITLE_COLOR, kwargs={"end": " "}) + print(client_settings or "-") + except Exception as e: + cprint(e, color="red", background=True) + + +# ------- + +_DumplableEntity = int | float | str | bool | None | list[Any] | dict[Any, Any] +DumplableType = _DumplableEntity | list["DumplableType"] | dict["DumplableType", "DumplableType"] + + +def _arange_dumpable_object(obj: Any) -> DumplableType: + # 基本データ型の場合、そのまま返す + if isinstance(obj, (int, float, str, bool, type(None))): + return obj + + # リストの場合、再帰的に各要素を変換 + if isinstance(obj, list): + return [_arange_dumpable_object(item) for item in obj] + + # 辞書の場合、再帰的に各キーと値を変換 + if isinstance(obj, dict): + return {_arange_dumpable_object(key): _arange_dumpable_object(value) for key, value in obj.items()} + + # それ以外の型の場合、型情報を含めて文字列に変換 + return f"<{type(obj).__name__}>{str(obj)}" diff --git a/neollm/types/__init__.py b/neollm/types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f96e71d9e6cafc8794729a4ea471074693a3fe72 --- /dev/null +++ b/neollm/types/__init__.py @@ -0,0 +1,4 @@ +from neollm.types.info import * # NOQA +from neollm.types.mytypes import * # NOQA +from neollm.types.openai.chat_completion import * # NOQA +from neollm.types.openai.chat_completion_chunk import * # NOQA diff --git a/neollm/types/_model.py b/neollm/types/_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3070da8c68474c7b398db77b2147a8dd005b7ea3 --- /dev/null +++ b/neollm/types/_model.py @@ -0,0 +1,8 @@ +from typing import Any + +from openai._models import BaseModel + + +class DictableBaseModel(BaseModel): # openaiのBaseModelをDictAccessできるようにした + def __getitem__(self, item: str) -> Any: + return getattr(self, item) diff --git a/neollm/types/info.py b/neollm/types/info.py new file mode 100644 index 0000000000000000000000000000000000000000..3ffeba19a4139af2d08702ef2167b99315dd8e7c --- /dev/null +++ b/neollm/types/info.py @@ -0,0 +1,82 @@ +from typing import Literal + +from neollm.types._model import DictableBaseModel + +PrintColor = Literal["black", "red", "green", "yellow", "blue", "magenta", "cyan", "white"] + + +class TimeInfo(DictableBaseModel): + total: float = 0.0 + """処理時間合計 preprocess + main + postprocess""" + preprocess: float = 0.0 + """前処理時間""" + main: float = 0.0 + """メイン処理時間""" + postprocess: float = 0.0 + """後処理時間""" + + def __repr__(self) -> str: + return ( + f"TimeInfo(total={self.total:.3f}, preprocess={self.preprocess:.3f}, main={self.main:.3f}, " + f"postprocess={self.postprocess:.3f})" + ) + + +class TokenInfo(DictableBaseModel): + input: int + """入力部分のトークン数""" + output: int + """出力部分のトークン数""" + total: int + """合計トークン数""" + + def __add__(self, other: "TokenInfo") -> "TokenInfo": + if not isinstance(other, TokenInfo): + raise TypeError(f"{other} is not TokenInfo") + return TokenInfo( + input=self.input + other.input, output=self.output + other.output, total=self.total + other.total + ) + + def __iadd__(self, other: "TokenInfo") -> "TokenInfo": + if not isinstance(other, TokenInfo): + raise TypeError(f"{other} is not TokenInfo") + self.input += other.input + self.output += other.output + self.total += other.total + return self + + +class PriceInfo(DictableBaseModel): + input: float + """入力部分の費用 (USD)""" + output: float + """出力部分の費用 (USD)""" + total: float + """合計費用 (USD)""" + + def __add__(self, other: "PriceInfo") -> "PriceInfo": + if not isinstance(other, PriceInfo): + raise TypeError(f"{other} is not PriceInfo") + return PriceInfo( + input=self.input + other.input, output=self.output + other.output, total=self.total + other.total + ) + + def __iadd__(self, other: "PriceInfo") -> "PriceInfo": + if not isinstance(other, PriceInfo): + raise TypeError(f"{other} is not PriceInfo") + self.input += other.input + self.output += other.output + self.total += other.total + return self + + def __repr__(self) -> str: + return f"PriceInfo(input={self.input:.3f}, output={self.output:.3f}, total={self.total:.3f})" + + +class APIPricing(DictableBaseModel): + """APIの価格設定に関する情報を表すクラス。""" + + input: float + """入力 1k tokens 当たりのAPI利用料 (USD)""" + output: float + """出力 1k tokens 当たりのAPI利用料 (USD)""" diff --git a/neollm/types/mytypes.py b/neollm/types/mytypes.py new file mode 100644 index 0000000000000000000000000000000000000000..2348f3b642ba057ac73c67707cda7c398c7d16ff --- /dev/null +++ b/neollm/types/mytypes.py @@ -0,0 +1,31 @@ +from typing import Any, Iterator, Literal, TypeVar + +import openai.types.chat as openai_types +from openai._streaming import Stream + +from neollm.types.openai.chat_completion import ChatCompletion +from neollm.types.openai.chat_completion_chunk import ChatCompletionChunk + +Role = Literal["system", "user", "assistant", "tool", "function"] +# Settings +LLMSettings = dict[str, Any] +ClientSettings = dict[str, Any] +# Message +Message = openai_types.ChatCompletionMessageParam +Messages = list[Message] +Tools = Any +Functions = Any +# Response +Response = ChatCompletion +Chunk = ChatCompletionChunk +StreamResponse = Iterator[Chunk] +# IO +InputType = TypeVar("InputType") +OutputType = TypeVar("OutputType") +StreamOutputType = Any + +# OpenAI -------------------------------------------- +OpenAIResponse = openai_types.ChatCompletion +OpenAIChunk = openai_types.ChatCompletionChunk +OpenAIStreamResponse = Stream[OpenAIChunk] # OpneAI StreamResponse +OpenAIMessages = list[openai_types.ChatCompletionMessageParam] diff --git a/neollm/types/openai/__init__.py b/neollm/types/openai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9d18b3d0bac608c5501f5480f1863c14112f3218 --- /dev/null +++ b/neollm/types/openai/__init__.py @@ -0,0 +1,2 @@ +from neollm.types.openai.chat_completion import * # NOQA +from neollm.types.openai.chat_completion_chunk import * # NOQA diff --git a/neollm/types/openai/chat_completion.py b/neollm/types/openai/chat_completion.py new file mode 100644 index 0000000000000000000000000000000000000000..5151e71bf6a00eaca607eabc8e827e5956a3f2f6 --- /dev/null +++ b/neollm/types/openai/chat_completion.py @@ -0,0 +1,170 @@ +from typing import List, Literal, Optional + +from openai.types.chat import ChatCompletionAssistantMessageParam +from openai.types.chat.chat_completion_assistant_message_param import ( + FunctionCall as FunctionCallParams, +) +from openai.types.chat.chat_completion_message_tool_call_param import ( + ChatCompletionMessageToolCallParam, +) +from openai.types.chat.chat_completion_message_tool_call_param import ( + Function as FunctionParams, +) +from pydantic import field_validator + +from neollm.types._model import DictableBaseModel + + +class CompletionUsage(DictableBaseModel): + completion_tokens: int + """Number of tokens in the generated completion.""" + + prompt_tokens: int + """Number of tokens in the prompt.""" + + total_tokens: int + """Total number of tokens used in the request (prompt + completion).""" + + # ADDED: gpt4v preview用(Noneを許容するため) + @field_validator("completion_tokens", "prompt_tokens", "total_tokens", mode="before") + def validate_name(cls, v: int | None) -> int: + return v or 0 + + +class CompletionUsageForCustomPriceCalculation(DictableBaseModel): + completion_tokens: int + """Number of tokens in the generated completion.""" + + prompt_tokens: int + """Number of tokens in the prompt.""" + + total_tokens: int + """Total number of tokens used in the request (prompt + completion).""" + + # ADDED: gpt4v preview用(Noneを許容するため) + @field_validator("completion_tokens", "prompt_tokens", "total_tokens", mode="before") + def validate_name(cls, v: int | None) -> int: + return v or 0 + + +class Function(DictableBaseModel): + arguments: str + """ + The arguments to call the function with, as generated by the model in JSON + format. Note that the model does not always generate valid JSON, and may + hallucinate parameters not defined by your function schema. Validate the + arguments in your code before calling your function. + """ + + name: str + """The name of the function to call.""" + + +class ChatCompletionMessageToolCall(DictableBaseModel): + id: str + """The ID of the tool call.""" + + function: Function + """The function that the model called.""" + + type: Literal["function"] + """The type of the tool. Currently, only `function` is supported.""" + + +class FunctionCall(DictableBaseModel): + arguments: str + """ + The arguments to call the function with, as generated by the model in JSON + format. Note that the model does not always generate valid JSON, and may + hallucinate parameters not defined by your function schema. Validate the + arguments in your code before calling your function. + """ + + name: str + """The name of the function to call.""" + + +class ChatCompletionMessage(DictableBaseModel): + content: Optional[str] + """The contents of the message.""" + + role: Literal["assistant"] + """The role of the author of this message.""" + + function_call: Optional[FunctionCall] = None + """Deprecated and replaced by `tool_calls`. + + The name and arguments of a function that should be called, as generated by the + model. + """ + + tool_calls: Optional[List[ChatCompletionMessageToolCall]] = None + """The tool calls generated by the model, such as function calls.""" + + def to_typeddict_message(self) -> ChatCompletionAssistantMessageParam: + message_dict = ChatCompletionAssistantMessageParam(role=self.role, content=self.content) + if self.function_call is not None: + message_dict["function_call"] = FunctionCallParams( + arguments=self.function_call.arguments, name=self.function_call.name + ) + if self.tool_calls is not None: + message_dict["tool_calls"] = [ + ChatCompletionMessageToolCallParam( + id=tool_call.id, + function=FunctionParams(arguments=tool_call.function.arguments, name=tool_call.function.name), + type=tool_call.type, + ) + for tool_call in self.tool_calls + ] + return message_dict + + +FinishReason = Literal["stop", "length", "tool_calls", "content_filter", "function_call"] + + +class Choice(DictableBaseModel): + finish_reason: FinishReason | None = None # ADDED: gpt4v preview用 + """The reason the model stopped generating tokens. + + This will be `stop` if the model hit a natural stop point or a