LlamaIndex handles document pre-processing by automating the steps needed to prepare raw data for efficient indexing and retrieval with large language models (LLMs). The process focuses on structuring documents into smaller, searchable units while preserving context and metadata. This ensures the LLM can access relevant information quickly during queries. The workflow typically involves loading documents, splitting them into chunks, and enriching them with metadata, all configurable to suit specific use cases.
First, LlamaIndex uses data loaders to ingest documents from various sources (PDFs, web pages, databases) and convert them into plain text. For example, a PDF might be parsed to extract text while ignoring images or complex layouts. Once loaded, the text splitter breaks documents into smaller chunks (nodes) to fit LLM context windows. The splitter can use simple rules (e.g., splitting at paragraph breaks) or token-based methods to avoid cutting sentences mid-way. For codebases, developers might configure the splitter to separate functions or classes, ensuring logical code blocks remain intact. Each chunk is stored as a node with metadata like source file names or section headers, aiding in traceability during retrieval.
Next, node parsers structure these chunks into nodes with customizable metadata. Developers can attach context such as document titles, timestamps, or keywords. For instance, a research paper might have nodes tagged with section names (Abstract, Methodology) for precise retrieval. LlamaIndex doesn’t perform heavy NLP tasks like entity recognition but integrates with libraries like spaCy or NLTK if those steps are needed. The final output is a structured index optimized for LLM queries. By balancing automation with configurability, LlamaIndex streamlines preprocessing while letting developers tailor it to their data’s unique needs.