LlamaIndex supports incremental indexing by enabling developers to update existing indexes with new data without rebuilding from scratch. This approach saves computational resources and time, especially when working with large or frequently updated datasets. The system tracks which documents have already been processed and efficiently integrates new or modified content into the index, ensuring that queries reflect the most up-to-date information. Incremental indexing is particularly useful for applications like real-time document retrieval or dynamic knowledge bases.
The framework achieves this through two primary mechanisms. First, it maintains a registry of document metadata, including unique identifiers and timestamps, to determine whether a document is new or updated. For example, when adding a folder of files, LlamaIndex checks modification times or content hashes to detect changes. Second, it integrates with vector databases like FAISS or Pinecone, which support appending new embeddings. When a new document is added, LlamaIndex processes it into text chunks, generates embeddings, and inserts them into the existing vector store. This avoids reprocessing unchanged data while preserving semantic relationships between old and new content. Developers can use methods like index.add_documents(new_docs) to trigger this process.
A practical example involves a customer support knowledge base that receives daily updates. Instead of reindexing thousands of articles nightly, LlamaIndex identifies new or modified articles using metadata checks. It converts only these changes into embeddings and appends them to the index. This reduces processing time from hours to minutes. Additionally, if a document is deleted, LlamaIndex can flag its embeddings as inactive rather than rebuilding the entire index. This balance between efficiency and accuracy makes incremental indexing a key feature for maintaining responsive applications without sacrificing performance as data scales.