provided stop + sequence, `length` if the maximum number of tokens specified in the request was + reached, `content_filter` if content was omitted due to a flag from our content + filters, `tool_calls` if the model called a tool, or `function_call` + (deprecated) if the model called a function. + """ + + index: int + """The index of the choice in the list of choices.""" + + message: ChatCompletionMessage + """A chat completion message generated by the model.""" + + +class ChatCompletion(DictableBaseModel): + id: str + """A unique identifier for the chat completion.""" + + choices: List[Choice] + """A list of chat completion choices. + + Can be more than one if `n` is greater than 1. + """ + + created: int + """The Unix timestamp (in seconds) of when the chat completion was created.""" + + model: str + """The model used for the chat completion.""" + + object: Literal["chat.completion"] | str + """The object type, which is always `chat.completion`.""" + + system_fingerprint: Optional[str] = None + """This fingerprint represents the backend configuration that the model runs with. + + Can be used in conjunction with the `seed` request parameter to understand when + backend changes have been made that might impact determinism. + """ + + usage: Optional[CompletionUsage] = None + """Usage statistics for the completion request.""" diff --git a/neollm/types/openai/chat_completion_chunk.py b/neollm/types/openai/chat_completion_chunk.py new file mode 100644 index 0000000000000000000000000000000000000000..e98020830a46a2a3ac1a0152332bbf8eddcf22a6 --- /dev/null +++ b/neollm/types/openai/chat_completion_chunk.py @@ -0,0 +1,109 @@ +from typing import List, Literal, Optional + +from pydantic import field_validator + +from neollm.types._model import DictableBaseModel +from neollm.utils.utils import cprint + + +class ChoiceDeltaFunctionCall(DictableBaseModel): + arguments: Optional[str] = None + """ + The arguments to call the function with, as generated by the model in JSON + format. Note that the model does not always generate valid JSON, and may + hallucinate parameters not defined by your function schema. Validate the + arguments in your code before calling your function. + """ + + name: Optional[str] = None + """The name of the function to call.""" + + +class ChoiceDeltaToolCallFunction(DictableBaseModel): + arguments: Optional[str] = None + """ + The arguments to call the function with, as generated by the model in JSON + format. Note that the model does not always generate valid JSON, and may + hallucinate parameters not defined by your function schema. Validate the + arguments in your code before calling your function. + """ + + name: Optional[str] = None + """The name of the function to call.""" + + +class ChoiceDeltaToolCall(DictableBaseModel): + index: int + + id: Optional[str] = None + """The ID of the tool call.""" + + function: Optional[ChoiceDeltaToolCallFunction] = None + + type: Optional[Literal["function"]] = None + """The type of the tool. Currently, only `function` is supported.""" + + +class ChoiceDelta(DictableBaseModel): + content: Optional[str] = None + """The contents of the chunk message.""" + + function_call: Optional[ChoiceDeltaFunctionCall] = None + """Deprecated and replaced by `tool_calls`. + + The name and arguments of a function that should be called, as generated by the + model. + """ + + role: Optional[Literal["system", "user", "assistant", "tool"]] = None + """The role of the author of this message.""" + + tool_calls: Optional[List[ChoiceDeltaToolCall]] = None + + +class ChunkChoice(DictableBaseModel): # chat_completionと同名なため、改名(Choice->ChunkChoice) + delta: ChoiceDelta + """A chat completion delta generated by streamed model responses.""" + + finish_reason: Optional[Literal["stop", "length", "tool_calls", "content_filter", "function_call"]] + """The reason the model stopped generating tokens. + + This will be `stop` if the model hit a natural stop point or a provided stop + sequence, `length` if the maximum number of tokens specified in the request was + reached, `content_filter` if content was omitted due to a flag from our content + filters, `tool_calls` if the model called a tool, or `function_call` + (deprecated) if the model called a function. + """ + + index: int + """The index of the choice in the list of choices.""" + + +class ChatCompletionChunk(DictableBaseModel): + id: str + """A unique identifier for the chat completion. Each chunk has the same ID.""" + + choices: List[ChunkChoice] + """A list of chat completion choices. + + Can be more than one if `n` is greater than 1. + """ + + created: int + """The Unix timestamp (in seconds) of when the chat completion was created. + + Each chunk has the same timestamp. + """ + + model: str + """The model to generate the completion.""" + + object: Literal["chat.completion.chunk"] = "chat.completion.chunk" # for azure + """The object type, which is always `chat.completion.chunk`.""" + + # ADDED: azure用 (""を許容するため) + @field_validator("object", mode="before") + def validate_name(cls, v: str) -> Literal["chat.completion.chunk"]: + if v != "" and v != "chat.completion.chunk": + cprint(f"ChatCompletionChunk.object is not 'chat.completion.chunk': {v}", "yellow") + return "chat.completion.chunk" diff --git a/neollm/utils/inference.py b/neollm/utils/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..490793397f55a27c72e3f26fa0798b2784aee8e8 --- /dev/null +++ b/neollm/utils/inference.py @@ -0,0 +1,70 @@ +import csv +import glob +import json +from concurrent.futures import Future, ThreadPoolExecutor +from typing import Any, Callable, TypeVar + +_T = TypeVar("_T") + + +def execute_parallel(func: Callable[..., _T], kwargs_list: list[dict[str, Any]], max_workers: int) -> list[_T]: + """並行処理を行う + + Args: + func (Callable): 並行処理したい関数 + kwargs_list (list[dict[str, Any]]): 関数の引数(dict型)のリスト + max_workers (int): 並行処理数 + + Returns: + list[Any]: 関数の戻り値のリスト + """ + response_list: list[Future[_T]] = [] + with ThreadPoolExecutor(max_workers=max_workers) as e: + for kwargs in kwargs_list: + response: Future[_T] = e.submit(func, **kwargs) + response_list.append(response) + return [r.result() for r in response_list] + + +def _load_json_file(file_path: str) -> Any: + # TODO: Docstring追加 + with open(file_path, "r", encoding="utf-8") as json_file: + data = json.load(json_file) + return data + + +def make_log_csv(log_dir: str, csv_file_name: str = "log.csv") -> None: + """ログデータのcsvを保存 + + Args: + log_dir (str): ログデータが保存されているディレクトリ + csv_file_name (str, optional): 保存するcsvファイル名. Defaults to "log.csv". + """ + # ディレクトリ内のJSONファイルのリストを取得 + # TODO: エラーキャッチ + json_files = sorted([f for f in glob.glob(f"{log_dir}/*.json")], key=lambda x: int(x.split("/")[-1].split(".")[0])) + + # すべてのJSONファイルからユニークなキーを取得 + columns = [] + data_list: list[dict[Any, Any]] = [] + keys_set = set() + for json_file in json_files: + data = _load_json_file(json_file) + if isinstance(data, dict): + for key in data.keys(): + if key not in keys_set: + keys_set.add(key) + columns.append(key) + data_list.append(data) + + # CSVファイルを作成し、ヘッダーを書き込む + with open(csv_file_name, "w", encoding="utf-8", newline="") as csv_file: + writer = csv.writer(csv_file) + writer.writerow(columns) + + # JSONファイルからデータを読み取り、CSVファイルに書き込む + for data in data_list: + row = [data.get(key, "") for key in columns] + writer.writerow(row) + + print(f"saved csv file: {csv_file_name}") diff --git a/neollm/utils/postprocess.py b/neollm/utils/postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..b9653b00042da5754120aa6fdf5c9ff699538bb8 --- /dev/null +++ b/neollm/utils/postprocess.py @@ -0,0 +1,120 @@ +import json +from typing import Any, overload + + +# string --------------------------------------- +def _extract_string(text: str, start_string: str | None = None, end_string: str | None = None) -> str: + """ + テキストから必要な文字列を抽出する + + Args: + text (str): 抽出するテキスト + + Returns: + str: 抽出された必要な文字列 + """ + # 最初の文字 + if start_string is not None and start_string in text: + idx_head = text.index(start_string) + text = text[idx_head:] + # 最後の文字 + if end_string is not None and end_string in text: + idx_tail = len(text) - text[::-1].index(end_string[::-1]) + text = text[:idx_tail] + return text + + +def _delete_first_chapter_tag(text: str, first_character_tag: str | list[str]) -> str: + """_summary_ + + Args: + text (str): テキスト + first_character_tag (str | list[str]): 最初にある余分な文字列 + + Returns: + str: 除去済みのテキスト + """ + # first_character_tagのlist化 + if isinstance(first_character_tag, str): + first_character_tag = [first_character_tag] + # 最初のチャプタータグの消去 + for first_character_i in first_character_tag: + if text.startswith(first_character_i): + text = text[len(first_character_i) :] + break + return text.strip() + + +def strip_string( + text: str, + first_character: str | list[str] = ["", ""], + start_string: str | None = None, + end_string: str | None = None, + strip_quotes: str | list[str] = ["'", '"'], +) -> str: + """stringの前後の余分な文字を削除する + + Args: + text (str): ChatGPTの出力文字列 + first_character (str, optional): 出力の先頭につく文字 Defaults to ["", ""]. + start_string (str, optional): 出力の先頭につく文字 Defaults to None. + end_string (str, optional): 出力の先頭につく文字 Defaults to None. + strip_quotes (str, optional): 前後の余分な'"を消す. Defaults to ["'", '"']. + + Returns: + str: 余分な文字列を消去した文字列 + + Examples: + >>> strip_string("'''ChatGPT is smart!'''", "") + ChatGPT is smart! + >>> strip_string('{"a": 1}', start_string="{", end_string="}") + {"a": 1} + >>> strip_string(" `neoAI`", strip_quotes="`") + neoAI + """ + # 余分な文字列消去 + text = _delete_first_chapter_tag(text, first_character) + # 前後の'" を消す + if isinstance(strip_quotes, str): + strip_quotes = [strip_quotes] + for quote in strip_quotes: + text = text.strip(quote).strip() + text = _extract_string(text, start_string, end_string) + return text.strip() + + +# dict --------------------------------------- + + +@overload +def json2dict(json_string: str, error_key: None) -> dict[Any, Any] | str: ... + + +@overload +def json2dict(json_string: str, error_key: str) -> dict[Any, Any]: ... + + +def json2dict(json_string: