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import random |
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import cohere |
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import os |
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import openai |
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import time |
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import backoff |
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PRICE_PER_1K_TOKENS = { |
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"gpt-4": {"prompt": 0.03, "completion": 0.06}, |
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"gpt-4-1106-preview": {"prompt": 0.01, "completion": 0.03}, |
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"gpt-3.5-turbo": {"prompt": 0.001, "completion": 0.002}, |
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"ada": {"embed": 0.0004}, |
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"text-embedding-ada-002": {"embed": 0.0001}, |
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} |
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EMBEDDING_DIMENSIONS = { |
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"ada": 1536, |
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"text-embedding-ada-002": 1536, |
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} |
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def estimate_cost_of_text_generation_api_call( |
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model: str, response: dict, verbose: bool |
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) -> float: |
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completion_tokens = response.usage.completion_tokens |
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prompt_tokens = response.usage.prompt_tokens |
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total_tokens = response.usage.total_tokens |
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prompt_cost = prompt_tokens / 1000 * PRICE_PER_1K_TOKENS[model]["prompt"] |
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completion_cost = ( |
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completion_tokens / 1000 * PRICE_PER_1K_TOKENS[model]["completion"] |
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) |
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cost = prompt_cost + completion_cost |
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if verbose: |
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summary = f"""\ |
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Used {prompt_tokens} prompt tokens, {completion_tokens} completion tokens, {total_tokens} total tokens |
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Esimated cost: {cost:.4f} USD |
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""" |
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print(summary) |
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return cost |
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@backoff.on_exception(backoff.expo, (openai.RateLimitError, openai.APIConnectionError)) |
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def call_openai_with_exponetial_backoff(**kwargs): |
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rand_sleep_in_secs = 5 * random.random() |
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time.sleep(rand_sleep_in_secs) |
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return openai.chat.completions.create(**kwargs) |
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def init_openai_with_api_key(): |
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openai.api_key = os.environ.get("OPENAI_API_KEY") |
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def init_cohere_with_api_key(): |
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COHERE_API_KEY = os.environ.get("COHERE_API_KEY") |
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co = cohere.Client(COHERE_API_KEY) |
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return co |
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