mirror of
https://github.com/OpenBMB/ChatDev.git
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130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
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# Licensed under the Apache License, Version 2.0 (the “License”);
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an “AS IS” BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
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from abc import ABC, abstractmethod
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from typing import Any, Dict
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import openai
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import tiktoken
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from retry import retry
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from camel.typing import ModelType
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from chatdev.utils import log_and_print_online
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class ModelBackend(ABC):
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r"""Base class for different model backends.
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May be OpenAI API, a local LLM, a stub for unit tests, etc."""
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@abstractmethod
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def run(self, *args, **kwargs) -> Dict[str, Any]:
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r"""Runs the query to the backend model.
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Raises:
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RuntimeError: if the return value from OpenAI API
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is not a dict that is expected.
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Returns:
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Dict[str, Any]: All backends must return a dict in OpenAI format.
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"""
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pass
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class OpenAIModel(ModelBackend):
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r"""OpenAI API in a unified ModelBackend interface."""
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def __init__(self, model_type: ModelType, model_config_dict: Dict) -> None:
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super().__init__()
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self.model_type = model_type
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self.model_config_dict = model_config_dict
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@retry(tries=-1, delay=0, max_delay=None, backoff=1, jitter=0)
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def run(self, *args, **kwargs) -> Dict[str, Any]:
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string = "\n".join([message["content"] for message in kwargs["messages"]])
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encoding = tiktoken.encoding_for_model(self.model_type.value)
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num_prompt_tokens = len(encoding.encode(string))
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gap_between_send_receive = 15 * len(kwargs["messages"])
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num_prompt_tokens += gap_between_send_receive
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num_max_token_map = {
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"gpt-3.5-turbo": 4096,
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"gpt-3.5-turbo-16k": 16384,
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"gpt-3.5-turbo-0613": 4096,
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"gpt-3.5-turbo-16k-0613": 16384,
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"gpt-4": 8192,
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"gpt-4-0613": 8192,
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"gpt-4-32k": 32768,
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}
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num_max_token = num_max_token_map[self.model_type.value]
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num_max_completion_tokens = num_max_token - num_prompt_tokens
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self.model_config_dict['max_tokens'] = num_max_completion_tokens
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response = openai.ChatCompletion.create(*args, **kwargs,
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model=self.model_type.value,
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**self.model_config_dict)
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log_and_print_online(
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"**[OpenAI_Usage_Info Receive]**\nprompt_tokens: {}\ncompletion_tokens: {}\ntotal_tokens: {}\n".format(
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response["usage"]["prompt_tokens"], response["usage"]["completion_tokens"],
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response["usage"]["total_tokens"]))
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if not isinstance(response, Dict):
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raise RuntimeError("Unexpected return from OpenAI API")
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return response
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class StubModel(ModelBackend):
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r"""A dummy model used for unit tests."""
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def __init__(self, *args, **kwargs) -> None:
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super().__init__()
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def run(self, *args, **kwargs) -> Dict[str, Any]:
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ARBITRARY_STRING = "Lorem Ipsum"
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return dict(
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id="stub_model_id",
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usage=dict(),
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choices=[
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dict(finish_reason="stop",
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message=dict(content=ARBITRARY_STRING, role="assistant"))
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],
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)
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class ModelFactory:
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r"""Factory of backend models.
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Raises:
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ValueError: in case the provided model type is unknown.
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"""
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@staticmethod
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def create(model_type: ModelType, model_config_dict: Dict) -> ModelBackend:
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default_model_type = ModelType.GPT_3_5_TURBO
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if model_type in {
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ModelType.GPT_3_5_TURBO, ModelType.GPT_4, ModelType.GPT_4_32k,
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None
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}:
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model_class = OpenAIModel
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elif model_type == ModelType.STUB:
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model_class = StubModel
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else:
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raise ValueError("Unknown model")
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if model_type is None:
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model_type = default_model_type
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# log_and_print_online("Model Type: {}".format(model_type))
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inst = model_class(model_type, model_config_dict)
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return inst
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