str, error_key: str | None = "error") -> dict[Any, Any] | str: + """ + JSON文字列をPython dictに変換する + + Args: + json_string (str): 変換するJSON文字列 + error_key (str, optional): エラーキーの値として代入する文字列. Defaults to "error". + + Returns: + dict: 変換されたPython dict + """ + try: + python_dict = json.loads(_extract_string(json_string, start_string="{", end_string="}"), strict=False) + except ValueError: + if error_key is None: + return json_string + python_dict = {error_key: json_string} + if isinstance(python_dict, dict): + return python_dict + return {error_key: python_dict} + + +# calender +# YYYY年MM月YY日 -> YYYY-MM-DD diff --git a/neollm/utils/preprocess.py b/neollm/utils/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..5a01ad6a6cda9f89b98b5f090c3aeb647b7cd318 --- /dev/null +++ b/neollm/utils/preprocess.py @@ -0,0 +1,107 @@ +import json +import re +from typing import Any, Callable + + +# dict2json -------------------------------- +def dict2json(python_dict: dict[str, Any]) -> str: + """ + Python dictをJSON文字列に変換する + + Args: + python_dict (dict): 変換するPython dict + + Returns: + str: 変換されたJSON文字列 + """ + # ensure_ascii: 日本語とかを出力するため + json_string = json.dumps(python_dict, indent=2, ensure_ascii=False) + return json_string + + +# optimize token -------------------------------- +def optimize_token(text: str, funcs: list[Callable[[str], str]] | None = None) -> str: + """ + テキストのトークンを最適化をする + + Args: + text (str): 最適化するテキスト + + Returns: + str: 最適化されたテキスト + """ + funcs = funcs or [minimize_newline, zenkaku_to_hankaku, remove_trailing_spaces] + for func in funcs: + text = func(text) + return text.strip() + + +def _replace_consecutive(text: str, pattern: str, replacing_text: str) -> str: + """ + テキスト内の連続するパターンに対して、指定された置換テキストで置換する + + Args: + text (str): テキスト + pattern (str): 置換するパターン + replacing_text (str): 置換テキスト + + Returns: + str: 置換されたテキスト + """ + p = re.compile(pattern) + matches = [(m.start(), m.end()) for m in p.finditer(text)][::-1] + + text_replaced = list(text) + + for i_start, i_end in matches: + text_replaced[i_start:i_end] = [replacing_text] + return "".join(text_replaced) + + +def minimize_newline(text: str) -> str: + """ + テキスト内の連続する改行を2以下にする + + Args: + text (str): テキスト + + Returns: + str: 改行を最小限にしたテキスト + """ + return _replace_consecutive(text, pattern="\n{2,}", replacing_text="\n\n") + + +def zenkaku_to_hankaku(text: str) -> str: + """ + テキスト内の全角文字を半角文字に変換する + + Args: + text (str): テキスト + + Returns: + str: 半角文字に変換されたテキスト + """ + mapping_dict = {"　": " ", "：": ": ", "‎": " ", "．": "。", "，": "、", "￥": "¥"} + hankaku_text = "" + for char in text: + # A-Za-z0-9!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ + if char in mapping_dict: + hankaku_text += mapping_dict[char] + elif 65281 <= ord(char) <= 65374: + hankaku_text += chr(ord(char) - 65248) + else: + hankaku_text += char + return hankaku_text + + +def remove_trailing_spaces(text: str) -> str: + """ + テキスト内の各行の末尾のスペースを削除する + + Args: + text (str): テキスト + + Returns: + str: スペースを削除したテキスト + """ + return "\n".join([line.rstrip() for line in text.split("\n")]) diff --git a/neollm/utils/prompt_checker.py b/neollm/utils/prompt_checker.py new file mode 100644 index 0000000000000000000000000000000000000000..437f40bfecee7b7e593a7b49d2375d6697ac4cb3 --- /dev/null +++ b/neollm/utils/prompt_checker.py @@ -0,0 +1,110 @@ +from __future__ import annotations + +from typing import Any + +from typing_extensions import TypedDict + +from neollm import MyL3M2, MyLLM +from neollm.types import LLMSettings, Messages, Response + +_MyLLM = MyLLM[Any, Any] +_MyL3M2 = MyL3M2[Any, Any] + + +class PromptCheckerInput(TypedDict): + myllm: _MyLLM | _MyL3M2 + model: str + platform: str + llm_settings: LLMSettings | None + + +class APromptCheckerInput(TypedDict): + myllm: _MyLLM + + +class APromptChecker(MyLLM[APromptCheckerInput, str]): + def _preprocess(self, inputs: APromptCheckerInput) -> Messages: + system_prompt = ( + "あなたは、AIへの指示(プロンプト)をより良くすることが仕事です。\n" + "あなたは言語能力が非常に高く、仕事も丁寧なので小さなミスも気づくことができる天才です。" + "誤字脱字・論理的でない点・指示が不明確な点を箇条書きで指摘し、より良いプロンプトを提案してください。\n" + "# 出力例: \n" + "[指示の誤字脱字/文法ミス]\n" + "- ...\n" + "- ...\n" + "[指示が論理的でない点]\n" + "- ...\n" + "- ...\n" + "[指示が不明確な点]\n" + "- ...\n" + "- ...\n" + "[その他気になる点]\n" + "- ...\n" + "- ...\n" + "[提案]\n" + "- ...\n" + "- ...\n" + ) + if inputs["myllm"].messages is None: + return [] + user_prompt = "# プロンプト\n" + "\n".join( + # [f"<{message['role']}>\n{message['content']}\n" for message in inputs.messages] + [str(message) for message in inputs["myllm"].messages] + ) + messages: Messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + return messages + + def _postprocess(self, response: Response) -> str: + if response.choices[0].message.content is None: + return "contentがないンゴ" + return response.choices[0].message.content + + def _ruleprocess(self, inputs: APromptCheckerInput) -> str | None: + if inputs["myllm"].messages is None: + return "ruleprocessが走って、リクエストしてないよ！" + return None + + def __call__(self, inputs: APromptCheckerInput) -> str: + outputs: str = super().__call__(inputs) + return outputs + + +class PromptsChecker(MyL3M2[PromptCheckerInput, None]): + def _link(self, inputs: PromptCheckerInput) -> None: + if isinstance(inputs["myllm"], MyL3M2): + for myllm in inputs["myllm"].myllm_list: + prompts_checker = PromptsChecker(parent=self, verbose=True) + prompts_checker( + inputs={ + "myllm": myllm, + "model": inputs["model"], + "platform": inputs["platform"], + "llm_settings": inputs["llm_settings"], + } + ) + elif isinstance(inputs["myllm"], MyLLM): + a_prompt_checker = APromptChecker( + parent=self, + llm_settings=inputs["llm_settings"], + verbose=True, + platform=inputs["platform"], + model=inputs["model"], + ) + a_prompt_checker(inputs={"myllm": inputs["myllm"]}) + + def __call__(self, inputs: PromptCheckerInput) -> None: + super().__call__(inputs) + + +def check_prompt( + myllm: _MyLLM | _MyL3M2, + llm_settings: LLMSettings | None = None, + model: str = "gpt-3.5-turbo", + platform: str = "openai", +) -> MyL3M2[Any, Any]: + prompt_checker_2 = PromptsChecker(verbose=True) + prompt_checker_2(inputs={"myllm": myllm, "llm_settings": llm_settings, "model": model, "platform": platform}) + return prompt_checker_2 diff --git a/neollm/utils/tokens.py b/neollm/utils/tokens.py new file mode 100644 index 0000000000000000000000000000000000000000..2794936fb4d3c8638c4b21479597184ba05c2fd8 --- /dev/null +++ b/neollm/utils/tokens.py @@ -0,0 +1,229 @@ +import json +import textwrap +from typing import Any + +import tiktoken + +from neollm.types import Function # , Functions, Messages + + +def normalize_model_name(model_name: str) -> str: + """model_nameのトークン数計測のための標準化 + + Args: + model_name (str): model_name + OpenAI: gpt-3.5-turbo-0613, gpt-3.5-turbo-16k-0613, gpt-4-0613, gpt-4-32k-0613 + OpenAIFT: ft:gpt-3.5-turbo:org_id + Azure: gpt-35-turbo-0613, gpt-35-turbo-16k-0613, gpt-4-0613, gpt-4-32k-0613 + + Returns: + str: 標準化されたmodel_name + + Raises: + ValueError: model_nameが不適切 + """ + # 参考: https://platform.openai.com/docs/models/gpt-3-5 + NEWEST_MAP = [ + ("gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613"), + ("gpt-3.5-turbo", "gpt-3.5-turbo-0613"), + ("gpt-4-32k", "gpt-4-32k-0613"), + ("gpt-4", "gpt-4-0613"), + ] + ALL_VERSION_MODELS = [ + # gpt-3.5-turbo + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-3.5-turbo-0301", # Legacy + # gpt-4 + "gpt-4-0613", + "gpt-4-32k-0613", + "gpt-4-0314", # Legacy + "gpt-4-32k-0314", # Legacy + ] + # Azure表記 → OpenAI表記に統一 + model_name = model_name.replace("gpt-35", "gpt-3.5") + # 最新モデルを正式名称に & 新モデル, FTモデルをキャッチ + if model_name not in ALL_VERSION_MODELS: + for key, model_name_version in NEWEST_MAP: + if key in model_name: + model_name = model_name_version + break + # Return + if model_name in ALL_VERSION_MODELS: + return model_name + raise ValueError("model_name は以下から選んで．\n" + ",".join(ALL_VERSION_MODELS)) + + +def count_tokens(messages: Any | None = None, model_name: str | None = None, functions: Any | None = None) -> int: + """トークン数計測 + + Args: + messages (Messages): GPTAPIの入力のmessages + model_name (str | None, optional): モデル名. Defaults to None. + functions (Functions | None, optional): GPTAPIの入力のfunctions. Defaults to None. + + Returns: + int: トークン数 + """ + model_name = normalize_model_name(model_name or "cl100k_base") + num_tokens = _count_messages_tokens(messages, model_name) + if functions is not None: + num_tokens += _count_functions_tokens(functions, model_name) + return num_tokens + + +# https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb +def _count_messages_tokens(messages: Any | None, model_name: str) -> int: + """メッセージのトークン数を計算 + + Args: + messages (Messages): ChatGPT等APIに入力するmessages + model_name (str, optional): 使用するモデルの名前 + "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-4-0314", "gpt-4-32k-0314" + "gpt-4-0613", "gpt-4-32k-0613", "gpt-3.5-turbo", "gpt-4" + + Returns: + int: トークン数の合計 + """ + if messages is None: + return 0 + # setting model + encoding_model = tiktoken.encoding_for_model(model_name) # "cl100k_base" + + # config + if model_name == "gpt-3.5-turbo-0301": + tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n + tokens_per_name = -1 # if there's a name, the role is omitted + else: + tokens_per_message = 3 + tokens_per_name = 1 + + # count tokens + num_tokens = 3 # every reply is primed with <|start|>assistant<|message|> + for message in messages: + num_tokens += tokens_per_message + for key, value in message.items(): + if isinstance(value, str): + num_tokens += len(encoding_model.encode(value)) + if key == "name": + num_tokens += tokens_per_name + return num_tokens + + +# https://gist.github.com/CGamesPlay/dd4f108f27e2eec145eedf5c717318f5 +def _count_functions_tokens(functions: Any, model_name: str | None = None) -> int: + """ + functionsのトークン数計測 + + Args: + functions (Functions): GPTAPIの入力のfunctions + model_name (str | None, optional): モデル名. Defaults to None. + + Returns: + _type_: トークン数 + """ + encoding_model = tiktoken.encoding_for_model(model_name or "cl100k_base") # "cl100k_base" + num_tokens = 3 + len(encoding_model.encode(__functions2string(functions))) + return num_tokens + + +# functionsのstring化、補助関数 --------------------------------------------------------------------------- +def __functions2string(functions: Any) -> str: + """functionsの文字列化 + + Args: + functions (Functions): GPTAPIの入力のfunctions + + Returns: + str: functionsの文字列 + """ + prefix = "# Tools\n\n## functions\n\nnamespace functions {\n\n} // namespace functions\n" + functions_string = prefix + "".join(__function2string(function) for function in functions) + return functions_string + + +def __function2string(function: Function) -> str: + """functionの文字列化 + + Args: + function (Function): GPTAPIのfunctionの要素 + + Returns: + str: functionの文字列 + """ + object_string = __format_object(function["parameters"]) + if object_string is not None: + object_string = "_: " + object_string + else: + object_string = "" + + functions_string: str = ( + f"// {function['description']}\ntype {function['name']} = (" + object_string + ") => any;\n\n" + ) + return functions_string + + +def __format_object(schema: dict[str, Any], indent: int = 0) -> str | None: + if "properties" not in schema or len(schema["properties"]) == 0: + if schema.get("additionalProperties", False): + return "object" + return None + + result = "{\n" + for key, value in dict(schema["properties"]).items(): + # value <- resolve_ref(value) + value_rendered = __format_schema(value, indent + 1) + if value_rendered is None: + continue + # description + if "description" in value: + description = "".join( + " " * indent + f"// {description_i}\n" + for description_i in textwrap.dedent(value["description"]).strip().split("\n") + ) + # optional + optional = "" if key in schema.get("required", {}) else "?" + # default + default_comment = "" if "default" not in value else f" // default: {__format_default(value)}" + # add string + result += description + " " * indent + f"{key}{optional}: {value_rendered},{default_comment}\n" + result += (" " * (indent - 1)) + "}" + return result + + +# よくわからん +# def resolve_ref(schema): +# if schema.get("$ref") is not None: +# ref = schema["$ref"][14:] +# schema = json_schema["definitions"][ref] +# return schema + + +def __format_schema(schema: dict[str, Any], indent: int) -> str | None: + # schema <- resolve_ref(schema) + if "enum" in schema: + return __format_enum(schema) + elif schema["type"] == "object": + return __format_object(schema, indent) + elif schema["type"] in {"integer", "number"}: + return "number" + elif schema["type"] in {"string"}: + return "string" + elif schema["type"] == "array": + return str(__format_schema(schema["items"], indent)) + "[]" + else: + raise ValueError("unknown schema type " + schema["type"]) + + +def __format_enum(schema: dict[str, Any]) -> str: + # "A" | "B" | "C" + return " | ".join(json.dumps(element, ensure_ascii=False) for element in schema["enum"]) + + +def __format_default(schema: dict[str, Any]) -> str: + default = schema["default"] + if schema["type"] == "number" and float(default).is_integer(): + # numberの時、0 → 0.0 + return f"{default:.1f}" + else: + return str(default) diff --git a/neollm/utils/utils.py b/neollm/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb5c01626eb718a36a67724b8b87289f237e1fa --- /dev/null +++ b/neollm/utils/utils.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import os +from typing import Any + +from typing_extensions import TypedDict + +from neollm.types import PrintColor + + +class CPrintParam(TypedDict, total=False): + text: Any + color: PrintColor | None + background: bool + light: bool + bold: bool + italic: bool + underline: bool + kwargs: dict[str, Any] + + +def cprint( + *text: Any, + color: PrintColor | None = None, + background: bool = False, + light: bool = False, + bold: bool = False, + italic: bool = False, + underline: bool = False, + kwargs: dict[str, Any] = {}, +) -> None: + """ + 色付けなどリッチにprint + + Args: + *text: 表示するテキスト。 + color (PrintColor): テキストの色: 'black', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white'。 + background (bool): 背景色 + light (bool): 淡い色にするか + bold (bool): 太字 + italic (bool): 斜体 + underline (bool): 下線 + **kwargs: printの引数 + """ + # ANSIエスケープシーケンスを使用して、テキストを書式設定して表示する + format_string = "" + + # 色の設定 + color2code: dict[PrintColor, int] = { + "black": 30, + "red": 31, + "green": 32, + "yellow": 33, + "blue": 34, + "magenta": 35, + "cyan": 36, + "white": 37, + } + if color is not None and color in color2code: + code = color2code[color] + if background: + code += 10 + elif light: + code += 60 + format_string += f"\033[{code}m" + if bold: + format_string += "\033[1m" + if italic: + format_string += "\033[3m" + if underline: + format_string += "\033[4m" + + # テキストの表示 + for text_i in text: + print(format_string + str(text_i) + "\033[0m", **kwargs) + + +def ensure_env_var(var_name: str | None = None, default: str | None = None) -> str: + if var_name is None: + return "" + if os.environ.get(var_name, "") == "": + if default is None: + raise ValueError(f"{var_name}をenvで設定しよう") + cprint(f"WARNING: {var_name}が設定されていません。{default}を使用します。", color="yellow", background=True) + os.environ[var_name] = default + return os.environ[var_name] + + +def suport_unrecomended_env_var(old_key: str, new_key: str) -> None: + """非推奨の環境変数をサポートする + + Args: + old_key (str): 非推奨の環境変数名 + new_key (str): 推奨の環境変数名 + """ + if os.getenv(old_key) is not None and os.getenv(new_key) is None: + cprint(f"WARNING: {old_key}ではなく、{new_key}にしてね", color="yellow", background=True) + os.environ[new_key] = os.environ[old_key] diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..54855ab7a557fce861190c751b8f25c8783e74b9 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3694 @@ +# This file is 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a/project/.env.template b/project/.env.template new file mode 100644 index 0000000000000000000000000000000000000000..67bff8ac66db7349b007ce439387f868c3bc6256 --- /dev/null +++ b/project/.env.template @@ -0,0 +1,24 @@ +LLM_PLATFORM=azure + +# OpenAIkey +OPENAI_API_KEY=sk-XXX + +# Azure OpenAIkey +AZURE_OPENAI_API_KEY=XXX # AZURE_OPENAI_AD_TOKEN=YYY +AZURE_OPENAI_ENDPOINT=https://neoai-pjname.openai.azure.com/ # (not-recomended): AZURE_API_BASE +OPENAI_API_VERSION=2024-02-01 # (not-recomended): AZURE_API_VERSION + +# ENGINE +# 1106 ---------------------------------------------------------- +AZURE_ENGINE_GPT35T_1106=xxx +AZURE_ENGINE_GPT4T_1106=xxx +# 0613 ---------------------------------------------------------- +AZURE_ENGINE_GPT35T_0613=neoai-pjname-gpt-35 # (not-recomended): AZURE_ENGINE_GPT35, AZURE_ENGINE_GPT35_0613 +AZURE_ENGINE_GPT35T_16K_0613=neoai-pjname-gpt-35-16k # (not-recomended): AZURE_ENGINE_GPT35_16k, AZURE_ENGINE_GPT35_16K_0613 +AZURE_ENGINE_GPT4_0613=neoai-pjname-gpt4 # (not-recomended): AZURE_ENGINE_GPT4 +AZURE_ENGINE_GPT4_32K_0613=neoai-pjname-gpt4-32k # (not recomended): AZURE_ENGINE_GPT4_32k + +# Anthropic +ANTHROPIC_API_KEY=xxx + +# GCP diff --git a/project/ex_module/ex_profile_extractor.py b/project/ex_module/ex_profile_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..7d7b09ad8edd4f0e3ae1564cf7f55e06871ebad8 --- /dev/null +++ b/project/ex_module/ex_profile_extractor.py @@ -0,0 +1,113 @@ +from typing import Any, Literal, TypedDict + +from neollm import MyLLM +from neollm.types import Functions +from neollm.utils.postprocess import json2dict +from neollm.utils.preprocess import optimize_token + + +class ProfileExtractorInputType(TypedDict): + text: str + + +class ProfileExtractorOuputType(TypedDict): + name: str + birth_year: int + domain: str + lang: Literal["ENG", "JPN"] + + +class ProfileExtractor(MyLLM): + """情報を抽出するMyLLM + + Notes: + inputs: + >>> {"text": str} + outpus: + >>> {"text_translated": str | None(うまくいかなかった場合)} + """ + + def _preprocess(self, inputs: ProfileExtractorInputType): + system_prompt = "より情報を抽出する。存在しない場合nullとする" + user_prompt = "\n" f"'''{inputs['text'].strip()}'''" + messages = [ + {"role": "system", "content": optimize_token(system_prompt)}, + {"role": "user", "content": optimize_token(user_prompt)}, + ] + return messages + + def _check_input( + self, inputs: ProfileExtractorInputType, messages + ) -> tuple[bool, ProfileExtractorOuputType | None]: + # 入力がない場合の処理 + if inputs["text"].strip() == "": + # requestしない, ルールベースのoutput + return False, {"name": "", "birth_year": -1, "domain": "", "lang": "JPN"} + # 入力が多い時に16kを使う + if self.llm.count_tokens(messages) >= 1600: + self.model = "gpt-3.5-turbo-16k" + else: + self.model = "gpt-3.5-turbo" + # requestする, _ + return True, None + + def _postprocess(self, response) -> ProfileExtractorOuputType: + if dict(response["choices"][0]["message"]).get("function_call"): + try: + extracted_data = json2dict(response["choices"][0]["message"]["function_call"]["arguments"]) + except Exception: + extracted_data = {} + else: + extracted_data = {} + + lang_ = extracted_data.get("lang") + if lang_ in {"ENG", "JPN"}: + lang = lang_ + else: + lang = "JPN" + + outputs: ProfileExtractorOuputType = { + "name": str(extracted_data.get("name") or ""), + "birth_year": int(extracted_data.get("birth_year") or -1), + "domain": str(extracted_data.get("domain") or ""), + "lang": lang, + } + return outputs + + # Function Callingを使う場合必要 + def _add_functions(self, inputs: Any) -> Functions | None: + functions: Functions = [ + { + "name": "extract_profile", + "description": "extract profile of a person", + "parameters": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "名前", + }, + "domain": { + "type": "string", + "description": "研究ドメイン カンマ区切り", + }, + "birth_year": { + "type": "integer", + "description": "the year of the birth YYYY", + }, + "lang": { + "type": "string", + "description": "the language of the text", + "enum": ["ENG", "JPN"], + }, + }, + "required": ["name", "birth_year", "domain", "lang"], + }, + } + ] + return functions + + # 型定義のために必要 + def __call__(self, inputs: ProfileExtractorInputType) -> ProfileExtractorOuputType: + outputs: ProfileExtractorOuputType = super().