mirror of
https://github.com/aljazceru/Auto-GPT.git
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2619740daa
* Extract retry logic, unify embedding functions * Add some docstrings * Remove embedding creation from API manager * Add test suite for retry handler * Make api manager fixture * Fix typing * Streamline tests
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
from typing import List
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import openai
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from autogpt.config import Config
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from autogpt.logs import logger
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from autogpt.modelsinfo import COSTS
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cfg = Config()
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openai.api_key = cfg.openai_api_key
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print_total_cost = cfg.debug_mode
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class ApiManager:
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def __init__(self, debug=False):
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self.total_prompt_tokens = 0
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self.total_completion_tokens = 0
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self.total_cost = 0
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self.total_budget = 0
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self.debug = debug
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def reset(self):
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self.total_prompt_tokens = 0
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self.total_completion_tokens = 0
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self.total_cost = 0
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self.total_budget = 0.0
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def create_chat_completion(
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self,
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messages: list, # type: ignore
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model: str | None = None,
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temperature: float = cfg.temperature,
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max_tokens: int | None = None,
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deployment_id=None,
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) -> str:
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"""
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Create a chat completion and update the cost.
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Args:
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messages (list): The list of messages to send to the API.
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model (str): The model to use for the API call.
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temperature (float): The temperature to use for the API call.
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max_tokens (int): The maximum number of tokens for the API call.
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Returns:
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str: The AI's response.
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"""
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if deployment_id is not None:
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response = openai.ChatCompletion.create(
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deployment_id=deployment_id,
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model=model,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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else:
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response = openai.ChatCompletion.create(
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model=model,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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if self.debug:
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logger.debug(f"Response: {response}")
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prompt_tokens = response.usage.prompt_tokens
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completion_tokens = response.usage.completion_tokens
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self.update_cost(prompt_tokens, completion_tokens, model)
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return response
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def update_cost(self, prompt_tokens, completion_tokens, model):
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"""
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Update the total cost, prompt tokens, and completion tokens.
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Args:
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prompt_tokens (int): The number of tokens used in the prompt.
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completion_tokens (int): The number of tokens used in the completion.
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model (str): The model used for the API call.
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"""
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self.total_prompt_tokens += prompt_tokens
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self.total_completion_tokens += completion_tokens
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self.total_cost += (
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prompt_tokens * COSTS[model]["prompt"]
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+ completion_tokens * COSTS[model]["completion"]
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) / 1000
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if print_total_cost:
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print(f"Total running cost: ${self.total_cost:.3f}")
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def set_total_budget(self, total_budget):
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"""
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Sets the total user-defined budget for API calls.
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Args:
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prompt_tokens (int): The number of tokens used in the prompt.
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"""
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self.total_budget = total_budget
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def get_total_prompt_tokens(self):
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"""
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Get the total number of prompt tokens.
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Returns:
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int: The total number of prompt tokens.
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"""
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return self.total_prompt_tokens
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def get_total_completion_tokens(self):
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"""
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Get the total number of completion tokens.
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Returns:
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int: The total number of completion tokens.
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"""
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return self.total_completion_tokens
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def get_total_cost(self):
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"""
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Get the total cost of API calls.
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Returns:
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float: The total cost of API calls.
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"""
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return self.total_cost
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def get_total_budget(self):
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"""
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Get the total user-defined budget for API calls.
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Returns:
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float: The total budget for API calls.
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"""
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return self.total_budget
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api_manager = ApiManager(cfg.debug_mode)
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