53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import re
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def parse_section(text, section_name):
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# Define a pattern to extract the named section along with its content
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section_pattern = rf'#### {section_name}\n(.*?)(?=####|$)'
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# Find the specific section content
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section_content = re.search(section_pattern, text, re.DOTALL)
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if section_content:
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# If the section is found, extract the content
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content = section_content.group(1)
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# Define a pattern to find segments that follow the format **xx:**
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segments_pattern = r'\*\*([^*]+):\*\*'
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# Use findall method to extract all matches in the section content
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segments = re.findall(segments_pattern, content)
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return segments
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else:
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# If the section is not found, return an empty list
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return []
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def prompt_cost(model_type: str, num_prompt_tokens: float, num_completion_tokens: float):
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input_cost_map = {
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"gpt-3.5-turbo": 0.0015,
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"gpt-3.5-turbo-16k": 0.003,
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"gpt-3.5-turbo-0613": 0.0015,
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"gpt-3.5-turbo-16k-0613": 0.003,
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"gpt-4": 0.03,
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"gpt-4-0613": 0.03,
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"gpt-4-32k": 0.06,
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}
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output_cost_map = {
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"gpt-3.5-turbo": 0.002,
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"gpt-3.5-turbo-16k": 0.004,
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"gpt-3.5-turbo-0613": 0.002,
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"gpt-3.5-turbo-16k-0613": 0.004,
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"gpt-4": 0.06,
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"gpt-4-0613": 0.06,
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"gpt-4-32k": 0.12,
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}
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if model_type not in input_cost_map or model_type not in output_cost_map:
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return -1
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return num_prompt_tokens * input_cost_map[model_type] / 1000.0 + num_completion_tokens * output_cost_map[model_type] / 1000.0
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