__call__(inputs) + return outputs diff --git a/project/ex_module/ex_translated_profile_extractor.py b/project/ex_module/ex_translated_profile_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..4b285060399b9d2deb6598c130b13f9a3a0f96f2 --- /dev/null +++ b/project/ex_module/ex_translated_profile_extractor.py @@ -0,0 +1,49 @@ +from typing import TypedDict + +from ex_profile_extractor import ProfileExtractor, ProfileExtractorInputType +from ex_translator import Translator + +from neollm import MyL3M2 + + +class TranslatedProfileExtractorOutputType(TypedDict): + name_ENG: str + name_JPN: str + domain_ENG: str + domain_JPN: str + birth_year: int + + +class TranslatedProfileExtractor(MyL3M2): + def _link(self, inputs: ProfileExtractorInputType) -> TranslatedProfileExtractorOutputType: + # Profile Extract + profile_extractor = ProfileExtractor(parent=self, silent_list=["llm_settings", "inputs", "messages"]) + profile = profile_extractor(inputs) + # Translator name + translator_name = Translator(parent=self, silent_list=["llm_settings", "inputs", "messages"]) + translated_name = translator_name(inputs={"text": profile["name"]})["text_translated"] + # Translate domain + translator_domain = Translator(parent=self, silent_list=["llm_settings", "inputs", "messages"]) + translated_domain = translator_domain(inputs={"text": profile["domain"]})["text_translated"] + + outputs: TranslatedProfileExtractorOutputType = { + "name_ENG": profile["name"], + "name_JPN": profile["name"], + "domain_ENG": profile["domain"], + "domain_JPN": profile["domain"], + "birth_year": profile["birth_year"], + } + + if profile["lang"] == "ENG": + outputs["name_JPN"] = translated_name + outputs["domain_JPN"] = translated_domain + else: + outputs["name_ENG"] = translated_name + outputs["domain_ENG"] = translated_domain + + return outputs + + # 型定義のために必要 + def __call__(self, inputs: ProfileExtractorInputType) -> TranslatedProfileExtractorOutputType: + outputs: TranslatedProfileExtractorOutputType = super().__call__(inputs) + return outputs diff --git a/project/ex_module/ex_translator.py b/project/ex_module/ex_translator.py new file mode 100644 index 0000000000000000000000000000000000000000..38850f3820ca86c7cf405de8088519e403429869 --- /dev/null +++ b/project/ex_module/ex_translator.py @@ -0,0 +1,62 @@ +from typing import TypedDict + +from neollm import MyLLM +from neollm.types import Messages, OpenAIResponse +from neollm.utils.postprocess import strip_string +from neollm.utils.preprocess import optimize_token + + +class TranslatorInputType(TypedDict): + text: str + + +class TranslatorOuputType(TypedDict): + text_translated: str + + +class Translator(MyLLM): + """情報を抽出するMyLLM + + Notes: + inputs: + >>> {"text": str} + outpus: + >>> {"text_translated": str | None(うまくいかなかった場合)} + """ + + def _preprocess(self, inputs: TranslatorInputType) -> Messages: + system_prompt = ( + "You are a good translator. Translate Japanese into English or English into Japanese.\n" + "# output_format:\n\n{translated text in English or Japanese}" + ) + user_prompt = "\n" f"'''{inputs['text'].strip()}'''" + messages: Messages = [ + {"role": "system", "content": optimize_token(system_prompt)}, + {"role": "user", "content": optimize_token(user_prompt)}, + ] + return messages + + def _ruleprocess(self, inputs: TranslatorInputType) -> None | TranslatorOuputType: + # 入力がない場合の処理 + if inputs["text"].strip() == "": + return {"text_translated": ""} + return None + + def _update_settings(self) -> None: + # 入力が多い時に16kを使う + if self.messages is not None: + if self.llm.count_tokens(self.messages) >= 1600: + self.model = "gpt-3.5-turbo-16k" + else: + self.model = "gpt-3.5-turbo" + + def _postprocess(self, response: OpenAIResponse) -> TranslatorOuputType: + text_translated: str = str(response.choices[0].message["content"]) + text_translated = strip_string(text=text_translated, first_character=["", ""]) + outputs: TranslatorOuputType = {"text_translated": text_translated} + return outputs + + # 型定義のために必要 + def __call__(self, inputs: TranslatorInputType) -> TranslatorOuputType: + outputs: TranslatorOuputType = super().__call__(inputs) + return outputs diff --git a/project/neollm-tutorial.ipynb b/project/neollm-tutorial.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0c431594052dfb1bbcf3297b6e60bbb275061dfe --- /dev/null +++ b/project/neollm-tutorial.ipynb @@ -0,0 +1,713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# settings\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. install neollm\n", + "\n", + "[Document インストール方法](https://www.notion.so/c760d96f1b4240e6880a32bee96bba35)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# githubのssh接続してね\n", + "# versionは適宜変更してね\n", + "%pip install git+https://github.com/neoAI-inc/neo-llm-module.git@v1.2.6\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2 環境変数の設定方法\n", + "\n", + "[Document env ファイルの作り方](https://www.notion.so/env-32ebb04105684a77bbc730c39865df34)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "環境変数読み込み成功\n" + ] + } + ], + "source": [ + "from dotenv import load_dotenv\n", + "\n", + "env_path = \".env\" # .envのpath 適宜変更\n", + "if load_dotenv(env_path):\n", + " print(\"環境変数読み込み成功\")\n", + "else:\n", + " print(\"path違うよ〜\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# neoLLM 　使い方\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "neollm は、前処理・LLM のリクエスト・後処理を 1 つのクラスにした、Pytorch 的な記法で書ける neoAI の LLM 統一ライブラリ。\n", + "\n", + "大きく 2 種類のクラスがあり、MyLLM は 1 つのリクエスト、MyL3M2 は複数のリクエストを受け持つことができる。\n", + "\n", + "![概観図](../asset/external_view.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### モデルの定義\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[43mWARNING: AZURE_API_BASEではなく、AZURE_OPENAI_ENDPOINTにしてね\u001b[0m\n", + "\u001b[43mWARNING: AZURE_API_VERSIONではなく、OPENAI_API_VERSIONにしてね\u001b[0m\n" + ] + } + ], + "source": [ + "from neollm import MyLLM\n", + "\n", + "# 例: 翻訳をするclass\n", + "# _preprocess, _postprocessを必ず書く\n", + "\n", + "\n", + "class Translator(MyLLM):\n", + " # _preprocessは、前処理をしてMessageを作る関数\n", + " def _preprocess(self, inputs: str):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": \"英語を日本語に翻訳するAIです。\"},\n", + " {\"role\": \"user\", \"content\": inputs},\n", + " ]\n", + " return messages\n", + "\n", + " # _postprocessは、APIのResponseを後処理をして、欲しいものを返す関数\n", + " def _postprocess(self, response):\n", + " text_translated: str = str(response.choices[0].message.content)\n", + " return text_translated" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### モデルの呼び出し\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Translator) ----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "\"Hello, We are neoAI.\"\n", + "\u001b[34m[messages]\u001b[0m\n", + " \u001b[32msystem\u001b[0m\n", + " 英語を日本語に翻訳するAIです。\n", + " \u001b[32muser\u001b[0m\n", + " Hello, We are neoAI.\n", + " \u001b[32massistant\u001b[0m\n", + " こんにちは、私たちはneoAIです。\n", + "\u001b[34m[outputs]\u001b[0m\n", + "\"こんにちは、私たちはneoAIです。\"\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'azure', 'temperature': 1, 'model': 'gpt-3.5-turbo-0613', 'engine': 'neoai-free-swd-gpt-35-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 1.6s; 45(36+9)tokens; $6.8e-05; ¥0.0095\n", + "----------------------------------------------------------------------------------------------------\n", + "こんにちは、私たちはneoAIです。\n" + ] + } + ], + "source": [ + "# 初期化 (platformやmodelなど設定をしておく)\n", + "# 詳細: https://www.notion.so/neollm-MyLLM-581cd7562df9473b91c981d88469c452?pvs=4#ac5361a5e3fa46a48441fdd538858fee\n", + "translator = Translator(\n", + " platform=\"azure\", # azure or openai\n", + " model=\"gpt-3.5-turbo-0613\", # gpt-3.5-turbo-1106, gpt-4-turbo-1106\n", + " llm_settings={\"temperature\": 1}, # llmの設定 dictで渡す\n", + ")\n", + "\n", + "# 呼び出し\n", + "# preprocessでinputsとしたものを入力として、postprocessで処理したものを出力とする。\n", + "translated_text = translator(inputs=\"Hello, We are neoAI.\")\n", + "print(translated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "時間 1.5658628940582275\n", + "token数 TokenInfo(input=36, output=9, total=45)\n", + "token数合計 45\n", + "値段(USD) PriceInfo(input=5.4e-05, output=1.8e-05, total=6.75e-05)\n", + "値段数合計(USD) 6.75e-05\n" + ] + } + ], + "source": [ + "# 処理時間\n", + "print(\"時間\", translator.time)\n", + "# トークン数\n", + "print(\"token数\", translator.token)\n", + "print(\"token数合計\", translator.token.total)\n", + "# 値段の取得\n", + "print(\"値段(USD)\", translator.price)\n", + "print(\"値段数合計(USD)\", translator.price.total)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inputs Hello, We are neoAI.\n", + "messages [{'role': 'system', 'content': '英語を日本語に翻訳するAIです。'}, {'role': 'user', 'content': 'Hello, We are neoAI.'}]\n", + "response ChatCompletion(id='chatcmpl-8T5MkidV9bhqewdzcUwO1PioHOSHi', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='こんにちは、私たちはneoAIです。', role='assistant', function_call=None, tool_calls=None), content_filter_results={'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}})], created=1701942830, model='gpt-35-turbo', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=9, prompt_tokens=36, total_tokens=45), prompt_filter_results=[{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}])\n", + "outputs こんにちは、私たちはneoAIです。\n", + "chat_history [{'role': 'system', 'content': '英語を日本語に翻訳するAIです。'}, {'role': 'user', 'content': 'Hello, We are neoAI.'}, {'content': 'こんにちは、私たちはneoAIです。', 'role': 'assistant'}]\n" + ] + } + ], + "source": [ + "# その他property\n", + "print(\"inputs\", translator.inputs)\n", + "print(\"messages\", translator.messages)\n", + "print(\"response\", translator.response)\n", + "print(\"outputs\", translator.outputs)\n", + "\n", + "print(\"chat_history\", translator.chat_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# neoLLM 　例\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1-1 MyLLM (ex. 翻訳)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from neollm import MyLLM\n", + "from neollm.utils.preprocess import optimize_token\n", + "from neollm.utils.postprocess import strip_string\n", + "\n", + "\n", + "class Translator(MyLLM):\n", + " def _preprocess(self, inputs):\n", + " system_prompt = (\n", + " \"You are a good translator. Translate Japanese into English or English into Japanese.