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➕ refactor: summarize error message without line number
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@@ -19,21 +19,26 @@ def answer_yes_no_question(text, question):
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question=question,
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text=text,
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)
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# count words "yes" and "no" in pros and cons
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yes_count = pros_and_cons.lower().count('yes')
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no_count = pros_and_cons.lower().count('no')
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return yes_count > no_count
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return ask_gpt(
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question_prompt,
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boolean_parser,
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text=text,
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question=question,
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pros_and_cons=pros_and_cons,
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)
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# return ask_gpt(
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# question_prompt,
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# boolean_parser,
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# text=text,
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# question=question,
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# pros_and_cons=pros_and_cons,
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# )
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pros_and_cons_prompt = '''\
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# Context
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{text}
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# Question
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{question}
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Note: You must not answer the question. Instead, give up to 5 bullet points (10 words) arguing why the question should be answered with true or false.'''
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Note: You must not answer the question. Instead, give up to 5 bullet points (10 words) arguing why the question should be answered with yes or no. \
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Each bullet point ends with either "- yes" or "- no".'''
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question_prompt = '''\
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# Context
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21
test/unit/test_yes_no_question.py
Normal file
21
test/unit/test_yes_no_question.py
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@@ -0,0 +1,21 @@
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import os
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from dev_gpt.apis.gpt import GPTSession
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from dev_gpt.options.generate.chains.question_answering import answer_yes_no_question
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def test_answer_yes_no_question(tmpdir):
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os.environ['VERBOSE'] = 'true'
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GPTSession(os.path.join(str(tmpdir), 'log.json'), model='gpt-3.5-turbo')
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assert answer_yes_no_question(
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'''\
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Microservice description:
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```
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The microservice takes a stock symbol as input and returns a summary of the company's stock performance over the past 30 days, \
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including the average closing price and the company name. \
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The summary is returned as a string. \
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The microservice uses the Yahoo Finance API to fetch the stock data and Python libraries to calculate the average closing price and generate the summary. \
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The request parameter is "stock_symbol" and the response parameter is "summary".
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```
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''', 'Does the microservice send requests to an API beside the google_custom_search and gpt_3_5_turbo?'
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) == True
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