rag_intent_classifier

A production-ready, offline intent classifier that can sit right before any RAG system, domain-aware router, or any routing layer where fast intent decisions are needed. It runs before retrieval or downstream routing so your application can detect follow-up, escalation, urgent, human-handoff, negative-sentiment, and general-support requests early and route them intelligently.

Why this package exists

Most RAG pipelines, domain-aware routing systems, and other decision-heavy workflows focus on retrieval or downstream handling, but they often skip the most important first step: User intent classification.

This package helps you identify whether a user query is:

• a normal domain request,
• a follow-up question,
• a request to connect to a human,
• an escalation,
• an urgent issue,
• or negative sentiment that should not trigger normal search.
• 150 default labels (The full mapping is stored in the bundled model asset at rag_intent_classifier/models/label_map.joblib)

That makes retrieval more accurate and reduces unnecessary document search.

What it does

• Classifies intents using MiniLM embeddings and packaged classical ML models
• Returns human-readable labels for downstream routing
• Works offline with no external API dependency
• Supports a simple Python API and CLI

Supported meta-intent labels

The current release surfaces these labels for routing-heavy RAG scenarios:

• follow_up
• connect_to_human
• escalate_issue
• negative_sentiment
• urgent_attention_required
• general_customer_support
• and additional 150 intents

These are especially useful for routing to a human, skipping standard retrieval, when to send chat history, narrowing search to the right support context, etc.

Quick comparison

This model is trained on a total of 17k+ labelled rows, with 3.8k+ validation rows and 5.7k+ test rows to support reliable intent routing.

Note: This classifier is built on general-purpose intent data and is intended as a strong starting point. For production use in a specific domain such as insurance, banking, sports, HR, consulting, or customer support, it is recommended to fine-tune or retrain the model on domain-specific examples for better accuracy and more relevant routing.

Architecture in a RAG pipeline

The package acts as the first decision layer in a RAG system.

Intent Classifier architecture

In practice, once the intent is classified, the retrieval step becomes smaller and more precise: the system can route to the right workflow, focus on the most relevant evidence and reduce the search area/scope dramatically.

Included capabilities

• 156 intents total (150 CLINC + 6 additional meta-intents)
• MiniLM embeddings
• Multiple packaged classifier models
• Offline inference
• Human-readable labels
• Simple Python API and CLI

Installation

Python 3.9 to 3.12 is the currently supported range for this package. This release uses a pinned dependency stack for reproducible installs and model compatibility. Python 3.13 is not currently targeted for this release.

pip install rag_intent_classifier

Quick start

from rag_intent_classifier import infer_intent

print(infer_intent("How do I renew my policy?"))

CLI usage

rag_intent_classifier "How do I renew my policy?"

Development install

git clone https://github.com/yuvarajd2588/rag_intent_classifier.git
cd rag_intent_classifier
pip install -e .