\\n\"\n", + " \"# output_format:\\n\\n{translated text in English or Japanese}\"\n", + " )\n", + " user_prompt = \"\\n\" f\"'''{inputs['text'].strip()}'''\"\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": optimize_token(system_prompt)},\n", + " {\"role\": \"user\", \"content\": optimize_token(user_prompt)},\n", + " ]\n", + " return messages\n", + "\n", + " def _ruleprocess(self, inputs):\n", + " # 例外処理\n", + " if inputs[\"text\"].strip() == \"\":\n", + " return {\"text_translated\": \"\"}\n", + " # APIリクエストを送る場合はNone\n", + " return None\n", + "\n", + " def _update_settings(self):\n", + " # 入力によってAPIの設定を変更する\n", + "\n", + " # トークン数: self.llm.count_tokens(self.messsage)\n", + "\n", + " # モデル変更: self.model = \"gpt-3.5-turbo-16k\"\n", + "\n", + " # パラメータ変更: self.llm_settings = {\"temperature\": 0.2}\n", + "\n", + " # 入力が多い時に16kを使う(1106の場合はやらなくていい)\n", + " if self.messages is not None:\n", + " if self.llm.count_tokens(self.messages) >= 1600:\n", + " self.model = \"gpt-3.5-turbo-16k-0613\"\n", + " else:\n", + " self.model = \"gpt-3.5-turbo-0613\"\n", + "\n", + " def _postprocess(self, response):\n", + " text_translated: str = str(response.choices[0].message.content)\n", + " text_translated = strip_string(text=text_translated, first_character=[\"\", \"\"])\n", + " outputs = {\"text_translated\": text_translated}\n", + " return outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Translator) ----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "{\n", + " \"text\": \"大規模LLMモデル\"\n", + "}\n", + "\u001b[34m[messages]\u001b[0m\n", + " \u001b[32msystem\u001b[0m\n", + " You are a good translator. Translate Japanese into English or English into Japanese.\n", + " # output_format:\n", + " \n", + " {translated text in English or Japanese}\n", + " \u001b[32muser\u001b[0m\n", + " \n", + " '''大規模LLMモデル'''\n", + " \u001b[32massistant\u001b[0m\n", + " \n", + " \"Large-Scale LLM Model\"\n", + "\u001b[34m[outputs]\u001b[0m\n", + "{\n", + " \"text_translated\": \"Large-Scale LLM Model\"\n", + "}\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'azure', 'temperature': 1, 'model': 'gpt-3.5-turbo-0613', 'engine': 'neoai-free-swd-gpt-35-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 1.5s; 66(55+11)tokens; $9.9e-05; ¥0.014\n", + "----------------------------------------------------------------------------------------------------\n", + "{'text_translated': 'Large-Scale LLM Model'}\n" + ] + } + ], + "source": [ + "translator = Translator(\n", + " llm_settings={\"temperature\": 1}, # defaultは、{\"temperature\": 0}\n", + " model=\"gpt-3.5-turbo-0613\", # defaultは、DEFAULT_MODEL_NAME\n", + " platform=\"azure\", # defaultは、LLM_PLATFORM\n", + " verbose=True,\n", + " silent_list=[], # 表示しないもの\n", + ")\n", + "output_1 = translator(inputs={\"text\": \"大規模LLMモデル\"})\n", + "print(output_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[43mWARNING: model_nameに日付を指定してください\u001b[0m\n", + "model_name: gpt-3.5-turbo -> gpt-3.5-turbo-0613\n", + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Translator) ----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "{\n", + " \"text\": \"Large LLM Model\"\n", + "}\n", + "\u001b[34m[messages]\u001b[0m\n", + " \u001b[32msystem\u001b[0m\n", + " You are a good translator. Translate Japanese into English or English into Japanese.\n", + " # output_format:\n", + " \n", + " {translated text in English or Japanese}\n", + " \u001b[32muser\u001b[0m\n", + " \n", + " '''Large LLM Model'''\n", + "\u001b[43mWARNING: model_nameに日付を指定してください\u001b[0m\n", + "model_name: gpt-3.5-turbo -> gpt-3.5-turbo-0613\n", + " \u001b[32massistant\u001b[0m\n", + " \n", + " 大きなLLMモデル\n", + "\u001b[34m[outputs]\u001b[0m\n", + "{\n", + " \"text_translated\": \"大きなLLMモデル\"\n", + "}\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'openai', 'temperature': 0, 'model': 'gpt-3.5-turbo-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 0.9s; 61(49+12)tokens; $9.2e-05; ¥0.013\n", + "----------------------------------------------------------------------------------------------------\n", + "{'text_translated': '大きなLLMモデル'}\n" + ] + } + ], + "source": [ + "translator = Translator(\n", + " platform=\"openai\", # <- 変えてみる\n", + " verbose=True,\n", + ")\n", + "output_1 = translator(inputs={\"text\": \"Large LLM Model\"})\n", + "print(output_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[43mWARNING: model_nameに日付を指定してください\u001b[0m\n", + "model_name: gpt-3.5-turbo -> gpt-3.5-turbo-0613\n", + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Translator) ----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "{\n", + " \"text\": \"\"\n", + "}\n", + "\u001b[34m[outputs]\u001b[0m\n", + "{\n", + " \"text_translated\": \"\"\n", + "}\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'azure', 'temperature': 0, 'model': 'gpt-3.5-turbo-0613', 'engine': 'neoai-free-swd-gpt-35-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 0.0s; 0(0+0)tokens; $0; ¥0\n", + "----------------------------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "{'text_translated': ''}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ルールベースが起動\n", + "data = {\"text\": \"\"}\n", + "translator = Translator(verbose=True)\n", + "translator(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[43mWARNING: model_nameに日付を指定してください\u001b[0m\n", + "model_name: gpt-3.5-turbo -> gpt-3.5-turbo-0613\n", + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Translator) ----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "{\n", + " \"text\": \"こんにちは!!\\nこんにちは?こんにちは?\"\n", + "}\n", + "\u001b[34m[messages]\u001b[0m\n", + " \u001b[32msystem\u001b[0m\n", + " You are a good translator. Translate Japanese into English or English into Japanese.\n", + " # output_format:\n", + " \n", + " {translated text in English or Japanese}\n", + " \u001b[32muser\u001b[0m\n", + " \n", + " '''こんにちは!!\n", + " こんにちは?こんにちは?'''\n", + "\u001b[43mWARNING: model_nameに日付を指定してください\u001b[0m\n", + "model_name: gpt-3.5-turbo -> gpt-3.5-turbo-0613\n", + " \u001b[32massistant\u001b[0m\n", + " \n", + " Hello!!\n", + " Hello? Hello?\n", + "\u001b[34m[outputs]\u001b[0m\n", + "{\n", + " \"text_translated\": \"Hello!!\\nHello? Hello?\"\n", + "}\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'azure', 'temperature': 0, 'model': 'gpt-3.5-turbo-0613', 'engine': 'neoai-free-swd-gpt-35-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 1.4s; 60(51+9)tokens; $9e-05; ¥0.013\n", + "----------------------------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "{'text_translated': 'Hello!!\\nHello? Hello?'}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = {\"text\": \"こんにちは!!\\nこんにちは?こんにちは?\"}\n", + "translator = Translator(verbose=True)\n", + "translator(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 情報抽出\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from neollm import MyLLM\n", + "from neollm.utils.preprocess import optimize_token, dict2json\n", + "from neollm.utils.postprocess import json2dict\n", + "\n", + "\n", + "class Extractor(MyLLM):\n", + " def _preprocess(self, inputs):\n", + " system_prompt = \"から、にしたがって、情報を抽出しなさい。\"\n", + " output_format = {\"date\": \"yy-mm-dd形式 日付\", \"event\": \"起きたことを簡潔に。\"}\n", + " user_prompt = (\n", + " \"\\n\"\n", + " \"```\\n\"\n", + " f\"{inputs['info'].strip()}\\n\"\n", + " \"```\\n\"\n", + " \"\\n\"\n", + " \"\\n\"\n", + " \"```json\\n\"\n", + " f\"{dict2json(output_format)}\\n\"\n", + " \"```\"\n", + " )\n", + "\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": optimize_token(system_prompt)},\n", + " {\"role\": \"user\", \"content\": optimize_token(user_prompt)},\n", + " ]\n", + " return messages\n", + "\n", + " def _ruleprocess(self, inputs):\n", + " # 例外処理\n", + " if inputs[\"info\"].strip() == \"\":\n", + " return {\"date\": \"\", \"event\": \"\"}\n", + " # APIリクエストを送る場合はNone\n", + " return None\n", + "\n", + " def _postprocess(self, response):\n", + " return json2dict(response.choices[0].message.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Extractor) -----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "{\n", + " \"info\": \"2021年6月13日に、neoAIのサービスが始まりました。\"\n", + "}\n", + "\u001b[34m[messages]\u001b[0m\n", + " \u001b[32msystem\u001b[0m\n", + " から、にしたがって、情報を抽出しなさい。\n", + " \u001b[32muser\u001b[0m\n", + " \n", + " ```\n", + " 2021年6月13日に、neoAIのサービスが始まりました。\n", + " ```\n", + " \n", + " \n", + " ```json\n", + " {\n", + " \"date\": \"yy-mm-dd形式 日付\",\n", + " \"event\": \"起きたことを簡潔に。\"\n", + " }\n", + " ```\n", + " \u001b[32massistant\u001b[0m\n", + " ```json\n", + " {\n", + " \"date\": \"2021-06-13\",\n", + " \"event\": \"neoAIのサービスが始まりました。\"\n", + " }\n", + " ```\n", + "\u001b[34m[outputs]\u001b[0m\n", + "{\n", + " \"date\": \"2021-06-13\",\n", + " \"event\": \"neoAIのサービスが始まりました。\"\n", + "}\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'azure', 'temperature': 0, 'model': 'gpt-3.5-turbo-0613', 'engine': 'neoai-free-swd-gpt-35-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 1.6s; 143(106+37)tokens; $0.00021; ¥0.03\n", + "----------------------------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "{'date': '2021-06-13', 'event': 'neoAIのサービスが始まりました。'}" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "extractor = Extractor(model=\"gpt-3.5-turbo-0613\")\n", + "\n", + "extractor(inputs={\"info\": \"2021年6月13日に、neoAIのサービスが始まりました。\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[41mPARENT\u001b[0m\n", + "MyLLM(Extractor) -----------------------------------------------------------------------------------\n", + "\u001b[34m[inputs]\u001b[0m\n", + "{\n", + " \"info\": \"1998年4月1日に、neoAI大学が設立されました。\"\n", + "}\n", + "\u001b[34m[messages]\u001b[0m\n", + " \u001b[32msystem\u001b[0m\n", + " から、にしたがって、情報を抽出しなさい。\n", + " \u001b[32muser\u001b[0m\n", + " \n", + " ```\n", + " 1998年4月1日に、neoAI大学が設立されました。\n", + " ```\n", + " \n", + " \n", + " ```json\n", + " {\n", + " \"date\": \"yy-mm-dd形式 日付\",\n", + " \"event\": \"起きたことを簡潔に。