From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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46 lines
2.3 KiB
46 lines
2.3 KiB
import os |
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import json |
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from openai import OpenAI |
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from agents.deals import Deal, QualityDealSelection |
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class ScannerAgent: |
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MODEL = "gpt-4o-mini" |
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SYSTEM_PROMPT = """You identify and summarize the 5 most detailed deals from a list, by selecting deals that have the most detailed, high quality description and the most clear price. |
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Respond strictly in JSON with no explanation, using this format. You should provide the price as a number derived from the description. If the price of a deal isn't clear, do not include that deal in your response. |
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Most important is that you respond with the 5 deals that have the most detailed product description with price. It's not important to mention the terms of the deal; most important is a thorough description of the product. |
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{"quality_deals": [ |
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{ |
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"product_description": "Your clearly expressed summary of the product in 4-5 sentences. Details of the item are much more important than why it's a good deal. Avoid mentioning discounts and coupons; focus on the item itself. There should be a paragpraph of text for each item you choose.", |
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"price": 99.99, |
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"url": "the url as provided" |
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}, |
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... |
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]}""" |
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USER_PROMPT_PREFIX = """Respond with the most promising 5 deals from this list, selecting those which have the most detailed, high quality product description and a clear price. |
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Respond strictly in JSON, and only JSON. You should rephrase the description to be a summary of the product itself, not the terms of the deal. |
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Remember to respond with a paragraph of text in the product_description field for each of the 5 items that you select. |
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Deals: |
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""" |
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def __init__(self): |
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self.openai = OpenAI() |
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def scan(self) -> QualityDealSelection: |
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deals = Deal.fetch() |
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user_prompt = self.USER_PROMPT_PREFIX + '\n\n'.join([deal.describe() for deal in deals]) |
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completion = self.openai.beta.chat.completions.parse( |
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model=self.MODEL, |
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messages=[ |
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{"role": "system", "content": self.SYSTEM_PROMPT}, |
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{"role": "user", "content": user_prompt} |
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], |
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response_format=QualityDealSelection |
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) |
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result = completion.choices[0].message.parsed |
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return result
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