To handle multiple indexing sources with LlamaIndex, you need to first set up a structured approach that allows integration and management of these different sources. LlamaIndex is designed to take in various data inputs, such as text documents, databases, or APIs, and create an index that can be queried efficiently. You can start by defining each source you want to index and how you will gather data from them. For instance, if you have a SQL database, a set of CSV files, and a web API, you will need to write the necessary code to extract data from each source reliably.
Once you have established connections to your different data sources, the next step is to preprocess the data from each source to ensure consistency. This may involve cleaning the data, normalizing formats, or filtering out unnecessary information. For example, if you're combining data from an API and a CSV file, ensure that the fields you are interested in align in terms of names and data types. After preprocessing, you would use LlamaIndex functions to build indexes for each source. Each indexed source should have unique identifiers or keys to prevent overlap and confusion when querying.
Finally, you can implement a unified querying mechanism that abstracts the different sources. This might involve creating a layer where queries can be routed to the appropriate index based on the request parameters. For instance, if a user searches for a specific document, your application would identify whether that document exists in the database or in the CSV file's index and then retrieve it accordingly. This allows your application to seamlessly handle data from multiple indexing sources, providing a smooth user experience while maintaining organized, distinct datasets.