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Yes, you can achieve this by using LlamaIndex with an SQL database. First, you need to build an SQL index using the
GPTSQLStructStoreIndex.from_documents()
method, as shown in this
source (
https://gpt-index.readthedocs.io/en/latest/examples/query_engine/SQLRouterQueryEngine.html):
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
sql_index = GPTSQLStructStoreIndex.from_documents(
[],
sql_database=sql_database,
table_name="city_stats",
)
Then, you can run a query on the index using the
query()
method. If the index is not capable of answering the query, you can fetch the data from the database, add it to the index, and then run the query again. Here's an example of how you can achieve this:
query = "xyz"
# Check if the index is capable of answering the query
response = index.query(query, mode="default")
if response: # If the index is capable of answering the query
print(response)
else:
# Fetch data from the database
# Add the data to the index
# Run the query on the index
response = index.query(query, mode="default")
print(response)
Keep in mind that this is a general example, and you might need to adapt it to your specific use case and data fetching process.
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