LlamaIndex handles large documents like PDFs by breaking them into manageable chunks, processing their content into structured data, and creating efficient indexes for retrieval. The system first parses the document to extract text and metadata (e.g., page numbers, sections), then splits the content into smaller segments called “nodes.” These nodes are designed to preserve context while avoiding memory constraints. For example, a 100-page PDF might be divided into 500-word chunks with overlapping text to maintain continuity between sections. This chunking process ensures the system can handle documents that exceed typical memory limits while retaining meaningful relationships between sections.
After parsing and chunking, LlamaIndex converts the text into numerical representations (embeddings) using language models like OpenAI’s text-embedding-ada-002 or open-source alternatives. These embeddings capture semantic meaning, enabling similarity-based searches. The nodes and their embeddings are stored in a vector database (e.g., FAISS, Pinecone) for efficient querying. Metadata such as document titles or page ranges is also indexed, allowing developers to filter results by specific criteria. For instance, a query about “privacy policies” could retrieve nodes from page 10-15 of a PDF contract, with the system using both semantic relevance and metadata to prioritize results.
LlamaIndex also supports hybrid approaches that combine keyword-based and vector-based retrieval. Developers can configure the system to index terms like “liability clause” alongside embeddings, ensuring precise matches and semantic relevance are both considered. Customization options include adjusting chunk sizes, choosing embedding models, and defining metadata fields. For example, a legal document might use smaller chunks (200 words) to isolate specific clauses, while a research paper could use larger chunks (1,000 words) to retain broader context. These tools allow developers to balance performance, accuracy, and resource usage based on their specific use case.