Extracting Specific Values from User Prompts with Semantic Kernel
Semantic Kernel is a powerful tool for building intelligent applications, allowing you to combine natural language understanding with code execution. A common task in these applications is extracting specific values from user prompts. This article explores how to achieve this with Semantic Kernel, focusing on identifying and extracting relevant information from user input.
Scenario: Extracting Order Information
Let's say we're building a chatbot that manages customer orders. A user might provide a prompt like: "I want to order 2 pizzas with pepperoni and extra cheese, please." Our goal is to extract the key information from this prompt, like the number of pizzas, the topping, and any special requests.
Here's a basic Semantic Kernel code snippet that demonstrates the extraction process:
from semantic_kernel import Kernel
from semantic_kernel.skill_definition import sk_function, sk_tool
kernel = Kernel()
@sk_function(description="Extracts order information from a user prompt.")
def extract_order_info(text: str):
"""
Extracts key order information from a user prompt.
"""
# Implement logic here to identify and extract order details
# For example, using regular expressions or natural language processing techniques.
return {"quantity": 2, "topping": "pepperoni", "extra": "cheese"}
kernel.import_skill(extract_order_info)
# Example usage
user_prompt = "I want to order 2 pizzas with pepperoni and extra cheese, please."
order_info = kernel.run_skill(extract_order_info, {"text": user_prompt})
print(order_info)
This code demonstrates the fundamental structure of extracting information with Semantic Kernel:
- Define a Skill Function: The
extract_order_infofunction represents a skill that takes user input as a string and returns extracted data as a dictionary. - Implement Extraction Logic: This is where the core logic lies. You'll need to implement methods for identifying and extracting the relevant data from the user prompt. Techniques like regular expressions, natural language processing (NLP), and semantic parsing can be employed here.
- Import Skill: You register the
extract_order_infofunction as a skill within your Kernel instance. - Run the Skill: You provide the user prompt as input to the skill, and Semantic Kernel executes the logic, returning the extracted data.
Advanced Techniques for Extraction
The above example showcases a basic extraction approach. However, for more complex prompts or scenarios, consider these advanced techniques:
- Natural Language Processing (NLP): Libraries like spaCy or NLTK can be integrated with Semantic Kernel to analyze the text, identify key entities (like "pizzas", "pepperoni", "cheese"), and perform sentiment analysis.
- Semantic Parsing: Convert the user prompt into a structured representation, allowing you to extract the information based on its logical structure.
- Knowledge Graph Integration: Incorporate external knowledge bases (like DBpedia or Wikidata) into your extraction process to enhance entity recognition and resolve ambiguities in the user input.
Real-world Applications
Extracting values from user prompts is essential in numerous scenarios:
- Chatbots and Conversational Agents: Extract user preferences, order details, or support inquiries from chatbot interactions.
- Data Extraction from Documents: Analyze documents to identify specific data points like dates, locations, or product names.
- Personalized Recommendations: Extract user interests and preferences to tailor recommendations for products, services, or content.
Conclusion
Semantic Kernel provides a powerful framework for building intelligent applications that can understand and extract information from user prompts. By combining core functionalities with advanced NLP and knowledge graph techniques, you can enhance the accuracy and robustness of your extraction process.
Remember to explore available libraries and techniques for extracting data efficiently, and don't hesitate to experiment with different methods to find the best approach for your specific use case.
