Can LlamaIndex be used for document classification tasks?

Yes, LlamaIndex can be used for document classification tasks, though it isn’t specifically designed for this purpose. LlamaIndex is primarily a framework for structuring and retrieving data to improve interactions with large language models (LLMs). Its core strength lies in organizing documents into searchable indexes, which allows LLMs to efficiently query and extract relevant information. While classification isn’t its primary focus, developers can leverage its indexing capabilities to build pipelines that preprocess documents, retrieve context, and feed that data into classification workflows. For example, you could index documents using LlamaIndex, extract embeddings or keywords, and then use those features to train a classifier or directly prompt an LLM to categorize the content.

To implement document classification, developers can use LlamaIndex to preprocess and structure documents into a format that simplifies feature extraction. For instance, LlamaIndex can split documents into sections, generate summaries, or create vector embeddings that capture semantic meaning. These outputs can then serve as inputs for traditional machine learning models (e.g., SVM, logistic regression) or LLM-based classifiers. For example, after indexing a set of legal documents, you might extract embeddings for each document and train a classifier to categorize them into contract types (e.g., NDAs, employment agreements). Alternatively, you could use LlamaIndex’s retrieval capabilities to fetch similar documents from a labeled dataset and infer categories via similarity comparisons.

However, there are limitations. LlamaIndex doesn’t include built-in classification algorithms, so developers must integrate it with other tools or write custom logic. For instance, you might pair it with a library like scikit-learn for model training or use OpenAI’s API to prompt an LLM like GPT-4 to classify text based on retrieved context. A practical workflow could involve indexing documents with LlamaIndex, querying the index to retrieve top-k relevant examples, and using those examples in a few-shot prompt to an LLM (e.g., “Here are five finance reports; classify this new document into ‘budget’ or ‘forecast’”). While this approach works, it requires careful tuning of prompts and indexing parameters to ensure accurate results. For simpler use cases, traditional classification methods may be more efficient, but LlamaIndex adds value when dealing with large, unstructured datasets that benefit from semantic search or LLM-based reasoning.