In our recent project, we had two primary use cases:
- We needed to classify user queries and route them to the appropriate backend bot.
- We had to manage dependencies in a dynamic bot flow, where each step depended on previous user inputs.
To address these use cases efficiently, we turned to classifier chains—a powerful machine learning technique for multi-label classification. In this article, we’ll walk you through how we used classifier chains to handle the classification of queries and manage the bot flow in a way that remained invisible to the user while being robust on the backend.
What Are Classifier Chains?
A classifier chain is a machine learning technique that tackles multi-label classification problems, where a single data point can belong to multiple categories. In a classifier chain, several binary classifiers are arranged in a sequence, where each classifier builds on the predictions of the previous one. This approach is particularly useful when there are dependencies between labels, meaning that the classification of one label can influence the classification of the next.
In our case, classifier chains worked perfectly because:
- We needed to classify user queries and route them to the appropriate backend bot, with some queries requiring a simple response and others needing authorization and access to databases.
- Bot flows also had dependencies—whether a user passed a verification step or not, the next step in the flow would change accordingly.
How Classifier Chains Helped Us
1. Classifying User Queries
The first challenge we faced was classifying incoming user queries. We needed to decide which backend bot should handle the query—one bot for generic queries (like documentation-based responses) and another bot for queries requiring authorization and interaction with databases.
Here’s how we tackled it:
Step 1: The first classifier predicted the broadest category of the query using LLM —whether it was a generic query (e.g., related to documentation) or a complex query that required authentication and database interaction.
Step 2: The second classifier refined this classification. For instance, if the query was related to documentation, it would further categorize it into specific documentation types. If the query required authorization, it would identify whether the query was a database-related request (e.g., a user query that required reading or updating data from a database).
Step 3: The final prediction would determine exactly where to route the query. This ensured that a simple query would be handled by the documentation bot, while more complex queries would be sent to the sql bot for further action, such as validating the user’s identity or querying the database, etc.
By chaining these classifiers together, each step in the classification process was informed by the previous one. This allowed us to make highly accurate routing decisions, ensuring that the right backend bot handled each query without any confusion.
2. Managing Dependencies in Bot Flows
The second challenge we faced was managing the dynamic bot flow. Our bot needed to guide the user through a sequence of steps, with each step being influenced by the user’s previous input. For example, if the bot asked a user for verification, the next step could vary depending on whether the user passed the verification or not.
Here’s how classifier chains helped with this:
Each step in the bot flow depended on the output of the previous step, meaning that classifier chains played a key role in managing these interdependencies. The bot would dynamically adjust the next action based on the previous responses and classifications.
Step 1: LLM first understood the type of query – whether it needs authorization or not.
Step 2: If the user query required authorization, the bot would first classify the query as requiring authorization and then kicks in the flow of doing validation or authorization process.
Step 3: Once this step was completed, the next step would either continue based on successful authentication or direct the user to re-enter their credentials or do not allow users to access any further information.
Step 4: The classifier chain ensured that this sequence was consistent, with each step being informed by the previous classification and user input.
This dynamic flow control helped us create a more responsive and context-aware bot, able to adapt its behaviour as the user progressed through different stages of interaction.
How We Implemented It: A Sneak Peek Into Our Code
To integrate classifier chains into our system, we used LangChain, a framework that makes it easy to implement classification workflows, along with scikit-learn for the machine learning models. Below is a simplified version of how we set up classifier chains for classifying user queries:
import langchain
from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from langchain.chains import ClassifierChain
# Generate synthetic data to simulate our query classification task
X, y = make_multilabel_classification(n_samples=1000, n_features=20, n_classes=5, random_state=42)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Initialize a logistic regression model (our base classifier)
model = LogisticRegression(solver='liblinear')
# Set up the classifier chain
chain = ClassifierChain(base_estimator=model)
# Train the model with the training data
chain.fit(X_train, y_train)
# Make predictions using the test data
y_pred = chain.predict(X_test)
# Evaluate the model's accuracy
from sklearn.metrics import accuracy_score
print("Accuracy: ", accuracy_score(y_test, y_pred))
In this code, we use synthetic data to simulate our classification task. Each classifier in the chain is responsible for predicting a different aspect of the query, with each classifier’s output influencing the next. This chain ensures that user queries are accurately classified and routed to the right backend bot.
What We Learned and Best Practices
During the implementation of classifier chains, we followed several best practices to ensure the system’s performance and efficiency:
- Feature Selection: We carefully selected features that helped improve the model’s accuracy and relevance to the task at hand.
- Fine-Tuning the Model: We tuned our base classifier (logistic regression) to optimize its performance and accuracy.
- Data Preprocessing: Ensuring that our data was clean and well-prepared before training was crucial for achieving reliable results.
By focusing on these areas, we ensured that our classifier chains could handle growing complexity in the queries and bot flows.
Final Thoughts
Using classifier chains in our project was a game-changer. Whether we were classifying user queries to determine which bot to route them to, or managing the dependencies in a dynamic bot flow, classifier chains helped us make more accurate and efficient decisions. The key benefit was that we could handle complex routing logic and bot flows without exposing this complexity to the user, delivering a smooth and intelligent experience.
By leveraging the power of machine learning and classifier chains, we were able to build a smarter, more responsive system capable of handling both generic and authorization-dependent queries seamlessly. The result? A more efficient backend architecture and a better user experience.
Classifier chains are a powerful tool for handling multi-label classification and managing complex dependencies, and they were a perfect fit for our project. By combining this technique with robust frameworks like LangChain and scikit-learn, we were able to build a flexible, dynamic system that easily adapts to changing user inputs and query requirements.
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