⚡ Quick Answer

Indexing is the process of organizing and storing information in a structured format so that an AI system, search engine, or database can find and retrieve relevant information efficiently. In AI applications, indexing can involve processing documents, creating searchable representations, and preparing content for fast retrieval.

What Is Indexing?

In terms of simple explanation, Indexing is how you organize information so that it can be found quickly. That means instead of having to go through and search every single document or record one at a time when someone performs a search, an index is created that contains links to the information that you need to retrieve in order to answer the search question.

In many AI applications, like Knowledge Management or Search-based applications, the content has to be indexed first, in order to enable the retrieval. As part of the processing, the information is split into smaller chunks of information, to make it easier to index and retrieve afterwards.

As opposed to storing information, an index contains an organized representation of the same information. An index in a database or a document repository therefore always refers to the same information, whereas the information itself can be organized in a completely different manner.

How Does Indexing Work?

Prior to a user searching for information, the information is indexed. This means that the information has been processed and the necessary structures have been set up to facilitate retrieval.

Collecting and Processing Information

Information for the purpose of creating an index is collected from the relevant sources. Depending on the system, these can be documents, webpages, databases, FAQs, product information, internal company policies and more.

Depending on the information sources that are available to a system, content is processed to create the information that is required for the indexing. The processed information is used for indexing purposes by AI systems.

Creating an Index

Once all information has been processed, an index is created to locate relevant information as quickly as possible.

Word-based indexing can be used for search systems such as Google’s as well as for the retrieval system. A special type of indexing that is often used for AI-based search systems, is the so-called vector-based indexing. This type of indexing, uses so-called embeddings, which are numeric representations of text. This allows for highly efficient semantic search, where similar text is found even though it uses slightly different words.

Organizing Information for Retrieval

Information in the index needs to be preserved so that the retrieval system can find the relevant information. Additional metadata like the title of a document, categories, timestamps, source information and access rights can be stored in the index as well.

Indexing information is one of the core factors of the retrieval quality for most AI applications. How well information has been indexed beforehand strongly affects the search results quality.

Updating the Index

The Index has to be updated as information changes over time, new documents have to be added, outdated information has to be removed and modified content has to be reprocessed.

However, the index must also be updated when information in the underlying information is modified. New information must be added while outdated information is removed. Furthermore, modified information must be reprocessed.

What Are the Types of Indexing?

Keyword Indexing

The keyword indexing strategy is used to organize information by word and term. Thus, a keyword search will find documents that contain the exact terms searched for. This approach is particularly useful for situations in which information contains specific terms (such as names), product IDs, and technical phrases and terms.

Keyword Indexing is widely used in many search systems, document search, and even database systems. It is especially effective when searching for exact terms and phrases, such as names, product IDs, and other unique identifiers, as well as specific technical terms and jargon.

Vector Indexing

Indexing Vector information. This is usually generated by an embedding model and then indexed using a vector search algorithm. Information can then be searched for not only by matching the exact keywords in a query but also by information of a semantic nature and be found even though it was written using completely different words.

For example, a user might search for a document containing the phrase “I would like to purchase a yacht” while the actual document contains the words “Broking”, “Sales”, “Yachts” and “New Boat Sales”.

This type of index is widely used in semantic search, in AI assistants and in RAG (Retrieve And Generate) systems to retrieve conceptually similar information to what has been searched for.

Database Indexing

Database Indexing: By creating special data structures for databases, indexing helps databases quickly locate relevant records and execute queries more efficiently. Typically, B-tree and hash indexes are used for database indexing.

B-tree indexes and hash indexes are examples of database indexes, but there are many more and usually the database administrator decides which one to use depending on the used database, the typical queries and the kind of data stored in the database.

What Are the Benefits of Indexing?

The primary use of an index is to aid search in large stores of information and to do so in a manner that is much faster than would be possible were the information to be searched on a document by document basis.

Organizing large information repositories in an efficient manner is also an important aspect that is enabled by indexing. Hence, typical applications of indexing are search engines, enterprise search engines, databases, and AI-based retrieval systems.

The quality of indexing can significantly affect the retrieval quality of AI systems. Therefore, the same attention is to be paid to good quality indexing for enterprise search, RAGs, and other knowledge retrieval applications.

What Are the Applications of Indexing?

Search Engines

Indexing is also a central feature of search engines that index web pages and other online documents, to enable fast retrieval of relevant documents for a given search query.

Enterprise Search

Enterprises can also index internal documents like policies, reports, knowledge articles, and more, allowing employees to quickly search for information across large document collections without having to manually read through each document.

AI and RAG

RAG systems for example index knowledge sources of an organization prior to user interaction with the AI system. For this purpose, documents are processed in chunks, embedded into vectors and stored in a vector index. When a user issues a question, a retrieval system searches the index for relevant information and provides this context to the respective AI model.

How well the retrieval system can find the required information in the knowledge sources depends on the quality of the indexing process.

Databases

Databases can use indexes to speed up queries against data stored in structured tables. As opposed to searching through the entirety of a table, an index can help to locate required information in a much faster manner.

Document Retrieval

A document management system can also index a large number of files in a database. Such an index enables a search for keyword, metadata, or even semantic retrieval of documents and content.

Indexing vs. Vector Indexing

Feature Indexing Vector Indexing
Purpose Organizes information for efficient retrieval Organizes vector representations for similarity-based retrieval
Data representation Can use words, fields, metadata, or other structures Numerical vectors or embeddings
Search approach Can support keyword, structured, or other retrieval methods Primarily supports semantic or similarity search
Exact matching Can support exact term matching Not primarily designed for exact keyword matching
Semantic search May or may not support semantic retrieval Designed to support semantic similarity
Common technologies Search indexes and database indexes Vector indexes and vector databases
Common applications Databases, search engines, document retrieval Semantic search, AI assistants, RAG
Best suited for Efficient retrieval across structured or searchable information Finding information based on semantic similarity

 

 

Frequently Asked Questions

Indexing in AI: Indexing AI information in order to perform efficient search and retrieval using AI systems. This includes indexing documents and generating document embeddings that are then stored and used for search.

By creating an index of relevant information within a knowledge collection that has been processed and retrieved prior to a search query, a RAG system is able to efficiently search for relevant information to provide the best possible answer.

Vector indexing refers to the process of indexing numerical vector representations (i.e. embeddings) of information so that similar vectors can be efficiently found. This form of indexing is increasingly used for semantic search as well as general AI retrieval applications.

Indexing: preparing information for retrieval, and searching: using an index to retrieve information from a store of information.

Yes. It enables search systems and databases to localize search within seconds rather than having to search through the entire database for every search.