Query Intent Detection in Multi-Model Databases

1. Introduction

Query intent detection is a critical component in multi-model databases, allowing systems to interpret user queries accurately.

2. Key Concepts

3. Step-by-Step Process

1. Collect user queries and label them with intents.
2. Preprocess the text data (tokenization, normalization).
3. Train an NLP model for intent classification.
4. Integrate the intent detection model with the database queries.
5. Evaluate the model's performance and iterate.

3.1 Code Example: Intent Classification

import nltk
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline

# Sample data
data = [
    ("Get user information", "get_user_info"),
    ("Show me the sales report", "get_sales_report"),
    ("Find all products", "get_all_products"),
]

# Split data into training and labels
texts, labels = zip(*data)

# Create a model
model = make_pipeline(CountVectorizer(), MultinomialNB())

# Train the model
model.fit(texts, labels)

# Predict intent
print(model.predict(["How do I get a report?"]))
            

4. Best Practices

• Ensure a diverse dataset for training to capture various intents.
• Apply continuous monitoring to improve the model over time.
• Use user feedback to refine the intent detection system.
• Regularly update the NLP model to keep up with language changes.

5. FAQ