GPT-4o and LangGraph Tutorial: Build a TNT-LLM Application

Microsoft's TNT-LLM: Revolutionizing Taxonomy Generation and Text Classification

Microsoft has unveiled TNT-LLM, a groundbreaking system automating taxonomy creation and text classification, surpassing traditional methods in both speed and accuracy. This innovative approach leverages the power of large language models (LLMs) to streamline and scale the generation of taxonomies and classifiers, minimizing manual intervention. This is particularly beneficial for applications like Bing Copilot, where managing dynamic and diverse textual data is paramount.

This article demonstrates TNT-LLM's implementation using GPT-4o and LangGraph for efficient news article clustering. For further information on GPT-4o and LangGraph, consult these resources:

• What Is OpenAI's GPT-4o?
• GPT-4o API Tutorial: Getting Started with OpenAI's API
• LangGraph Tutorial: What Is LangGraph and How to Use It?

The original TnT-LLM research paper, "TnT-LLM: Text Mining at Scale with Large Language Models," provides comprehensive details on the system.

Understanding TNT-LLM

TNT-LLM (Taxonomy and Text Classification using Large Language Models) is a two-stage framework designed for generating and classifying taxonomies from textual data.

Phase 1: Taxonomy Generation

This initial phase utilizes a sample of text documents and a specific instruction (e.g., "generate a taxonomy to cluster news articles"). An LLM summarizes each document, extracting key information. Through iterative refinement, the LLM builds, modifies, and refines the taxonomy, resulting in a structured hierarchy of labels and descriptions for effective news article categorization.

Source: Mengting Wan et al.

Phase 2: Text Classification

The second phase employs the generated taxonomy to label a larger dataset. The LLM applies these labels, creating training data for a lightweight classifier (like logistic regression). This trained classifier efficiently labels the entire dataset or performs real-time classification.

Source: Mengting Wan et al.

TNT-LLM's adaptable nature makes it suitable for various text classification tasks, including intent detection and topic categorization.

Advantages of TNT-LLM

TNT-LLM offers significant advantages for large-scale text mining and classification:

• Automated Taxonomy Generation: Automates the creation of detailed and interpretable taxonomies from raw text, eliminating the need for extensive manual effort and domain expertise.
• Scalable Classification: Enables scalable text classification using lightweight models that handle large datasets and real-time classification efficiently.
• Cost-Effectiveness: Optimizes resource usage through tiered LLM utilization (e.g., GPT-4 for taxonomy generation, GPT-3.5-Turbo for summarization, and logistic regression for final classification).
• High-Quality Outputs: Iterative taxonomy generation ensures high-quality, relevant, and accurate categorizations.
• Minimal Human Intervention: Reduces manual input, minimizing potential biases and inconsistencies.
• Flexibility: Adapts to diverse text classification tasks and domains, supporting integration with various LLMs, embedding methods, and classifiers.

Implementing TNT-LLM

A step-by-step implementation guide follows:

Installation:

Install necessary packages:

pip install langgraph langchain langchain_openai

Set environment variables for API keys and model names:

export AZURE_OPENAI_API_KEY='your_api_key_here'
export AZURE_OPENAI_MODEL='your_deployment_name_here'
export AZURE_OPENAI_ENDPOINT='deployment_endpoint'

Core Concepts:

• Documents: Raw text data (articles, chat logs) structured using the Doc class.
• Taxonomies: Clusters of categorized intents or topics, managed by the TaxonomyGenerationState class.

Building a Simple TNT-LLM Application:

The following sections detail the implementation steps, using code snippets to illustrate key processes. Due to the length of the original code, a complete reproduction here is impractical. However, the following provides a structured overview of the process:

• Step 0: Define Graph State Class, Load Datasets, and Initialize GPT-4o: This involves defining the data structures and loading the news articles dataset. A GPT-4o model is initialized for use throughout the pipeline.

• Step 1: Summarize Documents: Each document is summarized using an LLM prompt.

• Step 2: Create Minibatches: Summarized documents are divided into minibatches for parallel processing.

• Step 3: Generate Initial Taxonomy: An initial taxonomy is generated from the first minibatch.

• Step 4: Update Taxonomy: The taxonomy is iteratively updated as subsequent minibatches are processed.

• Step 5: Review Taxonomy: The final taxonomy is reviewed for accuracy and relevance.

• Step 6: Orchestrating the TNT-LLM Pipeline with StateGraph: A StateGraph orchestrates the execution of the various steps.

• Step 7: Clustering and Displaying TNT-LLM's News Article Taxonomy: The final taxonomy is displayed, showing the clusters of news articles.

Conclusion

TNT-LLM offers a powerful and efficient solution for large-scale text mining and classification. Its automation capabilities significantly reduce the time and resources required for analyzing unstructured text data, enabling data-driven decision-making across various domains. The potential for further development and application across industries is substantial. For those interested in further LLM application development, a course on "Developing LLM Applications with LangChain" is recommended.

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