| | from functools import lru_cache |
| | from typing import Optional |
| |
|
| | from pydantic_settings import BaseSettings |
| |
|
| | EXTRACT_INFO_SYSTEM = "You are an expert in structured data extraction. You will be given an image or a set of images of a product and should extract its properties into the given structure." |
| |
|
| | EXTRACT_INFO_HUMAN = ( |
| | """Output properties of the main product (or {product_taxonomy}) shown in the images. You should use the following product data to assist you, if available: |
| | |
| | {product_data} |
| | |
| | If an attribute appears in both the image and the product data, use the value from the product data.""" |
| | ).replace(" ", "") |
| |
|
| | FOLLOW_SCHEMA_SYSTEM = "You are an expert in structured data extraction. You will be given an dictionary of attributes of a product and should output the its properties into the given structure." |
| |
|
| | FOLLOW_SCHEMA_HUMAN = """Convert following attributes to structured schema. Keep all the keys and number of values. Only replace the values themselves. : |
| | |
| | {json_info}""" |
| |
|
| | GET_PERCENTAGE_SYSTEM = "You are a fashion assistant. You have to assign percentages of cerntainty to each attribute of a product based on the image and product data. You will be given an image or a set of images of a product and set of attributes and should output the percentages of certainty into the given structure." |
| |
|
| | GET_PERCENTAGE_HUMAN = """For each allowed value in each attribute, assign a percentage of certainty (from 0 to 100) that the product fits that value. |
| | For attributes of type list[string], there can be multiple values, and multiple percentages of 100 are possible. |
| | You should use the following product data to assist you, if available: |
| | {product_data} |
| | If an attribute appears in both the image and the product data, use the value from the product data. |
| | """ |
| |
|
| | REEVALUATE_SYSTEM = "You are a fashion assistant. You have to reevaluate the attributes of a product based on the image and product data. You will be given an image or a set of images of a product and set of attributes and should output the reevaluated attributes into the given structure." |
| |
|
| | REEVALUATE_HUMAN = """Reevaluate the following attributes of the main product (or {product_taxonomy}) shown in the images. Here are the attributes to reevaluate: |
| | {product_data} |
| | |
| | If an attribute has type of string, do not need to reevaluate the values, just the attribute itself. If an attribute has type of list[string], reevaluate the top three values. |
| | """ |
| |
|
| | class Prompts(BaseSettings): |
| | EXTRACT_INFO_SYSTEM_MESSAGE: str = EXTRACT_INFO_SYSTEM |
| |
|
| | EXTRACT_INFO_HUMAN_MESSAGE: str = EXTRACT_INFO_HUMAN |
| |
|
| | FOLLOW_SCHEMA_SYSTEM_MESSAGE: str = FOLLOW_SCHEMA_SYSTEM |
| |
|
| | FOLLOW_SCHEMA_HUMAN_MESSAGE: str = FOLLOW_SCHEMA_HUMAN |
| |
|
| | GET_PERCENTAGE_SYSTEM_MESSAGE: str = GET_PERCENTAGE_SYSTEM |
| |
|
| | GET_PERCENTAGE_HUMAN_MESSAGE: str = GET_PERCENTAGE_HUMAN |
| |
|
| | REEVALUATE_SYSTEM_MESSAGE: str = REEVALUATE_SYSTEM |
| | |
| | REEVALUATE_HUMAN_MESSAGE: str = REEVALUATE_HUMAN |
| |
|
| |
|
| | |
| | @lru_cache |
| | def get_prompts() -> Prompts: |
| | """ |
| | Create and cache a Prompts instance. |
| | Returns the same instance for subsequent calls. |
| | """ |
| | prompts = Prompts() |
| | return prompts |
| |
|