\"\n", + " }\n", + " ```\n", + " \u001b[32massistant\u001b[0m\n", + " \n", + " ```json\n", + " {\n", + " \"date\": \"1998-04-01\",\n", + " \"event\": \"neoAI大学の設立\"\n", + " }\n", + " ```\n", + "\u001b[34m[outputs]\u001b[0m\n", + "{\n", + " \"date\": \"1998-04-01\",\n", + " \"event\": \"neoAI大学の設立\"\n", + "}\n", + "\u001b[34m[client_settings]\u001b[0m -\n", + "\u001b[34m[llm_settings]\u001b[0m {'platform': 'azure', 'temperature': 0, 'model': 'gpt-3.5-turbo-0613', 'engine': 'neoai-free-swd-gpt-35-0613'}\n", + "\u001b[34m[metadata]\u001b[0m 1.6s; 139(104+35)tokens; $0.00021; ¥0.029\n", + "----------------------------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "{'date': '1998-04-01', 'event': 'neoAI大学の設立'}" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "extractor = Extractor(model=\"gpt-3.5-turbo-0613\")\n", + "\n", + "extractor(inputs={\"info\": \"1998年4月1日に、neoAI大学が設立されました。\"})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b1f690006203b2902b11d9e0239af432701d18eb --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,81 @@ +[tool.poetry] +name = "neollm" +version = "1.3.3" +description = "neo LLM Module for Python 3.10" +authors = ["KoshiroTerasawa "] +readme = "README.md" +packages = [{ include = "neollm" }] + +[tool.poetry.dependencies] +python = "^3.10" +python-dotenv = "^1.0.0" +pydantic = "^2.4.2" +openai = "^1.1.1" +google-cloud-aiplatform = "^1.48.0" +anthropic = { version = "^0.18.1", extras = ["vertex"] } +typing-extensions = "^4.8.0" +google-generativeai = "0.5.2" +tiktoken = "0.7.0" + + +[tool.poetry.group.dev.dependencies] +isort = "^5.12.0" +black = "24.3.0" +mypy = "^1.8.0" +pyproject-flake8 = "^6.1.0" +ipykernel = "^6.26.0" +jupyter = "^1.0.0" +jupyter-client = "^8.6.0" +pytest = "^8.1.1" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 119 +exclude = ''' +/( + \venv + | \.git + | \.hg + | __pycache__ + | \.mypy_cache +)/ +''' + +[tool.isort] +profile = "black" +multi_line_output = 3 + +[tool.flake8] +max-line-length = 119 +extend-ignore = ["E203", "W503", "E501", "E704"] +exclude = [".venv", ".git", "__pycache__", ".mypy_cache", ".hg"] +max-complexity = 15 + +[tool.mypy] +ignore_missing_imports = true +# follow_imports = normal +disallow_any_unimported = false +disallow_any_expr = false # 式でのAny禁止 +disallow_any_decorated = false +disallow_any_explicit = false # 変数でAny禁止 +disallow_any_generics = true # ジェネリックで書かないの禁止 +disallow_subclassing_any = true # Anyのサブクラス禁止 + +disallow_untyped_calls = true # 型なし関数呼び出し禁止 `a: int = f()` +disallow_untyped_defs = true # 型なし関数定義禁止 `def f(a: int) -> int` +disallow_incomplete_defs = true # 一部の型定義を禁止 `def f(a: int, b)` +check_untyped_defs = true +disallow_untyped_decorators = true +no_implicit_optional = true + +warn_redundant_casts = true +warn_unused_ignores = true +warn_return_any = true +warn_unreachable = true # 辿りつかないコードの検出 +allow_redefinition = false # 変数の再定義を禁止 + +show_error_context = true +show_column_numbers = true diff --git a/test/llm/claude/test_claude_llm.py b/test/llm/claude/test_claude_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..d8faa7fa7d117d7ce10ccc76b283201ce85f0e38 --- /dev/null +++ b/test/llm/claude/test_claude_llm.py @@ -0,0 +1,37 @@ +# from neollm.llm.gpt.azure_llm import ( +# AzureGPT4_0613, +# AzureGPT4T_0125, +# AzureGPT4T_1106, +# AzureGPT4T_20240409, +# AzureGPT4VT_1106, +# AzureGPT35FT, +# AzureGPT35T16k_0613, +# AzureGPT35T_0125, +# AzureGPT35T_0613, +# AzureGPT35T_1106, +# AzureGPT432k_0613, +# ) +# from neollm.types.info import APIPricing + + +# def test_check_price() -> None: +# # https://azure.microsoft.com/ja-jp/pricing/details/cognitive-services/openai-service/ + +# # これからのモデル -------------------------------------------------------- +# assert AzureGPT4T_20240409.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# # Updated -------------------------------------------------------- +# # GPT3.5T +# assert AzureGPT35T_0125.dollar_per_ktoken == APIPricing(input=0.0005, output=0.0015) +# # GPT4 +# assert AzureGPT4T_0125.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# assert AzureGPT4VT_1106.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# assert AzureGPT4T_1106.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# assert AzureGPT4_0613.dollar_per_ktoken == APIPricing(input=0.03, output=0.06) +# assert AzureGPT432k_0613.dollar_per_ktoken == APIPricing(input=0.06, output=0.12) +# # FT +# assert AzureGPT35FT.dollar_per_ktoken == APIPricing(input=0.0005, output=0.0015) +# # Legacy --------------------------------------------------------- +# # AzureGPT35T_0301 なし +# assert AzureGPT35T_0613.dollar_per_ktoken == APIPricing(input=0.0015, output=0.002) +# assert AzureGPT35T16k_0613.dollar_per_ktoken == APIPricing(input=0.003, output=0.004) +# assert AzureGPT35T_1106.dollar_per_ktoken == APIPricing(input=0.001, output=0.002) diff --git a/test/llm/gpt/test_azure_llm.py b/test/llm/gpt/test_azure_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..5517a6157812aff7f2aae3d88f99ec7cb259f9f1 --- /dev/null +++ b/test/llm/gpt/test_azure_llm.py @@ -0,0 +1,92 @@ +from neollm.llm.gpt.azure_llm import ( + AzureGPT4_0613, + AzureGPT4O_20240513, + AzureGPT4T_0125, + AzureGPT4T_1106, + AzureGPT4T_20240409, + AzureGPT4VT_1106, + AzureGPT35FT, + AzureGPT35T16k_0613, + AzureGPT35T_0125, + AzureGPT35T_0613, + AzureGPT35T_1106, + AzureGPT432k_0613, + get_azure_llm, +) +from neollm.types.info import APIPricing + + +def test_get_azure_llm() -> None: + + # no date + assert get_azure_llm("gpt-3.5-turbo", {}).__class__ == AzureGPT35T_0613 + assert get_azure_llm("gpt-35-turbo", {}).__class__ == AzureGPT35T_0613 + assert get_azure_llm("gpt-3.5-turbo-16k", {}).__class__ == AzureGPT35T16k_0613 + assert get_azure_llm("gpt-35-turbo-16k", {}).__class__ == AzureGPT35T16k_0613 + assert get_azure_llm("gpt-4", {}).__class__ == AzureGPT4_0613 + assert get_azure_llm("gpt-4-32k", {}).__class__ == AzureGPT432k_0613 + assert get_azure_llm("gpt-4-turbo", {}).__class__ == AzureGPT4T_1106 + assert get_azure_llm("gpt-4v-turbo", {}).__class__ == AzureGPT4VT_1106 + assert get_azure_llm("gpt-4o", {}).__class__ == AzureGPT4O_20240513 + # with date + assert get_azure_llm("gpt-4o-2024-05-13", {}).__class__ == AzureGPT4O_20240513 + assert get_azure_llm("gpt-4-turbo-2024-04-09", {}).__class__ == AzureGPT4T_20240409 + assert get_azure_llm("gpt-3.5-turbo-0125", {}).__class__ == AzureGPT35T_0125 + assert get_azure_llm("gpt-35-turbo-0125", {}).__class__ == AzureGPT35T_0125 + assert get_azure_llm("gpt-4-turbo-0125", {}).__class__ == AzureGPT4T_0125 + assert get_azure_llm("gpt-3.5-turbo-1106", {}).__class__ == AzureGPT35T_1106 + assert get_azure_llm("gpt-35-turbo-1106", {}).__class__ == AzureGPT35T_1106 + assert get_azure_llm("gpt-4-turbo-1106", {}).__class__ == AzureGPT4T_1106 + assert get_azure_llm("gpt-4v-turbo-1106", {}).__class__ == AzureGPT4VT_1106 + assert get_azure_llm("gpt-3.5-turbo-0613", {}).__class__ == AzureGPT35T_0613 + assert get_azure_llm("gpt-35-turbo-0613", {}).__class__ == AzureGPT35T_0613 + assert get_azure_llm("gpt-3.5-turbo-16k-0613", {}).__class__ == AzureGPT35T16k_0613 + assert get_azure_llm("gpt-35-turbo-16k-0613", {}).__class__ == AzureGPT35T16k_0613 + assert get_azure_llm("gpt-4-0613", {}).__class__ == AzureGPT4_0613 + assert get_azure_llm("gpt-4-32k-0613", {}).__class__ == AzureGPT432k_0613 + # ft + assert get_azure_llm("ft:gpt-3.5-turbo-1106-XXXX", {}).__class__ == AzureGPT35FT + + +def test_check_price() -> None: + # https://azure.microsoft.com/ja-jp/pricing/details/cognitive-services/openai-service/ + + # これからのモデル -------------------------------------------------------- + assert AzureGPT4T_20240409.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) + # Updated -------------------------------------------------------- + # GPT3.5T + assert AzureGPT35T_0125.dollar_per_ktoken == APIPricing(input=0.0005, output=0.0015) + # GPT4 + assert AzureGPT4O_20240513.dollar_per_ktoken == APIPricing(input=0.005, output=0.015) + assert AzureGPT4T_0125.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) + assert AzureGPT4VT_1106.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) + assert AzureGPT4T_1106.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) + assert AzureGPT4_0613.dollar_per_ktoken == APIPricing(input=0.03, output=0.06) + assert AzureGPT432k_0613.dollar_per_ktoken == APIPricing(input=0.06, output=0.12) + # FT + assert AzureGPT35FT.dollar_per_ktoken == APIPricing(input=0.0005, output=0.0015) + # Legacy --------------------------------------------------------- + # AzureGPT35T_0301 なし + assert AzureGPT35T_0613.dollar_per_ktoken == APIPricing(input=0.0015, output=0.002) + assert AzureGPT35T16k_0613.dollar_per_ktoken == APIPricing(input=0.003, output=0.004) + assert AzureGPT35T_1106.dollar_per_ktoken == APIPricing(input=0.001, output=0.002) + + +def test_check_context_window() -> None: + # https://learn.microsoft.com/ja-jp/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-preview-models + assert AzureGPT4T_20240409.context_window == 128_000 + + assert AzureGPT4T_0125.context_window == 128_000 + assert AzureGPT35T_0125.context_window == 16_385 + + assert AzureGPT4O_20240513.context_window == 128_000 + assert AzureGPT4T_1106.context_window == 128_000 + assert AzureGPT4VT_1106.context_window == 128_000 + assert AzureGPT35T_1106.context_window == 16_385 + + assert AzureGPT35T_0613.context_window == 4_096 + assert AzureGPT4_0613.context_window == 8_192 + assert AzureGPT35T16k_0613.context_window == 16_385 + assert AzureGPT432k_0613.context_window == 32_768 + + assert AzureGPT35FT.context_window == 4_096 diff --git a/test/llm/gpt/test_openai_llm.py b/test/llm/gpt/test_openai_llm.py new file mode 100644 index 0000000000000000000000000000000000000000..d8faa7fa7d117d7ce10ccc76b283201ce85f0e38 --- /dev/null +++ b/test/llm/gpt/test_openai_llm.py @@ -0,0 +1,37 @@ +# from neollm.llm.gpt.azure_llm import ( +# AzureGPT4_0613, +# AzureGPT4T_0125, +# AzureGPT4T_1106, +# AzureGPT4T_20240409, +# AzureGPT4VT_1106, +# AzureGPT35FT, +# AzureGPT35T16k_0613, +# AzureGPT35T_0125, +# AzureGPT35T_0613, +# AzureGPT35T_1106, +# AzureGPT432k_0613, +# ) +# from neollm.types.info import APIPricing + + +# def test_check_price() -> None: +# # https://azure.microsoft.com/ja-jp/pricing/details/cognitive-services/openai-service/ + +# # これからのモデル -------------------------------------------------------- +# assert AzureGPT4T_20240409.