Batch processing in LlamaIndex involves handling multiple documents or queries simultaneously to improve efficiency when working with large datasets. This is particularly useful when you need to index or retrieve information from numerous files without processing them one at a time. The core idea is to leverage LlamaIndex’s built-in tools to manage data in bulk, reducing redundant operations and optimizing resource usage.
To implement batch processing, start by using LlamaIndex’s SimpleDirectoryReader to load multiple documents from a directory. For example, if you have a folder containing hundreds of text files, this reader can ingest all files at once, converting them into LlamaIndex’s Document objects. Once loaded, you can use the VectorStoreIndex class to create embeddings and index these documents in a single operation. If your documents require preprocessing (like splitting text into smaller chunks), use a NodeParser (e.g., SimpleNodeParser) to generate nodes in batches. For instance, parsing 1,000 documents into nodes might take minutes instead of hours if done sequentially. When querying, use asynchronous methods or batch inference APIs (if supported by your LLM) to process multiple queries in parallel, reducing latency.
Considerations include memory management and API rate limits. For example, embedding 10,000 text chunks at once might exhaust GPU memory, so you may need to split batches into smaller groups (e.g., 500 chunks per batch). LlamaIndex’s ServiceContext allows configuring components like LLMs and embedding models to handle batches, and tools like ThreadPoolExecutor can parallelize tasks. Always test batch sizes to balance speed and stability. Batch processing is ideal for scenarios like indexing corporate knowledge bases or analyzing logs, where processing individual items would be impractical.