To index documents from a relational database using LlamaIndex, start by setting up your connection to the database. LlamaIndex integrates easily with common relational databases such as PostgreSQL and MySQL. You'll need to use the appropriate database driver in your programming language of choice, such as psycopg2 for PostgreSQL or PyMySQL for MySQL. After establishing the connection, define the schema, including the tables and columns you want to index. This step is crucial because it determines which data will be extracted and how it will be structured for indexing.
Once your connection is established and your schema is defined, you can use LlamaIndex's API to extract the data from your specified tables. You typically perform queries to get the relevant documents or records that you want to index. For instance, if you're dealing with a customer database, you might query the customer table to fetch all relevant records, including names, addresses, and other pertinent information. The fetched data can then be transformed into a format that LlamaIndex can understand, often requiring some mapping of database fields to LlamaIndex documents or nodes.
Finally, after transforming your data, use LlamaIndex's indexing functions to create the index. This step involves feeding the prepared documents into LlamaIndex, which will organize them for efficient searching and retrieval. Depending on the size of the data, you may need to handle batching to avoid memory issues. Once the documents are indexed, you can perform searches or queries against them, leveraging LlamaIndex's capabilities to retrieve relevant results quickly. Make sure to continuously update the index as the underlying database changes to maintain the accuracy of your indexed data.