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# # Updated -------------------------------------------------------- +# # GPT3.5T +# assert AzureGPT35T_0125.dollar_per_ktoken == APIPricing(input=0.0005, output=0.0015) +# # GPT4 +# assert AzureGPT4T_0125.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# assert AzureGPT4VT_1106.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# assert AzureGPT4T_1106.dollar_per_ktoken == APIPricing(input=0.01, output=0.03) +# assert AzureGPT4_0613.dollar_per_ktoken == APIPricing(input=0.03, output=0.06) +# assert AzureGPT432k_0613.dollar_per_ktoken == APIPricing(input=0.06, output=0.12) +# # FT +# assert AzureGPT35FT.dollar_per_ktoken == APIPricing(input=0.0005, output=0.0015) +# # Legacy --------------------------------------------------------- +# # AzureGPT35T_0301 なし +# assert AzureGPT35T_0613.dollar_per_ktoken == APIPricing(input=0.0015, output=0.002) +# assert AzureGPT35T16k_0613.dollar_per_ktoken == APIPricing(input=0.003, output=0.004) +# assert AzureGPT35T_1106.dollar_per_ktoken == APIPricing(input=0.001, output=0.002) diff --git a/test/llm/platform.py b/test/llm/platform.py new file mode 100644 index 0000000000000000000000000000000000000000..3024a1a79d6776d028d59563c6ab4cf0a11900ed --- /dev/null +++ b/test/llm/platform.py @@ -0,0 +1,32 @@ +import pytest + +from neollm.llm.platform import Platform + + +class TestPlatform: + def test_str(self) -> None: + assert Platform.AZURE == "azure" # type: ignore + assert Platform.OPENAI == "openai" # type: ignore + assert Platform.ANTHROPIC == "anthropic" # type: ignore + assert Platform.GCP == "gcp" # type: ignore + + def test_init(self) -> None: + assert Platform("azure") == Platform.AZURE + assert Platform("openai") == Platform.OPENAI + assert Platform("anthropic") == Platform.ANTHROPIC + assert Platform("gcp") == Platform.GCP + + assert Platform("Azure ") == Platform.AZURE + assert Platform(" OpenAI") == Platform.OPENAI + assert Platform("Anthropic ") == Platform.ANTHROPIC + assert Platform("GcP") == Platform.GCP + + def test_from_string(self) -> None: + assert Platform.from_string("azure") == Platform.AZURE + assert Platform.from_string("openai") == Platform.OPENAI + assert Platform.from_string("anthropic") == Platform.ANTHROPIC + assert Platform.from_string("gcp") == Platform.GCP + + def test_from_string_error(self) -> None: + with pytest.raises(ValueError): + Platform.from_string("error") diff --git a/test/myllm/test_neollm.py b/test/myllm/test_neollm.py new file mode 100644 index 0000000000000000000000000000000000000000..5057278f91b03fc708015691fb887f95cf48833d --- /dev/null +++ b/test/myllm/test_neollm.py @@ -0,0 +1,116 @@ +import pytest # noqa F401 +from dotenv import load_dotenv + +from neollm import MyLLM +from neollm.types import Messages, Response +from neollm.utils.inference import execute_parallel + +env_file_path = "project/.env" +assert load_dotenv(env_file_path, override=True) + + +class SampleMyLLM(MyLLM[str, str]): + def _preprocess(self, inputs: str) -> Messages: + messages: Messages = [ + {"role": "system", "content": ("1+1={int}")}, + {"role": "user", "content": str(inputs) + "\n{int}"}, + ] + return messages + + def _postprocess(self, response: Response) -> str: + return response.choices[0].message.content or "" + + +class TestMyLLM: + def call_myllm(self, model: str, platform: str) -> None: + myllm = SampleMyLLM(model=model, platform=platform, llm_settings={"max_tokens": 1}) + myllm("1+1") + + def call_stream_myllm(self, model: str, platform: str) -> None: + myllm = SampleMyLLM(model=model, platform=platform) + myllm.call_stream("1+1") + + def test_azure(self) -> None: + kwargs = [ + {"model": model, "platform": "azure"} + for model in [ + # "gpt-4o-2024-05-13", # 2024/05/15時点、AOAIで使用不可（リージョン不明） + # "gpt-4-turbo-2024-04-09", # スウェーデンにない + # "gpt-3.5-turbo-0125",# スウェーデンにない + # "gpt-4-turbo-0125",# スウェーデンにない + "gpt-3.5-turbo-1106", + "gpt-4-turbo-1106", + # "gpt-4v-turbo-1106", # スウェーデンにない + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0613", + "gpt-4-32k-0613", + # w/o date + "gpt-4-turbo", + "gpt-3.5-turbo", + # "gpt-4v-turbo", # スウェーデンにない + "gpt-3.5-turbo-16k", + "gpt-4", + "gpt-4-32k", + ] + ] + execute_parallel(self.call_myllm, kwargs, max_workers=10) + execute_parallel(self.call_stream_myllm, kwargs, max_workers=10) + + def test_openai(self) -> None: + kwargs = [ + {"model": model, "platform": "openai"} + for model in [ + "gpt-4o-2024-05-13", + "gpt-4-turbo-2024-04-09", + "gpt-3.5-turbo-0125", + "gpt-4-turbo-0125", + "gpt-3.5-turbo-1106", + "gpt-4-turbo-1106", + "gpt-4v-turbo-1106", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0613", + # "gpt-4-32k-0613", #　使えなくなってる + # w/o date + "gpt-4-turbo", + "gpt-3.5-turbo", + "gpt-4v-turbo", + "gpt-3.5-turbo-16k", + "gpt-4", + # "gpt-4-32k", #　使えなくなってる + ] + ] + execute_parallel(self.call_myllm, kwargs, max_workers=10) + execute_parallel(self.call_stream_myllm, kwargs, max_workers=10) + + def test_anthropic(self) -> None: + kwargs = [ + {"model": model, "platform": "anthropic"} + for model in [ + "claude-3-opus", + "claude-3-sonnet", + "claude-3-haiku", + "claude-3-opus-20240229", + "claude-3-sonnet-20240229", + "claude-3-haiku-20240307", + ] + ] + execute_parallel(self.call_myllm, kwargs, max_workers=10) + execute_parallel(self.call_stream_myllm, kwargs, max_workers=10) + + # TODO! google + def _test_gcp(self) -> None: + kwargs = [ + {"model": model, "platform": "gcp"} + for model in [ + "claude-3-opus", + "claude-3-sonnet", + "claude-3-haiku", + "claude-3-opus@20240229", + "claude-3-sonnet@20240229", + "claude-3-haiku@20240307", + ] + ] + execute_parallel(self.call_myllm, kwargs, max_workers=10) + execute_parallel(self.call_stream_myllm, kwargs, max_workers=10) diff --git a/test/types/test_info.py b/test/types/test_info.py new file mode 100644 index 0000000000000000000000000000000000000000..abaaca523e6ff183436a73a9ac4dc66b9676ed25 --- /dev/null +++ b/test/types/test_info.py @@ -0,0 +1,82 @@ +import pytest # noqa F401 + +from neollm.types import APIPricing, PriceInfo, TimeInfo, TokenInfo + + +class TestTimeInfo: + def test_init_default(self): + time_info = TimeInfo() + assert time_info.total == 0.0 + assert time_info.preprocess == 0.0 + assert time_info.main == 0.0 + assert time_info.postprocess == 0.0 + + def test_init(self): + time_info = TimeInfo(total=1.0, preprocess=0.5, main=0.3, postprocess=0.2) + assert time_info.total == 1.0 + assert time_info.preprocess == 0.5 + assert time_info.main == 0.3 + assert time_info.postprocess == 0.2 + + def test_repr(self): + time_info = TimeInfo(total=1.000001, preprocess=0.5, main=0.3, postprocess=0.2) + assert repr(time_info) == "TimeInfo(total=1.000, preprocess=0.500, main=0.300, postprocess=0.200)" + + +class TestTokenInfo: + def test_init(self): + token_info = TokenInfo(input=100, output=200, total=300) + assert token_info.input == 100 + assert token_info.output == 200 + assert token_info.total == 300 + + def test_add(self): + token_info1 = TokenInfo(input=100, output=200, total=300) + token_info2 = TokenInfo(input=10, output=20, total=30) + token_info3 = token_info1 + token_info2 + assert token_info3.input == 110 + assert token_info3.output == 220 + assert token_info3.total == 330 + + def test_iadd(self): + token_info1 = TokenInfo(input=100, output=200, total=300) + token_info2 = TokenInfo(input=10, output=20, total=30) + token_info1 += token_info2 + assert token_info1.input == 110 + assert token_info1.output == 220 + assert token_info1.total == 330 + + +class TestPriceInfo: + def test_init(self): + price_info = PriceInfo(input=0.001, output=0.002, total=0.003) + assert price_info.input == 0.001 + assert price_info.output == 0.002 + assert price_info.total == 0.003 + + def test_add(self): + price_info1 = PriceInfo(input=0.001, output=0.002, total=0.003) + price_info2 = PriceInfo(input=0.0001, output=0.0002, total=0.0003) + price_info3 = price_info1 + price_info2 + assert price_info3.input == 0.0011 + assert price_info3.output == 0.0022 + assert price_info3.total == 0.0033 + + def test_iadd(self): + price_info1 = PriceInfo(input=0.001, output=0.002, total=0.003) + price_info2 = PriceInfo(input=0.0001, output=0.0002, total=0.0003) + price_info1 += price_info2 + assert price_info1.input == 0.0011 + assert price_info1.output == 0.0022 + assert price_info1.total == 0.0033 + + def test_repr(self): + price_info = PriceInfo(input=0.0010001, output=0.002, total=0.003) + assert repr(price_info) == "PriceInfo(input=0.001, output=0.002, total=0.003)" + + +class TestAPIPricing: + def test_init(self): + api_pricing = APIPricing(input=0.0010, output=0.0020) + assert api_pricing.input == 0.0010 + assert api_pricing.output == 0.0020 diff --git a/test/types/test_model.py b/test/types/test_model.py new file mode 100644 index 0000000000000000000000000000000000000000..e82d36669e60852da01c4ff80fd9432d2afbbc54 --- /dev/null +++ b/test/types/test_model.py @@ -0,0 +1,16 @@ +import pytest # noqa F401 + +from neollm.types._model import DictableBaseModel + + +class TestDictableBaseModel: + def test_getitem(self): + class TestModel(DictableBaseModel): + a: int + b: str + + model = TestModel(a=1, b="test") + assert model["a"] == 1 + assert model["b"] == "test" + assert model.a == 1 + assert model.b == "test" diff --git a/test/utils/test_preprocess.py b/test/utils/test_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..9a3473af5c2f438caec14506196b84955829cc27 --- /dev/null +++ b/test/utils/test_preprocess.py @@ -0,0 +1,12 @@ +import pytest # noqa E402 + +from neollm.utils.preprocess import dict2json, optimize_token + + +def test_dict2json() -> None: + assert dict2json({"a": 1, "b": 2.1}) == '{\n "a": 1,\n "b": 2.1\n}' + assert dict2json({"あいうえお": False, "漢字": None}) == '{\n "あいうえお": false,\n "漢字": null\n}' + + +def test_optimize_token() -> None: + assert optimize_token("\na\n\n\nb \n") == "a\n\nb"