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from typing import Any, Dict
from openai import OpenAI
from string import Template
import logging
import json
class LLMHandler:
def __init__(self, api_key, model_name, default_temperature, default_max_tokens, default_system_message=None):
self.client = OpenAI(api_key=api_key)
self.model_name = model_name
self.default_temperature = default_temperature
self.default_max_tokens = default_max_tokens
self.default_system_message = default_system_message
def generate(
self,
prompt,
model_name,
temperature=None,
max_tokens=None,
system_message=None):
# optional parameters
model_name = model_name or self.model_name
temperature = temperature or self.default_temperature
max_tokens = max_tokens or self.default_max_tokens
system_message = system_message or self.default_system_message
# prepare messages
messages = [{"role": "user", "content": prompt}]
if system_message:
messages.insert(0, {"role": "system", "content": system_message})
# get response
logging.info(f"Generating text with model {model_name}, temperature {temperature}, and max tokens {max_tokens}...")
response = self.client.chat.completions.create(
model=model_name,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
seed=1234,
response_format={ "type": "json_object" }
)
return response.choices[0].message.content
def format_personalization(personalization):
return '\n'.join([f"- {key}: {value}" for key, value in personalization.items()])
def build_prompt(
context: Dict[str, Any]) -> str:
"""
Create a detailed prompt incorporating all personalization factors
:param section_type: Type of section to generate
:param context: Context data including items and customer info
:param additional_textual_context: additional context for personalization
:return: Formatted prompt string
"""
# Create a detailed user prompt with all context and personalization
user_prompt = f"""
Generate personalized newsletter content based on the following information:
Customer Purchase Context:
- Previously bought items: {context.pop('bought_items', [])}
- Recommended items: {context.pop('recommended_items', [])}
Personalization Parameters:
{format_personalization(context)}
Required Sections:
1. greeting: A warm personalized welcome that considers all personalization parameters. Start with "Hello [customer name],"
2. intro: Acknowledge previous purchases and build connection warmly. Mention the previous purchases. Only acknoweldge previous purchases that are actually present in the context.
3. recommendations: Present recommended items naturally, explaining why they match the customer's preferences and previous purchases.
4. closing: Wrap up maintaining the established tone.
General reequirements:
- Write in first person.
- Don't be too formal, maintain a friendly, warm and engaging tone, avoid stuff like "I hope you are doing well".
- The reader should feel like they have a special dedicated fashion assistant, who is also a friend.
- When mentioning items, use the item name and avoid mentioning colors that are not present in the image, or using adjectives like "vibrant".
- The goal should be to make the reader feel special and excited about the items they are being recommended.
Please generate all sections in a single, coherent response that maintains consistency in tone and style throughout. Begin each section with the section name in all caps.
"""
return user_prompt
def initialize_newsletter(newsletter_meta_info, transactions, recommendations):
# get the paths to the template files
newsletter_example_path = newsletter_meta_info['newsletter_example_path']
# load the template from file
with open(newsletter_example_path, "r") as f:
newsletter_text = Template(f.read())
newsletter_text = newsletter_text.safe_substitute(
brand_logo=newsletter_meta_info.get("brand_logo", ""),
brand_name=newsletter_meta_info.get("brand_name", ""),
)
# iterate over the transactions and replace the placeholders with the actual content
for i, transaction in enumerate(transactions):
newsletter_text = newsletter_text.replace("${transaction_url}", transaction['product_image_url'], 1)
newsletter_text = newsletter_text.replace("${transaction_name}", transaction['product_name'], 1)
# iterate over the recommendations and replace the placeholders with the actual content
for i, recommendation in enumerate(recommendations):
newsletter_text = newsletter_text.replace("${recommendation_url}", recommendation['product_image_url'], 1)
newsletter_text = newsletter_text.replace("${recommendation_name}", recommendation['product_name'], 1)
return newsletter_text
def integrate_personalized_text(newsletter_text, customer_info, textual_sections):
# get dctionary from the textual sections json
textual_sections = json.loads(textual_sections)
logging.info(textual_sections)
newsletter_text = Template(newsletter_text)
newsletter_text = newsletter_text.safe_substitute(
**textual_sections
)
# make sure the greeting line is h1, so replace Hello ${customer_name} with <h1>Hello ${customer_name}</h1>
customer_name = customer_info.get("customer name")
newsletter_text = newsletter_text.replace(f"Hello {customer_name},", f"<h1>Hello {customer_name},</h1>")
return newsletter_text
def build_context(recommendations, transactions, additional_context, customer_info):
# clean recommendations and transactions
for rec in recommendations:
rec.pop('product_image_url', None)
rec.pop('article_id', None)
rec.pop('sales_channel_id', None)
rec.pop('transaction_date', None)
for tr in transactions:
tr.pop('product_image_url', None)
tr.pop('article_id', None)
tr.pop('sales_channel_id', None)
tr.pop('transaction_date', None)
tr.pop('price', None)
context = {
'recommended_items': recommendations,
'bought_items': transactions,
'customer_name': customer_info.get('customer name', 'Unknown'),
'customer_age': customer_info.get('customer age', 'Unknown'),
'last_visit': customer_info.get('last_visit', 'Unknown'),
# 'preferred_categories': customer_info.get('preferred_categories', 'Unknown'),
# 'shopping_frequency': customer_info.get('shopping_frequency', 'Unknown'),
# 'style_preferences': customer_info.get('style_preferences', 'Unknown'),
# 'size_preferences': customer_info.get('size_preferences', 'Unknown'),
'preferences': additional_context if len(additional_context) > 1 else 'No additional preferences',
}
return context
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