I have seen many applications augmenting LLM/GPT responses with unstructured text data using frameworks like LangChain/Vector databases. The idea is basically querying similar document chunks and plug them into the prompts.
But for structured data stored in SQL-like databases, there doesn't seem to be a widly adopted way.
I have seen exapmles from the LangChain library using GPT to generate a SQL query and execute it using Python, but this would create security issues if the LLM generates queries that tamper with the database.
Thanks for any inspirations or input.
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Ask HN: How to augment LLM/GPT with structured data in a safe way? · HackerTrans
But for structured data stored in SQL-like databases, there doesn't seem to be a widly adopted way.
I have seen exapmles from the LangChain library using GPT to generate a SQL query and execute it using Python, but this would create security issues if the LLM generates queries that tamper with the database.
Thanks for any inspirations or input.