LlamaIndex performs full-text search by indexing text data in a way that allows for efficient retrieval of relevant results based on user queries. This process begins with the ingestion of documents, where LlamaIndex extracts the text content from a variety of sources, such as PDFs, web pages, or plain text files. Once the text is extracted, it is analyzed and transformed into a format suitable for indexing. This typically involves breaking down the text into smaller components, such as tokens or words, and creating a structure that allows for quick searching.
The core of LlamaIndex's search functionality lies in its inverted index. This data structure maps each unique term in the indexed text to the documents that contain it. When a developer performs a search query, LlamaIndex looks up the terms in the inverted index to quickly identify the relevant documents. This is much faster than scanning through every document in the dataset, particularly when dealing with large volumes of text. LlamaIndex can also support features like phrase searching and Boolean queries, where users can combine terms using operators like AND, OR, and NOT to refine their search results.
Another key aspect of full-text search in LlamaIndex is the scoring and ranking of results. Once potential matches are found, LlamaIndex evaluates their relevance based on various factors, such as term frequency (how often the search terms appear in the documents) and document length. This scoring process helps ensure that the most relevant documents are presented higher in the search results. For developers, this means that once they set up LlamaIndex, users can perform powerful searches with confidence that the results returned will be meaningful and closely related to their query.