Getting Started with Mirascope: Removing Semantic Duplicates using an LLM
Mirascope is a versatile library that offers a straightforward interface for interacting with various Large Language Model (LLM) providers, including well-known names like OpenAI and Google. It streamlines tasks such as text generation and data extraction, making it easier to build AI-driven workflows.
Understanding Semantic Duplicates
Semantic duplicates are entries that convey the same meaning but are expressed in different ways. For businesses, especially those relying on customer feedback, identifying and removing these duplicates can lead to clearer insights. Consider a scenario where multiple customers praise the sound quality of a product in different words; without deduplication, this valuable feedback could be overlooked or misrepresented.
Installing Mirascope
To get started with Mirascope, you need to install it along with OpenAI support. Use the following command:
pip install "mirascope[openai]"Setting Up Your OpenAI Key
To utilize OpenAI’s capabilities, you’ll need an API key. Follow these steps:
- Visit the OpenAI API Keys page.
- Generate a new key. Note that new users may need to add billing information and make a minimum payment of $5 to activate access.
Once you have your key, set it up in your environment:
import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass('Enter OpenAI API Key: ')Defining Customer Reviews
Next, create a list of customer reviews that captures various sentiments. This list should include both positive and negative feedback:
customer_reviews = [
"Sound quality is amazing!",
"Audio is crystal clear and very immersive.",
"Incredible sound, especially the bass response.",
"Battery doesn't last as advertised.",
"Needs charging too often.",
"Battery drains quickly -- not ideal for travel.",
"Setup was super easy and straightforward.",
"Very user-friendly, even for my parents.",
"Simple interface and smooth experience.",
"Feels cheap and plasticky.",
"Build quality could be better.",
"Broke within the first week of use.",
"People say they can't hear me during calls.",
"Mic quality is terrible on Zoom meetings.",
"Great product for the price!"
]Creating a Pydantic Schema
To structure the output from the deduplication process, define a Pydantic model:
from pydantic import BaseModel, Field
class DeduplicatedReviews(BaseModel):
duplicates: list[list[str]] = Field(
..., description="A list of semantically equivalent customer review groups"
)
reviews: list[str] = Field(
..., description="The deduplicated list of core customer feedback themes"
)Implementing Semantic Deduplication
Using Mirascope’s integration with OpenAI, define a function to handle semantic deduplication:
from mirascope.core import openai, prompt_template
@openai.call(model="gpt-4o", response_model=DeduplicatedReviews)
@prompt_template(
"""
SYSTEM:
You are an AI assistant helping to analyze customer reviews.
Your task is to group semantically similar reviews together -- even if they are worded differently.
- Use your understanding of meaning, tone, and implication to group duplicates.
- Return two lists:
1. A deduplicated list of the key distinct review sentiments.
2. A list of grouped duplicates that share the same underlying feedback.
USER:
{reviews}
"""
)
def deduplicate_customer_reviews(reviews: list[str]): ...Executing the Deduplication Function
Now, run the deduplication function and observe the results:
response = deduplicate_customer_reviews(customer_reviews)
# Ensure response format
assert isinstance(response, DeduplicatedReviews)
# Print Output
print("Distinct Customer Feedback:")
for item in response.reviews:
print("-", item)
print("Grouped Duplicates:")
for group in response.duplicates:
print("-", group)The output will provide a clear summary of customer feedback, highlighting distinct insights and grouping similar sentiments. This process not only reduces redundancy but also enhances the clarity of the feedback.
Case Study: Real-World Application
Consider a tech company that recently launched a new audio product. By using Mirascope to analyze customer reviews, they discovered that while many customers praised the sound quality, they also frequently mentioned battery issues. By understanding this, the company could prioritize product improvements and tailor their marketing strategies accordingly.
Conclusion
Utilizing Mirascope for semantic deduplication can significantly streamline the process of analyzing customer feedback. By leveraging AI to identify and group similar sentiments, businesses can gain clearer insights, leading to better decision-making and improved customer satisfaction.
FAQ
- What is Mirascope? Mirascope is a library that provides an interface for working with various LLM providers, enabling tasks such as text generation and data extraction.
- How do I install Mirascope? You can install Mirascope using the command
pip install "mirascope[openai]". - What are semantic duplicates? Semantic duplicates are entries that express the same meaning in different wording.
- Why is deduplication important? Deduplication helps clarify insights by eliminating redundancy in customer feedback, making analysis more effective.
- Can I use Mirascope for other LLMs? Yes, Mirascope supports various LLM providers, allowing for a wide range of applications.
