How it works
User Input → text2SQL Model → SQL Query
Based on the user’s question, the model generates an SQL query.
Here’s what happens during this process:
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NLP
- Analyze the user input
- Extract the intent and the key elements they’re looking for
- Convert it into a structured format
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Generate the SQL Query
- Map the extracted elements into SQL
- Generate the query
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Execute the Query on the DB
- Run the query
- Return the result
3 Techniques to Make This Work
Prompt Engineering
Crafting a well-structured prompt is essential for getting good results with text2SQL.
A good technique here is few-shot learning — providing a few examples to help the model be more precise when generating the SQL.
The prompt alone won’t bring out the best from the solution. It needs to be combined with the next techniques to achieve better performance.
Fine-tuning
Fine-tuning a base model to perform better with our specific data in the text2SQL context.
Basically, this means training the model with our data catalog, data dictionary, etc., and feeding it examples of what should be returned based on different queries.
It’s a solid approach but more complex to implement. It’s totally fine to start with RAG or Prompt Engineering and evolve to Fine-tuning later.
RAG
Load your data catalog into a vector store and embed it into the user query context.
The idea of using RAG here is to help the model bring in the correct database, schema, table, etc., into the query.
You’re giving the model visibility into your data catalog so it can be more accurate when executing text2SQL.
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