| from openai import OpenAI |
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| def openai_complete_if_cache( |
| model="gpt-4o-mini", prompt=None, system_prompt=None, history_messages=[], **kwargs |
| ) -> str: |
| openai_client = OpenAI() |
|
|
| messages = [] |
| if system_prompt: |
| messages.append({"role": "system", "content": system_prompt}) |
| messages.extend(history_messages) |
| messages.append({"role": "user", "content": prompt}) |
|
|
| response = openai_client.chat.completions.create( |
| model=model, messages=messages, **kwargs |
| ) |
| return response.choices[0].message.content |
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|
|
| if __name__ == "__main__": |
| description = "" |
| prompt = f""" |
| Given the following description of a dataset: |
| |
| {description} |
| |
| Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset. |
| |
| Output the results in the following structure: |
| - User 1: [user description] |
| - Task 1: [task description] |
| - Question 1: |
| - Question 2: |
| - Question 3: |
| - Question 4: |
| - Question 5: |
| - Task 2: [task description] |
| ... |
| - Task 5: [task description] |
| - User 2: [user description] |
| ... |
| - User 5: [user description] |
| ... |
| """ |
|
|
| result = openai_complete_if_cache(model="gpt-4o-mini", prompt=prompt) |
|
|
| file_path = "./queries.txt" |
| with open(file_path, "w") as file: |
| file.write(result) |
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| print(f"Queries written to {file_path}") |
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