Information Retrieval Basics

What is Information Retrieval?

Information Retrieval (IR) is the process of finding relevant information from a large collection of unstructured data (usually documents) that satisfies an information need. The most prominent example of an IR system is a web search engine like Google, which indexes web pages and ranks them based on a user's query. Recently, Large Language Models (LLMs) have become a popular interface for accessing information. Their ability to understand and organize information in a semantic space makes them powerful tools, often used in conjunction with traditional IR systems.

Core Components and System Anatomy

An IR system is fundamentally composed of a document collection to be searched, a query representing the user's information need, and a result set containing a ranked list of relevant documents. At an abstract level, this can be viewed as a K-Nearest Neighbor (KNN) problem: given a query, find the K most similar documents.

Building a modern search system begins with data representation. You might use sparse representations like BM25, which represent text based on keywords and are excellent for direct term matching. Alternatively, you could use dense representations, or embeddings, from neural networks. These encode text into a semantic vector, capturing meaning and enabling searches for concepts, with cosine similarity typically used to measure distance.

To search efficiently, this data must be indexed. The choice of index follows the representation. For sparse data, an inverted index is standard, mapping keywords to the documents containing them. For dense vectors, Approximate Nearest Neighbor (ANN) indexes like HNSW, Ball Trees, or KD-Trees are used to find the closest vectors quickly in high-dimensional space. Most high-performance systems use a two-stage architecture to balance speed and accuracy. The first stage uses a fast method to generate a large set of candidates with high recall. The second stage then uses a more complex model to re-rank these candidates for high precision.

Evaluation, Applications, and Tools

To measure the effectiveness of an IR system, you can use metrics like Precision@K, which measures the fraction of relevant documents in the top K results, or Normalized Discounted Cumulative Gain (nDCG), which rewards systems for placing more relevant documents higher in the ranking.

Key applications for IR are widespread. Beyond classic web search, IR is used for log analysis in systems like Elasticsearch to find patterns and anomalies. A major modern application is Retrieval-Augmented Generation (RAG), where an IR system retrieves relevant context to help an LLM generate more accurate and factual responses.

Several libraries are available for building these systems. For traditional sparse IR, you might use the Lucene library or toolkits built on it like Anserini and PyTerrier. When working with dense vector search, Faiss from Meta AI is a prominent choice. For building complete, full-stack systems, Elasticsearch is a widely used distributed search and analytics engine.