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"""
Module: answer_augmentation
Description: Uses an LLM to enhance real estate listing descriptions for better personalization.
"""
import json
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
class LlmAugmentation:
"""Handles LLM-based augmentation of real estate listings."""
def __init__(self):
"""
Initializes the LLM model using LangChain.
"""
# Load API credentials securely (avoid hardcoding API keys)
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
# os.environ["OPENAI_API_BASE"] = "https://openai.vocareum.com/v1"
if not os.environ["OPENAI_API_KEY"]:
raise ValueError("❌ OpenAI API key is missing. Set VOCAREUM_OPENAI_API_KEY in environment variables.")
self.llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0.9
)
# Define the LLM prompt template
self.llm_prompt = PromptTemplate.from_template(
"""
Your role is to enhance real estate listing descriptions.
You will receive {n_answers} real estate listings retrieved using similarity search.
Each listing matches the buyer’s preferences in terms of location, property features, amenities, and neighborhood.
Your task:
- **Enhance the property descriptions**: Subtly emphasize features of the property to make it appealing to possible buyers.
- **Maintain factual integrity**: Do not add fictional information. Keep the facts unchanged while improving appeal.
- **Do NOT** use bullet points or numered lists. Separate the listings by a new blank line.
- **Recommended lenght**: Use between 80 to 120 words.
Here are the listings:
{listings}
Now, rewrite the descriptions to highlight the aspects most relevant to the buyer.
"""
)
def generate_augmented_descriptions(self, listings):
"""
Uses the LLM to augment property descriptions for better personalization.
Args:
listings (list): A list of retrieved real estate listings (dictionaries).
Returns:
list: A list of augmented listing descriptions.
"""
try:
if not listings:
raise ValueError("Listings data is empty. Cannot generate augmented descriptions.")
# Format listings into readable JSON
listings_text = json.dumps(listings, indent=2)
# Generate augmented descriptions using LangChain's LLM
response = self.llm.invoke(
self.llm_prompt.format(n_answers=len(listings), listings=listings_text)
)
# Ensure response is valid
if not response or not hasattr(response, "content"):
raise ValueError("Received invalid response from LLM.")
return response.content.strip()
except Exception as e:
print(f"❌ Error generating augmented descriptions: {e}")
return []
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