To perform batch processing in LlamaIndex, you will primarily use the library's data loading and indexing capabilities. Batch processing allows you to handle multiple entries at once, which can improve the efficiency of both data ingestion and querying. This process generally involves preparing your data in bulk, feeding it into LlamaIndex, and then managing the indexing, retrieval, or updating processes as needed.
First, you need to format your data properly. LlamaIndex typically works with datasets that are structured in a way that's compliant with the library's input requirements. If you have a CSV file, for instance, you can read the entire file into memory using libraries like pandas. From there, organize the data in a list or dictionary format that LlamaIndex can process. It’s important to ensure that your data is clean and de-duplicated to prevent indexing redundant entries.
Once your data is ready, you can call batch processing methods provided by LlamaIndex. These methods generally allow you to pass a collection of items in one go rather than processing them one by one. For example, you might use a function like index_batch(data) to add all your entries to the index at once. After indexing, you can enhance performance further by parallelizing queries. This means that when you retrieve or update items, you can do so in chunks rather than individually, which can significantly reduce processing time in scenarios with large datasets. Always remember to check the documentation of LlamaIndex for the latest optimization tips and examples to ensure that you are using the library effectively.