How do you implement RAG with structured data and SQL databases?
Structured data RAG (Text-to-SQL RAG) enables natural language queries over relational databases. Architecture: natural language query is processed by a schema-aware LLM that generates SQL, the SQL is executed against the database, and results are formatted into a natural language response. Schema representation: provide the LLM with table definitions (CREATE TABLE statements), column descriptions and data types, foreign key relationships, and sample data (3-5 rows per table to illustrate content). Implementation: construct a prompt containing the database schema, question, and SQL generation instructions. Use few-shot examples of question-SQL pairs for the target database to improve accuracy. Execute generated SQL against a read-only database connection with timeout and row limits. Post-process results into a readable answer. Safety: always use read-only database connections, implement query timeout limits (5-10 seconds), limit result row counts (max 1000), whitelist allowed tables and operations, validate generated SQL before execution, and log all queries for audit. Error handling: if SQL fails, pass the error message back to the LLM for self-correction (allow 2-3 retry attempts), provide helpful error messages to users, and fall back to canned responses for unsupported query types. Advanced techniques: query decomposition for complex questions (break into multiple SQL queries), implement caching for frequent queries, use query history to improve generation quality, and implement query plan analysis to prevent expensive full-table scans. Hybrid approach: combine structured data retrieval with document-based RAG, using the structured data for quantitative questions and documents for qualitative context. Evaluation: measure SQL generation accuracy, execution success rate, and answer correctness against a test set of question-answer pairs.